Codebase list python-geopandas / 1a32544
New upstream version 0.4.0 Bas Couwenberg 5 years ago
102 changed file(s) with 5925 addition(s) and 2915 deletion(s). Raw diff Collapse all Expand all
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.gitignore less more
0 # Byte-compiled / optimized / DLL files
1 __pycache__/
2 *.py[cod]
3 *$py.class
4
5 # C extensions
6 *.so
7
8 # Distribution / packaging
9 .Python
10 env/
11 build/
12 dist/
13 eggs/
14 .eggs/
15 lib/
16 lib64/
17 parts/
18 sdist/
19 var/
20 wheels/
21 *.egg-info/
22 .installed.cfg
23 *.egg
24
25 # Unit test / coverage reports
26 htmlcov/
27 .tox/
028 .coverage
1 *.pyc
2 build
3 dist
4 doc/_build
29 .coverage.*
30 .cache
31 nosetests.xml
32 coverage.xml
33 *.cover
34 .hypothesis/
35
36 # Sphinx documentation
37 doc/_build/
38
39 # Jupyter Notebook
40 .ipynb_checkpoints
41
42 # projects
43 .spyderproject
44 .spyproject
45 .idea
46 .ropeproject
47
48 *.py~
49 .DS_Store
50
51 examples/nybb_*.zip
52
53 doc/source/gallery
54 doc/source/savefig
55 doc/source/reference
56
557 geopandas.egg-info
658 geopandas/version.py
7 *.py~
8 doc/_static/world_*
9 examples/nybb_*.zip
10 .cache/
59
60 .asv
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.travis.yml less more
11
22 sudo: false
33
4 cache: pip
5
6 addons:
7 apt:
8 packages:
9 - libgdal1h
10 - gdal-bin
11 - libgdal-dev
12 - libspatialindex-dev
134
145 matrix:
156 include:
167 # Only one test for these Python versions
17 - python: 3.4
18 env: PANDAS=0.18.1 MATPLOTLIB=1.4.3 SHAPELY=1.5.17
198 - python: 3.5
20 env: PANDAS=0.19.2 MATPLOTLIB=1.5.3
9 env: PANDAS=0.20.2 MATPLOTLIB=1.5.3
2110
2211 # Python 2.7 and 3.6 test all supported Pandas versions
2312 - python: 2.7
24 env: PANDAS=0.16.2 MATPLOTLIB=1.4.3 SHAPELY=1.5.17
25 - python: 2.7
26 env: PANDAS=0.17.1 MATPLOTLIB=1.4.3 SHAPELY=1.5.17
27 - python: 2.7
28 env: PANDAS=0.18.1 MATPLOTLIB=1.5.3
29 - python: 2.7
30 env: PANDAS=0.19.2 MATPLOTLIB=1.5.3
13 env: PANDAS=0.19.2 MATPLOTLIB=1.5.3 SHAPELY=1.5
3114 - python: 2.7
3215 env: PANDAS=0.20.2 MATPLOTLIB=2.0.2
3316 - python: 2.7
34 env: PANDAS=master MATPLOTLIB=master
17 env: PANDAS=0.23.2 MATPLOTLIB=2.1.2
18 - python: 2.7
19 env: PANDAS=master MATPLOTLIB=2.2.2
3520
3621 - python: 3.6
37 env: PANDAS=0.16.2 MATPLOTLIB=1.4.3 SHAPELY=1.5.17
38 - python: 3.6
39 env: PANDAS=0.17.1 MATPLOTLIB=1.4.3 SHAPELY=1.5.17
40 - python: 3.6
41 env: PANDAS=0.18.1 MATPLOTLIB=1.5.3
42 - python: 3.6
43 env: PANDAS=0.19.2 MATPLOTLIB=1.5.3
22 env: PANDAS=0.19.2 MATPLOTLIB=1.5.3 SHAPELY=1.5 CHANNEL=conda-forge
4423 - python: 3.6
4524 env: PANDAS=0.20.2 MATPLOTLIB=2.0.2
4625 - python: 3.6
26 env: PANDAS=0.23.2 MATPLOTLIB=2.2.2
27 - python: 3.6
4728 env: PANDAS=master MATPLOTLIB=master
4829
49 before_install:
50 - pip install -U pip
30 install:
31 # Install conda
32 - wget http://repo.continuum.io/miniconda/Miniconda-latest-Linux-x86_64.sh -O miniconda.sh
33 - bash miniconda.sh -b -p $HOME/miniconda
34 - export PATH="$HOME/miniconda/bin:$PATH"
35 - conda config --set always_yes yes --set changeps1 no
36 - conda update conda
37 - if [ -n "$CHANNEL" ]; then conda config --add channels $CHANNEL; fi
5138
52 install:
53 - pip install numpy
54 - if [[ $MATPLOTLIB == 'master' ]]; then pip install git+https://github.com/matplotlib/matplotlib.git; else pip install matplotlib==$MATPLOTLIB; fi
55 - if [ -n "$SHAPELY" ]; then pip install shapely==$SHAPELY; fi
39 # Install dependencies
40 - conda create -n test-geopandas -c conda-forge python=$TRAVIS_PYTHON_VERSION pytest matplotlib six psycopg2 sqlalchemy codecov pytest-cov mock
41 - source activate test-geopandas
42 - if [[ $MATPLOTLIB == 'master' ]]; then pip install git+https://github.com/matplotlib/matplotlib.git; fi
43 - if [ -n "$SHAPELY" ]; then conda install shapely=$SHAPELY; else conda install shapely; fi
44 - conda install pysal pyproj fiona descartes rtree
5645
57 - pip install -r requirements.txt
58 - pip install -r requirements.test.txt
59
60 - if [[ $PANDAS == 'master' ]]; then pip install git+https://github.com/pydata/pandas.git; else pip install pandas==$PANDAS; fi
46 - if [[ $PANDAS != 'master' ]]; then conda install pandas==$PANDAS; fi
47 - if [[ $PANDAS == 'master' ]]; then conda install pandas cython; pip install git+https://github.com/pydata/pandas.git; fi
6148
6249 script:
6350 - py.test geopandas --cov geopandas -v --cov-report term-missing
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CHANGELOG.md less more
00 Changes
11 =======
2
3 Version 0.4.0 (July 15, 2017)
4 -----------------------------
5
6 Improvements:
7
8 * Improved `overlay` function (better performance, several incorrect behaviours fixed) (#429)
9 * Pass keywords to control legend behavior (`legend_kwds`) to `plot` (#434)
10 * Add basic support for reading remote datasets in `read_file` (#531)
11 * Pass kwargs for `buffer` operation on GeoSeries (#535)
12 * Expose all geopy services as options in geocoding (#550)
13 * Faster write speeds to GeoPackage (#605)
14 * Permit `read_file` filtering with a bounding box from a GeoDataFrame (#613)
15 * Set CRS on GeoDataFrame returned by `read_postgis` (#627)
16 * Permit setting markersize for Point GeoSeries plots with column values (#633)
17 * Started an example gallery (#463, #690, #717)
18 * Support for plotting MultiPoints (#683)
19 * Testing functionalty (e.g. `assert_geodataframe_equal`) is now publicly exposed (#707)
20 * Add `explode` method to GeoDataFrame (similar to the GeoSeries method) (#671)
21 * Set equal aspect on active axis on multi-axis figures (#718)
22 * Pass array of values to column argument in `plot` (#770)
23
24 Bug fixes :
25
26 * Ensure that colorbars are plotted on the correct axis (#523)
27 * Handle plotting empty GeoDataFrame (#571)
28 * Save z-dimension when writing files (#652)
29 * Handle reading empty shapefiles (#653)
30 * Correct dtype for empty result of spatial operations (#685)
31 * Fix empty `sjoin` handling for pandas>=0.23 (#762)
32
233
334 Version 0.3.0 (August 29, 2017)
435 -------------------------------
4071 * Addition of the ``sindex`` attribute, a Spatial Index using the optional
4172 dependency ``rtree`` (``libspatialindex``) that can be used to speed up
4273 certain operations such as overlays (#140, #141).
43 * Addition of the ``GeoSeries.ix`` coordinate indexer to slice a GeoSeries based
74 * Addition of the ``GeoSeries.cx`` coordinate indexer to slice a GeoSeries based
4475 on a bounding box of the coordinates (#55).
4576 * Improvements to plotting: ability to specify edge colors (#173), support for
4677 the ``vmin``, ``vmax``, ``figsize``, ``linewidth`` keywords (#207), legends
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README.md less more
0 GeoPandas [![build status](https://secure.travis-ci.org/geopandas/geopandas.png?branch=master)](https://travis-ci.org/geopandas/geopandas) [![Coverage Status](https://codecov.io/gh/geopandas/geopandas/branch/master/graph/badge.svg)](https://codecov.io/gh/geopandas/geopandas) [![Join the chat at https://gitter.im/geopandas/geopandas](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/geopandas/geopandas?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
0 GeoPandas [![build status](https://secure.travis-ci.org/geopandas/geopandas.png?branch=master)](https://travis-ci.org/geopandas/geopandas) [![Coverage Status](https://codecov.io/gh/geopandas/geopandas/branch/master/graph/badge.svg)](https://codecov.io/gh/geopandas/geopandas) [![Join the chat at https://gitter.im/geopandas/geopandas](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/geopandas/geopandas?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) [![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/geopandas/geopandas/master)
11 =========
22
33 Python tools for geographic data
3434 - ``pandas``
3535 - ``shapely``
3636 - ``fiona``
37 - ``descartes``
3837 - ``pyproj``
3938
40 Further, [``rtree``](https://github.com/Toblerity/rtree) is an optional
41 dependency. ``rtree`` requires the C library [``libspatialindex``](https://github.com/libspatialindex/libspatialindex). If using brew, you can install using ``brew install Spatialindex``.
39 Further, ``descartes`` and ``matplotlib`` are optional dependencies, required
40 for plotting, and [``rtree``](https://github.com/Toblerity/rtree) is an optional
41 dependency, required for spatial joins. ``rtree`` requires the C library [``libspatialindex``](https://github.com/libspatialindex/libspatialindex). If using brew, you can install using ``brew install Spatialindex``.
4242
4343
4444 **Install**
4545
46 Then, installation works as normal: ``pip install geopandas``
46 GeoPandas depends on several low-level libraries for geospatial analysis. Depending on the system and package
47 manager that you use, this may cause dependency conflicts if you are not careful.
4748
49 *Using `conda`*
50
51 We suggest that you use the [anaconda distribution](https://conda.io/docs/user-guide/install/download.html)
52 to install GeoPandas (``miniconda`` is fine as well).
53
54 Use ``conda`` and the ``conda-forge`` channel to install GeoPandas on a clean environment:
55
56 ```bash
57 conda create -n geopandas
58 source activate geopandas # 'activate geopandas' on Windows
59 conda install -c conda-forge geopandas
60 ```
61
62 **NOTE:** Creating a new environment is not strictly necessary, but installing other geospatial packages
63 from a *different* channel than ``conda-forge`` may cause dependency conflicts, so we recommend starting
64 fresh if possible. See the [conda-forge gotcha page](https://conda-forge.org/docs/conda-forge_gotchas.html)
65 for more information.
66
67 *Using `pip`*
68
69 GeoPandas is also pip-installable. If you choose to use `pip`, make sure that you have the proper non-python
70 libraries installed and linked properly.
71
72 ```bash
73 pip install geopandas
74 ```
4875
4976 Examples
5077 --------
5279 >>> p1 = Polygon([(0, 0), (1, 0), (1, 1)])
5380 >>> p2 = Polygon([(0, 0), (1, 0), (1, 1), (0, 1)])
5481 >>> p3 = Polygon([(2, 0), (3, 0), (3, 1), (2, 1)])
55 >>> g = GeoSeries([p1, p2, p3])
82 >>> g = geopandas.GeoSeries([p1, p2, p3])
5683 >>> g
5784 0 POLYGON ((0.0000000000000000 0.000000000000000...
5885 1 POLYGON ((0.0000000000000000 0.000000000000000...
87114 GeoPandas also implements alternate constructors that can read any data format recognized by [fiona](http://toblerity.github.io/fiona). To read a zip file containing an ESRI shapefile with the [boroughs boundaries of New York City](https://data.cityofnewyork.us/City-Government/Borough-Boundaries/tqmj-j8zm) (GeoPandas includes this as an example dataset):
88115
89116 >>> nybb_path = geopandas.datasets.get_path('nybb')
90 >>> boros = GeoDataFrame.from_file(nybb_path)
117 >>> boros = geopandas.read_file(nybb_path)
91118 >>> boros.set_index('BoroCode', inplace=True)
92119 >>> boros.sort()
93120 >>> boros
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appveyor.yml less more
3333 - conda update -q conda
3434 - conda config --add channels conda-forge
3535 - conda info -a
36 - "conda create -q -n test-environment python=%PYTHON_VERSION%"
37 - activate test-environment
38 - conda install pandas shapely=1.5 fiona pyproj rtree matplotlib descartes
39 - conda install pytest mock
36 - "conda create --quiet --name test-environment python=%PYTHON_VERSION% pandas --file requirements.txt --file requirements.test.txt"
4037
4138 test_script:
4239 - activate test-environment
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benchmarks/transform.py less more
0 from geopandas import GeoDataFrame, GeoSeries, read_file, datasets
1 import numpy as np
2 import pandas as pd
3 from shapely.geometry import Point
4
5
6 class CRS:
7
8 def setup(self):
9 nybb = read_file(datasets.get_path('nybb'))
10 self.long_nybb = GeoDataFrame(pd.concat(10 * [nybb]),
11 crs=nybb.crs)
12
13 num_points = 20000
14 longitudes = np.random.rand(num_points) - 120
15 latitudes = np.random.rand(num_points) + 38
16 self.point_df = GeoSeries([Point(x, y) for (x, y)
17 in zip(longitudes, latitudes)])
18 self.point_df.crs = {"init": "epsg:4326"}
19
20 def time_transform_wgs84(self):
21 self.long_nybb.to_crs({"init": "epsg:4326"})
22
23 def time_transform_many_points(self):
24 self.point_df.to_crs({"init": "epsg:32610"})
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doc/Makefile less more
3939
4040 clean:
4141 -rm -rf $(BUILDDIR)/*
42 -rm -rf source/reference/*
4243
4344 html:
4445 $(SPHINXBUILD) -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html
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doc/environment.yml less more
11 channels:
22 - conda-forge
33 dependencies:
4 - python=3.5
4 - python=3.6
55 - pandas
66 - shapely
77 - fiona
1414 - pysal
1515 - sphinx
1616 - sphinx_rtd_theme
17 - numpydoc
1718 - ipython
1819 - pillow
20 - mock
21 - cartopy
22 - contextily
23 - geoplot
24 - sphinx-gallery
25 - jinja2
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doc/nyc_boros.py less more
0 """
1 Visualizing NYC Boroughs
2 ------------------------
3
4 Visualize the Boroughs of New York City with Geopandas.
5
6 This example generates many images that are used in the documentation. See
7 the `Geometric Manipulations <geometric_manipulations>` example for more
8 details.
9
10 First we'll import a dataset containing each borough in New York City. We'll
11 use the ``datasets`` module to handle this quickly.
12 """
13 import numpy as np
14 import matplotlib.pyplot as plt
15 from shapely.geometry import Point
16 from geopandas import GeoSeries, GeoDataFrame
17 import geopandas as gpd
18
19 np.random.seed(1)
20 DPI = 100
21
22 path_nybb = gpd.datasets.get_path('nybb')
23 boros = GeoDataFrame.from_file(path_nybb)
24 boros = boros.set_index('BoroCode')
25 boros
26
27 ##############################################################################
28 # Next, we'll plot the raw data
29 ax = boros.plot()
30 plt.xticks(rotation=90)
31 plt.savefig('nyc.png', dpi=DPI, bbox_inches='tight')
32
33 ##############################################################################
34 # We can easily retrieve the convex hull of each shape. This corresponds to
35 # the outer edge of the shapes.
36 boros.geometry.convex_hull.plot()
37 plt.xticks(rotation=90)
38
39 # Grab the limits which we'll use later
40 xmin, xmax = plt.gca().get_xlim()
41 ymin, ymax = plt.gca().get_ylim()
42
43 plt.savefig('nyc_hull.png', dpi=DPI, bbox_inches='tight')
44
45 ##############################################################################
46 # We'll generate some random dots scattered throughout our data, and will
47 # use them to perform some set operations with our boroughs. We can use
48 # GeoPandas to perform unions, intersections, etc.
49
50 N = 2000 # number of random points
51 R = 2000 # radius of buffer in feet
52
53 #xmin, xmax, ymin, ymax = 900000, 1080000, 120000, 280000
54 xc = (xmax - xmin) * np.random.random(N) + xmin
55 yc = (ymax - ymin) * np.random.random(N) + ymin
56 pts = GeoSeries([Point(x, y) for x, y in zip(xc, yc)])
57 mp = pts.buffer(R).unary_union
58 boros_with_holes = boros.geometry - mp
59 boros_with_holes.plot()
60 plt.xticks(rotation=90)
61 plt.savefig('boros_with_holes.png', dpi=DPI, bbox_inches='tight')
62
63 ##############################################################################
64 # Finally, we'll show the holes that were taken out of our boroughs.
65
66 holes = boros.geometry & mp
67 holes.plot()
68 plt.xticks(rotation=90)
69 plt.savefig('holes.png', dpi=DPI, bbox_inches='tight')
70 plt.show()
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doc/source/_static/custom.css less more
0 /*This ensures that clickable links in the SG examples are the right color*/
1 div.section a span {
2 color: #2980B9 !important
3 }
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doc/source/_templates/autosummary.rst less more
0 {{ fullname | escape | underline}}
1
2
3 .. currentmodule:: {{ module }}
4
5 .. auto{{ objtype }}:: {{ objname }}
6
7 {% if objtype in ['class', 'method', 'function'] %}
8
9 .. include:: {{module}}.{{objname}}.examples
10
11 .. raw:: html
12
13 <div class="clear"></div>
14
15 {% endif %}
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doc/source/about.rst less more
0 About
1 =====
2
3 Known issues
4 ------------
5
6 - The ``geopy`` API has changed significantly over recent versions,
7 ``geopy 1.10.0`` is currently supported.
8
9 .. toctree::
10 :maxdepth: 2
11
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doc/source/aggregation_with_dissolve.rst less more
00 .. ipython:: python
11 :suppress:
22
3 import geopandas as gpd
3 import geopandas
44 import matplotlib
55 orig = matplotlib.rcParams['figure.figsize']
66 matplotlib.rcParams['figure.figsize'] = [orig[0] * 1.5, orig[1]]
2525
2626 .. ipython:: python
2727
28 world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))
28 world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres'))
2929 world = world[['continent', 'geometry']]
3030 continents = world.dissolve(by='continent')
3131
3838
3939 .. ipython:: python
4040
41 world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))
41 world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres'))
4242 world = world[['continent', 'geometry', 'pop_est']]
4343 continents = world.dissolve(by='continent', aggfunc='sum')
4444
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doc/source/conf.py less more
2525 # Add any Sphinx extension module names here, as strings. They can be extensions
2626 # coming with Sphinx (named 'sphinx.ext.*') or your custom ones.
2727 extensions = ['IPython.sphinxext.ipython_console_highlighting',
28 'IPython.sphinxext.ipython_directive']
28 'IPython.sphinxext.ipython_directive',
29 'sphinx_gallery.gen_gallery',
30 'sphinx.ext.autosummary',
31 'sphinx.ext.intersphinx',
32 'sphinx.ext.autodoc',
33 'numpydoc',
34 ]
35
36 # Fix issue with warnings from numpydoc (see discussion in PR #534)
37 numpydoc_show_class_members = False
38
39 def setup(app):
40 app.add_stylesheet('custom.css') # may also be an URL
2941
3042 # Add any paths that contain templates here, relative to this directory.
3143
3244 templates_path = ['_templates']
45
46 autosummary_generate = True
47
48 # Sphinx gallery configuration
49 sphinx_gallery_conf = {
50 'examples_dirs': ['../../examples'],
51 'filename_pattern': '^((?!sgskip).)*$',
52 'gallery_dirs': ['gallery'],
53 'doc_module': ('geopandas',),
54 'reference_url': {'matplotlib': 'http://matplotlib.org',
55 'numpy': 'http://docs.scipy.org/doc/numpy/reference',
56 'scipy': 'http://docs.scipy.org/doc/scipy/reference',
57 'geopandas': None},
58 'backreferences_dir': 'reference'
59 }
3360
3461 # The suffix of source filenames.
3562 source_suffix = '.rst'
4269
4370 # General information about the project.
4471 project = u'GeoPandas'
45 copyright = u'2013-2016, GeoPandas developers'
72 copyright = u'2013–2017, GeoPandas developers'
4673
4774 # The version info for the project you're documenting, acts as replacement for
4875 # |version| and |release|, also used in various other places throughout the
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doc/source/data_structures.rst less more
22 .. ipython:: python
33 :suppress:
44
5 import geopandas as gpd
5 import geopandas
66 import matplotlib
77 orig = matplotlib.rcParams['figure.figsize']
88 matplotlib.rcParams['figure.figsize'] = [orig[0] * 1.5, orig[1]]
9292
9393 .. ipython:: python
9494
95 world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))
95 world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres'))
9696
9797 world.head()
9898 #Plot countries
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doc/source/geocoding.rst less more
0 .. currentmodule:: geopandas
1
2 .. ipython:: python
3 :suppress:
4
5 import geopandas
6
07
18 Geocoding
29 ==========
310
4 [TO BE COMPLETED]
11 ``geopandas`` supports geocoding (i.e., converting place names to
12 location on Earth) through `geopy`_, an optional dependency of ``geopandas``.
13 The following example shows how to use the `Google geocoding API
14 <https://developers.google.com/maps/documentation/geocoding/start>`_ to get the
15 locations of boroughs in New York City, and plots those locations along
16 with the detailed borough boundary file included within ``geopandas``.
17
18 .. _geopy: http://geopy.readthedocs.io/
19
20 .. ipython:: python
21
22 boros = geopandas.read_file(geopandas.datasets.get_path("nybb"))
23 boros.BoroName
24 boro_locations = geopandas.tools.geocode(boros.BoroName, provider="google")
25 boro_locations
26
27 import matplotlib.pyplot as plt
28 fig, ax = plt.subplots()
29
30 boros.to_crs({"init": "epsg:4326"}).plot(ax=ax, color="white", edgecolor="black");
31 @savefig boro_centers_over_bounds.png
32 boro_locations.plot(ax=ax, color="red");
533
634
7 .. function:: geopandas.geocode.geocode(strings, provider='googlev3', **kwargs)
35 The argument to ``provider`` can either be a string referencing geocoding
36 services, such as ``'google'``, ``'bing'``, ``'yahoo'``, and
37 ``'openmapquest'``, or an instance of a ``Geocoder`` from ``geopy``. See
38 ``geopy.geocoders.SERVICE_TO_GEOCODER`` for the full list.
39 For many providers, parameters such as API keys need to be passed as
40 ``**kwargs`` in the ``geocode`` call.
841
9 Geocode a list of strings and return a GeoDataFrame containing the
10 resulting points in its ``geometry`` column. Available
11 ``provider``s include ``googlev3``, ``bing``, ``google``, ``yahoo``,
12 ``mapquest``, and ``openmapquest``. ``**kwargs`` will be passed as
13 parameters to the appropriate geocoder.
14
15 Requires `geopy`_. Please consult the Terms of Service for the
16 chosen provider.
42 Please consult the Terms of Service for the chosen provider.
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doc/source/geometric_manipulations.rst less more
0 .. _geometric_manipulations:
1
02 Geometric Manipulations
13 ========================
24
5961
6062 Shear/Skew the geometries of the GeoSeries by angles along x and y dimensions.
6163
62 .. method:: GeoSeries.translate(self, angle, origin='center', use_radians=False)
64 .. method:: GeoSeries.translate(self, xoff=0.0, yoff=0.0, zoff=0.0)
6365
6466 Shift the coordinates of the GeoSeries.
6567
117119
118120 >>> nybb_path = geopandas.datasets.get_path('nybb')
119121 >>> boros = GeoDataFrame.from_file(nybb_path)
120 >>> boros.set_index('BoroCode', inplace=True)
121 >>> boros.sort()
122 >>> boros = boros.set_index('BoroCode')
123 >>> boros = boros.sort_index()
122124 >>> boros
123125 BoroName Shape_Area Shape_Leng \
124126 BoroCode
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2222 operations in python that would otherwise require a spatial database
2323 such as PostGIS.
2424
25
2526 .. toctree::
26 :maxdepth: 2
27 :maxdepth: 1
28 :caption: Getting Started
2729
2830 Installation <install>
31 Examples Gallery <gallery/index>
32
33 .. toctree::
34 :maxdepth: 1
35 :caption: User Guide
36
2937 Data Structures <data_structures>
3038 Reading and Writing Files <io>
3139 Indexing and Selecting Data <indexing>
3644 Aggregation with dissolve <aggregation_with_dissolve>
3745 Merging Data <mergingdata>
3846 Geocoding <geocoding>
47
48 .. toctree::
49 :maxdepth: 1
50 :caption: Reference Guide
51
3952 Reference to All Attributes and Methods <reference>
53
54
55 .. toctree::
56 :maxdepth: 1
57 :caption: Developer
58
4059 Contributing to GeoPandas <contributing>
41 About <about>
60
4261
4362 Indices and tables
4463 ==================
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22 .. ipython:: python
33 :suppress:
44
5 import geopandas as gpd
5 import geopandas
66
77
88 Indexing and Selecting Data
2222
2323 .. ipython:: python
2424
25 world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))
25 world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres'))
2626 southern_world = world.cx[:, :0]
2727 @savefig world_southern.png
2828 southern_world.plot(figsize=(10, 3));
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33 Installing GeoPandas
44 ---------------------
55
6 To install the released version, you can use pip::
6 To install the released version, we recommend to use `conda`_ (from the conda-forge
7 channel)::
8
9 conda install -c conda-forge geopandas
10
11 Alternatively, you can also install GeoPandas with pip, but then you need
12 to make sure that all dependencies are installed correctly:
713
814 pip install geopandas
9
10 or you can install the conda package from the conda-forge channel::
11
12 conda install -c conda-forge geopandas
1315
1416 You may install the latest development version by cloning the
1517 `GitHub` repository and using the setup script::
8486 .. _libspatialindex: https://github.com/libspatialindex/libspatialindex
8587
8688 .. _Travis CI: https://travis-ci.org/geopandas/geopandas
89
90 .. _conda: https://conda-forge.org/
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0 .. _io:
01
12 Reading and Writing Files
23 =========================================
89
910 *geopandas* can read almost any vector-based spatial data format including ESRI shapefile, GeoJSON files and more using the command::
1011
11 gpd.read_file()
12 geopandas.read_file()
1213
1314 which returns a GeoDataFrame object. (This is possible because *geopandas* makes use of the great `fiona <http://toblerity.org/fiona/manual.html>`_ library, which in turn makes use of a massive open-source program called `GDAL/OGR <http://www.gdal.org/>`_ designed to facilitate spatial data transformations).
1415
1819
1920 Among other things, one can explicitly set the driver (shapefile, GeoJSON) with the ``driver`` keyword, or pick a single layer from a multi-layered file with the ``layer`` keyword.
2021
22 Where supported in ``fiona``, *geopandas* can also load resources directly from
23 a web URL, for example for GeoJSON files from `geojson.xyz <http://geojson.xyz/>`_::
24
25 url = "http://d2ad6b4ur7yvpq.cloudfront.net/naturalearth-3.3.0/ne_110m_land.geojson"
26 df = geopandas.read_file(url)
27
2128 *geopandas* can also get data from a PostGIS database using the ``read_postgis()`` command.
2229
2330
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22 .. ipython:: python
33 :suppress:
44
5 import geopandas as gpd
5 import geopandas
66 import matplotlib
77 orig = matplotlib.rcParams['figure.figsize']
88 matplotlib.rcParams['figure.figsize'] = [orig[0] * 1.5, orig[1]]
9
9 import matplotlib.pyplot as plt
10 plt.close('all')
1011
1112
1213 Mapping Tools
1920
2021 .. ipython:: python
2122
22 world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))
23 cities = gpd.read_file(gpd.datasets.get_path('naturalearth_cities'))
23 world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres'))
24 cities = geopandas.read_file(geopandas.datasets.get_path('naturalearth_cities'))
2425
2526 We can now plot those GeoDataFrames:
2627
3637 Note that in general, any options one can pass to `pyplot <http://matplotlib.org/api/pyplot_api.html>`_ in ``matplotlib`` (or `style options that work for lines <http://matplotlib.org/api/lines_api.html>`_) can be passed to the ``plot()`` method.
3738
3839
39 Chloropleth Maps
40 Choropleth Maps
4041 -----------------
4142
42 *geopandas* makes it easy to create Chloropleth maps (maps where the color of each shape is based on the value of an associated variable). Simply use the plot command with the ``column`` argument set to the column whose values you want used to assign colors.
43 *geopandas* makes it easy to create Choropleth maps (maps where the color of each shape is based on the value of an associated variable). Simply use the plot command with the ``column`` argument set to the column whose values you want used to assign colors.
4344
4445 .. ipython:: python
4546
6162 world.plot(column='gdp_per_cap', cmap='OrRd');
6263
6364
64 The way color maps are scaled can also be manipulated with the ``scheme`` option (if you have ``pysal`` installed, which can be accomplished via ``conda install pysal``). By default, ``scheme`` is set to 'equal_intervals', but it can also be adjusted to any other `pysal option <http://pysal.org/1.2/library/esda/mapclassify.html>`_, like 'quantiles', 'percentiles', etc.
65 The way color maps are scaled can also be manipulated with the ``scheme`` option (if you have ``pysal`` installed, which can be accomplished via ``conda install pysal``). The ``scheme`` option can be set to 'equal_interval', 'quantiles' or 'percentiles'. See the `PySAL documentation <http://pysal.readthedocs.io/en/latest/library/esda/mapclassify.html>`_ for further details about these map classification schemes.
6566
6667 .. ipython:: python
6768
7273 Maps with Layers
7374 -----------------
7475
75 There are two strategies for making a map with multiple layers -- one more succinct, and one that is a littel more flexible.
76 There are two strategies for making a map with multiple layers -- one more succinct, and one that is a little more flexible.
7677
7778 Before combining maps, however, remember to always ensure they share a common CRS (so they will align).
7879
127128 :suppress:
128129
129130 matplotlib.rcParams['figure.figsize'] = orig
131
132
133 .. ipython:: python
134 :suppress:
135
136 import matplotlib.pyplot as plt
137 plt.close('all')
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22 .. ipython:: python
33 :suppress:
44
5 import geopandas as gpd
5 import geopandas
66
77
88 Merging Data
1818
1919 .. ipython:: python
2020
21 world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))
22 cities = gpd.read_file(gpd.datasets.get_path('naturalearth_cities'))
21 world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres'))
22 cities = geopandas.read_file(geopandas.datasets.get_path('naturalearth_cities'))
2323
2424 # For attribute join
2525 country_shapes = world[['geometry', 'iso_a3']]
6767
6868 # Execute spatial join
6969
70 cities_with_country = gpd.sjoin(cities, countries, how="inner", op='intersects')
70 cities_with_country = geopandas.sjoin(cities, countries, how="inner", op='intersects')
7171 cities_with_country.head()
7272
7373
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22 .. ipython:: python
33 :suppress:
44
5 import geopandas as gpd
5 import geopandas
66
77
88 Managing Projections
1515
1616 CRS are important because the geometric shapes in a GeoSeries or GeoDataFrame object are simply a collection of coordinates in an arbitrary space. A CRS tells Python how those coordinates related to places on the Earth.
1717
18 CRS are referred to using codes called `proj4 strings <https://en.wikipedia.org/wiki/PROJ.4>`_. You can find the codes for most commonly used projections from `www.spatialreference.org <http://spatialreference.org/>`_ or `remotesensing.org <http://www.remotesensing.org/geotiff/proj_list/>`_.
18 CRS are referred to using codes called `proj4 strings <https://en.wikipedia.org/wiki/PROJ.4>`_. You can find the codes for most commonly used projections from `www.spatialreference.org <http://spatialreference.org/>`_.
1919
2020 The same CRS can often be referred to in many ways. For example, one of the most commonly used CRS is the WGS84 latitude-longitude projection. One `proj4` representation of this projection is: ``"+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs"``. But common projections can also be referred to by `EPSG` codes, so this same projection can also called using the `proj4` string ``"+init=epsg:4326"``.
2121
5353 .. ipython:: python
5454
5555 # load example data
56 world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))
56 world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres'))
5757
5858 # Check original projection
5959 # (it's Platte Carre! x-y are long and lat)
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0
0 .. _reference:
11
22 Reference
33 ===========================
44
5 GeoSeries
6 ---------
7
58 The following Shapely methods and attributes are available on
69 ``GeoSeries`` objects:
710
8 .. attribute:: GeoSeries.area
11 .. autoattribute:: geopandas.GeoSeries.area
912
10 Returns a ``Series`` containing the area of each geometry in the ``GeoSeries``.
13 .. autoattribute:: geopandas.GeoSeries.bounds
1114
12 .. attribute:: GeoSeries.bounds
15 .. autoattribute:: geopandas.GeoSeries.length
1316
14 Returns a ``DataFrame`` with columns ``minx``, ``miny``, ``maxx``,
15 ``maxy`` values containing the bounds for each geometry.
16 (see ``GeoSeries.total_bounds`` for the limits of the entire series).
17 .. autoattribute:: geopandas.GeoSeries.geom_type
1718
18 .. attribute:: GeoSeries.length
19 .. automethod:: geopandas.GeoSeries.distance
1920
20 Returns a ``Series`` containing the length of each geometry.
21 .. automethod:: geopandas.GeoSeries.representative_point
2122
22 .. attribute:: GeoSeries.geom_type
23 .. autoattribute:: geopandas.GeoSeries.exterior
2324
24 Returns a ``Series`` of strings specifying the `Geometry Type` of
25 each object.
26
27 .. method:: GeoSeries.distance(other)
28
29 Returns a ``Series`` containing the minimum distance to the `other`
30 ``GeoSeries`` (elementwise) or geometric object.
31
32 .. method:: GeoSeries.representative_point()
33
34 Returns a ``GeoSeries`` of (cheaply computed) points that are
35 guaranteed to be within each geometry.
36
37 .. attribute:: GeoSeries.exterior
38
39 Returns a ``GeoSeries`` of LinearRings representing the outer
40 boundary of each polygon in the GeoSeries. (Applies to GeoSeries
41 containing only Polygons).
42
43 .. attribute:: GeoSeries.interiors
44
45 Returns a ``GeoSeries`` of InteriorRingSequences representing the
46 inner rings of each polygon in the GeoSeries. (Applies to GeoSeries
47 containing only Polygons).
25 .. autoattribute:: geopandas.GeoSeries.interiors
4826
4927 `Unary Predicates`
5028
51 .. attribute:: GeoSeries.is_empty
29 .. autoattribute:: geopandas.GeoSeries.is_empty
5230
53 Returns a ``Series`` of ``dtype('bool')`` with value ``True`` for
54 empty geometries.
31 .. autoattribute:: geopandas.GeoSeries.is_ring
5532
56 .. attribute:: GeoSeries.is_ring
33 .. autoattribute:: geopandas.GeoSeries.is_simple
5734
58 Returns a ``Series`` of ``dtype('bool')`` with value ``True`` for
59 features that are closed.
60
61 .. attribute:: GeoSeries.is_simple
62
63 Returns a ``Series`` of ``dtype('bool')`` with value ``True`` for
64 geometries that do not cross themselves (meaningful only for
65 `LineStrings` and `LinearRings`).
66
67 .. attribute:: GeoSeries.is_valid
68
69 Returns a ``Series`` of ``dtype('bool')`` with value ``True`` for
70 geometries that are valid.
35 .. autoattribute:: geopandas.GeoSeries.is_valid
7136
7237 `Binary Predicates`
7338
74 .. method:: GeoSeries.almost_equals(other[, decimal=6])
39 .. automethod:: geopandas.GeoSeries.geom_almost_equals
7540
76 Returns a ``Series`` of ``dtype('bool')`` with value ``True`` if
77 each object is approximately equal to the `other` at all
78 points to specified `decimal` place precision. (See also :meth:`equals`)
41 .. automethod:: geopandas.GeoSeries.contains
7942
80 .. method:: GeoSeries.contains(other)
43 .. automethod:: geopandas.GeoSeries.crosses
8144
82 Returns a ``Series`` of ``dtype('bool')`` with value ``True`` if
83 each object's `interior` contains the `boundary` and
84 `interior` of the other object and their boundaries do not touch at all.
45 .. automethod:: geopandas.GeoSeries.disjoint
8546
86 .. method:: GeoSeries.crosses(other)
47 .. automethod:: geopandas.GeoSeries.geom_equals
8748
88 Returns a ``Series`` of ``dtype('bool')`` with value ``True`` if
89 the `interior` of each object intersects the `interior` of
90 the other but does not contain it, and the dimension of the intersection is
91 less than the dimension of the one or the other.
49 .. automethod:: geopandas.GeoSeries.intersects
9250
93 .. method:: GeoSeries.disjoint(other)
51 .. automethod:: geopandas.GeoSeries.touches
9452
95 Returns a ``Series`` of ``dtype('bool')`` with value ``True`` if
96 the `boundary` and `interior` of each object does not
97 intersect at all with those of the other.
98
99 .. method:: GeoSeries.equals(other)
100
101 Returns a ``Series`` of ``dtype('bool')`` with value ``True`` if
102 if the set-theoretic `boundary`, `interior`, and `exterior`
103 of each object coincides with those of the other.
104
105 .. method:: GeoSeries.intersects(other)
106
107 Returns a ``Series`` of ``dtype('bool')`` with value ``True`` if
108 if the `boundary` and `interior` of each object intersects in
109 any way with those of the other.
110
111 .. method:: GeoSeries.touches(other)
112
113 Returns a ``Series`` of ``dtype('bool')`` with value ``True`` if
114 the objects have at least one point in common and their
115 interiors do not intersect with any part of the other.
116
117 .. method:: GeoSeries.within(other)
118
119 Returns a ``Series`` of ``dtype('bool')`` with value ``True`` if
120 each object's `boundary` and `interior` intersect only
121 with the `interior` of the other (not its `boundary` or `exterior`).
122 (Inverse of :meth:`contains`)
53 .. automethod:: geopandas.GeoSeries.within
12354
12455 `Set-theoretic Methods`
12556
126 .. method:: GeoSeries.difference(other)
57 .. automethod:: geopandas.GeoSeries.difference
12758
128 Returns a ``GeoSeries`` of the points in each geometry that
129 are not in the *other* object.
59 .. automethod:: geopandas.GeoSeries.intersection
13060
131 .. method:: GeoSeries.intersection(other)
61 .. automethod:: geopandas.GeoSeries.symmetric_difference
13262
133 Returns a ``GeoSeries`` of the intersection of each object with the `other`
134 geometric object.
135
136 .. method:: GeoSeries.symmetric_difference(other)
137
138 Returns a ``GeoSeries`` of the points in each object not in the `other`
139 geometric object, and the points in the `other` not in this object.
140
141 .. method:: GeoSeries.union(other)
142
143 Returns a ``GeoSeries`` of the union of points from each object and the
144 `other` geometric object.
63 .. automethod:: geopandas.GeoSeries.union
14564
14665 `Constructive Methods`
14766
148 .. method:: GeoSeries.buffer(distance, resolution=16)
67 .. automethod:: geopandas.GeoSeries.buffer
14968
150 Returns a ``GeoSeries`` of geometries representing all points within a given `distance`
151 of each geometric object.
69 .. autoattribute:: geopandas.GeoSeries.boundary
15270
153 .. attribute:: GeoSeries.boundary
71 .. autoattribute:: geopandas.GeoSeries.centroid
15472
155 Returns a ``GeoSeries`` of lower dimensional objects representing
156 each geometries's set-theoretic `boundary`.
73 .. autoattribute:: geopandas.GeoSeries.convex_hull
15774
158 .. attribute:: GeoSeries.centroid
75 .. autoattribute:: geopandas.GeoSeries.envelope
15976
160 Returns a ``GeoSeries`` of points for each geometric centroid.
161
162 .. attribute:: GeoSeries.convex_hull
163
164 Returns a ``GeoSeries`` of geometries representing the smallest
165 convex `Polygon` containing all the points in each object unless the
166 number of points in the object is less than three. For two points,
167 the convex hull collapses to a `LineString`; for 1, a `Point`.
168
169 .. attribute:: GeoSeries.envelope
170
171 Returns a ``GeoSeries`` of geometries representing the point or
172 smallest rectangular polygon (with sides parallel to the coordinate
173 axes) that contains each object.
174
175 .. method:: GeoSeries.simplify(tolerance, preserve_topology=True)
176
177 Returns a ``GeoSeries`` containing a simplified representation of
178 each object.
77 .. automethod:: geopandas.GeoSeries.simplify
17978
18079 `Affine transformations`
18180
182 .. method:: GeoSeries.rotate(self, angle, origin='center', use_radians=False)
81 .. automethod:: geopandas.GeoSeries.rotate
18382
184 Rotate the coordinates of the GeoSeries.
83 .. automethod:: geopandas.GeoSeries.scale
18584
186 .. method:: GeoSeries.scale(self, xfact=1.0, yfact=1.0, zfact=1.0, origin='center')
85 .. automethod:: geopandas.GeoSeries.skew
18786
188 Scale the geometries of the GeoSeries along each (x, y, z) dimensio.
189
190 .. method:: GeoSeries.skew(self, angle, origin='center', use_radians=False)
191
192 Shear/Skew the geometries of the GeoSeries by angles along x and y dimensions.
193
194 .. method:: GeoSeries.translate(self, angle, origin='center', use_radians=False)
195
196 Shift the coordinates of the GeoSeries.
87 .. automethod:: geopandas.GeoSeries.translate
19788
19889 `Aggregating methods`
19990
200 .. attribute:: GeoSeries.unary_union
201
202 Return a geometry containing the union of all geometries in the ``GeoSeries``.
91 .. autoattribute:: geopandas.GeoSeries.unary_union
20392
20493 Additionally, the following methods are implemented:
20594
206 .. method:: GeoSeries.from_file()
95 .. automethod:: geopandas.GeoSeries.from_file
20796
208 Load a ``GeoSeries`` from a file from any format recognized by
209 `fiona`_.
97 .. automethod:: geopandas.GeoSeries.to_crs
21098
211 .. method:: GeoSeries.to_crs(crs=None, epsg=None)
99 .. automethod:: geopandas.GeoSeries.plot
212100
213 Transform all geometries in a GeoSeries to a different coordinate
214 reference system. The ``crs`` attribute on the current GeoSeries
215 must be set. Either ``crs`` in dictionary form or an EPSG code may
216 be specified for output.
101 .. autoattribute:: geopandas.GeoSeries.total_bounds
217102
218 This method will transform all points in all objects. It has no
219 notion or projecting entire geometries. All segments joining points
220 are assumed to be lines in the current projection, not geodesics.
221 Objects crossing the dateline (or other projection boundary) will
222 have undesirable behavior.
223
224 .. method:: GeoSeries.plot(colormap='Set1', alpha=0.5, axes=None)
225
226 Generate a plot of the geometries in the ``GeoSeries``.
227 ``colormap`` can be any recognized by matplotlib, but discrete
228 colormaps such as ``Accent``, ``Dark2``, ``Paired``, ``Pastel1``,
229 ``Pastel2``, ``Set1``, ``Set2``, or ``Set3`` are recommended.
230 Wraps the ``plot_series()`` function.
231
232 .. attribute:: GeoSeries.total_bounds
233
234 Returns a tuple containing ``minx``, ``miny``, ``maxx``,
235 ``maxy`` values for the bounds of the series as a whole.
236 See ``GeoSeries.bounds`` for the bounds of the geometries contained
237 in the series.
238
239 .. attribute:: GeoSeries.__geo_interface__
240
241 Implements the `geo_interface`_. Returns a python data structure
242 to represent the ``GeoSeries`` as a GeoJSON-like ``FeatureCollection``.
243 Note that the features will have an empty ``properties`` dict as they don't
244 have associated attributes (geometry only).
103 .. autoattribute:: geopandas.GeoSeries.__geo_interface__
245104
246105 Methods of pandas ``Series`` objects are also available, although not
247106 all are applicable to geometric objects and some may return a
258117
259118 Currently, the following methods are implemented for a ``GeoDataFrame``:
260119
261 .. classmethod:: GeoDataFrame.from_file(filename, **kwargs)
120 .. automethod:: geopandas.GeoDataFrame.from_file
262121
263 Load a ``GeoDataFrame`` from a file from any format recognized by
264 `fiona`_. See ``read_file()``.
122 .. automethod:: geopandas.GeoDataFrame.from_postgis
265123
266 .. classmethod:: GeoDataFrame.from_postgis(sql, con, geom_col='geom', crs=None, index_col=None, coerce_float=True, params=None)
124 .. automethod:: geopandas.GeoDataFrame.to_crs
267125
268 Load a ``GeoDataFrame`` from a file from a PostGIS database.
269 See ``read_postgis()``.
126 .. automethod:: geopandas.GeoDataFrame.to_file
270127
271 .. method:: GeoSeries.to_crs(crs=None, epsg=None, inplace=False)
128 .. automethod:: geopandas.GeoDataFrame.to_json
272129
273 Transform all geometries in the ``geometry`` column of a
274 GeoDataFrame to a different coordinate reference system. The
275 ``crs`` attribute on the current GeoSeries must be set. Either
276 ``crs`` in dictionary form or an EPSG code may be specified for
277 output. If ``inplace=True`` the geometry column will be replaced in
278 the current dataframe, otherwise a new GeoDataFrame will be returned.
130 .. automethod:: geopandas.GeoDataFrame.plot
279131
280 This method will transform all points in all objects. It has no
281 notion or projecting entire geometries. All segments joining points
282 are assumed to be lines in the current projection, not geodesics.
283 Objects crossing the dateline (or other projection boundary) will
284 have undesirable behavior.
285
286 .. method:: GeoSeries.to_file(filename, driver="ESRI Shapefile", **kwargs)
287
288 Write the ``GeoDataFrame`` to a file. By default, an ESRI shapefile
289 is written, but any OGR data source supported by Fiona can be
290 written. ``**kwargs`` are passed to the Fiona driver.
291
292 .. method:: GeoSeries.to_json(**kwargs)
293
294 Returns a GeoJSON representation of the ``GeoDataFrame`` as a string.
295
296 .. method:: GeoDataFrame.plot(column=None, colormap=None, alpha=0.5, categorical=False, legend=False, axes=None)
297
298 Generate a plot of the geometries in the ``GeoDataFrame``. If the
299 ``column`` parameter is given, colors plot according to values in
300 that column, otherwise calls ``GeoSeries.plot()`` on the
301 ``geometry`` column. Wraps the ``plot_dataframe()`` function.
302
303 .. attribute:: GeoDataFrame.__geo_interface__
304
305 Implements the `geo_interface`_. Returns a python data structure
306 to represent the ``GeoDataFrame`` as a GeoJSON-like ``FeatureCollection``.
132 .. autoattribute:: geopandas.GeoDataFrame.__geo_interface__
307133
308134 All pandas ``DataFrame`` methods are also available, although they may
309135 not operate in a meaningful way on the ``geometry`` column and may not
310136 return a ``GeoDataFrame`` result even when it would be appropriate to
311137 do so.
138
139 API Pages
140 ---------
141
142 .. currentmodule:: geopandas
143 .. autosummary::
144 :template: autosummary.rst
145 :toctree: reference/
146
147 GeoDataFrame
148 GeoSeries
149 overlay
150 read_file
151 sjoin
152 tools.geocode
153 datasets.get_path
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00 .. ipython:: python
11 :suppress:
22
3 import geopandas as gpd
3 import geopandas
4 import matplotlib.pyplot as plt
5 plt.close('all')
46
57
68 Set-Operations with Overlay
3739 .. ipython:: python
3840
3941 from shapely.geometry import Polygon
40 polys1 = gpd.GeoSeries([Polygon([(0,0), (2,0), (2,2), (0,2)]),
41 Polygon([(2,2), (4,2), (4,4), (2,4)])])
42 polys2 = gpd.GeoSeries([Polygon([(1,1), (3,1), (3,3), (1,3)]),
43 Polygon([(3,3), (5,3), (5,5), (3,5)])])
44
45 df1 = gpd.GeoDataFrame({'geometry': polys1, 'df1':[1,2]})
46 df2 = gpd.GeoDataFrame({'geometry': polys2, 'df2':[1,2]})
42 polys1 = geopandas.GeoSeries([Polygon([(0,0), (2,0), (2,2), (0,2)]),
43 Polygon([(2,2), (4,2), (4,4), (2,4)])])
44 polys2 = geopandas.GeoSeries([Polygon([(1,1), (3,1), (3,3), (1,3)]),
45 Polygon([(3,3), (5,3), (5,5), (3,5)])])
46
47 df1 = geopandas.GeoDataFrame({'geometry': polys1, 'df1':[1,2]})
48 df2 = geopandas.GeoDataFrame({'geometry': polys2, 'df2':[1,2]})
4749
4850 These two GeoDataFrames have some overlapping areas:
4951
6365
6466 .. ipython:: python
6567
66 res_union = gpd.overlay(df1, df2, how='union')
68 res_union = geopandas.overlay(df1, df2, how='union')
6769 res_union
6870
6971 ax = res_union.plot(alpha=0.5, cmap='tab10')
7779
7880 .. ipython:: python
7981
80 res_intersection = gpd.overlay(df1, df2, how='intersection')
82 res_intersection = geopandas.overlay(df1, df2, how='intersection')
8183 res_intersection
8284
8385 ax = res_intersection.plot(cmap='tab10')
9092
9193 .. ipython:: python
9294
93 res_symdiff = gpd.overlay(df1, df2, how='symmetric_difference')
95 res_symdiff = geopandas.overlay(df1, df2, how='symmetric_difference')
9496 res_symdiff
9597
9698 ax = res_symdiff.plot(cmap='tab10')
103105
104106 .. ipython:: python
105107
106 res_difference = gpd.overlay(df1, df2, how='difference')
108 res_difference = geopandas.overlay(df1, df2, how='difference')
107109 res_difference
108110
109111 ax = res_difference.plot(cmap='tab10')
116118
117119 .. ipython:: python
118120
119 res_identity = gpd.overlay(df1, df2, how='identity')
121 res_identity = geopandas.overlay(df1, df2, how='identity')
120122 res_identity
121123
122124 ax = res_identity.plot(cmap='tab10')
132134
133135 .. ipython:: python
134136
135 world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))
136 capitals = gpd.read_file(gpd.datasets.get_path('naturalearth_cities'))
137 world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres'))
138 capitals = geopandas.read_file(geopandas.datasets.get_path('naturalearth_cities'))
137139
138140 # Select South Amarica and some columns
139141 countries = world[world['continent'] == "South America"]
168170
169171 .. ipython:: python
170172
171 country_cores = gpd.overlay(countries, capitals, how='intersection')
173 country_cores = geopandas.overlay(countries, capitals, how='intersection')
172174 @savefig country_cores.png width=5in
173175 country_cores.plot(alpha=0.5, edgecolor='k', cmap='tab10');
174176
176178
177179 .. ipython:: python
178180
179 country_peripheries = gpd.overlay(countries, capitals, how='difference')
181 country_peripheries = geopandas.overlay(countries, capitals, how='difference')
180182 @savefig country_peripheries.png width=5in
181183 country_peripheries.plot(alpha=0.5, edgecolor='k', cmap='tab10');
182184
183185
186 .. ipython:: python
187 :suppress:
188
189 import matplotlib.pyplot as plt
190 plt.close('all')
184191
185192
186193 More Examples
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examples/README.txt less more
0 .. _gallery:
1
2 Examples Gallery
3 ----------------
4
5 The following examples show off the functionality in GeoPandas. They highlight
6 many of the things you can do with this package, and show off some
7 best-practices.
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examples/cartopy_convert.py less more
0 """
1 Plotting with CartoPy and GeoPandas
2 -----------------------------------
3
4 Converting between GeoPandas and CartoPy for visualizing data.
5
6 `CartoPy <http://scitools.org.uk/cartopy/>`_ is a Python library
7 that specializes in creating geospatial
8 visualizations. It has a slightly different way of representing
9 Coordinate Reference Systems (CRS) as well as constructing plots.
10 This example steps through a round-trip transfer of data
11 between GeoPandas and CartoPy.
12
13 First we'll load in the data using GeoPandas.
14 """
15 # sphinx_gallery_thumbnail_number = 7
16 import matplotlib.pyplot as plt
17 import geopandas
18 from cartopy import crs as ccrs
19
20 path = geopandas.datasets.get_path('naturalearth_lowres')
21 df = geopandas.read_file(path)
22 # Add a column we'll use later
23 df['gdp_pp'] = df['gdp_md_est'] / df['pop_est']
24
25 ####################################################################
26 # First we'll visualize the map using GeoPandas
27 df.plot()
28
29 ###############################################################################
30 # Plotting with CartoPy
31 # =====================
32 #
33 # Cartopy also handles Shapely objects well, but it uses a different system for
34 # CRS. To plot this data with CartoPy, we'll first need to project it into a
35 # new CRS. We'll use a CRS defined within CartoPy and use the GeoPandas
36 # ``to_crs`` method to make the transformation.
37
38 # Define the CartoPy CRS object.
39 crs = ccrs.AzimuthalEquidistant()
40
41 # This can be converted into a `proj4` string/dict compatible with GeoPandas
42 crs_proj4 = crs.proj4_init
43 df_ae = df.to_crs(crs_proj4)
44
45 # Here's what the plot looks like in GeoPandas
46 df_ae.plot()
47
48 ###############################################################################
49 # Now that our data is in a CRS based off of CartoPy, we can easily
50 # plot it.
51
52 fig, ax = plt.subplots(subplot_kw={'projection': crs})
53 ax.add_geometries(df_ae['geometry'], crs=crs)
54
55 ###############################################################################
56 # Note that we could have easily done this with an EPSG code like so:
57 crs_epsg = ccrs.epsg('3857')
58 df_epsg = df.to_crs(epsg='3857')
59
60 # Generate a figure with two axes, one for CartoPy, one for GeoPandas
61 fig, axs = plt.subplots(1, 2, subplot_kw={'projection': crs_epsg},
62 figsize=(10, 5))
63 # Make the CartoPy plot
64 axs[0].add_geometries(df_epsg['geometry'], crs=crs_epsg,
65 facecolor='white', edgecolor='black')
66 # Make the GeoPandas plot
67 df_epsg.plot(ax=axs[1], color='white', edgecolor='black')
68
69 ###############################################################################
70 # CartoPy to GeoPandas
71 # ====================
72 #
73 # Next we'll perform a CRS projection in CartoPy, and then convert it
74 # back into a GeoPandas object.
75
76 crs_new = ccrs.AlbersEqualArea()
77 new_geometries = [crs_new.project_geometry(ii, src_crs=crs)
78 for ii in df_ae['geometry'].values]
79
80 fig, ax = plt.subplots(subplot_kw={'projection': crs_new})
81 ax.add_geometries(new_geometries, crs=crs_new)
82
83 ###############################################################################
84 # Now that we've created new Shapely objects with the CartoPy CRS,
85 # we can use this to create a GeoDataFrame.
86
87 df_aea = geopandas.GeoDataFrame(df['gdp_pp'], geometry=new_geometries,
88 crs=crs_new.proj4_init)
89 df_aea.plot()
90
91 ###############################################################################
92 # We can even combine these into the same figure. Here we'll plot the
93 # shapes of the countries with CartoPy. We'll then calculate the centroid
94 # of each with GeoPandas and plot it on top.
95
96 # Generate a CartoPy figure and add the countries to it
97 fig, ax = plt.subplots(subplot_kw={'projection': crs_new})
98 ax.add_geometries(new_geometries, crs=crs_new)
99
100 # Calculate centroids and plot
101 df_aea_centroids = df_aea.geometry.centroid
102 df_aea_centroids.plot(ax=ax, markersize=5, color='r')
103
104 plt.show()
+842
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examples/choropleths.ipynb less more
00 {
1 "metadata": {
2 "name": ""
3 },
4 "nbformat": 3,
5 "nbformat_minor": 0,
6 "worksheets": [
7 {
8 "cells": [
9 {
10 "cell_type": "markdown",
11 "metadata": {},
12 "source": [
13 "## Protoyping choropleth classification schemes from PySAL for use with GeoPandas\n",
14 "\n",
15 "\n",
16 "Under the hood, if PySAL is not available or if an unsupported classification scheme is specified, the choropleth classification would fall back to GeoPandas defaults."
1 "cells": [
2 {
3 "cell_type": "markdown",
4 "metadata": {},
5 "source": [
6 "# Choropleth classification schemes from PySAL for use with GeoPandas\n",
7 "<img src=\"http://pysal.readthedocs.io/en/latest/_static/images/socal_3.jpg\" width=\"200\" align=\"right\" alt=\"PySAL image\" title=\"PySAL image\">\n",
8 "PySAL is a [Spatial Analysis Library](), which packages fast spatial algorithms used in various fields. These include Exploratory spatial data analysis, spatial inequality analysis, spatial analysis on networks, spatial dynamics, and many more.\n",
9 "\n",
10 "It is used under the hood in geopandas when plotting measures with a set of colors. There are many ways to classify data into different bins, depending on a number of classification schemes.\n",
11 "\n",
12 "<img src=\"http://alumni.media.mit.edu/~tpminka/courses/36-350.2001/lectures/day11/boston-kmeans.png\" width=\"300\">\n",
13 "\n",
14 "For example, if we have 20 countries whose average annual temperature varies between 5C and 25C, we can classify them in 4 bins by:\n",
15 "* Quantiles\n",
16 " - Separates the rows into equal parts, 5 countries per bin.\n",
17 "* Equal Intervals\n",
18 " - Separates the measure's interval into equal parts, 5C per bin.\n",
19 "* Natural Breaks (Fischer Jenks)\n",
20 " - This algorithm tries to split the rows into naturaly occurring clusters. The numbers per bin will depend on how the observations are located on the interval."
21 ]
22 },
23 {
24 "cell_type": "code",
25 "execution_count": 1,
26 "metadata": {
27 "ExecuteTime": {
28 "end_time": "2017-12-15T21:29:37.736444Z",
29 "start_time": "2017-12-15T21:29:37.716444Z"
30 },
31 "collapsed": true
32 },
33 "outputs": [],
34 "source": [
35 "%matplotlib inline\n",
36 "import geopandas as gpd\n",
37 "import matplotlib.pyplot as plt"
38 ]
39 },
40 {
41 "cell_type": "code",
42 "execution_count": 2,
43 "metadata": {
44 "ExecuteTime": {
45 "end_time": "2017-12-15T21:29:39.866422Z",
46 "start_time": "2017-12-15T21:29:39.846422Z"
47 }
48 },
49 "outputs": [
50 {
51 "name": "stdout",
52 "output_type": "stream",
53 "text": [
54 "Observations, Attributes: (49, 21)\n"
1755 ]
1856 },
1957 {
20 "cell_type": "code",
21 "collapsed": false,
22 "input": [
23 "import geopandas as gp"
24 ],
25 "language": "python",
26 "metadata": {},
27 "outputs": [],
28 "prompt_number": 9
58 "data": {
59 "text/html": [
60 "<div>\n",
61 "<style>\n",
62 " .dataframe thead tr:only-child th {\n",
63 " text-align: right;\n",
64 " }\n",
65 "\n",
66 " .dataframe thead th {\n",
67 " text-align: left;\n",
68 " }\n",
69 "\n",
70 " .dataframe tbody tr th {\n",
71 " vertical-align: top;\n",
72 " }\n",
73 "</style>\n",
74 "<table border=\"1\" class=\"dataframe\">\n",
75 " <thead>\n",
76 " <tr style=\"text-align: right;\">\n",
77 " <th></th>\n",
78 " <th>AREA</th>\n",
79 " <th>PERIMETER</th>\n",
80 " <th>COLUMBUS_</th>\n",
81 " <th>COLUMBUS_I</th>\n",
82 " <th>POLYID</th>\n",
83 " <th>NEIG</th>\n",
84 " <th>HOVAL</th>\n",
85 " <th>INC</th>\n",
86 " <th>CRIME</th>\n",
87 " <th>OPEN</th>\n",
88 " <th>...</th>\n",
89 " <th>DISCBD</th>\n",
90 " <th>X</th>\n",
91 " <th>Y</th>\n",
92 " <th>NSA</th>\n",
93 " <th>NSB</th>\n",
94 " <th>EW</th>\n",
95 " <th>CP</th>\n",
96 " <th>THOUS</th>\n",
97 " <th>NEIGNO</th>\n",
98 " <th>geometry</th>\n",
99 " </tr>\n",
100 " </thead>\n",
101 " <tbody>\n",
102 " <tr>\n",
103 " <th>0</th>\n",
104 " <td>0.309441</td>\n",
105 " <td>2.440629</td>\n",
106 " <td>2</td>\n",
107 " <td>5</td>\n",
108 " <td>1</td>\n",
109 " <td>5</td>\n",
110 " <td>80.467003</td>\n",
111 " <td>19.531</td>\n",
112 " <td>15.725980</td>\n",
113 " <td>2.850747</td>\n",
114 " <td>...</td>\n",
115 " <td>5.03</td>\n",
116 " <td>38.799999</td>\n",
117 " <td>44.070000</td>\n",
118 " <td>1.0</td>\n",
119 " <td>1.0</td>\n",
120 " <td>1.0</td>\n",
121 " <td>0.0</td>\n",
122 " <td>1000.0</td>\n",
123 " <td>1005.0</td>\n",
124 " <td>POLYGON ((8.624129295349121 14.23698043823242,...</td>\n",
125 " </tr>\n",
126 " <tr>\n",
127 " <th>1</th>\n",
128 " <td>0.259329</td>\n",
129 " <td>2.236939</td>\n",
130 " <td>3</td>\n",
131 " <td>1</td>\n",
132 " <td>2</td>\n",
133 " <td>1</td>\n",
134 " <td>44.567001</td>\n",
135 " <td>21.232</td>\n",
136 " <td>18.801754</td>\n",
137 " <td>5.296720</td>\n",
138 " <td>...</td>\n",
139 " <td>4.27</td>\n",
140 " <td>35.619999</td>\n",
141 " <td>42.380001</td>\n",
142 " <td>1.0</td>\n",
143 " <td>1.0</td>\n",
144 " <td>0.0</td>\n",
145 " <td>0.0</td>\n",
146 " <td>1000.0</td>\n",
147 " <td>1001.0</td>\n",
148 " <td>POLYGON ((8.252790451049805 14.23694038391113,...</td>\n",
149 " </tr>\n",
150 " <tr>\n",
151 " <th>2</th>\n",
152 " <td>0.192468</td>\n",
153 " <td>2.187547</td>\n",
154 " <td>4</td>\n",
155 " <td>6</td>\n",
156 " <td>3</td>\n",
157 " <td>6</td>\n",
158 " <td>26.350000</td>\n",
159 " <td>15.956</td>\n",
160 " <td>30.626781</td>\n",
161 " <td>4.534649</td>\n",
162 " <td>...</td>\n",
163 " <td>3.89</td>\n",
164 " <td>39.820000</td>\n",
165 " <td>41.180000</td>\n",
166 " <td>1.0</td>\n",
167 " <td>1.0</td>\n",
168 " <td>1.0</td>\n",
169 " <td>0.0</td>\n",
170 " <td>1000.0</td>\n",
171 " <td>1006.0</td>\n",
172 " <td>POLYGON ((8.653305053710938 14.00809001922607,...</td>\n",
173 " </tr>\n",
174 " <tr>\n",
175 " <th>3</th>\n",
176 " <td>0.083841</td>\n",
177 " <td>1.427635</td>\n",
178 " <td>5</td>\n",
179 " <td>2</td>\n",
180 " <td>4</td>\n",
181 " <td>2</td>\n",
182 " <td>33.200001</td>\n",
183 " <td>4.477</td>\n",
184 " <td>32.387760</td>\n",
185 " <td>0.394427</td>\n",
186 " <td>...</td>\n",
187 " <td>3.70</td>\n",
188 " <td>36.500000</td>\n",
189 " <td>40.520000</td>\n",
190 " <td>1.0</td>\n",
191 " <td>1.0</td>\n",
192 " <td>0.0</td>\n",
193 " <td>0.0</td>\n",
194 " <td>1000.0</td>\n",
195 " <td>1002.0</td>\n",
196 " <td>POLYGON ((8.459499359130859 13.82034969329834,...</td>\n",
197 " </tr>\n",
198 " <tr>\n",
199 " <th>4</th>\n",
200 " <td>0.488888</td>\n",
201 " <td>2.997133</td>\n",
202 " <td>6</td>\n",
203 " <td>7</td>\n",
204 " <td>5</td>\n",
205 " <td>7</td>\n",
206 " <td>23.225000</td>\n",
207 " <td>11.252</td>\n",
208 " <td>50.731510</td>\n",
209 " <td>0.405664</td>\n",
210 " <td>...</td>\n",
211 " <td>2.83</td>\n",
212 " <td>40.009998</td>\n",
213 " <td>38.000000</td>\n",
214 " <td>1.0</td>\n",
215 " <td>1.0</td>\n",
216 " <td>1.0</td>\n",
217 " <td>0.0</td>\n",
218 " <td>1000.0</td>\n",
219 " <td>1007.0</td>\n",
220 " <td>POLYGON ((8.685274124145508 13.63951969146729,...</td>\n",
221 " </tr>\n",
222 " </tbody>\n",
223 "</table>\n",
224 "<p>5 rows × 21 columns</p>\n",
225 "</div>"
226 ],
227 "text/plain": [
228 " AREA PERIMETER COLUMBUS_ COLUMBUS_I POLYID NEIG HOVAL \\\n",
229 "0 0.309441 2.440629 2 5 1 5 80.467003 \n",
230 "1 0.259329 2.236939 3 1 2 1 44.567001 \n",
231 "2 0.192468 2.187547 4 6 3 6 26.350000 \n",
232 "3 0.083841 1.427635 5 2 4 2 33.200001 \n",
233 "4 0.488888 2.997133 6 7 5 7 23.225000 \n",
234 "\n",
235 " INC CRIME OPEN \\\n",
236 "0 19.531 15.725980 2.850747 \n",
237 "1 21.232 18.801754 5.296720 \n",
238 "2 15.956 30.626781 4.534649 \n",
239 "3 4.477 32.387760 0.394427 \n",
240 "4 11.252 50.731510 0.405664 \n",
241 "\n",
242 " ... DISCBD X \\\n",
243 "0 ... 5.03 38.799999 \n",
244 "1 ... 4.27 35.619999 \n",
245 "2 ... 3.89 39.820000 \n",
246 "3 ... 3.70 36.500000 \n",
247 "4 ... 2.83 40.009998 \n",
248 "\n",
249 " Y NSA NSB EW CP THOUS NEIGNO \\\n",
250 "0 44.070000 1.0 1.0 1.0 0.0 1000.0 1005.0 \n",
251 "1 42.380001 1.0 1.0 0.0 0.0 1000.0 1001.0 \n",
252 "2 41.180000 1.0 1.0 1.0 0.0 1000.0 1006.0 \n",
253 "3 40.520000 1.0 1.0 0.0 0.0 1000.0 1002.0 \n",
254 "4 38.000000 1.0 1.0 1.0 0.0 1000.0 1007.0 \n",
255 "\n",
256 " geometry \n",
257 "0 POLYGON ((8.624129295349121 14.23698043823242,... \n",
258 "1 POLYGON ((8.252790451049805 14.23694038391113,... \n",
259 "2 POLYGON ((8.653305053710938 14.00809001922607,... \n",
260 "3 POLYGON ((8.459499359130859 13.82034969329834,... \n",
261 "4 POLYGON ((8.685274124145508 13.63951969146729,... \n",
262 "\n",
263 "[5 rows x 21 columns]"
264 ]
265 },
266 "execution_count": 2,
267 "metadata": {},
268 "output_type": "execute_result"
269 }
270 ],
271 "source": [
272 "# We use a PySAL example shapefile\n",
273 "import pysal as ps\n",
274 "\n",
275 "pth = ps.examples.get_path(\"columbus.shp\")\n",
276 "tracts = gpd.GeoDataFrame.from_file(pth)\n",
277 "print('Observations, Attributes:',tracts.shape)\n",
278 "tracts.head()"
279 ]
280 },
281 {
282 "cell_type": "markdown",
283 "metadata": {},
284 "source": [
285 "## Plotting the CRIME variable\n",
286 "In this example, we are taking a look at neighbourhood-level statistics for the city of Columbus, OH. We'd like to have an idea of how the crime rate variable is distributed around the city.\n",
287 "\n",
288 "From the [shapefile's metadata](https://github.com/pysal/pysal/blob/master/pysal/examples/columbus/columbus.html):\n",
289 ">**CRIME**: residential burglaries and vehicle thefts per 1000 households"
290 ]
291 },
292 {
293 "cell_type": "code",
294 "execution_count": 3,
295 "metadata": {},
296 "outputs": [
297 {
298 "data": {
299 "image/png": 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Lxjt16tQ5EdE1BCEBna00uoC/ALtExLWSvgM8FRFfajZNV1dXzJ49u+159fT00N3d3e9Y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\n3e38taQyuRBA0q9J5bSWj5A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300 "text/plain": [
301 "<matplotlib.figure.Figure at 0x7efc284d1cf8>"
302 ]
303 },
304 "metadata": {},
305 "output_type": "display_data"
306 }
307 ],
308 "source": [
309 "# Let's take a look at how the CRIME variable is distributed with a histogram\n",
310 "tracts['CRIME'].hist(bins=20)\n",
311 "plt.xlabel('CRIME\\nResidential burglaries and vehicle thefts per 1000 households')\n",
312 "plt.ylabel('Number of neighbourhoods')\n",
313 "plt.title('Distribution of neighbourhoods by crime rate in Columbus, OH')\n",
314 "plt.show()"
315 ]
316 },
317 {
318 "cell_type": "markdown",
319 "metadata": {},
320 "source": [
321 "Now let's see what it looks like without a classification scheme:"
322 ]
323 },
324 {
325 "cell_type": "code",
326 "execution_count": 4,
327 "metadata": {
328 "ExecuteTime": {
329 "end_time": "2017-12-15T21:29:54.097280Z",
330 "start_time": "2017-12-15T21:29:53.766283Z"
331 }
332 },
333 "outputs": [
334 {
335 "data": {
336 "text/plain": [
337 "<matplotlib.axes._subplots.AxesSubplot at 0x7efc2841c2b0>"
338 ]
339 },
340 "execution_count": 4,
341 "metadata": {},
342 "output_type": "execute_result"
29343 },
30344 {
31 "cell_type": "code",
32 "collapsed": false,
33 "input": [
34 "# we use PySAL for loading a test shapefile\n",
35 "# replace this cell if you have a local shapefile and want to use GeoPandas readers\n",
36 "import pysal as ps \n",
37 "pth = ps.examples.get_path(\"columbus.shp\")\n",
38 "tracts = gp.GeoDataFrame.from_file(pth)"
39 ],
40 "language": "python",
41 "metadata": {},
42 "outputs": [],
43 "prompt_number": 10
345 "data": {
346 "image/png": 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uWT9lYSfjmbPibbh6waj7uNHFzFGFPT9QGBb/sJMpxx7f5vsLFizg7t/9lvk/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\nSUVORK5CYII=\n",
347 "text/plain": [
348 "<matplotlib.figure.Figure at 0x7efc35af9128>"
349 ]
350 },
351 "metadata": {},
352 "output_type": "display_data"
353 }
354 ],
355 "source": [
356 "tracts.plot(column='CRIME', cmap='OrRd', edgecolor='k', legend=True)"
357 ]
358 },
359 {
360 "cell_type": "markdown",
361 "metadata": {},
362 "source": [
363 "All the 49 neighbourhoods are colored along a white-to-dark-red gradient, but the human eye can have a hard time comparing the color of shapes that are distant one to the other. In this case, it is especially hard to rank the peripheral districts colored in beige.\n",
364 "\n",
365 "Instead, we'll classify them in color bins."
366 ]
367 },
368 {
369 "cell_type": "markdown",
370 "metadata": {},
371 "source": [
372 "## Classification by quantiles\n",
373 ">QUANTILES will create attractive maps that place an equal number of observations in each class: If you have 30 counties and 6 data classes, you’ll have 5 counties in each class. The problem with quantiles is that you can end up with classes that have very different numerical ranges (e.g., 1-4, 4-9, 9-250)."
374 ]
375 },
376 {
377 "cell_type": "code",
378 "execution_count": 5,
379 "metadata": {
380 "ExecuteTime": {
381 "end_time": "2017-12-15T21:30:30.408917Z",
382 "start_time": "2017-12-15T21:30:30.088920Z"
383 }
384 },
385 "outputs": [
386 {
387 "data": {
388 "text/plain": [
389 "<matplotlib.axes._subplots.AxesSubplot at 0x7efc24b22278>"
390 ]
391 },
392 "execution_count": 5,
393 "metadata": {},
394 "output_type": "execute_result"
44395 },
45396 {
46 "cell_type": "code",
47 "collapsed": false,
48 "input": [
49 "tracts.plot(column='CRIME', scheme='QUANTILES', k=3, colormap='OrRd')"
50 ],
51 "language": "python",
52 "metadata": {},
53 "outputs": [
54 {
55 "metadata": {},
56 "output_type": "pyout",
57 "prompt_number": 11,
58 "text": [
59 "<matplotlib.axes.AxesSubplot at 0x10cedbed0>"
60 ]
61 },
62 {
63 "metadata": {},
64 "output_type": "display_data",
65 "png": 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Xhbi4ODw8PXj8CftuoNqjRVQUfx08Znc5L40rhVcxd/uJs2dp37zqVqBX047D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\n7vaA/n3p+thjdHv8cc6eO2fxC1EqlTIqKop9v/2OdxN/fH18OP7HQZu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66 "text": [
67 "<matplotlib.figure.Figure at 0x10d8ae3d0>"
68 ]
69 }
70 ],
71 "prompt_number": 11
397 "data": {
398 "image/png": 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+Y9myZVgsFoKDgx2VAkJCiIuLIzCw6Qiqxx+cwyOjUjDqXbO7miw2Hv5mBxa7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\n",
399 "text/plain": [
400 "<matplotlib.figure.Figure at 0x7efc24ab8278>"
401 ]
402 },
403 "metadata": {},
404 "output_type": "display_data"
405 }
406 ],
407 "source": [
408 "# Splitting the data in three shows some spatial clustering around the center\n",
409 "tracts.plot(column='CRIME', scheme='quantiles', k=3, cmap='OrRd', edgecolor='k', legend=True)"
410 ]
411 },
412 {
413 "cell_type": "code",
414 "execution_count": 6,
415 "metadata": {
416 "ExecuteTime": {
417 "end_time": "2017-12-15T21:28:00.376417Z",
418 "start_time": "2017-12-15T21:27:57.039Z"
419 }
420 },
421 "outputs": [
422 {
423 "data": {
424 "text/plain": [
425 "<matplotlib.axes._subplots.AxesSubplot at 0x7efc24ab8e80>"
426 ]
427 },
428 "execution_count": 6,
429 "metadata": {},
430 "output_type": "execute_result"
72431 },
73432 {
74 "cell_type": "code",
75 "collapsed": false,
76 "input": [
77 "tracts.plot(column='CRIME', scheme='Unrecognized', k=3, colormap='OrRd')"
78 ],
79 "language": "python",
80 "metadata": {},
81 "outputs": [
82 {
83 "output_type": "stream",
84 "stream": "stdout",
85 "text": [
86 "Unrecognized scheme: Unrecognized\n",
87 "Using Quantiles instead\n"
88 ]
89 },
90 {
91 "metadata": {},
92 "output_type": "pyout",
93 "prompt_number": 12,
94 "text": [
95 "<matplotlib.axes.AxesSubplot at 0x10f177290>"
96 ]
97 },
98 {
99 "metadata": {},
100 "output_type": "display_data",
101 "png": 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Xhbi4ODw8PXj8CftuoNqjRVQUfx08Znc5L40rhVcxd/uJs2dp37zqVqBX047D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\n7vaA/n3p+thjdHv8cc6eO2fxC1EqlTIqKop9v/2OdxN/fH18OP7HQZu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102 "text": [
103 "<matplotlib.figure.Figure at 0x10d41ae50>"
104 ]
105 }
106 ],
107 "prompt_number": 12
433 "data": {
434 "image/png": 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Ibhvejd1njqUgPsPl+YL4DPYMGMNtwzxfibXldIEvv/wyTz/9NL179+bTTz/l9ddfB+D1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435 "text/plain": [
436 "<matplotlib.figure.Figure at 0x7efc24a41080>"
437 ]
438 },
439 "metadata": {},
440 "output_type": "display_data"
441 }
442 ],
443 "source": [
444 "# We can also see where the top and bottom halves are located\n",
445 "tracts.plot(column='CRIME', scheme='quantiles', k=2, cmap='OrRd', edgecolor='k', legend=True)"
446 ]
447 },
448 {
449 "cell_type": "markdown",
450 "metadata": {},
451 "source": [
452 "## Classification by equal intervals\n",
453 ">EQUAL INTERVAL divides the data into equal size classes (e.g., 0-10, 10-20, 20-30, etc.) and works best on data that is generally spread across the entire range. CAUTION: Avoid equal interval if your data are skewed to one end or if you have one or two really large outlier values."
454 ]
455 },
456 {
457 "cell_type": "code",
458 "execution_count": 7,
459 "metadata": {
460 "ExecuteTime": {
461 "end_time": "2017-12-15T21:28:00.376417Z",
462 "start_time": "2017-12-15T21:27:57.045Z"
463 }
464 },
465 "outputs": [
466 {
467 "data": {
468 "text/plain": [
469 "<matplotlib.axes._subplots.AxesSubplot at 0x7efc24a0b4a8>"
470 ]
471 },
472 "execution_count": 7,
473 "metadata": {},
474 "output_type": "execute_result"
108475 },
109476 {
110 "cell_type": "code",
111 "collapsed": false,
112 "input": [],
113 "language": "python",
114 "metadata": {},
115 "outputs": [],
116 "prompt_number": 12
477 "data": {
478 "image/png": 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VLomfnx8JPeLImP0spZU1+Gm1BHhpe2qIycjR0ioWfLmGQKM/JqOeoAA9QQEG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\nSUVORK5CYII=\n",
479 "text/plain": [
480 "<matplotlib.figure.Figure at 0x7efc249ca2e8>"
481 ]
482 },
483 "metadata": {},
484 "output_type": "display_data"
485 }
486 ],
487 "source": [
488 "tracts.plot(column='CRIME', scheme='equal_interval', k=4, cmap='OrRd', edgecolor='k', legend=True)"
489 ]
490 },
491 {
492 "cell_type": "code",
493 "execution_count": 15,
494 "metadata": {
495 "ExecuteTime": {
496 "end_time": "2017-12-15T21:28:00.386417Z",
497 "start_time": "2017-12-15T21:27:57.048Z"
498 }
499 },
500 "outputs": [
501 {
502 "data": {
503 "text/plain": [
504 "<matplotlib.axes._subplots.AxesSubplot at 0x7efc28468588>"
505 ]
506 },
507 "execution_count": 15,
508 "metadata": {},
509 "output_type": "execute_result"
117510 },
118511 {
119 "cell_type": "code",
120 "collapsed": false,
121 "input": [
122 "tracts.plot(column='CRIME', scheme='QUANTILES', k=1, colormap='OrRd')"
123 ],
124 "language": "python",
125 "metadata": {},
126 "outputs": [
127 {
128 "output_type": "stream",
129 "stream": "stdout",
130 "text": [
131 "Invalid k: 1\n",
132 "2<=k<=9, setting k=5 (default)\n"
133 ]
134 },
135 {
136 "metadata": {},
137 "output_type": "pyout",
138 "prompt_number": 13,
139 "text": [
140 "<matplotlib.axes.AxesSubplot at 0x10f4bca90>"
141 ]
142 },
143 {
144 "metadata": {},
145 "output_type": "display_data",
146 "png": 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BuHKIYRzhqjZy1Cj+9a9/NXiO0WgkMzOLT79YzBtvvUtEkD/H/vthg2X+Ts/A2MKbgyvk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\nwt7WGt+mnrTu/STPDu5Fm1al10JQazT/2Vk9CM7eZMaNG8cff/xBp74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147 "text": [
148 "<matplotlib.figure.Figure at 0x10f4cb9d0>"
149 ]
150 }
151 ],
152 "prompt_number": 13
512 "data": {
513 "image/png": 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eq48ZEbM51Du9JCR0vyUfS5TAKroMs9lMVlYolKq99btmvzqHeZ9/gcPhpMnh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\nfNoZu7ddZ7aPZObwzJcry93u88+/wOFDBxl23+vM/eiJMuO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gYCD5Xor6OU2QxUheXsWE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\n",
514 "text/plain": [
515 "<matplotlib.figure.Figure at 0x7efc284da550>"
516 ]
517 },
518 "metadata": {},
519 "output_type": "display_data"
520 }
521 ],
522 "source": [
523 "# No legend here as we'd be out of space\n",
524 "tracts.plot(column='CRIME', scheme='equal_interval', k=12, cmap='OrRd', edgecolor='k')"
525 ]
526 },
527 {
528 "cell_type": "markdown",
529 "metadata": {},
530 "source": [
531 "## Classificaton by natural breaks\n",
532 ">NATURAL BREAKS is a kind of “optimal” classification scheme that finds class breaks that will minimize within-class variance and maximize between-class differences. One drawback of this approach is each dataset generates a unique classification solution, and if you need to make comparison across maps, such as in an atlas or a series (e.g., one map each for 1980, 1990, 2000) you might want to use a single scheme that can be applied across all of the maps."
533 ]
534 },
535 {
536 "cell_type": "code",
537 "execution_count": 9,
538 "metadata": {
539 "ExecuteTime": {
540 "end_time": "2017-12-15T21:28:00.376417Z",
541 "start_time": "2017-12-15T21:27:57.042Z"
542 }
543 },
544 "outputs": [
545 {
546 "data": {
547 "text/plain": [
548 "<matplotlib.axes._subplots.AxesSubplot at 0x7efc23709fd0>"
549 ]
550 },
551 "execution_count": 9,
552 "metadata": {},
553 "output_type": "execute_result"
153554 },
154555 {
155 "cell_type": "code",
156 "collapsed": false,
157 "input": [
158 "tracts.plot(column='CRIME', scheme='fisher_jenks', k=8, colormap='OrRd')"
159 ],
160 "language": "python",
161 "metadata": {},
162 "outputs": [
163 {
164 "metadata": {},
165 "output_type": "pyout",
166 "prompt_number": 14,
167 "text": [
168 "<matplotlib.axes.AxesSubplot at 0x10fb36250>"
169 ]
170 },
171 {
172 "metadata": {},
173 "output_type": "display_data",
174 "png": 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YCzeECRMm4OHpwVNPP+O0Nlo0b86uoyfsLuflpianuNgJParakdR02jd3Xupm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\nYThenu7IZFIkEgkO9nYEB9ZDJpOh1WpJupvCrTuJ7P/9GPsPHeX6zdt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175 "text": [
176 "<matplotlib.figure.Figure at 0x10f19a050>"
177 ]
178 }
179 ],
180 "prompt_number": 14
556 "data": {
557 "image/png": 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69uxJfsFhKiurCAjwJyjAnzsGdOOKTJehLFpEmJ8PVaa2B91uKwatWpnBXuAo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\nVzzj+9yfF+Jr9GPKlClX7EX2fGJjY7E6FLILzEQGVOz3kGu2khjmeo4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558 "text/plain": [
559 "<matplotlib.figure.Figure at 0x7efc24868630>"
560 ]
561 },
562 "metadata": {},
563 "output_type": "display_data"
564 }
565 ],
566 "source": [
567 "# Compare this to the previous 3-bin figure with quantiles\n",
568 "tracts.plot(column='CRIME', scheme='fisher_jenks', k=3, cmap='OrRd', edgecolor='k', legend=True)"
569 ]
570 },
571 {
572 "cell_type": "markdown",
573 "metadata": {},
574 "source": [
575 "## Other classification schemes in PySAL\n",
576 "\n",
577 "Geopandas includes only the most used classifiers found in PySAL. In order to use the others, you will need to add them as additional columns to your GeoDataFrame.\n",
578 "\n",
579 ">The max-p algorithm determines the number of regions (p) endogenously based on a set of areas, a matrix of attributes on each area and a floor constraint. The floor constraint defines the minimum bound that a variable must reach for each region; for example, a constraint might be the minimum population each region must have. max-p further enforces a contiguity constraint on the areas within regions."
580 ]
581 },
582 {
583 "cell_type": "code",
584 "execution_count": 10,
585 "metadata": {},
586 "outputs": [
587 {
588 "data": {
589 "text/html": [
590 "<div>\n",
591 "<style>\n",
592 " .dataframe thead tr:only-child th {\n",
593 " text-align: right;\n",
594 " }\n",
595 "\n",
596 " .dataframe thead th {\n",
597 " text-align: left;\n",
598 " }\n",
599 "\n",
600 " .dataframe tbody tr th {\n",
601 " vertical-align: top;\n",
602 " }\n",
603 "</style>\n",
604 "<table border=\"1\" class=\"dataframe\">\n",
605 " <thead>\n",
606 " <tr style=\"text-align: right;\">\n",
607 " <th></th>\n",
608 " <th>AREA</th>\n",
609 " <th>PERIMETER</th>\n",
610 " <th>COLUMBUS_</th>\n",
611 " <th>COLUMBUS_I</th>\n",
612 " <th>POLYID</th>\n",
613 " <th>NEIG</th>\n",
614 " <th>HOVAL</th>\n",
615 " <th>INC</th>\n",
616 " <th>CRIME</th>\n",
617 " <th>OPEN</th>\n",
618 " <th>...</th>\n",
619 " <th>X</th>\n",
620 " <th>Y</th>\n",
621 " <th>NSA</th>\n",
622 " <th>NSB</th>\n",
623 " <th>EW</th>\n",
624 " <th>CP</th>\n",
625 " <th>THOUS</th>\n",
626 " <th>NEIGNO</th>\n",
627 " <th>geometry</th>\n",
628 " <th>Max_P</th>\n",
629 " </tr>\n",
630 " </thead>\n",
631 " <tbody>\n",
632 " <tr>\n",
633 " <th>0</th>\n",
634 " <td>0.309441</td>\n",
635 " <td>2.440629</td>\n",
636 " <td>2</td>\n",
637 " <td>5</td>\n",
638 " <td>1</td>\n",
639 " <td>5</td>\n",
640 " <td>80.467003</td>\n",
641 " <td>19.531</td>\n",
642 " <td>15.725980</td>\n",
643 " <td>2.850747</td>\n",
644 " <td>...</td>\n",
645 " <td>38.799999</td>\n",
646 " <td>44.070000</td>\n",
647 " <td>1.0</td>\n",
648 " <td>1.0</td>\n",
649 " <td>1.0</td>\n",
650 " <td>0.0</td>\n",
651 " <td>1000.0</td>\n",
652 " <td>1005.0</td>\n",
653 " <td>POLYGON ((8.624129295349121 14.23698043823242,...</td>\n",
654 " <td>0</td>\n",
655 " </tr>\n",
656 " <tr>\n",
657 " <th>1</th>\n",
658 " <td>0.259329</td>\n",
659 " <td>2.236939</td>\n",
660 " <td>3</td>\n",
661 " <td>1</td>\n",
662 " <td>2</td>\n",
663 " <td>1</td>\n",
664 " <td>44.567001</td>\n",
665 " <td>21.232</td>\n",
666 " <td>18.801754</td>\n",
667 " <td>5.296720</td>\n",
668 " <td>...</td>\n",
669 " <td>35.619999</td>\n",
670 " <td>42.380001</td>\n",
671 " <td>1.0</td>\n",
672 " <td>1.0</td>\n",
673 " <td>0.0</td>\n",
674 " <td>0.0</td>\n",
675 " <td>1000.0</td>\n",
676 " <td>1001.0</td>\n",
677 " <td>POLYGON ((8.252790451049805 14.23694038391113,...</td>\n",
678 " <td>0</td>\n",
679 " </tr>\n",
680 " <tr>\n",
681 " <th>2</th>\n",
682 " <td>0.192468</td>\n",
683 " <td>2.187547</td>\n",
684 " <td>4</td>\n",
685 " <td>6</td>\n",
686 " <td>3</td>\n",
687 " <td>6</td>\n",
688 " <td>26.350000</td>\n",
689 " <td>15.956</td>\n",
690 " <td>30.626781</td>\n",
691 " <td>4.534649</td>\n",
692 " <td>...</td>\n",
693 " <td>39.820000</td>\n",
694 " <td>41.180000</td>\n",
695 " <td>1.0</td>\n",
696 " <td>1.0</td>\n",
697 " <td>1.0</td>\n",
698 " <td>0.0</td>\n",
699 " <td>1000.0</td>\n",
700 " <td>1006.0</td>\n",
701 " <td>POLYGON ((8.653305053710938 14.00809001922607,...</td>\n",
702 " <td>2</td>\n",
703 " </tr>\n",
704 " <tr>\n",
705 " <th>3</th>\n",
706 " <td>0.083841</td>\n",
707 " <td>1.427635</td>\n",
708 " <td>5</td>\n",
709 " <td>2</td>\n",
710 " <td>4</td>\n",
711 " <td>2</td>\n",
712 " <td>33.200001</td>\n",
713 " <td>4.477</td>\n",
714 " <td>32.387760</td>\n",
715 " <td>0.394427</td>\n",
716 " <td>...</td>\n",
717 " <td>36.500000</td>\n",
718 " <td>40.520000</td>\n",
719 " <td>1.0</td>\n",
720 " <td>1.0</td>\n",
721 " <td>0.0</td>\n",
722 " <td>0.0</td>\n",
723 " <td>1000.0</td>\n",
724 " <td>1002.0</td>\n",
725 " <td>POLYGON ((8.459499359130859 13.82034969329834,...</td>\n",
726 " <td>2</td>\n",
727 " </tr>\n",
728 " <tr>\n",
729 " <th>4</th>\n",
730 " <td>0.488888</td>\n",
731 " <td>2.997133</td>\n",
732 " <td>6</td>\n",
733 " <td>7</td>\n",
734 " <td>5</td>\n",
735 " <td>7</td>\n",
736 " <td>23.225000</td>\n",
737 " <td>11.252</td>\n",
738 " <td>50.731510</td>\n",
739 " <td>0.405664</td>\n",
740 " <td>...</td>\n",
741 " <td>40.009998</td>\n",
742 " <td>38.000000</td>\n",
743 " <td>1.0</td>\n",
744 " <td>1.0</td>\n",
745 " <td>1.0</td>\n",
746 " <td>0.0</td>\n",
747 " <td>1000.0</td>\n",
748 " <td>1007.0</td>\n",
749 " <td>POLYGON ((8.685274124145508 13.63951969146729,...</td>\n",
750 " <td>3</td>\n",
751 " </tr>\n",
752 " </tbody>\n",
753 "</table>\n",
754 "<p>5 rows × 22 columns</p>\n",
755 "</div>"
756 ],
757 "text/plain": [
758 " AREA PERIMETER COLUMBUS_ COLUMBUS_I POLYID NEIG HOVAL \\\n",
759 "0 0.309441 2.440629 2 5 1 5 80.467003 \n",
760 "1 0.259329 2.236939 3 1 2 1 44.567001 \n",
761 "2 0.192468 2.187547 4 6 3 6 26.350000 \n",
762 "3 0.083841 1.427635 5 2 4 2 33.200001 \n",
763 "4 0.488888 2.997133 6 7 5 7 23.225000 \n",
764 "\n",
765 " INC CRIME OPEN ... X Y NSA NSB EW \\\n",
766 "0 19.531 15.725980 2.850747 ... 38.799999 44.070000 1.0 1.0 1.0 \n",
767 "1 21.232 18.801754 5.296720 ... 35.619999 42.380001 1.0 1.0 0.0 \n",
768 "2 15.956 30.626781 4.534649 ... 39.820000 41.180000 1.0 1.0 1.0 \n",
769 "3 4.477 32.387760 0.394427 ... 36.500000 40.520000 1.0 1.0 0.0 \n",
770 "4 11.252 50.731510 0.405664 ... 40.009998 38.000000 1.0 1.0 1.0 \n",
771 "\n",
772 " CP THOUS NEIGNO geometry \\\n",
773 "0 0.0 1000.0 1005.0 POLYGON ((8.624129295349121 14.23698043823242,... \n",
774 "1 0.0 1000.0 1001.0 POLYGON ((8.252790451049805 14.23694038391113,... \n",
775 "2 0.0 1000.0 1006.0 POLYGON ((8.653305053710938 14.00809001922607,... \n",
776 "3 0.0 1000.0 1002.0 POLYGON ((8.459499359130859 13.82034969329834,... \n",
777 "4 0.0 1000.0 1007.0 POLYGON ((8.685274124145508 13.63951969146729,... \n",
778 "\n",
779 " Max_P \n",
780 "0 0 \n",
781 "1 0 \n",
782 "2 2 \n",
783 "3 2 \n",
784 "4 3 \n",
785 "\n",
786 "[5 rows x 22 columns]"
787 ]
788 },
789 "execution_count": 10,
790 "metadata": {},
791 "output_type": "execute_result"
792 }
793 ],
794 "source": [
795 "def max_p(values, k):\n",
796 " \"\"\"\n",
797 " Given a list of values and `k` bins,\n",
798 " returns a list of their Maximum P bin number.\n",
799 " \"\"\"\n",
800 " from pysal.esda.mapclassify import Max_P_Classifier\n",
801 " binning = Max_P_Classifier(values, k=k)\n",
802 " return binning.yb\n",
803 "\n",
804 "tracts['Max_P'] = max_p(tracts['CRIME'].values, k=5)\n",
805 "tracts.head()"
806 ]
807 },
808 {
809 "cell_type": "code",
810 "execution_count": 11,
811 "metadata": {},
812 "outputs": [
813 {
814 "data": {
815 "text/plain": [
816 "<matplotlib.axes._subplots.AxesSubplot at 0x7efc23a79f98>"
817 ]
818 },
819 "execution_count": 11,
820 "metadata": {},
821 "output_type": "execute_result"
181822 },
182823 {
183 "cell_type": "code",
184 "collapsed": false,
185 "input": [
186 "tracts.plot(column='CRIME', scheme='equal_interval', k=7, colormap='OrRd')"
187 ],
188 "language": "python",
189 "metadata": {},
190 "outputs": [
191 {
192 "metadata": {},
193 "output_type": "pyout",
194 "prompt_number": 15,
195 "text": [
196 "<matplotlib.axes.AxesSubplot at 0x10fd88790>"
197 ]
198 },
199 {
200 "metadata": {},
201 "output_type": "display_data",
202 "png": 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T1xgMBrKO5vLZV8t56/0v6BDox5FfPmqyzLaMTAxtvDm4Qi6nsrKyTfsgXDxEsBcuaTfe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\nNE0Ee+GikJCQgIe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FJRIJlTodGw41DLxmi4Xf9+49Z7BXK+oH+2BvD/afsG0Hqk7+/pgs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\n0y3xODrHqwAAAABJRU5ErkJggg==\n",
203 "text": [
204 "<matplotlib.figure.Figure at 0x10fd92f50>"
205 ]
206 }
207 ],
208 "prompt_number": 15
209 },
210 {
211 "cell_type": "code",
212 "collapsed": false,
213 "input": [
214 "tracts.plot(column='CRIME', scheme='equal_interval', k=12, colormap='OrRd')"
215 ],
216 "language": "python",
217 "metadata": {},
218 "outputs": [
219 {
220 "output_type": "stream",
221 "stream": "stdout",
222 "text": [
223 "Invalid k: 12\n",
224 "2<=k<=9, setting k=5 (default)\n"
225 ]
226 },
227 {
228 "metadata": {},
229 "output_type": "pyout",
230 "prompt_number": 16,
231 "text": [
232 "<matplotlib.axes.AxesSubplot at 0x10fd9bc90>"
233 ]
234 },
235 {
236 "metadata": {},
237 "output_type": "display_data",
238 "png": 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1oY+HG6EB/nVm2hUaJoK9cEWYPHkybu5uPDbTthuotoiMiOCPpKM2l/Nw0ZBX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\nN+o4e6OOExd/m/SMTPLytGRl5yt7Kr1bIBKBg8oWfy93mrdtxivPt6V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239 "text": [
240 "<matplotlib.figure.Figure at 0x10fb62310>"
241 ]
242 }
243 ],
244 "prompt_number": 16
245 },
246 {
247 "cell_type": "markdown",
248 "metadata": {},
249 "source": [
250 "## Notes\n",
251 "\n",
252 "This is only using a subset of the classifiers in PySAL. specifically those that take only an attribute and a value of k as an argument. This simplifies the number of new default parameters that would be required in GeoPandas.DataFrame.plot().\n",
253 "\n",
254 "It is of course possible to add other classifiers with the cost of additional kw args."
255 ]
256 },
257 {
258 "cell_type": "code",
259 "collapsed": false,
260 "input": [],
261 "language": "python",
262 "metadata": {},
263 "outputs": []
264 }
265 ],
266 "metadata": {}
824 "data": {
825 "image/png": 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SXevUaDMOb9vRXF3Bfq+PWmsjp3azB0FLqKrKI/94irzc7gvDfW/1Lvqu+rDN\nNrkrF7Fw1c6g+v/jH//IY4891mbEXWcIRa+vA1NbOnFg40tKuR1YCpwYgvE0eigmk9/O6HJ1LNfq\n/AVvs+Hn9Xx/43iizYZwTO0oUqLN2N1e1G5exX5QZyMvt2+Pqyzr9Xr5/U23s6tyD28sWNBt82jw\nKUTVV7XZJqqhmgZfx6Vs0aJFJCcnh9VDIiiBFUIcmllhEnDU+l0I0UsIYWp6nQicBmwIZjyNY4fY\n2Fjq6vZ36Jq7732gOc+rqnaNOT/KZEARgipv9wrsl41uzhp/RrfO4UisVisnj5xAeWUVixZ92PzF\n2R3E6FQaY9vewGqMSSJG1/H/x2XLlvH++++TnZ3NxRdfzJdffsnll18e7FRbpN1NLiHEfGA0kCiE\nKAfuB84RQhTgd9Mqo8mDQAgxFLhWSjkbKASeFUKo+IX8ESmlJrDHOUVFA/hp/QZSUwN3f/J6vby+\nqpQ31pTi9qmY9TqizUbiLEZ6RRiJjzSRGGEi3mIgxqQn0uh/DEyLY0RO8Oak+AgT21weUo3d5w6+\ny2BizKgR3TZ+S+zcVUGDtZGVqxZ1e5DB+YP7sG7IuZQsfr7VNtuGTmTykMxWz7fG3/72N/72t78B\nsHTpUv7xj3/w6quvBj3XlgjEi+CSFg63+NNKKVcCs5teLwfC58io0SMZM3oM/333A8aPGxPwNTu3\nH/zedbvd7NpVQdmucsorKqjcvZe9e6uoqt7Hlvp6bDYbzloHDnsDmz9YQ91D0w4Lqe0IydFmSp0e\nTou2BHV9Z7F5VfY1NDLi1NAU2AsVRqPfTNPd4gpw5eh8Jq48j7SNy1rc6KpK68/2oRN5alRw6QrD\njRbJpRFSbvjDH8jPz+f+u/6H9PTeHb7eaDSSm5tDbm5Ou22TU7L4cVcNp2YHt4pNi41gV3XoXXMC\nZdF+G1lZfYiLi+22ObSE0WDE7XZ39zQAf5KXp2YO50Yeoe/KReSuXERUQzWNMUlsGzrRL64zh3c6\n2GD06NGMHj06NJM+BE1gNUJKQkIC/QsK2FFaFpTAdoSc3FyWbK0KWmAzYiMoq2i73lM4WWx1MWFS\nz7K/2mw2vln+HW63p7un0syYgmQW3TaOeV9lsXDVec2RXJOHZPLUKC1docZvjJiYGOrD4LR9JBPO\nOpOP57/C3eOKgro+I9bM6i7aVGuJMoOZW0eP7LbxD+WPt93Nq2+8hRCC/v37c+Ws4IJFwkVWQiQP\nXFDCAxccW4GhmsBqhJyIiAgcjvBXS50963c89vcncHl9mPQdD3tNjTZjC5P/Y3s4VZUqayMjTg1d\nieiOIKXE5XJhtTay9OtlvPuCP71bAAAgAElEQVTBR6xcuQqv10tubm6X2V+llD3C1tsa7eVqaQ9N\nYDVCTkREBPYuqJaa2SeD2MgIVpTVMDK347HoKdEWuqum68f77aT1TiUxMSHoPv76yOP85a+PYtAp\nGHQ6DHodBoO/tIzP58PXnFRb+l+rElX6n31NK3e9TuDxSV6YN4/s7OwQ/XSBYTabqampISEhoUeK\nrJSSmpqaTuWI0ARWI+RYLBYaGqxdMlZuv34s2bo3KIFNjTbj8HVPNNcHtTb2ehpIS+vblMhD+p+b\n/jmwcJLIwzJ9HHgvAbvTzf3ji5k1rC+NLi+Nbi9WlxcpJUadgkmva3pWMOqVo47pFIU6u5ucRz7i\n0ssuC/nP+MUXX3DnLX8kOioSg8HQXKImKzeP/kUD6d27NyUlJfTk0Hiz2UxGRkbQ12sCqxFyCgoK\nuP3u+zCbTcye9buwjnXW2eN5/6Xng0ptmBJtxtFO8cNwUaEoXDksi2mDMhH4S9gIRNOz30XqwJpO\niIPvj2zXPzkGo15HSpCBYN/uqObkwSdiNLZcO+xIGhsbuezCqURFRzHklNMoKioiMjKSdevWYbVa\ncblc1O6ronTLZj5f+jUzhmZzQXEk3qZVs1eVlFatYfOm71hQuZ8TRp/Fv59r3cf1WEcTWI2Qc+tt\nt3HuxImcdtppTJsyKaxuSLNnXc5DD/8dh8eLxdCxP+eUaDN2jw+vqqLvQlusW1Wpcri484wBpMZ0\njw/uAb4urWHkuAvbb4g/IGT6BeeT2LCL4VGxbHz/ed6Z24jbpzIoJYo4kw6jAhlmA6clR/LvO88l\nObr12+sfd9bw+y+WhepH6ZFoAqsRFvr378+5557DM88+z9133BK2cdJ696ZXTCTfle7jjH6p7V9w\nCCa9DrNeYZfbS04r1V/DweIGB4mR5m4XV4Bvdtbz+Jj2g0KklPz+mqvxVm7l2StOCTq441B6x1jY\nWbkbp9PZ43Lhhoru2ULV+E3wpz/dxVP/9xw1NeH1Nc3LL+DLrW0nBGmNhEgz211d6/P5cZ2dsfnh\n9REOBKvTw8bKfQwbNqzdtn976CF+XPwJCy45KSTiCv6yPdnxUaxYsSIk/fVENIHVCBuFhYVcdull\nXP37Wzrt7tIWEyeezce/7g7q2pQYC6WurrXDbpWCsXndn5JzWWk1Q04obnf1+Oorr/DsU//k/StO\nDnm2s0aXl8TExJD22ZPQBFYjrPztkUfYsXMXc59/KWxjzJ55ORv37Mfm8nb42vTYCCrcHb8uWFRV\nZa/TFZTXQ6j5prSGkePGt93mm2+45cbref+KU0iLjQj5HHSK6PaUkeFEE1iNsGIymZg//w3u/cuj\nvDr/TcCf0OWhR/9JRUVwq84jSUxMICE2muVlHXf3yYi1sKcLBXap1UmM2UCfuO4P7/x6ZwOjx7Qe\nqrt9+3YunDKZly4cQnELpW86S2ltI1VWR5f733Yl2iaXRtjp378/ixcv5vzzJ3PLHffR2GjD4XBQ\nVNg/ZPkKktPSuXPRWt7M2InL68PtU3F7/Y+KejuVtXZ80u8q5JP+hyolKtC7C9MVflRn54x+3W9/\ntbu9/LRrL8OHD2/xfENDA+edNZ67RuYxviA88921307/fnk9Ltl4KNEEVqNLGDhwIL/+uom6ujoi\nIiK4Zs4cGm3BlZc5FLvdTlZWIY12OzpAVllRJOgAvZQoUvKLlIwDMvD/wRuanvXARmC96LobuY1S\ncnde95sHvi/bxwmF/YmIOPq23+PxcPHUKYxKNXL9aeErF7Pf4SYqKips/fcENIHV6DIMBgPJyX5x\n6Wj9rvkL3ubLr76hsdGGtbERu82Ow2anoqKSPkLlif69mbBxN5O9vsPsXhL4Hn+topa2cnoB9i6y\nAaqqSpXT0yPsr1/v2MfIsUeXRpdScs3sK5F7tvPP34U3T8KP5XUMGX5eWMfobgISWCHEPGAiUCWl\nLG469iAwGX9Vgypg5oEaXEdcOwO4p+ntX6WU4dvt0Dhm6KjAXnfNHxhkMZBk0BEvBH0EWASYFYVx\nGb1I0uvQCdgr4dAbWif+jYbW9smjAKevawT2O5sLs15HTnwPsL/uauBPN4896vg//v4oP3+7mMVX\njQiZO1ZrLKto5M5bekY2sXAR6Ar2ReBp4OVDjv1dSnkvgBDiRvylu6899CIhRDz+EjND8S8mVgkh\n3pdS1nVy3hrHOHq9Hl87K8eGhgbeeucD1q77CZvbzVP5KRiV1pOC5EWY2NLoPExg7fhNAq0RBbi6\naAW7qM7GyLyUbk9s4vL6WFW6h1NPPfWw459//jn33nMP6249m0hT+G9u7W4fvXr1Cvs43UlAv0Up\n5ddCiOwjjh2a8DOSw1JSNDMB+FxKWQsghPgcOAuYH8xkNX5bjBp9Nnu2baMg0sxdGQltiitAscXI\nj42Hp0m0AQZFgVZENALw4jcTRIQ5XHa9F27LD7xWWbj4YWcNhf1yiYmJAfwhsPfdcxcvzH0Wr08l\nPbZrIswyY81s27aNk0/unpSNXUGnvqaEEA8BVwD1QEvxdunArkPelzcda6mvOcAcgMzMjhcw0zj2\nUVWVyt17cDldOF0uNm/ewn/7pZBtCsy5vdCkZ6lBD56Dblc2wNDGilEBjECp08OAiPBWT61yuxnZ\ntwfYX7dXk5Wbz44dO1BVlSsumU6kfR+rbjyDgkcWUd3oIrNX+FeweXFGtm3bFvZxupNOfWVLKe+W\nUvYBXgNuaKFJS3/ZLYb0SCnnSimHSimHJiV1f5SLRtczdfoVZOedQPEJwxh20kgKI0wBiytAvsWA\n/YiIMTt+AW2LSCHY7gyvL+yqRieKEOQndb9LUl5iNFW/rmHUyUMYWDSASSkqH84YTkq0BYtBT7XN\n1SXzOJC39ngmVF9TrwMf4re3Hko5/pLfB8gAloZoTI1jmKysLOY9/x9qa+uwWMxYzBa++PxLzo2L\nYFSshShFIVIn2OxwE6NTiFYULAoobdzG55kNNHp9uDkoqnagzudjHn576zgg/ojrohWFnWGuQbWw\nzsbpud1vfwWYXpLJ9BL/XeKRFQUsRj3VjeGvRgFQ2uDhnNzcLhmruwhaYIUQ/aSUW5reTgKOrqkL\nnwIPCyEOWLLHA38KdkyN44eZs2Zx3e9/T+matUQpCj4g2ufjO7eHb+sdeAGvlHjxu6n48N/6KIc8\ndEKgCP+zTgj0QqDg920d1DTOCYAFv6lgLX6XrXOOmEsMUOEO70rqJ4/khh5gfz2SIwU/wqBnXxet\nYBPMOrZu3twlY3UXgbppzce/Ek0UQpTjX6meI4QowP/3X0aTB4EQYihwrZRytpSytsmd68emrv5y\nYMNL47eN2WzmxuuvZ+2zzzLGG9jtuYp/Q8rT9OyVEo8EL7L5+FdCUCFls8DG4ndhAdin0+Ft4ZY0\nWlXZ6wmviWCvx9Mj7K/tEWnUUWXtmhXstOLe3Pre2zzw4INdMl53EKgXwSUtHG4xDbmUciUw+5D3\n84B5Qc1O47jm6muvZdQLLzDK6w1oM+DAhlRbNtUKKdnRyjkd/pXwkURJya4wLmDX2134VMmAlPAl\nHg8VvSwGqrpoBSslmE3h3VjsbrRILo1uo7i4mIw+fdi+aROhCshMBX7S6aCFlaoOOFQ6vMB+/OaD\nPW4P79Y2okp/3StfU90rX1O+AhVQ5YFn2XQO1KZaWgfyGhxoI+UBs4ZkmdXJ4D4JKO24mfUEPD4V\nexeV0emfHMMvm74/rhNuawKr0a1cc+ONzL39dvJstpD0lwLYW9mZ1kvJgVF2A8/hD0LQAT6fyn+q\nG1EO1LyCptd+O6UCTccOnjtwTBx27PBrFSGwqpIYU8fLinc15fvt/LCzhqemDG2/cQhIjjaTFhfF\n1q1bKSoqwuVyHXdCqwmsRrdy6aWXcvutt2LH7/TfWeI4uDI9MsGeTspmE8EWIF1RuEpVqQJeEoIP\nCzpWciZQztu8J2wZqULJrAUrmFiUQVFq+EwZqqqypqKOzzfv4YedNZTu3cdl06aws3IPHp+P+gYr\nOl3P/zIKFE1gNbqVuLg4zjnrLH5+7z1CEc8jgGj8gmlqcumSTQ+Hz4cKPK3TYVdVBjRdEwm4w1Rx\nwaVKypxuLhrUs4NnNlc18H1pNWtvPTsk/amqytrKJiEtq2FbrZ19Nid1dhcmvY6ClFhOTO/FP847\nkcKUWApTBlLy5GKqqqro3bvnfxkFiiawGt3ONTfcwNQPP2SDx++LeqTUSQ5GrDSfE6L5uE9KVEVg\nbHI5cqkqqVIy0Ofz38Ifcr0CKD5/xq30JlG14LeXOlUVc4jDZX+yu4g1GkiM6tm3vrMWrOCSITnk\nJnYsEEJVVX7aXc8Hv5SzrnI/22pt7LO5qLU5Mep05CfHcGJGPOPyezMgNZai1FgSI1ve2MqIj6a8\nvFwTWA2NUDJ69GjqPR4ygYSmY+KI5yNfI2Xz+58BnyK4OMkfW7/e4eYnq4sTA4wSag6XdXnpbwlt\nddklVhcFYagGEEpW76rhp8o63rzitFbbqKrK+j31fLZpNyvKatja4GGf00tdfSM+VcVg0HPpoAzO\n6JfCgBS/kCYF+KXy9bYqFm7cw46q/dTWHl9enJrAanQ7Op2OOVdeybYXXmBEELfqe3Q6CmPMzEr2\nC+yqRicrbO6WfbJaIUIIdrg8IRVYr5S8U2PllYkjQtZnOLj6vyuZMzyf9NgIVFVlw956Pt20xy+k\n9W6qHV7qrI3odXoK8nMpGTqaOScUUzSggKLC/iz9ehn3/Olu/j01uM2xK95cxSWzZvPRI9MCqnB7\nLKEJ7DGGlJKXX34Zh8OBXq9Hp9MRHx/P5MmTu3tqnWLSBRdw23//Cw0N7Tc+Ai80mwcA8i1GGrxe\nVAJPthGlKJQHUTSxLb61OjEZ9Jw7oMX8Rt3OngYH//rmV9ZV1OKSCm899hl1DTaEIsjvl8vgE09n\n9qCBzUKanJzUYqhvZysGx0dbuPSyyygpKelUPz0RTWCPMVwuFzNnzmRq7wR/sgwEX9Q28POvm8jK\nyuru6QVNTEwMapBx+j4OF9honUK0Tkep10ffAPuIBnaH2P/z1RobZxWlhbTPYNnX6OTdn8v5fPMe\nNlZb2dvgwOp041ElGem9ue7mP1A0oD9FhQWkpCR3KGdCZwU2KcpCVVVVp/roqWgCe4xhNptJT0xg\nTqyJPk1Jke3SX175WBbYqKgoHEF+UH2A4YilakGEiW0N9oAFNkpKqkIYLvthnY2f7S7+O/HEkPUZ\nKPvtbt5bX86nm3bzS5WVvQ12Ghxu+ibGMDwniZtGpDKkTzwrd9Vy9ye/sOmnFS3W5goUeYg9PBgi\njTpsIfKD7mloAnsMkpeTQ9m+smaBHSw8fPXF51x++eXdPLPg6d+/P3vtdnz4Hf87gg8wHVG4cKDF\nwOcdsDZEqSo13tC4aq23u7i/vJb/m34yydFd5z2wr9FJyT8/oabRSVZ8NMNzkrjh1DwGZ/RiYO84\nTPqDv9nKeju3f7CW5577v06JayhweHxYLF2T5Lur0QT2GCS/qIiyz7dxYOtkSKSZe5Yu7c4pdRqL\nxUJSfDwLqqpazTXQj4NZsg7FB0dVO+hv1vP+Ecm32yIS2KF2XmC3Oz1ctb2am0b35/IhOZ3uryPM\nfusHBqX14u0ZIzAbWv+aklIy+60fGTxkMNMvnNLpcTtrIthnc/Hss8/y1ptv4nA4cDgc2O12AOLj\n44mPjychIYH4hASSk5OZNm0aRmNovT3ChSawxyD5RcX89OkHB99bDFTs2ktNTQ0JCQltXNmzURQw\n945jaGbiUed21dn4vryWQfajE5H4pMR8xD1qgdmIrQO1tiIBW4DFD6vdXn5xuNnk8LDD7aHC7aPG\nJ7F6VRyq3447d9lW/rN8GzrFn0pRp1PQKwKdoqDXCQwHnvUKqdEm3p41KuC5tsSeBgdfbtnL8j+c\n2aa4AixYW8YPu+oo++rbTo15gM6aCDbtrmHAMD3DTzqBCIsFi8WCxWJGSkld3X5qauuoratjx5aN\nPPTQX8nNzT1mysxoAnsMkp+fz6JDbqT1QlASF8OyZcuYNGlSN86sc2Rn9uHeE2M4o9/RIavfl+1j\n8vNft3idDzAdsYLNNOlxqSqN+BNtt0ck/kCDA/xgdfLvvfU0quBA0uhVsflUnKqKCkQJQZyi0AuI\n9flIx58a8RXg5dxkIhQFt5T+h9r0LP0RY57m9xK328tTv9Swp8FBakzwt8lXLljBhP5pFLfjc1tl\ndfL7t1fy1FP/bK7J1Vkkks7kEY+KiOCPN1zD0CHt26t/WLUGtYuKVIYCTWCPQfLz8yl1uPHHIPkZ\npnh57YV5x7TAFpecyE+7V7UosKnRZlzelnf5VSTGI2yweiHIMBnZ7HQzOICxIwB3k4lgtc3JNdur\nOAF/di4zfgGObXpYACFlixm7zIog22QgsZ1V5AGqPF6e2VtP9oPvNVdrEAcSzjSJlmg62JaG+VTJ\nqlvOane811aXkpaezozftZSBNDgy0tPYUVVH4RNfMC4rlttGF5IVH8jXmh8pZcD5B4QQnTZJdCWa\nwB6D9O3bl92NjXhkTHNBv0viI5m8eDFLlixhzJiW6k/2fEqGnMR3L7V825oSbcbh8bXo2+qVYG7B\n4bUo0kRpBwTWIyW/2t3M3lbNaCE4JYgPssAv+IHgVFVmbd/HyLxU/jtzhD9VopT4pEQ2pUtUVdmc\nHrEtLAYdcQEESUSb9Oh1oQ0HHj1yBKW/rmHRx5/xyutvMvTppVTfNzHg64UQeANMuq4oyvG1ghVC\nzAMmAlVSyuKmY38HzgPcwDZglpRyfwvXlgJW/HdxXill1+RBO84xGo30Tkigwu1tLgoYoVO4I8HM\ntbNm8v2atcdkvfmMjAwqre4Wz1kMekwGhRekQOfxcjb+1ITgz796pBcBQLFJx9pWcsMeiQn/H+ll\nW/cyXAhOCfJDLBD4AtTlTQ4PdT6Vj64e1WatsVCSEm2m0WoNeb+pqSnMnvU7zjpzLIUlwzt0rU4R\nuAOsiSaEOKYENpD/1ReBI+89PgeKpZQnAJtpu87WGClliSauoaVfbl/Kjog8Ghtj4VSvnX5ZmTz6\n8MPNO7HHCr1792aP1dHq+fevHMXNZw9EjYs4rACcD4mphb/kfLMRR4BJrl348xGUCMHpnfgAC0HA\nAisBg07pMnEFSI4y43C0/jvuLIoiaH+9ffQ1LndgVRQU5dgyEbT7Pyul/BqoPeLYZ1LKA5/u7/FX\ni9XoQvKLiil1Hf6tL4Tg9sQIXkyLZslTj9Mvsw9/+fOf+fXXlupR9jwaGhqIbKNM9+i8FG4a2Z/e\nsZGH+cqqEiwtiFS+xdAcMtseLykK2YrCmaraqR1xBX+NsEDwyc5tDgVDtMmAw9XyXUIoEEIcnQ6t\nHXSiAytYjr8VbHtcCXzcyjkJfCaEWCWEmNNWJ0KIOUKIlUKIldXV1SGY1vFNQfFAdrbikp9rNvBE\nahRPxhvZMvd/GTNsKMW5ffnzffexdOnSHhs188svv1CUGNluO/WIFYzK0V4EAPF6HWZFoaKd/j4E\n7FJyQSfFFQ6kTwysrT/dYtcq7LMrttEvL1QFeo6mo5tQG/bU4/J4cLsDE31FUY6pFWynNrmEEHfj\nz7XxWitNTpNSVgohkoHPhRC/Nq2Ij0JKOReYCzB06NBj5zfYTRQUFLBAbfv7sSjCSFGEkTuTIllj\ns7HkP89w69z/x6919RTk5DBizBj6DzyBk08+mSFDhnTRzFtn/bo1FCa0H/k0KC2Ob3fua36vApZW\ndCo/wsRWq4M+rfT1K7AOuEpKQlF+TwiBL0AB8OH/svB6VfT68JsJKuvtzPt+K8u/+SJsY/jNHUf/\n/JX1dj76tZJl26vZUONgr8NHbYP/i76wfz798nID6t/j9WIwtH6X09MIWmCFEDPwb36Nla18pUgp\nK5ueq4QQ7wLDgJadGTU6RH5+PjvsDg511WoNRQiGRJkZ0uQ540q28IujnjULF/D1+29zV72dqtq6\nbo+OWbXie6YOb7+09YjsRN5bU8bXLg9u/N/wplbsmAMtBr5uxa7rBhYKwdlSNm+YdRaB36shELJN\neoxS0ueBd9n94NQQzaB1Hlq8keKiIkoGDQzbGEIIvD6VP7zzI2v3WNntUKltdOB0usjJyWJwyWAu\nmTaI4qJCBhYV0rt3aocSyzidTkzHUCXaoARWCHEWcAcwSkrZ4k6KECISUKSU1qbX44G/BD1TjcPI\nzMykzuHC7lOJ6KDbjUkRDI40Mbgps/yvHsm3337LGWec0eo1D95/H7srd9O3oICcnJzmR1xcXIc+\nIK2xf/9+NmzeyikXD2i37fiCVJxSslGvkGM2MNmgJ7mVFWB/s4FPjQY4wsan4q87nyYEJSG85RT4\nN90CIcWg5795KYza0J4Ro/OU77fz8o/b+GH5krCOY7U24vV6KI/L5ayxJQwsGsDA4kJysrNCUmvL\n5XIfXwIrhJgPjAYShRDlwP34vQZM+G/7Ab6XUl4rhEgD/iOlPAe/F827Tef1wOtSyk/C8lP8BtHp\ndOSkp1HqcjEgonMrz9P1Km++/jrr16/nm88+ZdPmzfTNy+O9j/ymdSklf//7P5gTZ+LnDxU+FToq\n3D52WRsRio7stN5k5+SQU1BA3375ZGZmkpGRQZ8+fUhOTg5ol/y7775jaE5qu2GeACnRFl65dDiX\nv7acM2IsXJbUekRSf7MB2xFuWruBd8xGrE43o0Ngdz0UIQQdSWkQqRN4pERV1bB6E/x18QYGnTCQ\n4uL2v8A6g5QSs8nM+2+3ZjXsHC6X6/gSWCllSyEfz7fSthI4p+n1dlrOzaERIs49fwrzF7zCg50U\n2NFRJqY9/zxjknoxwaLQz+vj1VX1zecrKytxe9ycFBXDQIuxecUqZST1PpUKt53yLeuo+GUVP6Cw\nSOjY4/Gx2+7E6nKRGh9Peu/eZGRmkpXXjz7Z2fTp04eMjAwyMjJISUlh06ZNFCUGntVpUnEGb808\nnUtfWcYXNjf/LzO+xXpa2SYDNp+KHb8p4WMh2KlXuOX0Ap5bthldC7kNOoPfRBC4wuqFQAfU2t1h\nq9u1s87Gayu3s+qHb8LS/6EoitJhN62O4DzGSntrkVzHMPf8+c8UvPgiG+zuTq1iCy0GXs5NZkik\nCSEEv9jdfHRIBH9ycjL33X8/d/7f/2GptTLFLJieEIVBCOL0OuL0OopaHD8KlyrZ6/Gyt3EPe9ZV\nsOfHb/hB0fMBCns8KnvsDhpcLiLNJu4Yld+heU8o6M3628/l0te+44wtVWQZdJweYWB6QhQJBv+f\nth5JvEHPi1LFimBCYRqvjiuiJL0Xc7/dHBI3mkPxmwg6hlmnsMfqCJvAPvjFBk48cRCF/Tv2+w0G\nnV6HDEFWstY47lawGj2X2NhY/vb449xxy828mK4jIcD49yMRQjD0kA+3XkBdfUPzbavBYOCue+7l\nzrvuZunSpfzxumuJ3V/Leb3ad6kyKYJMk4HMVv1bI3Grkj+WVvNLVccjjHrHWFh8zRi+3VHN0m1V\nLNpYyb9/3Y1eUVCEP0Y/ymTgkiG53DQin5yEg18cnc0C1RId2eQ6gEUR7G5wUhyGYqqltY28sXoH\na1YeDEG22+189e1yJow7I+RmiXCvYI87G6xGz2bWlVeyfctm5jz7b+alRRMbAneffLOB+Bord91x\nOw898ig6nQ63280rr7xCYmIiv5t9NaueeJTzQjB/8OdyPScukqe37gnqekURjMxNZmRuMveNL8br\nU6lzuFGlRKcoJEQYW9yIU6XscHLv9vBHcnVMYCJ1ujYj2DrDA5/9QlZ2Fs/8+3m+/WY5pdt20OBw\noAIvzP3fkCZ9AdDrdITTTdXn86HXHzuydezMVKNV/vLw37BarVw3/zWeS4smspPJPIQQPJkSye0v\nzmPC999zxdVzeOCuP9Hb7cCF4Of9VrKjQpsFf1ychXsq66i1u4iP6NwKRa9TAioZrUrJfsBOU4as\nTo3qRyACihw7lDiDjor60AhsZb2d+WvK+PTX3WzcXU+1zYkB+HTHTvp4vQwE0oBPdDo++nRxyAX2\nWAsECDeawB4HCCF44n+fZk6jjVkfvMdfEy3kd7L8dJJBx3NpUTy9/Vee/p9buCtKx6mJ0QDsiDex\nzRVYaGOgmBWFZIuJd38u56qTA3M67ywD0+NZvLOGD5u8DK4D4jvZZ0c3uQDi9Dr2WJ0dHktVVT7b\ntIe3f97Fih372Flrw+HzkawoZAEjVJUMIAbgiGxV6T4fa35c1eEx20MT2MPRBPY4QQjB3Bde4D/P\nncpVt93KJTFGZsdHHlVKpSPoheCPSUfbWXPMBnLMoY+mmR1r5q6P1nHp4CwshvD/aX55/djm15bb\n5rdaqqZDSNlhG2y8TqG6sX2BXb+7jrfW7eKrrVVsqWqgxu7CJASZikKWz8ep+PPX6gKI1U8Dvtq9\nu2MTDQC9XqcJ7CFoAnscIYTg6jlzOPucc7j6it9x8do1PJhoaWWHv+dxUWI0z9c7uOfjn/nHeSUh\nCWAIhF11NlT8SbU7i98PtmMCE6/AjsbD3cUq6+28tW4XX2zezfrKeqqsDlQpSdPp6KOqjJGSNCC6\nlcTf7ZEC2D1eqqqqSU5O6vD1rdFaqOxvFU1gj0MyMjL4aPGXvPLyy1x34x+4IMrDdQkRLSZE6Wk8\n0TuWOT9up7LBwUsXn4xRH+ptqKNZsbMGE/7ors6OJpAEWvy7yu3lG6uTFVYnpXU2Ch76gHqHm0aX\nB6eUpCoKmcBQVSUdv/lCBCGmLaEDEhWFt99bxHVzZoWkTwC9Xh/WTa5jja5LRKnRpQghuGLGDH7e\ntJm9J5zEtF31LA/CztfVDIgw8X7fRL7bspexzy5hR01j2Mc8qyCV6EgT8xWl82svebQXQY3Hy7u1\njdy7q4bpW/YyakMlJ67bydiNlTxZUYd0eRni8VFc18jZTjdZQlAEXKOqnK2qnAAkEJpNuEPpA3y2\nOLShs5oN9nBET/xlDJV7nckAACAASURBVB06VK5cubK7p3HcIKXkvffe4+brf0++6ua2Xmb6mHr2\nzYtbVbluZx1r7U5mDcvlobNPIDoMdt8D2N1eEu56ixto2hQKpg/gPzqFLKMOn1DY6/HR4PXhlpJe\nQpCqKKT4fCQDSUAcLa9wvgG24M8DGireURR0UpIpJclAMrAeWJ+exratP4VsHK/XizE6FdWxr/3G\nQRCdlE1FRUXICjYGixBiVSBFBHr2p0wjJAghmDJlCmeffTaPP/YYFz/2KBfHWbghIaLL7Jwdxago\nPJ+dwA6nmz/+tIs315Yx98JhTCoOT273CKMeo07B4VPbFdhG/HWSdgJVgE2nw+Hz4QTwqUQ6Jf2k\nZAB+Ie0FKB2wlfYCHAGWugmUainZIyXWjHSW7K3C5vFgBJSq0OZeVkJxF9AKUkrsdjsREaF1EQwn\nmsD+hjCbzdx9333MvOoqCvJyuSzOTHwX2Dg7Q47ZyMK+SbxU3cDM+d8TYdIzom8yo/smcUpWIsWp\nsXhVyYa99fyyp57UGDNjclOCKuxn0utw+A7uwDcCW/ELabUQ2BQFu8+HB4hrWpH28/lI9vlIAjYI\nwVohuKqTGfdjAFeI7ywvkZJngPv+/CeuuOxi7HY7n36+hIaGhpCOcyAyTEoZ8i9vu92OyWTSAg00\nejbp6ekYdPpjygA/IymGyxKiWNrgYHFZNU9tq+Zerxeb24sqJTEmA3EGPfVeH6kxFr64dkxAwQYA\nZbWNfLZpDw6vj4WA0OmahfTArX2+z0dSk5C2tiJdBZ2q53UAA0dXbegsMf+/vfMOj6Lq/vjnbks2\nvVcgoQcC0gJSBOldFAGlSFEUsaOv5bUgqIAiKqAggmD9KQoiKuVVQAQEASnSO6HFkISWutk69/fH\nBqQkZJPMJgH38zz7TNmZe88ku9+9c+bcc3Amb35k9Bh6dutCWFgofe/spWofl+MOgc3JycXf31/V\nNt2NR2D/pTgUBW0ldQ8UhU6joXOQL52D/gmoOm21E6DR4FswRVhRFEacOE+7mavY9nR3fAzOj7jZ\namdrynk2nzjLlpTzHEzL5nRWPtlmKw4gRKNBoyjogU4FQhqE67f2CpAlJU1UuE59QXtq0xA4ICWd\nu/Zhx/YNbujBycXKr2rnOcjJzcXf36/4AysRHoG9QXE4HJhMJhwOB0FBQSU+X1EUMu0KvhqB5gYT\n2suJNlz5EdZoNHwWF0KP5DPUmvATDockz2rDIiVGAcEaDREIYhwObsHpIw0AhKJwUAgWS8leIbi7\nhCNIK87QJzUkRYf6I9iL9HY4mHHgEJPenspLzz/tlj5KWpfLVbKzczwjWA/q4XA4SE5OZvfu3ezZ\nvZsjR46QnJzM0eRkMjIyMBqNWK1W9u/fT82aJZte2vrWFgzZsYNsk4lwHx+ijF5E6jSEO2xEaiSR\nei2Rel3BUovuBhJhjUbD+zFB9Duczt1ANBAIaCXgKHpsWFdKbgf2CkFJgzktqPdl0lPylIeuYgT6\nSclr4ydxb7+7qFmzulv6cUfl15zcXAL8KzZ6oKS4UtHgE5zumwwpZYOCfVOAO3D+cB8F7pdSZhZy\nbndgOs4f97lSyrdUtP2mZffu3cycMYP533xDcHAQtzSoT4P6CXRo24KRwwZQs3p1YmKi0Gg0DH/w\nMVauWEHNRx4pUR8r1jpLo1ksFlJTU0lJSfnndewY65OTSUk5xYm//ybJW8c7kWrMcyo/6vp4EeOl\nJ89iK1F+ATOgK4U4WHCWn1Yjyt7dvvEaQBMh6Ni5N8eO7lb9Vt6dI1g/v5vPRfAZMAP44rJ9K4EX\npZR2IcRknCVkXrj8JCGEFpgJdAFSgC1CiJ+klPvUMPxmw2azsXjxYmbOnMHhw4d5eOQw9v+1gZiY\n6ycJ7dzxdn5ctorRJRTYi3h5eV2qr1UYu3fvpl/7dqVqu6K5J9iXL9KzaFGCL7tFCM5JyZ9AU1wf\nlaopsA7Un1RwNZ0UhVnpGTz21HPM+uBdVdt2/hlK9nfIzMxkx6497Nt3kIOHj3Li5ClOp6WTlZ1N\nTm4epjwTuXl51K9fT1Vb3Y0rJWPWCSHir9q34rLNTUD/Qk5tARwpKB2DEOIb4E7AI7CXcfr0aebM\nns3sObOpXbMGjz38AH3v7OVyaeJO7dsy5rlXcDgcqhSVu5patWpxMjsXh/S/4R6KJfjoySnhF/1W\nKTEIwZ/ASim5B6jtwnlW1Bt5OnDO5v+tYKkULK+3fnFbKTj/4tJx2XEIgdQIJAKE8wHeZ/M+Z/h9\nA2l5a3OVrAcQl1wEVquVAwcPsWv3Pg4cOkzysROkpKRyITOT7Jxc8kwm8vJM2Gw2QoKDiIqKpFqV\nWOLjqtG6ZXNiY6KJiYkiNiaa5GMnmDB5uop2uh813EYPAN8Wsj8WOHXZdgpwa1GNCCFGAaPAWTH1\nZmfjxo1MmzqVFStXMnDAXfzy0wIalqIgXUxMNJER4ezYsYNmzZqpbqfRaCQ8OIhUq6PSz/66mlnp\nOTTRaKAEt/zBQEcp6Qgs0mrZ53C4LLBq+anNOOuHmQN80OAsu+5cOvPNaoVTzIUALaJgP2gQ6ATo\nC146BHpkwbZAJ5zvaxHO94VgeVY+r0+YwvIlC1SxHcDo7U2Nes0w5ZsxmUz4+voQER5OldgY4uOq\n0qF9W6rERhMT7RTO2JhoQkNDinVV6PV6Uv5OUc3O8qBM3xghxMs4PwuFlZAs7NNW5HBCSjkHmAPO\nqbJlsasys3//fp579ln27t3L0088zJwPJhMYWDbHfaf2bfl11Sq3CCxA7Ro1OJ5x7IYSWKuisDfP\nzMgytBHmcHDU1f4oe6KYi/jiLNvzWfUwlVosmjgvHWPWqxuylZOby8KvPyGhTm2ioiJUK/ESEx1F\nauppt1fgVZNSWymEGI7z4dcQWbjDJQVnPomLVAFSS9vfjc6ZM2d47NFHadeuLZ1ub8XBXRt58rFR\nZRZXgM4d27Fq1SoVrCycug0acNziao6oysGnGTkEaTRElKGNEAqmrLqAFdCq9GDHB3BIsJVDnpBm\nvl5gt/Pd9z+p1qaXwUDLFknExVVVtX6Wl5cXQUGBfPbZZ+zdu5eTJ09y8OBBrFZrpU0wUyqBLYgO\neAHoI6U0FXHYFqC2EKK6EMIADATU+y/eICiKwswZM6hfvz56jcKBHRt5+slHMBjUy9F6e9s2bNy0\nCbPZPdmy6jZoyAl5Y/lfF14wkVTGUKEQIN/FNqwUTEpQAQ1gEILc64SUqYVGCO4O9ePd995Xr1Hh\nnjAtgInjX2LZkh/oe9edtGnTml49e2A0Grn3nnvc0l9ZcSVMaz7QHggTQqQA43BGDXgBKwumw22S\nUo4WQsTgDMfqWRBh8DjwC867p0+klHvddB2VkpSUFB64/36yszP5fdUSEuq64s0rOUFBgSTWT2Dj\nxo106NBB1bbT0tJYuXwZXm5/rq0er586R6bVRsMythMCmKVEofiRiBkwqDiK0msE2Q6F4HLIFdEn\nyMg3O3er1p67wrQAHnpgGA89MOyKfWazmYZJ7Vi+fDk9e/Z0S7+lpdgRrJRykJQyWkqpl1JWkVLO\nk1LWklJWlVI2LniNLjg2VUrZ87Jzl0sp60gpa0opJ7rzQioTUkq++r//o2nTptx+WwvW/7rUbeJ6\nkU7t27Jq5UrV2pNSMvODD2hQpzaxe7fzcqhRtbbdycfpmXx/Po9hgGuZCIrGiHNk4ErivXyNBjX/\nQnohyCmHESxALS89FoeDVNVKyLhPYAvD29ub99+dxFNPPYnFYin+hHLkxnlqcYNw9uxZHnlkNPv3\n7eOXn76lSeNbyqXfzh3b8eKrb1LUr5iUkszMTM6cOcOZM2fIyMhwLtPTL+3LzMxk9pw5VK1aFbPZ\nzLixYxnqq2VU+I0R3P3T+TxmpGUzBGdJFDUI0mg4oSjF+nLzhVBVYDXA79lmTlntWBSJRZGYpcSq\nSCzSuW2VEqvk0rpNkdikc91LI+gQYKR3sC/exTwQEkIQbtCz+c/tqiSAEbjPRVAUPbp1pv7cz3n3\nnXd46eWXy7Xv6+ERWBVZumQJD49+mMH39OPLj6fj7V3WMdT1ycg4w6Y/t7L9r13s3L2X7X/9xbCh\nQ8nLyyMrO4usrCwyMzPJysomMzMTo9FIRHgY4eFhhIeFOtfDQomvEsHJ40c5eeok4eHO+kxGo5Ff\n166ly+3tqJllolNg5c7B+b/MPMaeOsfdQJyK7YYLgSvjOhOg5jN/jV7LB+lZ1Aj1Q6fVYNBq0Wu1\neBmc6wadBi+dBi+tFm+dhsBL285ltsXOZwfTmHA6lSgfL+72NfBAuD+6IsQ20qBn34GD6gisG10E\n12PalAkktenCgHvuoXZt994xuopHYFUgJyeHZ55+mlWrVjL/89m0u611mdvMzc1l95797Nt/kENH\njrJ7zz5Op6djMuWTlZ1DTk4OVquNqMgI4uOqUbdOLca/8jwR4WEEBgQQFBRYsAy4tF3UE92jyceY\nNGUaq1b9esWPQqNGjVi+6ld6dOyIXgjaBZSvmyDDZmd9jplArea6Av/N2Rze/PsCdwIJKtsQ5nBw\nzIXj8qVEzXG+QQi+HNyKQU3jy9TOmVwzP+xJYcpvB/j0cDotvHQ8EhlIwlVl3Q0aod7ttRsfcl2P\n6vFxvPbK8wwceC9//LFR1QiG0uIR2DKybt06RowYTsfbb2Pnn2sJCCg624/dbictPYNjx0+w/8Bh\nDh85yvETp0hNSyMrM5vcvDxy8/IwmUxYrVYCAwKIjIggNjaas+fOc+FCFhPHv0T1+Dji46oSFRWp\nSjzg8Acf55WXX6FRo0bXvJeUlMSSFSu4o2sXJgtBK3/3jcqtimR7noUN+Xb+sEpOm620bnkrO7ds\noWOAsdD8oh+czmReRjb9cSZ02Qmc0elQtFq0djsahwM9zgQqAufTfqsQ2A0GLFotJ60WvAGhSDSK\nggGueJ0HLgjBX1LiA/gVvHy58stjlhI18zwJwGIvu0iF+3nzUMtaPHhrTdYfO8MHGw4zZN/f+Bt0\nNNVr+W90EBEGZ25gu12dUDxRzj7Yy3ls9EhWr1nP8889x/T3VYyMKCUegS0lWVlZPHD//Xy/eDEt\nkpqSmZnFXfcMJTc3D1N+PmazBYvFgsVqxWqxYrFasFiseBkM+Pv7ER4eRpXYGKpVjaXRLYlER0US\nEx1FdFQk0VGRhIeHXSGeM2bN5ZPPv2LIoAGqX8uR5GP0H1B0uy1btuT7Zcu5u2dP3hOQ5GIia1f5\n6XweK22CPzNzSKhZkx6D+jKvVy+aN2+OVqulZmws+/Nt1L+q/PjwI+lsyXOOur7X64kOC6Nxkyb0\nuu02/Pz8MJlMmEwm8nJzycvOxmazERQaSmBQEIGBgWzfvp3tn3/OK7HB5CuSPEUhT4FcCfmKJF9R\nCFYkBruD3VJicijkOxTMBb5OLaDTCPRCYHcorBCCA1JSD+f02rL89AlFYrGrl1NLCEHbGhG0rRGB\n1e5gbXIG038/TLfDp6lm9EKrSBxqPVQToCgVI7BCCOZ9NI0mrTrSsWNH7rzrrgqx4yIegS0FiqLQ\ntWsX9u/fT/t2txEaEkxoaAgJdWsTHBRIUFAgQYEXlwEEBwcRFBhIQIB/qctd+Pv7YXbTE9JqVatw\n6tQpYmNjizymbdu2zF+8mIF97+IDIWjsq97t19QLZl6e9CbfDh5MaGjoNe/f2a8fqxd9RX0fA5l2\nB79l5/OzVXDC4MPIgfcxYsQIGjVqVOJcoX/88Qc7li9lcFjJM4UpUpKvSEwFwtzzwGmSpOS8VsvS\ngvpc/lotQQ4HNXEmuy7JlBKhKFjcFEVg0GnpUieaLnWiycgx8/rKPXyxJZnMTHXKx1SUD/YiwcFB\nfP3pR/QdOIImTZtW6NR7j8CWgheef56UU6fw9fXlt19+KJc+A/z9sVisbmm7bu1aLF2yhJYtW173\nuM6dO/PlgoUMHTCAWTHQwEcdkY3z8yExMbFQcQXoO2AAQz7/lN3SxI7MXDq1v51Hhg2nT58++PqW\nPo1ivXr1OJKVg4wuefFHjRD4agW+WghHiwFoDvgXVD/IBVIcDk4KwV4h+E1R8BKCACGIURQSgXic\n88xTgXTgDHABsHnpOaco2N0gsNlmG7kWG3lWB3lWZ8mdPomxrD2awYqVvzLm2ZfQaDQIoUGjEWg0\nmsteAq1Gi9AIhBBotVo0QoNGe/n7GhwOBx99/CneRm+sVisWixWbzcZ9gwbQtMm1bih30LpVC555\nYjSDBg1kzZq1LidPUhuPwJaQmTNmsGTJTyz4v3nc0W9wufUbEOCP1eoegX174qu0aNeNNm3a0KOY\nQO0ePXoweswYFsybpZ7AaiSHDh2iY8eOhb7fpk0b7nngQW5t3ZqePXuqlhM0ODgYf18fTtscxBjK\n/lW4fMzmh/OBW4KUICUOIE1KTknJSa2WRQ4HVpyZroJ9vIgN8iE+1I92Ib7EBftSLciHdjXLMtH3\nWn47kk7PuWsJCw7C12jE18eIr68vvr6+BMRUZ/9fOziafBxFShRFQUqJLFh3vq7aL5V/thWJIiVS\nUahbuxYrVq/BoDeg1+vR651/27ade9Otc0fi46qi0+nQarXodFq0Gi06vQ4/Xx98fHzx9/PF39+f\ngAA/jN7eZGZlkZqaRlrGGTIyznL23DnOnT9PVlY2Tz32MP3v7lPo9T73zOOs+X0DY195hbcmT1b1\nb+kqHoEtAZ99+ikTJ01kw+pl+Pr4uO2WvTD8/fyw2mxuaTs6OopRDwxl/fr1xQoswK9LlzDUW71k\nG9WknYP7ip7kp9VqeXfaNNX6u5yE2rU5euZ4mQVWcJ1MRjgnLMQWvFoWjHJfBy5M6I+fd/mMrnIt\ndrq2b8fSlauvee/cuXPUqFGDH7/7P7clUtn+107enDKdA4eOoCgKDocDu91xad1sNmM2WzBbnM8v\nzBYLeXl5aLVaYmOiCQoKJCQoiNDQEGrVqEHq6TReHj+xSIHVaDR8MXcmTVt3ol27dvTs5b4ij0Xh\nEVgXyM3N5fHHH2Pzpk2sWLKQ6vFx2O12zGYLdrvdrWWEv/rmO77/YSk7du3B5iaBBedEBI2m+GmZ\nhw4d4siRI9xWs/Db+dIQ56Xj593qTdUsCQ0aNyF56WHaqjBNoKReRwl4l2PZ9Oslwg4NDSU4OIgj\nR5OpU7uWW/pv2qQRC7/+pETnGAKiSD+xv1D/enp6BnF1m3D+/HlCQgqvWxEeHsbXn37EgPtGsnXr\nVqpUqVIq20vLjZHzqwLZuXMnSUnNEIqNrRtW0iDRmVFdp9Ph5eVFSop7E4S99/4sUk+n8eKzT7Fr\nyzq39aMoSrEJu7Ozs3l93Di6+3uhVyn3aZ5D4YTFzpHkZFXaKyn1GzcmWY1xRgn/HBdlTqcrv6+g\n4PqVBpKaJbFx89Zys6c4zp8/j6LIIl1CkZERNL6lASNHj2HBdz+w8tff2LptBydOnLoi5Kztba14\n8tEHGTRooGqhaK7iEdgikFLy4cyZdO7ciVdeGMOncz645oFKQIA/ycdPuNWOmKhImjdrzIMPDKVK\nbIzb+lEUBc1lAiul0y/6+eef8/CoUdxyS0NiYmJY/vP/yDSXzTXikJKNOWZeysij05Gz7E9owtRZ\nH5X1EkpF/fr1SVYhGkogSjSCLf8wfOeDuesJ7H1Dh/LBrLmVJvVfQEAAUsrriuL4V57nSPIxXnx1\nAsMffJyuvftRr0krQmNqM2Dw/aSlpQPw32efwtugY9yrr5aX+YBHYAvlwoUL9O/Xj7lz5/DHb8u5\nb1DhqdCCgwI5ceJUoe+pRZXYGFL+VisJR9E4HAqHDh3izUmTuKN3byIiIujSpTP/W/YjiXWrM+/D\n9zifepg5M6fyl6Z0D7eSzTamncml67ELvK8LpN2zL3H4xAmWrV5dYVmQ6tevz5Gs3DKLigZnRICr\nSNxfd+tqiquV1adPH/LNFlatXluOVhWN8y7RwIUL19RTvUT3rp3YvfV3ju7bSuqxvZw/fRTT+RR+\nWbKQfLOF6glNiYyrh19YHGvWbeDbBQvKdZaZxwd7FRs3bmTQoIHc2as7X38647rT7cJCQ/lbtQxE\nhRMbG82uve4vY1azRjyr164nMjSA4YP78dH0t4iNvbbgYo9unRiaZ+K42Yt47+Jz2mbaHSzPNLHE\nIkh3SAYPG8ovIx+kYcOyJhNUh/DwcHR6PWftCuH60vtDjVotWXY74S4eXzECe/0RrEaj4b8v/JdJ\nb0+jS6f25WfYdfD28uLc+QtERLj6l3XS8tYkln7/NQ2T2vLe1Om0bt0aH5+Sh+OVFY/AFqAoCm9P\nnszUaVP5eOZ79Ondo9hzwsJCSE1Nc6tdUZERZGWVPgA8LS2d9z+cQ5WYGB4dXXQBlQeGD+GB4UOK\nbc/X15fOHW9nzqYNTKpW+IMuqyJZl5PPErNkc5aJnt27Mfnh0XTq1MmtDwRLS0KtmhzJSi2TwAbp\nNWSWYAhbES6C4nywAAMHDWLsq2NZvWYdHStBNWGDl9d1R7DF4e3tjU6nK1O8dFnwuAiA9PR0enTv\nzrKlP7F1/SqXxBUgMiKCjDNn3GpbZEQ4eXlFFY24FkVR+GXlavreM4xqtRsRV7cJK1ev5fmXX+Ps\n2XOq2HTfoAFsEVeOXqWU7DJZmJCRS8ejZ1kYFse9EyZzKi2N+d8vplu3bpVSXAESGzXmqLlsERoR\nGmfWLROuiWfFuQiub51er2f2R7MZPGI0Bw8dLifLikar0WCxlt7nP+TefsyZPVtFi0qGKxUNPsFZ\neytDStmgYN8AYDxQD2ghpSz00aMQ4jiQg7N6sF1KmaSO2eqxdu1aBg8ezAPDBjHu5edKJAIR4WFu\n/xBGRUZgMl1fYDMzM5k5+xMW/7iMI0ePodVquaN3d95/9006dWiLv78/ffoNYcRDj7N08fwy29Sr\nRxdGPGTipNkbvUawJCufpfkKitGX4aMeZsLwEVSvXr3M/ZQXiU2asGnFkjK14aMVbAR2aQQOKTFo\nNeg1GvRaDdqCiq5anFNgNQ4FxWpHAfp/9jveOg3eei3eOi1GvRajXoe3XouPXouvQYfRoMXPoMO3\n4OXnpcPfS4+fQYe/lw5vF2N4XU3C0q17dyZOmEjPuwbxx2/LiYxUd8KDq5jNZs6dO0/DxJJXW75I\nm1Yt+GrBYhWtKhmu/Gc+A2YAX1y2bw9wN+DKT0MHKaUrSeHLFSkl7737LlPemcIXc2fStXPJS62E\nhYaQk5PrBuv+ITIiAlN+/jX7N/yxmZmz57Fx8xZST6dTP6EuA+7uQ68eXbilYeI1vqbJE8eR1LoT\nJ0+lUK1q2WIB/fz86HD7bQxd9zt2nZ7+/fvz+UOjaNWqVbn7uNQgMTGRr5Wy2Z2lwCtdGjC+W0Ns\nDoUci40cs51si40ci41ss3M7x2Ij22Ij+WwuH/5xmOhWXcg3mzFbLGSZzeSbzVgsViy5FwPuTVht\nNiwWK1arc8qpzWbHZrdhtzsuPWHXarVotRq0motL5/TVS+vO4SuBQcEuXc/IBx/k5MmT9O43hDW/\n/FAht9i/rFxNaGgIYWGlj7k2GAxuq1XnCsUKrJRynRAi/qp9+4Eb8ssEzvytI0c+wLHko2xe+wtx\ncVWLP6kQQkODCxU/NYmICMNkyic3N5e5n37Jt9/9yKHDR7HZbPTo1plJr71Cty4dCQm5/henXkId\n7ryjJ8MffIzffvmxTDYpisKpv1MZOeYZxo0b5/bE4u6mXr16HMkxQWTpk4pnajRUD3GKkF6rIcTH\ni5DrTCXe8fcFvtqTxofvTyl1nxex2+2X5vxbLM6sbVbbP9tWqw2L1cKplFReHj/J5XbHv/YaJ06e\nYNDwh1n87efFxkmrjc1mL3NOV29vL7KyslSyqOS42ykmgRVCCAnMllLOKepAIcQoYBTg1uw3Bw4c\n4O67+9KmZXN+X7WkTOIQEqyuwCqKwuEjR9m6fQd79h7g4OEjpKSk4ufrS1iVOtSoHk+/u3ozbcpE\nkpo1LvEHfuJrL5HYtA0HDx2mbp3SZ3xftHgJ3kYfJk2adMP+yF5OdHQ0Nik5b3cQUsqZVbmKQnyI\n6zkSbI7iJ3a4ik6nQ6fT4eNz/R8Ih8PB6CeeJScnx6XMY0II5sz5mB49uvPM82OZ/q7r4qwGvn4+\nZUoCrigKffoPpX+//ipaVTLcLbBtpJSpQogInBVoD0gpC52OVCC+cwCSkpLcEum86LvvGP3II7z5\n+ss8eP/QMrcXGhKCuYRB94qicOz4Cb78eiEbNm4m48xZMrOyyMnJJTc3F51OR3RUFNXjq1GzRnVa\ntUgiPq4a7W5rVWZfWPX4OO4bdA8PPfoM61aVzue4/a+dPP7Mf/n22wU3hbiCU0gSatQgOf8MIX6F\ni55dUdiUZ8FHIwjQaPDXajBqNHhrwKDRkGu1Ex/s+m20xe5w25z/otBqtdRLqMPevXuLzZx2EYPB\nwKJF39OmTWumz5jNU48/7GYr/yHAr2wJjux2O8eOn+C9qVNVtKpkuFVgpZSpBcsMIcRioAXgvvme\nRWC323npxRdZsHAB//thPknNmqjSrre3F/mmfH5cspys7Gxyc/LIys7h/IULZGZlkZWdQ15uHnkm\nE0ajkeycXHbv2YePjw/p6ek8O+YxalSPI65aVapVrUK1qlWuWxFBDe7pdycjHnq8VOeuWr2WIfeP\nZtaHs2jfvr26hlUgGRkZODQaXj6TS2R6NpE6DXFeOmp46alr1FPdoOPhUxfYY3Ng0OuwWO1YbXYc\nioLDoSAE6HVawv2Kjwu+iJoj2JLQMLEeu3fvdllgAYKCgli+/H+0bt2asNAQtyR9L4zAAH+sVvfl\n3ygP3CawQghfQCOlzClY74ozgVC5kp6ezsCB92LQadi6fmWJHeZms5lPv5jPzl17OHTkKOkZZ8jM\nyiI7O4f8fDPBQYE8+Z+X8Pb2wujtjdFoJCDAn8DAAAL8/akSE83X3y6iabNmTJz0Frfccgv79u3j\nheefZcqbr7npZvyCrAAAGXFJREFUqoumfkIdzpcwrnDNuvWMnzCFv0+n8dmnn7mUcetGIDk5mSlv\nT+abb+bTv2sSzfo24e+0CxxPPceuv8/yv/QLZJy8gNlqQ6fVsOuHN6hV7cp6tVI6KwFE3z6Gbacu\n0NbFFIMWe8UIbIP6CezetavE58XFxbFixQq6dOkMUC4i6+/v77YMcuWFK2Fa84H2QJgQIgUYh7NU\n0QdAOLBMCLFDStlNCBEDzJVS9sRZOXlxwW2kDvhaSvmzey6jcDZt2sSAAf0Zcd9Axr/yfKk+0K9P\nmsLMjz6hU4d2tGyRRM0a8dSoHkeN+HhiY6OLDev6be3vLPpxGQsXfnfpSayiKBV2ex0dHYWUksNH\njlK7Vs3rHrtt+w5efHUiycdPMO7VcQwaPLjSxrKWhL///pv/PP0Uq1at4qH+7dj74+tEhQUWeXy+\n2YrZYiM48FoXgBACnU5LzWqRbDxxxmWBtTkUtOXsIgBo2KA+S3+ZUapzExMTWblyFV26dEYii5xC\nrhZCc+O7oFyJIhhUxFvXBJcVuAR6FqwnA+WTvvxaO5j14YeMf20882ZN445e3UvdVk5OLu1vv43v\nv/28VOfP+Gger4599YowF71ez8lTKS6JnNoIIahZI56Vq9YU2reUks1/buO992exYdOfvDr2VR4Y\nObLCMsK7gz/++IOjB3Zw9OdJ+PsWn6bQ6G3AWMy04Ho1Yth54qTLNtgUibYcUxVexOki2IOUslQ/\n8hdFtnv3bhw/cYqXX3jmpvHFu4ObbiaXyWRi+PBhfPTRh/zx2/IyiStA1Sqx/LVzV6lj6bKzc6l6\nVVTErbfeyv0j7mfYg6XzhZaVRg0b8MfmLVfsM5lMzP30S5q17sR9Ix+lZeu2HD58hIdHj76pxBUg\nISGB3HybS+LqKvVrRpN8wfWIEqvDUSEugqioSKRUSE9PL3UbiYmJbN78Jz8tX8nDj/9HRetuPm4q\ngT169CitWrVEsZnZtPZnatWsUeY2n336cfRaHW+89W6pzs83mzEar/wiazQaHn/iCfYfOFQhqeEa\n35LIwUNHyMvLY9n/VvDoU89RrU5jlvxvNW++9TaHDh3mmf/8p9iwnxuV2rVrc+xUGjaberlBa1WL\n4KzF9fZsDgWdtvzdLUIIGibWZ3cZE5zHxMSwevVvfPXNd+S7ORb8RuamEdilS5bQqlUrRt1/H19+\nMks1X6FGo2HIoP78sXHLdY/Lzs4hMzOL/Px8HAUlQRRF4cjRZKpWvXYiQ1hYGFJK1fIDlIR6CXU4\neuw4UfGJvPP+bKrF12bbtu38+NNPdOvWrdzDh8obb29vqsREcfSUenkkalWLJMvkekiR1aGg1VbM\n37lhYj32qFBBws/Pj/r167Ft+04VrFKfypDX9sZ/YoHzYdYdffqg1+t58dUJPPXsSzgcDr754mPu\nHdC3zO0HBgSQZ8or8n2LxUJEtQS8vb0LZtJY0Gg06PV6atSoTnx8/DXnvD15MtFRkfj5lf8UxPr1\n6uLl5cWJEydLXOr6ZqFeQgIHjp0moca1KRlLQ82q4eSarTT7YDWKFCiAIsEhQUE6CwaCcynBYrfj\nZVTPRVESGiQmsHmbOiV6bmtzG7//sYnb2rge9lUeHD5ylJGjx2Cs4FmGN4XAtmjRgsOHD+Pj44Ov\nry8+Pj489OCDqs2yCgwIwJRXdFs5Obn4+vpy7tw/o1G73Y7FYil0JL1s2TKmvz+dLb+vvMZ9UB7E\nx1Xj/PkLN0VEQGmpW78BB5JPQCd12vMxegGCIaNGExQUiE6nQ6/XodPq0Ov16HTaSzOudDodm7ds\nY868L65oIycnhxMnTnEy5W9SU9NITUvjzJmz5OaZmDltsmoum4aJ9Zn7edmT/gC079CBWTM/4MXn\nxqjSXlk4eSqFt6ZM5+eVq0lLS6dHt87oDRX7/OCm+IZpNBpq1bqyUJvNZrtULris7NqzF+U6ad7y\n8kz4+l754b/4RSqMJk2aYLPZWfj9j4SFheDv50enDu1UK0ddHGlp6fj7+9/wOQTKQtWq1di2Rt36\nUwaDnoED7qZq1dhij83IOMPptHRComuSZ8rHarWi1Wrw8fEhwN8ZRx0SHERISAirVq/hrjt6cOcd\nxccf5+Tk8OXXC7FYnAU57Q5nQhjny4HNZiMrO5u9e/eVOpLgctq2bcvQoUPLPP26pNjtdpYs+4UF\ni35g/4FDZJw5y/kLF2jbuhXjXn6OPr26k5ubR6sOrqUedRc3hcBeza5du1i5ahUvPD26zG29OPYN\nPv1yPj//uKDIY9LSM4iIcH0aa0xMDF9+8QU//PADmVt38e2CBfz6v+/LLcHx1u07SGrW7F8bXjNp\n0gSmvvsuc8aXfbr05Rj0erJzclw6tu+dvahVszr+/v4Muf9hOt7elrcnjS/0f1K7QXNy84p2UV3O\npj+3Mfm9Gdzdt+8VI2ad3gsfHz06nY7IWB0ffthBlf9/SEgI06dN47ZOvRlx30BGjriPhLrqCq3d\nbmft7xtYtHgpm7ds43R6BpkXMgkKCqRj+7aMHHEfDRvUo2njRlfMhLyQmVnhETA3pcDO/fhjEuvV\nJbF+QrHH9r13GDt2OuMCFUVxLqWCVCSKlJjzzaz55UeaNW1cZBu5eXlkZmaSlZVFYGDRAeuX071H\nD7r36MGXX3zB6t9WY/Q2Foy63f+B2Lp9B82bN3d7P5WRr7/+mi/mzWbbgleoElV4qefSotfrXE5f\n6e3tTfOkpoAzaZBOpytS8Ly9vMm7jovqckwmE40bNWLqtGmuGa0C9z/wALe1bcu8uXPp0L0vVWKj\nadm8GY0bNSChTm28vAzodDq0Wi2hIcFER0chhMBut3Pu3HnOnD3Hlm1/sWHjZo4cSeZCVjZms4X8\n/Hy8DAZ8Q6riH+DPrc2bMXBAX5KaNaZu7VrExFzff15e36frcVMK7FuTJ3Nnnz4MGTGa/n3vQK/X\n0SCxHjVrXJsE+vDho3Rs35b+fe8o+BBonEuN02cWH1e12CQrHdu3pUeXjtxxR2/Wrl1XopFB8xYt\nGDxoMI+MeYFjx45z/9BBTHtnYomvuSRs2baD0Y8+4dY+KiMnT55kzFNPsGzm46qLK4BBr3N5BHs5\nOq3uulNCA4MCeO6lcbwyfiJCiCteCIHm4jrOjFlx8eWf7Lx27dq8NXkyb0yYwPr16/lr+3bWbtjK\nx59+jc1uw1HgqsjIOIPdbsdo9CY9PYOQEGe+13PnznFLg/rcdlsrYqOjCAwIKHCTBJNQtzbBwUEl\ntslud3gE1h34+Pjw408/8d8XXmD+d0uw2Wxs3LSJOTPepe+dva44tkXzZpy/cIEe3TqXuj8hBNPf\nnUTV2o04duwYNWq4Hn+bkJDAtOnTARg8eJDbowqklE4XQVKlKy7hdmbP/ohB3ZNolhjvlvYNOm2p\nErAbDLrrZo1a/M3npJ5Ow+FwoCiKMxJBUf7Zlsql/WvWbeCv3fvLchllQq/X06FDBzp0KDqBfUZG\nBvn5+VSpUuXSZIsOHdrz4nNPqeom84RpuREfHx/e/+CDS9tbt27lrrvu4tDho7zw7JOX9t/RsxuP\njXm+zA5/jUZDi6SmbNu2rUQCe5EdO3awevVqPtq1udQ2uMKfW7YTHBxMTEyMW/upnAhCg9w3ecLL\nULoRrF5vuG7WqPDwMMLDw1xqKy8vjx27D5TYhvKksOcVfr5+5Oa65md2FX8/P3JK8f9Qk5s7ovwy\nkpKS2Lx5MxPfnkpq6j+ltrt16YDZbOazL8sethIZEU5GRkapzn3h+ecZ+9//uD1d4SdffM39I+7/\nVz7g8vb2xmJVb/bW1Wg0olT5SzVaDQ7FoYoNRqOxQkuklBZ/f3/Vyy8FBPiTne0R2HIjNjaWegkJ\nHE0+fmmfj48Pc2ZO5YlnXuTY8RNlaj8iPJSzpagyu2LFCo4dS2bUyGFl6r848vLyWPj9TwwbPtyt\n/VRWvLy8sNrcUzBbURROn8miXkKdEp9rt9kx6NTxFXp7ed1wAmu1Wtmzdw+BgQGqtuvv7xzBKkpF\nFEl38q8SWIDQ0FBycq/8pex/dx9aJDVh/MTS10eSUmI2WzhTCoH96qv/w2yx8OLYN/h5xa/kuRiS\nU1KmTJ1Bl86diY0tPk7zZsRsNmPQu+cj//HCdZw7f4G4qlVL7Puz2W3oVIrZ1uv1ZaoCUBFMeOMN\nqsZG06tHV1Xb1el01KxRnZ07K24q779OYKtWrcrjT7/Icy+OY/2GTZfyBjz3zOP8/MuqUrc7/9tF\nLFy8hKFDSx5bOW/eJyxYsJCg0CjefHcGkXH1ad/1Tia89S6bNm+9VDm0LBxNPsaMjz7hnXdLl7Tm\nZiAjPY3wYPe4YKSUBAcGkNi0Dd5BsSTc0pK1v29w6VyHw4FepRGs2WK+oSaQbN26lY9mf8THM99z\ni9uqd4+uLF1StpLsZeFfJ7AfzZ7Nd4sWYfQL5rFnXiSmRgNGjn6KnTv34CjDrURWdjbdu3Xn1hKU\n4riITqejZcuWvDJ2LGvXriMtLY0XXnyZC9lmHn7yOcKq1OWue4YxY9ZcDhw8XOIRkpSSx8a8wPPP\nPVdo4pl/C+t/X0tQgA8Hj51m/9FU9h75m92HUth54CSWMpYmGT2wA2c3TCP7zxkcWjYRjWLi9w2b\nXDrX4VDQ6dVJXZifb75hsqCZzWaGDRvK9CkTi41pLS29e3Zh2bJlbmnbFVypaPAJ0BvIkFI2KNg3\nABgP1ANaSCkLnXMohOgOTAe0OCsdvKWS3aVGCEHTpk1p2rQpr7/xBseOHePHH35g4XcLycrKplOP\nu2nfrjXt27ahRfOmLpcNNhgMqt2a+fn50aNHD3r0cE7zS09PZ/Xq1axauZK3p85AURQ6d2hH547t\n6NS+HdHRUddtb9acTzl3IYunn3lGFftuRKSUaHV6Js5bhUajueKVlp7B+Ed688jAokOLSkLV6FBi\nI4J4Z9pMvv9h6aXsZBcz9FutNqxWKzabHZvNxrlz52nZQp2wOZMpv0LyW5SGsa+8QmJCHQbec7fb\n+ritdUuSjyXzzfz5DBxUVO0A9+GK4+czYAZweWaKPcDdwOyiThJCaIGZQBcgBdgihPhJSrmv1Na6\ngerVqzPm6acZ8/TTZGVlsX79etb89hv/efE19h84QPNmTZyC264NtzZvVqTgqimwVxMZGcmgQYMY\nNGgQUkqOHDnCqpUrWbxkJU/+52VioqMuCe7tbVtfkSFr/4FDjJvwNhs2bKjwoOuKRAjBtu07Cn1v\n7NixnD6jrp+ualQIf1+wc/+wwVfMEpRS4uNjdCYlMhrx9fXB18eHWxomqtJvvvnGENgNGzbwf1/9\nH7v+XOvWiBaDwcCqZYvoM2AoBw8e5NVx48o1gsaVkjHrhBDxV+3bDxRnaAvgSEHpGIQQ3wB3ApVK\nYC8nMDCQXr160auXczJCVlYWGzZsYM1vv/Hsi6+zb/9+WiQ1vSS4LZKaXvJ3GfT6MtVwdxUhBLVr\n16Z27do88uijOBwOtm/fzsoVK3hvxscMHDaKxrc0pHPHtnRs35annx/LhDfeoE6dkj/d/rewe8c2\n7r09TtU246uEczjdxhOPPqRqu8WRn39tgvfKRl5eHsOHD2PW9Ckux/eWhVsaJrJ57c906tmP4OBg\nnnzqKbf3eRF3TjSIBU5dtp0C3FrUwUKIUcAogGpXlVipKAIDA+nZsyc9C6qoZmdnXxrhPvfSG+zd\nt+/SCNdud1TI01utVkvz5s1p3rw5L738MiaTifXr17Nq5Uqeem4sDRIbMOrh8qtlfyPibTRid6gT\nhwqwPzmVuYs2lKg0tlo4KqgUTUl4e/JkWjZvyl19yq86cWRkBEu//4rWHXpSs2ZNevXuXS79ulNg\nCxveFvl0Rko5B5gDkJSUVPFz3AohICDgGsG9OMJds2YdzZo1q2ALnXG9Xbt2pWtXdUNebmYa3NKE\n7fvXM6R3K1Xaaz/iHQYPuocpk8ar0l5J0Gq1lyJjKit79uyha8c25d5vfFw1vp//GXf0v48dO3aU\nS7iiO6MIUoDLH1lXAVLd2F+5ExAQQI8ePZj89tts/vNPPpw1q6JN8lAK+vTpww+rd6oyd/3shRwy\ns3OZMmk8BsP1K9G6A61Wq9qsMHfx+BNPMGXqTGzXSXDjLlremsQjDw1nTDm5CdwpsFuA2kKI6kII\nAzAQ+MmN/XnwUCoaNmyI0cef5et2lbmtJWt2EB9XtULEFQoE1l65BbZDhw7UqFHzmooO5cVLzz/N\njh1/lUt8bLECK4SYD2wE6gohUoQQI4UQfYUQKUArYJkQ4peCY2OEEMsBpJR24HHgF2A/sEBKuddd\nF+LBQ2kRQvDetPd56q1vMeWX/kHl+1+u5L9TF9O1szrhXqWhMiQ4cYX3pk7ltUnvsGr12nLv29vb\nmw+nv81TY55y+4PpYgVWSjlIShktpdRLKatIKedJKRcXrHtJKSOllN0Kjk2VUva87NzlUso6Usqa\nUkr3Jjn14KEMdO/enZat2zJm8relchVs3HmE/077nqnvTOKD9you3DssLISzZ89WWP+u0qBBAxYt\nWsTgEQ+zZev2cu+/S6f21Ktbm4/c7Nb7183k8uChKGZ/PI9Ne0/z0bdrSnzu/qOp1IiP475B91Ro\n2fPwsDDOnFWvHLk7adu2LXNmz6H/kJFkZJS/zU8/MZpvv/3WrX3ctPlgPXgoKf7+/vzw01Jat2pB\nUmI8zRu6Xhkg9UwWoaHBLh/vcDjIz88nP9+MyZRPvtn8z3Z+fqHr+eaCY/PzC9bNl8672EZ2do7q\naf/cyV19+7JlyxYGDhvFiqULy7XSceuWzdm1ezfZ2dkEBKibyesiojJk/b6apKQkuXWruhU/PXhw\nlUWLFvHsmMf5cOxgbDY7Fpsdi9WO2WLDYrVjtdkurVusdiw2B6s27eVslpX27W4rED0zpnyTUxjz\nC0TwolDm52Oz2TAajRiNRnx8jP+sG30w+hgxel98z+ey43wwFmxfvv/q7cjIyBsqY5rD4aBnjx7E\nVY3mzddfISQkuNxmWzVq0Z5PPv2sxCGWQohtUspi5zd7RrAePFxFv379OHzoIO/O/xkvL6+Clzfe\nRm8MXt54eQc4t/2M+Hl7E+rlxaC67TCbzdSpU6dQ0btaEL28vP6VSc8LQ6vVsvC777hvyBBqJjYn\nPz+fqKhIIiPCCfD3x9/fDz9fX/z9fQnw9ycyIpyY6ChioqOoUiWG+Lhqpf5bKori1lGzZwTrwYOH\nSkV+fj5paWmkp6eTk5NDbm7upWVWZiZpaWmkpqZy+vRpjp84jsmUz63NmxJXreqlvA//vOSV++Q/\nNc2klKxY9Rt//vkn9erVK5GNnhGsBw8ebkiMRiPVq1enenXXfOCnT59m06ZNpKamotVqr8mWdvVL\nCHFp/f6Ro9yap8MjsB48eLihiY6Opm/fvhVtRqF4wrQ8ePDgwU14BNaDBw8e3IRHYD148ODBTXgE\n1oMHDx7chEdgPXjw4MFNeATWgwcPHtyER2A9ePDgwU14BNaDBw8e3ESlnCorhDgDnFCpuTCg8ifI\nLB7PdVQuPNdRuSjv64iTUoYXd1ClFFg1EUJsdWXOcGXHcx2VC891VC4q63V4XAQePHjw4CY8AuvB\ngwcPbuLfILBzKtoAlfBcR+XCcx2Vi0p5HTe9D9aDBw8eKop/wwjWgwcPHiqEm1pghRBBQojvhBAH\nhBD7hRCtKtqmkiKEqCuE2HHZK1sIMaai7SoNQoinhRB7hRB7hBDzhRDeFW1TaRBCPFVwDXtvpP+F\nEOITIUSGEGLPZftChBArhRCHC5auV26sIIq4jgEF/w9FCFFpogluaoEFpgM/SykTgEbA/gq2p8RI\nKQ9KKRtLKRsDzQATsLiCzSoxQohY4EkgSUrZANACAyvWqpIjhGgAPAS0wPmZ6i2EqF2xVrnMZ0D3\nq/b9F/hVSlkb+LVgu7LzGddexx7gbmBduVtzHW5agRVCBADtgHkAUkqrlDKzYq0qM52Ao1JKtSZh\nlDc6wCiE0AE+QGoF21Ma6gGbpJQmKaUdWAtUznT6VyGlXAecv2r3ncDnBeufA3eVq1GloLDrkFLu\nl1IerCCTiuSmFVigBnAG+FQI8ZcQYq4QwreijSojA4H5FW1EaZBS/g28A5wETgNZUsoVFWtVqdgD\ntBNChAohfICeQNUKtqksREopTwMULCMq2J6biptZYHVAU2CWlLIJkMeNcftTKEIIA9AHWFjRtpSG\nAt/enUB1IAbwFULcV7FWlRwp5X5gMrAS+BnYCdgr1CgPlZabWWBTgBQp5eaC7e9wCu6NSg9gu5Qy\nvaINKSWdgWNSyjNSShvwPdC6gm0qFVLKeVLKplLKdjhvVQ9XtE1lIF0IEQ1QsMyoYHtuKm5agZVS\npgGnhBB1C3Z1AvZVoEllZRA3qHuggJNASyGEjxBC4Px/3HAPHQGEEBEFy2o4H6zcyP+Xn4DhBevD\ngR8r0Jabjpt6ooEQojEwFzAAycD9UsoLFWtVySnw9Z0CakgpsyrantIihHgNuBfnLfVfwINSSkvF\nWlVyhBC/A6GADXhGSvlrBZvkEkKI+UB7nJmn0oFxwA/AAqAazh/BAVLKqx+EVSqKuI7zwAdAOJAJ\n7JBSdqsoGy9yUwusBw8ePFQkN62LwIMHDx4qGo/AevDgwYOb8AisBw8ePLgJj8B68ODBg5vwCKwH\nDx48uAmPwHrw4MGDm/AIrAcPHjy4CY/AevDgwYOb+H8SZfw4fub+HAAAAABJRU5ErkJggg==\n",
826 "text/plain": [
827 "<matplotlib.figure.Figure at 0x7efc236ae518>"
828 ]
829 },
830 "metadata": {},
831 "output_type": "display_data"
832 }
833 ],
834 "source": [
835 "tracts.plot(column='Max_P', cmap='OrRd', edgecolor='k', categorical=True, legend=True)"
836 ]
267837 }
268 ]
269 }⏎
838 ],
839 "metadata": {
840 "kernelspec": {
841 "display_name": "Python 3",
842 "language": "python",
843 "name": "python3"
844 },
845 "language_info": {
846 "codemirror_mode": {
847 "name": "ipython",
848 "version": 3
849 },
850 "file_extension": ".py",
851 "mimetype": "text/x-python",
852 "name": "python",
853 "nbconvert_exporter": "python",
854 "pygments_lexer": "ipython3",
855 "version": "3.6.1"
856 }
857 },
858 "nbformat": 4,
859 "nbformat_minor": 1
860 }
+89
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examples/create_geopandas_from_pandas.py less more
0 """
1 Creating a GeoDataFrame from a DataFrame with coordinates
2 ---------------------------------------------------------
3
4 This example shows how to create a ``GeoDataFrame`` when starting from
5 a *regular* ``DataFrame`` that has coordinates either WKT
6 (`well-known text <https://en.wikipedia.org/wiki/Well-known_text>`_)
7 format, or in
8 two columns.
9
10 """
11 import pandas as pd
12 import geopandas
13 from shapely.geometry import Point
14 import matplotlib.pyplot as plt
15
16 ###############################################################################
17 # From longitudes and latitudes
18 # =============================
19 #
20 # First, let's consider a ``DataFrame`` containing cities and their respective
21 # longitudes and latitudes.
22
23 df = pd.DataFrame(
24 {'City': ['Buenos Aires', 'Brasilia', 'Santiago', 'Bogota', 'Caracas'],
25 'Country': ['Argentina', 'Brazil', 'Chile', 'Colombia', 'Venezuela'],
26 'Latitude': [-34.58, -15.78, -33.45, 4.60, 10.48],
27 'Longitude': [-58.66, -47.91, -70.66, -74.08, -66.86]})
28
29 ###############################################################################
30 # A ``GeoDataFrame`` needs a ``shapely`` object, so we create a new column
31 # **Coordinates** as a tuple of **Longitude** and **Latitude** :
32
33 df['Coordinates'] = list(zip(df.Longitude, df.Latitude))
34
35 ###############################################################################
36 # Then, we transform tuples to ``Point`` :
37
38 df['Coordinates'] = df['Coordinates'].apply(Point)
39
40 ###############################################################################
41 # Now, we can create the ``GeoDataFrame`` by setting ``geometry`` with the
42 # coordinates created previously.
43
44 gdf = geopandas.GeoDataFrame(df, geometry='Coordinates')
45
46 ###############################################################################
47 # ``gdf`` looks like this :
48
49 print(gdf.head())
50
51 ###############################################################################
52 # Finally, we plot the coordinates over a country-level map.
53
54 world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres'))
55
56 # We restrict to South America.
57 ax = world[world.continent == 'South America'].plot(
58 color='white', edgecolor='black')
59
60 # We can now plot our GeoDataFrame.
61 gdf.plot(ax=ax, color='red')
62
63 plt.show()
64
65 ###############################################################################
66 # From WKT format
67 # ===============
68 # Here, we consider a ``DataFrame`` having coordinates in WKT format.
69
70 df = pd.DataFrame(
71 {'City': ['Buenos Aires', 'Brasilia', 'Santiago', 'Bogota', 'Caracas'],
72 'Country': ['Argentina', 'Brazil', 'Chile', 'Colombia', 'Venezuela'],
73 'Coordinates': ['POINT(-34.58 -58.66)', 'POINT(-15.78 -47.91)',
74 'POINT(-33.45 -70.66)', 'POINT(4.60 -74.08)',
75 'POINT(10.48 -66.86)']})
76
77 ###############################################################################
78 # We use ``shapely.wkt`` sub-module to parse wkt format:
79 from shapely import wkt
80
81 df['Coordinates'] = df['Coordinates'].apply(wkt.loads)
82
83 ###############################################################################
84 # The ``GeoDataFrame`` is constructed as follows :
85
86 gdf = geopandas.GeoDataFrame(df, geometry='Coordinates')
87
88 print(gdf.head())
+0
-48
examples/nyc_boros.py less more
0 """
1 Generate example images for GeoPandas documentation.
2
3 TODO: autogenerate these from docs themselves
4
5 Kelsey Jordahl
6 Time-stamp: <Tue May 6 12:17:29 EDT 2014>
7 """
8 import numpy as np
9 import matplotlib.pyplot as plt
10 from shapely.geometry import Point
11 from geopandas import GeoSeries, GeoDataFrame
12
13 np.random.seed(1)
14 DPI = 100
15
16 # http://www1.nyc.gov/assets/planning/download/zip/data-maps/open-data/nybb_16a.zip
17 boros = GeoDataFrame.from_file('nybb.shp')
18 boros.set_index('BoroCode', inplace=True)
19 boros.sort()
20 boros.plot()
21 plt.xticks(rotation=90)
22 plt.savefig('nyc.png', dpi=DPI, bbox_inches='tight')
23 #plt.show()
24 boros.geometry.convex_hull.plot()
25 plt.xticks(rotation=90)
26 plt.savefig('nyc_hull.png', dpi=DPI, bbox_inches='tight')
27 #plt.show()
28
29 N = 2000 # number of random points
30 R = 2000 # radius of buffer in feet
31 xmin, xmax = plt.gca().get_xlim()
32 ymin, ymax = plt.gca().get_ylim()
33 #xmin, xmax, ymin, ymax = 900000, 1080000, 120000, 280000
34 xc = (xmax - xmin) * np.random.random(N) + xmin
35 yc = (ymax - ymin) * np.random.random(N) + ymin
36 pts = GeoSeries([Point(x, y) for x, y in zip(xc, yc)])
37 mp = pts.buffer(R).unary_union
38 boros_with_holes = boros.geometry - mp
39 boros_with_holes.plot()
40 plt.xticks(rotation=90)
41 plt.savefig('boros_with_holes.png', dpi=DPI, bbox_inches='tight')
42 plt.show()
43 holes = boros.geometry & mp
44 holes.plot()
45 plt.xticks(rotation=90)
46 plt.savefig('holes.png', dpi=DPI, bbox_inches='tight')
47 plt.show()
+718
-598
examples/overlays.ipynb less more
00 {
1 "cells": [
2 {
3 "cell_type": "markdown",
4 "metadata": {},
5 "source": [
6 "Spatial overlays allow you to compare two GeoDataFrames containing polygon or multipolygon geometries \n",
7 "and create a new GeoDataFrame with the new geometries representing the spatial combination *and*\n",
8 "merged properties. This allows you to answer questions like\n",
9 "\n",
10 "> What are the demographics of the census tracts within 1000 ft of the highway?\n",
11 "\n",
12 "The basic idea is demonstrated by the graphic below but keep in mind that overlays operate at the dataframe level, \n",
13 "not on individual geometries, and the properties from both are retained"
14 ]
15 },
16 {
17 "cell_type": "code",
18 "execution_count": 1,
19 "metadata": {
20 "ExecuteTime": {
21 "end_time": "2017-12-15T21:09:34.256318Z",
22 "start_time": "2017-12-15T21:09:34.226318Z"
23 }
24 },
25 "outputs": [
26 {
27 "data": {
28 "text/html": [
29 "<img src=\"http://docs.qgis.org/testing/en/_images/overlay_operations.png\"/>"
30 ],
31 "text/plain": [
32 "<IPython.core.display.Image object>"
33 ]
34 },
35 "execution_count": 1,
36 "metadata": {},
37 "output_type": "execute_result"
38 }
39 ],
40 "source": [
41 "from IPython.core.display import Image\n",
42 "Image(url=\"http://docs.qgis.org/testing/en/_images/overlay_operations.png\")"
43 ]
44 },
45 {
46 "cell_type": "markdown",
47 "metadata": {},
48 "source": [
49 "Now we can load up two GeoDataFrames containing (multi)polygon geometries..."
50 ]
51 },
52 {
53 "cell_type": "code",
54 "execution_count": 2,
55 "metadata": {
56 "ExecuteTime": {
57 "end_time": "2017-12-15T21:09:36.236298Z",
58 "start_time": "2017-12-15T21:09:34.256318Z"
59 }
60 },
61 "outputs": [],
62 "source": [
63 "%matplotlib inline\n",
64 "from shapely.geometry import Point\n",
65 "from geopandas import datasets, GeoDataFrame, read_file\n",
66 "from geopandas.tools import overlay\n",
67 "\n",
68 "# NYC Boros\n",
69 "zippath = datasets.get_path('nybb')\n",
70 "polydf = read_file(zippath)\n",
71 "\n",
72 "# Generate some circles\n",
73 "b = [int(x) for x in polydf.total_bounds]\n",
74 "N = 10\n",
75 "polydf2 = GeoDataFrame([\n",
76 " {'geometry': Point(x, y).buffer(10000), 'value1': x + y, 'value2': x - y}\n",
77 " for x, y in zip(range(b[0], b[2], int((b[2] - b[0]) / N)),\n",
78 " range(b[1], b[3], int((b[3] - b[1]) / N)))])"
79 ]
80 },
81 {
82 "cell_type": "markdown",
83 "metadata": {},
84 "source": [
85 "The first dataframe contains multipolygons of the NYC boros"
86 ]
87 },
88 {
89 "cell_type": "code",
90 "execution_count": 3,
91 "metadata": {
92 "ExecuteTime": {
93 "end_time": "2017-12-15T21:09:36.526295Z",
94 "start_time": "2017-12-15T21:09:36.236298Z"
95 }
96 },
97 "outputs": [
98 {
99 "data": {
100 "text/plain": [
101 "<matplotlib.axes._subplots.AxesSubplot at 0x512ee48>"
102 ]
103 },
104 "execution_count": 3,
105 "metadata": {},
106 "output_type": "execute_result"
107 },
108 {
109 "data": {
110 "image/png": 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f52/Q0VaCQn09efL7s7j+5VRnmoyvp447z02irM7I27tH99G8u3L3ecmjVrBA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\nyQAAAABJRU5ErkJggg==\n",
111 "text/plain": [
112 "<matplotlib.figure.Figure at 0x512e2b0>"
113 ]
114 },
115 "metadata": {},
116 "output_type": "display_data"
117 }
118 ],
119 "source": [
120 "polydf.plot()"
121 ]
122 },
123 {
124 "cell_type": "markdown",
125 "metadata": {},
126 "source": [
127 "And the second GeoDataFrame is a sequentially generated set of circles in the same geographic space. We'll plot these with a [different color palette](https://matplotlib.org/examples/color/colormaps_reference.html)."
128 ]
129 },
130 {
131 "cell_type": "code",
132 "execution_count": 4,
133 "metadata": {
134 "ExecuteTime": {
135 "end_time": "2017-12-15T21:09:36.756293Z",
136 "start_time": "2017-12-15T21:09:36.526295Z"
137 }
138 },
139 "outputs": [
140 {
141 "data": {
142 "text/plain": [
143 "<matplotlib.axes._subplots.AxesSubplot at 0x512e0f0>"
144 ]
145 },
146 "execution_count": 4,
147 "metadata": {},
148 "output_type": "execute_result"
149 },
150 {
151 "data": {
152 "image/png": 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YNPrzTM4/e8D9jQlmiSbCVB/cy/9dch3lO1b3u29D00F+9sR3KVv7Bgmxacwr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\nzhKNMcZzlmiMMZ77/2SB+rI5WiaWAAAAAElFTkSuQmCC\n",
153 "text/plain": [
154 "<matplotlib.figure.Figure at 0x12caea58>"
155 ]
156 },
157 "metadata": {},
158 "output_type": "display_data"
159 }
160 ],
161 "source": [
162 "polydf2.plot(cmap='tab20b')"
163 ]
164 },
165 {
166 "cell_type": "markdown",
167 "metadata": {},
168 "source": [
169 "The `geopandas.tools.overlay` function takes three arguments:\n",
170 "\n",
171 "* df1\n",
172 "* df2\n",
173 "* how\n",
174 "\n",
175 "Where `how` can be one of:\n",
176 "\n",
177 " ['intersection',\n",
178 " 'union',\n",
179 " 'identity',\n",
180 " 'symmetric_difference',\n",
181 " 'difference']\n",
182 "\n",
183 "So let's identify the areas (and attributes) where both dataframes intersect using the `overlay` tool. "
184 ]
185 },
186 {
187 "cell_type": "code",
188 "execution_count": 5,
189 "metadata": {
190 "ExecuteTime": {
191 "end_time": "2017-12-15T21:09:39.796263Z",
192 "start_time": "2017-12-15T21:09:36.756293Z"
193 }
194 },
195 "outputs": [
196 {
197 "data": {
198 "text/plain": [
199 "<matplotlib.axes._subplots.AxesSubplot at 0x12978940>"
200 ]
201 },
202 "execution_count": 5,
203 "metadata": {},
204 "output_type": "execute_result"
205 },
206 {
207 "data": {
208 "image/png": 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209 "text/plain": [
210 "<matplotlib.figure.Figure at 0x4dd50f0>"
211 ]
212 },
213 "metadata": {},
214 "output_type": "display_data"
215 }
216 ],
217 "source": [
218 "from geopandas.tools import overlay\n",
219 "newdf = overlay(polydf, polydf2, how=\"intersection\")\n",
220 "newdf.plot(cmap='tab20b')"
221 ]
222 },
223 {
224 "cell_type": "markdown",
225 "metadata": {},
226 "source": [
227 "And take a look at the attributes; we see that the attributes from both of the original GeoDataFrames are retained. "
228 ]
229 },
230 {
231 "cell_type": "code",
232 "execution_count": 6,
233 "metadata": {
234 "ExecuteTime": {
235 "end_time": "2017-12-15T21:09:40.416257Z",
236 "start_time": "2017-12-15T21:09:39.796263Z"
237 }
238 },
239 "outputs": [
240 {
241 "data": {
242 "text/html": [
243 "<div>\n",
244 "<style>\n",
245 " .dataframe thead tr:only-child th {\n",
246 " text-align: right;\n",
247 " }\n",
248 "\n",
249 " .dataframe thead th {\n",
250 " text-align: left;\n",
251 " }\n",
252 "\n",
253 " .dataframe tbody tr th {\n",
254 " vertical-align: top;\n",
255 " }\n",
256 "</style>\n",
257 "<table border=\"1\" class=\"dataframe\">\n",
258 " <thead>\n",
259 " <tr style=\"text-align: right;\">\n",
260 " <th></th>\n",
261 " <th>BoroCode</th>\n",
262 " <th>BoroName</th>\n",
263 " <th>Shape_Leng</th>\n",
264 " <th>Shape_Area</th>\n",
265 " <th>geometry</th>\n",
266 " </tr>\n",
267 " </thead>\n",
268 " <tbody>\n",
269 " <tr>\n",
270 " <th>0</th>\n",
271 " <td>5</td>\n",
272 " <td>Staten Island</td>\n",
273 " <td>330470.010332</td>\n",
274 " <td>1.623820e+09</td>\n",
275 " <td>(POLYGON ((970217.0223999023 145643.3322143555...</td>\n",
276 " </tr>\n",
277 " <tr>\n",
278 " <th>1</th>\n",
279 " <td>4</td>\n",
280 " <td>Queens</td>\n",
281 " <td>896344.047763</td>\n",
282 " <td>3.045213e+09</td>\n",
283 " <td>(POLYGON ((1029606.076599121 156073.8142089844...</td>\n",
284 " </tr>\n",
285 " <tr>\n",
286 " <th>2</th>\n",
287 " <td>3</td>\n",
288 " <td>Brooklyn</td>\n",
289 " <td>741080.523166</td>\n",
290 " <td>1.937479e+09</td>\n",
291 " <td>(POLYGON ((1021176.479003906 151374.7969970703...</td>\n",
292 " </tr>\n",
293 " <tr>\n",
294 " <th>3</th>\n",
295 " <td>1</td>\n",
296 " <td>Manhattan</td>\n",
297 " <td>359299.096471</td>\n",
298 " <td>6.364715e+08</td>\n",
299 " <td>(POLYGON ((981219.0557861328 188655.3157958984...</td>\n",
300 " </tr>\n",
301 " <tr>\n",
302 " <th>4</th>\n",
303 " <td>2</td>\n",
304 " <td>Bronx</td>\n",
305 " <td>464392.991824</td>\n",
306 " <td>1.186925e+09</td>\n",
307 " <td>(POLYGON ((1012821.805786133 229228.2645874023...</td>\n",
308 " </tr>\n",
309 " </tbody>\n",
310 "</table>\n",
311 "</div>"
312 ],
313 "text/plain": [
314 " BoroCode BoroName Shape_Leng Shape_Area \\\n",
315 "0 5 Staten Island 330470.010332 1.623820e+09 \n",
316 "1 4 Queens 896344.047763 3.045213e+09 \n",
317 "2 3 Brooklyn 741080.523166 1.937479e+09 \n",
318 "3 1 Manhattan 359299.096471 6.364715e+08 \n",
319 "4 2 Bronx 464392.991824 1.186925e+09 \n",
320 "\n",
321 " geometry \n",
322 "0 (POLYGON ((970217.0223999023 145643.3322143555... \n",
323 "1 (POLYGON ((1029606.076599121 156073.8142089844... \n",
324 "2 (POLYGON ((1021176.479003906 151374.7969970703... \n",
325 "3 (POLYGON ((981219.0557861328 188655.3157958984... \n",
326 "4 (POLYGON ((1012821.805786133 229228.2645874023... "
327 ]
328 },
329 "execution_count": 6,
330 "metadata": {},
331 "output_type": "execute_result"
332 }
333 ],
334 "source": [
335 "polydf.head()"
336 ]
337 },
338 {
339 "cell_type": "code",
340 "execution_count": 7,
341 "metadata": {
342 "ExecuteTime": {
343 "end_time": "2017-12-15T21:09:40.446256Z",
344 "start_time": "2017-12-15T21:09:40.416257Z"
345 }
346 },
347 "outputs": [
348 {
349 "data": {
350 "text/html": [
351 "<div>\n",
352 "<style>\n",
353 " .dataframe thead tr:only-child th {\n",
354 " text-align: right;\n",
355 " }\n",
356 "\n",
357 " .dataframe thead th {\n",
358 " text-align: left;\n",
359 " }\n",
360 "\n",
361 " .dataframe tbody tr th {\n",
362 " vertical-align: top;\n",
363 " }\n",
364 "</style>\n",
365 "<table border=\"1\" class=\"dataframe\">\n",
366 " <thead>\n",
367 " <tr style=\"text-align: right;\">\n",
368 " <th></th>\n",
369 " <th>geometry</th>\n",
370 " <th>value1</th>\n",
371 " <th>value2</th>\n",
372 " </tr>\n",
373 " </thead>\n",
374 " <tbody>\n",
375 " <tr>\n",
376 " <th>0</th>\n",
377 " <td>POLYGON ((923175 120121, 923126.847266722 1191...</td>\n",
378 " <td>1033296</td>\n",
379 " <td>793054</td>\n",
380 " </tr>\n",
381 " <tr>\n",
382 " <th>1</th>\n",
383 " <td>POLYGON ((938595 135393, 938546.847266722 1344...</td>\n",
384 " <td>1063988</td>\n",
385 " <td>793202</td>\n",
386 " </tr>\n",
387 " <tr>\n",
388 " <th>2</th>\n",
389 " <td>POLYGON ((954015 150665, 953966.847266722 1496...</td>\n",
390 " <td>1094680</td>\n",
391 " <td>793350</td>\n",
392 " </tr>\n",
393 " <tr>\n",
394 " <th>3</th>\n",
395 " <td>POLYGON ((969435 165937, 969386.847266722 1649...</td>\n",
396 " <td>1125372</td>\n",
397 " <td>793498</td>\n",
398 " </tr>\n",
399 " <tr>\n",
400 " <th>4</th>\n",
401 " <td>POLYGON ((984855 181209, 984806.847266722 1802...</td>\n",
402 " <td>1156064</td>\n",
403 " <td>793646</td>\n",
404 " </tr>\n",
405 " </tbody>\n",
406 "</table>\n",
407 "</div>"
408 ],
409 "text/plain": [
410 " geometry value1 value2\n",
411 "0 POLYGON ((923175 120121, 923126.847266722 1191... 1033296 793054\n",
412 "1 POLYGON ((938595 135393, 938546.847266722 1344... 1063988 793202\n",
413 "2 POLYGON ((954015 150665, 953966.847266722 1496... 1094680 793350\n",
414 "3 POLYGON ((969435 165937, 969386.847266722 1649... 1125372 793498\n",
415 "4 POLYGON ((984855 181209, 984806.847266722 1802... 1156064 793646"
416 ]
417 },
418 "execution_count": 7,
419 "metadata": {},
420 "output_type": "execute_result"
421 }
422 ],
423 "source": [
424 "polydf2.head()"
425 ]
426 },
427 {
428 "cell_type": "code",
429 "execution_count": 8,
430 "metadata": {
431 "ExecuteTime": {
432 "end_time": "2017-12-15T21:09:40.586255Z",
433 "start_time": "2017-12-15T21:09:40.446256Z"
434 }
435 },
436 "outputs": [
437 {
438 "data": {
439 "text/html": [
440 "<div>\n",
441 "<style>\n",
442 " .dataframe thead tr:only-child th {\n",
443 " text-align: right;\n",
444 " }\n",
445 "\n",
446 " .dataframe thead th {\n",
447 " text-align: left;\n",
448 " }\n",
449 "\n",
450 " .dataframe tbody tr th {\n",
451 " vertical-align: top;\n",
452 " }\n",
453 "</style>\n",
454 "<table border=\"1\" class=\"dataframe\">\n",
455 " <thead>\n",
456 " <tr style=\"text-align: right;\">\n",
457 " <th></th>\n",
458 " <th>BoroCode</th>\n",
459 " <th>BoroName</th>\n",
460 " <th>Shape_Leng</th>\n",
461 " <th>Shape_Area</th>\n",
462 " <th>value1</th>\n",
463 " <th>value2</th>\n",
464 " <th>geometry</th>\n",
465 " </tr>\n",
466 " </thead>\n",
467 " <tbody>\n",
468 " <tr>\n",
469 " <th>0</th>\n",
470 " <td>5</td>\n",
471 " <td>Staten Island</td>\n",
472 " <td>330470.010332</td>\n",
473 " <td>1.623820e+09</td>\n",
474 " <td>1033296</td>\n",
475 " <td>793054</td>\n",
476 " <td>POLYGON ((916755.4256330276 129447.9617643995,...</td>\n",
477 " </tr>\n",
478 " <tr>\n",
479 " <th>1</th>\n",
480 " <td>5</td>\n",
481 " <td>Staten Island</td>\n",
482 " <td>330470.010332</td>\n",
483 " <td>1.623820e+09</td>\n",
484 " <td>1063988</td>\n",
485 " <td>793202</td>\n",
486 " <td>POLYGON ((938595 135393, 938546.847266722 1344...</td>\n",
487 " </tr>\n",
488 " <tr>\n",
489 " <th>2</th>\n",
490 " <td>5</td>\n",
491 " <td>Staten Island</td>\n",
492 " <td>330470.010332</td>\n",
493 " <td>1.623820e+09</td>\n",
494 " <td>1125372</td>\n",
495 " <td>793498</td>\n",
496 " <td>POLYGON ((961436.3049926758 175473.0296020508,...</td>\n",
497 " </tr>\n",
498 " <tr>\n",
499 " <th>3</th>\n",
500 " <td>5</td>\n",
501 " <td>Staten Island</td>\n",
502 " <td>330470.010332</td>\n",
503 " <td>1.623820e+09</td>\n",
504 " <td>1094680</td>\n",
505 " <td>793350</td>\n",
506 " <td>POLYGON ((954015 150665, 953966.847266722 1496...</td>\n",
507 " </tr>\n",
508 " <tr>\n",
509 " <th>4</th>\n",
510 " <td>2</td>\n",
511 " <td>Bronx</td>\n",
512 " <td>464392.991824</td>\n",
513 " <td>1.186925e+09</td>\n",
514 " <td>1309524</td>\n",
515 " <td>794386</td>\n",
516 " <td>POLYGON ((1043287.193237305 260300.0289916992,...</td>\n",
517 " </tr>\n",
518 " </tbody>\n",
519 "</table>\n",
520 "</div>"
521 ],
522 "text/plain": [
523 " BoroCode BoroName Shape_Leng Shape_Area value1 value2 \\\n",
524 "0 5 Staten Island 330470.010332 1.623820e+09 1033296 793054 \n",
525 "1 5 Staten Island 330470.010332 1.623820e+09 1063988 793202 \n",
526 "2 5 Staten Island 330470.010332 1.623820e+09 1125372 793498 \n",
527 "3 5 Staten Island 330470.010332 1.623820e+09 1094680 793350 \n",
528 "4 2 Bronx 464392.991824 1.186925e+09 1309524 794386 \n",
529 "\n",
530 " geometry \n",
531 "0 POLYGON ((916755.4256330276 129447.9617643995,... \n",
532 "1 POLYGON ((938595 135393, 938546.847266722 1344... \n",
533 "2 POLYGON ((961436.3049926758 175473.0296020508,... \n",
534 "3 POLYGON ((954015 150665, 953966.847266722 1496... \n",
535 "4 POLYGON ((1043287.193237305 260300.0289916992,... "
536 ]
537 },
538 "execution_count": 8,
539 "metadata": {},
540 "output_type": "execute_result"
541 }
542 ],
543 "source": [
544 "newdf.head()"
545 ]
546 },
547 {
548 "cell_type": "markdown",
549 "metadata": {},
550 "source": [
551 "Now let's look at the other `how` operations:"
552 ]
553 },
554 {
555 "cell_type": "code",
556 "execution_count": 9,
557 "metadata": {
558 "ExecuteTime": {
559 "end_time": "2017-12-15T21:09:44.026220Z",
560 "start_time": "2017-12-15T21:09:40.586255Z"
561 }
562 },
563 "outputs": [
564 {
565 "data": {
566 "text/plain": [
567 "<matplotlib.axes._subplots.AxesSubplot at 0x12a72e48>"
568 ]
569 },
570 "execution_count": 9,
571 "metadata": {},
572 "output_type": "execute_result"
573 },
574 {
575 "data": {
576 "image/png": 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JzezDrYwnuSwAHRaLFcaYks7gQwgd6WlZVDa8wfqSK1mWs4HsjGVkZ0QX8AP/LhUmNDRS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\nxudoodFoND5HC41Go/E5/w/hJcoDL8piaQAAAABJRU5ErkJggg==\n",
577 "text/plain": [
578 "<matplotlib.figure.Figure at 0x12ae5f98>"
579 ]
580 },
581 "metadata": {},
582 "output_type": "display_data"
583 }
584 ],
585 "source": [
586 "newdf = overlay(polydf, polydf2, how=\"union\")\n",
587 "newdf.plot(cmap='tab20b')"
588 ]
589 },
590 {
591 "cell_type": "code",
592 "execution_count": 10,
593 "metadata": {
594 "ExecuteTime": {
595 "end_time": "2017-12-15T21:09:47.366187Z",
596 "start_time": "2017-12-15T21:09:44.026220Z"
597 }
598 },
599 "outputs": [
600 {
601 "data": {
602 "text/plain": [
603 "<matplotlib.axes._subplots.AxesSubplot at 0x12bccda0>"
604 ]
605 },
606 "execution_count": 10,
607 "metadata": {},
608 "output_type": "execute_result"
609 },
610 {
611 "data": {
612 "image/png": 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EI350eivRcRqtcMDJkec/y4pL/p28kpumuXeJQdvGozF9SPDVDS+COl5S16Wj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\nhTJaCoViWPH/2UAwmuqzL+4AAAAASUVORK5CYII=\n",
613 "text/plain": [
614 "<matplotlib.figure.Figure at 0x12d79940>"
615 ]
616 },
617 "metadata": {},
618 "output_type": "display_data"
619 }
620 ],
621 "source": [
622 "newdf = overlay(polydf, polydf2, how=\"identity\")\n",
623 "newdf.plot(cmap='tab20b')"
624 ]
625 },
626 {
627 "cell_type": "code",
628 "execution_count": 11,
629 "metadata": {
630 "ExecuteTime": {
631 "end_time": "2017-12-15T21:09:50.556155Z",
632 "start_time": "2017-12-15T21:09:47.366187Z"
633 }
634 },
635 "outputs": [
636 {
637 "data": {
638 "text/plain": [
639 "<matplotlib.axes._subplots.AxesSubplot at 0x12dc2710>"
640 ]
641 },
642 "execution_count": 11,
643 "metadata": {},
644 "output_type": "execute_result"
645 },
646 {
647 "data": {
648 "image/png": 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DviOr0WoMzCm5GiE0KCEPrVv+H+H2nh38ho4gdd43MeedRsTXSMvmH6IE4r9f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\nxb9Vhmmp3E7T4cqO+xz86ENSMjLwtI0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HXLT3CTuBy4FRwD6nuna9d8DbwG1G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\n",
649 "text/plain": [
650 "<matplotlib.figure.Figure at 0x12d3a7f0>"
651 ]
652 },
653 "metadata": {},
654 "output_type": "display_data"
655 }
656 ],
657 "source": [
658 "newdf = overlay(polydf, polydf2, how=\"symmetric_difference\")\n",
659 "newdf.plot(cmap='tab20b')"
660 ]
661 },
662 {
663 "cell_type": "code",
664 "execution_count": 12,
665 "metadata": {
666 "ExecuteTime": {
667 "end_time": "2017-12-15T21:09:53.566125Z",
668 "start_time": "2017-12-15T21:09:50.556155Z"
669 }
670 },
671 "outputs": [
672 {
673 "data": {
674 "text/plain": [
675 "<matplotlib.axes._subplots.AxesSubplot at 0x12e13630>"
676 ]
677 },
678 "execution_count": 12,
679 "metadata": {},
680 "output_type": "execute_result"
681 },
682 {
683 "data": {
684 "image/png": 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1njGJk0mMySIiNB6tJgir3UKrqZ6ahmLKqg9gtna/TmQ0GZxRWAWvM6DTklJuAvr9rXDN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\ndxB/dtdC9K6c8of//Q6HThK0nXrrreQ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685 "text/plain": [
686 "<matplotlib.figure.Figure at 0x12d3aac8>"
687 ]
688 },
689 "metadata": {},
690 "output_type": "display_data"
691 }
692 ],
693 "source": [
694 "newdf = overlay(polydf, polydf2, how=\"difference\")\n",
695 "newdf.plot(cmap='tab20b')"
696 ]
697 }
698 ],
1699 "metadata": {
2 "name": "",
3 "signature": "sha256:f2cfcb83c09747f8bcf7a08abe582a16207e6bcfb0547e4cb69755830bcae172"
700 "kernelspec": {
701 "display_name": "Python 3",
702 "language": "python",
703 "name": "python3"
704 },
705 "language_info": {
706 "codemirror_mode": {
707 "name": "ipython",
708 "version": 3
709 },
710 "file_extension": ".py",
711 "mimetype": "text/x-python",
712 "name": "python",
713 "nbconvert_exporter": "python",
714 "pygments_lexer": "ipython3",
715 "version": "3.6.1"
716 }
4717 },
5 "nbformat": 3,
6 "nbformat_minor": 0,
7 "worksheets": [
8 {
9 "cells": [
10 {
11 "cell_type": "markdown",
12 "metadata": {},
13 "source": [
14 "Spatial overlays allow you to compare two GeoDataFrames containing polygon or multipolygon geometries \n",
15 "and create a new GeoDataFrame with the new geometries representing the spatial combination *and*\n",
16 "merged properties. This allows you to answer questions like\n",
17 "\n",
18 "> What are the demographics of the census tracts within 1000 ft of the highway?\n",
19 "\n",
20 "The basic idea is demonstrated by the graphic below but keep in mind that overlays operate at the dataframe level, \n",
21 "not on individual geometries, and the properties from both are retained"
22 ]
23 },
24 {
25 "cell_type": "code",
26 "collapsed": false,
27 "input": [
28 "from IPython.core.display import Image \n",
29 "Image(url=\"http://docs.qgis.org/testing/en/_images/overlay_operations.png\") "
30 ],
31 "language": "python",
32 "metadata": {},
33 "outputs": [
34 {
35 "html": [
36 "<img src=\"http://docs.qgis.org/testing/en/_images/overlay_operations.png\"/>"
37 ],
38 "metadata": {},
39 "output_type": "pyout",
40 "prompt_number": 1,
41 "text": [
42 "<IPython.core.display.Image at 0x1038a8f10>"
43 ]
44 }
45 ],
46 "prompt_number": 1
47 },
48 {
49 "cell_type": "markdown",
50 "metadata": {},
51 "source": [
52 "Now we can load up two GeoDataFrames containing (multi)polygon geometries..."
53 ]
54 },
55 {
56 "cell_type": "code",
57 "collapsed": false,
58 "input": [
59 "%matplotlib inline\n",
60 "import os\n",
61 "from shapely.geometry import Point\n",
62 "from geopandas import GeoDataFrame, read_file\n",
63 "from geopandas.tools import overlay\n",
64 "\n",
65 "# NYC Boros\n",
66 "zippath = os.path.abspath('../examples/nybb_14aav.zip')\n",
67 "polydf = read_file('/nybb_14a_av/nybb.shp', vfs='zip://' + zippath)\n",
68 "\n",
69 "# Generate some circles\n",
70 "b = [int(x) for x in polydf.total_bounds]\n",
71 "N = 10\n",
72 "polydf2 = GeoDataFrame([\n",
73 " {'geometry' : Point(x, y).buffer(10000), 'value1': x + y, 'value2': x - y}\n",
74 " for x, y in zip(range(b[0], b[2], int((b[2]-b[0])/N)),\n",
75 " range(b[1], b[3], int((b[3]-b[1])/N)))])"
76 ],
77 "language": "python",
78 "metadata": {},
79 "outputs": [],
80 "prompt_number": 2
81 },
82 {
83 "cell_type": "markdown",
84 "metadata": {},
85 "source": [
86 "The first dataframe contains multipolygons of the NYC boros"
87 ]
88 },
89 {
90 "cell_type": "code",
91 "collapsed": false,
92 "input": [
93 "polydf.plot()"
94 ],
95 "language": "python",
96 "metadata": {},
97 "outputs": [
98 {
99 "metadata": {},
100 "output_type": "pyout",
101 "prompt_number": 3,
102 "text": [
103 "<matplotlib.axes.AxesSubplot at 0x1038b1550>"
104 ]
105 },
106 {
107 "metadata": {},
108 "output_type": "display_data",
109 "png": 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OLehBzQrJe4Vjlx1DrVbQtU4d9HR0NGBh+ihoYMDDR49o1KgRBeVQDgMHDqSQmRlLjxzB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\nsJUDHUQhegAAAABJRU5ErkJggg==\n",
110 "text": [
111 "<matplotlib.figure.Figure at 0x1038ab350>"
112 ]
113 }
114 ],
115 "prompt_number": 3
116 },
117 {
118 "cell_type": "markdown",
119 "metadata": {},
120 "source": [
121 "And the second GeoDataFrame is a randomly generated set of circles in the same geographic space (TODO ...use real data)"
122 ]
123 },
124 {
125 "cell_type": "code",
126 "collapsed": false,
127 "input": [
128 "polydf2.plot()"
129 ],
130 "language": "python",
131 "metadata": {},
132 "outputs": [
133 {
134 "metadata": {},
135 "output_type": "pyout",
136 "prompt_number": 4,
137 "text": [
138 "<matplotlib.axes.AxesSubplot at 0x10fb4ad90>"
139 ]
140 },
141 {
142 "metadata": {},
143 "output_type": "display_data",
144 "png": 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t+fyokCJyVeRyXnv1Nfat3KdbEchoNOK9wpuBAwbqoifkPqRFbQ4mMDCQ3bt3\nExQUREJCAo6OjtSuXZsOHTpQqJBq7JaYmIhLWRdem/oaXm9lvKe1bYu3sezjZVwNuypvGucQsvO3\nlIVsQFxcHFOnfcmvy5cTeTOSkpUrUti5NFa2+bkfE0v0ooXcjriBR6OGfPLRBLp27cr0r6czdvxY\nmnRpQnGX9H+/J/JqJAs/WMjMGTPFUISnIjmVHMTy5csZNWYM9mXL0HzcO9R9tRfWKdzc0SGhHPx+\nHqeXrKBO7dqsW72aUaNHcdLvJHOOzcHewfKuB+JuxzGm6Rjq16jPhvUb9NgdIZOQFwotJ0+Yyrtj\nRrN46VI6z/ySxm8OStMyd6Kj+X3IKEIPHmHThj/4dNKnXLpyic83f0656uXSvO6QiyF82vVTKpat\nyF+7/pJcSg5DXigUnmDMuLH8smoVww/vTLOhABR0dOS1P5bT8J036fJSV76f+T2d2nZiVKNRzBs7\nj/jY+GcuHx8bz/xx8xnVcBQdPDuw96+9YihCqkhOJZuzdu1aBr/xBsMPbadMnfR/I2fjux8Q8ucO\nAi7+w7Fjxxj3/jgunL9AzWY1qdmqJuVrlse+qD1xMXEEnwvm3P5znD9yHvfq7nz/7ffSzUEORoo/\nlpNrTeXevXu4li9Hs0/G88K7wzOkZTQa+b+6Lej5YjvmzpkDgJ+fH0uWLOHo8aOEXw3n/r372NrZ\n4uzsTLPGzRgyZIh87CsXIKZiObnWVKZNm8b//bKUMeeP6dLQLGj/YX716sO18HCKFCmiwxYKOQGJ\nqQj/sfjXX2j0zpu6tVyt2Ko5juVc+fnnn3XRE4SUEFPJpkRGRnIl6DINhwzQVbeyVwc2bdmsq6Yg\nmCOmkk05cOAAjs6lsdP5cxaVXmyJf4C+H1sXBHPEVLIply9fxr5USd11i1etxPWwcN11BcGEmEo2\nJSEhAStr/d+iyJdPTrnwfJErLJtSvHhx7t2+rbvunVtRODg56q4rCCbEVLIpDRs2JCokTPde20KO\n+eDs4qKrpiCYI6aSTalTpw5WBiuCDx7VVTdg2y5eaNZMV01BMEdMJZtiMBho3749R2bP103zTnQ0\nQd6HGD4sY61zBeFZSIvaLMLf35/9+/cTFqaKOKVKlaJJkyY0aNDgv8Zu586dw6NxI0ad3EfJalUz\nvM4Nb48j4bQfJ48dz7CWkHOQZvqWk2NMJTIykkmTp7Bu/e/cjomhmHMFCjiUIJ/BwL3YW0RdC8bK\nykCXTp3sGnEZAAAV10lEQVT44ospVKlShVcHvsZBP19GnvTOUMvay4eOsrTDyxzcv196wc9jSM9v\nuZTp06fz+dQvKVWxLq3fnkO1F3pgsLJ6ZB6j0ciVv/dzYv331K5Tj8GDB/HjD/Oo26A+q/q9Qb9V\ni9NlLJGBQSzvMYAP3n9fDEV47khO5TmTmJhItx4vc/jocbzeX0zVZmnrJ/ZawBn++LIfxQvbsmrF\nb3Ts0hmHWtV5dd0ybAul/WPrQfsOsaLXIHr37Mmin+Sdn7yIvFCYizAajXTxeolT5wIYttAvzYYC\nUKZKPYb9fJYEuxK83KsPh/YfwD46lu8qN+D4wl9SrWqOvR7BmsFvs6xLbz4YO1YMRcg0JKfyHJk4\n8RPmL1rGmwvOUKho+jqcTkpMZNno5tSp7MymjRuY+8Ncvvz6a+4nJVGxXWvKt2pByWpVyV+wAP9G\n3CD85Bku/7WfkOMnadykMfPn/iB9ouRxJFBrOdnSVAIDA6lVuw79p+/ErXaLDGnFRV1n3mB3Vi3/\nlW7dumE0Gtm4cSOr167hjK8vtyIjeZD4gIKFCuLq6kqrFi0YPmw4lSpV0mlvhJyMmIrlZEtT6flK\nL/6JSKTP1I266O1d/Ck3T23kvN9ZXfSEvIOYiuVkO1O5d+8ejk7FGTT7AGWq1NdFM+FuPN+9UhKf\n48ekOCNYRHYM1C4GIgBfs7TJQBhwWhs6m037GAgALgIdzNI9NI0AYLZZui2wWks/Cph/O2Iw4K8N\nae9GPovZsmULBR2cdDMUgPwFCuHi3pDly1fopikIz4O0mMoSoNNjaQ+B74D62rBNS68B9NV+OwHz\nSHbI+cBQoIo2mDSHAre0tO+B6Vq6E/AZ0FgbJgFF07xnWcihQ4coUaG27rql3Rtzwuek7rqCoCdp\nMZUDQHQK6Sllp7oDK4FEIBi4BDQBygCFAVP78F+AHtp4N2CZNr4eaKuNdwR2AjHasIsnzS1bEhIa\nhn1xN911CxUtiY/PCd11BUFPMtJO5V3gb2ARyTkIZ1SxyEQY4JJCeriWjvYbqo0/AG4DxZ6hle1J\nSEjAyia/7rrxMTe4HZOSvwtC9iG9zfTnA59r418AM1HFmCxh8uTJ/417enpm+YevHIsW5cq1KN11\nCxYtiUejJrrrCrkLb29vvL29s2z96TWVG2bjC4E/tfFwwNVsWllUDiNcG3883bSMG3BV2x4HVIwl\nHPA0W8YV2JPSxpibSnagRo3qePus1133RtBZalaprLuukLt4/ME6ZcqUTF1/eos/ZczGe5JcM7QJ\n6AfkByqggq/HgetALCq+kg8YCGw0W2awNt4L+Esb34mqPSoKOALtgR3p3N5MpXv37kQEnSPx/j1d\nda+dP0yH9u101RQEvUlLTmUl0Boojop9TELlIOqhaoEuA6Zef84Da7TfB8BIbR608aVAAWArsF1L\nXwT8iqpSvoUyJYAoVNHKFJmcggrYZnuqVauGs7MLJ//8kaa9xuqiGXruCHExN+jXr1/qMwtCFiKN\n39LBxYsXOXbsGDdu3MDKyopy5crRokULSpcu/d88c+b8H5O//IZ3fgvCysYmw+tcNvoFPD3cWbJk\nUYa1hLyFtKi1nEwxleDgYCZNmsLmzVuJj4/D0dGNAgUcefjQSHz8DaKjw3FxcWXgwP5MmDCBAgUK\n4F69JkXdPfF6L2NdQvpsWsCBJR8THBSIo6P0hC9YhpiK5TxXU0lKSmLMmHEsWrQIN7eWeHi8S+XK\nnTAYHu1gKSEhHl/fFZw6NZf4+DBmzJhOs2ZNadKsOZ5Dp9Oox4h0rf/yqb2s+bQbv/2ylFdeeUWP\nXRLyGGIqlvPcTCU6OprWrdtw/Xoc3bsvx8WlcZqW+/vvX9m5czQ9e3ajX7/e9OnXn8avvIfnEMui\n8Gd3/sq22SOZ+vkUxo9/Lz27IAhiKunguZhKfHw89eo15OHDMvTtuxUbGzuLlo+Ovsxvv3nStm1z\nRo9+h5d79cbOqSwdRs2lbI1ntzWJvhrEzh/GEHp2P/PnzWXgwIEZ2RUhjyN91GYTevXqx/379gwe\nvAMrK8sDrY6OFRg06ACLFnnQpEkjgi4FMHbseyx/vy1OzhUo37ATbrVb4uhcCYPBits3Qgg9d5gr\nJ3dy7dIZ2rZrz+6L53GRD38JOQzJqaTA+vXrGTRoKCNGXMTevnTqCzwDf//NbNzYn8DAAEqXLs3t\n27f5+eef2bJ1O/4BAcTevs3Dhw8pZG9PhQoVaPuiJ2+/PRxXV9fUxQUhDUjxx3J0N5WqVWvg7NwH\nT8/Juuj99lsbWrasINXBQpaQHftTyVOcPHmS0NAQWrT4SDfNFi0+Zf3630lKStJNUxCyK2Iqj7F8\n+XJcXZtjY1NAN80KFV4ErNm/f79umoKQXRFTeYzjx09Rpoz+HzAvWbJmlr45KgiZhZjKY1y/foNi\nxfR/E9hgKISP9Nom5AGkSvkxEhMTsLYuqLtuRIQvISE3ddcVhOyGmMpj2Nvbc+dOpO66Zcs2pmXL\nErrrCkJ2Q4o/j1GpUnkiIv7WXTc29hI1a9bQXVcQshtiKo/Rpo0nV68e0FUzISGOGzf+oWvXrrrq\nCkJ2REzlMQYPHkxk5CVu3Dinm+aJE/Nxc6tAhQoVdNMUhOxKnoup+Pv7c+rUKaKjo7Gzs6Nq1ao0\natSI/PlV7/eOjo507tyFv/76gP79t2Z4fUlJCfj4zGLatP9lWEsQcgJ5opm+n58fkydPYffuv7h3\n7x4ODsWxtS3AgwcPiIuL4v79u9SrV58xY96lX79+XLt2japVq9Gly2Jq1uydoY3bsmU4sbEHuHDB\nD4NBMoZC5iPv/ljOU00lLi6O118fwubNm6lcuTEeHt0pV67eEzd3dPRVfHz+xM9vJ6VKleC3337h\n9OkzjBv3Aa+9tgdnZ490bdiJE/Px9v6I48ePULNmzXRpCEJGEVOxnBRNxd/fnxdfbIO1dVG6d/8Y\nJ6fUuxBISkpk164F/P33VmbN+p7g4BDmzPmBHj1WUqVK51SXN2f//qkcPTqdNWtWSoBWyFLEVCzn\nCVO5fPkyHh6NqFixKV26vGdxseOffw7zxx9f8v33M7l/P4GPP55I1ao9adfuW+ztSz1z2WvXzrBj\nx0ji4gJZv34NrVu3tniHBEFPxFQs5xFTSUxMpEaNWhQs6EbPnp+kW1QZy1T27fOmSJEivPXW25w4\ncQI3txZUrNgFV9fmODiUIykpgaioQIKD93L58hZu3rxAr169mTt3Ng4ODnrsnyBkCDEVy3nEVCZM\nmMCSJSsYNmxRunpsM2fHjrlERvryzz8XMBgMnDt3jvnzf8Tb+wChoaHcvRuPwWCgcGEHKlWqTJcu\n7RkxYgQlSkjLWSH7IKZiOf+ZSnx8PKVKlaFHj0+pXLlRhoWTkhKZM6cfc+fOkn5ihRyLdNKUAX76\n6ScKFy6mi6EAWFnZULt2J+bMmauLniDkBXKVqWzYsJGKFZvqqtmoUTfOnj1DQkKCrrqCkFvJVaZy\n4cIFKlXSJ5diwsGhFHZ29hw5ckRXXUHIreQqU4mJiaZkSf3frzEYrDlz5ozuuoKQG8lVppKUlISt\nrX59y5qIibkhORVBSCO5ylT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145 "text": [
146 "<matplotlib.figure.Figure at 0x10fb7da50>"
147 ]
148 }
149 ],
150 "prompt_number": 4
151 },
152 {
153 "cell_type": "markdown",
154 "metadata": {},
155 "source": [
156 "The `geopandas.tools.overlay` function takes three arguments:\n",
157 "\n",
158 "* df1\n",
159 "* df2\n",
160 "* how\n",
161 "\n",
162 "Where `how` can be one of:\n",
163 "\n",
164 " ['intersection',\n",
165 " 'union',\n",
166 " 'identity',\n",
167 " 'symmetric_difference',\n",
168 " 'difference']\n",
169 "\n",
170 "So let's identify the areas (and attributes) where both dataframes intersect using the `overlay` tool. "
171 ]
172 },
173 {
174 "cell_type": "code",
175 "collapsed": false,
176 "input": [
177 "from geopandas.tools import overlay\n",
178 "newdf = overlay(polydf, polydf2, how=\"intersection\")\n",
179 "newdf.plot()"
180 ],
181 "language": "python",
182 "metadata": {},
183 "outputs": [
184 {
185 "metadata": {},
186 "output_type": "pyout",
187 "prompt_number": 5,
188 "text": [
189 "<matplotlib.axes.AxesSubplot at 0x10ce02510>"
190 ]
191 },
192 {
193 "metadata": {},
194 "output_type": "display_data",
195 "png": 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Uq0JKYmIiq1atYvXa1QScC6CIcRFsy9tibmlOYkIiQeeCOHr0aNJmdtrExcVR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\nPwqYoORrh48/RR6Gjw8JCSE8/C5166a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196 "text": [
197 "<matplotlib.figure.Figure at 0x10ce02250>"
198 ]
199 }
200 ],
201 "prompt_number": 5
202 },
203 {
204 "cell_type": "markdown",
205 "metadata": {},
206 "source": [
207 "And take a look at the attributes; we see that the attributes from both of the original GeoDataFrames are retained. "
208 ]
209 },
210 {
211 "cell_type": "code",
212 "collapsed": false,
213 "input": [
214 "polydf.head()"
215 ],
216 "language": "python",
217 "metadata": {},
218 "outputs": [
219 {
220 "html": [
221 "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
222 "<table border=\"1\" class=\"dataframe\">\n",
223 " <thead>\n",
224 " <tr style=\"text-align: right;\">\n",
225 " <th></th>\n",
226 " <th>BoroCode</th>\n",
227 " <th>BoroName</th>\n",
228 " <th>Shape_Area</th>\n",
229 " <th>Shape_Leng</th>\n",
230 " <th>geometry</th>\n",
231 " </tr>\n",
232 " </thead>\n",
233 " <tbody>\n",
234 " <tr>\n",
235 " <th>0</th>\n",
236 " <td> 5</td>\n",
237 " <td> Staten Island</td>\n",
238 " <td> 1.623847e+09</td>\n",
239 " <td> 330454.175933</td>\n",
240 " <td> (POLYGON ((970217.0223999023 145643.3322143555...</td>\n",
241 " </tr>\n",
242 " <tr>\n",
243 " <th>1</th>\n",
244 " <td> 3</td>\n",
245 " <td> Brooklyn</td>\n",
246 " <td> 1.937810e+09</td>\n",
247 " <td> 741227.337073</td>\n",
248 " <td> (POLYGON ((1021176.479003906 151374.7969970703...</td>\n",
249 " </tr>\n",
250 " <tr>\n",
251 " <th>2</th>\n",
252 " <td> 4</td>\n",
253 " <td> Queens</td>\n",
254 " <td> 3.045079e+09</td>\n",
255 " <td> 896875.396449</td>\n",
256 " <td> (POLYGON ((1029606.076599121 156073.8142089844...</td>\n",
257 " </tr>\n",
258 " <tr>\n",
259 " <th>3</th>\n",
260 " <td> 1</td>\n",
261 " <td> Manhattan</td>\n",
262 " <td> 6.364308e+08</td>\n",
263 " <td> 358400.912836</td>\n",
264 " <td> (POLYGON ((981219.0557861328 188655.3157958984...</td>\n",
265 " </tr>\n",
266 " <tr>\n",
267 " <th>4</th>\n",
268 " <td> 2</td>\n",
269 " <td> Bronx</td>\n",
270 " <td> 1.186822e+09</td>\n",
271 " <td> 464475.145651</td>\n",
272 " <td> (POLYGON ((1012821.805786133 229228.2645874023...</td>\n",
273 " </tr>\n",
274 " </tbody>\n",
275 "</table>\n",
276 "</div>"
277 ],
278 "metadata": {},
279 "output_type": "pyout",
280 "prompt_number": 6,
281 "text": [
282 " BoroCode BoroName Shape_Area Shape_Leng \\\n",
283 "0 5 Staten Island 1.623847e+09 330454.175933 \n",
284 "1 3 Brooklyn 1.937810e+09 741227.337073 \n",
285 "2 4 Queens 3.045079e+09 896875.396449 \n",
286 "3 1 Manhattan 6.364308e+08 358400.912836 \n",
287 "4 2 Bronx 1.186822e+09 464475.145651 \n",
288 "\n",
289 " geometry \n",
290 "0 (POLYGON ((970217.0223999023 145643.3322143555... \n",
291 "1 (POLYGON ((1021176.479003906 151374.7969970703... \n",
292 "2 (POLYGON ((1029606.076599121 156073.8142089844... \n",
293 "3 (POLYGON ((981219.0557861328 188655.3157958984... \n",
294 "4 (POLYGON ((1012821.805786133 229228.2645874023... "
295 ]
296 }
297 ],
298 "prompt_number": 6
299 },
300 {
301 "cell_type": "code",
302 "collapsed": false,
303 "input": [
304 "polydf2.head()"
305 ],
306 "language": "python",
307 "metadata": {},
308 "outputs": [
309 {
310 "html": [
311 "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
312 "<table border=\"1\" class=\"dataframe\">\n",
313 " <thead>\n",
314 " <tr style=\"text-align: right;\">\n",
315 " <th></th>\n",
316 " <th>geometry</th>\n",
317 " <th>value1</th>\n",
318 " <th>value2</th>\n",
319 " </tr>\n",
320 " </thead>\n",
321 " <tbody>\n",
322 " <tr>\n",
323 " <th>0</th>\n",
324 " <td> POLYGON ((923175 120121, 923126.847266722 1191...</td>\n",
325 " <td> 1033296</td>\n",
326 " <td> 793054</td>\n",
327 " </tr>\n",
328 " <tr>\n",
329 " <th>1</th>\n",
330 " <td> POLYGON ((938595 135393, 938546.847266722 1344...</td>\n",
331 " <td> 1063988</td>\n",
332 " <td> 793202</td>\n",
333 " </tr>\n",
334 " <tr>\n",
335 " <th>2</th>\n",
336 " <td> POLYGON ((954015 150665, 953966.847266722 1496...</td>\n",
337 " <td> 1094680</td>\n",
338 " <td> 793350</td>\n",
339 " </tr>\n",
340 " <tr>\n",
341 " <th>3</th>\n",
342 " <td> POLYGON ((969435 165937, 969386.847266722 1649...</td>\n",
343 " <td> 1125372</td>\n",
344 " <td> 793498</td>\n",
345 " </tr>\n",
346 " <tr>\n",
347 " <th>4</th>\n",
348 " <td> POLYGON ((984855 181209, 984806.847266722 1802...</td>\n",
349 " <td> 1156064</td>\n",
350 " <td> 793646</td>\n",
351 " </tr>\n",
352 " </tbody>\n",
353 "</table>\n",
354 "</div>"
355 ],
356 "metadata": {},
357 "output_type": "pyout",
358 "prompt_number": 7,
359 "text": [
360 " geometry value1 value2\n",
361 "0 POLYGON ((923175 120121, 923126.847266722 1191... 1033296 793054\n",
362 "1 POLYGON ((938595 135393, 938546.847266722 1344... 1063988 793202\n",
363 "2 POLYGON ((954015 150665, 953966.847266722 1496... 1094680 793350\n",
364 "3 POLYGON ((969435 165937, 969386.847266722 1649... 1125372 793498\n",
365 "4 POLYGON ((984855 181209, 984806.847266722 1802... 1156064 793646"
366 ]
367 }
368 ],
369 "prompt_number": 7
370 },
371 {
372 "cell_type": "code",
373 "collapsed": false,
374 "input": [
375 "newdf.head()"
376 ],
377 "language": "python",
378 "metadata": {},
379 "outputs": [
380 {
381 "html": [
382 "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
383 "<table border=\"1\" class=\"dataframe\">\n",
384 " <thead>\n",
385 " <tr style=\"text-align: right;\">\n",
386 " <th></th>\n",
387 " <th>BoroCode</th>\n",
388 " <th>BoroName</th>\n",
389 " <th>Shape_Area</th>\n",
390 " <th>Shape_Leng</th>\n",
391 " <th>value1</th>\n",
392 " <th>value2</th>\n",
393 " <th>geometry</th>\n",
394 " </tr>\n",
395 " </thead>\n",
396 " <tbody>\n",
397 " <tr>\n",
398 " <th>0</th>\n",
399 " <td> 5</td>\n",
400 " <td> Staten Island</td>\n",
401 " <td> 1.623847e+09</td>\n",
402 " <td> 330454.175933</td>\n",
403 " <td> 1033296</td>\n",
404 " <td> 793054</td>\n",
405 " <td> POLYGON ((916755.4256330276 129447.9617643995,...</td>\n",
406 " </tr>\n",
407 " <tr>\n",
408 " <th>1</th>\n",
409 " <td> 5</td>\n",
410 " <td> Staten Island</td>\n",
411 " <td> 1.623847e+09</td>\n",
412 " <td> 330454.175933</td>\n",
413 " <td> 1063988</td>\n",
414 " <td> 793202</td>\n",
415 " <td> POLYGON ((938595 135393, 938546.847266722 1344...</td>\n",
416 " </tr>\n",
417 " <tr>\n",
418 " <th>2</th>\n",
419 " <td> 5</td>\n",
420 " <td> Staten Island</td>\n",
421 " <td> 1.623847e+09</td>\n",
422 " <td> 330454.175933</td>\n",
423 " <td> 1125372</td>\n",
424 " <td> 793498</td>\n",
425 " <td> POLYGON ((961436.3049926758 175473.0296020508,...</td>\n",
426 " </tr>\n",
427 " <tr>\n",
428 " <th>3</th>\n",
429 " <td> 5</td>\n",
430 " <td> Staten Island</td>\n",
431 " <td> 1.623847e+09</td>\n",
432 " <td> 330454.175933</td>\n",
433 " <td> 1094680</td>\n",
434 " <td> 793350</td>\n",
435 " <td> POLYGON ((954015 150665, 953966.847266722 1496...</td>\n",
436 " </tr>\n",
437 " <tr>\n",
438 " <th>4</th>\n",
439 " <td> 2</td>\n",
440 " <td> Bronx</td>\n",
441 " <td> 1.186822e+09</td>\n",
442 " <td> 464475.145651</td>\n",
443 " <td> 1309524</td>\n",
444 " <td> 794386</td>\n",
445 " <td> POLYGON ((1043287.193237305 260300.0289916992,...</td>\n",
446 " </tr>\n",
447 " </tbody>\n",
448 "</table>\n",
449 "</div>"
450 ],
451 "metadata": {},
452 "output_type": "pyout",
453 "prompt_number": 8,
454 "text": [
455 " BoroCode BoroName Shape_Area Shape_Leng value1 value2 \\\n",
456 "0 5 Staten Island 1.623847e+09 330454.175933 1033296 793054 \n",
457 "1 5 Staten Island 1.623847e+09 330454.175933 1063988 793202 \n",
458 "2 5 Staten Island 1.623847e+09 330454.175933 1125372 793498 \n",
459 "3 5 Staten Island 1.623847e+09 330454.175933 1094680 793350 \n",
460 "4 2 Bronx 1.186822e+09 464475.145651 1309524 794386 \n",
461 "\n",
462 " geometry \n",
463 "0 POLYGON ((916755.4256330276 129447.9617643995,... \n",
464 "1 POLYGON ((938595 135393, 938546.847266722 1344... \n",
465 "2 POLYGON ((961436.3049926758 175473.0296020508,... \n",
466 "3 POLYGON ((954015 150665, 953966.847266722 1496... \n",
467 "4 POLYGON ((1043287.193237305 260300.0289916992,... "
468 ]
469 }
470 ],
471 "prompt_number": 8
472 },
473 {
474 "cell_type": "markdown",
475 "metadata": {},
476 "source": [
477 "Now let's look at the other `how` operations:"
478 ]
479 },
480 {
481 "cell_type": "code",
482 "collapsed": false,
483 "input": [
484 "newdf = overlay(polydf, polydf2, how=\"union\")\n",
485 "newdf.plot()"
486 ],
487 "language": "python",
488 "metadata": {},
489 "outputs": [
490 {
491 "metadata": {},
492 "output_type": "pyout",
493 "prompt_number": 9,
494 "text": [
495 "<matplotlib.axes.AxesSubplot at 0x10ce3a610>"
496 ]
497 },
498 {
499 "metadata": {},
500 "output_type": "display_data",
501 "png": 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SE4mMDGfV/9rjtmUol9cO4Ps5x6hWsy43b90FrGjY8DciI6OIi4tkydLuNG7c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\nIyMjIyMjIyMjIyMjIyMjIyMjI1Mk/g8f25H9uqiQuwAAAABJRU5ErkJggg==\n",
502 "text": [
503 "<matplotlib.figure.Figure at 0x10cdaf190>"
504 ]
505 }
506 ],
507 "prompt_number": 9
508 },
509 {
510 "cell_type": "code",
511 "collapsed": false,
512 "input": [
513 "newdf = overlay(polydf, polydf2, how=\"identity\")\n",
514 "newdf.plot()"
515 ],
516 "language": "python",
517 "metadata": {},
518 "outputs": [
519 {
520 "metadata": {},
521 "output_type": "pyout",
522 "prompt_number": 10,
523 "text": [
524 "<matplotlib.axes.AxesSubplot at 0x113aeda90>"
525 ]
526 },
527 {
528 "metadata": {},
529 "output_type": "display_data",
530 "png": 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gyJEjeHt78913W/PVT1CQD7t2TadMGSsCAuKoXc8mpaxCJUsMDHTp1q1bukBW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\nXqZ3AAAAAElFTkSuQmCC\n",
531 "text": [
532 "<matplotlib.figure.Figure at 0x10cda3b10>"
533 ]
534 }
535 ],
536 "prompt_number": 10
537 },
538 {
539 "cell_type": "code",
540 "collapsed": false,
541 "input": [
542 "newdf = overlay(polydf, polydf2, how=\"symmetric_difference\")\n",
543 "newdf.plot()"
544 ],
545 "language": "python",
546 "metadata": {},
547 "outputs": [
548 {
549 "metadata": {},
550 "output_type": "pyout",
551 "prompt_number": 11,
552 "text": [
553 "<matplotlib.axes.AxesSubplot at 0x113473b50>"
554 ]
555 },
556 {
557 "metadata": {},
558 "output_type": "display_data",
559 "png": 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pKSnMnDmTmzdv4urqSmxCAk4eHgCM79Il1/H2lSYc69C+PeZVq7JxoyTTTKc6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\nrkJggg==\n",
560 "text": [
561 "<matplotlib.figure.Figure at 0x113c66e10>"
562 ]
563 }
564 ],
565 "prompt_number": 11
566 },
567 {
568 "cell_type": "code",
569 "collapsed": false,
570 "input": [
571 "newdf = overlay(polydf, polydf2, how=\"difference\")\n",
572 "newdf.plot()"
573 ],
574 "language": "python",
575 "metadata": {},
576 "outputs": [
577 {
578 "metadata": {},
579 "output_type": "pyout",
580 "prompt_number": 12,
581 "text": [
582 "<matplotlib.axes.AxesSubplot at 0x113c7cc10>"
583 ]
584 },
585 {
586 "metadata": {},
587 "output_type": "display_data",
588 "png": 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sjL6JPradbLNc34HlB/gY+pHg4GBu37jNlSNXWD56OTVr16R7t+60rN2Szi06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\nuhMcHJwlokx+krG3uPfEABo3La8WOXOmn2DOtJMAlCptjO/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589 "text": [
590 "<matplotlib.figure.Figure at 0x113464590>"
591 ]
592 }
593 ],
594 "prompt_number": 12
595 }
596 ],
597 "metadata": {}
598 }
599 ]
600 }⏎
718 "nbformat": 4,
719 "nbformat_minor": 1
720 }
+68
-0
examples/plotting_basemap_background.py less more
0 """
1 Adding a background map to plots
2 --------------------------------
3
4 This example shows how you can add a background basemap to plots created
5 with the geopandas ``.plot()`` method. This makes use of the
6 `contextily <https://github.com/darribas/contextily>`__ package to retrieve
7 web map tiles from several sources (OpenStreetMap, Stamen).
8
9 """
10 # sphinx_gallery_thumbnail_number = 3
11 import geopandas
12
13 ###############################################################################
14 # Let's use the NYC borough boundary data that is available in geopandas
15 # datasets. Plotting this gives the following result:
16
17 df = geopandas.read_file(geopandas.datasets.get_path('nybb'))
18 ax = df.plot(figsize=(10, 10), alpha=0.5, edgecolor='k')
19
20 ###############################################################################
21 # Convert the data to Web Mercator
22 # ================================
23 #
24 # Web map tiles are typically provided in
25 # `Web Mercator <https://en.wikipedia.org/wiki/Web_Mercator>`__
26 # (`EPSG 3857 <https://epsg.io/3857>`__), so we need to make sure to convert
27 # our data first to the same CRS to combine our polygons and background tiles
28 # in the same map:
29
30 df = df.to_crs(epsg=3857)
31
32 ###############################################################################
33 # Contextily helper function
34 # ==========================
35 #
36 # We define a small helper function that uses
37 # `contextily <https://github.com/darribas/contextily>`__ to add a map
38 # as background to an existing plot:
39
40 import contextily as ctx
41
42 def add_basemap(ax, zoom, url='http://tile.stamen.com/terrain/tileZ/tileX/tileY.png'):
43 xmin, xmax, ymin, ymax = ax.axis()
44 basemap, extent = ctx.bounds2img(xmin, ymin, xmax, ymax, zoom=zoom, url=url)
45 ax.imshow(basemap, extent=extent, interpolation='bilinear')
46 # restore original x/y limits
47 ax.axis((xmin, xmax, ymin, ymax))
48
49 ###############################################################################
50 # Add background tiles to plot
51 # ============================
52 #
53 # Now we can use the above function to easily add a background map to our
54 # plot. The `zoom` keyword is required and let's you specify the detail of the
55 # map tiles (be careful to not specify a too high `zoom` level, as this can
56 # result in a large download):
57
58 ax = df.plot(figsize=(10, 10), alpha=0.5, edgecolor='k')
59 add_basemap(ax, zoom=10)
60
61 ###############################################################################
62 # By default, contextily uses the Stamen Terrain style. We can specify a
63 # different style using ``ctx.sources``:
64
65 ax = df.plot(figsize=(10, 10), alpha=0.5, edgecolor='k')
66 add_basemap(ax, zoom=11, url=ctx.sources.ST_TONER_LITE)
67 ax.set_axis_off()
+88
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examples/plotting_with_geoplot.py less more
0 """
1 Plotting with Geoplot and GeoPandas
2 -----------------------------------
3
4 `Geoplot <https://residentmario.github.io/geoplot/index.html>`_ is a Python
5 library providing a selection of easy-to-use geospatial visualizations. It is
6 built on top of the lower-level `CartoPy <http://scitools.org.uk/cartopy/>`_,
7 covered in a separate section of this tutorial, and is designed to work with
8 GeoPandas input.
9
10 This example is a brief tour of the `geoplot` API. For more details on the
11 library refer to `its documentation
12 <https://residentmario.github.io/geoplot/index.html>`_.
13
14 First we'll load in the data using GeoPandas.
15 """
16 import geopandas
17
18 path = geopandas.datasets.get_path('naturalearth_lowres')
19 df = geopandas.read_file(path)
20 # Add a column we'll use later
21 df['gdp_pp'] = df['gdp_md_est'] / df['pop_est']
22
23 boroughs = geopandas.read_file(geopandas.datasets.get_path('nybb')).to_crs(epsg='4326')
24 injurious_collisions = geopandas.read_file(
25 "https://github.com/ResidentMario/geoplot-data/raw/master/nyc-injurious-collisions.geojson")
26
27
28 ###############################################################################
29 # Plotting with Geoplot
30 # =====================
31 #
32 # We start out by replicating the basic GeoPandas world plot using Geoplot.
33 import geoplot
34
35 geoplot.polyplot(df, figsize=(8, 4))
36
37 ###############################################################################
38 # Geoplot can re-project data into any of the map projections provided by
39 # CartoPy (see the list
40 # `here <http://scitools.org.uk/cartopy/docs/latest/crs/projections.html>`_).
41
42 import geoplot.crs as gcrs
43 ax = geoplot.polyplot(df, projection=gcrs.Orthographic(), figsize=(8, 4))
44 ax.set_global()
45 ax.outline_patch.set_visible(True)
46
47 ###############################################################################
48 # ``polyplot`` is trivial and can only plot the geometries you pass to it. If
49 # you want to use color as a visual variable, specify a ``choropleth``. Here
50 # we sort GDP per person by country into five buckets by color.
51
52 geoplot.choropleth(df, hue='gdp_pp', cmap='Greens', figsize=(8, 4))
53
54 ###############################################################################
55 # If you want to use size as a visual variable, you want a ``cartogram``. Here
56 # are population estimates for countries in Africa.
57
58 geoplot.cartogram(df[df['continent'] == 'Africa'],
59 scale='pop_est', limits=(0.2, 1), figsize=(7, 8))
60
61 ###############################################################################
62 # If we have data in the shape of points in space, we may generate a
63 # three-dimensional heatmap on it using ``kdeplot``. This example also
64 # demonstrates how easy it is to stack plots on top of one another.
65
66 ax = geoplot.kdeplot(injurious_collisions.sample(1000),
67 shade=True, shade_lowest=False,
68 clip=boroughs.geometry)
69 geoplot.polyplot(boroughs, ax=ax)
70
71 ###############################################################################
72 # Alternatively, we may partition the space into neighborhoods automatically,
73 # using Voronoi tessellation.
74
75 ax = geoplot.voronoi(
76 injurious_collisions.sample(1000),
77 hue='NUMBER OF PERSONS INJURED', cmap='Reds', scheme='fisher_jenks',
78 clip=boroughs.geometry,
79 linewidth=0)
80 geoplot.polyplot(boroughs, ax=ax)
81
82 ###############################################################################
83 # Again, these are just some of the plots you can make with Geoplot. There are
84 # several other possibilities not covered in this brief introduction. For more
85 # examples, refer to the
86 # `Gallery <https://residentmario.github.io/geoplot/gallery.html>`_ in the
87 # Geoplot documentation.
+1106
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examples/spatial_joins.ipynb less more
00 {
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2 "name": "",
3 "signature": "sha256:48efe66abdde37459952c96981b795279e6c34f5d4ebbec82ba5614f7e90d199"
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7 "worksheets": [
8 {
9 "cells": [
1 "cells": [
2 {
3 "cell_type": "markdown",
4 "metadata": {},
5 "source": [
6 "# Spatial Joins\n",
7 "\n",
8 "A *spatial join* uses [binary predicates](http://toblerity.org/shapely/manual.html#binary-predicates) \n",
9 "such as `intersects` and `crosses` to combine two `GeoDataFrames` based on the spatial relationship \n",
10 "between their geometries.\n",
11 "\n",
12 "A common use case might be a spatial join between a point layer and a polygon layer where you want to retain the point geometries and grab the attributes of the intersecting polygons.\n"
13 ]
14 },
15 {
16 "cell_type": "code",
17 "execution_count": 1,
18 "metadata": {
19 "ExecuteTime": {
20 "end_time": "2017-12-15T21:26:04.391570Z",
21 "start_time": "2017-12-15T21:26:04.361570Z"
22 }
23 },
24 "outputs": [
1025 {
11 "cell_type": "markdown",
26 "data": {
27 "text/html": [
28 "<img src=\"https://web.natur.cuni.cz/~langhamr/lectures/vtfg1/mapinfo_1/about_gis/Image23.gif\"/>"
29 ],
30 "text/plain": [
31 "<IPython.core.display.Image object>"
32 ]
33 },
34 "execution_count": 1,
1235 "metadata": {},
13 "source": [
14 "#Spatial Joins\n",
15 "\n",
16 "A *spatial join* uses [binary predicates](http://toblerity.org/shapely/manual.html#binary-predicates) \n",
17 "such as `intersects` and `crosses` to combine two `GeoDataFrames` based on the spatial relationship \n",
18 "between their geometries.\n",
19 "\n",
20 "A common use case might be a spatial join between a point layer and a polygon layer where you want to retain the point geometries and grab the attributes of the intersecting polygons.\n"
21 ]
36 "output_type": "execute_result"
37 }
38 ],
39 "source": [
40 "from IPython.core.display import Image \n",
41 "Image(url='https://web.natur.cuni.cz/~langhamr/lectures/vtfg1/mapinfo_1/about_gis/Image23.gif') "
42 ]
43 },
44 {
45 "cell_type": "markdown",
46 "metadata": {},
47 "source": [
48 "\n",
49 "## Types of spatial joins\n",
50 "\n",
51 "We currently support the following methods of spatial joins. We refer to the *left_df* and *right_df* which are the correspond to the two dataframes passed in as args.\n",
52 "\n",
53 "### Left outer join\n",
54 "\n",
55 "In a LEFT OUTER JOIN (`how='left'`), we keep *all* rows from the left and duplicate them if necessary to represent multiple hits between the two dataframes. We retain attributes of the right if they intersect and lose right rows that don't intersect. A left outer join implies that we are interested in retaining the geometries of the left. \n",
56 "\n",
57 "This is equivalent to the PostGIS query:\n",
58 "```\n",
59 "SELECT pts.geom, pts.id as ptid, polys.id as polyid \n",
60 "FROM pts\n",
61 "LEFT OUTER JOIN polys\n",
62 "ON ST_Intersects(pts.geom, polys.geom);\n",
63 "\n",
64 " geom | ptid | polyid \n",
65 "--------------------------------------------+------+--------\n",
66 " 010100000040A9FBF2D88AD03F349CD47D796CE9BF | 4 | 10\n",
67 " 010100000048EABE3CB622D8BFA8FBF2D88AA0E9BF | 3 | 10\n",
68 " 010100000048EABE3CB622D8BFA8FBF2D88AA0E9BF | 3 | 20\n",
69 " 0101000000F0D88AA0E1A4EEBF7052F7E5B115E9BF | 2 | 20\n",
70 " 0101000000818693BA2F8FF7BF4ADD97C75604E9BF | 1 | \n",
71 "(5 rows)\n",
72 "```\n",
73 "\n",
74 "### Right outer join\n",
75 "\n",
76 "In a RIGHT OUTER JOIN (`how='right'`), we keep *all* rows from the right and duplicate them if necessary to represent multiple hits between the two dataframes. We retain attributes of the left if they intersect and lose left rows that don't intersect. A right outer join implies that we are interested in retaining the geometries of the right. \n",
77 "\n",
78 "This is equivalent to the PostGIS query:\n",
79 "```\n",
80 "SELECT polys.geom, pts.id as ptid, polys.id as polyid \n",
81 "FROM pts\n",
82 "RIGHT OUTER JOIN polys\n",
83 "ON ST_Intersects(pts.geom, polys.geom);\n",
84 "\n",
85 " geom | ptid | polyid \n",
86 "----------+------+--------\n",
87 " 01...9BF | 4 | 10\n",
88 " 01...9BF | 3 | 10\n",
89 " 02...7BF | 3 | 20\n",
90 " 02...7BF | 2 | 20\n",
91 " 00...5BF | | 30\n",
92 "(5 rows)\n",
93 "```\n",
94 "\n",
95 "### Inner join\n",
96 "\n",
97 "In an INNER JOIN (`how='inner'`), we keep rows from the right and left only where their binary predicate is `True`. We duplicate them if necessary to represent multiple hits between the two dataframes. We retain attributes of the right and left only if they intersect and lose all rows that do not. An inner join implies that we are interested in retaining the geometries of the left. \n",
98 "\n",
99 "This is equivalent to the PostGIS query:\n",
100 "```\n",
101 "SELECT pts.geom, pts.id as ptid, polys.id as polyid \n",
102 "FROM pts\n",
103 "INNER JOIN polys\n",
104 "ON ST_Intersects(pts.geom, polys.geom);\n",
105 "\n",
106 " geom | ptid | polyid \n",
107 "--------------------------------------------+------+--------\n",
108 " 010100000040A9FBF2D88AD03F349CD47D796CE9BF | 4 | 10\n",
109 " 010100000048EABE3CB622D8BFA8FBF2D88AA0E9BF | 3 | 10\n",
110 " 010100000048EABE3CB622D8BFA8FBF2D88AA0E9BF | 3 | 20\n",
111 " 0101000000F0D88AA0E1A4EEBF7052F7E5B115E9BF | 2 | 20\n",
112 "(4 rows) \n",
113 "```"
114 ]
115 },
116 {
117 "cell_type": "markdown",
118 "metadata": {},
119 "source": [
120 "## Spatial Joins between two GeoDataFrames\n",
121 "\n",
122 "Let's take a look at how we'd implement these using `GeoPandas`. First, load up the NYC test data into `GeoDataFrames`:"
123 ]
124 },
125 {
126 "cell_type": "code",
127 "execution_count": 2,
128 "metadata": {
129 "ExecuteTime": {
130 "end_time": "2017-12-15T21:26:07.191542Z",
131 "start_time": "2017-12-15T21:26:04.391570Z"
132 }
133 },
134 "outputs": [],
135 "source": [
136 "%matplotlib inline\n",
137 "from shapely.geometry import Point\n",
138 "from geopandas import datasets, GeoDataFrame, read_file\n",
139 "from geopandas.tools import overlay\n",
140 "\n",
141 "# NYC Boros\n",
142 "zippath = datasets.get_path('nybb')\n",
143 "polydf = read_file(zippath)\n",
144 "\n",
145 "# Generate some points\n",
146 "b = [int(x) for x in polydf.total_bounds]\n",
147 "N = 8\n",
148 "pointdf = GeoDataFrame([\n",
149 " {'geometry': Point(x, y), 'value1': x + y, 'value2': x - y}\n",
150 " for x, y in zip(range(b[0], b[2], int((b[2] - b[0]) / N)),\n",
151 " range(b[1], b[3], int((b[3] - b[1]) / N)))])\n",
152 "\n",
153 "# Make sure they're using the same projection reference\n",
154 "pointdf.crs = polydf.crs"
155 ]
156 },
157 {
158 "cell_type": "code",
159 "execution_count": 3,
160 "metadata": {
161 "ExecuteTime": {
162 "end_time": "2017-12-15T21:26:07.211542Z",
163 "start_time": "2017-12-15T21:26:07.191542Z"
164 }
165 },
166 "outputs": [
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361 "2 3 Brooklyn 741080.523166 1.937479e+09 \n",
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370 "4 (POLYGON ((1012821.805786133 229228.2645874023... "
371 ]
372 },
373 "execution_count": 4,
374 "metadata": {},
375 "output_type": "execute_result"
376 }
377 ],
378 "source": [
379 "polydf"
380 ]
381 },
382 {
383 "cell_type": "code",
384 "execution_count": 5,
385 "metadata": {
386 "ExecuteTime": {
387 "end_time": "2017-12-15T21:26:08.271531Z",
388 "start_time": "2017-12-15T21:26:07.921534Z"
389 }
390 },
391 "outputs": [
392 {
393 "data": {
394 "text/plain": [
395 "<matplotlib.axes._subplots.AxesSubplot at 0x4f1b0b8>"
396 ]
397 },
398 "execution_count": 5,
399 "metadata": {},
400 "output_type": "execute_result"
22401 },
23402 {
24 "cell_type": "code",
25 "collapsed": false,
26 "input": [
27 "from IPython.core.display import Image \n",
28 "Image(url=\"https://dl.dropboxusercontent.com/u/6769420/sjoin_test.png\") "
29 ],
30 "language": "python",
403 "data": {
404 "image/png": 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405 "text/plain": [
406 "<matplotlib.figure.Figure at 0x4efcc88>"
407 ]
408 },
31409 "metadata": {},
32 "outputs": [
33 {
34 "html": [
35 "<img src=\"https://dl.dropboxusercontent.com/u/6769420/sjoin_test.png\"/>"
36 ],
37 "metadata": {},
38 "output_type": "pyout",
39 "prompt_number": 2,
40 "text": [
41 "<IPython.core.display.Image at 0x7fe191420650>"
42 ]
43 }
44 ],
45 "prompt_number": 2
410 "output_type": "display_data"
411 }
412 ],
413 "source": [
414 "pointdf.plot()"
415 ]
416 },
417 {
418 "cell_type": "code",
419 "execution_count": 6,
420 "metadata": {
421 "ExecuteTime": {
422 "end_time": "2017-12-15T21:26:10.561508Z",
423 "start_time": "2017-12-15T21:26:08.271531Z"
424 }
425 },
426 "outputs": [
427 {
428 "data": {
429 "text/plain": [
430 "<matplotlib.axes._subplots.AxesSubplot at 0x12991208>"
431 ]
432 },
433 "execution_count": 6,
434 "metadata": {},
435 "output_type": "execute_result"
46436 },
47437 {
48 "cell_type": "markdown",
438 "data": {
439 "image/png": 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f52/Q0VaCQn09efL7s7j+5VRnmoyvp447z02irM7I27tH99G8u3L3ecmjVrBA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\nkCaF+eCpG1v7WaDceFxGVnkD5z397Zi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440 "text/plain": [
441 "<matplotlib.figure.Figure at 0x129caf98>"
442 ]
443 },
49444 "metadata": {},
50 "source": [
51 "\n",
52 "## Types of spatial joins\n",
53 "\n",
54 "We currently support the following methods of spatial joins. We refer to the *left_df* and *right_df* which are the correspond to the two dataframes passed in as args.\n",
55 "\n",
56 "### Left outer join\n",
57 "\n",
58 "In a LEFT OUTER JOIN (`how='left'`), we keep *all* rows from the left and duplicate them if necessary to represent multiple hits between the two dataframes. We retain attributes of the right if they intersect and lose right rows that don't intersect. A left outer join implies that we are interested in retaining the geometries of the left. \n",
59 "\n",
60 "This is equivalent to the PostGIS query:\n",
61 "```\n",
62 "SELECT pts.geom, pts.id as ptid, polys.id as polyid \n",
63 "FROM pts\n",
64 "LEFT OUTER JOIN polys\n",
65 "ON ST_Intersects(pts.geom, polys.geom);\n",
66 "\n",
67 " geom | ptid | polyid \n",
68 "--------------------------------------------+------+--------\n",
69 " 010100000040A9FBF2D88AD03F349CD47D796CE9BF | 4 | 10\n",
70 " 010100000048EABE3CB622D8BFA8FBF2D88AA0E9BF | 3 | 10\n",
71 " 010100000048EABE3CB622D8BFA8FBF2D88AA0E9BF | 3 | 20\n",
72 " 0101000000F0D88AA0E1A4EEBF7052F7E5B115E9BF | 2 | 20\n",
73 " 0101000000818693BA2F8FF7BF4ADD97C75604E9BF | 1 | \n",
74 "(5 rows)\n",
75 "```\n",
76 "\n",
77 "### Right outer join\n",
78 "\n",
79 "In a RIGHT OUTER JOIN (`how='right'`), we keep *all* rows from the right and duplicate them if necessary to represent multiple hits between the two dataframes. We retain attributes of the left if they intersect and lose left rows that don't intersect. A right outer join implies that we are interested in retaining the geometries of the right. \n",
80 "\n",
81 "This is equivalent to the PostGIS query:\n",
82 "```\n",
83 "SELECT polys.geom, pts.id as ptid, polys.id as polyid \n",
84 "FROM pts\n",
85 "RIGHT OUTER JOIN polys\n",
86 "ON ST_Intersects(pts.geom, polys.geom);\n",
87 "\n",
88 " geom | ptid | polyid \n",
89 "----------+------+--------\n",
90 " 01...9BF | 4 | 10\n",
91 " 01...9BF | 3 | 10\n",
92 " 02...7BF | 3 | 20\n",
93 " 02...7BF | 2 | 20\n",
94 " 00...5BF | | 30\n",
95 "(5 rows)\n",
96 "```\n",
97 "\n",
98 "### Inner join\n",
99 "\n",
100 "In an INNER JOIN (`how='inner'`), we keep rows from the right and left only where their binary predicate is `True`. We duplicate them if necessary to represent multiple hits between the two dataframes. We retain attributes of the right and left only if they intersect and lose all rows that do not. An inner join implies that we are interested in retaining the geometries of the left. \n",
101 "\n",
102 "This is equivalent to the PostGIS query:\n",
103 "```\n",
104 "SELECT pts.geom, pts.id as ptid, polys.id as polyid \n",
105 "FROM pts\n",
106 "INNER JOIN polys\n",
107 "ON ST_Intersects(pts.geom, polys.geom);\n",
108 "\n",
109 " geom | ptid | polyid \n",
110 "--------------------------------------------+------+--------\n",
111 " 010100000040A9FBF2D88AD03F349CD47D796CE9BF | 4 | 10\n",
112 " 010100000048EABE3CB622D8BFA8FBF2D88AA0E9BF | 3 | 10\n",
113 " 010100000048EABE3CB622D8BFA8FBF2D88AA0E9BF | 3 | 20\n",
114 " 0101000000F0D88AA0E1A4EEBF7052F7E5B115E9BF | 2 | 20\n",
115 "(4 rows) \n",
116 "```"
117 ]
118 },
445 "output_type": "display_data"
446 }
447 ],
448 "source": [
449 "polydf.plot()"
450 ]
451 },
452 {
453 "cell_type": "markdown",
454 "metadata": {},
455 "source": [
456 "## Joins"
457 ]
458 },
459 {
460 "cell_type": "code",
461 "execution_count": 7,
462 "metadata": {
463 "ExecuteTime": {
464 "end_time": "2017-12-15T21:26:12.951484Z",
465 "start_time": "2017-12-15T21:26:10.561508Z"
466 }
467 },
468 "outputs": [
119469 {
120 "cell_type": "markdown",
470 "data": {
471 "text/html": [
472 "<div>\n",
473 "<style>\n",
474 " .dataframe thead tr:only-child th {\n",
475 " text-align: right;\n",
476 " }\n",
477 "\n",
478 " .dataframe thead th {\n",
479 " text-align: left;\n",
480 " }\n",
481 "\n",
482 " .dataframe tbody tr th {\n",
483 " vertical-align: top;\n",
484 " }\n",
485 "</style>\n",
486 "<table border=\"1\" class=\"dataframe\">\n",
487 " <thead>\n",
488 " <tr style=\"text-align: right;\">\n",
489 " <th></th>\n",
490 " <th>geometry</th>\n",
491 " <th>value1</th>\n",
492 " <th>value2</th>\n",
493 " <th>index_right</th>\n",
494 " <th>BoroCode</th>\n",
495 " <th>BoroName</th>\n",
496 " <th>Shape_Leng</th>\n",
497 " <th>Shape_Area</th>\n",
498 " </tr>\n",
499 " </thead>\n",
500 " <tbody>\n",
501 " <tr>\n",
502 " <th>0</th>\n",
503 " <td>POINT (913175 120121)</td>\n",
504 " <td>1033296</td>\n",
505 " <td>793054</td>\n",
506 " <td>NaN</td>\n",
507 " <td>NaN</td>\n",
508 " <td>NaN</td>\n",
509 " <td>NaN</td>\n",
510 " <td>NaN</td>\n",
511 " </tr>\n",
512 " <tr>\n",
513 " <th>1</th>\n",
514 " <td>POINT (932450 139211)</td>\n",
515 " <td>1071661</td>\n",
516 " <td>793239</td>\n",
517 " <td>0.0</td>\n",
518 " <td>5.0</td>\n",
519 " <td>Staten Island</td>\n",
520 " <td>330470.010332</td>\n",
521 " <td>1.623820e+09</td>\n",
522 " </tr>\n",
523 " <tr>\n",
524 " <th>2</th>\n",
525 " <td>POINT (951725 158301)</td>\n",
526 " <td>1110026</td>\n",
527 " <td>793424</td>\n",
528 " <td>0.0</td>\n",
529 " <td>5.0</td>\n",
530 " <td>Staten Island</td>\n",
531 " <td>330470.010332</td>\n",
532 " <td>1.623820e+09</td>\n",
533 " </tr>\n",
534 " <tr>\n",
535 " <th>3</th>\n",
536 " <td>POINT (971000 177391)</td>\n",
537 " <td>1148391</td>\n",
538 " <td>793609</td>\n",
539 " <td>NaN</td>\n",
540 " <td>NaN</td>\n",
541 " <td>NaN</td>\n",
542 " <td>NaN</td>\n",
543 " <td>NaN</td>\n",
544 " </tr>\n",
545 " <tr>\n",
546 " <th>4</th>\n",
547 " <td>POINT (990275 196481)</td>\n",
548 " <td>1186756</td>\n",
549 " <td>793794</td>\n",
550 " <td>NaN</td>\n",
551 " <td>NaN</td>\n",
552 " <td>NaN</td>\n",
553 " <td>NaN</td>\n",
554 " <td>NaN</td>\n",
555 " </tr>\n",
556 " <tr>\n",
557 " <th>5</th>\n",
558 " <td>POINT (1009550 215571)</td>\n",
559 " <td>1225121</td>\n",
560 " <td>793979</td>\n",
561 " <td>1.0</td>\n",
562 " <td>4.0</td>\n",
563 " <td>Queens</td>\n",
564 " <td>896344.047763</td>\n",
565 " <td>3.045213e+09</td>\n",
566 " </tr>\n",
567 " <tr>\n",
568 " <th>6</th>\n",
569 " <td>POINT (1028825 234661)</td>\n",
570 " <td>1263486</td>\n",
571 " <td>794164</td>\n",
572 " <td>4.0</td>\n",
573 " <td>2.0</td>\n",
574 " <td>Bronx</td>\n",
575 " <td>464392.991824</td>\n",
576 " <td>1.186925e+09</td>\n",
577 " </tr>\n",
578 " <tr>\n",
579 " <th>7</th>\n",
580 " <td>POINT (1048100 253751)</td>\n",
581 " <td>1301851</td>\n",
582 " <td>794349</td>\n",
583 " <td>NaN</td>\n",
584 " <td>NaN</td>\n",
585 " <td>NaN</td>\n",
586 " <td>NaN</td>\n",
587 " <td>NaN</td>\n",
588 " </tr>\n",
589 " <tr>\n",
590 " <th>8</th>\n",
591 " <td>POINT (1067375 272841)</td>\n",
592 " <td>1340216</td>\n",
593 " <td>794534</td>\n",
594 " <td>NaN</td>\n",
595 " <td>NaN</td>\n",
596 " <td>NaN</td>\n",
597 " <td>NaN</td>\n",
598 " <td>NaN</td>\n",
599 " </tr>\n",
600 " </tbody>\n",
601 "</table>\n",
602 "</div>"
603 ],
604 "text/plain": [
605 " geometry value1 value2 index_right BoroCode \\\n",
606 "0 POINT (913175 120121) 1033296 793054 NaN NaN \n",
607 "1 POINT (932450 139211) 1071661 793239 0.0 5.0 \n",
608 "2 POINT (951725 158301) 1110026 793424 0.0 5.0 \n",
609 "3 POINT (971000 177391) 1148391 793609 NaN NaN \n",
610 "4 POINT (990275 196481) 1186756 793794 NaN NaN \n",
611 "5 POINT (1009550 215571) 1225121 793979 1.0 4.0 \n",
612 "6 POINT (1028825 234661) 1263486 794164 4.0 2.0 \n",
613 "7 POINT (1048100 253751) 1301851 794349 NaN NaN \n",
614 "8 POINT (1067375 272841) 1340216 794534 NaN NaN \n",
615 "\n",
616 " BoroName Shape_Leng Shape_Area \n",
617 "0 NaN NaN NaN \n",
618 "1 Staten Island 330470.010332 1.623820e+09 \n",
619 "2 Staten Island 330470.010332 1.623820e+09 \n",
620 "3 NaN NaN NaN \n",
621 "4 NaN NaN NaN \n",
622 "5 Queens 896344.047763 3.045213e+09 \n",
623 "6 Bronx 464392.991824 1.186925e+09 \n",
624 "7 NaN NaN NaN \n",
625 "8 NaN NaN NaN "
626 ]
627 },
628 "execution_count": 7,
121629 "metadata": {},
122 "source": [
123 "## Spatial Joins between two GeoDataFrames\n",
124 "\n",
125 "Let's take a look at how we'd implement these using `GeoPandas`. First, load up the NYC test data into `GeoDataFrames`:"
126 ]
127 },
630 "output_type": "execute_result"
631 }
632 ],
633 "source": [
634 "from geopandas.tools import sjoin\n",
635 "join_left_df = sjoin(pointdf, polydf, how=\"left\")\n",
636 "join_left_df\n",
637 "# Note the NaNs where the point did not intersect a boro"
638 ]
639 },
640 {
641 "cell_type": "code",
642 "execution_count": 8,
643 "metadata": {
644 "ExecuteTime": {
645 "end_time": "2017-12-15T21:26:13.871475Z",
646 "start_time": "2017-12-15T21:26:12.951484Z"
647 }
648 },
649 "outputs": [
128650 {
129 "cell_type": "code",
130 "collapsed": false,
131 "input": [
132 "import os\n",
133 "from shapely.geometry import Point\n",
134 "from geopandas import GeoDataFrame, read_file\n",
135 "from geopandas.tools import overlay\n",
136 "\n",
137 "# NYC Boros\n",
138 "zippath = os.path.abspath('../examples/nybb_14aav.zip')\n",
139 "polydf = read_file('/nybb_14a_av/nybb.shp', vfs='zip://' + zippath)\n",
140 "\n",
141 "# Generate some points\n",
142 "b = [int(x) for x in polydf.total_bounds]\n",
143 "N = 8\n",
144 "pointdf = GeoDataFrame([\n",
145 " {'geometry' : Point(x, y), 'value1': x + y, 'value2': x - y}\n",
146 " for x, y in zip(range(b[0], b[2], int((b[2]-b[0])/N)),\n",
147 " range(b[1], b[3], int((b[3]-b[1])/N)))])"
148 ],
149 "language": "python",
651 "data": {
652 "text/html": [
653 "<div>\n",
654 "<style>\n",
655 " .dataframe thead tr:only-child th {\n",
656 " text-align: right;\n",
657 " }\n",
658 "\n",
659 " .dataframe thead th {\n",
660 " text-align: left;\n",
661 " }\n",
662 "\n",
663 " .dataframe tbody tr th {\n",
664 " vertical-align: top;\n",
665 " }\n",
666 "</style>\n",
667 "<table border=\"1\" class=\"dataframe\">\n",
668 " <thead>\n",
669 " <tr style=\"text-align: right;\">\n",
670 " <th></th>\n",
671 " <th>index_left</th>\n",
672 " <th>value1</th>\n",
673 " <th>value2</th>\n",
674 " <th>BoroCode</th>\n",
675 " <th>BoroName</th>\n",
676 " <th>Shape_Leng</th>\n",
677 " <th>Shape_Area</th>\n",
678 " <th>geometry</th>\n",
679 " </tr>\n",
680 " <tr>\n",
681 " <th>index_right</th>\n",
682 " <th></th>\n",
683 " <th></th>\n",
684 " <th></th>\n",
685 " <th></th>\n",
686 " <th></th>\n",
687 " <th></th>\n",
688 " <th></th>\n",
689 " <th></th>\n",
690 " </tr>\n",
691 " </thead>\n",
692 " <tbody>\n",
693 " <tr>\n",
694 " <th>0</th>\n",
695 " <td>1.0</td>\n",
696 " <td>1071661.0</td>\n",
697 " <td>793239.0</td>\n",
698 " <td>5</td>\n",
699 " <td>Staten Island</td>\n",
700 " <td>330470.010332</td>\n",
701 " <td>1.623820e+09</td>\n",
702 " <td>(POLYGON ((970217.0223999023 145643.3322143555...</td>\n",
703 " </tr>\n",
704 " <tr>\n",
705 " <th>0</th>\n",
706 " <td>2.0</td>\n",
707 " <td>1110026.0</td>\n",
708 " <td>793424.0</td>\n",
709 " <td>5</td>\n",
710 " <td>Staten Island</td>\n",
711 " <td>330470.010332</td>\n",
712 " <td>1.623820e+09</td>\n",
713 " <td>(POLYGON ((970217.0223999023 145643.3322143555...</td>\n",
714 " </tr>\n",
715 " <tr>\n",
716 " <th>1</th>\n",
717 " <td>5.0</td>\n",
718 " <td>1225121.0</td>\n",
719 " <td>793979.0</td>\n",
720 " <td>4</td>\n",
721 " <td>Queens</td>\n",
722 " <td>896344.047763</td>\n",
723 " <td>3.045213e+09</td>\n",
724 " <td>(POLYGON ((1029606.076599121 156073.8142089844...</td>\n",
725 " </tr>\n",
726 " <tr>\n",
727 " <th>4</th>\n",
728 " <td>6.0</td>\n",
729 " <td>1263486.0</td>\n",
730 " <td>794164.0</td>\n",
731 " <td>2</td>\n",
732 " <td>Bronx</td>\n",
733 " <td>464392.991824</td>\n",
734 " <td>1.186925e+09</td>\n",
735 " <td>(POLYGON ((1012821.805786133 229228.2645874023...</td>\n",
736 " </tr>\n",
737 " <tr>\n",
738 " <th>2</th>\n",
739 " <td>NaN</td>\n",
740 " <td>NaN</td>\n",
741 " <td>NaN</td>\n",
742 " <td>3</td>\n",
743 " <td>Brooklyn</td>\n",
744 " <td>741080.523166</td>\n",
745 " <td>1.937479e+09</td>\n",
746 " <td>(POLYGON ((1021176.479003906 151374.7969970703...</td>\n",
747 " </tr>\n",
748 " <tr>\n",
749 " <th>3</th>\n",
750 " <td>NaN</td>\n",
751 " <td>NaN</td>\n",
752 " <td>NaN</td>\n",
753 " <td>1</td>\n",
754 " <td>Manhattan</td>\n",
755 " <td>359299.096471</td>\n",
756 " <td>6.364715e+08</td>\n",
757 " <td>(POLYGON ((981219.0557861328 188655.3157958984...</td>\n",
758 " </tr>\n",
759 " </tbody>\n",
760 "</table>\n",
761 "</div>"
762 ],
763 "text/plain": [
764 " index_left value1 value2 BoroCode BoroName \\\n",
765 "index_right \n",
766 "0 1.0 1071661.0 793239.0 5 Staten Island \n",
767 "0 2.0 1110026.0 793424.0 5 Staten Island \n",
768 "1 5.0 1225121.0 793979.0 4 Queens \n",
769 "4 6.0 1263486.0 794164.0 2 Bronx \n",
770 "2 NaN NaN NaN 3 Brooklyn \n",
771 "3 NaN NaN NaN 1 Manhattan \n",
772 "\n",
773 " Shape_Leng Shape_Area \\\n",
774 "index_right \n",
775 "0 330470.010332 1.623820e+09 \n",
776 "0 330470.010332 1.623820e+09 \n",
777 "1 896344.047763 3.045213e+09 \n",
778 "4 464392.991824 1.186925e+09 \n",
779 "2 741080.523166 1.937479e+09 \n",
780 "3 359299.096471 6.364715e+08 \n",
781 "\n",
782 " geometry \n",
783 "index_right \n",
784 "0 (POLYGON ((970217.0223999023 145643.3322143555... \n",
785 "0 (POLYGON ((970217.0223999023 145643.3322143555... \n",
786 "1 (POLYGON ((1029606.076599121 156073.8142089844... \n",
787 "4 (POLYGON ((1012821.805786133 229228.2645874023... \n",
788 "2 (POLYGON ((1021176.479003906 151374.7969970703... \n",
789 "3 (POLYGON ((981219.0557861328 188655.3157958984... "
790 ]
791 },
792 "execution_count": 8,
150793 "metadata": {},
151 "outputs": [],
152 "prompt_number": 19
153 },
794 "output_type": "execute_result"
795 }
796 ],
797 "source": [
798 "join_right_df = sjoin(pointdf, polydf, how=\"right\")\n",
799 "join_right_df\n",
800 "# Note Staten Island is repeated"
801 ]
802 },
803 {
804 "cell_type": "code",
805 "execution_count": 9,
806 "metadata": {
807 "ExecuteTime": {
808 "end_time": "2017-12-15T21:26:13.961474Z",
809 "start_time": "2017-12-15T21:26:13.881475Z"
810 }
811 },
812 "outputs": [
154813 {
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156 "collapsed": true,
157 "input": [
158 "pointdf"
159 ],
160 "language": "python",
814 "data": {
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816 "<div>\n",
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822 " .dataframe thead th {\n",
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825 "\n",
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830 "<table border=\"1\" class=\"dataframe\">\n",
831 " <thead>\n",
832 " <tr style=\"text-align: right;\">\n",
833 " <th></th>\n",
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835 " <th>value1</th>\n",
836 " <th>value2</th>\n",
837 " <th>index_right</th>\n",
838 " <th>BoroCode</th>\n",
839 " <th>BoroName</th>\n",
840 " <th>Shape_Leng</th>\n",
841 " <th>Shape_Area</th>\n",
842 " </tr>\n",
843 " </thead>\n",
844 " <tbody>\n",
845 " <tr>\n",
846 " <th>1</th>\n",
847 " <td>POINT (932450 139211)</td>\n",
848 " <td>1071661</td>\n",
849 " <td>793239</td>\n",
850 " <td>0</td>\n",
851 " <td>5</td>\n",
852 " <td>Staten Island</td>\n",
853 " <td>330470.010332</td>\n",
854 " <td>1.623820e+09</td>\n",
855 " </tr>\n",
856 " <tr>\n",
857 " <th>2</th>\n",
858 " <td>POINT (951725 158301)</td>\n",
859 " <td>1110026</td>\n",
860 " <td>793424</td>\n",
861 " <td>0</td>\n",
862 " <td>5</td>\n",
863 " <td>Staten Island</td>\n",
864 " <td>330470.010332</td>\n",
865 " <td>1.623820e+09</td>\n",
866 " </tr>\n",
867 " <tr>\n",
868 " <th>5</th>\n",
869 " <td>POINT (1009550 215571)</td>\n",
870 " <td>1225121</td>\n",
871 " <td>793979</td>\n",
872 " <td>1</td>\n",
873 " <td>4</td>\n",
874 " <td>Queens</td>\n",
875 " <td>896344.047763</td>\n",
876 " <td>3.045213e+09</td>\n",
877 " </tr>\n",
878 " <tr>\n",
879 " <th>6</th>\n",
880 " <td>POINT (1028825 234661)</td>\n",
881 " <td>1263486</td>\n",
882 " <td>794164</td>\n",
883 " <td>4</td>\n",
884 " <td>2</td>\n",
885 " <td>Bronx</td>\n",
886 " <td>464392.991824</td>\n",
887 " <td>1.186925e+09</td>\n",
888 " </tr>\n",
889 " </tbody>\n",
890 "</table>\n",
891 "</div>"
892 ],
893 "text/plain": [
894 " geometry value1 value2 index_right BoroCode \\\n",
895 "1 POINT (932450 139211) 1071661 793239 0 5 \n",
896 "2 POINT (951725 158301) 1110026 793424 0 5 \n",
897 "5 POINT (1009550 215571) 1225121 793979 1 4 \n",
898 "6 POINT (1028825 234661) 1263486 794164 4 2 \n",
899 "\n",
900 " BoroName Shape_Leng Shape_Area \n",
901 "1 Staten Island 330470.010332 1.623820e+09 \n",
902 "2 Staten Island 330470.010332 1.623820e+09 \n",
903 "5 Queens 896344.047763 3.045213e+09 \n",
904 "6 Bronx 464392.991824 1.186925e+09 "
905 ]
906 },
907 "execution_count": 9,
161908 "metadata": {},
162 "outputs": [
163 {
164 "html": [
165 "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
166 "<table border=\"1\" class=\"dataframe\">\n",
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169 " <th></th>\n",
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171 " <th>value1</th>\n",
172 " <th>value2</th>\n",
173 " </tr>\n",
174 " </thead>\n",
175 " <tbody>\n",
176 " <tr>\n",
177 " <th>0</th>\n",
178 " <td> POINT (913175 120121)</td>\n",
179 " <td> 1033296</td>\n",
180 " <td> 793054</td>\n",
181 " </tr>\n",
182 " <tr>\n",
183 " <th>1</th>\n",
184 " <td> POINT (932450 139211)</td>\n",
185 " <td> 1071661</td>\n",
186 " <td> 793239</td>\n",
187 " </tr>\n",
188 " <tr>\n",
189 " <th>2</th>\n",
190 " <td> POINT (951725 158301)</td>\n",
191 " <td> 1110026</td>\n",
192 " <td> 793424</td>\n",
193 " </tr>\n",
194 " <tr>\n",
195 " <th>3</th>\n",
196 " <td> POINT (971000 177391)</td>\n",
197 " <td> 1148391</td>\n",
198 " <td> 793609</td>\n",
199 " </tr>\n",
200 " <tr>\n",
201 " <th>4</th>\n",
202 " <td> POINT (990275 196481)</td>\n",
203 " <td> 1186756</td>\n",
204 " <td> 793794</td>\n",
205 " </tr>\n",
206 " <tr>\n",
207 " <th>5</th>\n",
208 " <td> POINT (1009550 215571)</td>\n",
209 " <td> 1225121</td>\n",
210 " <td> 793979</td>\n",
211 " </tr>\n",
212 " <tr>\n",
213 " <th>6</th>\n",
214 " <td> POINT (1028825 234661)</td>\n",
215 " <td> 1263486</td>\n",
216 " <td> 794164</td>\n",
217 " </tr>\n",
218 " <tr>\n",
219 " <th>7</th>\n",
220 " <td> POINT (1048100 253751)</td>\n",
221 " <td> 1301851</td>\n",
222 " <td> 794349</td>\n",
223 " </tr>\n",
224 " <tr>\n",
225 " <th>8</th>\n",
226 " <td> POINT (1067375 272841)</td>\n",
227 " <td> 1340216</td>\n",
228 " <td> 794534</td>\n",
229 " </tr>\n",
230 " </tbody>\n",
231 "</table>\n",
232 "</div>"
233 ],
234 "metadata": {},
235 "output_type": "pyout",
236 "prompt_number": 20,
237 "text": [
238 " geometry value1 value2\n",
239 "0 POINT (913175 120121) 1033296 793054\n",
240 "1 POINT (932450 139211) 1071661 793239\n",
241 "2 POINT (951725 158301) 1110026 793424\n",
242 "3 POINT (971000 177391) 1148391 793609\n",
243 "4 POINT (990275 196481) 1186756 793794\n",
244 "5 POINT (1009550 215571) 1225121 793979\n",
245 "6 POINT (1028825 234661) 1263486 794164\n",
246 "7 POINT (1048100 253751) 1301851 794349\n",
247 "8 POINT (1067375 272841) 1340216 794534"
248 ]
249 }
250 ],
251 "prompt_number": 20
252 },
909 "output_type": "execute_result"
910 }
911 ],
912 "source": [
913 "join_inner_df = sjoin(pointdf, polydf, how=\"inner\")\n",
914 "join_inner_df\n",
915 "# Note the lack of NaNs; dropped anything that didn't intersect"
916 ]
917 },
918 {
919 "cell_type": "markdown",
920 "metadata": {},
921 "source": [
922 "We're not limited to using the `intersection` binary predicate. Any of the `Shapely` geometry methods that return a Boolean can be used by specifying the `op` kwarg."
923 ]
924 },
925 {
926 "cell_type": "code",
927 "execution_count": 10,
928 "metadata": {
929 "ExecuteTime": {
930 "end_time": "2017-12-15T21:26:14.191472Z",
931 "start_time": "2017-12-15T21:26:13.961474Z"
932 }
933 },
934 "outputs": [
253935 {
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255 "collapsed": true,
256 "input": [
257 "polydf"
258 ],
259 "language": "python",
936 "data": {
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940 " .dataframe thead tr:only-child th {\n",
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958 " <th>value2</th>\n",
959 " <th>index_right</th>\n",
960 " <th>BoroCode</th>\n",
961 " <th>BoroName</th>\n",
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963 " <th>Shape_Area</th>\n",
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967 " <tr>\n",
968 " <th>0</th>\n",
969 " <td>POINT (913175 120121)</td>\n",
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978 " <tr>\n",
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980 " <td>POINT (932450 139211)</td>\n",
981 " <td>1071661</td>\n",
982 " <td>793239</td>\n",
983 " <td>0.0</td>\n",
984 " <td>5.0</td>\n",
985 " <td>Staten Island</td>\n",
986 " <td>330470.010332</td>\n",
987 " <td>1.623820e+09</td>\n",
988 " </tr>\n",
989 " <tr>\n",
990 " <th>2</th>\n",
991 " <td>POINT (951725 158301)</td>\n",
992 " <td>1110026</td>\n",
993 " <td>793424</td>\n",
994 " <td>0.0</td>\n",
995 " <td>5.0</td>\n",
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998 " <td>1.623820e+09</td>\n",
999 " </tr>\n",
1000 " <tr>\n",
1001 " <th>3</th>\n",
1002 " <td>POINT (971000 177391)</td>\n",
1003 " <td>1148391</td>\n",
1004 " <td>793609</td>\n",
1005 " <td>NaN</td>\n",
1006 " <td>NaN</td>\n",
1007 " <td>NaN</td>\n",
1008 " <td>NaN</td>\n",
1009 " <td>NaN</td>\n",
1010 " </tr>\n",
1011 " <tr>\n",
1012 " <th>4</th>\n",
1013 " <td>POINT (990275 196481)</td>\n",
1014 " <td>1186756</td>\n",
1015 " <td>793794</td>\n",
1016 " <td>NaN</td>\n",
1017 " <td>NaN</td>\n",
1018 " <td>NaN</td>\n",
1019 " <td>NaN</td>\n",
1020 " <td>NaN</td>\n",
1021 " </tr>\n",
1022 " <tr>\n",
1023 " <th>5</th>\n",
1024 " <td>POINT (1009550 215571)</td>\n",
1025 " <td>1225121</td>\n",
1026 " <td>793979</td>\n",
1027 " <td>1.0</td>\n",
1028 " <td>4.0</td>\n",
1029 " <td>Queens</td>\n",
1030 " <td>896344.047763</td>\n",
1031 " <td>3.045213e+09</td>\n",
1032 " </tr>\n",
1033 " <tr>\n",
1034 " <th>6</th>\n",
1035 " <td>POINT (1028825 234661)</td>\n",
1036 " <td>1263486</td>\n",
1037 " <td>794164</td>\n",
1038 " <td>4.0</td>\n",
1039 " <td>2.0</td>\n",
1040 " <td>Bronx</td>\n",
1041 " <td>464392.991824</td>\n",
1042 " <td>1.186925e+09</td>\n",
1043 " </tr>\n",
1044 " <tr>\n",
1045 " <th>7</th>\n",
1046 " <td>POINT (1048100 253751)</td>\n",
1047 " <td>1301851</td>\n",
1048 " <td>794349</td>\n",
1049 " <td>NaN</td>\n",
1050 " <td>NaN</td>\n",
1051 " <td>NaN</td>\n",
1052 " <td>NaN</td>\n",
1053 " <td>NaN</td>\n",
1054 " </tr>\n",
1055 " <tr>\n",
1056 " <th>8</th>\n",
1057 " <td>POINT (1067375 272841)</td>\n",
1058 " <td>1340216</td>\n",
1059 " <td>794534</td>\n",
1060 " <td>NaN</td>\n",
1061 " <td>NaN</td>\n",
1062 " <td>NaN</td>\n",
1063 " <td>NaN</td>\n",
1064 " <td>NaN</td>\n",
1065 " </tr>\n",
1066 " </tbody>\n",
1067 "</table>\n",
1068 "</div>"
1069 ],
1070 "text/plain": [
1071 " geometry value1 value2 index_right BoroCode \\\n",
1072 "0 POINT (913175 120121) 1033296 793054 NaN NaN \n",
1073 "1 POINT (932450 139211) 1071661 793239 0.0 5.0 \n",
1074 "2 POINT (951725 158301) 1110026 793424 0.0 5.0 \n",
1075 "3 POINT (971000 177391) 1148391 793609 NaN NaN \n",
1076 "4 POINT (990275 196481) 1186756 793794 NaN NaN \n",
1077 "5 POINT (1009550 215571) 1225121 793979 1.0 4.0 \n",
1078 "6 POINT (1028825 234661) 1263486 794164 4.0 2.0 \n",
1079 "7 POINT (1048100 253751) 1301851 794349 NaN NaN \n",
1080 "8 POINT (1067375 272841) 1340216 794534 NaN NaN \n",
1081 "\n",
1082 " BoroName Shape_Leng Shape_Area \n",
1083 "0 NaN NaN NaN \n",
1084 "1 Staten Island 330470.010332 1.623820e+09 \n",
1085 "2 Staten Island 330470.010332 1.623820e+09 \n",
1086 "3 NaN NaN NaN \n",
1087 "4 NaN NaN NaN \n",
1088 "5 Queens 896344.047763 3.045213e+09 \n",
1089 "6 Bronx 464392.991824 1.186925e+09 \n",
1090 "7 NaN NaN NaN \n",
1091 "8 NaN NaN NaN "
1092 ]
1093 },
1094 "execution_count": 10,
2601095 "metadata": {},
261 "outputs": [
262 {
263 "html": [
264 "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
265 "<table border=\"1\" class=\"dataframe\">\n",
266 " <thead>\n",
267 " <tr style=\"text-align: right;\">\n",
268 " <th></th>\n",
269 " <th>BoroCode</th>\n",
270 " <th>BoroName</th>\n",
271 " <th>Shape_Area</th>\n",
272 " <th>Shape_Leng</th>\n",
273 " <th>geometry</th>\n",
274 " </tr>\n",
275 " </thead>\n",
276 " <tbody>\n",
277 " <tr>\n",
278 " <th>0</th>\n",
279 " <td> 5</td>\n",
280 " <td> Staten Island</td>\n",
281 " <td> 1.623847e+09</td>\n",
282 " <td> 330454.175933</td>\n",
283 " <td> (POLYGON ((970217.0223999023 145643.3322143555...</td>\n",
284 " </tr>\n",
285 " <tr>\n",
286 " <th>1</th>\n",
287 " <td> 3</td>\n",
288 " <td> Brooklyn</td>\n",
289 " <td> 1.937810e+09</td>\n",
290 " <td> 741227.337073</td>\n",
291 " <td> (POLYGON ((1021176.479003906 151374.7969970703...</td>\n",
292 " </tr>\n",
293 " <tr>\n",
294 " <th>2</th>\n",
295 " <td> 4</td>\n",
296 " <td> Queens</td>\n",
297 " <td> 3.045079e+09</td>\n",
298 " <td> 896875.396449</td>\n",
299 " <td> (POLYGON ((1029606.076599121 156073.8142089844...</td>\n",
300 " </tr>\n",
301 " <tr>\n",
302 " <th>3</th>\n",
303 " <td> 1</td>\n",
304 " <td> Manhattan</td>\n",
305 " <td> 6.364308e+08</td>\n",
306 " <td> 358400.912836</td>\n",
307 " <td> (POLYGON ((981219.0557861328 188655.3157958984...</td>\n",
308 " </tr>\n",
309 " <tr>\n",
310 " <th>4</th>\n",
311 " <td> 2</td>\n",
312 " <td> Bronx</td>\n",
313 " <td> 1.186822e+09</td>\n",
314 " <td> 464475.145651</td>\n",
315 " <td> (POLYGON ((1012821.805786133 229228.2645874023...</td>\n",
316 " </tr>\n",
317 " </tbody>\n",
318 "</table>\n",
319 "</div>"
320 ],
321 "metadata": {},
322 "output_type": "pyout",
323 "prompt_number": 21,
324 "text": [
325 " BoroCode BoroName Shape_Area Shape_Leng \\\n",
326 "0 5 Staten Island 1.623847e+09 330454.175933 \n",
327 "1 3 Brooklyn 1.937810e+09 741227.337073 \n",
328 "2 4 Queens 3.045079e+09 896875.396449 \n",
329 "3 1 Manhattan 6.364308e+08 358400.912836 \n",
330 "4 2 Bronx 1.186822e+09 464475.145651 \n",
331 "\n",
332 " geometry \n",
333 "0 (POLYGON ((970217.0223999023 145643.3322143555... \n",
334 "1 (POLYGON ((1021176.479003906 151374.7969970703... \n",
335 "2 (POLYGON ((1029606.076599121 156073.8142089844... \n",
336 "3 (POLYGON ((981219.0557861328 188655.3157958984... \n",
337 "4 (POLYGON ((1012821.805786133 229228.2645874023... "
338 ]
339 }
340 ],
341 "prompt_number": 21
342 },
343 {
344 "cell_type": "code",
345 "collapsed": true,
346 "input": [
347 "pointdf.plot()"
348 ],
349 "language": "python",
350 "metadata": {},
351 "outputs": [
352 {
353 "metadata": {},
354 "output_type": "pyout",
355 "prompt_number": 22,
356 "text": [
357 "<matplotlib.axes.AxesSubplot at 0x7fe17c79de90>"
358 ]
359 },
360 {
361 "metadata": {},
362 "output_type": "display_data",
363 "png": 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364 "text": [
365 "<matplotlib.figure.Figure at 0x7fe17c8344d0>"
366 ]
367 }
368 ],
369 "prompt_number": 22
370 },
371 {
372 "cell_type": "code",
373 "collapsed": false,
374 "input": [
375 "polydf.plot()"
376 ],
377 "language": "python",
378 "metadata": {},
379 "outputs": [
380 {
381 "metadata": {},
382 "output_type": "pyout",
383 "prompt_number": 23,
384 "text": [
385 "<matplotlib.axes.AxesSubplot at 0x7fe17c48f8d0>"
386 ]
387 },
388 {
389 "metadata": {},
390 "output_type": "display_data",
391 "png": 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CgizNyZMncXZ2Zninqj8kgAFBn3gV8IHyDkUIe/8Sl3kd0NOTBkC3vF6x8dAd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\nJCQkJCQ+Pv4PeOABo584nw0AAAAASUVORK5CYII=\n",
392 "text": [
393 "<matplotlib.figure.Figure at 0x7fe17c7a93d0>"
394 ]
395 }
396 ],
397 "prompt_number": 23
398 },
399 {
400 "cell_type": "markdown",
401 "metadata": {},
402 "source": [
403 "## Joins"
404 ]
405 },
406 {
407 "cell_type": "code",
408 "collapsed": false,
409 "input": [
410 "from geopandas.tools import sjoin\n",
411 "join_left_df = sjoin(pointdf, polydf, how=\"left\")\n",
412 "join_left_df\n",
413 "# Note the NaNs where the point did not intersect a boro"
414 ],
415 "language": "python",
416 "metadata": {},
417 "outputs": [
418 {
419 "html": [
420 "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
421 "<table border=\"1\" class=\"dataframe\">\n",
422 " <thead>\n",
423 " <tr style=\"text-align: right;\">\n",
424 " <th></th>\n",
425 " <th>geometry</th>\n",
426 " <th>value1</th>\n",
427 " <th>value2</th>\n",
428 " <th>BoroCode</th>\n",
429 " <th>BoroName</th>\n",
430 " <th>Shape_Area</th>\n",
431 " <th>Shape_Leng</th>\n",
432 " </tr>\n",
433 " </thead>\n",
434 " <tbody>\n",
435 " <tr>\n",
436 " <th>0</th>\n",
437 " <td> POINT (913175 120121)</td>\n",
438 " <td> 1033296</td>\n",
439 " <td> 793054</td>\n",
440 " <td>NaN</td>\n",
441 " <td> None</td>\n",
442 " <td> NaN</td>\n",
443 " <td> NaN</td>\n",
444 " </tr>\n",
445 " <tr>\n",
446 " <th>1</th>\n",
447 " <td> POINT (932450 139211)</td>\n",
448 " <td> 1071661</td>\n",
449 " <td> 793239</td>\n",
450 " <td> 5</td>\n",
451 " <td> Staten Island</td>\n",
452 " <td> 1.623847e+09</td>\n",
453 " <td> 330454.175933</td>\n",
454 " </tr>\n",
455 " <tr>\n",
456 " <th>2</th>\n",
457 " <td> POINT (951725 158301)</td>\n",
458 " <td> 1110026</td>\n",
459 " <td> 793424</td>\n",
460 " <td> 5</td>\n",
461 " <td> Staten Island</td>\n",
462 " <td> 1.623847e+09</td>\n",
463 " <td> 330454.175933</td>\n",
464 " </tr>\n",
465 " <tr>\n",
466 " <th>3</th>\n",
467 " <td> POINT (971000 177391)</td>\n",
468 " <td> 1148391</td>\n",
469 " <td> 793609</td>\n",
470 " <td>NaN</td>\n",
471 " <td> None</td>\n",
472 " <td> NaN</td>\n",
473 " <td> NaN</td>\n",
474 " </tr>\n",
475 " <tr>\n",
476 " <th>4</th>\n",
477 " <td> POINT (990275 196481)</td>\n",
478 " <td> 1186756</td>\n",
479 " <td> 793794</td>\n",
480 " <td>NaN</td>\n",
481 " <td> None</td>\n",
482 " <td> NaN</td>\n",
483 " <td> NaN</td>\n",
484 " </tr>\n",
485 " <tr>\n",
486 " <th>5</th>\n",
487 " <td> POINT (1009550 215571)</td>\n",
488 " <td> 1225121</td>\n",
489 " <td> 793979</td>\n",
490 " <td> 4</td>\n",
491 " <td> Queens</td>\n",
492 " <td> 3.045079e+09</td>\n",
493 " <td> 896875.396449</td>\n",
494 " </tr>\n",
495 " <tr>\n",
496 " <th>6</th>\n",
497 " <td> POINT (1028825 234661)</td>\n",
498 " <td> 1263486</td>\n",
499 " <td> 794164</td>\n",
500 " <td> 2</td>\n",
501 " <td> Bronx</td>\n",
502 " <td> 1.186822e+09</td>\n",
503 " <td> 464475.145651</td>\n",
504 " </tr>\n",
505 " <tr>\n",
506 " <th>7</th>\n",
507 " <td> POINT (1048100 253751)</td>\n",
508 " <td> 1301851</td>\n",
509 " <td> 794349</td>\n",
510 " <td>NaN</td>\n",
511 " <td> None</td>\n",
512 " <td> NaN</td>\n",
513 " <td> NaN</td>\n",
514 " </tr>\n",
515 " <tr>\n",
516 " <th>8</th>\n",
517 " <td> POINT (1067375 272841)</td>\n",
518 " <td> 1340216</td>\n",
519 " <td> 794534</td>\n",
520 " <td>NaN</td>\n",
521 " <td> None</td>\n",
522 " <td> NaN</td>\n",
523 " <td> NaN</td>\n",
524 " </tr>\n",
525 " </tbody>\n",
526 "</table>\n",
527 "</div>"
528 ],
529 "metadata": {},
530 "output_type": "pyout",
531 "prompt_number": 24,
532 "text": [
533 " geometry value1 value2 BoroCode BoroName \\\n",
534 "0 POINT (913175 120121) 1033296 793054 NaN None \n",
535 "1 POINT (932450 139211) 1071661 793239 5 Staten Island \n",
536 "2 POINT (951725 158301) 1110026 793424 5 Staten Island \n",
537 "3 POINT (971000 177391) 1148391 793609 NaN None \n",
538 "4 POINT (990275 196481) 1186756 793794 NaN None \n",
539 "5 POINT (1009550 215571) 1225121 793979 4 Queens \n",
540 "6 POINT (1028825 234661) 1263486 794164 2 Bronx \n",
541 "7 POINT (1048100 253751) 1301851 794349 NaN None \n",
542 "8 POINT (1067375 272841) 1340216 794534 NaN None \n",
543 "\n",
544 " Shape_Area Shape_Leng \n",
545 "0 NaN NaN \n",
546 "1 1.623847e+09 330454.175933 \n",
547 "2 1.623847e+09 330454.175933 \n",
548 "3 NaN NaN \n",
549 "4 NaN NaN \n",
550 "5 3.045079e+09 896875.396449 \n",
551 "6 1.186822e+09 464475.145651 \n",
552 "7 NaN NaN \n",
553 "8 NaN NaN "
554 ]
555 }
556 ],
557 "prompt_number": 24
558 },
559 {
560 "cell_type": "code",
561 "collapsed": false,
562 "input": [
563 "join_right_df = sjoin(pointdf, polydf, how=\"right\")\n",
564 "join_right_df\n",
565 "# Note Staten Island is repeated"
566 ],
567 "language": "python",
568 "metadata": {},
569 "outputs": [
570 {
571 "html": [
572 "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
573 "<table border=\"1\" class=\"dataframe\">\n",
574 " <thead>\n",
575 " <tr style=\"text-align: right;\">\n",
576 " <th></th>\n",
577 " <th>BoroCode</th>\n",
578 " <th>BoroName</th>\n",
579 " <th>Shape_Area</th>\n",
580 " <th>Shape_Leng</th>\n",
581 " <th>geometry</th>\n",
582 " <th>value1</th>\n",
583 " <th>value2</th>\n",
584 " </tr>\n",
585 " </thead>\n",
586 " <tbody>\n",
587 " <tr>\n",
588 " <th>0</th>\n",
589 " <td> 5</td>\n",
590 " <td> Staten Island</td>\n",
591 " <td> 1.623847e+09</td>\n",
592 " <td> 330454.175933</td>\n",
593 " <td> (POLYGON ((970217.0223999023 145643.3322143555...</td>\n",
594 " <td> 1071661</td>\n",
595 " <td> 793239</td>\n",
596 " </tr>\n",
597 " <tr>\n",
598 " <th>1</th>\n",
599 " <td> 5</td>\n",
600 " <td> Staten Island</td>\n",
601 " <td> 1.623847e+09</td>\n",
602 " <td> 330454.175933</td>\n",
603 " <td> (POLYGON ((970217.0223999023 145643.3322143555...</td>\n",
604 " <td> 1110026</td>\n",
605 " <td> 793424</td>\n",
606 " </tr>\n",
607 " <tr>\n",
608 " <th>2</th>\n",
609 " <td> 3</td>\n",
610 " <td> Brooklyn</td>\n",
611 " <td> 1.937810e+09</td>\n",
612 " <td> 741227.337073</td>\n",
613 " <td> (POLYGON ((1021176.479003906 151374.7969970703...</td>\n",
614 " <td> NaN</td>\n",
615 " <td> NaN</td>\n",
616 " </tr>\n",
617 " <tr>\n",
618 " <th>3</th>\n",
619 " <td> 4</td>\n",
620 " <td> Queens</td>\n",
621 " <td> 3.045079e+09</td>\n",
622 " <td> 896875.396449</td>\n",
623 " <td> (POLYGON ((1029606.076599121 156073.8142089844...</td>\n",
624 " <td> 1225121</td>\n",
625 " <td> 793979</td>\n",
626 " </tr>\n",
627 " <tr>\n",
628 " <th>4</th>\n",
629 " <td> 1</td>\n",
630 " <td> Manhattan</td>\n",
631 " <td> 6.364308e+08</td>\n",
632 " <td> 358400.912836</td>\n",
633 " <td> (POLYGON ((981219.0557861328 188655.3157958984...</td>\n",
634 " <td> NaN</td>\n",
635 " <td> NaN</td>\n",
636 " </tr>\n",
637 " <tr>\n",
638 " <th>5</th>\n",
639 " <td> 2</td>\n",
640 " <td> Bronx</td>\n",
641 " <td> 1.186822e+09</td>\n",
642 " <td> 464475.145651</td>\n",
643 " <td> (POLYGON ((1012821.805786133 229228.2645874023...</td>\n",
644 " <td> 1263486</td>\n",
645 " <td> 794164</td>\n",
646 " </tr>\n",
647 " </tbody>\n",
648 "</table>\n",
649 "</div>"
650 ],
651 "metadata": {},
652 "output_type": "pyout",
653 "prompt_number": 25,
654 "text": [
655 " BoroCode BoroName Shape_Area Shape_Leng \\\n",
656 "0 5 Staten Island 1.623847e+09 330454.175933 \n",
657 "1 5 Staten Island 1.623847e+09 330454.175933 \n",
658 "2 3 Brooklyn 1.937810e+09 741227.337073 \n",
659 "3 4 Queens 3.045079e+09 896875.396449 \n",
660 "4 1 Manhattan 6.364308e+08 358400.912836 \n",
661 "5 2 Bronx 1.186822e+09 464475.145651 \n",
662 "\n",
663 " geometry value1 value2 \n",
664 "0 (POLYGON ((970217.0223999023 145643.3322143555... 1071661 793239 \n",
665 "1 (POLYGON ((970217.0223999023 145643.3322143555... 1110026 793424 \n",
666 "2 (POLYGON ((1021176.479003906 151374.7969970703... NaN NaN \n",
667 "3 (POLYGON ((1029606.076599121 156073.8142089844... 1225121 793979 \n",
668 "4 (POLYGON ((981219.0557861328 188655.3157958984... NaN NaN \n",
669 "5 (POLYGON ((1012821.805786133 229228.2645874023... 1263486 794164 "
670 ]
671 }
672 ],
673 "prompt_number": 25
674 },
675 {
676 "cell_type": "code",
677 "collapsed": false,
678 "input": [
679 "join_inner_df = sjoin(pointdf, polydf, how=\"inner\")\n",
680 "join_inner_df\n",
681 "# Note the lack of NaNs; dropped anything that didn't intersect"
682 ],
683 "language": "python",
684 "metadata": {},
685 "outputs": [
686 {
687 "html": [
688 "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
689 "<table border=\"1\" class=\"dataframe\">\n",
690 " <thead>\n",
691 " <tr style=\"text-align: right;\">\n",
692 " <th></th>\n",
693 " <th>geometry</th>\n",
694 " <th>value1</th>\n",
695 " <th>value2</th>\n",
696 " <th>BoroCode</th>\n",
697 " <th>BoroName</th>\n",
698 " <th>Shape_Area</th>\n",
699 " <th>Shape_Leng</th>\n",
700 " </tr>\n",
701 " </thead>\n",
702 " <tbody>\n",
703 " <tr>\n",
704 " <th>0</th>\n",
705 " <td> POINT (932450 139211)</td>\n",
706 " <td> 1071661</td>\n",
707 " <td> 793239</td>\n",
708 " <td> 5</td>\n",
709 " <td> Staten Island</td>\n",
710 " <td> 1.623847e+09</td>\n",
711 " <td> 330454.175933</td>\n",
712 " </tr>\n",
713 " <tr>\n",
714 " <th>1</th>\n",
715 " <td> POINT (951725 158301)</td>\n",
716 " <td> 1110026</td>\n",
717 " <td> 793424</td>\n",
718 " <td> 5</td>\n",
719 " <td> Staten Island</td>\n",
720 " <td> 1.623847e+09</td>\n",
721 " <td> 330454.175933</td>\n",
722 " </tr>\n",
723 " <tr>\n",
724 " <th>2</th>\n",
725 " <td> POINT (1009550 215571)</td>\n",
726 " <td> 1225121</td>\n",
727 " <td> 793979</td>\n",
728 " <td> 4</td>\n",
729 " <td> Queens</td>\n",
730 " <td> 3.045079e+09</td>\n",
731 " <td> 896875.396449</td>\n",
732 " </tr>\n",
733 " <tr>\n",
734 " <th>3</th>\n",
735 " <td> POINT (1028825 234661)</td>\n",
736 " <td> 1263486</td>\n",
737 " <td> 794164</td>\n",
738 " <td> 2</td>\n",
739 " <td> Bronx</td>\n",
740 " <td> 1.186822e+09</td>\n",
741 " <td> 464475.145651</td>\n",
742 " </tr>\n",
743 " </tbody>\n",
744 "</table>\n",
745 "</div>"
746 ],
747 "metadata": {},
748 "output_type": "pyout",
749 "prompt_number": 27,
750 "text": [
751 " geometry value1 value2 BoroCode BoroName \\\n",
752 "0 POINT (932450 139211) 1071661 793239 5 Staten Island \n",
753 "1 POINT (951725 158301) 1110026 793424 5 Staten Island \n",
754 "2 POINT (1009550 215571) 1225121 793979 4 Queens \n",
755 "3 POINT (1028825 234661) 1263486 794164 2 Bronx \n",
756 "\n",
757 " Shape_Area Shape_Leng \n",
758 "0 1.623847e+09 330454.175933 \n",
759 "1 1.623847e+09 330454.175933 \n",
760 "2 3.045079e+09 896875.396449 \n",
761 "3 1.186822e+09 464475.145651 "
762 ]
763 }
764 ],
765 "prompt_number": 27
766 },
767 {
768 "cell_type": "markdown",
769 "metadata": {},
770 "source": [
771 "We're not limited to using the `intersection` binary predicate. Any of the `Shapely` geometry methods that return a Boolean can be used by specifying the `op` kwarg."
772 ]
773 },
774 {
775 "cell_type": "code",
776 "collapsed": false,
777 "input": [
778 "sjoin(pointdf, polydf, how=\"left\", op=\"within\")"
779 ],
780 "language": "python",
781 "metadata": {},
782 "outputs": [
783 {
784 "html": [
785 "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
786 "<table border=\"1\" class=\"dataframe\">\n",
787 " <thead>\n",
788 " <tr style=\"text-align: right;\">\n",
789 " <th></th>\n",
790 " <th>geometry</th>\n",
791 " <th>value1</th>\n",
792 " <th>value2</th>\n",
793 " <th>BoroCode</th>\n",
794 " <th>BoroName</th>\n",
795 " <th>Shape_Area</th>\n",
796 " <th>Shape_Leng</th>\n",
797 " </tr>\n",
798 " </thead>\n",
799 " <tbody>\n",
800 " <tr>\n",
801 " <th>0</th>\n",
802 " <td> POINT (913175 120121)</td>\n",
803 " <td> 1033296</td>\n",
804 " <td> 793054</td>\n",
805 " <td>NaN</td>\n",
806 " <td> None</td>\n",
807 " <td> NaN</td>\n",
808 " <td> NaN</td>\n",
809 " </tr>\n",
810 " <tr>\n",
811 " <th>1</th>\n",
812 " <td> POINT (932450 139211)</td>\n",
813 " <td> 1071661</td>\n",
814 " <td> 793239</td>\n",
815 " <td> 5</td>\n",
816 " <td> Staten Island</td>\n",
817 " <td> 1.623847e+09</td>\n",
818 " <td> 330454.175933</td>\n",
819 " </tr>\n",
820 " <tr>\n",
821 " <th>2</th>\n",
822 " <td> POINT (951725 158301)</td>\n",
823 " <td> 1110026</td>\n",
824 " <td> 793424</td>\n",
825 " <td> 5</td>\n",
826 " <td> Staten Island</td>\n",
827 " <td> 1.623847e+09</td>\n",
828 " <td> 330454.175933</td>\n",
829 " </tr>\n",
830 " <tr>\n",
831 " <th>3</th>\n",
832 " <td> POINT (971000 177391)</td>\n",
833 " <td> 1148391</td>\n",
834 " <td> 793609</td>\n",
835 " <td>NaN</td>\n",
836 " <td> None</td>\n",
837 " <td> NaN</td>\n",
838 " <td> NaN</td>\n",
839 " </tr>\n",
840 " <tr>\n",
841 " <th>4</th>\n",
842 " <td> POINT (990275 196481)</td>\n",
843 " <td> 1186756</td>\n",
844 " <td> 793794</td>\n",
845 " <td>NaN</td>\n",
846 " <td> None</td>\n",
847 " <td> NaN</td>\n",
848 " <td> NaN</td>\n",
849 " </tr>\n",
850 " <tr>\n",
851 " <th>5</th>\n",
852 " <td> POINT (1009550 215571)</td>\n",
853 " <td> 1225121</td>\n",
854 " <td> 793979</td>\n",
855 " <td> 4</td>\n",
856 " <td> Queens</td>\n",
857 " <td> 3.045079e+09</td>\n",
858 " <td> 896875.396449</td>\n",
859 " </tr>\n",
860 " <tr>\n",
861 " <th>6</th>\n",
862 " <td> POINT (1028825 234661)</td>\n",
863 " <td> 1263486</td>\n",
864 " <td> 794164</td>\n",
865 " <td> 2</td>\n",
866 " <td> Bronx</td>\n",
867 " <td> 1.186822e+09</td>\n",
868 " <td> 464475.145651</td>\n",
869 " </tr>\n",
870 " <tr>\n",
871 " <th>7</th>\n",
872 " <td> POINT (1048100 253751)</td>\n",
873 " <td> 1301851</td>\n",
874 " <td> 794349</td>\n",
875 " <td>NaN</td>\n",
876 " <td> None</td>\n",
877 " <td> NaN</td>\n",
878 " <td> NaN</td>\n",
879 " </tr>\n",
880 " <tr>\n",
881 " <th>8</th>\n",
882 " <td> POINT (1067375 272841)</td>\n",
883 " <td> 1340216</td>\n",
884 " <td> 794534</td>\n",
885 " <td>NaN</td>\n",
886 " <td> None</td>\n",
887 " <td> NaN</td>\n",
888 " <td> NaN</td>\n",
889 " </tr>\n",
890 " </tbody>\n",
891 "</table>\n",
892 "</div>"
893 ],
894 "metadata": {},
895 "output_type": "pyout",
896 "prompt_number": 32,
897 "text": [
898 " geometry value1 value2 BoroCode BoroName \\\n",
899 "0 POINT (913175 120121) 1033296 793054 NaN None \n",
900 "1 POINT (932450 139211) 1071661 793239 5 Staten Island \n",
901 "2 POINT (951725 158301) 1110026 793424 5 Staten Island \n",
902 "3 POINT (971000 177391) 1148391 793609 NaN None \n",
903 "4 POINT (990275 196481) 1186756 793794 NaN None \n",
904 "5 POINT (1009550 215571) 1225121 793979 4 Queens \n",
905 "6 POINT (1028825 234661) 1263486 794164 2 Bronx \n",
906 "7 POINT (1048100 253751) 1301851 794349 NaN None \n",
907 "8 POINT (1067375 272841) 1340216 794534 NaN None \n",
908 "\n",
909 " Shape_Area Shape_Leng \n",
910 "0 NaN NaN \n",
911 "1 1.623847e+09 330454.175933 \n",
912 "2 1.623847e+09 330454.175933 \n",
913 "3 NaN NaN \n",
914 "4 NaN NaN \n",
915 "5 3.045079e+09 896875.396449 \n",
916 "6 1.186822e+09 464475.145651 \n",
917 "7 NaN NaN \n",
918 "8 NaN NaN "
919 ]
920 }
921 ],
922 "prompt_number": 32
1096 "output_type": "execute_result"
9231097 }
9241098 ],
925 "metadata": {}
1099 "source": [
1100 "sjoin(pointdf, polydf, how=\"left\", op=\"within\")"
1101 ]
9261102 }
927 ]
928 }⏎
1103 ],
1104 "metadata": {
1105 "kernelspec": {
1106 "display_name": "Python 3",
1107 "language": "python",
1108 "name": "python3"
1109 },
1110 "language_info": {
1111 "codemirror_mode": {
1112 "name": "ipython",
1113 "version": 3
1114 },
1115 "file_extension": ".py",
1116 "mimetype": "text/x-python",
1117 "name": "python",
1118 "nbconvert_exporter": "python",
1119 "pygments_lexer": "ipython3",
1120 "version": "3.6.1"
1121 }
1122 },
1123 "nbformat": 4,
1124 "nbformat_minor": 1
1125 }
+2
-2
geopandas/_version.py less more
2222 # setup.py/versioneer.py will grep for the variable names, so they must
2323 # each be defined on a line of their own. _version.py will just call
2424 # get_keywords().
25 git_refnames = " (tag: v0.3.0)"
26 git_full = "e48af9b2f990c333bc6c59c0f566cd919a3338d1"
25 git_refnames = " (tag: v0.4.0)"
26 git_full = "33c7a7cc572d5209dfb3da6dd1f967ebd0e34b2d"
2727 keywords = {"refnames": git_refnames, "full": git_full}
2828 return keywords
2929
+289
-85
geopandas/base.py less more
3939 # TODO: think about merging with _geo_op
4040 def _series_op(this, other, op, **kwargs):
4141 """Geometric operation that returns a pandas Series"""
42 null_val = False if op != 'distance' else np.nan
42 null_val = False if op not in ['distance', 'project'] else np.nan
4343
4444 if isinstance(other, GeoPandasBase):
4545 this = this.geometry
4747 return Series([getattr(this_elem, op)(other_elem, **kwargs)
4848 if not this_elem.is_empty | other_elem.is_empty else null_val
4949 for this_elem, other_elem in zip(this, other)],
50 index=this.index)
50 index=this.index, dtype=np.dtype(type(null_val)))
5151 else:
5252 return Series([getattr(s, op)(other, **kwargs) if s else null_val
53 for s in this.geometry], index=this.index)
53 for s in this.geometry],
54 index=this.index, dtype=np.dtype(type(null_val)))
5455
5556
5657 def _geo_unary_op(this, op):
6263 def _series_unary_op(this, op, null_value=False):
6364 """Unary operation that returns a Series"""
6465 return Series([getattr(geom, op, null_value) for geom in this.geometry],
65 index=this.index)
66 index=this.index, dtype=np.dtype(type(null_value)))
6667
6768
6869 class GeoPandasBase(object):
9798
9899 @property
99100 def area(self):
100 """Return the area of each geometry in the GeoSeries"""
101 """Returns a ``Series`` containing the area of each geometry in the
102 ``GeoSeries``."""
101103 return _series_unary_op(self, 'area', null_value=np.nan)
102104
103105 @property
104106 def geom_type(self):
105 """Return the geometry type of each geometry in the GeoSeries"""
107 """Returns a ``Series`` of strings specifying the `Geometry Type` of each
108 object."""
106109 return _series_unary_op(self, 'geom_type', null_value=None)
107110
108111 @property
112115
113116 @property
114117 def length(self):
115 """Return the length of each geometry in the GeoSeries"""
118 """Returns a ``Series`` containing the length of each geometry."""
116119 return _series_unary_op(self, 'length', null_value=np.nan)
117120
118121 @property
119122 def is_valid(self):
120 """Return True for each valid geometry, else False"""
123 """Returns a ``Series`` of ``dtype('bool')`` with value ``True`` for
124 geometries that are valid."""
121125 return _series_unary_op(self, 'is_valid', null_value=False)
122126
123127 @property
124128 def is_empty(self):
125 """Return True for each empty geometry, False for non-empty"""
129 """Returns a ``Series`` of ``dtype('bool')`` with value ``True`` for
130 empty geometries."""
126131 return _series_unary_op(self, 'is_empty', null_value=False)
127132
128133 @property
129134 def is_simple(self):
130 """Return True for each simple geometry, else False"""
135 """Returns a ``Series`` of ``dtype('bool')`` with value ``True`` for
136 geometries that do not cross themselves.
137
138 This is meaningful only for `LineStrings` and `LinearRings`.
139 """
131140 return _series_unary_op(self, 'is_simple', null_value=False)
132141
133142 @property
134143 def is_ring(self):
135 """Return True for each geometry that is a closed ring, else False"""
144 """Returns a ``Series`` of ``dtype('bool')`` with value ``True`` for
145 features that are closed."""
136146 # operates on the exterior, so can't use _series_unary_op()
137147 return Series([geom.exterior.is_ring for geom in self.geometry],
138148 index=self.index)
139149
150 @property
151 def has_z(self):
152 """Returns a ``Series`` of ``dtype('bool')`` with value ``True`` for
153 features that have a z-component."""
154 # operates on the exterior, so can't use _series_unary_op()
155 return _series_unary_op(self, 'has_z', null_value=False)
156
140157 #
141158 # Unary operations that return a GeoSeries
142159 #
143160
144161 @property
145162 def boundary(self):
146 """Return the bounding geometry for each geometry"""
163 """Returns a ``GeoSeries`` of lower dimensional objects representing
164 each geometries's set-theoretic `boundary`."""
147165 return _geo_unary_op(self, 'boundary')
148166
149167 @property
150168 def centroid(self):
151 """Return the centroid of each geometry in the GeoSeries"""
169 """Returns a ``GeoSeries`` of points representing the centroid of each
170 geometry."""
152171 return _geo_unary_op(self, 'centroid')
153172
154173 @property
155174 def convex_hull(self):
156 """Return the convex hull of each geometry"""
175 """Returns a ``GeoSeries`` of geometries representing the convex hull
176 of each geometry.
177
178 The convex hull of a geometry is the smallest convex `Polygon`
179 containing all the points in each geometry, unless the number of points
180 in the geometric object is less than three. For two points, the convex
181 hull collapses to a `LineString`; for 1, a `Point`."""
157182 return _geo_unary_op(self, 'convex_hull')
158183
159184 @property
160185 def envelope(self):
161 """Return a bounding rectangle for each geometry"""
186 """Returns a ``GeoSeries`` of geometries representing the envelope of
187 each geometry.
188
189 The envelope of a geometry is the bounding rectangle. That is, the
190 point or smallest rectangular polygon (with sides parallel to the
191 coordinate axes) that contains the geometry."""
162192 return _geo_unary_op(self, 'envelope')
163193
164194 @property
165195 def exterior(self):
166 """Return the outer boundary of each polygon"""
196 """Returns a ``GeoSeries`` of LinearRings representing the outer
197 boundary of each polygon in the GeoSeries.
198
199 Applies to GeoSeries containing only Polygons.
200 """
167201 # TODO: return empty geometry for non-polygons
168202 return _geo_unary_op(self, 'exterior')
169203
170204 @property
171205 def interiors(self):
172 """Return the interior rings of each polygon"""
206 """Returns a ``GeoSeries`` of InteriorRingSequences representing the
207 inner rings of each polygon in the GeoSeries.
208
209 Applies to GeoSeries containing only Polygons.
210 """
173211 # TODO: return empty list or None for non-polygons
174212 return _series_unary_op(self, 'interiors', null_value=False)
175213
176214 def representative_point(self):
177 """Return a GeoSeries of points guaranteed to be in each geometry"""
215 """Returns a ``GeoSeries`` of (cheaply computed) points that are
216 guaranteed to be within each geometry.
217 """
178218 return gpd.GeoSeries([geom.representative_point()
179219 for geom in self.geometry],
180220 index=self.index)
190230
191231 @property
192232 def unary_union(self):
193 """Return the union of all geometries"""
233 """Returns a geometry containing the union of all geometries in the
234 ``GeoSeries``."""
194235 return unary_union(self.geometry.values)
195236
196237 #
198239 #
199240
200241 def contains(self, other):
201 """Return True for all geometries that contain *other*, else False"""
242 """Returns a ``Series`` of ``dtype('bool')`` with value ``True`` for
243 each geometry that contains `other`.
244
245 An object is said to contain `other` if its `interior` contains the
246 `boundary` and `interior` of the other object and their boundaries do
247 not touch at all.
248
249 This is the inverse of :meth:`within` in the sense that the expression
250 ``a.contains(b) == b.within(a)`` always evaluates to ``True``.
251
252 Parameters
253 ----------
254 other : GeoSeries or geometric object
255 The GeoSeries (elementwise) or geometric object to test if is
256 contained.
257 """
202258 return _series_op(self, other, 'contains')
203259
204260 def geom_equals(self, other):
205 """Return True for all geometries that equal *other*, else False"""
261 """Returns a ``Series`` of ``dtype('bool')`` with value ``True`` for
262 each geometry equal to `other`.
263
264 An object is said to be equal to `other` if its set-theoretic
265 `boundary`, `interior`, and `exterior` coincides with those of the
266 other.
267
268 Parameters
269 ----------
270 other : GeoSeries or geometric object
271 The GeoSeries (elementwise) or geometric object to test for
272 equality.
273 """
206274 return _series_op(self, other, 'equals')
207275
208276 def geom_almost_equals(self, other, decimal=6):
209 """Return True for all geometries that is approximately equal to *other*, else False"""
277 """Returns a ``Series`` of ``dtype('bool')`` with value ``True`` if
278 each geometry is approximately equal to `other`.
279
280 Approximate equality is tested at all points to the specified `decimal`
281 place precision. See also :meth:`equals`.
282
283 Parameters
284 ----------
285 other : GeoSeries or geometric object
286 The GeoSeries (elementwise) or geometric object to compare to.
287 decimal : int
288 Decimal place presion used when testing for approximate equality.
289 """
210290 # TODO: pass precision argument
211291 return _series_op(self, other, 'almost_equals', decimal=decimal)
212292
213293 def geom_equals_exact(self, other, tolerance):
214 """Return True for all geometries that equal *other* to a given tolerance, else False"""
294 """Return True for all geometries that equal *other* to a given
295 tolerance, else False"""
215296 # TODO: pass tolerance argument.
216297 return _series_op(self, other, 'equals_exact', tolerance=tolerance)
217298
218299 def crosses(self, other):
219 """Return True for all geometries that cross *other*, else False"""
300 """Returns a ``Series`` of ``dtype('bool')`` with value ``True`` for
301 each geometry that cross `other`.
302
303 An object is said to cross `other` if its `interior` intersects the
304 `interior` of the other but does not contain it, and the dimension of
305 the intersection is less than the dimension of the one or the other.
306
307 Parameters
308 ----------
309 other : GeoSeries or geometric object
310 The GeoSeries (elementwise) or geometric object to test if is
311 crossed.
312 """
220313 return _series_op(self, other, 'crosses')
221314
222315 def disjoint(self, other):
223 """Return True for all geometries that are disjoint with *other*, else False"""
316 """Returns a ``Series`` of ``dtype('bool')`` with value ``True`` for
317 each geometry disjoint to `other`.
318
319 An object is said to be disjoint to `other` if its `boundary` and
320 `interior` does not intersect at all with those of the other.
321
322 Parameters
323 ----------
324 other : GeoSeries or geometric object
325 The GeoSeries (elementwise) or geometric object to test if is
326 disjoint.
327 """
224328 return _series_op(self, other, 'disjoint')
225329
226330 def intersects(self, other):
227 """Return True for all geometries that intersect *other*, else False"""
331 """Returns a ``Series`` of ``dtype('bool')`` with value ``True`` for
332 each geometry that intersects `other`.
333
334 An object is said to intersect `other` if its `boundary` and `interior`
335 intersects in any way with those of the other.
336
337 Parameters
338 ----------
339 other : GeoSeries or geometric object
340 The GeoSeries (elementwise) or geometric object to test if is
341 intersected.
342 """
228343 return _series_op(self, other, 'intersects')
229344
230345 def overlaps(self, other):
232347 return _series_op(self, other, 'overlaps')
233348
234349 def touches(self, other):
235 """Return True for all geometries that touch *other*, else False"""
350 """Returns a ``Series`` of ``dtype('bool')`` with value ``True`` for
351 each geometry that touches `other`.
352
353 An object is said to touch `other` if it has at least one point in
354 common with `other` and its interior does not intersect with any part
355 of the other.
356
357 Parameters
358 ----------
359 other : GeoSeries or geometric object
360 The GeoSeries (elementwise) or geometric object to test if is
361 touched.
362 """
236363 return _series_op(self, other, 'touches')
237364
238365 def within(self, other):
239 """Return True for all geometries that are within *other*, else False"""
366 """Returns a ``Series`` of ``dtype('bool')`` with value ``True`` for
367 each geometry that is within `other`.
368
369 An object is said to be within `other` if its `boundary` and `interior`
370 intersects only with the `interior` of the other (not its `boundary` or
371 `exterior`).
372
373 This is the inverse of :meth:`contains` in the sense that the
374 expression ``a.within(b) == b.contains(a)`` always evaluates to
375 ``True``.
376
377 Parameters
378 ----------
379 other : GeoSeries or geometric object
380 The GeoSeries (elementwise) or geometric object to test if each
381 geometry is within.
382
383 """
240384 return _series_op(self, other, 'within')
241385
242386 def distance(self, other):
243 """Return distance of each geometry to *other*"""
387 """Returns a ``Series`` containing the distance to `other`.
388
389 Parameters
390 ----------
391 other : Geoseries or geometric object
392 The Geoseries (elementwise) or geometric object to find the
393 distance to.
394 """
244395 return _series_op(self, other, 'distance')
245396
246397 #
248399 #
249400
250401 def difference(self, other):
251 """Return the set-theoretic difference of each geometry with *other*"""
402 """Returns a ``GeoSeries`` of the points in each geometry that
403 are not in `other`.
404
405 Parameters
406 ----------
407 other : Geoseries or geometric object
408 The Geoseries (elementwise) or geometric object to find the
409 difference to.
410 """
252411 return _geo_op(self, other, 'difference')
253412
254413 def symmetric_difference(self, other):
255 """Return the symmetric difference of each geometry with *other*"""
414 """Returns a ``GeoSeries`` of the symmetric difference of points in
415 each geometry with `other`.
416
417 For each geometry, the symmetric difference consists of points in the
418 geometry not in `other`, and points in `other` not in the geometry.
419
420 Parameters
421 ----------
422 other : Geoseries or geometric object
423 The Geoseries (elementwise) or geometric object to find the
424 symmetric difference to.
425 """
256426 return _geo_op(self, other, 'symmetric_difference')
257427
258428 def union(self, other):
259 """Return the set-theoretic union of each geometry with *other*"""
429 """Returns a ``GeoSeries`` of the union of points in each geometry with
430 `other`.
431
432 Parameters
433 ----------
434 other : Geoseries or geometric object
435 The Geoseries (elementwise) or geometric object to find the union
436 with.
437 """
260438 return _geo_op(self, other, 'union')
261439
262440 def intersection(self, other):
263 """Return the set-theoretic intersection of each geometry with *other*"""
441 """Returns a ``GeoSeries`` of the intersection of points in each
442 geometry with `other`.
443
444 Parameters
445 ----------
446 other : Geoseries or geometric object
447 The Geoseries (elementwise) or geometric object to find the
448 intersection with.
449 """
264450 return _geo_op(self, other, 'intersection')
265451
266452 #
269455
270456 @property
271457 def bounds(self):
272 """Return a DataFrame of minx, miny, maxx, maxy values of geometry objects"""
458 """Returns a ``DataFrame`` with columns ``minx``, ``miny``, ``maxx``,
459 ``maxy`` values containing the bounds for each geometry.
460
461 See ``GeoSeries.total_bounds`` for the limits of the entire series.
462 """
273463 bounds = np.array([geom.bounds for geom in self.geometry])
274464 return DataFrame(bounds,
275465 columns=['minx', 'miny', 'maxx', 'maxy'],
277467
278468 @property
279469 def total_bounds(self):
280 """Return a single bounding box (minx, miny, maxx, maxy) for all geometries
281
282 This is a shortcut for calculating the min/max x and y bounds individually.
283 """
284
470 """Returns a tuple containing ``minx``, ``miny``, ``maxx``, ``maxy``
471 values for the bounds of the series as a whole.
472
473 See ``GeoSeries.bounds`` for the bounds of the geometries contained in
474 the series.
475 """
285476 b = self.bounds
286477 return np.array((b['minx'].min(),
287478 b['miny'].min(),
294485 self._generate_sindex()
295486 return self._sindex
296487
297 def buffer(self, distance, resolution=16):
298 return gpd.GeoSeries([geom.buffer(distance, resolution)
488 def buffer(self, distance, resolution=16, **kwargs):
489 """Returns a ``GeoSeries`` of geometries representing all points within
490 a given `distance` of each geometric object.
491
492 See http://shapely.readthedocs.io/en/latest/manual.html#object.buffer
493 for details.
494
495 Parameters
496 ----------
497 distance : float
498 The radius of the buffer.
499 resolution: float
500 Optional, the resolution of the buffer around each vertex.
501 """
502 return gpd.GeoSeries([geom.buffer(distance, resolution, **kwargs)
299503 for geom in self.geometry],
300504 index=self.index, crs=self.crs)
301505
302506 def simplify(self, *args, **kwargs):
507 """Returns a ``GeoSeries`` containing a simplified representation of
508 each geometry.
509
510 See http://shapely.readthedocs.io/en/latest/manual.html#object.simplify
511 for details
512
513 Parameters
514 ----------
515 tolerance : float
516 All points in a simplified geometry will be no more than
517 `tolerance` distance from the original.
518 preserve_topology: bool
519 False uses a quicker algorithm, but may produce self-intersecting
520 or otherwise invalid geometries.
521 """
303522 return gpd.GeoSeries([geom.simplify(*args, **kwargs)
304523 for geom in self.geometry],
305524 index=self.index, crs=self.crs)
342561 index=self.index, crs=self.crs)
343562
344563 def translate(self, xoff=0.0, yoff=0.0, zoff=0.0):
345 """
346 Shift the coordinates of the GeoSeries.
564 """Returns a ``GeoSeries`` with translated geometries.
565
566 See http://shapely.readthedocs.io/en/latest/manual.html#shapely.affinity.translate
567 for details.
347568
348569 Parameters
349570 ----------
351572 Amount of offset along each dimension.
352573 xoff, yoff, and zoff for translation along the x, y, and z
353574 dimensions respectively.
354
355 See shapely manual for more information:
356 http://toblerity.org/shapely/manual.html#affine-transformations
357 """
358
575 """
359576 return gpd.GeoSeries([affinity.translate(s, xoff, yoff, zoff)
360577 for s in self.geometry],
361578 index=self.index, crs=self.crs)
362579
363580 def rotate(self, angle, origin='center', use_radians=False):
364 """
365 Rotate the coordinates of the GeoSeries.
581 """Returns a ``GeoSeries`` with rotated geometries.
582
583 See http://shapely.readthedocs.io/en/latest/manual.html#shapely.affinity.rotate
584 for details.
366585
367586 Parameters
368587 ----------
376595 object or a coordinate tuple (x, y).
377596 use_radians : boolean
378597 Whether to interpret the angle of rotation as degrees or radians
379
380 See shapely manual for more information:
381 http://toblerity.org/shapely/manual.html#affine-transformations
382 """
383
598 """
384599 return gpd.GeoSeries([affinity.rotate(s, angle, origin=origin,
385600 use_radians=use_radians) for s in self.geometry],
386601 index=self.index, crs=self.crs)
387602
388603 def scale(self, xfact=1.0, yfact=1.0, zfact=1.0, origin='center'):
389 """
390 Scale the geometries of the GeoSeries along each (x, y, z) dimension.
604 """Returns a ``GeoSeries`` with scaled geometries.
605
606 The geometries can be scaled by different factors along each
607 dimension. Negative scale factors will mirror or reflect coordinates.
608
609 See http://shapely.readthedocs.io/en/latest/manual.html#shapely.affinity.scale
610 for details.
391611
392612 Parameters
393613 ----------
397617 The point of origin can be a keyword 'center' for the 2D bounding
398618 box center (default), 'centroid' for the geometry's 2D centroid, a
399619 Point object or a coordinate tuple (x, y, z).
400
401 Note: Negative scale factors will mirror or reflect coordinates.
402
403 See shapely manual for more information:
404 http://toblerity.org/shapely/manual.html#affine-transformations
405 """
406
620 """
407621 return gpd.GeoSeries([affinity.scale(s, xfact, yfact, zfact,
408622 origin=origin) for s in self.geometry], index=self.index,
409623 crs=self.crs)
410624
411625 def skew(self, xs=0.0, ys=0.0, origin='center', use_radians=False):
412 """
413 Shear/Skew the geometries of the GeoSeries by angles along x and y dimensions.
626 """Returns a ``GeoSeries`` with skewed geometries.
627
628 The geometries are sheared by angles along the x and y dimensions.
629
630 See http://shapely.readthedocs.io/en/latest/manual.html#shapely.affinity.skew
631 for details.
414632
415633 Parameters
416634 ----------
424642 object or a coordinate tuple (x, y).
425643 use_radians : boolean
426644 Whether to interpret the shear angle(s) as degrees or radians
427
428 See shapely manual for more information:
429 http://toblerity.org/shapely/manual.html#affine-transformations
430 """
431
645 """
432646 return gpd.GeoSeries([affinity.skew(s, xs, ys, origin=origin,
433647 use_radians=use_radians) for s in self.geometry],
434648 index=self.index, crs=self.crs)
444658 Returns
445659 ------
446660 A GeoSeries with a MultiIndex. The levels of the MultiIndex are the
447 original index and an integer.
661 original index and a zero-based integer index that counts the
662 number of single geometries within a multi-part geometry.
448663
449664 Example
450665 -------
472687 idxs = [(idx, 0)]
473688 index.extend(idxs)
474689 geometries.extend(geoms)
475 return gpd.GeoSeries(geometries,
476 index=MultiIndex.from_tuples(index)).__finalize__(self)
690 index = MultiIndex.from_tuples(index, names=self.index.names + [None])
691 return gpd.GeoSeries(geometries, index=index).__finalize__(self)
477692
478693
479694 class _CoordinateIndexer(_NDFrameIndexer):
504719 ys.stop if ys.stop is not None else ymax)
505720 idx = obj.intersects(bbox)
506721 return obj[idx]
507
508
509 def _array_input(arr):
510 if isinstance(arr, (MultiPoint, MultiLineString, MultiPolygon)):
511 # Prevent against improper length detection when input is a
512 # Multi*
513 geom = arr
514 arr = np.empty(1, dtype=object)
515 arr[0] = geom
516
517 return arr
+161
-59
geopandas/geodataframe.py less more
00 import json
11
22 import numpy as np
3 import pandas as pd
34 from pandas import DataFrame, Series
45 from shapely.geometry import mapping, shape
56 from shapely.geometry.base import BaseGeometry
154155
155156 @classmethod
156157 def from_file(cls, filename, **kwargs):
157 """
158 Alternate constructor to create a GeoDataFrame from a file.
159
160 Example:
161 df = geopandas.GeoDataFrame.from_file('nybb.shp')
162
163 Wraps geopandas.read_file(). For additional help, see read_file()
164
158 """Alternate constructor to create a ``GeoDataFrame`` from a file.
159
160 Can load a ``GeoDataFrame`` from a file in any format recognized by
161 `fiona`. See http://toblerity.org/fiona/manual.html for details.
162
163 Parameters
164 ----------
165
166 filename : str
167 File path or file handle to read from. Depending on which kwargs
168 are included, the content of filename may vary. See
169 http://toblerity.org/fiona/README.html#usage for usage details.
170 kwargs : key-word arguments
171 These arguments are passed to fiona.open, and can be used to
172 access multi-layer data, data stored within archives (zip files),
173 etc.
174
175 Examples
176 --------
177
178 >>> df = geopandas.GeoDataFrame.from_file('nybb.shp')
165179 """
166180 return geopandas.io.file.read_file(filename, **kwargs)
167181
168182 @classmethod
169 def from_features(cls, features, crs=None):
183 def from_features(cls, features, crs=None, columns=None):
170184 """
171185 Alternate constructor to create GeoDataFrame from an iterable of
172186 features or a feature collection.
182196 ``__geo_interface__``.
183197 crs : str or dict (optional)
184198 Coordinate reference system to set on the resulting frame.
199 columns : list of column names, optional
200 Optionally specify the column names to include in the output frame.
201 This does not overwrite the property names of the input, but can
202 ensure a consistent output format.
185203
186204 Returns
187205 -------
214232 d = {'geometry': shape(f['geometry']) if f['geometry'] else None}
215233 d.update(f['properties'])
216234 rows.append(d)
217 df = GeoDataFrame.from_dict(rows)
235 df = GeoDataFrame(rows, columns=columns)
218236 df.crs = crs
219237 return df
220238
221239 @classmethod
222 def from_postgis(cls, sql, con, geom_col='geom', crs=None, index_col=None,
223 coerce_float=True, params=None):
224 """
225 Alternate constructor to create a GeoDataFrame from a sql query
240 def from_postgis(cls, sql, con, geom_col='geom', crs=None,
241 hex_encoded=True, index_col=None, coerce_float=True,
242 params=None):
243 """Alternate constructor to create a ``GeoDataFrame`` from a sql query
226244 containing a geometry column.
227245
228 Example:
229 df = geopandas.GeoDataFrame.from_postgis(con,
230 "SELECT geom, highway FROM roads;")
231
232 Wraps geopandas.read_postgis(). For additional help, see read_postgis()
233
234 """
235 return geopandas.io.sql.read_postgis(sql, con, geom_col, crs, index_col,
236 coerce_float, params)
246 Parameters
247 ----------
248 sql : string
249 con : DB connection object or SQLAlchemy engine
250 geom_col : string, default 'geom'
251 column name to convert to shapely geometries
252 crs : optional
253 Coordinate reference system to use for the returned GeoDataFrame
254 hex_encoded : bool, optional
255 Whether the geometry is in a hex-encoded string. Default is True,
256 standard for postGIS.
257 index_col : string or list of strings, optional, default: None
258 Column(s) to set as index(MultiIndex)
259 coerce_float : boolean, default True
260 Attempt to convert values of non-string, non-numeric objects (like
261 decimal.Decimal) to floating point, useful for SQL result sets
262 params : list, tuple or dict, optional, default: None
263 List of parameters to pass to execute method.
264
265 Examples
266 --------
267 >>> sql = "SELECT geom, highway FROM roads;"
268 >>> df = geopandas.GeoDataFrame.from_postgis(sql, con)
269 """
270
271 df = geopandas.io.sql.read_postgis(
272 sql, con, geom_col=geom_col, crs=crs, hex_encoded=hex_encoded,
273 index_col=index_col, coerce_float=coerce_float, params=params)
274 return df
237275
238276 def to_json(self, na='null', show_bbox=False, **kwargs):
239277 """
240 Returns a GeoJSON string representation of the GeoDataFrame.
278 Returns a GeoJSON representation of the ``GeoDataFrame`` as a string.
241279
242280 Parameters
243281 ----------
244282 na : {'null', 'drop', 'keep'}, default 'null'
245 Indicates how to output missing (NaN) values in the GeoDataFrame
246 * null: output the missing entries as JSON null
247 * drop: remove the property from the feature. This applies to
248 each feature individually so that features may have
249 different properties
250 * keep: output the missing entries as NaN
251
252 show_bbox : include bbox (bounds) in the geojson
253
283 Indicates how to output missing (NaN) values in the GeoDataFrame.
284 See below.
285 show_bbox : bool, optional, default: False
286 Include bbox (bounds) in the geojson
287
288 Notes
289 -----
254290 The remaining *kwargs* are passed to json.dumps().
255291
292 Missing (NaN) values in the GeoDataFrame can be represented as follows:
293
294 - ``null``: output the missing entries as JSON null.
295 - ``drop``: remove the property from the feature. This applies to each
296 feature individually so that features may have different properties.
297 - ``keep``: output the missing entries as NaN.
256298 """
257299 return json.dumps(self._to_geo(na=na, show_bbox=show_bbox), **kwargs)
258300
259301 @property
260302 def __geo_interface__(self):
261 """
262 Returns a python feature collection (i.e. the geointerface)
263 representation of the GeoDataFrame.
303 """Returns a ``GeoDataFrame`` as a python feature collection.
304
305 Implements the `geo_interface`. The returned python data structure
306 represents the ``GeoDataFrame`` as a GeoJSON-like
307 ``FeatureCollection``.
264308
265309 This differs from `_to_geo()` only in that it is a property with
266310 default args instead of a method
267
268311 """
269312 return self._to_geo(na='null', show_bbox=True)
270313
340383
341384 def to_file(self, filename, driver="ESRI Shapefile", schema=None,
342385 **kwargs):
343 """
344 Write this GeoDataFrame to an OGR data source
345
346 A dictionary of supported OGR providers is available via:
386 """Write the ``GeoDataFrame`` to a file.
387
388 By default, an ESRI shapefile is written, but any OGR data source
389 supported by Fiona can be written. A dictionary of supported OGR
390 providers is available via:
391
347392 >>> import fiona
348393 >>> fiona.supported_drivers
349394
351396 ----------
352397 filename : string
353398 File path or file handle to write to.
354 driver : string, default 'ESRI Shapefile'
399 driver : string, default: 'ESRI Shapefile'
355400 The OGR format driver used to write the vector file.
356 schema : dict, default None
401 schema : dict, default: None
357402 If specified, the schema dictionary is passed to Fiona to
358403 better control how the file is written.
359404
360 The *kwargs* are passed to fiona.open and can be used to write
361 to multi-layer data, store data within archives (zip files), etc.
405 Notes
406 -----
407 The extra keyword arguments ``**kwargs`` are passed to fiona.open and
408 can be used to write to multi-layer data, store data within archives
409 (zip files), etc.
362410 """
363411 from geopandas.io.file import to_file
364412 to_file(self, filename, driver, schema, **kwargs)
365413
366414 def to_crs(self, crs=None, epsg=None, inplace=False):
367 """Transform geometries to a new coordinate reference system
368
369 This method will transform all points in all objects. It has
370 no notion or projecting entire geometries. All segments
371 joining points are assumed to be lines in the current
372 projection, not geodesics. Objects crossing the dateline (or
373 other projection boundary) will have undesirable behavior.
374
375 `to_crs` passes the `crs` argument to the `Proj` function from the
376 `pyproj` library (with the option `preserve_units=True`). It can
377 therefore accept proj4 projections in any format
378 supported by `Proj`, including dictionaries, or proj4 strings.
379
415 """Transform geometries to a new coordinate reference system.
416
417 Transform all geometries in a GeoSeries to a different coordinate
418 reference system. The ``crs`` attribute on the current GeoSeries must
419 be set. Either ``crs`` in string or dictionary form or an EPSG code
420 may be specified for output.
421
422 This method will transform all points in all objects. It has no notion
423 or projecting entire geometries. All segments joining points are
424 assumed to be lines in the current projection, not geodesics. Objects
425 crossing the dateline (or other projection boundary) will have
426 undesirable behavior.
427
428 Parameters
429 ----------
430 crs : dict or str
431 Output projection parameters as string or in dictionary form.
432 epsg : int
433 EPSG code specifying output projection.
434 inplace : bool, optional, default: False
435 Whether to return a new GeoDataFrame or do the transformation in
436 place.
380437 """
381438 if inplace:
382439 df = self
464521 return GeoDataFrame(data).__finalize__(self)
465522
466523 def plot(self, *args, **kwargs):
467
524 """Generate a plot of the geometries in the ``GeoDataFrame``.
525
526 If the ``column`` parameter is given, colors plot according to values
527 in that column, otherwise calls ``GeoSeries.plot()`` on the
528 ``geometry`` column.
529
530 Wraps the ``plot_dataframe()`` function, and documentation is copied
531 from there.
532 """
468533 return plot_dataframe(self, *args, **kwargs)
469534
470535 plot.__doc__ = plot_dataframe.__doc__
517582
518583 return aggregated
519584
585 # overrides GeoPandasBase method
586 def explode(self):
587 """
588 Explode muti-part geometries into multiple single geometries.
589
590 Each row containing a multi-part geometry will be split into
591 multiple rows with single geometries, thereby increasing the vertical
592 size of the GeoDataFrame.
593
594 The index of the input geodataframe is no longer unique and is
595 replaced with a multi-index (original index with additional level
596 indicating the multiple geometries: a new zero-based index for each
597 single part geometry per multi-part geometry).
598
599 Returns
600 -------
601 GeoDataFrame
602 Exploded geodataframe with each single geometry
603 as a separate entry in the geodataframe.
604
605 """
606 df_copy = self.copy()
607
608 exploded_geom = df_copy.geometry.explode().reset_index(level=-1)
609 exploded_index = exploded_geom.columns[0]
610
611 df = pd.concat(
612 [df_copy.drop(df_copy._geometry_column_name, axis=1),
613 exploded_geom], axis=1)
614 # reset to MultiIndex, otherwise df index is only first level of
615 # exploded GeoSeries index.
616 df.set_index(exploded_index, append=True, inplace=True)
617 df.index.names = list(self.index.names) + [None]
618 geo_df = df.set_geometry(self._geometry_column_name)
619 return geo_df
620
621
520622 def _dataframe_set_geometry(self, col, drop=False, inplace=False, crs=None):
521623 if inplace:
522624 raise ValueError("Can't do inplace setting when converting from"
+41
-22
geopandas/geoseries.py less more
3131 return arr.view(GeoSeries)
3232
3333 def __init__(self, *args, **kwargs):
34 # fix problem for scalar geometries passed
34 # fix problem for scalar geometries passed, ensure the list of
35 # scalars is of correct length if index is specified
3536 if len(args) == 1 and isinstance(args[0], BaseGeometry):
36 args = ([args[0]],)
37 n = len(kwargs.get('index', [1]))
38 args = ([args[0]] * n,)
3739
3840 crs = kwargs.pop('crs', None)
3941
6870
6971 @classmethod
7072 def from_file(cls, filename, **kwargs):
71 """
72 Alternate constructor to create a GeoSeries from a file
73 """Alternate constructor to create a ``GeoSeries`` from a file.
74
75 Can load a ``GeoSeries`` from a file from any format recognized by
76 `fiona`. See http://toblerity.org/fiona/manual.html for details.
7377
7478 Parameters
7579 ----------
7680
7781 filename : str
7882 File path or file handle to read from. Depending on which kwargs
79 are included, the content of filename may vary, see:
80 http://toblerity.github.io/fiona/README.html#usage
81 for usage details.
83 are included, the content of filename may vary. See
84 http://toblerity.org/fiona/README.html#usage for usage details.
8285 kwargs : key-word arguments
8386 These arguments are passed to fiona.open, and can be used to
8487 access multi-layer data, data stored within archives (zip files),
8588 etc.
86
8789 """
8890 import fiona
8991 geoms = []
9799
98100 @property
99101 def __geo_interface__(self):
100 """Returns a GeoSeries as a python feature collection
102 """Returns a ``GeoSeries`` as a python feature collection.
103
104 Implements the `geo_interface`. The returned python data structure
105 represents the ``GeoSeries`` as a GeoJSON-like ``FeatureCollection``.
106 Note that the features will have an empty ``properties`` dict as they
107 don't have associated attributes (geometry only).
101108 """
102109 from geopandas import GeoDataFrame
103110 return GeoDataFrame({'geometry': self}).__geo_interface__
249256 return False
250257
251258 def plot(self, *args, **kwargs):
259 """Generate a plot of the geometries in the ``GeoSeries``.
260
261 Wraps the ``plot_series()`` function, and documentation is copied from
262 there.
263 """
252264 return plot_series(self, *args, **kwargs)
253265
254266 plot.__doc__ = plot_series.__doc__
258270 #
259271
260272 def to_crs(self, crs=None, epsg=None):
261 """Transform geometries to a new coordinate reference system
262
263 This method will transform all points in all objects. It has
264 no notion or projecting entire geometries. All segments
265 joining points are assumed to be lines in the current
266 projection, not geodesics. Objects crossing the dateline (or
267 other projection boundary) will have undesirable behavior.
268
269 `to_crs` passes the `crs` argument to the `Proj` function from the
270 `pyproj` library (with the option `preserve_units=True`). It can
271 therefore accept proj4 projections in any format
272 supported by `Proj`, including dictionaries, or proj4 strings.
273
273 """Returns a ``GeoSeries`` with all geometries transformed to a new
274 coordinate reference system.
275
276 Transform all geometries in a GeoSeries to a different coordinate
277 reference system. The ``crs`` attribute on the current GeoSeries must
278 be set. Either ``crs`` in string or dictionary form or an EPSG code
279 may be specified for output.
280
281 This method will transform all points in all objects. It has no notion
282 or projecting entire geometries. All segments joining points are
283 assumed to be lines in the current projection, not geodesics. Objects
284 crossing the dateline (or other projection boundary) will have
285 undesirable behavior.
286
287 Parameters
288 ----------
289 crs : dict or str
290 Output projection parameters as string or in dictionary form.
291 epsg : int
292 EPSG code specifying output projection.
274293 """
275294 from fiona.crs import from_epsg
276295 if self.crs is None:
+82
-24
geopandas/io/file.py less more
11
22 import fiona
33 import numpy as np
4 import six
45
5 from geopandas import GeoDataFrame
6 from geopandas import GeoDataFrame, GeoSeries
7
8 # Adapted from pandas.io.common
9 if six.PY3:
10 from urllib.request import urlopen as _urlopen
11 from urllib.parse import urlparse as parse_url
12 from urllib.parse import uses_relative, uses_netloc, uses_params
13 else:
14 from urllib2 import urlopen as _urlopen
15 from urlparse import urlparse as parse_url
16 from urlparse import uses_relative, uses_netloc, uses_params
17
18 _VALID_URLS = set(uses_relative + uses_netloc + uses_params)
19 _VALID_URLS.discard('')
620
721
8 def read_file(filename, **kwargs):
22 def _is_url(url):
23 """Check to see if *url* has a valid protocol."""
24 try:
25 return parse_url(url).scheme in _VALID_URLS
26 except:
27 return False
28
29
30 def read_file(filename, bbox=None, **kwargs):
931 """
10 Returns a GeoDataFrame from a file.
32 Returns a GeoDataFrame from a file or URL.
1133
12 *filename* is either the absolute or relative path to the file to be
13 opened and *kwargs* are keyword args to be passed to the `open` method
14 in the fiona library when opening the file. For more information on
15 possible keywords, type: ``import fiona; help(fiona.open)``
34 Parameters
35 ----------
36 filename: str
37 Either the absolute or relative path to the file or URL to
38 be opened.
39 bbox : tuple | GeoDataFrame or GeoSeries, default None
40 Filter features by given bounding box, GeoSeries, or GeoDataFrame.
41 CRS mis-matches are resolved if given a GeoSeries or GeoDataFrame.
42 **kwargs:
43 Keyword args to be passed to the `open` or `BytesCollection` method
44 in the fiona library when opening the file. For more information on
45 possible keywords, type:
46 ``import fiona; help(fiona.open)``
47
48 Examples
49 --------
50 >>> df = geopandas.read_file("nybb.shp")
51
52 Returns
53 -------
54 geodataframe : GeoDataFrame
1655 """
17 bbox = kwargs.pop('bbox', None)
18 with fiona.open(filename, **kwargs) as f:
19 crs = f.crs
56 if _is_url(filename):
57 req = _urlopen(filename)
58 path_or_bytes = req.read()
59 reader = fiona.BytesCollection
60 else:
61 path_or_bytes = filename
62 reader = fiona.open
63
64 with reader(path_or_bytes, **kwargs) as features:
65 crs = features.crs
2066 if bbox is not None:
21 assert len(bbox)==4
22 f_filt = f.filter(bbox=bbox)
67 if isinstance(bbox, GeoDataFrame) or isinstance(bbox, GeoSeries):
68 bbox = tuple(bbox.to_crs(crs).total_bounds)
69 assert len(bbox) == 4
70 f_filt = features.filter(bbox=bbox)
2371 else:
24 f_filt = f
25 gdf = GeoDataFrame.from_features(f_filt, crs=crs)
72 f_filt = features
2673
27 # re-order with column order from metadata, with geometry last
28 columns = list(f.meta["schema"]["properties"]) + ["geometry"]
29 gdf = gdf[columns]
74 columns = list(features.meta["schema"]["properties"]) + ["geometry"]
75 gdf = GeoDataFrame.from_features(f_filt, crs=crs, columns=columns)
3076
3177 return gdf
3278
61107 with fiona.drivers():
62108 with fiona.open(filename, 'w', driver=driver, crs=df.crs,
63109 schema=schema, **kwargs) as colxn:
64 for feature in df.iterfeatures():
65 colxn.write(feature)
110 colxn.writerecords(df.iterfeatures())
66111
67112
68113 def infer_schema(df):
71116 except ImportError:
72117 from ordereddict import OrderedDict
73118
74 def convert_type(in_type):
119 def convert_type(column, in_type):
75120 if in_type == object:
76121 return 'str'
77122 out_type = type(np.asscalar(np.zeros(1, in_type))).__name__
78123 if out_type == 'long':
79124 out_type = 'int'
125 if out_type == 'bool':
126 raise ValueError('column "{}" is boolean type, '.format(column) +
127 'which is unsupported in file writing. '
128 'Consider casting the column to int type.')
80129 return out_type
81130
82131 properties = OrderedDict([
83 (col, convert_type(_type)) for col, _type in
132 (col, convert_type(col, _type)) for col, _type in
84133 zip(df.columns, df.dtypes) if col != df._geometry_column_name
85134 ])
86135
136 if df.empty:
137 raise ValueError("Cannot write empty DataFrame to file.")
138
87139 geom_type = _common_geom_type(df)
140
88141 if not geom_type:
89142 raise ValueError("Geometry column cannot contain mutiple "
90143 "geometry types when writing to file.")
100153 # Point, LineString, or Polygon
101154 geom_types = df.geometry.geom_type.unique()
102155
103 from os.path import commonprefix # To find longest common prefix
104 geom_type = commonprefix([g[::-1] for g in geom_types if g])[::-1] # Reverse
156 from os.path import commonprefix
157 # use reversed geom types and commonprefix to find the common suffix,
158 # then reverse the result to get back to a geom type
159 geom_type = commonprefix([g[::-1] for g in geom_types if g])[::-1]
105160 if not geom_type:
106 geom_type = None
161 return None
162
163 if df.geometry.has_z.any():
164 geom_type = "3D " + geom_type
107165
108166 return geom_type
+42
-23
geopandas/io/sql.py less more
0 import binascii
1
2 from pandas import read_sql
0 import pandas as pd
31 import shapely.wkb
42
5 from geopandas import GeoSeries, GeoDataFrame
3 from geopandas import GeoDataFrame
64
75
8 def read_postgis(sql, con, geom_col='geom', crs=None, index_col=None,
9 coerce_float=True, params=None):
6 def read_postgis(sql, con, geom_col='geom', crs=None, hex_encoded=True,
7 index_col=None, coerce_float=True, params=None):
108 """
119 Returns a GeoDataFrame corresponding to the result of the query
1210 string, which must contain a geometry column.
1311
14 Examples:
15 sql = "SELECT geom, kind FROM polygons;"
16 df = geopandas.read_postgis(sql, con)
17
1812 Parameters
1913 ----------
20 sql: string
21 con: DB connection object or SQLAlchemy engine
22 geom_col: string, default 'geom'
14 sql : string
15 SQL query to execute in selecting entries from database, or name
16 of the table to read from the database.
17 con : DB connection object or SQLAlchemy engine
18 Active connection to the database to query.
19 geom_col : string, default 'geom'
2320 column name to convert to shapely geometries
24 crs: optional
25 CRS to use for the returned GeoDataFrame
21 crs : dict or str, optional
22 CRS to use for the returned GeoDataFrame; if not set, tries to
23 determine CRS from the SRID associated with the first geometry in
24 the database, and assigns that to all geometries.
25 hex_encoded : bool, optional
26 Whether the geometry is in a hex-encoded string. Default is True,
27 standard for postGIS. Use hex_encoded=False for sqlite databases.
2628
2729 See the documentation for pandas.read_sql for further explanation
2830 of the following parameters:
2931 index_col, coerce_float, params
3032
33 Returns
34 -------
35 GeoDataFrame
36
37 Example
38 -------
39 >>> sql = "SELECT geom, kind FROM polygons;"
40 >>> df = geopandas.read_postgis(sql, con)
3141 """
32 df = read_sql(sql, con, index_col=index_col, coerce_float=coerce_float,
33 params=params)
42
43 df = pd.read_sql(sql, con, index_col=index_col, coerce_float=coerce_float,
44 params=params)
3445
3546 if geom_col not in df:
36 raise ValueError("Query missing geometry column '{0}'".format(
37 geom_col))
47 raise ValueError("Query missing geometry column '{}'".format(geom_col))
3848
39 wkb_geoms = df[geom_col]
49 def load_geom(x):
50 if isinstance(x, bytes):
51 return shapely.wkb.loads(x, hex=hex_encoded)
52 else:
53 return shapely.wkb.loads(str(x), hex=hex_encoded)
54 geoms = df[geom_col].apply(load_geom)
55 df[geom_col] = geoms
4056
41 s = wkb_geoms.apply(lambda x: shapely.wkb.loads(binascii.unhexlify(x.encode())))
42
43 df[geom_col] = GeoSeries(s)
57 if crs is None:
58 if len(geoms) > 0:
59 srid = shapely.geos.lgeos.GEOSGetSRID(geoms[0]._geom)
60 # if no defined SRID in geodatabase, returns SRID of 0
61 if srid != 0:
62 crs = {"init": "epsg:{}".format(srid)}
4463
4564 return GeoDataFrame(df, crs=crs, geometry=geom_col)
+125
-9
geopandas/io/tests/test_io.py less more
00 from __future__ import absolute_import
11
2 from collections import OrderedDict
3
24 import fiona
5 import pytest
6 from shapely.geometry import box
37
48 import geopandas
59 from geopandas import read_postgis, read_file
10 from geopandas.tests.util import connect, create_postgis, validate_boro_df
611
7 import pytest
8 from geopandas.tests.util import connect, create_db, validate_boro_df
12
13 @pytest.fixture
14 def nybb_df():
15 nybb_path = geopandas.datasets.get_path('nybb')
16 df = read_file(nybb_path)
17
18 return df
919
1020
1121 class TestIO:
1828
1929 def test_read_postgis_default(self):
2030 con = connect('test_geopandas')
21 if con is None or not create_db(self.df):
31 if con is None or not create_postgis(self.df):
2232 raise pytest.skip()
2333
2434 try:
2838 con.close()
2939
3040 validate_boro_df(df)
41 # no crs defined on the created geodatabase, and none specified
42 # by user; should not be set to 0, as from get_srid failure
43 assert df.crs is None
3144
3245 def test_read_postgis_custom_geom_col(self):
3346 con = connect('test_geopandas')
34 if con is None or not create_db(self.df):
47 geom_col = "the_geom"
48 if con is None or not create_postgis(self.df, geom_col=geom_col):
3549 raise pytest.skip()
3650
3751 try:
38 sql = """SELECT
39 borocode, boroname, shape_leng, shape_area,
40 geom AS __geometry__
41 FROM nybb;"""
42 df = read_postgis(sql, con, geom_col='__geometry__')
52 sql = "SELECT * FROM nybb;"
53 df = read_postgis(sql, con, geom_col=geom_col)
4354 finally:
4455 con.close()
4556
4657 validate_boro_df(df)
58
59 def test_read_postgis_select_geom_as(self):
60 """Tests that a SELECT {geom} AS {some_other_geom} works."""
61 con = connect('test_geopandas')
62 orig_geom = "geom"
63 out_geom = "the_geom"
64 if con is None or not create_postgis(self.df, geom_col=orig_geom):
65 raise pytest.skip()
66
67 try:
68 sql = """SELECT borocode, boroname, shape_leng, shape_area,
69 {} as {} FROM nybb;""".format(orig_geom, out_geom)
70 df = read_postgis(sql, con, geom_col=out_geom)
71 finally:
72 con.close()
73
74 validate_boro_df(df)
75
76 def test_read_postgis_get_srid(self):
77 """Tests that an SRID can be read from a geodatabase (GH #451)."""
78 crs = {"init": "epsg:4269"}
79 df_reproj = self.df.to_crs(crs)
80 created = create_postgis(df_reproj, srid=4269)
81 con = connect('test_geopandas')
82 if con is None or not created:
83 raise pytest.skip()
84
85 try:
86 sql = "SELECT * FROM nybb;"
87 df = read_postgis(sql, con)
88 finally:
89 con.close()
90
91 validate_boro_df(df)
92 assert(df.crs == crs)
93
94 def test_read_postgis_override_srid(self):
95 """Tests that a user specified CRS overrides the geodatabase SRID."""
96 orig_crs = self.df.crs
97 created = create_postgis(self.df, srid=4269)
98 con = connect('test_geopandas')
99 if con is None or not created:
100 raise pytest.skip()
101
102 try:
103 sql = "SELECT * FROM nybb;"
104 df = read_postgis(sql, con, crs=orig_crs)
105 finally:
106 con.close()
107
108 validate_boro_df(df)
109 assert(df.crs == orig_crs)
47110
48111 def test_read_file(self):
49112 df = self.df.rename(columns=lambda x: x.lower())
52115 # get lower case columns, and exclude geometry column from comparison
53116 lower_columns = [c.lower() for c in self.columns]
54117 assert (df.columns[:-1] == lower_columns).all()
118
119 @pytest.mark.web
120 def test_remote_geojson_url(self):
121 url = ("https://raw.githubusercontent.com/geopandas/geopandas/"
122 "master/examples/null_geom.geojson")
123 gdf = read_file(url)
124 assert isinstance(gdf, geopandas.GeoDataFrame)
55125
56126 def test_filtered_read_file(self):
57127 full_df_shape = self.df.shape
62132 filtered_df_shape = filtered_df.shape
63133 assert full_df_shape != filtered_df_shape
64134 assert filtered_df_shape == (2, 5)
135
136 def test_filtered_read_file_with_gdf_boundary(self):
137 full_df_shape = self.df.shape
138 nybb_filename = geopandas.datasets.get_path('nybb')
139 bbox = geopandas.GeoDataFrame(
140 geometry=[box(1031051.7879884212, 224272.49231459625,
141 1047224.3104931959, 244317.30894023244)],
142 crs=self.crs)
143 filtered_df = read_file(nybb_filename, bbox=bbox)
144 filtered_df_shape = filtered_df.shape
145 assert full_df_shape != filtered_df_shape
146 assert filtered_df_shape == (2, 5)
147
148 def test_filtered_read_file_with_gdf_boundary_mismatched_crs(self):
149 full_df_shape = self.df.shape
150 nybb_filename = geopandas.datasets.get_path('nybb')
151 bbox = geopandas.GeoDataFrame(
152 geometry=[box(1031051.7879884212, 224272.49231459625,
153 1047224.3104931959, 244317.30894023244)],
154 crs=self.crs)
155 bbox.to_crs(epsg=4326, inplace=True)
156 filtered_df = read_file(nybb_filename, bbox=bbox)
157 filtered_df_shape = filtered_df.shape
158 assert full_df_shape != filtered_df_shape
159 assert filtered_df_shape == (2, 5)
160
161 def test_empty_shapefile(self, tmpdir):
162
163 # create empty shapefile
164 meta = {'crs': {},
165 'crs_wkt': '',
166 'driver': 'ESRI Shapefile',
167 'schema':
168 {'geometry': 'Point',
169 'properties': OrderedDict([('A', 'int:9'),
170 ('Z', 'float:24.15')])}}
171
172 fname = str(tmpdir.join("test_empty.shp"))
173
174 with fiona.drivers():
175 with fiona.open(fname, 'w', **meta) as _:
176 pass
177
178 empty = read_file(fname)
179 assert isinstance(empty, geopandas.GeoDataFrame)
180 assert all(empty.columns == ['A', 'Z', 'geometry'])
+99
-70
geopandas/plotting.py less more
00 from __future__ import print_function
1
21 from distutils.version import LooseVersion
32 import warnings
43
54 import numpy as np
6
5 import pandas as pd
76
87 def _flatten_multi_geoms(geoms, colors=None):
98 """
7776
7877 collection : matplotlib.collections.Collection that was plotted
7978 """
80 from descartes.patch import PolygonPatch
79
80 try:
81 from descartes.patch import PolygonPatch
82 except ImportError:
83 raise ImportError("The descartes package is required"
84 " for plotting polygons in geopandas.")
8185 from matplotlib.collections import PatchCollection
8286
8387 geoms, values = _flatten_multi_geoms(geoms, values)
163167 cmap=None, vmin=None, vmax=None,
164168 marker='o', markersize=None, **kwargs):
165169 """
166 Plots a collection of Point geometries to `ax`
170 Plots a collection of Point and MultiPoint geometries to `ax`
167171
168172 Parameters
169173 ----------
170
171174 ax : matplotlib.axes.Axes
172175 where shapes will be plotted
173
174 geoms : sequence of `N` Points
176 geoms : sequence of `N` Points or MultiPoints
175177
176178 values : a sequence of `N` values, optional
177179 Values mapped to colors using vmin, vmax, and cmap.
178180 Cannot be specified together with `color`.
179
180181 markersize : scalar or array-like, optional
181182 Size of the markers. Note that under the hood ``scatter`` is
182183 used, so the specified value will be proportional to the
189190 if values is not None and color is not None:
190191 raise ValueError("Can only specify one of 'values' and 'color' kwargs")
191192
192 x = geoms.x.values
193 y = geoms.y.values
193 geoms, values = _flatten_multi_geoms(geoms, values)
194 if None in values:
195 values = None
196 x = [p.x for p in geoms]
197 y = [p.y for p in geoms]
194198
195199 # matplotlib 1.4 does not support c=None, and < 2.0 does not support s=None
196200 if values is not None:
211215
212216 Parameters
213217 ----------
214
215218 s : Series
216 The GeoSeries to be plotted. Currently Polygon,
219 The GeoSeries to be plotted. Currently Polygon,
217220 MultiPolygon, LineString, MultiLineString and Point
218221 geometries can be plotted.
219
220222 cmap : str (default None)
221 The name of a colormap recognized by matplotlib. Any
223 The name of a colormap recognized by matplotlib. Any
222224 colormap will work, but categorical colormaps are
223 generally recommended. Examples of useful discrete
225 generally recommended. Examples of useful discrete
224226 colormaps include:
225227
226228 tab10, tab20, Accent, Dark2, Paired, Pastel1, Set1, Set2
227229
228230 color : str (default None)
229231 If specified, all objects will be colored uniformly.
230
231232 ax : matplotlib.pyplot.Artist (default None)
232233 axes on which to draw the plot
233
234234 figsize : pair of floats (default None)
235235 Size of the resulting matplotlib.figure.Figure. If the argument
236236 ax is given explicitly, figsize is ignored.
237
238237 **style_kwds : dict
239238 Color options to be passed on to the actual plot function, such
240239 as ``edgecolor``, ``facecolor``, ``linewidth``, ``markersize``,
242241
243242 Returns
244243 -------
245
246 matplotlib axes instance
244 ax : matplotlib axes instance
247245 """
248246 if 'colormap' in style_kwds:
249247 warnings.warn("'colormap' is deprecated, please use 'cmap' instead "
257255 import matplotlib.pyplot as plt
258256 if ax is None:
259257 fig, ax = plt.subplots(figsize=figsize)
260 ax.set_aspect('equal')
258 ax.set_aspect('equal')
259
260 if s.empty:
261 warnings.warn("The GeoSeries you are attempting to plot is "
262 "empty. Nothing has been displayed.", UserWarning)
263 return ax
261264
262265 # if cmap is specified, create range of colors based on cmap
263266 values = None
273276 | (geom_types == 'MultiPolygon'))
274277 line_idx = np.asarray((geom_types == 'LineString')
275278 | (geom_types == 'MultiLineString'))
276 point_idx = np.asarray(geom_types == 'Point')
279 point_idx = np.asarray((geom_types == 'Point')
280 | (geom_types == 'MultiPoint'))
277281
278282 # plot all Polygons and all MultiPolygon components in the same collection
279283 polys = s.geometry[poly_idx]
308312
309313 def plot_dataframe(df, column=None, cmap=None, color=None, ax=None,
310314 categorical=False, legend=False, scheme=None, k=5,
311 vmin=None, vmax=None, figsize=None, **style_kwds):
315 vmin=None, vmax=None, markersize=None, figsize=None,
316 legend_kwds=None, **style_kwds):
312317 """
313318 Plot a GeoDataFrame.
314319
318323
319324 Parameters
320325 ----------
321
322326 df : GeoDataFrame
323327 The GeoDataFrame to be plotted. Currently Polygon,
324328 MultiPolygon, LineString, MultiLineString and Point
325329 geometries can be plotted.
326
327 column : str (default None)
328 The name of the column to be plotted. Ignored if `color` is also set.
329
330 column : str, np.array, pd.Series (default None)
331 The name of the dataframe column, np.array, or pd.Series to be plotted.
332 If np.array or pd.Series are used then it must have same length as
333 dataframe. Values are used to color the plot. Ignored if `color` is
334 also set.
330335 cmap : str (default None)
331336 The name of a colormap recognized by matplotlib.
332
337 color : str (default None)
338 If specified, all objects will be colored uniformly.
339 ax : matplotlib.pyplot.Artist (default None)
340 axes on which to draw the plot
333341 categorical : bool (default False)
334342 If False, cmap will reflect numerical values of the
335343 column being plotted. For non-numerical columns, this
336344 will be set to True.
337
338 color : str (default None)
339 If specified, all objects will be colored uniformly.
340
341345 legend : bool (default False)
342346 Plot a legend. Ignored if no `column` is given, or if `color` is given.
343
344 ax : matplotlib.pyplot.Artist (default None)
345 axes on which to draw the plot
346
347347 scheme : str (default None)
348348 Name of a choropleth classification scheme (requires PySAL).
349349 A pysal.esda.mapclassify.Map_Classifier object will be used
350350 under the hood. Supported schemes: 'Equal_interval', 'Quantiles',
351351 'Fisher_Jenks'
352
353 k : int (default 5)
352 k : int (default 5)
354353 Number of classes (ignored if scheme is None)
355
356354 vmin : None or float (default None)
357355 Minimum value of cmap. If None, the minimum data value
358356 in the column to be plotted is used.
359
360357 vmax : None or float (default None)
361358 Maximum value of cmap. If None, the maximum data value
362359 in the column to be plotted is used.
363
364 figsize
360 markersize : str or float or sequence (default None)
361 Only applies to point geometries within a frame.
362 If a str, will use the values in the column of the frame specified
363 by markersize to set the size of markers. Otherwise can be a value
364 to apply to all points, or a sequence of the same length as the
365 number of points.
366 figsize : tuple of integers (default None)
365367 Size of the resulting matplotlib.figure.Figure. If the argument
366368 axes is given explicitly, figsize is ignored.
369 legend_kwds : dict (default None)
370 Keyword arguments to pass to ax.legend()
367371
368372 **style_kwds : dict
369373 Color options to be passed on to the actual plot function, such
372376
373377 Returns
374378 -------
375 matplotlib axes instance
379 ax : matplotlib axes instance
376380
377381 """
378382 if 'colormap' in style_kwds:
383387 warnings.warn("'axes' is deprecated, please use 'ax' instead "
384388 "(for consistency with pandas)", FutureWarning)
385389 ax = style_kwds.pop('axes')
386 if column and color:
390 if column is not None and color is not None:
387391 warnings.warn("Only specify one of 'column' or 'color'. Using "
388392 "'color'.", UserWarning)
389393 column = None
391395 import matplotlib
392396 import matplotlib.pyplot as plt
393397
398 if ax is None:
399 fig, ax = plt.subplots(figsize=figsize)
400 ax.set_aspect('equal')
401
402 if df.empty:
403 warnings.warn("The GeoDataFrame you are attempting to plot is "
404 "empty. Nothing has been displayed.", UserWarning)
405 return ax
406
407 if isinstance(markersize, str):
408 markersize = df[markersize].values
409
394410 if column is None:
395411 return plot_series(df.geometry, cmap=cmap, color=color, ax=ax,
396 figsize=figsize, **style_kwds)
397
398 if df[column].dtype is np.dtype('O'):
412 figsize=figsize, markersize=markersize,
413 **style_kwds)
414
415 # To accept pd.Series and np.arrays as column
416 if isinstance(column, (np.ndarray, pd.Series)):
417 if column.shape[0] != df.shape[0]:
418 raise ValueError("The dataframe and given column have different "
419 "number of rows.")
420 else:
421 values = np.asarray(column)
422 else:
423 values = np.asarray(df[column])
424
425 if values.dtype is np.dtype('O'):
399426 categorical = True
400427
401428 # Define `values` as a Series
402429 if categorical:
403430 if cmap is None:
404 if LooseVersion(matplotlib.__version__) >= '2.0':
431 if LooseVersion(matplotlib.__version__) >= '2.0.1':
405432 cmap = 'tab10'
433 elif LooseVersion(matplotlib.__version__) >= '2.0.0':
434 # Erroneous name.
435 cmap = 'Vega10'
406436 else:
407437 cmap = 'Set1'
408 categories = list(set(df[column].values))
438 categories = list(set(values))
409439 categories.sort()
410440 valuemap = dict([(k, v) for (v, k) in enumerate(categories)])
411 values = np.array([valuemap[k] for k in df[column]])
412 else:
413 values = df[column]
441 values = np.array([valuemap[k] for k in values])
442
414443 if scheme is not None:
415444 binning = __pysal_choro(values, scheme, k=k)
416445 # set categorical to True for creating the legend
420449 for i in range(len(binedges)-1)]
421450 values = np.array(binning.yb)
422451
423 if ax is None:
424 fig, ax = plt.subplots(figsize=figsize)
425 ax.set_aspect('equal')
426
427452 mn = values.min() if vmin is None else vmin
428453 mx = values.max() if vmax is None else vmax
429454
432457 | (geom_types == 'MultiPolygon'))
433458 line_idx = np.asarray((geom_types == 'LineString')
434459 | (geom_types == 'MultiLineString'))
435 point_idx = np.asarray(geom_types == 'Point')
460 point_idx = np.asarray((geom_types == 'Point')
461 | (geom_types == 'MultiPoint'))
436462
437463 # plot all Polygons and all MultiPolygon components in the same collection
438464 polys = df.geometry[poly_idx]
449475 # plot all Points in the same collection
450476 points = df.geometry[point_idx]
451477 if not points.empty:
452 plot_point_collection(ax, points, values[point_idx],
453 vmin=mn, vmax=mx, cmap=cmap, **style_kwds)
478 if isinstance(markersize, np.ndarray):
479 markersize = markersize[point_idx]
480 plot_point_collection(ax, points, values[point_idx], vmin=mn, vmax=mx,
481 markersize=markersize, cmap=cmap,
482 **style_kwds)
454483
455484 if legend and not color:
456485 from matplotlib.lines import Line2D
466495 Line2D([0], [0], linestyle="none", marker="o",
467496 alpha=style_kwds.get('alpha', 1), markersize=10,
468497 markerfacecolor=n_cmap.to_rgba(value)))
469 ax.legend(patches, categories, numpoints=1, loc='best')
498 if legend_kwds is None:
499 legend_kwds = {}
500 legend_kwds.setdefault('numpoints', 1)
501 legend_kwds.setdefault('loc', 'best')
502 ax.legend(patches, categories, **legend_kwds)
470503 else:
471504 n_cmap.set_array([])
472 ax.get_figure().colorbar(n_cmap)
505 ax.get_figure().colorbar(n_cmap, ax=ax)
473506
474507 plt.draw()
475508 return ax
481514
482515 Parameters
483516 ----------
484
485517 values
486518 Series to be plotted
487
488 scheme
489 pysal.esda.mapclassify classificatin scheme
490 ['Equal_interval'|'Quantiles'|'Fisher_Jenks']
491
492 k
519 scheme : str
520 One of pysal.esda.mapclassify classification schemes
521 Options are 'Equal_interval', 'Quantiles', 'Fisher_Jenks'
522 k : int
493523 number of classes (2 <= k <=9)
494524
495525 Returns
496526 -------
497
498527 binning
499528 Binning objects that holds the Series with values replaced with
500529 class identifier and the bins.
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0 """
1 Testing functionality for geopandas objects.
2 """
3
4 from geopandas import GeoSeries, GeoDataFrame
5
6
7 def geom_equals(this, that):
8 """
9 Test for geometric equality. Empty geometries are considered equal.
10
11 Parameters
12 ----------
13 this, that : arrays of Geo objects (or anything that has an `is_empty`
14 attribute)
15 """
16
17 return (this.geom_equals(that) | (this.is_empty & that.is_empty)).all()
18
19
20 def geom_almost_equals(this, that):
21 """
22 Test for 'almost' geometric equality. Empty geometries considered equal.
23
24 This method allows small difference in the coordinates, but this
25 requires coordinates be in the same order for all components of a geometry.
26
27 Parameters
28 ----------
29 this, that : arrays of Geo objects (or anything that has an `is_empty`
30 property)
31 """
32
33 return (this.geom_almost_equals(that) |
34 (this.is_empty & that.is_empty)).all()
35
36
37 def assert_geoseries_equal(left, right,
38 check_dtype=False,
39 check_index_type=False,
40 check_series_type=True,
41 check_less_precise=False,
42 check_geom_type=False,
43 check_crs=True):
44 """
45 Test util for checking that two GeoSeries are equal.
46
47 Parameters
48 ----------
49 left, right : two GeoSeries
50 check_dtype : bool, default False
51 If True, check geo dtype [only included so it's a drop-in replacement
52 for assert_series_equal].
53 check_index_type : bool, default False
54 Check that index types are equal.
55 check_series_type : bool, default True
56 Check that both are same type (*and* are GeoSeries). If False,
57 will attempt to convert both into GeoSeries.
58 check_less_precise : bool, default False
59 If True, use geom_almost_equals. if False, use geom_equals.
60 check_geom_type : bool, default False
61 If True, check that all the geom types are equal.
62 check_crs: bool, default True
63 If `check_series_type` is True, then also check that the
64 crs matches.
65 """
66 assert len(left) == len(right), "%d != %d" % (len(left), len(right))
67
68 if check_index_type:
69 assert isinstance(left.index, type(right.index))
70
71 if check_dtype:
72 assert left.dtype == right.dtype, "dtype: %s != %s" % (left.dtype,
73 right.dtype)
74
75 if check_series_type:
76 assert isinstance(left, GeoSeries)
77 assert isinstance(left, type(right))
78
79 if check_crs:
80 assert(left.crs == right.crs)
81 else:
82 if not isinstance(left, GeoSeries):
83 left = GeoSeries(left)
84 if not isinstance(right, GeoSeries):
85 right = GeoSeries(right, index=left.index)
86
87 assert left.index.equals(right.index), "index: %s != %s" % (left.index,
88 right.index)
89
90 if check_geom_type:
91 assert (left.type == right.type).all(), "type: %s != %s" % (left.type,
92 right.type)
93
94 if check_less_precise:
95 assert geom_almost_equals(left, right)
96 else:
97 assert geom_equals(left, right)
98
99
100 def assert_geodataframe_equal(left, right,
101 check_dtype=True,
102 check_index_type='equiv',
103 check_column_type='equiv',
104 check_frame_type=True,
105 check_like=False,
106 check_less_precise=False,
107 check_geom_type=False,
108 check_crs=True):
109 """
110 Check that two GeoDataFrames are equal/
111
112 Parameters
113 ----------
114 left, right : two GeoDataFrames
115 check_dtype : bool, default True
116 Whether to check the DataFrame dtype is identical.
117 check_index_type, check_column_type : bool, default 'equiv'
118 Check that index types are equal.
119 check_frame_type : bool, default True
120 Check that both are same type (*and* are GeoDataFrames). If False,
121 will attempt to convert both into GeoDataFrame.
122 check_like : bool, default False
123 If true, ignore the order of rows & columns
124 check_less_precise : bool, default False
125 If True, use geom_almost_equals. if False, use geom_equals.
126 check_geom_type : bool, default False
127 If True, check that all the geom types are equal.
128 check_crs: bool, default True
129 If `check_frame_type` is True, then also check that the
130 crs matches.
131 """
132 try:
133 # added from pandas 0.20
134 from pandas.testing import assert_frame_equal, assert_index_equal
135 except ImportError:
136 from pandas.util.testing import assert_frame_equal, assert_index_equal
137
138 # instance validation
139 if check_frame_type:
140 assert isinstance(left, GeoDataFrame)
141 assert isinstance(left, type(right))
142
143 if check_crs:
144 assert left.crs == right.crs
145 else:
146 if not isinstance(left, GeoDataFrame):
147 left = GeoDataFrame(left)
148 if not isinstance(right, GeoDataFrame):
149 right = GeoDataFrame(right)
150
151 # shape comparison
152 assert left.shape == right.shape, (
153 'GeoDataFrame shape mismatch, left: {lshape!r}, right: {rshape!r}.\n'
154 'Left columns: {lcols!r}, right columns: {rcols!r}'.format(
155 lshape=left.shape, rshape=right.shape,
156 lcols=left.columns, rcols=right.columns))
157
158 if check_like:
159 left, right = left.reindex_like(right), right
160
161 # column comparison
162 assert_index_equal(left.columns, right.columns, exact=check_column_type,
163 obj='GeoDataFrame.columns')
164
165 # geometry comparison
166 assert_geoseries_equal(
167 left.geometry, right.geometry, check_dtype=check_dtype,
168 check_less_precise=check_less_precise,
169 check_geom_type=check_geom_type, check_crs=False)
170
171 # drop geometries and check remaining columns
172 left2 = left.drop([left._geometry_column_name], axis=1)
173 right2 = left.drop([right._geometry_column_name], axis=1)
174 assert_frame_equal(left2, right2, check_dtype=check_dtype,
175 check_index_type=check_index_type, obj='GeoDataFrame')
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0 PROJCS["Lambert_Conformal_Conic",GEOGCS["GCS_North_American_1983",DATUM["D_North_American_1983",SPHEROID["GRS_1980",6378137,298.257222101]],PRIMEM["Greenwich",0],UNIT["Degree",0.017453292519943295]],PROJECTION["Lambert_Conformal_Conic"],PARAMETER["standard_parallel_1",40.66666666666666],PARAMETER["standard_parallel_2",41.03333333333333],PARAMETER["latitude_of_origin",40.16666666666666],PARAMETER["central_meridian",-74],PARAMETER["false_easting",984250],PARAMETER["false_northing",0],UNIT["Foot_US",0.30480060960121924]]⏎
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0 PROJCS["Lambert_Conformal_Conic",GEOGCS["GCS_North_American_1983",DATUM["D_North_American_1983",SPHEROID["GRS_1980",6378137,298.257222101]],PRIMEM["Greenwich",0],UNIT["Degree",0.017453292519943295]],PROJECTION["Lambert_Conformal_Conic"],PARAMETER["standard_parallel_1",40.66666666666666],PARAMETER["standard_parallel_2",41.03333333333333],PARAMETER["latitude_of_origin",40.16666666666666],PARAMETER["central_meridian",-74],PARAMETER["false_easting",984250],PARAMETER["false_northing",0],UNIT["Foot_US",0.30480060960121924]]⏎
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0 PROJCS["Lambert_Conformal_Conic",GEOGCS["GCS_GRS 1980(IUGG, 1980)",DATUM["D_unknown",SPHEROID["GRS80",6378137,298.257222101]],PRIMEM["Greenwich",0],UNIT["Degree",0.017453292519943295]],PROJECTION["Lambert_Conformal_Conic"],PARAMETER["standard_parallel_1",40.66666666666666],PARAMETER["standard_parallel_2",41.03333333333333],PARAMETER["latitude_of_origin",40.16666666666666],PARAMETER["central_meridian",-74],PARAMETER["false_easting",984250],PARAMETER["false_northing",0],UNIT["Foot_US",0.30480060960121924]]⏎
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0 PROJCS["unnamed",GEOGCS["GRS 1980(IUGG, 1980)",DATUM["unknown",SPHEROID["GRS80",6378137,298.257222101],TOWGS84[0,0,0,0,0,0,0]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433]],PROJECTION["Lambert_Conformal_Conic_2SP"],PARAMETER["standard_parallel_1",40.66666666666666],PARAMETER["standard_parallel_2",41.03333333333333],PARAMETER["latitude_of_origin",40.16666666666666],PARAMETER["central_meridian",-74],PARAMETER["false_easting",984250],PARAMETER["false_northing",0],UNIT["Foot_US",0.3048006096012192]]
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0 PROJCS["Lambert_Conformal_Conic",GEOGCS["GCS_GRS 1980(IUGG, 1980)",DATUM["D_unknown",SPHEROID["GRS80",6378137,298.257222101]],PRIMEM["Greenwich",0],UNIT["Degree",0.017453292519943295]],PROJECTION["Lambert_Conformal_Conic"],PARAMETER["standard_parallel_1",40.66666666666666],PARAMETER["standard_parallel_2",41.03333333333333],PARAMETER["latitude_of_origin",40.16666666666666],PARAMETER["central_meridian",-74],PARAMETER["false_easting",984250],PARAMETER["false_northing",0],UNIT["Foot_US",0.30480060960121924]]⏎
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0 PROJCS["unnamed",GEOGCS["GRS 1980(IUGG, 1980)",DATUM["unknown",SPHEROID["GRS80",6378137,298.257222101],TOWGS84[0,0,0,0,0,0,0]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433]],PROJECTION["Lambert_Conformal_Conic_2SP"],PARAMETER["standard_parallel_1",40.66666666666666],PARAMETER["standard_parallel_2",41.03333333333333],PARAMETER["latitude_of_origin",40.16666666666666],PARAMETER["central_meridian",-74],PARAMETER["false_easting",984250],PARAMETER["false_northing",0],UNIT["Foot_US",0.3048006096012192]]
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geopandas/tests/data/overlay/nybb_qgis/qgis-symmetric_difference.prj less more
0 PROJCS["Lambert_Conformal_Conic",GEOGCS["GCS_GRS 1980(IUGG, 1980)",DATUM["D_unknown",SPHEROID["GRS80",6378137,298.257222101]],PRIMEM["Greenwich",0],UNIT["Degree",0.017453292519943295]],PROJECTION["Lambert_Conformal_Conic"],PARAMETER["standard_parallel_1",40.66666666666666],PARAMETER["standard_parallel_2",41.03333333333333],PARAMETER["latitude_of_origin",40.16666666666666],PARAMETER["central_meridian",-74],PARAMETER["false_easting",984250],PARAMETER["false_northing",0],UNIT["Foot_US",0.30480060960121924]]⏎
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geopandas/tests/data/overlay/nybb_qgis/qgis-symmetric_difference.qpj less more
0 PROJCS["unnamed",GEOGCS["GRS 1980(IUGG, 1980)",DATUM["unknown",SPHEROID["GRS80",6378137,298.257222101],TOWGS84[0,0,0,0,0,0,0]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433]],PROJECTION["Lambert_Conformal_Conic_2SP"],PARAMETER["standard_parallel_1",40.66666666666666],PARAMETER["standard_parallel_2",41.03333333333333],PARAMETER["latitude_of_origin",40.16666666666666],PARAMETER["central_meridian",-74],PARAMETER["false_easting",984250],PARAMETER["false_northing",0],UNIT["Foot_US",0.3048006096012192]]
geopandas/tests/data/overlay/nybb_qgis/qgis-symmetric_difference.shp less more
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geopandas/tests/data/overlay/nybb_qgis/qgis-symmetric_difference.shx less more
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geopandas/tests/data/overlay/nybb_qgis/qgis-union.dbf less more
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geopandas/tests/data/overlay/nybb_qgis/qgis-union.prj less more
0 PROJCS["Lambert_Conformal_Conic",GEOGCS["GCS_GRS 1980(IUGG, 1980)",DATUM["D_unknown",SPHEROID["GRS80",6378137,298.257222101]],PRIMEM["Greenwich",0],UNIT["Degree",0.017453292519943295]],PROJECTION["Lambert_Conformal_Conic"],PARAMETER["standard_parallel_1",40.66666666666666],PARAMETER["standard_parallel_2",41.03333333333333],PARAMETER["latitude_of_origin",40.16666666666666],PARAMETER["central_meridian",-74],PARAMETER["false_easting",984250],PARAMETER["false_northing",0],UNIT["Foot_US",0.30480060960121924]]⏎
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geopandas/tests/data/overlay/nybb_qgis/qgis-union.qpj less more
0 PROJCS["unnamed",GEOGCS["GRS 1980(IUGG, 1980)",DATUM["unknown",SPHEROID["GRS80",6378137,298.257222101],TOWGS84[0,0,0,0,0,0,0]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433]],PROJECTION["Lambert_Conformal_Conic_2SP"],PARAMETER["standard_parallel_1",40.66666666666666],PARAMETER["standard_parallel_2",41.03333333333333],PARAMETER["latitude_of_origin",40.16666666666666],PARAMETER["central_meridian",-74],PARAMETER["false_easting",984250],PARAMETER["false_northing",0],UNIT["Foot_US",0.3048006096012192]]
geopandas/tests/data/overlay/nybb_qgis/qgis-union.shp less more
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geopandas/tests/data/overlay/nybb_qgis/qgis-union.shx less more
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geopandas/tests/data/overlay/overlap/df1_df2_overlap-difference.geojson less more
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geopandas/tests/data/overlay/overlap/df1_df2_overlap-intersection.geojson less more
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geopandas/tests/data/overlay/overlap/df1_df2_overlap-symmetric_difference.geojson less more
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geopandas/tests/data/overlay/overlap/df1_df2_overlap-union.geojson less more
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geopandas/tests/data/overlay/overlap/df1_overlap.geojson less more
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geopandas/tests/data/overlay/overlap/df2_overlap.geojson less more
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geopandas/tests/data/overlay/polys/df1.geojson less more
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geopandas/tests/data/overlay/polys/df1_df2-difference-inverse.geojson less more
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geopandas/tests/data/overlay/polys/df1_df2-difference.geojson less more
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geopandas/tests/data/overlay/polys/df1_df2-intersection.geojson less more
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geopandas/tests/data/overlay/polys/df1_df2-symmetric_difference.geojson less more
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geopandas/tests/data/overlay/polys/df1_df2-union.geojson less more
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geopandas/tests/data/overlay/polys/df2.geojson less more
0 {
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-13
geopandas/tests/test_datasets.py less more
00 from __future__ import absolute_import
1
2 import pytest
13
24 from geopandas import read_file, GeoDataFrame
35 from geopandas.datasets import get_path
46
5
6 class TestDatasets:
7
8 def test_read_paths(self):
9
10 gdf = read_file(get_path('naturalearth_lowres'))
11 assert isinstance(gdf, GeoDataFrame)
12
13 gdf = read_file(get_path('naturalearth_cities'))
14 assert isinstance(gdf, GeoDataFrame)
15
16 gdf = read_file(get_path('nybb'))
17 assert isinstance(gdf, GeoDataFrame)
7 @pytest.mark.parametrize("test_dataset",
8 ['naturalearth_lowres',
9 'naturalearth_cities',
10 'nybb'])
11 def test_read_paths(test_dataset):
12 assert isinstance(read_file(get_path(test_dataset)), GeoDataFrame)
+73
-56
geopandas/tests/test_dissolve.py less more
00 from __future__ import absolute_import
1
2 import pytest
13
24 import numpy as np
35 import pandas as pd
79
810 from pandas.util.testing import assert_frame_equal
911
12 @pytest.fixture
13 def nybb_polydf():
14 nybb_filename = geopandas.datasets.get_path('nybb')
15 nybb_polydf = read_file(nybb_filename)
16 nybb_polydf = nybb_polydf[['geometry', 'BoroName', 'BoroCode']]
17 nybb_polydf = nybb_polydf.rename(columns={'geometry': 'myshapes'})
18 nybb_polydf = nybb_polydf.set_geometry('myshapes')
19 nybb_polydf['manhattan_bronx'] = 5
20 nybb_polydf.loc[3:4, 'manhattan_bronx'] = 6
21 return nybb_polydf
1022
11 class TestDataFrame:
1223
13 def setup_method(self):
24 @pytest.fixture
25 def merged_shapes(nybb_polydf):
26 # Merged geometry
27 manhattan_bronx = nybb_polydf.loc[3:4, ]
28 others = nybb_polydf.loc[0:2, ]
1429
15 nybb_filename = geopandas.datasets.get_path('nybb')
16 self.polydf = read_file(nybb_filename)
17 self.polydf = self.polydf[['geometry', 'BoroName', 'BoroCode']]
30 collapsed = [others.geometry.unary_union,
31 manhattan_bronx.geometry.unary_union]
32 merged_shapes = GeoDataFrame(
33 {'myshapes': collapsed}, geometry='myshapes',
34 index=pd.Index([5, 6], name='manhattan_bronx'))
1835
19 self.polydf = self.polydf.rename(columns={'geometry': 'myshapes'})
20 self.polydf = self.polydf.set_geometry('myshapes')
36 return merged_shapes
2137
22 self.polydf['manhattan_bronx'] = 5
23 self.polydf.loc[3:4, 'manhattan_bronx'] = 6
2438
25 # Merged geometry
26 manhattan_bronx = self.polydf.loc[3:4, ]
27 others = self.polydf.loc[0:2, ]
39 @pytest.fixture
40 def first(merged_shapes):
41 first = merged_shapes.copy()
42 first['BoroName'] = ['Staten Island', 'Manhattan']
43 first['BoroCode'] = [5, 1]
44 return first
2845
29 collapsed = [others.geometry.unary_union,
30 manhattan_bronx.geometry.unary_union]
31 merged_shapes = GeoDataFrame(
32 {'myshapes': collapsed}, geometry='myshapes',
33 index=pd.Index([5, 6], name='manhattan_bronx'))
3446
35 # Different expected results
36 self.first = merged_shapes.copy()
37 self.first['BoroName'] = ['Staten Island', 'Manhattan']
38 self.first['BoroCode'] = [5, 1]
47 @pytest.fixture
48 def expected_mean(merged_shapes):
49 test_mean = merged_shapes.copy()
50 test_mean['BoroCode'] = [4, 1.5]
51 return test_mean
3952
40 self.mean = merged_shapes.copy()
41 self.mean['BoroCode'] = [4, 1.5]
4253
43 def test_geom_dissolve(self):
44 test = self.polydf.dissolve('manhattan_bronx')
45 assert test.geometry.name == 'myshapes'
46 assert test.geom_almost_equals(self.first).all()
54 def test_geom_dissolve(nybb_polydf, first):
55 test = nybb_polydf.dissolve('manhattan_bronx')
56 assert test.geometry.name == 'myshapes'
57 assert test.geom_almost_equals(first).all()
4758
48 def test_dissolve_retains_existing_crs(self):
49 assert self.polydf.crs is not None
50 test = self.polydf.dissolve('manhattan_bronx')
51 assert test.crs is not None
5259
53 def test_dissolve_retains_nonexisting_crs(self):
54 self.polydf.crs = None
55 test = self.polydf.dissolve('manhattan_bronx')
56 assert test.crs is None
60 def test_dissolve_retains_existing_crs(nybb_polydf):
61 assert nybb_polydf.crs is not None
62 test = nybb_polydf.dissolve('manhattan_bronx')
63 assert test.crs is not None
5764
58 def test_first_dissolve(self):
59 test = self.polydf.dissolve('manhattan_bronx')
60 assert_frame_equal(self.first, test, check_column_type=False)
6165
62 def test_mean_dissolve(self):
63 test = self.polydf.dissolve('manhattan_bronx', aggfunc='mean')
64 assert_frame_equal(self.mean, test, check_column_type=False)
66 def test_dissolve_retains_nonexisting_crs(nybb_polydf):
67 nybb_polydf.crs = None
68 test = nybb_polydf.dissolve('manhattan_bronx')
69 assert test.crs is None
6570
66 test = self.polydf.dissolve('manhattan_bronx', aggfunc=np.mean)
67 assert_frame_equal(self.mean, test, check_column_type=False)
6871
69 def test_multicolumn_dissolve(self):
70 multi = self.polydf.copy()
71 multi['dup_col'] = multi.manhattan_bronx
72 multi_test = multi.dissolve(['manhattan_bronx', 'dup_col'],
73 aggfunc='first')
72 def first_dissolve(nybb_polydf, first):
73 test = nybb_polydf.dissolve('manhattan_bronx')
74 assert_frame_equal(first, test, check_column_type=False)
7475
75 first = self.first.copy()
76 first['dup_col'] = first.index
77 first = first.set_index([first.index, 'dup_col'])
7876
79 assert_frame_equal(multi_test, first, check_column_type=False)
77 def test_mean_dissolve(nybb_polydf, first, expected_mean):
78 test = nybb_polydf.dissolve('manhattan_bronx', aggfunc='mean')
79 assert_frame_equal(expected_mean, test, check_column_type=False)
8080
81 def test_reset_index(self):
82 test = self.polydf.dissolve('manhattan_bronx', as_index=False)
83 comparison = self.first.reset_index()
84 assert_frame_equal(comparison, test, check_column_type=False)
81 test = nybb_polydf.dissolve('manhattan_bronx', aggfunc=np.mean)
82 assert_frame_equal(expected_mean, test, check_column_type=False)
83
84
85 def test_multicolumn_dissolve(nybb_polydf, first):
86 multi = nybb_polydf.copy()
87 multi['dup_col'] = multi.manhattan_bronx
88 multi_test = multi.dissolve(['manhattan_bronx', 'dup_col'],
89 aggfunc='first')
90
91 first_copy = first.copy()
92 first_copy['dup_col'] = first_copy.index
93 first_copy = first_copy.set_index([first_copy.index, 'dup_col'])
94
95 assert_frame_equal(multi_test, first_copy, check_column_type=False)
96
97
98 def test_reset_index(nybb_polydf, first):
99 test = nybb_polydf.dissolve('manhattan_bronx', as_index=False)
100 comparison = first.reset_index()
101 assert_frame_equal(comparison, test, check_column_type=False)
+71
-65
geopandas/tests/test_geocode.py less more
00 from __future__ import absolute_import
11
2 import sys
23 import numpy as np
34 import pandas as pd
45 from fiona.crs import from_epsg
1314 from geopandas.tests.util import mock, assert_geoseries_equal
1415
1516
16 def _skip_if_no_geopy():
17 try:
18 import geopy
19 except ImportError:
20 raise pytest.skip("Geopy not installed. Skipping tests.")
21 except SyntaxError:
22 raise pytest.skip("Geopy is known to be broken on Python 3.2. "
23 "Skipping tests.")
17 geopy = pytest.importorskip("geopy")
2418
2519
2620 class ForwardMock(mock.MagicMock):
5751 return super(ReverseMock, self).__call__(*args, **kwargs)
5852
5953
60 class TestGeocode:
61 def setup_method(self):
62 _skip_if_no_geopy()
63 self.locations = ['260 Broadway, New York, NY',
64 '77 Massachusetts Ave, Cambridge, MA']
65 self.points = [Point(-71.0597732, 42.3584308),
66 Point(-77.0365305, 38.8977332)]
54 @pytest.fixture
55 def locations():
56 locations = ['260 Broadway, New York, NY',
57 '77 Massachusetts Ave, Cambridge, MA']
58 return locations
6759
68 def test_prepare_result(self):
69 # Calls _prepare_result with sample results from the geocoder call
70 # loop
71 p0 = Point(12.3, -45.6) # Treat these as lat/lon
72 p1 = Point(-23.4, 56.7)
73 d = {'a': ('address0', p0.coords[0]),
74 'b': ('address1', p1.coords[0])}
7560
76 df = _prepare_geocode_result(d)
77 assert type(df) is GeoDataFrame
78 assert from_epsg(4326) == df.crs
79 assert len(df) == 2
80 assert 'address' in df
61 @pytest.fixture
62 def points():
63 points = [Point(-71.0597732, 42.3584308),
64 Point(-77.0365305, 38.8977332)]
65 return points
8166
82 coords = df.loc['a']['geometry'].coords[0]
83 test = p0.coords[0]
84 # Output from the df should be lon/lat
85 assert coords[0] == pytest.approx(test[1])
86 assert coords[1] == pytest.approx(test[0])
8767
88 coords = df.loc['b']['geometry'].coords[0]
89 test = p1.coords[0]
90 assert coords[0] == pytest.approx(test[1])
91 assert coords[1] == pytest.approx(test[0])
68 def test_prepare_result():
69 # Calls _prepare_result with sample results from the geocoder call
70 # loop
71 p0 = Point(12.3, -45.6) # Treat these as lat/lon
72 p1 = Point(-23.4, 56.7)
73 d = {'a': ('address0', p0.coords[0]),
74 'b': ('address1', p1.coords[0])}
9275
93 def test_prepare_result_none(self):
94 p0 = Point(12.3, -45.6) # Treat these as lat/lon
95 d = {'a': ('address0', p0.coords[0]),
96 'b': (None, None)}
76 df = _prepare_geocode_result(d)
77 assert type(df) is GeoDataFrame
78 assert from_epsg(4326) == df.crs
79 assert len(df) == 2
80 assert 'address' in df
9781
98 df = _prepare_geocode_result(d)
99 assert type(df) is GeoDataFrame
100 assert from_epsg(4326) == df.crs
101 assert len(df) == 2
102 assert 'address' in df
82 coords = df.loc['a']['geometry'].coords[0]
83 test = p0.coords[0]
84 # Output from the df should be lon/lat
85 assert coords[0] == pytest.approx(test[1])
86 assert coords[1] == pytest.approx(test[0])
10387
104 row = df.loc['b']
105 assert len(row['geometry'].coords) == 0
106 assert np.isnan(row['address'])
107
108 def test_bad_provider_forward(self):
109 with pytest.raises(ValueError):
110 geocode(['cambridge, ma'], 'badprovider')
88 coords = df.loc['b']['geometry'].coords[0]
89 test = p1.coords[0]
90 assert coords[0] == pytest.approx(test[1])
91 assert coords[1] == pytest.approx(test[0])
11192
112 def test_bad_provider_reverse(self):
113 with pytest.raises(ValueError):
114 reverse_geocode(['cambridge, ma'], 'badprovider')
93 def test_prepare_result_none():
94 p0 = Point(12.3, -45.6) # Treat these as lat/lon
95 d = {'a': ('address0', p0.coords[0]),
96 'b': (None, None)}
11597
116 def test_forward(self):
98 df = _prepare_geocode_result(d)
99 assert type(df) is GeoDataFrame
100 assert from_epsg(4326) == df.crs
101 assert len(df) == 2
102 assert 'address' in df
103
104 row = df.loc['b']
105 assert len(row['geometry'].coords) == 0
106 assert np.isnan(row['address'])
107
108 def test_bad_provider_forward():
109 from geopy.exc import GeocoderNotFound
110 with pytest.raises(GeocoderNotFound):
111 geocode(['cambridge, ma'], 'badprovider')
112
113 def test_bad_provider_reverse():
114 from geopy.exc import GeocoderNotFound
115 with pytest.raises(GeocoderNotFound):
116 reverse_geocode(['cambridge, ma'], 'badprovider')
117
118 def test_forward(locations, points):
119 from geopy.geocoders import GoogleV3
120 for provider in ['googlev3', GoogleV3]:
117121 with mock.patch('geopy.geocoders.googlev3.GoogleV3.geocode',
118122 ForwardMock()) as m:
119 g = geocode(self.locations, provider='googlev3', timeout=2)
120 assert len(self.locations) == m.call_count
123 g = geocode(locations, provider=provider, timeout=2)
124 assert len(locations) == m.call_count
121125
122 n = len(self.locations)
126 n = len(locations)
123127 assert isinstance(g, GeoDataFrame)
124128 expected = GeoSeries(
125129 [Point(float(x) + 0.5, float(x)) for x in range(n)],
126130 crs=from_epsg(4326))
127131 assert_geoseries_equal(expected, g['geometry'])
128132 assert_series_equal(g['address'],
129 pd.Series(self.locations, name='address'))
133 pd.Series(locations, name='address'))
130134
131 def test_reverse(self):
135 def test_reverse(locations, points):
136 from geopy.geocoders import GoogleV3
137 for provider in ['googlev3', GoogleV3]:
132138 with mock.patch('geopy.geocoders.googlev3.GoogleV3.reverse',
133139 ReverseMock()) as m:
134 g = reverse_geocode(self.points, provider='googlev3', timeout=2)
135 assert len(self.points) == m.call_count
140 g = reverse_geocode(points, provider=provider, timeout=2)
141 assert len(points) == m.call_count
136142
137143 assert isinstance(g, GeoDataFrame)
138144
139 expected = GeoSeries(self.points, crs=from_epsg(4326))
145 expected = GeoSeries(points, crs=from_epsg(4326))
140146 assert_geoseries_equal(expected, g['geometry'])
141147 address = pd.Series(
142 ['address' + str(x) for x in range(len(self.points))],
148 ['address' + str(x) for x in range(len(points))],
143149 name='address')
144150 assert_series_equal(g['address'], address)
+224
-17
geopandas/tests/test_geodataframe.py less more
1212 import geopandas
1313 from geopandas import GeoDataFrame, read_file, GeoSeries
1414
15 import pytest
16 from pandas.util.testing import (
17 assert_frame_equal, assert_index_equal, assert_series_equal)
1518 from geopandas.tests.util import (
16 assert_geoseries_equal, connect, create_db, PACKAGE_DIR,
19 assert_geoseries_equal, connect, create_postgis, PACKAGE_DIR,
1720 validate_boro_df)
18 from pandas.util.testing import assert_frame_equal
19 import pytest
2021
2122
2223 class TestDataFrame:
308309 assert len(df3) == 2
309310 assert np.alltrue(df3['Name'].values == self.line_paths)
310311
312 def test_to_file_bool(self):
313 """Test error raise when writing with a boolean column (GH #437)."""
314
315 # still want a temp dir in case this test passes
316 tempfilename = os.path.join(self.tempdir, 'boros.shp')
317 df_with_bool = self.df.copy()
318 df_with_bool['bool_column'] = True
319 with pytest.raises(ValueError):
320 df_with_bool.to_file(tempfilename)
321
322 def test_to_file_with_point_z(self):
323 """Test that 3D geometries are retained in writes (GH #612)."""
324
325 tempfilename = os.path.join(self.tempdir, 'test_3Dpoint.shp')
326 point3d = Point(0, 0, 500)
327 point2d = Point(1, 1)
328 df = GeoDataFrame({'a': [1, 2]}, geometry=[point3d, point2d], crs={})
329 df.to_file(tempfilename)
330 df_read = GeoDataFrame.from_file(tempfilename)
331 assert_geoseries_equal(df.geometry, df_read.geometry)
332
333 def test_to_file_with_poly_z(self):
334 """Test that 3D geometries are retained in writes (GH #612)."""
335
336 tempfilename = os.path.join(self.tempdir, 'test_3Dpoly.shp')
337 poly3d = Polygon([[0, 0, 5], [0, 1, 5], [1, 1, 5], [1, 0, 5]])
338 poly2d = Polygon([[0, 0], [0, 1], [1, 1], [1, 0]])
339 df = GeoDataFrame({'a': [1, 2]}, geometry=[poly3d, poly2d], crs={})
340 df.to_file(tempfilename)
341 df_read = GeoDataFrame.from_file(tempfilename)
342 assert_geoseries_equal(df.geometry, df_read.geometry)
343
311344 def test_to_file_types(self):
312345 """ Test various integer type columns (GH#93) """
313346 tempfilename = os.path.join(self.tempdir, 'int.shp')
327360 with pytest.raises(ValueError):
328361 s.to_file(tempfilename)
329362
363 def test_empty_to_file(self):
364 input_empty_df = GeoDataFrame()
365 tempfilename = os.path.join(self.tempdir, 'test.shp')
366 with pytest.raises(
367 ValueError, match="Cannot write empty DataFrame to file."):
368 input_empty_df.to_file(tempfilename)
369
330370 def test_to_file_schema(self):
331371 """
332372 Ensure that the file is written according to the schema
374414 lonlat = df2.to_crs(epsg=4326)
375415 utm = lonlat.to_crs(epsg=26918)
376416 assert all(df2['geometry'].geom_almost_equals(utm['geometry'],
417 decimal=2))
418
419 def test_transform_inplace(self):
420 df2 = self.df2.copy()
421 df2.crs = {'init': 'epsg:26918', 'no_defs': True}
422 lonlat = df2.to_crs(epsg=4326)
423 df2.to_crs(epsg=4326, inplace=True)
424 assert all(df2['geometry'].geom_almost_equals(lonlat['geometry'],
377425 decimal=2))
378426
379427 def test_to_crs_geo_column_name(self):
395443 crs = f.crs
396444
397445 df = GeoDataFrame.from_features(features, crs=crs)
398 df.rename(columns=lambda x: x.lower(), inplace=True)
399 validate_boro_df(df)
446 validate_boro_df(df, case_sensitive=True)
400447 assert df.crs == crs
401448
402449 def test_from_features_unaligned_properties(self):
448495
449496 def test_from_postgis_default(self):
450497 con = connect('test_geopandas')
451 if con is None or not create_db(self.df):
498 if con is None or not create_postgis(self.df):
452499 raise pytest.skip()
453500
454501 try:
457504 finally:
458505 con.close()
459506
460 validate_boro_df(df)
507 validate_boro_df(df, case_sensitive=False)
461508
462509 def test_from_postgis_custom_geom_col(self):
463510 con = connect('test_geopandas')
464 if con is None or not create_db(self.df):
511 geom_col = "the_geom"
512 if con is None or not create_postgis(self.df, geom_col=geom_col):
465513 raise pytest.skip()
466514
467515 try:
468 sql = """SELECT
469 borocode, boroname, shape_leng, shape_area,
470 geom AS __geometry__
471 FROM nybb;"""
472 df = GeoDataFrame.from_postgis(sql, con, geom_col='__geometry__')
516 sql = "SELECT * FROM nybb;"
517 df = GeoDataFrame.from_postgis(sql, con, geom_col=geom_col)
473518 finally:
474519 con.close()
475520
476 validate_boro_df(df)
521 validate_boro_df(df, case_sensitive=False)
477522
478523 def test_dataframe_to_geodataframe(self):
479524 df = pd.DataFrame({"A": range(len(self.df)), "location":
494539 assert 'location' in df
495540
496541 # should be a copy
497 df.ix[0, "A"] = 100
498 assert gf.ix[0, "A"] == 0
499 assert gf2.ix[0, "A"] == 0
542 df.loc[0, "A"] = 100
543 assert gf.loc[0, "A"] == 0
544 assert gf2.loc[0, "A"] == 0
500545
501546 with pytest.raises(ValueError):
502547 df.set_geometry('location', inplace=True)
525570 unpickled = pd.read_pickle(filename)
526571 assert_frame_equal(self.df, unpickled)
527572 assert self.df.crs == unpickled.crs
573
574
575 def check_geodataframe(df, geometry_column='geometry'):
576 assert isinstance(df, GeoDataFrame)
577 assert isinstance(df.geometry, GeoSeries)
578 assert isinstance(df[geometry_column], GeoSeries)
579 assert df._geometry_column_name == geometry_column
580
581
582 class TestConstructor:
583
584 def test_dict(self):
585 data = {"A": range(3), "B": np.arange(3.0),
586 "geometry": [Point(x, x) for x in range(3)]}
587 df = GeoDataFrame(data)
588 check_geodataframe(df)
589
590 # with specifying other kwargs
591 df = GeoDataFrame(data, index=list('abc'))
592 check_geodataframe(df)
593 assert_index_equal(df.index, pd.Index(list('abc')))
594
595 df = GeoDataFrame(data, columns=['B', 'A', 'geometry'])
596 check_geodataframe(df)
597 assert_index_equal(df.columns, pd.Index(['B', 'A', 'geometry']))
598
599 df = GeoDataFrame(data, columns=['A', 'geometry'])
600 check_geodataframe(df)
601 assert_index_equal(df.columns, pd.Index(['A', 'geometry']))
602 assert_series_equal(df['A'], pd.Series(range(3), name='A'))
603
604 def test_dict_of_series(self):
605
606 data = {"A": pd.Series(range(3)), "B": pd.Series(np.arange(3.0)),
607 "geometry": GeoSeries([Point(x, x) for x in range(3)])}
608
609 df = GeoDataFrame(data)
610 check_geodataframe(df)
611
612 df = GeoDataFrame(data, index=pd.Index([1, 2]))
613 check_geodataframe(df)
614 assert_index_equal(df.index, pd.Index([1, 2]))
615 assert df['A'].tolist() == [1, 2]
616
617 # one non-series -> length is not correct
618 data = {"A": pd.Series(range(3)), "B": np.arange(3.0),
619 "geometry": GeoSeries([Point(x, x) for x in range(3)])}
620 with pytest.raises(ValueError):
621 GeoDataFrame(data, index=[1, 2])
622
623 def test_dict_specified_geometry(self):
624
625 data = {"A": range(3), "B": np.arange(3.0),
626 "other_geom": [Point(x, x) for x in range(3)]}
627
628 df = GeoDataFrame(data, geometry='other_geom')
629 check_geodataframe(df, 'other_geom')
630
631 with pytest.raises(ValueError):
632 df = GeoDataFrame(data, geometry='geometry')
633
634 # when no geometry specified -> works but raises error once
635 # trying to access geometry
636 df = GeoDataFrame(data)
637
638 with pytest.raises(AttributeError):
639 _ = df.geometry
640
641 df = df.set_geometry('other_geom')
642 check_geodataframe(df, 'other_geom')
643
644 # combined with custom args
645 df = GeoDataFrame(data, geometry='other_geom',
646 columns=['B', 'other_geom'])
647 check_geodataframe(df, 'other_geom')
648 assert_index_equal(df.columns, pd.Index(['B', 'other_geom']))
649 assert_series_equal(df['B'], pd.Series(np.arange(3.), name='B'))
650
651 df = GeoDataFrame(data, geometry='other_geom',
652 columns=['other_geom', 'A'])
653 check_geodataframe(df, 'other_geom')
654 assert_index_equal(df.columns, pd.Index(['other_geom', 'A']))
655 assert_series_equal(df['A'], pd.Series(range(3), name='A'))
656
657 def test_array(self):
658 data = {"A": range(3), "B": np.arange(3.0),
659 "geometry": [Point(x, x) for x in range(3)]}
660 a = np.array([data['A'], data['B'], data['geometry']], dtype=object).T
661
662 df = GeoDataFrame(a, columns=['A', 'B', 'geometry'])
663 check_geodataframe(df)
664
665 df = GeoDataFrame(a, columns=['A', 'B', 'other_geom'],
666 geometry='other_geom')
667 check_geodataframe(df, 'other_geom')
668
669 def test_from_frame(self):
670 data = {"A": range(3), "B": np.arange(3.0),
671 "geometry": [Point(x, x) for x in range(3)]}
672 gpdf = GeoDataFrame(data)
673 pddf = pd.DataFrame(data)
674 check_geodataframe(gpdf)
675 assert type(pddf) == pd.DataFrame
676
677 for df in [gpdf, pddf]:
678
679 res = GeoDataFrame(df)
680 check_geodataframe(res)
681
682 res = GeoDataFrame(df, index=pd.Index([0, 2]))
683 check_geodataframe(res)
684 assert_index_equal(res.index, pd.Index([0, 2]))
685 assert res['A'].tolist() == [0, 2]
686
687 res = GeoDataFrame(df, columns=['geometry', 'B'])
688 check_geodataframe(res)
689 assert_index_equal(res.columns, pd.Index(['geometry', 'B']))
690
691 with pytest.raises(ValueError):
692 GeoDataFrame(df, geometry='other_geom')
693
694 def test_from_frame_specified_geometry(self):
695 data = {"A": range(3), "B": np.arange(3.0),
696 "other_geom": [Point(x, x) for x in range(3)]}
697
698 gpdf = GeoDataFrame(data, geometry='other_geom')
699 check_geodataframe(gpdf, 'other_geom')
700 pddf = pd.DataFrame(data)
701
702 for df in [gpdf, pddf]:
703 res = GeoDataFrame(df, geometry='other_geom')
704 check_geodataframe(res, 'other_geom')
705
706 # when passing GeoDataFrame with custom geometry name to constructor
707 # an invalid geodataframe is the result TODO is this desired ?
708 df = GeoDataFrame(gpdf)
709 with pytest.raises(AttributeError):
710 df.geometry
711
712 def test_only_geometry(self):
713 df = GeoDataFrame(geometry=[Point(x, x) for x in range(3)])
714 check_geodataframe(df)
715
716 df = GeoDataFrame({'geometry': [Point(x, x) for x in range(3)]})
717 check_geodataframe(df)
718
719 df = GeoDataFrame({'other_geom': [Point(x, x) for x in range(3)]},
720 geometry='other_geom')
721 check_geodataframe(df, 'other_geom')
722
723 def test_no_geometries(self):
724 # keeps GeoDataFrame class (no DataFrame)
725 data = {"A": range(3), "B": np.arange(3.0)}
726 df = GeoDataFrame(data)
727 assert type(df) == GeoDataFrame
728
729 def test_empty(self):
730 df = GeoDataFrame()
731 assert type(df) == GeoDataFrame
732
733 df = GeoDataFrame({'A': [], 'B': []}, geometry=[])
734 assert type(df) == GeoDataFrame
+98
-21
geopandas/tests/test_geom_methods.py less more
1717 import pytest
1818 from numpy.testing import assert_array_equal
1919 from pandas.util.testing import assert_series_equal, assert_frame_equal
20
21
22 def assert_array_dtype_equal(a, b, *args, **kwargs):
23 a = np.asanyarray(a)
24 b = np.asanyarray(b)
25 assert a.dtype == b.dtype
26 assert_array_equal(a, b, *args, **kwargs)
2027
2128
2229 class TestGeomMethods:
3138 self.nested_squares = Polygon(self.sq.boundary,
3239 [self.inner_sq.boundary])
3340 self.p0 = Point(5, 5)
41 self.p3d = Point(5, 5, 5)
3442 self.g0 = GeoSeries([self.t1, self.t2, self.sq, self.inner_sq,
3543 self.nested_squares, self.p0])
3644 self.g1 = GeoSeries([self.t1, self.sq])
3846 self.g3 = GeoSeries([self.t1, self.t2])
3947 self.g3.crs = {'init': 'epsg:4326', 'no_defs': True}
4048 self.g4 = GeoSeries([self.t2, self.t1])
49 self.g4.crs = {'init': 'epsg:4326', 'no_defs': True}
50 self.g_3d = GeoSeries([self.p0, self.p3d])
4151 self.na = GeoSeries([self.t1, self.t2, Polygon()])
4252 self.na_none = GeoSeries([self.t1, None])
4353 self.a1 = self.g1.copy()
5262 self.l2 = LineString([(0, 0), (1, 0), (1, 1), (0, 1)])
5363 self.g5 = GeoSeries([self.l1, self.l2])
5464 self.g6 = GeoSeries([self.p0, self.t3])
65 self.empty = GeoSeries([])
66 self.empty.crs = {'init': 'epsg:4326', 'no_defs': True}
67 self.empty_poly = Polygon()
5568
5669 # Crossed lines
5770 self.l3 = LineString([(0, 0), (1, 1)])
185198 result = getattr(gdf, op)
186199 fcmp(result, expected)
187200
201 def test_crs_warning(self):
202 # operations on geometries should warn for different CRS
203 no_crs_g3 = self.g3.copy()
204 no_crs_g3.crs = None
205 with pytest.warns(UserWarning):
206 self._test_binary_topological('intersection', self.g3,
207 self.g3, no_crs_g3)
208
188209 def test_intersection(self):
189210 self._test_binary_topological('intersection', self.t1,
190211 self.g1, self.g2)
212 self._test_binary_topological('intersection', self.empty_poly,
213 self.g1, self.empty)
191214
192215 def test_union_series(self):
193216 self._test_binary_topological('union', self.sq, self.g1, self.g2)
252275
253276 def test_contains(self):
254277 expected = [True, False, True, False, False, False]
255 assert_array_equal(expected, self.g0.contains(self.t1))
278 assert_array_dtype_equal(expected, self.g0.contains(self.t1))
256279
257280 def test_length(self):
258281 expected = Series(np.array([2 + np.sqrt(2), 4]), index=self.g1.index)
265288
266289 def test_crosses(self):
267290 expected = [False, False, False, False, False, False]
268 assert_array_equal(expected, self.g0.crosses(self.t1))
291 assert_array_dtype_equal(expected, self.g0.crosses(self.t1))
269292
270293 expected = [False, True]
271 assert_array_equal(expected, self.crossed_lines.crosses(self.l3))
294 assert_array_dtype_equal(expected, self.crossed_lines.crosses(self.l3))
272295
273296 def test_disjoint(self):
274297 expected = [False, False, False, False, False, True]
275 assert_array_equal(expected, self.g0.disjoint(self.t1))
298 assert_array_dtype_equal(expected, self.g0.disjoint(self.t1))
276299
277300 def test_distance(self):
278301 expected = Series(np.array([np.sqrt((5 - 1)**2 + (5 - 1)**2), np.nan]),
279302 self.na_none.index)
280 assert_array_equal(expected, self.na_none.distance(self.p0))
303 assert_array_dtype_equal(expected, self.na_none.distance(self.p0))
281304
282305 expected = Series(np.array([np.sqrt(4**2 + 4**2), np.nan]),
283306 self.g6.index)
284 assert_array_equal(expected, self.g6.distance(self.na_none))
307 assert_array_dtype_equal(expected, self.g6.distance(self.na_none))
285308
286309 def test_intersects(self):
287310 expected = [True, True, True, True, True, False]
288 assert_array_equal(expected, self.g0.intersects(self.t1))
311 assert_array_dtype_equal(expected, self.g0.intersects(self.t1))
289312
290313 expected = [True, False]
291 assert_array_equal(expected, self.na_none.intersects(self.t2))
314 assert_array_dtype_equal(expected, self.na_none.intersects(self.t2))
315
316 expected = np.array([], dtype=bool)
317 assert_array_dtype_equal(expected, self.empty.intersects(self.t1))
318
319 expected = np.array([], dtype=bool)
320 assert_array_dtype_equal(
321 expected, self.empty.intersects(self.empty_poly))
322
323 expected = [False] * 6
324 assert_array_dtype_equal(expected, self.g0.intersects(self.empty_poly))
292325
293326 def test_overlaps(self):
294327 expected = [True, True, False, False, False, False]
295 assert_array_equal(expected, self.g0.overlaps(self.inner_sq))
328 assert_array_dtype_equal(expected, self.g0.overlaps(self.inner_sq))
296329
297330 expected = [False, False]
298 assert_array_equal(expected, self.g4.overlaps(self.t1))
331 assert_array_dtype_equal(expected, self.g4.overlaps(self.t1))
299332
300333 def test_touches(self):
301334 expected = [False, True, False, False, False, False]
302 assert_array_equal(expected, self.g0.touches(self.t1))
335 assert_array_dtype_equal(expected, self.g0.touches(self.t1))
303336
304337 def test_within(self):
305338 expected = [True, False, False, False, False, False]
306 assert_array_equal(expected, self.g0.within(self.t1))
339 assert_array_dtype_equal(expected, self.g0.within(self.t1))
307340
308341 expected = [True, True, True, True, True, False]
309 assert_array_equal(expected, self.g0.within(self.sq))
342 assert_array_dtype_equal(expected, self.g0.within(self.sq))
310343
311344 def test_is_valid(self):
312345 expected = Series(np.array([True] * len(self.g1)), self.g1.index)
324357 expected = Series(np.array([True] * len(self.g1)), self.g1.index)
325358 self._test_unary_real('is_simple', expected, self.g1)
326359
360 def test_has_z(self):
361 expected = Series([False, True], self.g_3d.index)
362 self._test_unary_real('has_z', expected, self.g_3d)
363
327364 def test_xy_points(self):
328365 expected_x = [-73.9847, -74.0446]
329366 expected_y = [40.7484, 40.6893]
330367
331 assert_array_equal(expected_x, self.landmarks.geometry.x)
332 assert_array_equal(expected_y, self.landmarks.geometry.y)
368 assert_array_dtype_equal(expected_x, self.landmarks.geometry.x)
369 assert_array_dtype_equal(expected_y, self.landmarks.geometry.y)
333370
334371 def test_xy_polygons(self):
335372 # accessing x attribute in polygon geoseries should raise an error
339376 with pytest.raises(ValueError):
340377 _ = self.gdf1.geometry.y
341378
379 def test_centroid(self):
380 polygon = Polygon([(-1, -1), (1, -1), (1, 1), (-1, 1)])
381 point = Point(0, 0)
382 polygons = GeoSeries([polygon for i in range(3)])
383 points = GeoSeries([point for i in range(3)])
384 assert_geoseries_equal(polygons.centroid, points)
385
386 def test_convex_hull(self):
387 # the convex hull of a square should be the same as the square
388 squares = GeoSeries([self.sq for i in range(3)])
389 assert_geoseries_equal(squares, squares.convex_hull)
390
342391 def test_exterior(self):
343392 exp_exterior = GeoSeries([LinearRing(p.boundary) for p in self.g3])
344393 for expected, computed in zip(exp_exterior, self.g3.exterior):
422471 res = res.skew(ys=-skew, origin=o)
423472 assert geom_almost_equals(expected, res)
424473
474 def test_buffer(self):
475 original = GeoSeries([Point(0, 0)])
476 expected = GeoSeries([Polygon(((5, 0), (0, -5), (-5, 0), (0, 5),
477 (5, 0)))])
478 calculated = original.buffer(5, resolution=1)
479 assert geom_almost_equals(expected, calculated)
480
481 def test_buffer_args(self):
482 args = dict(cap_style=3, join_style=2, mitre_limit=2.5)
483 calculated_series = self.g0.buffer(10, **args)
484 for original, calculated in zip(self.g0, calculated_series):
485 expected = original.buffer(10, **args)
486 assert calculated.equals(expected)
487
425488 def test_envelope(self):
426489 e = self.g3.envelope
427490 assert np.all(e.geom_equals(self.sq))
437500 'col1': range(len(self.landmarks))})
438501 assert tuple(df.total_bounds) == bbox
439502
440 def test_explode(self):
503 def test_explode_geoseries(self):
441504 s = GeoSeries([MultiPoint([(0, 0), (1, 1)]),
442505 MultiPoint([(2, 2), (3, 3), (4, 4)])])
443
506 s.index.name = 'test_index_name'
507 expected_index_name = ['test_index_name', None]
444508 index = [(0, 0), (0, 1), (1, 0), (1, 1), (1, 2)]
445509 expected = GeoSeries([Point(0, 0), Point(1, 1), Point(2, 2),
446510 Point(3, 3), Point(4, 4)],
447 index=MultiIndex.from_tuples(index))
448
511 index=MultiIndex.from_tuples(
512 index, names=expected_index_name))
449513 assert_geoseries_equal(expected, s.explode())
450514
451 df = self.gdf1[:2].set_geometry(s)
452 assert_geoseries_equal(expected, df.explode())
515 @pytest.mark.parametrize("index_name", [None, 'test'])
516 def test_explode_geodataframe(self, index_name):
517 s = GeoSeries([MultiPoint([Point(1, 2), Point(2, 3)]), Point(5, 5)])
518 df = GeoDataFrame({'col': [1, 2], 'geometry': s})
519 df.index.name = index_name
520
521 test_df = df.explode()
522
523 expected_s = GeoSeries([Point(1, 2), Point(2, 3), Point(5, 5)])
524 expected_df = GeoDataFrame({'col': [1, 1, 2], 'geometry': expected_s})
525 expected_index = MultiIndex(levels=[[0, 1], [0, 1]],
526 labels=[[0, 0, 1], [0, 1, 0]],
527 names=[index_name, None])
528 expected_df = expected_df.set_index(expected_index)
529 assert_frame_equal(test_df, expected_df)
453530
454531 #
455532 # Test '&', '|', '^', and '-'
+283
-92
geopandas/tests/test_overlay.py less more
00 from __future__ import absolute_import
11
2 from shapely.geometry import Point
2 import os
3 from distutils.version import LooseVersion
4
5 import pandas as pd
6 from shapely.geometry import Point, Polygon
37
48 import geopandas
5 from geopandas import GeoDataFrame, read_file, overlay
9 from geopandas import GeoDataFrame, GeoSeries, read_file, overlay
10 from geopandas.testing import assert_geodataframe_equal, assert_geoseries_equal
611
712 import pytest
813
914
10 class TestDataFrame:
11
12 def setup_method(self):
13 N = 10
14
15 nybb_filename = geopandas.datasets.get_path('nybb')
16
17 self.polydf = read_file(nybb_filename)
18 self.crs = {'init': 'epsg:4326'}
19 b = [int(x) for x in self.polydf.total_bounds]
20 self.polydf2 = GeoDataFrame(
15 DATA = os.path.join(
16 os.path.abspath(os.path.dirname(__file__)), 'data', 'overlay')
17
18
19 if str(pd.__version__) < LooseVersion('0.23'):
20 CONCAT_KWARGS = {}
21 else:
22 CONCAT_KWARGS = {'sort': False}
23
24
25 @pytest.fixture
26 def dfs(request):
27 s1 = GeoSeries([Polygon([(0, 0), (2, 0), (2, 2), (0, 2)]),
28 Polygon([(2, 2), (4, 2), (4, 4), (2, 4)])])
29 s2 = GeoSeries([Polygon([(1, 1), (3, 1), (3, 3), (1, 3)]),
30 Polygon([(3, 3), (5, 3), (5, 5), (3, 5)])])
31 df1 = GeoDataFrame({'col1': [1, 2], 'geometry': s1})
32 df2 = GeoDataFrame({'col2': [1, 2], 'geometry': s2})
33 return df1, df2
34
35
36 @pytest.fixture(params=['default-index', 'int-index', 'string-index'])
37 def dfs_index(request, dfs):
38 df1, df2 = dfs
39 if request.param == 'int-index':
40 df1.index = [1, 2]
41 df2.index = [0, 2]
42 if request.param == 'string-index':
43 df1.index = ['row1', 'row2']
44 return df1, df2
45
46
47 @pytest.fixture(params=['union', 'intersection', 'difference',
48 'symmetric_difference', 'identity'])
49 def how(request):
50 return request.param
51
52
53 @pytest.fixture(params=[True, False])
54 def use_sindex(request):
55 return request.param
56
57
58 def test_overlay(dfs_index, how, use_sindex):
59 """
60 Basic overlay test with small dummy example dataframes (from docs).
61 Results obtained using QGIS 2.16 (Vector -> Geoprocessing Tools ->
62 Intersection / Union / ...), saved to GeoJSON
63 """
64 df1, df2 = dfs_index
65 result = overlay(df1, df2, how=how, use_sindex=use_sindex)
66
67 # construction of result
68
69 def _read(name):
70 expected = read_file(
71 os.path.join(DATA, 'polys', 'df1_df2-{0}.geojson'.format(name)))
72 expected.crs = None
73 return expected
74
75 if how == 'identity':
76 expected_intersection = _read('intersection')
77 expected_difference = _read('difference')
78 expected = pd.concat([
79 expected_intersection,
80 expected_difference
81 ], ignore_index=True, **CONCAT_KWARGS)
82 else:
83 expected = _read(how)
84
85 # TODO needed adaptations to result
86 if how == 'union':
87 result = result.sort_values(['col1', 'col2']).reset_index(drop=True)
88 elif how == 'difference':
89 result = result.reset_index(drop=True)
90
91 assert_geodataframe_equal(result, expected, check_column_type=False)
92
93 # for difference also reversed
94 if how == 'difference':
95 result = overlay(df2, df1, how=how, use_sindex=use_sindex)
96 result = result.reset_index(drop=True)
97 expected = _read('difference-inverse')
98 assert_geodataframe_equal(result, expected, check_column_type=False)
99
100
101 def test_overlay_nybb(how):
102 polydf = read_file(geopandas.datasets.get_path('nybb'))
103
104 # construct circles dataframe
105 N = 10
106 b = [int(x) for x in polydf.total_bounds]
107 polydf2 = GeoDataFrame(
21108 [{'geometry': Point(x, y).buffer(10000), 'value1': x + y,
22109 'value2': x - y}
23110 for x, y in zip(range(b[0], b[2], int((b[2]-b[0])/N)),
24111 range(b[1], b[3], int((b[3]-b[1])/N)))],
25 crs=self.crs)
26 self.pointdf = GeoDataFrame(
27 [{'geometry': Point(x, y), 'value1': x + y, 'value2': x - y}
28 for x, y in zip(range(b[0], b[2], int((b[2]-b[0])/N)),
29 range(b[1], b[3], int((b[3]-b[1])/N)))],
30 crs=self.crs)
31
32 # TODO this appears to be necessary;
33 # why is the sindex not generated automatically?
34 self.polydf2._generate_sindex()
35
36 self.union_shape = (180, 7)
37
38 def test_union(self):
39 df = overlay(self.polydf, self.polydf2, how="union")
40 assert type(df) is GeoDataFrame
41 assert df.shape == self.union_shape
42 assert 'value1' in df.columns and 'Shape_Area' in df.columns
43
44 def test_union_no_index(self):
45 # explicitly ignore indicies
46 dfB = overlay(self.polydf, self.polydf2, how="union", use_sindex=False)
47 assert dfB.shape == self.union_shape
48
49 # remove indicies from df
50 self.polydf._sindex = None
51 self.polydf2._sindex = None
52 dfC = overlay(self.polydf, self.polydf2, how="union")
53 assert dfC.shape == self.union_shape
54
55 def test_intersection(self):
56 df = overlay(self.polydf, self.polydf2, how="intersection")
57 assert df['BoroName'][0] is not None
58 assert df.shape == (68, 7)
59
60 def test_identity(self):
61 df = overlay(self.polydf, self.polydf2, how="identity")
62 assert df.shape == (154, 7)
63
64 def test_symmetric_difference(self):
65 df = overlay(self.polydf, self.polydf2, how="symmetric_difference")
66 assert df.shape == (122, 7)
67
68 def test_difference(self):
69 df = overlay(self.polydf, self.polydf2, how="difference")
70 assert df.shape == (86, 7)
71
72 def test_bad_how(self):
73 with pytest.raises(ValueError):
74 overlay(self.polydf, self.polydf, how="spandex")
75
76 def test_nonpoly(self):
77 with pytest.raises(TypeError):
78 overlay(self.pointdf, self.polydf, how="union")
79
80 def test_duplicate_column_name(self):
81 polydf2r = self.polydf2.rename(columns={'value2': 'Shape_Area'})
82 df = overlay(self.polydf, polydf2r, how="union")
83 assert 'Shape_Area_2' in df.columns and 'Shape_Area' in df.columns
84
85 def test_geometry_not_named_geometry(self):
86 # Issue #306
87 # Add points and flip names
88 polydf3 = self.polydf.copy()
89 polydf3 = polydf3.rename(columns={'geometry': 'polygons'})
90 polydf3 = polydf3.set_geometry('polygons')
91 polydf3['geometry'] = self.pointdf.geometry.loc[0:4]
92 assert polydf3.geometry.name == 'polygons'
93
94 df = overlay(polydf3, self.polydf2, how="union")
95 assert type(df) is GeoDataFrame
96
97 df2 = overlay(self.polydf, self.polydf2, how="union")
98 assert df.geom_almost_equals(df2).all()
99
100 def test_geoseries_warning(self):
101 # Issue #305
102 with pytest.raises(NotImplementedError):
103 overlay(self.polydf, self.polydf2.geometry, how="union")
112 crs=polydf.crs)
113
114 result = overlay(polydf, polydf2, how=how)
115
116 cols = ['BoroCode', 'BoroName', 'Shape_Leng', 'Shape_Area',
117 'value1', 'value2']
118 if how == 'difference':
119 cols = cols[:-2]
120
121 # expected result
122
123 if how == 'identity':
124 # read union one, further down below we take the appropriate subset
125 expected = read_file(os.path.join(
126 DATA, 'nybb_qgis', 'qgis-union.shp'))
127 else:
128 expected = read_file(os.path.join(
129 DATA, 'nybb_qgis', 'qgis-{0}.shp'.format(how)))
130
131 # The result of QGIS for 'union' contains incorrect geometries:
132 # 24 is a full original circle overlapping with unioned geometries, and
133 # 27 is a completely duplicated row)
134 if how == 'union':
135 expected = expected.drop([24, 27])
136 expected.reset_index(inplace=True, drop=True)
137 # Eliminate observations without geometries (issue from QGIS)
138 expected = expected[expected.is_valid]
139 expected.reset_index(inplace=True, drop=True)
140
141 if how == 'identity':
142 expected = expected[expected.BoroCode.notnull()].copy()
143
144 # Order GeoDataFrames
145 expected = expected.sort_values(cols).reset_index(drop=True)
146
147 # TODO needed adaptations to result
148 result = result.sort_values(cols).reset_index(drop=True)
149
150 if how in ('union', 'identity'):
151 # concat < 0.23 sorts, so changes the order of the columns
152 # but at least we ensure 'geometry' is the last column
153 assert result.columns[-1] == 'geometry'
154 assert len(result.columns) == len(expected.columns)
155 result = result.reindex(columns=expected.columns)
156
157 assert_geodataframe_equal(result, expected, check_crs=False,
158 check_column_type=False,)
159
160
161 def test_overlay_overlap(how):
162 """
163 Overlay test with overlapping geometries in both dataframes.
164 Test files are created with::
165
166 import geopandas
167 from geopandas import GeoSeries, GeoDataFrame
168 from shapely.geometry import Point, Polygon, LineString
169
170 s1 = GeoSeries([Point(0, 0), Point(1.5, 0)]).buffer(1, resolution=2)
171 s2 = GeoSeries([Point(1, 1), Point(2, 2)]).buffer(1, resolution=2)
172
173 df1 = GeoDataFrame({'geometry': s1, 'col1':[1,2]})
174 df2 = GeoDataFrame({'geometry': s2, 'col2':[1, 2]})
175
176 ax = df1.plot(alpha=0.5)
177 df2.plot(alpha=0.5, ax=ax, color='C1')
178
179 df1.to_file('geopandas/geopandas/tests/data/df1_overlap.geojson',
180 driver='GeoJSON')
181 df2.to_file('geopandas/geopandas/tests/data/df2_overlap.geojson',
182 driver='GeoJSON')
183
184 and then overlay results are obtained from using QGIS 2.16
185 (Vector -> Geoprocessing Tools -> Intersection / Union / ...),
186 saved to GeoJSON.
187 """
188 df1 = read_file(os.path.join(DATA, 'overlap', 'df1_overlap.geojson'))
189 df2 = read_file(os.path.join(DATA, 'overlap', 'df2_overlap.geojson'))
190
191 result = overlay(df1, df2, how=how)
192
193 if how == 'identity':
194 raise pytest.skip()
195
196 expected = read_file(os.path.join(
197 DATA, 'overlap', 'df1_df2_overlap-{0}.geojson'.format(how)))
198
199 if how == 'union':
200 # the QGIS result has the last row duplicated, so removing this
201 expected = expected.iloc[:-1]
202
203 # TODO needed adaptations to result
204 result = result.reset_index(drop=True)
205 if how == 'union':
206 result = result.sort_values(['col1', 'col2']).reset_index(drop=True)
207
208 assert_geodataframe_equal(result, expected, check_column_type=False,
209 check_less_precise=True)
210
211
212 @pytest.mark.parametrize('other_geometry', [False, True])
213 def test_geometry_not_named_geometry(dfs, how, other_geometry):
214 # Issue #306
215 # Add points and flip names
216 df1, df2 = dfs
217 df3 = df1.copy()
218 df3 = df3.rename(columns={'geometry': 'polygons'})
219 df3 = df3.set_geometry('polygons')
220 if other_geometry:
221 df3['geometry'] = df1.centroid.geometry
222 assert df3.geometry.name == 'polygons'
223
224 res1 = overlay(df1, df2, how=how)
225 res2 = overlay(df3, df2, how=how)
226
227 assert df3.geometry.name == 'polygons'
228
229 if how == 'difference':
230 # in case of 'difference', column names of left frame are preserved
231 assert res2.geometry.name == 'polygons'
232 if other_geometry:
233 assert 'geometry' in res2.columns
234 assert_geoseries_equal(res2['geometry'], df3['geometry'],
235 check_series_type=False)
236 res2 = res2.drop(['geometry'], axis=1)
237 res2 = res2.rename(columns={'polygons': 'geometry'})
238 res2 = res2.set_geometry('geometry')
239
240 # TODO if existing column is overwritten -> geometry not last column
241 if other_geometry and how == 'intersection':
242 res2 = res2.reindex(columns=res1.columns)
243 assert_geodataframe_equal(res1, res2)
244
245 df4 = df2.copy()
246 df4 = df4.rename(columns={'geometry': 'geom'})
247 df4 = df4.set_geometry('geom')
248 if other_geometry:
249 df4['geometry'] = df2.centroid.geometry
250 assert df4.geometry.name == 'geom'
251
252 res1 = overlay(df1, df2, how=how)
253 res2 = overlay(df1, df4, how=how)
254 assert_geodataframe_equal(res1, res2)
255
256
257 def test_bad_how(dfs):
258 df1, df2 = dfs
259 with pytest.raises(ValueError):
260 overlay(df1, df2, how="spandex")
261
262
263 def test_raise_nonpoly(dfs):
264 polydf, _ = dfs
265 pointdf = polydf.copy()
266 pointdf['geometry'] = pointdf.geometry.centroid
267
268 with pytest.raises(TypeError):
269 overlay(pointdf, polydf, how="union")
270
271
272 def test_duplicate_column_name(dfs):
273 df1, df2 = dfs
274 df2r = df2.rename(columns={'col2': 'col1'})
275 res = overlay(df1, df2r, how="union")
276 assert ('col1_1' in res.columns) and ('col1_2' in res.columns)
277
278
279 def test_geoseries_warning(dfs):
280 df1, df2 = dfs
281 # Issue #305
282 with pytest.raises(NotImplementedError):
283 overlay(df1, df2.geometry, how="union")
284
285
286 def test_preserve_crs(dfs, how):
287 df1, df2 = dfs
288 result = overlay(df1, df2, how=how)
289 assert result.crs is None
290 crs = {'init': 'epsg:4326'}
291 df1.crs = crs
292 df2.crs = crs
293 result = overlay(df1, df2, how=how)
294 assert result.crs == crs
+102
-17
geopandas/tests/test_plotting.py less more
88 matplotlib.use('Agg')
99 import matplotlib.pyplot as plt
1010 from shapely.affinity import rotate
11 from shapely.geometry import MultiPolygon, Polygon, LineString, Point
11 from shapely.geometry import MultiPolygon, Polygon, LineString, Point, MultiPoint
1212
1313 from geopandas import GeoSeries, GeoDataFrame, read_file
1414
3333 def setup_method(self):
3434 self.N = 10
3535 self.points = GeoSeries(Point(i, i) for i in range(self.N))
36
3637 values = np.arange(self.N)
3738 self.df = GeoDataFrame({'geometry': self.points, 'values': values})
39
40 multipoint1 = MultiPoint(self.points)
41 multipoint2 = rotate(multipoint1, 90)
42 self.df2 = GeoDataFrame({'geometry': [multipoint1, multipoint2],
43 'values': [0, 1]})
3844
3945 def test_figsize(self):
4046
104110 ax = self.df.plot(column='values', color='green')
105111 _check_colors(self.N, ax.collections[0].get_facecolors(), ['green']*self.N)
106112
107 def test_style_kwargs(self):
113 def test_markersize(self):
108114
109115 ax = self.points.plot(markersize=10)
110116 assert ax.collections[0].get_sizes() == [10]
114120
115121 ax = self.df.plot(column='values', markersize=10)
116122 assert ax.collections[0].get_sizes() == [10]
123
124 ax = self.df.plot(markersize='values')
125 assert (ax.collections[0].get_sizes() == self.df['values']).all()
126
127 ax = self.df.plot(column='values', markersize='values')
128 assert (ax.collections[0].get_sizes() == self.df['values']).all()
129
130 def test_style_kwargs(self):
131
132 ax = self.points.plot(edgecolors='k')
133 assert (ax.collections[0].get_edgecolor() == [0, 0, 0, 1]).all()
117134
118135 def test_legend(self):
119136 with warnings.catch_warnings(record=True) as _: # don't print warning
145162 # last point == top of colorbar
146163 np.testing.assert_array_equal(point_colors[-1], cbar_colors[-1])
147164
165 def test_empty_plot(self):
166 s = GeoSeries([])
167 with pytest.warns(UserWarning):
168 ax = s.plot()
169 assert len(ax.collections) == 0
170 df = GeoDataFrame([])
171 with pytest.warns(UserWarning):
172 ax = df.plot()
173 assert len(ax.collections) == 0
174
175 def test_multipoints(self):
176
177 # MultiPoints
178 ax = self.df2.plot()
179 _check_colors(4, ax.collections[0].get_facecolors(),
180 [MPL_DFT_COLOR] * 4)
181
182
183 ax = self.df2.plot(column='values')
184 cmap = plt.get_cmap()
185 expected_colors = [cmap(0)]* self.N + [cmap(1)] * self.N
186 _check_colors(2, ax.collections[0].get_facecolors(),
187 expected_colors)
148188
149189 class TestPointZPlotting:
150190
183223 _check_colors(self.N, ax.collections[0].get_colors(), ['green']*self.N)
184224
185225 def test_style_kwargs(self):
186
187226 # linestyle (style patterns depend on linewidth, therefore pin to 1)
188227 linestyle = 'dashed'
189 ax = self.lines.plot(linestyle=linestyle, linewidth=1)
190 exp_ls = _style_to_linestring_onoffseq(linestyle)
228 linewidth = 1
229
230 ax = self.lines.plot(linestyle=linestyle, linewidth=linewidth)
231 exp_ls = _style_to_linestring_onoffseq(linestyle, linewidth)
191232 for ls in ax.collections[0].get_linestyles():
192233 assert ls[0] == exp_ls[0]
193 assert tuple(ls[1]) == exp_ls[1]
194
195 ax = self.df.plot(linestyle=linestyle, linewidth=1)
234 assert ls[1] == exp_ls[1]
235
236 ax = self.df.plot(linestyle=linestyle, linewidth=linewidth)
196237 for ls in ax.collections[0].get_linestyles():
197238 assert ls[0] == exp_ls[0]
198 assert tuple(ls[1]) == exp_ls[1]
199
200 ax = self.df.plot(column='values', linestyle=linestyle, linewidth=1)
239 assert ls[1] == exp_ls[1]
240
241 ax = self.df.plot(column='values', linestyle=linestyle,
242 linewidth=linewidth)
201243 for ls in ax.collections[0].get_linestyles():
202244 assert ls[0] == exp_ls[0]
203 assert tuple(ls[1]) == exp_ls[1]
245 assert ls[1] == exp_ls[1]
204246
205247
206248 class TestPolygonPlotting:
270312 ax = self.polys.plot(facecolor='g', edgecolor='r', alpha=0.4)
271313 _check_colors(2, ax.collections[0].get_facecolors(), ['g'] * 2, alpha=0.4)
272314 _check_colors(2, ax.collections[0].get_edgecolors(), ['r'] * 2, alpha=0.4)
315
316 def test_legend_kwargs(self):
317
318 ax = self.df.plot(column='values', categorical=True, legend=True,
319 legend_kwds={'frameon': False})
320 assert ax.get_legend().get_frame_on() is False
273321
274322 def test_multipolygons(self):
275323
468516 # pass through of kwargs
469517 coll = plot_linestring_collection(ax, self.lines, linestyle='--',
470518 linewidth=1)
471 exp_ls = _style_to_linestring_onoffseq('dashed')
519 exp_ls = _style_to_linestring_onoffseq('dashed', 1)
472520 res_ls = coll.get_linestyle()[0]
473521 assert res_ls[0] == exp_ls[0]
474 assert tuple(res_ls[1]) == exp_ls[1]
522 assert res_ls[1] == exp_ls[1]
475523 ax.cla()
476524
477525 def test_linestrings_values(self):
581629 _check_colors(self.N, coll.get_facecolor(), exp_colors)
582630 _check_colors(self.N, coll.get_edgecolor(), ['g'] * self.N)
583631 ax.cla()
632
633
634 def test_column_values():
635 """
636 Check that the dataframe plot method returns same values with an
637 input string (column in df), pd.Series, or np.array
638 """
639 # Build test data
640 t1 = Polygon([(0, 0), (1, 0), (1, 1)])
641 t2 = Polygon([(1, 0), (2, 0), (2, 1)])
642 polys = GeoSeries([t1, t2], index=list('AB'))
643 df = GeoDataFrame({'geometry': polys, 'values': [0, 1]})
644
645 # Test with continous values
646 ax = df.plot(column='values')
647 colors = ax.collections[0].get_facecolors()
648 ax = df.plot(column=df['values'])
649 colors_series = ax.collections[0].get_facecolors()
650 np.testing.assert_array_equal(colors, colors_series)
651 ax = df.plot(column=df['values'].values)
652 colors_array = ax.collections[0].get_facecolors()
653 np.testing.assert_array_equal(colors, colors_array)
654
655 # Test with categorical values
656 ax = df.plot(column='values', categorical=True)
657 colors = ax.collections[0].get_facecolors()
658 ax = df.plot(column=df['values'], categorical=True)
659 colors_series = ax.collections[0].get_facecolors()
660 np.testing.assert_array_equal(colors, colors_series)
661 ax = df.plot(column=df['values'].values, categorical=True)
662 colors_array = ax.collections[0].get_facecolors()
663 np.testing.assert_array_equal(colors, colors_array)
664
665 # Check raised error: is df rows number equal to column legth?
666 with pytest.raises(ValueError, match="different number of rows"):
667 ax = df.plot(column=np.array([1, 2, 3]))
584668
585669
586670 def _check_colors(N, actual_colors, expected_colors, alpha=None):
602686 expected_colors : sequence of RGBA tuples
603687 alpha : float (optional)
604688 If set, this alpha transparency will be applied to the
605 `expected_colors`. (Any transparency on the `collecton` is assumed
689 `expected_colors`. (Any transparency on the `collection` is assumed
606690 to be set in its own facecolor RGBA tuples.)
607691 """
608692 import matplotlib.colors as colors
618702 '{} != {}'.format(actual, conv.to_rgba(expected, alpha=alpha))
619703
620704
621 def _style_to_linestring_onoffseq(linestyle):
705 def _style_to_linestring_onoffseq(linestyle, linewidth):
622706 """ Converts a linestyle string representation, namely one of:
623707 ['dashed', 'dotted', 'dashdot', 'solid'],
624708 documented in `Collections.set_linestyle`,
625709 to the form `onoffseq`.
626710 """
627711 if LooseVersion(matplotlib.__version__) >= '2.0':
628 return matplotlib.lines._get_dash_pattern(linestyle)
712 offset, dashes = matplotlib.lines._get_dash_pattern(linestyle)
713 return matplotlib.lines._scale_dashes(offset, dashes, linewidth)
629714 else:
630715 from matplotlib.backend_bases import GraphicsContextBase
631716 return GraphicsContextBase.dashd[linestyle]
+4
-4
geopandas/tests/test_sindex.py less more
9696 # First check that we gets hits from the boros frame.
9797 tree = self.boros.sindex
9898 hits = tree.intersection((1012821.80, 229228.26), objects=True)
99 res = [self.boros.ix[hit.object]['BoroName'] for hit in hits]
99 res = [self.boros.loc[hit.object]['BoroName'] for hit in hits]
100100 assert res == ['Bronx', 'Queens']
101101
102102 # Check that we only get the Bronx from this view.
103103 first = self.boros[self.boros['BoroCode'] < 3]
104104 tree = first.sindex
105105 hits = tree.intersection((1012821.80, 229228.26), objects=True)
106 res = [first.ix[hit.object]['BoroName'] for hit in hits]
106 res = [first.loc[hit.object]['BoroName'] for hit in hits]
107107 assert res == ['Bronx']
108108
109109 # Check that we only get Queens from this view.
110110 second = self.boros[self.boros['BoroCode'] >= 3]
111111 tree = second.sindex
112112 hits = tree.intersection((1012821.80, 229228.26), objects=True)
113 res = [second.ix[hit.object]['BoroName'] for hit in hits],
113 res = [second.loc[hit.object]['BoroName'] for hit in hits],
114114 assert res == ['Queens']
115115
116116 # Get both the Bronx and Queens again.
119119 assert merged.sindex.size == 5
120120 tree = merged.sindex
121121 hits = tree.intersection((1012821.80, 229228.26), objects=True)
122 res = [merged.ix[hit.object]['BoroName'] for hit in hits]
122 res = [merged.loc[hit.object]['BoroName'] for hit in hits]
123123 assert res == ['Bronx', 'Queens']
+35
-0
geopandas/tests/test_testing.py less more
0 import pytest
1
2 from shapely.geometry import Polygon
3
4 from geopandas import GeoSeries, GeoDataFrame
5 from geopandas.testing import (
6 assert_geoseries_equal, assert_geodataframe_equal)
7
8
9 s1 = GeoSeries([Polygon([(0, 0), (2, 0), (2, 2), (0, 2)]),
10 Polygon([(2, 2), (4, 2), (4, 4), (2, 4)])])
11 s2 = GeoSeries([Polygon([(0, 2), (0, 0), (2, 0), (2, 2)]),
12 Polygon([(2, 2), (4, 2), (4, 4), (2, 4)])])
13
14 df1 = GeoDataFrame({'col1': [1, 2], 'geometry': s1})
15 df2 = GeoDataFrame({'col1': [1, 2], 'geometry': s2})
16
17
18 def test_geoseries():
19 assert_geoseries_equal(s1, s2)
20
21 with pytest.raises(AssertionError):
22 assert_geoseries_equal(s1, s2, check_less_precise=True)
23
24
25 def test_geodataframe():
26 assert_geodataframe_equal(df1, df2)
27
28 with pytest.raises(AssertionError):
29 assert_geodataframe_equal(df1, df2, check_less_precise=True)
30
31 with pytest.raises(AssertionError):
32 assert_geodataframe_equal(df1, df2[['geometry', 'col1']])
33
34 assert_geodataframe_equal(df1, df2[['geometry', 'col1']], check_like=True)
+55
-116
geopandas/tests/util.py less more
00 import os.path
1 import sqlite3
12
2 from geopandas import GeoDataFrame, GeoSeries
3
3 import shapely.wkb
4 from geopandas import GeoDataFrame
5 from geopandas.testing import (
6 geom_equals, geom_almost_equals, assert_geoseries_equal) # flake8: noqa
47
58 HERE = os.path.abspath(os.path.dirname(__file__))
69 PACKAGE_DIR = os.path.dirname(os.path.dirname(HERE))
1316 class OperationalError(Exception):
1417 pass
1518
19 # mock not used here, but the import from here is used in other modules
1620 try:
1721 import unittest.mock as mock
1822 except ImportError:
1923 import mock
2024
2125
22 def validate_boro_df(df):
23 """ Tests a GeoDataFrame that has been read in from the nybb dataset."""
26 def validate_boro_df(df, case_sensitive=False):
27 """Tests a GeoDataFrame that has been read in from the nybb dataset."""
2428 assert isinstance(df, GeoDataFrame)
2529 # Make sure all the columns are there and the geometries
2630 # were properly loaded as MultiPolygons
2731 assert len(df) == 5
28 columns = ('borocode', 'boroname', 'shape_leng', 'shape_area')
29 for col in columns:
30 assert col in df.columns
32 columns = ('BoroCode', 'BoroName', 'Shape_Leng', 'Shape_Area')
33 if case_sensitive:
34 for col in columns:
35 assert col in df.columns
36 else:
37 for col in columns:
38 assert col.lower() in (dfcol.lower() for dfcol in df.columns)
3139 assert all(df.geometry.type == 'MultiPolygon')
3240
3341
4048 return con
4149
4250
43 def create_db(df):
51 def create_sqlite(df, filename, geom_col="geom"):
52 """
53 Create a sqlite database with the nybb table. This was the result of
54 using ogr2ogr to create a sqlite database from a shapefile, and then
55 the .dump command within sqlite to produce the commands to reproduce the
56 database, so may not representative of standard sqlite databases.
57 """
58
59 con = sqlite3.connect(filename)
60 cur = con.cursor()
61 cur.execute("CREATE TABLE IF NOT EXISTS 'nybb' "
62 "( ogc_fid INTEGER PRIMARY KEY AUTOINCREMENT, "
63 "'{}' BLOB, 'borocode' INTEGER, ".format(geom_col) +
64 "'boroname' VARCHAR(32), 'shape_leng' FLOAT, "
65 "'shape_area' FLOAT);")
66 sql_row = "INSERT INTO nybb VALUES({},X'{}',{},'{}',{},{});"
67 for i, row in df.iterrows():
68 cur.execute(sql_row.format(i, shapely.wkb.dumps(row['geometry'],
69 hex=True),
70 row['BoroCode'], row['BoroName'],
71 row['Shape_Leng'], row['Shape_Area']))
72 con.commit()
73 con.close()
74
75
76 def create_postgis(df, srid=None, geom_col="geom"):
4477 """
4578 Create a nybb table in the test_geopandas PostGIS database.
4679 Returns a boolean indicating whether the database table was successfully
4780 created
48
4981 """
5082 # Try to create the database, skip the db tests if something goes
5183 # wrong
5789 if con is None:
5890 return False
5991
92 if srid is not None:
93 geom_schema = "geometry(MULTIPOLYGON, {})".format(srid)
94 geom_insert = ("ST_SetSRID(ST_GeometryFromText(%s), {})".format(srid))
95 else:
96 geom_schema = "geometry"
97 geom_insert = "ST_GeometryFromText(%s)"
6098 try:
6199 cursor = con.cursor()
62100 cursor.execute("DROP TABLE IF EXISTS nybb;")
63101
64102 sql = """CREATE TABLE nybb (
65 geom geometry,
66 borocode integer,
67 boroname varchar(40),
68 shape_leng float,
69 shape_area float
70 );"""
103 {geom_col} {geom_schema},
104 borocode integer,
105 boroname varchar(40),
106 shape_leng float,
107 shape_area float
108 );""".format(geom_col=geom_col, geom_schema=geom_schema)
71109 cursor.execute(sql)
72110
73111 for i, row in df.iterrows():
74 sql = """INSERT INTO nybb VALUES (
75 ST_GeometryFromText(%s), %s, %s, %s, %s
76 );"""
112 sql = """INSERT INTO nybb VALUES ({}, %s, %s, %s, %s
113 );""".format(geom_insert)
77114 cursor.execute(sql, (row['geometry'].wkt,
78115 row['BoroCode'],
79116 row['BoroName'],
85122 con.close()
86123
87124 return True
88
89
90 def assert_seq_equal(left, right):
91 """
92 Poor man's version of assert_almost_equal which isn't working with Shapely
93 objects right now
94 """
95 assert (len(left) == len(right),
96 "Mismatched lengths: %d != %d" % (len(left), len(right)))
97
98 for elem_left, elem_right in zip(left, right):
99 assert elem_left == elem_right, "%r != %r" % (left, right)
100
101
102 def geom_equals(this, that):
103 """Test for geometric equality. Empty geometries are considered equal.
104
105 Parameters
106 ----------
107 this, that : arrays of Geo objects (or anything that has an `is_empty`
108 attribute)
109 """
110
111 return (this.geom_equals(that) | (this.is_empty & that.is_empty)).all()
112
113
114 def geom_almost_equals(this, that):
115 """Test for 'almost' geometric equality. Empty geometries considered equal.
116
117 Parameters
118 ----------
119 this, that : arrays of Geo objects (or anything that has an `is_empty`
120 property)
121 """
122
123 return (this.geom_almost_equals(that) |
124 (this.is_empty & that.is_empty)).all()
125
126
127 def assert_geoseries_equal(left, right, check_dtype=False,
128 check_index_type=False,
129 check_series_type=True,
130 check_less_precise=False,
131 check_geom_type=False,
132 check_crs=True):
133 """Test util for checking that two GeoSeries are equal.
134
135 Parameters
136 ----------
137 left, right : two GeoSeries
138 check_dtype : bool, default False
139 if True, check geo dtype [only included so it's a drop-in replacement
140 for assert_series_equal]
141 check_index_type : bool, default False
142 check that index types are equal
143 check_series_type : bool, default True
144 check that both are same type (*and* are GeoSeries). If False,
145 will attempt to convert both into GeoSeries.
146 check_less_precise : bool, default False
147 if True, use geom_almost_equals. if False, use geom_equals.
148 check_geom_type : bool, default False
149 if True, check that all the geom types are equal.
150 check_crs: bool, default True
151 if check_series_type is True, then also check that the
152 crs matches
153 """
154 assert len(left) == len(right), "%d != %d" % (len(left), len(right))
155
156 if check_index_type:
157 assert isinstance(left.index, type(right.index))
158
159 if check_dtype:
160 assert left.dtype == right.dtype, "dtype: %s != %s" % (left.dtype,
161 right.dtype)
162
163 if check_series_type:
164 assert isinstance(left, GeoSeries)
165 assert isinstance(left, type(right))
166
167 if check_crs:
168 assert(left.crs == right.crs)
169 else:
170 if not isinstance(left, GeoSeries):
171 left = GeoSeries(left)
172 if not isinstance(right, GeoSeries):
173 right = GeoSeries(right, index=left.index)
174
175 assert left.index.equals(right.index), "index: %s != %s" % (left.index,
176 right.index)
177
178 if check_geom_type:
179 assert (left.type == right.type).all(), "type: %s != %s" % (left.type,
180 right.type)
181
182 if check_less_precise:
183 assert geom_almost_equals(left, right)
184 else:
185 assert geom_equals(left, right)
+22
-34
geopandas/tools/geocoding.py less more
44 import numpy as np
55 import pandas as pd
66 from shapely.geometry import Point
7 from six import iteritems
7 from six import iteritems, string_types
88
9 import geopandas as gpd
9 import geopandas
1010
1111
1212 def _throttle_time(provider):
1616 require a maximum of 1 request per second.
1717 https://wiki.openstreetmap.org/wiki/Nominatim_usage_policy
1818 """
19 if provider == 'nominatim':
19 import geopy.geocoders
20 if provider == geopy.geocoders.Nominatim:
2021 return 1
2122 else:
2223 return 0
2930 Parameters
3031 ----------
3132 strings : list or Series of addresses to geocode
32 provider : geopy geocoder to use, default 'googlev3'
33 provider : str or geopy.geocoder
34 Specifies geocoding service to use, default is 'googlev3'.
35 Either the string name used by geopy (as specified in
36 geopy.geocoders.SERVICE_TO_GEOCODER) or a geopy Geocoder instance
37 (e.g., geopy.geocoders.GoogleV3) may be used.
38
3339 Some providers require additional arguments such as access keys
3440 See each geocoder's specific parameters in geopy.geocoders
35 * googlev3, default
36 * bing
37 * google
38 * yahoo
39 * mapquest
40 * openmapquest
4141
4242 Ensure proper use of the results by consulting the Terms of Service for
4343 your provider.
7373 points : list or Series of Shapely Point objects.
7474 x coordinate is longitude
7575 y coordinate is latitude
76 provider : geopy geocoder to use, default 'googlev3'
77 These are the same options as the geocode() function
76 provider : str or geopy.geocoder (opt)
77 Specifies geocoding service to use, default is 'googlev3'.
78 Either the string name used by geopy (as specified in
79 geopy.geocoders.SERVICE_TO_GEOCODER) or a geopy Geocoder instance
80 (e.g., geopy.geocoders.GoogleV3) may be used.
81
7882 Some providers require additional arguments such as access keys
7983 See each geocoder's specific parameters in geopy.geocoders
80 * googlev3, default
81 * bing
82 * google
83 * yahoo
84 * mapquest
85 * openmapquest
8684
8785 Ensure proper use of the results by consulting the Terms of Service for
8886 your provider.
108106
109107
110108 def _query(data, forward, provider, **kwargs):
109 # generic wrapper for calls over lists to geopy Geocoders
111110 import geopy
112111 from geopy.geocoders.base import GeocoderQueryError
112 from geopy.geocoders import get_geocoder_for_service
113113
114114 if not isinstance(data, pd.Series):
115115 data = pd.Series(data)
116116
117 # workaround changed name in 0.96
118 try:
119 Yahoo = geopy.geocoders.YahooPlaceFinder
120 except AttributeError:
121 Yahoo = geopy.geocoders.Yahoo
117 if isinstance(provider, string_types):
118 provider = get_geocoder_for_service(provider)
122119
123 coders = {'googlev3': geopy.geocoders.GoogleV3,
124 'bing': geopy.geocoders.Bing,
125 'yahoo': Yahoo,
126 'openmapquest': geopy.geocoders.OpenMapQuest,
127 'nominatim': geopy.geocoders.Nominatim}
128
129 if provider not in coders:
130 raise ValueError('Unknown geocoding provider: {0}'.format(provider))
131
132 coder = coders[provider](**kwargs)
120 coder = provider(**kwargs)
133121 results = {}
134122 for i, s in iteritems(data):
135123 try:
173161 d['address'].append(address)
174162 index.append(i)
175163
176 df = gpd.GeoDataFrame(d, index=index)
164 df = geopandas.GeoDataFrame(d, index=index)
177165 df.crs = from_epsg(4326)
178166
179167 return df
+205
-12
geopandas/tools/overlay.py less more
0 import warnings
1 from functools import reduce
2 from distutils.version import LooseVersion
3
4 import numpy as np
05 import pandas as pd
16 from shapely.ops import unary_union, polygonize
27 from shapely.geometry import MultiLineString
38
49 from geopandas import GeoDataFrame, GeoSeries
10
11
12 if str(pd.__version__) < LooseVersion('0.23'):
13 CONCAT_KWARGS = {}
14 else:
15 CONCAT_KWARGS = {'sort': False}
516
617
718 def _uniquify(columns):
5465
5566 return rings
5667
57 def overlay(df1, df2, how, use_sindex=True):
68
69 def _overlay_old(df1, df2, how, use_sindex=True, **kwargs):
5870 """Perform spatial overlay between two polygons.
5971
6072 Currently only supports data GeoDataFrames with polygons.
100112 mls2 = MultiLineString(rings2)
101113
102114 # Union and polygonize
103 try:
104 # calculating union (try the fast unary_union)
105 mm = unary_union([mls1, mls2])
106 except:
107 # unary_union FAILED
108 # see https://github.com/Toblerity/Shapely/issues/47#issuecomment-18506767
109 # calculating union again (using the slow a.union(b))
110 mm = mls1.union(mls2)
115 mm = unary_union([mls1, mls2])
111116 newpolys = polygonize(mm)
112117
113118 # determine spatial relationship
135140 prop1 = None
136141 prop2 = None
137142 for cand_id in candidates1:
138 cand = df1.ix[cand_id]
143 cand = df1.loc[cand_id]
139144 if cent.intersects(cand[df1.geometry.name]):
140145 df1_hit = True
141146 prop1 = cand
142147 break # Take the first hit
143148 for cand_id in candidates2:
144 cand = df2.ix[cand_id]
149 cand = df2.loc[cand_id]
145150 if cent.intersects(cand[df2.geometry.name]):
146151 df2_hit = True
147152 prop2 = cand
179184 out_series['geometry'] = newpoly
180185 collection.append(out_series)
181186
182 # Return geodataframe with new indicies
187 # Return geodataframe with new indices
183188 return GeoDataFrame(collection, index=range(len(collection)))
189
190
191 def _ensure_geometry_column(df):
192 """
193 Helper function to ensure the geometry column is called 'geometry'.
194 If another column with that name exists, it will be dropped.
195 """
196 if not df._geometry_column_name == 'geometry':
197 if 'geometry' in df.columns:
198 df.drop('geometry', axis=1, inplace=True)
199 df.rename(columns={df._geometry_column_name: 'geometry'},
200 copy=False, inplace=True)
201 df.set_geometry('geometry', inplace=True)
202
203
204 def _overlay_intersection(df1, df2):
205 """
206 Overlay Intersection operation used in overlay function
207 """
208 # Spatial Index to create intersections
209 spatial_index = df2.sindex
210 bbox = df1.geometry.apply(lambda x: x.bounds)
211 sidx = bbox.apply(lambda x: list(spatial_index.intersection(x)))
212 # Create pairs of geometries in both dataframes to be intersected
213 nei = []
214 for i, j in enumerate(sidx):
215 for k in j:
216 nei.append([i, k])
217 if nei != []:
218 pairs = pd.DataFrame(nei, columns=['__idx1', '__idx2'])
219 left = df1.geometry.take(pairs['__idx1'].values)
220 left.reset_index(drop=True, inplace=True)
221 right = df2.geometry.take(pairs['__idx2'].values)
222 right.reset_index(drop=True, inplace=True)
223 intersections = left.intersection(right).buffer(0)
224
225 # only keep actual intersecting geometries
226 pairs_intersect = pairs[~intersections.is_empty]
227 geom_intersect = intersections[~intersections.is_empty]
228
229 # merge data for intersecting geometries
230 df1 = df1.reset_index(drop=True)
231 df2 = df2.reset_index(drop=True)
232 dfinter = pairs_intersect.merge(
233 df1.drop(df1._geometry_column_name, axis=1),
234 left_on='__idx1', right_index=True)
235 dfinter = dfinter.merge(
236 df2.drop(df2._geometry_column_name, axis=1),
237 left_on='__idx2', right_index=True, suffixes=['_1', '_2'])
238
239 return GeoDataFrame(dfinter, geometry=geom_intersect, crs=df1.crs)
240 else:
241 return GeoDataFrame(
242 [],
243 columns=list(set(df1.columns).union(df2.columns)),
244 crs=df1.crs)
245
246
247 def _overlay_difference(df1, df2):
248 """
249 Overlay Difference operation used in overlay function
250 """
251 # Spatial Index to create intersections
252 spatial_index = df2.sindex
253 bbox = df1.geometry.apply(lambda x: x.bounds)
254 sidx = bbox.apply(lambda x: list(spatial_index.intersection(x)))
255 # Create differences
256 new_g = []
257 for geom, neighbours in zip(df1.geometry, sidx):
258 new = reduce(lambda x, y: x.difference(y).buffer(0),
259 [geom] + list(df2.geometry.iloc[neighbours]))
260 new_g.append(new)
261 differences = GeoSeries(new_g, index=df1.index)
262 geom_diff = differences[~differences.is_empty].copy()
263 dfdiff = df1[~differences.is_empty].copy()
264 dfdiff[dfdiff._geometry_column_name] = geom_diff
265 return dfdiff
266
267
268 def _overlay_symmetric_diff(df1, df2):
269 """
270 Overlay Symmetric Difference operation used in overlay function
271 """
272 dfdiff1 = _overlay_difference(df1, df2)
273 dfdiff2 = _overlay_difference(df2, df1)
274 dfdiff1['__idx1'] = range(len(dfdiff1))
275 dfdiff2['__idx2'] = range(len(dfdiff2))
276 dfdiff1['__idx2'] = np.nan
277 dfdiff2['__idx1'] = np.nan
278 # ensure geometry name (otherwise merge goes wrong)
279 _ensure_geometry_column(dfdiff1)
280 _ensure_geometry_column(dfdiff2)
281 # combine both 'difference' dataframes
282 dfsym = dfdiff1.merge(dfdiff2, on=['__idx1', '__idx2'], how='outer',
283 suffixes=['_1', '_2'])
284 geometry = dfsym.geometry_1.copy()
285 geometry[dfsym.geometry_1.isnull()] = \
286 dfsym.loc[dfsym.geometry_1.isnull(), 'geometry_2']
287 dfsym.drop(['geometry_1', 'geometry_2'], axis=1, inplace=True)
288 dfsym.reset_index(drop=True, inplace=True)
289 dfsym = GeoDataFrame(dfsym, geometry=geometry, crs=df1.crs)
290 return dfsym
291
292
293 def _overlay_union(df1, df2):
294 """
295 Overlay Union operation used in overlay function
296 """
297 dfinter = _overlay_intersection(df1, df2)
298 dfsym = _overlay_symmetric_diff(df1, df2)
299 dfunion = pd.concat([dfinter, dfsym], ignore_index=True, **CONCAT_KWARGS)
300 # keep geometry column last
301 columns = list(dfunion.columns)
302 columns.remove('geometry')
303 columns = columns + ['geometry']
304 return dfunion.reindex(columns=columns)
305
306
307 def overlay(df1, df2, how='intersection', make_valid=True, use_sindex=None):
308 """Perform spatial overlay between two polygons.
309
310 Currently only supports data GeoDataFrames with polygons.
311 Implements several methods that are all effectively subsets of
312 the union.
313
314 Parameters
315 ----------
316 df1 : GeoDataFrame with MultiPolygon or Polygon geometry column
317 df2 : GeoDataFrame with MultiPolygon or Polygon geometry column
318 how : string
319 Method of spatial overlay: 'intersection', 'union',
320 'identity', 'symmetric_difference' or 'difference'.
321
322 Returns
323 -------
324 df : GeoDataFrame
325 GeoDataFrame with new set of polygons and attributes
326 resulting from the overlay
327
328 """
329 if use_sindex is not None:
330 warnings.warn("'use_sindex' is deprecated. The overlay operation "
331 "always requires a spatial index (rtree).",
332 DeprecationWarning, stacklevel=2)
333
334 # Allowed operations
335 allowed_hows = [
336 'intersection',
337 'union',
338 'identity',
339 'symmetric_difference',
340 'difference', # aka erase
341 ]
342 # Error Messages
343 if how not in allowed_hows:
344 raise ValueError("`how` was '{0}' but is expected to be "
345 "in %s".format(how, allowed_hows))
346
347 if isinstance(df1, GeoSeries) or isinstance(df2, GeoSeries):
348 raise NotImplementedError("overlay currently only implemented for "
349 "GeoDataFrames")
350
351 accepted_types = ['Polygon', 'MultiPolygon']
352 if (not df1.geom_type.isin(accepted_types).all()
353 or not df2.geom_type.isin(accepted_types).all()):
354 raise TypeError("overlay only takes GeoDataFrames with (multi)polygon "
355 " geometries.")
356
357 # Computations
358 df1 = df1.copy()
359 df2 = df2.copy()
360 df1[df1._geometry_column_name] = df1.geometry.buffer(0)
361 df2[df2._geometry_column_name] = df2.geometry.buffer(0)
362
363 if how == 'difference':
364 return _overlay_difference(df1, df2)
365 elif how == 'intersection':
366 result = _overlay_intersection(df1, df2)
367 elif how == 'symmetric_difference':
368 result = _overlay_symmetric_diff(df1, df2)
369 elif how == 'union':
370 result = _overlay_union(df1, df2)
371 elif how == 'identity':
372 dfunion = _overlay_union(df1, df2)
373 result = dfunion[dfunion['__idx1'].notnull()].copy()
374 result.reset_index(drop=True, inplace=True)
375 result.drop(['__idx1', '__idx2'], axis=1, inplace=True)
376 return result
+12
-8
geopandas/tools/sjoin.py less more
0 from warnings import warn
1
02 import numpy as np
13 import pandas as pd
24 from shapely import prepared
2931
3032 allowed_hows = ['left', 'right', 'inner']
3133 if how not in allowed_hows:
32 raise ValueError("`how` was \"%s\" but is expected to be in %s" % \
33 (how, allowed_hows))
34 raise ValueError("`how` was \"%s\" but is expected to be in %s" %
35 (how, allowed_hows))
3436
3537 allowed_ops = ['contains', 'within', 'intersects']
3638 if op not in allowed_ops:
37 raise ValueError("`op` was \"%s\" but is expected to be in %s" % \
38 (op, allowed_ops))
39 raise ValueError("`op` was \"%s\" but is expected to be in %s" %
40 (op, allowed_ops))
3941
4042 if left_df.crs != right_df.crs:
41 print('Warning: CRS does not match!')
43 warn('CRS of frames being joined does not match!')
4244
4345 index_left = 'index_%s' % lsuffix
4446 index_right = 'index_%s' % rsuffix
5355 # index in geopandas may be any arbitrary dtype. so reset both indices now
5456 # and store references to the original indices, to be reaffixed later.
5557 # GH 352
56 left_df.index.name = index_left
57 right_df.index.name = index_right
58 left_df = left_df.copy(deep=True)
59 left_df.index = left_df.index.rename(index_left)
5860 left_df = left_df.reset_index()
61 right_df = right_df.copy(deep=True)
62 right_df.index = right_df.index.rename(index_right)
5963 right_df = right_df.reset_index()
6064
6165 if op == "within":
109113
110114 else:
111115 # when output from the join has no overlapping geometries
112 result = pd.DataFrame(columns=['_key_left', '_key_right'])
116 result = pd.DataFrame(columns=['_key_left', '_key_right'], dtype=float)
113117
114118 if op == "within":
115119 # within implemented as the inverse of contains; swap names
+54
-2
geopandas/tools/tests/test_sjoin.py less more
6666
6767 @pytest.mark.skipif(not base.HAS_SINDEX, reason='Rtree absent, skipping')
6868 class TestSpatialJoin:
69
70 @pytest.mark.parametrize('dfs', ['default-index', 'string-index'],
71 indirect=True)
72 def test_crs_mismatch(self, dfs):
73 index, df1, df2, expected = dfs
74 df1.crs = {'init': 'epsg:4326', 'no_defs': True}
75 with pytest.warns(UserWarning):
76 sjoin(df1, df2)
6977
7078 @pytest.mark.parametrize('dfs', ['default-index', 'string-index'],
7179 indirect=True)
107115 exp.index.name = None
108116
109117 assert_frame_equal(res, exp)
118
119 def test_empty_join(self):
120 # Check empty joins
121 polygons = geopandas.GeoDataFrame({'col2': [1, 2],
122 'geometry': [Polygon([(0, 0), (1, 0),
123 (1, 1), (0, 1)]),
124 Polygon([(1, 0), (2, 0),
125 (2, 1), (1, 1)])
126 ]})
127 not_in = geopandas.GeoDataFrame({'col1': [1],
128 'geometry': [Point(-0.5, 0.5)]})
129 empty = sjoin(not_in, polygons, how='left', op='intersects')
130 assert empty.index_right.isnull().all()
131 empty = sjoin(not_in, polygons, how='right', op='intersects')
132 assert empty.index_left.isnull().all()
133 empty = sjoin(not_in, polygons, how='inner', op='intersects')
134 assert empty.empty
110135
111136 @pytest.mark.parametrize('dfs', ['default-index', 'string-index'],
112137 indirect=True)
181206 # points within polygons
182207 df = sjoin(self.pointdf, self.polydf, how="left", op="within")
183208 assert df.shape == (21, 8)
184 assert df.ix[1]['BoroName'] == 'Staten Island'
209 assert df.loc[1]['BoroName'] == 'Staten Island'
185210
186211 # points contain polygons? never happens so we should have nulls
187212 df = sjoin(self.pointdf, self.polydf, how="left", op="contains")
188213 assert df.shape == (21, 8)
189 assert np.isnan(df.ix[1]['Shape_Area'])
214 assert np.isnan(df.loc[1]['Shape_Area'])
190215
191216 def test_sjoin_bad_op(self):
192217 # AttributeError: 'Point' object has no attribute 'spandex'
198223 df = sjoin(pointdf2, self.polydf, how="left")
199224 assert 'Shape_Area_left' in df.columns
200225 assert 'Shape_Area_right' in df.columns
226
227 @pytest.mark.parametrize('how', ['left', 'right', 'inner'])
228 def test_sjoin_named_index(self, how):
229 #original index names should be unchanged
230 pointdf2 = self.pointdf.copy()
231 pointdf2.index.name = 'pointid'
232 df = sjoin(pointdf2, self.polydf, how=how)
233 assert pointdf2.index.name == 'pointid'
234 assert self.polydf.index.name == None
201235
202236 def test_sjoin_values(self):
203237 # GH190
264298 def test_sjoin_outer(self):
265299 df = sjoin(self.pointdf, self.polydf, how="outer")
266300 assert df.shape == (21, 8)
301
302
303 @pytest.mark.skipif(not base.HAS_SINDEX, reason='Rtree absent, skipping')
304 class TestSpatialJoinNaturalEarth:
305
306 def setup_method(self):
307 world_path = geopandas.datasets.get_path("naturalearth_lowres")
308 cities_path = geopandas.datasets.get_path("naturalearth_cities")
309 self.world = read_file(world_path)
310 self.cities = read_file(cities_path)
311
312 def test_sjoin_inner(self):
313 # GH637
314 countries = self.world[["geometry", "name"]]
315 countries = countries.rename(columns={"name": "country"})
316 cities_with_country = sjoin(self.cities, countries, how="inner",
317 op="intersects")
318 assert cities_with_country.shape == (172, 4)
+3
-1
setup.py less more
2828 if os.environ.get('READTHEDOCS', False) == 'True':
2929 INSTALL_REQUIRES = []
3030 else:
31 INSTALL_REQUIRES = ['pandas', 'shapely', 'fiona', 'descartes', 'pyproj']
31 INSTALL_REQUIRES = ['pandas', 'shapely', 'fiona', 'pyproj']
3232
3333 # get all data dirs in the datasets module
3434 data_files = []
3939 data_files.append(os.path.join("datasets", item, '*'))
4040 elif item.endswith('.zip'):
4141 data_files.append(os.path.join("datasets", item))
42
43 data_files.append('tests/data/*')
4244
4345
4446 setup(name='geopandas',