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import json
import numpy as np
import pandas as pd
from pandas import DataFrame, Series
from shapely.geometry import mapping, shape, Point
from shapely.geometry.base import BaseGeometry
from six import string_types, PY3
from geopandas.base import GeoPandasBase, _CoordinateIndexer
from geopandas.geoseries import GeoSeries
from geopandas.plotting import plot_dataframe
class GeoDataFrame(GeoPandasBase, DataFrame):
A GeoDataFrame object is a pandas.DataFrame that has a column
with geometry. In addition to the standard DataFrame constructor arguments,
GeoDataFrame also accepts the following keyword arguments:
crs : str (optional)
Coordinate system
geometry : str or array (optional)
If str, column to use as geometry. If array, will be set as 'geometry'
column on GeoDataFrame.
# XXX: This will no longer be necessary in pandas 0.17
_internal_names = ['_data', '_cacher', '_item_cache', '_cache',
'is_copy', '_subtyp', '_index',
'_default_kind', '_default_fill_value', '_metadata',
'__array_struct__', '__array_interface__']
_metadata = ['crs', '_geometry_column_name']
_geometry_column_name = DEFAULT_GEO_COLUMN_NAME
def __init__(self, *args, **kwargs):
crs = kwargs.pop('crs', None)
geometry = kwargs.pop('geometry', None)
super(GeoDataFrame, self).__init__(*args, **kwargs) = crs
if geometry is not None:
self.set_geometry(geometry, inplace=True)
# Serialize metadata (will no longer be necessary in pandas 0.17+)
# See
def __getstate__(self):
meta = dict((k, getattr(self, k, None)) for k in self._metadata)
return dict(_data=self._data, _typ=self._typ,
_metadata=self._metadata, **meta)
def __setattr__(self, attr, val):
# have to special case geometry b/c pandas tries to use as column...
if attr == 'geometry':
object.__setattr__(self, attr, val)
super(GeoDataFrame, self).__setattr__(attr, val)
def _get_geometry(self):
if self._geometry_column_name not in self:
raise AttributeError("No geometry data set yet (expected in"
" column '%s'." % self._geometry_column_name)
return self[self._geometry_column_name]
def _set_geometry(self, col):
# TODO: Use pandas' core.common.is_list_like() here.
if not isinstance(col, (list, np.ndarray, Series)):
raise ValueError("Must use a list-like to set the geometry"
" property")
self.set_geometry(col, inplace=True)
geometry = property(fget=_get_geometry, fset=_set_geometry,
doc="Geometry data for GeoDataFrame")
def set_geometry(self, col, drop=False, inplace=False, crs=None):
Set the GeoDataFrame geometry using either an existing column or
the specified input. By default yields a new object.
The original geometry column is replaced with the input.
col : column label or array
drop : boolean, default True
Delete column to be used as the new geometry
inplace : boolean, default False
Modify the GeoDataFrame in place (do not create a new object)
crs : str/result of fion.get_crs (optional)
Coordinate system to use. If passed, overrides both DataFrame and
col's crs. Otherwise, tries to get crs from passed col values or
>>> df1 = df.set_geometry([Point(0,0), Point(1,1), Point(2,2)])
>>> df2 = df.set_geometry('geom1')
geodataframe : GeoDataFrame
# Most of the code here is taken from DataFrame.set_index()
if inplace:
frame = self
frame = self.copy()
if not crs:
crs = getattr(col, 'crs',
to_remove = None
geo_column_name = self._geometry_column_name
if isinstance(col, (Series, list, np.ndarray)):
level = col
elif hasattr(col, 'ndim') and col.ndim != 1:
raise ValueError("Must pass array with one dimension only.")
level = frame[col].values
except KeyError:
raise ValueError("Unknown column %s" % col)
if drop:
to_remove = col
geo_column_name = self._geometry_column_name
geo_column_name = col
if to_remove:
del frame[to_remove]
if isinstance(level, GeoSeries) and != crs:
# Avoids caching issues/crs sharing issues
level = level.copy() = crs
# Check that we are using a listlike of geometries
if not all(isinstance(item, BaseGeometry) or pd.isnull(item) for item in level):
raise TypeError("Input geometry column must contain valid geometry objects.")
frame[geo_column_name] = level
frame._geometry_column_name = geo_column_name = crs
if not inplace:
return frame
def from_file(cls, filename, **kwargs):
"""Alternate constructor to create a ``GeoDataFrame`` from a file.
Can load a ``GeoDataFrame`` from a file in any format recognized by
`fiona`. See for details.
filename : str
File path or file handle to read from. Depending on which kwargs
are included, the content of filename may vary. See for usage details.
kwargs : key-word arguments
These arguments are passed to, and can be used to
access multi-layer data, data stored within archives (zip files),
>>> df = geopandas.GeoDataFrame.from_file('nybb.shp')
return, **kwargs)
def from_features(cls, features, crs=None, columns=None):
Alternate constructor to create GeoDataFrame from an iterable of
features or a feature collection.
- Iterable of features, where each element must be a feature
dictionary or implement the __geo_interface__.
- Feature collection, where the 'features' key contains an
iterable of features.
- Object holding a feature collection that implements the
crs : str or dict (optional)
Coordinate reference system to set on the resulting frame.
columns : list of column names, optional
Optionally specify the column names to include in the output frame.
This does not overwrite the property names of the input, but can
ensure a consistent output format.
For more information about the ``__geo_interface__``, see
# Handle feature collections
if hasattr(features, "__geo_interface__"):
fs = features.__geo_interface__
fs = features
if isinstance(fs, dict) and fs.get('type') == 'FeatureCollection':
features_lst = fs['features']
features_lst = features
rows = []
for f in features_lst:
if hasattr(f, "__geo_interface__"):
f = f.__geo_interface__
f = f
d = {'geometry': shape(f['geometry']) if f['geometry'] else None}
df = GeoDataFrame(rows, columns=columns) = crs
return df
def from_postgis(cls, sql, con, geom_col='geom', crs=None,
index_col=None, coerce_float=True,
parse_dates=None, params=None):
Alternate constructor to create a ``GeoDataFrame`` from a sql query
containing a geometry column in WKB representation.
sql : string
con : DB connection object or SQLAlchemy engine
geom_col : string, default 'geom'
column name to convert to shapely geometries
crs : optional
Coordinate reference system to use for the returned GeoDataFrame
index_col : string or list of strings, optional, default: None
Column(s) to set as index(MultiIndex)
coerce_float : boolean, default True
Attempt to convert values of non-string, non-numeric objects (like
decimal.Decimal) to floating point, useful for SQL result sets
parse_dates : list or dict, default None
- List of column names to parse as dates.
- Dict of ``{column_name: format string}`` where format string is
strftime compatible in case of parsing string times, or is one of
(D, s, ns, ms, us) in case of parsing integer timestamps.
- Dict of ``{column_name: arg dict}``, where the arg dict
corresponds to the keyword arguments of
:func:`pandas.to_datetime`. Especially useful with databases
without native Datetime support, such as SQLite.
params : list, tuple or dict, optional, default None
List of parameters to pass to execute method.
>>> sql = "SELECT geom, highway FROM roads"
>>> sql = "SELECT ST_Binary(geom) AS geom, highway FROM roads"
>>> df = geopandas.GeoDataFrame.from_postgis(sql, con)
df =
sql, con, geom_col=geom_col, crs=crs,
index_col=index_col, coerce_float=coerce_float,
parse_dates=parse_dates, params=params)
return df
def to_json(self, na='null', show_bbox=False, **kwargs):
Returns a GeoJSON representation of the ``GeoDataFrame`` as a string.
na : {'null', 'drop', 'keep'}, default 'null'
Indicates how to output missing (NaN) values in the GeoDataFrame.
See below.
show_bbox : bool, optional, default: False
Include bbox (bounds) in the geojson
The remaining *kwargs* are passed to json.dumps().
Missing (NaN) values in the GeoDataFrame can be represented as follows:
- ``null``: output the missing entries as JSON null.
- ``drop``: remove the property from the feature. This applies to each
feature individually so that features may have different properties.
- ``keep``: output the missing entries as NaN.
return json.dumps(self._to_geo(na=na, show_bbox=show_bbox), **kwargs)
def __geo_interface__(self):
"""Returns a ``GeoDataFrame`` as a python feature collection.
Implements the `geo_interface`. The returned python data structure
represents the ``GeoDataFrame`` as a GeoJSON-like
This differs from `_to_geo()` only in that it is a property with
default args instead of a method
return self._to_geo(na='null', show_bbox=True)
def iterfeatures(self, na='null', show_bbox=False):
Returns an iterator that yields feature dictionaries that comply with
na : {'null', 'drop', 'keep'}, default 'null'
Indicates how to output missing (NaN) values in the GeoDataFrame
* null: ouput the missing entries as JSON null
* drop: remove the property from the feature. This applies to
each feature individually so that features may have
different properties
* keep: output the missing entries as NaN
show_bbox : include bbox (bounds) in the geojson. default False
if na not in ['null', 'drop', 'keep']:
raise ValueError('Unknown na method {0}'.format(na))
ids = np.array(self.index, copy=False)
geometries = np.array(self[self._geometry_column_name], copy=False)
properties_cols = self.columns.difference([self._geometry_column_name])
if len(properties_cols) > 0:
# convert to object to get python scalars.
properties = self[properties_cols].astype(object).values
if na == 'null':
properties[pd.isnull(self[properties_cols]).values] = None
for i, row in enumerate(properties):
geom = geometries[i]
if na == 'drop':
properties_items = dict((k, v) for k, v
in zip(properties_cols, row)
if not pd.isnull(v))
properties_items = dict((k, v) for k, v
in zip(properties_cols, row))
feature = {'id': str(ids[i]),
'type': 'Feature',
'properties': properties_items,
'geometry': mapping(geom) if geom else None}
if show_bbox:
feature['bbox'] = geom.bounds if geom else None
yield feature
for fid, geom in zip(ids, geometries):
feature = {'id': str(fid),
'type': 'Feature',
'properties': {},
'geometry': mapping(geom) if geom else None}
if show_bbox:
feature['bbox'] = geom.bounds if geom else None
yield feature
def _to_geo(self, **kwargs):
Returns a python feature collection (i.e. the geointerface)
representation of the GeoDataFrame.
geo = {'type': 'FeatureCollection',
'features': list(self.iterfeatures(**kwargs))}
if kwargs.get('show_bbox', False):
geo['bbox'] = tuple(self.total_bounds)
return geo
def to_file(self, filename, driver="ESRI Shapefile", schema=None,
"""Write the ``GeoDataFrame`` to a file.
By default, an ESRI shapefile is written, but any OGR data source
supported by Fiona can be written. A dictionary of supported OGR
providers is available via:
>>> import fiona
>>> fiona.supported_drivers
filename : string
File path or file handle to write to.
driver : string, default: 'ESRI Shapefile'
The OGR format driver used to write the vector file.
schema : dict, default: None
If specified, the schema dictionary is passed to Fiona to
better control how the file is written.
The extra keyword arguments ``**kwargs`` are passed to and
can be used to write to multi-layer data, store data within archives
(zip files), etc.
from import to_file
to_file(self, filename, driver, schema, **kwargs)
def to_crs(self, crs=None, epsg=None, inplace=False):
"""Transform geometries to a new coordinate reference system.
Transform all geometries in a GeoSeries to a different coordinate
reference system. The ``crs`` attribute on the current GeoSeries must
be set. Either ``crs`` in string or dictionary form or an EPSG code
may be specified for output.
This method will transform all points in all objects. It has no notion
or projecting entire geometries. All segments joining points are
assumed to be lines in the current projection, not geodesics. Objects
crossing the dateline (or other projection boundary) will have
undesirable behavior.
crs : dict or str
Output projection parameters as string or in dictionary form.
epsg : int
EPSG code specifying output projection.
inplace : bool, optional, default: False
Whether to return a new GeoDataFrame or do the transformation in
if inplace:
df = self
df = self.copy()
geom = df.geometry.to_crs(crs=crs, epsg=epsg)
df.geometry = geom =
if not inplace:
return df
def __getitem__(self, key):
If the result is a column containing only 'geometry', return a
GeoSeries. If it's a DataFrame with a 'geometry' column, return a
result = super(GeoDataFrame, self).__getitem__(key)
geo_col = self._geometry_column_name
if isinstance(key, string_types) and key == geo_col:
result.__class__ = GeoSeries =
elif isinstance(result, DataFrame) and geo_col in result:
result.__class__ = GeoDataFrame =
result._geometry_column_name = geo_col
elif isinstance(result, DataFrame) and geo_col not in result:
result.__class__ = DataFrame
return result
# Implement pandas methods
def merge(self, *args, **kwargs):
result = DataFrame.merge(self, *args, **kwargs)
geo_col = self._geometry_column_name
if isinstance(result, DataFrame) and geo_col in result:
result.__class__ = GeoDataFrame =
result._geometry_column_name = geo_col
elif isinstance(result, DataFrame) and geo_col not in result:
result.__class__ = DataFrame
return result
def _constructor(self):
return GeoDataFrame
def __finalize__(self, other, method=None, **kwargs):
"""propagate metadata from other to self """
# merge operation: using metadata of the left object
if method == 'merge':
for name in self._metadata:
object.__setattr__(self, name, getattr(other.left, name, None))
# concat operation: using metadata of the first object
elif method == 'concat':
for name in self._metadata:
object.__setattr__(self, name, getattr(other.objs[0], name, None))
for name in self._metadata:
object.__setattr__(self, name, getattr(other, name, None))
return self
def copy(self, deep=True):
Make a copy of this GeoDataFrame object
deep : boolean, default True
Make a deep copy, i.e. also copy data
copy : GeoDataFrame
# FIXME: this will likely be unnecessary in pandas >= 0.13
data = self._data
if deep:
data = data.copy()
return GeoDataFrame(data).__finalize__(self)
def plot(self, *args, **kwargs):
"""Generate a plot of the geometries in the ``GeoDataFrame``.
If the ``column`` parameter is given, colors plot according to values
in that column, otherwise calls ``GeoSeries.plot()`` on the
``geometry`` column.
Wraps the ``plot_dataframe()`` function, and documentation is copied
from there.
return plot_dataframe(self, *args, **kwargs)
plot.__doc__ = plot_dataframe.__doc__
def dissolve(self, by=None, aggfunc='first', as_index=True):
Dissolve geometries within `groupby` into single observation.
This is accomplished by applying the `unary_union` method
to all geometries within a groupself.
Observations associated with each `groupby` group will be aggregated
using the `aggfunc`.
by : string, default None
Column whose values define groups to be dissolved
aggfunc : function or string, default "first"
Aggregation function for manipulation of data associated
with each group. Passed to pandas `groupby.agg` method.
as_index : boolean, default True
If true, groupby columns become index of result.
# Process non-spatial component
data = self.drop(, axis=1)
aggregated_data = data.groupby(by=by).agg(aggfunc)
# Process spatial component
def merge_geometries(block):
merged_geom = block.unary_union
return merged_geom
g = self.groupby(by=by, group_keys=False)[].agg(merge_geometries)
# Aggregate
aggregated_geometry = GeoDataFrame(g,,
# Recombine
aggregated = aggregated_geometry.join(aggregated_data)
# Reset if requested
if not as_index:
aggregated = aggregated.reset_index()
return aggregated
# overrides GeoPandasBase method
def explode(self):
Explode muti-part geometries into multiple single geometries.
Each row containing a multi-part geometry will be split into
multiple rows with single geometries, thereby increasing the vertical
size of the GeoDataFrame.
The index of the input geodataframe is no longer unique and is
replaced with a multi-index (original index with additional level
indicating the multiple geometries: a new zero-based index for each
single part geometry per multi-part geometry).
Exploded geodataframe with each single geometry
as a separate entry in the geodataframe.
df_copy = self.copy()
exploded_geom = df_copy.geometry.explode().reset_index(level=-1)
exploded_index = exploded_geom.columns[0]
df = pd.concat(
[df_copy.drop(df_copy._geometry_column_name, axis=1),
exploded_geom], axis=1)
# reset to MultiIndex, otherwise df index is only first level of
# exploded GeoSeries index.
df.set_index(exploded_index, append=True, inplace=True)
df.index.names = list(self.index.names) + [None]
geo_df = df.set_geometry(self._geometry_column_name)
return geo_df
def points_from_xy(x, y, z=None):
Generate list of shapely Point geometries from x, y(, z) coordinates.
x, y, z : array
>>> geometry = geopandas.points_from_xy(x=[1, 0], y=[0, 1])
>>> geometry = geopandas.points_from_xy(df['x'], df['y'], df['z'])
>>> gdf = geopandas.GeoDataFrame(
df, geometry=geopandas.points_from_xy(df['x'], df['y']))
list : list
if not len(x) == len(y):
raise ValueError("x and y arrays must be equal length.")
if z is not None:
if not len(z) == len(x):
raise ValueError("z array must be same length as x and y.")
geom = [Point(i, j, k) for i, j, k in zip(x, y, z)]
geom = [Point(i, j) for i, j in zip(x, y)]
return geom
def _dataframe_set_geometry(self, col, drop=False, inplace=False, crs=None):
if inplace:
raise ValueError("Can't do inplace setting when converting from"
" DataFrame to GeoDataFrame")
gf = GeoDataFrame(self)
# this will copy so that BlockManager gets copied
return gf.set_geometry(col, drop=drop, inplace=False, crs=crs)
if PY3:
DataFrame.set_geometry = _dataframe_set_geometry
import types
DataFrame.set_geometry = types.MethodType(_dataframe_set_geometry, None,
GeoDataFrame._create_indexer('cx', _CoordinateIndexer)
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