.. _geometric_manipulations:

Geometric manipulations
========================

GeoPandas makes available all the tools for geometric manipulations in the `Shapely library <http://shapely.readthedocs.io/en/latest/manual.html>`_.

Note that documentation for all set-theoretic tools for creating new shapes using the relationship between two different spatial datasets -- like creating intersections, or differences -- can be found at :doc:`Set operations with overlay <set_operations>`.

Constructive methods
~~~~~~~~~~~~~~~~~~~~

.. method:: GeoSeries.buffer(distance, resolution=16)

  Returns a :class:`~geopandas.GeoSeries` of geometries representing all points within a given `distance`
  of each geometric object.

.. attribute:: GeoSeries.boundary

  Returns a :class:`~geopandas.GeoSeries` of lower dimensional objects representing
  each geometry's set-theoretic `boundary`.

.. attribute:: GeoSeries.centroid

  Returns a :class:`~geopandas.GeoSeries` of points for each geometric centroid.

.. attribute:: GeoSeries.concave_hull

  Returns a :class:`~geopandas.GeoSeries` of geometries representing the smallest
  concave `Polygon` containing all the points in each object unless the
  number of points in the object is less than three. For two points,
  the concave hull collapses to a `LineString`; for 1, a `Point`.

.. attribute:: GeoSeries.convex_hull

  Returns a :class:`~geopandas.GeoSeries` of geometries representing the smallest
  convex `Polygon` containing all the points in each object unless the
  number of points in the object is less than three. For two points,
  the convex hull collapses to a `LineString`; for 1, a `Point`.

.. method:: GeoSeries.constrained_delaunay_triangles

  Returns a :class:`~geopandas.GeoSeries` with the constrained Delaunay triangulation
  of polygons. A constrained Delaunay triangulation requires the edges of the input
  polygon(s) to be in the set of resulting triangle edges. An unconstrained
  delaunay triangulation only triangulates based on the vertices, hence triangle
  edges could cross polygon boundaries.

.. method:: GeoSeries.delaunay_triangles(tolerance, preserve_topology=True)

  Returns a :class:`~geopandas.GeoSeries` consisting of polygons (default) or linestrings
  (`only_edges=True`) representing the computed Delaunay triangulation around the vertices
  of an input geometry.

.. attribute:: GeoSeries.envelope

  Returns a :class:`~geopandas.GeoSeries` of geometries representing the point or
  smallest rectangular polygon (with sides parallel to the coordinate
  axes) that contains each object.

.. method:: GeoSeries.extract_unique_points

  Returns a :class:`~geopandas.GeoSeries` of geometries containing all distinct
  vertices of each input geometry as a multipoint.

.. method:: GeoSeries.offset_curve(distance, quad_segs=8, join_style="round", mitre_limit=5.0)

  Returns a :class:`~geopandas.GeoSeries` containing a `Linestring` or `MultiLineString`
  geometry at a distance from the object on its right or its left side.

.. method:: GeoSeries.remove_repeated_points

   Returns a :class:`~geopandas.GeoSeries` containing a copy of the input geometry
   with repeated points removed.

.. method:: GeoSeries.simplify(tolerance, preserve_topology=True)

  Returns a :class:`~geopandas.GeoSeries` containing a simplified representation of
  each object.

.. method:: GeoSeries.segmentize(max_segment_length)

  Returns a :class:`~geopandas.GeoSeries` with additional vertices added to line
  segments based on max_segment_length.

.. method:: GeoSeries.union_all()

  Return a geometry containing the union of all geometries in the :class:`~geopandas.GeoSeries`.


Affine transformations
~~~~~~~~~~~~~~~~~~~~~~~~

.. method:: GeoSeries.affine_transform(self, matrix)

  Transform the geometries of the :class:`~geopandas.GeoSeries` using an affine transformation matrix

.. method:: GeoSeries.rotate(self, angle, origin='center', use_radians=False)

  Rotate the coordinates of the :class:`~geopandas.GeoSeries`.

.. method:: GeoSeries.scale(self, xfact=1.0, yfact=1.0, zfact=1.0, origin='center')

 Scale the geometries of the :class:`~geopandas.GeoSeries` along each (x, y, z) dimension.

.. method:: GeoSeries.skew(self, angle, origin='center', use_radians=False)

  Shear/Skew the geometries of the :class:`~geopandas.GeoSeries` by angles along x and y dimensions.

.. method:: GeoSeries.translate(self, xoff=0.0, yoff=0.0, zoff=0.0)

  Shift the coordinates of the :class:`~geopandas.GeoSeries`.



Examples of geometric manipulations
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. sourcecode:: python

    >>> import geopandas
    >>> from geopandas import GeoSeries
    >>> from shapely.geometry import Polygon
    >>> p1 = Polygon([(0, 0), (1, 0), (1, 1)])
    >>> p2 = Polygon([(0, 0), (1, 0), (1, 1), (0, 1)])
    >>> p3 = Polygon([(2, 0), (3, 0), (3, 1), (2, 1)])
    >>> g = GeoSeries([p1, p2, p3])
    >>> g
    0         POLYGON ((0 0, 1 0, 1 1, 0 0))
    1    POLYGON ((0 0, 1 0, 1 1, 0 1, 0 0))
    2    POLYGON ((2 0, 3 0, 3 1, 2 1, 2 0))
    dtype: geometry

.. image:: ../../_static/test.png

Some geographic operations return normal pandas objects.  The :attr:`~geopandas.GeoSeries.area` property of a :class:`~geopandas.GeoSeries` will return a :class:`pandas.Series` containing the area of each item in the :class:`~geopandas.GeoSeries`:

.. sourcecode:: python

    >>> print(g.area)
    0    0.5
    1    1.0
    2    1.0
    dtype: float64

Other operations return GeoPandas objects:

.. sourcecode:: python

    >>> g.buffer(0.5)
    0    POLYGON ((-0.3535533905932737 0.35355339059327...
    1    POLYGON ((-0.5 0, -0.5 1, -0.4975923633360985 ...
    2    POLYGON ((1.5 0, 1.5 1, 1.502407636663901 1.04...
    dtype: geometry

.. image:: ../../_static/test_buffer.png

GeoPandas objects also know how to plot themselves. GeoPandas uses `matplotlib`_ for plotting. To generate a plot of a :class:`~geopandas.GeoSeries`, use:

.. sourcecode:: python

    >>> g.plot()

GeoPandas also implements alternate constructors that can read any data format recognized by `Pyogrio`_.  To read a zip file containing an ESRI shapefile with the `borough boundaries of New York City`_ (provided by the ``geodatasets`` package):

.. sourcecode:: python

    >>> import geodatasets
    >>> nybb_path = geodatasets.get_path('nybb')
    >>> boros = geopandas.read_file(nybb_path)
    >>> boros.set_index('BoroCode', inplace=True)
    >>> boros.sort_index(inplace=True)
    >>> boros
                   BoroName     Shape_Leng    Shape_Area  \
    BoroCode
    1             Manhattan  359299.096471  6.364715e+08
    2                 Bronx  464392.991824  1.186925e+09
    3              Brooklyn  741080.523166  1.937479e+09
    4                Queens  896344.047763  3.045213e+09
    5         Staten Island  330470.010332  1.623820e+09

                                                       geometry
    BoroCode
    1         MULTIPOLYGON (((981219.0557861328 188655.31579...
    2         MULTIPOLYGON (((1012821.805786133 229228.26458...
    3         MULTIPOLYGON (((1021176.479003906 151374.79699...
    4         MULTIPOLYGON (((1029606.076599121 156073.81420...
    5         MULTIPOLYGON (((970217.0223999023 145643.33221...

.. image:: ../../_static/nyc.png

.. sourcecode:: python

    >>> boros['geometry'].convex_hull
    BoroCode
    1    POLYGON ((977855.4451904297 188082.3223876953,...
    2    POLYGON ((1017949.977600098 225426.8845825195,...
    3    POLYGON ((988872.8212280273 146772.0317993164,...
    4    POLYGON ((1000721.531799316 136681.776184082, ...
    5    POLYGON ((915517.6877458114 120121.8812543372,...
    dtype: geometry

.. image:: ../../_static/nyc_hull.png

To demonstrate a more complex operation, generate a
:class:`~geopandas.GeoSeries` containing 2000 random points:

.. sourcecode:: python

    >>> import numpy as np
    >>> from shapely.geometry import Point
    >>> xmin, xmax, ymin, ymax = 900000, 1080000, 120000, 280000
    >>> xc = (xmax - xmin) * np.random.random(2000) + xmin
    >>> yc = (ymax - ymin) * np.random.random(2000) + ymin
    >>> pts = GeoSeries([Point(x, y) for x, y in zip(xc, yc)])

Now draw a circle with fixed radius around each point:

.. sourcecode:: python

    >>> circles = pts.buffer(2000)

You can collapse these circles into a single :class:`MultiPolygon`
geometry with

.. sourcecode:: python

    >>> mp = circles.union_all()

To extract the part of this geometry contained in each borough, you can
just use:

.. sourcecode:: python

    >>> holes = boros['geometry'].intersection(mp)

.. image:: ../../_static/holes.png

and to get the area outside of the holes:

.. sourcecode:: python

    >>> boros_with_holes = boros['geometry'].difference(mp)

.. image:: ../../_static/boros_with_holes.png

Note that this can be simplified a bit, since ``geometry`` is
available as an attribute on a :class:`~geopandas.GeoDataFrame`, and the
:meth:`~geopandas.GeoSeries.intersection` and :meth:`~geopandas.GeoSeries.difference` methods are implemented with the
"&" and "-" operators, respectively.  For example, the latter could
have been expressed simply as ``boros.geometry - mp``.

It's easy to do things like calculate the fractional area in each
borough that are in the holes:

.. sourcecode:: python

    >>> holes.area / boros.geometry.area
    BoroCode
    1    0.579939
    2    0.586833
    3    0.608174
    4    0.582172
    5    0.558075
    dtype: float64

.. _matplotlib: http://matplotlib.org
.. _pyogrio: http://pyogrio.readthedocs.io/en/latest/
.. _geopy: https://github.com/geopy/geopy
.. _geo_interface: https://gist.github.com/sgillies/2217756
.. _borough boundaries of New York City: https://data.cityofnewyork.us/City-Government/Borough-Boundaries/tqmj-j8zm

.. toctree::
   :maxdepth: 2
