import json import warnings import numpy as np import pandas as pd import shapely.errors from pandas import DataFrame, Series from pandas.core.accessor import CachedAccessor from shapely.geometry import mapping, shape from shapely.geometry.base import BaseGeometry from pyproj import CRS from geopandas.array import GeometryArray, GeometryDtype, from_shapely, to_wkb, to_wkt from geopandas.base import GeoPandasBase, is_geometry_type from geopandas.geoseries import GeoSeries import geopandas.io from geopandas.explore import _explore from . import _compat as compat from ._decorator import doc def _geodataframe_constructor_with_fallback(*args, **kwargs): """ A flexible constructor for GeoDataFrame._constructor, which falls back to returning a DataFrame (if a certain operation does not preserve the geometry column) """ df = GeoDataFrame(*args, **kwargs) geometry_cols_mask = df.dtypes == "geometry" if len(geometry_cols_mask) == 0 or geometry_cols_mask.sum() == 0: df = pd.DataFrame(df) return df def _ensure_geometry(data, crs=None): """ Ensure the data is of geometry dtype or converted to it. If input is a (Geo)Series, output is a GeoSeries, otherwise output is GeometryArray. If the input is a GeometryDtype with a set CRS, `crs` is ignored. """ if is_geometry_type(data): if isinstance(data, Series): data = GeoSeries(data) if data.crs is None and crs is not None: # Avoids caching issues/crs sharing issues data = data.copy() data.crs = crs return data else: if isinstance(data, Series): out = from_shapely(np.asarray(data), crs=crs) return GeoSeries(out, index=data.index, name=data.name) else: out = from_shapely(data, crs=crs) return out crs_mismatch_error = ( "CRS mismatch between CRS of the passed geometries " "and 'crs'. Use 'GeoDataFrame.set_crs(crs, " "allow_override=True)' to overwrite CRS or " "'GeoDataFrame.to_crs(crs)' to reproject geometries. " ) 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: Parameters ---------- crs : value (optional) Coordinate Reference System of the geometry objects. Can be anything accepted by :meth:`pyproj.CRS.from_user_input() `, such as an authority string (eg "EPSG:4326") or a WKT string. geometry : str or array (optional) If str, column to use as geometry. If array, will be set as 'geometry' column on GeoDataFrame. Examples -------- Constructing GeoDataFrame from a dictionary. >>> from shapely.geometry import Point >>> d = {'col1': ['name1', 'name2'], 'geometry': [Point(1, 2), Point(2, 1)]} >>> gdf = geopandas.GeoDataFrame(d, crs="EPSG:4326") >>> gdf col1 geometry 0 name1 POINT (1.00000 2.00000) 1 name2 POINT (2.00000 1.00000) Notice that the inferred dtype of 'geometry' columns is geometry. >>> gdf.dtypes col1 object geometry geometry dtype: object Constructing GeoDataFrame from a pandas DataFrame with a column of WKT geometries: >>> import pandas as pd >>> d = {'col1': ['name1', 'name2'], 'wkt': ['POINT (1 2)', 'POINT (2 1)']} >>> df = pd.DataFrame(d) >>> gs = geopandas.GeoSeries.from_wkt(df['wkt']) >>> gdf = geopandas.GeoDataFrame(df, geometry=gs, crs="EPSG:4326") >>> gdf col1 wkt geometry 0 name1 POINT (1 2) POINT (1.00000 2.00000) 1 name2 POINT (2 1) POINT (2.00000 1.00000) See also -------- GeoSeries : Series object designed to store shapely geometry objects """ _metadata = ["_geometry_column_name"] _internal_names = DataFrame._internal_names + ["geometry"] _internal_names_set = set(_internal_names) _geometry_column_name = None def __init__(self, data=None, *args, geometry=None, crs=None, **kwargs): with compat.ignore_shapely2_warnings(): if ( kwargs.get("copy") is None and isinstance(data, DataFrame) and not isinstance(data, GeoDataFrame) ): kwargs.update(copy=True) super().__init__(data, *args, **kwargs) # set_geometry ensures the geometry data have the proper dtype, # but is not called if `geometry=None` ('geometry' column present # in the data), so therefore need to ensure it here manually # but within a try/except because currently non-geometries are # allowed in that case # TODO do we want to raise / return normal DataFrame in this case? # if gdf passed in and geo_col is set, we use that for geometry if geometry is None and isinstance(data, GeoDataFrame): self._geometry_column_name = data._geometry_column_name if crs is not None and data.crs != crs: raise ValueError(crs_mismatch_error) if ( geometry is None and self.columns.nlevels == 1 and "geometry" in self.columns ): # Check for multiple columns with name "geometry". If there are, # self["geometry"] is a gdf and constructor gets recursively recalled # by pandas internals trying to access this if (self.columns == "geometry").sum() > 1: raise ValueError( "GeoDataFrame does not support multiple columns " "using the geometry column name 'geometry'." ) # only if we have actual geometry values -> call set_geometry try: if ( hasattr(self["geometry"].values, "crs") and self["geometry"].values.crs and crs and not self["geometry"].values.crs == crs ): raise ValueError(crs_mismatch_error) self["geometry"] = _ensure_geometry(self["geometry"].values, crs) except TypeError: pass else: geometry = "geometry" if geometry is not None: if ( hasattr(geometry, "crs") and geometry.crs and crs and not geometry.crs == crs ): raise ValueError(crs_mismatch_error) self.set_geometry(geometry, inplace=True, crs=crs) if geometry is None and crs: raise ValueError( "Assigning CRS to a GeoDataFrame without a geometry column is not " "supported. Supply geometry using the 'geometry=' keyword argument, " "or by providing a DataFrame with column name 'geometry'", ) 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) else: super().__setattr__(attr, val) def _get_geometry(self): if self._geometry_column_name not in self: if self._geometry_column_name is None: msg = ( "You are calling a geospatial method on the GeoDataFrame, " "but the active geometry column to use has not been set. " ) else: msg = ( "You are calling a geospatial method on the GeoDataFrame, " f"but the active geometry column ('{self._geometry_column_name}') " "is not present. " ) geo_cols = list(self.columns[self.dtypes == "geometry"]) if len(geo_cols) > 0: msg += ( f"\nThere are columns with geometry data type ({geo_cols}), and " "you can either set one as the active geometry with " 'df.set_geometry("name") or access the column as a ' 'GeoSeries (df["name"]) and call the method directly on it.' ) else: msg += ( "\nThere are no existing columns with geometry data type. You can " "add a geometry column as the active geometry column with " "df.set_geometry. " ) raise AttributeError(msg) return self[self._geometry_column_name] def _set_geometry(self, col): if not pd.api.types.is_list_like(col): raise ValueError("Must use a list-like to set the geometry property") self._persist_old_default_geometry_colname() 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. Parameters ---------- col : column label or array drop : boolean, default False 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 : pyproj.CRS, optional Coordinate system to use. The value can be anything accepted by :meth:`pyproj.CRS.from_user_input() `, such as an authority string (eg "EPSG:4326") or a WKT string. If passed, overrides both DataFrame and col's crs. Otherwise, tries to get crs from passed col values or DataFrame. Examples -------- >>> from shapely.geometry import Point >>> d = {'col1': ['name1', 'name2'], 'geometry': [Point(1, 2), Point(2, 1)]} >>> gdf = geopandas.GeoDataFrame(d, crs="EPSG:4326") >>> gdf col1 geometry 0 name1 POINT (1.00000 2.00000) 1 name2 POINT (2.00000 1.00000) Passing an array: >>> df1 = gdf.set_geometry([Point(0,0), Point(1,1)]) >>> df1 col1 geometry 0 name1 POINT (0.00000 0.00000) 1 name2 POINT (1.00000 1.00000) Using existing column: >>> gdf["buffered"] = gdf.buffer(2) >>> df2 = gdf.set_geometry("buffered") >>> df2.geometry 0 POLYGON ((3.00000 2.00000, 2.99037 1.80397, 2.... 1 POLYGON ((4.00000 1.00000, 3.99037 0.80397, 3.... Name: buffered, dtype: geometry Returns ------- GeoDataFrame See also -------- GeoDataFrame.rename_geometry : rename an active geometry column """ # Most of the code here is taken from DataFrame.set_index() if inplace: frame = self else: frame = self.copy() to_remove = None geo_column_name = self._geometry_column_name if geo_column_name is None: geo_column_name = "geometry" if isinstance(col, (Series, list, np.ndarray, GeometryArray)): level = col elif hasattr(col, "ndim") and col.ndim > 1: raise ValueError("Must pass array with one dimension only.") else: try: level = frame[col] except KeyError: raise ValueError("Unknown column %s" % col) except Exception: raise if isinstance(level, DataFrame): raise ValueError( "GeoDataFrame does not support setting the geometry column where " "the column name is shared by multiple columns." ) if drop: to_remove = col else: geo_column_name = col if to_remove: del frame[to_remove] if not crs: crs = getattr(level, "crs", None) if isinstance(level, (GeoSeries, GeometryArray)) and level.crs != crs: # Avoids caching issues/crs sharing issues level = level.copy() level.crs = crs # Check that we are using a listlike of geometries level = _ensure_geometry(level, crs=crs) # update _geometry_column_name prior to assignment # to avoid default is None warning frame._geometry_column_name = geo_column_name frame[geo_column_name] = level if not inplace: return frame def rename_geometry(self, col, inplace=False): """ Renames the GeoDataFrame geometry column to the specified name. By default yields a new object. The original geometry column is replaced with the input. Parameters ---------- col : new geometry column label inplace : boolean, default False Modify the GeoDataFrame in place (do not create a new object) Examples -------- >>> from shapely.geometry import Point >>> d = {'col1': ['name1', 'name2'], 'geometry': [Point(1, 2), Point(2, 1)]} >>> df = geopandas.GeoDataFrame(d, crs="EPSG:4326") >>> df1 = df.rename_geometry('geom1') >>> df1.geometry.name 'geom1' >>> df.rename_geometry('geom1', inplace=True) >>> df.geometry.name 'geom1' Returns ------- geodataframe : GeoDataFrame See also -------- GeoDataFrame.set_geometry : set the active geometry """ geometry_col = self.geometry.name if col in self.columns: raise ValueError(f"Column named {col} already exists") else: if not inplace: return self.rename(columns={geometry_col: col}).set_geometry( col, inplace ) self.rename(columns={geometry_col: col}, inplace=inplace) self.set_geometry(col, inplace=inplace) @property def crs(self): """ The Coordinate Reference System (CRS) represented as a ``pyproj.CRS`` object. Returns None if the CRS is not set, and to set the value it :getter: Returns a ``pyproj.CRS`` or None. When setting, the value can be anything accepted by :meth:`pyproj.CRS.from_user_input() `, such as an authority string (eg "EPSG:4326") or a WKT string. Examples -------- >>> gdf.crs # doctest: +SKIP Name: WGS 84 Axis Info [ellipsoidal]: - Lat[north]: Geodetic latitude (degree) - Lon[east]: Geodetic longitude (degree) Area of Use: - name: World - bounds: (-180.0, -90.0, 180.0, 90.0) Datum: World Geodetic System 1984 - Ellipsoid: WGS 84 - Prime Meridian: Greenwich See also -------- GeoDataFrame.set_crs : assign CRS GeoDataFrame.to_crs : re-project to another CRS """ try: return self.geometry.crs except AttributeError: raise AttributeError( "The CRS attribute of a GeoDataFrame without an active " "geometry column is not defined. Use GeoDataFrame.set_geometry " "to set the active geometry column." ) @crs.setter def crs(self, value): """Sets the value of the crs""" if self._geometry_column_name is None: raise ValueError( "Assigning CRS to a GeoDataFrame without a geometry column is not " "supported. Use GeoDataFrame.set_geometry to set the active " "geometry column.", ) if hasattr(self.geometry.values, "crs"): self.geometry.values.crs = value else: # column called 'geometry' without geometry raise ValueError( "Assigning CRS to a GeoDataFrame without an active geometry " "column is not supported. Use GeoDataFrame.set_geometry to set " "the active geometry column.", ) def __setstate__(self, state): # overriding DataFrame method for compat with older pickles (CRS handling) crs = None if isinstance(state, dict): if "crs" in state and "_crs" not in state: crs = state.pop("crs", None) else: crs = state.pop("_crs", None) crs = CRS.from_user_input(crs) if crs is not None else crs super().__setstate__(state) # for some versions that didn't yet have CRS at array level -> crs is set # at GeoDataFrame level with '_crs' (and not 'crs'), so without propagating # to the GeoSeries/GeometryArray try: if crs is not None: if self.geometry.values.crs is None: self.crs = crs except Exception: pass @classmethod def from_dict(cls, data, geometry=None, crs=None, **kwargs): """ Construct GeoDataFrame from dict of array-like or dicts by overriding DataFrame.from_dict method with geometry and crs Parameters ---------- data : dict Of the form {field : array-like} or {field : dict}. geometry : str or array (optional) If str, column to use as geometry. If array, will be set as 'geometry' column on GeoDataFrame. crs : str or dict (optional) Coordinate reference system to set on the resulting frame. kwargs : key-word arguments These arguments are passed to DataFrame.from_dict Returns ------- GeoDataFrame """ dataframe = DataFrame.from_dict(data, **kwargs) return cls(dataframe, geometry=geometry, crs=crs) @classmethod def from_file(cls, filename, **kwargs): """Alternate constructor to create a ``GeoDataFrame`` from a file. It is recommended to use :func:`geopandas.read_file` instead. Can load a ``GeoDataFrame`` from a file in any format recognized by `fiona`. See http://fiona.readthedocs.io/en/latest/manual.html for details. Parameters ---------- filename : str File path or file handle to read from. Depending on which kwargs are included, the content of filename may vary. See http://fiona.readthedocs.io/en/latest/README.html#usage for usage details. kwargs : key-word arguments These arguments are passed to fiona.open, and can be used to access multi-layer data, data stored within archives (zip files), etc. Examples -------- >>> import geodatasets >>> path = geodatasets.get_path('nybb') >>> gdf = geopandas.GeoDataFrame.from_file(path) >>> gdf # doctest: +SKIP BoroCode BoroName Shape_Leng Shape_Area \ geometry 0 5 Staten Island 330470.010332 1.623820e+09 MULTIPOLYGON ((\ (970217.022 145643.332, 970227.... 1 4 Queens 896344.047763 3.045213e+09 MULTIPOLYGON ((\ (1029606.077 156073.814, 102957... 2 3 Brooklyn 741080.523166 1.937479e+09 MULTIPOLYGON ((\ (1021176.479 151374.797, 102100... 3 1 Manhattan 359299.096471 6.364715e+08 MULTIPOLYGON ((\ (981219.056 188655.316, 980940.... 4 2 Bronx 464392.991824 1.186925e+09 MULTIPOLYGON ((\ (1012821.806 229228.265, 101278... The recommended method of reading files is :func:`geopandas.read_file`: >>> gdf = geopandas.read_file(path) See also -------- read_file : read file to GeoDataFame GeoDataFrame.to_file : write GeoDataFrame to file """ return geopandas.io.file._read_file(filename, **kwargs) @classmethod def from_features(cls, features, crs=None, columns=None): """ Alternate constructor to create GeoDataFrame from an iterable of features or a feature collection. Parameters ---------- features - 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 ``__geo_interface__``. 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. Returns ------- GeoDataFrame Notes ----- For more information about the ``__geo_interface__``, see https://gist.github.com/sgillies/2217756 Examples -------- >>> feature_coll = { ... "type": "FeatureCollection", ... "features": [ ... { ... "id": "0", ... "type": "Feature", ... "properties": {"col1": "name1"}, ... "geometry": {"type": "Point", "coordinates": (1.0, 2.0)}, ... "bbox": (1.0, 2.0, 1.0, 2.0), ... }, ... { ... "id": "1", ... "type": "Feature", ... "properties": {"col1": "name2"}, ... "geometry": {"type": "Point", "coordinates": (2.0, 1.0)}, ... "bbox": (2.0, 1.0, 2.0, 1.0), ... }, ... ], ... "bbox": (1.0, 1.0, 2.0, 2.0), ... } >>> df = geopandas.GeoDataFrame.from_features(feature_coll) >>> df geometry col1 0 POINT (1.00000 2.00000) name1 1 POINT (2.00000 1.00000) name2 """ # Handle feature collections if hasattr(features, "__geo_interface__"): fs = features.__geo_interface__ else: fs = features if isinstance(fs, dict) and fs.get("type") == "FeatureCollection": features_lst = fs["features"] else: features_lst = features rows = [] for feature in features_lst: # load geometry if hasattr(feature, "__geo_interface__"): feature = feature.__geo_interface__ row = { "geometry": shape(feature["geometry"]) if feature["geometry"] else None } # load properties properties = feature["properties"] if properties is None: properties = {} row.update(properties) rows.append(row) return cls(rows, columns=columns, crs=crs) @classmethod def from_postgis( cls, sql, con, geom_col="geom", crs=None, index_col=None, coerce_float=True, parse_dates=None, params=None, chunksize=None, ): """ Alternate constructor to create a ``GeoDataFrame`` from a sql query containing a geometry column in WKB representation. Parameters ---------- sql : string con : sqlalchemy.engine.Connection or sqlalchemy.engine.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. chunksize : int, default None If specified, return an iterator where chunksize is the number of rows to include in each chunk. Examples -------- PostGIS >>> from sqlalchemy import create_engine # doctest: +SKIP >>> db_connection_url = "postgresql://myusername:mypassword@myhost:5432/mydb" >>> con = create_engine(db_connection_url) # doctest: +SKIP >>> sql = "SELECT geom, highway FROM roads" >>> df = geopandas.GeoDataFrame.from_postgis(sql, con) # doctest: +SKIP SpatiaLite >>> sql = "SELECT ST_Binary(geom) AS geom, highway FROM roads" >>> df = geopandas.GeoDataFrame.from_postgis(sql, con) # doctest: +SKIP The recommended method of reading from PostGIS is :func:`geopandas.read_postgis`: >>> df = geopandas.read_postgis(sql, con) # doctest: +SKIP See also -------- geopandas.read_postgis : read PostGIS database to GeoDataFrame """ df = geopandas.io.sql._read_postgis( sql, con, geom_col=geom_col, crs=crs, index_col=index_col, coerce_float=coerce_float, parse_dates=parse_dates, params=params, chunksize=chunksize, ) return df def to_json( self, na="null", show_bbox=False, drop_id=False, to_wgs84=False, **kwargs ): """ Returns a GeoJSON representation of the ``GeoDataFrame`` as a string. Parameters ---------- 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 drop_id : bool, default: False Whether to retain the index of the GeoDataFrame as the id property in the generated GeoJSON. Default is False, but may want True if the index is just arbitrary row numbers. to_wgs84: bool, optional, default: False If the CRS is set on the active geometry column it is exported as WGS84 (EPSG:4326) to meet the `2016 GeoJSON specification `_. Set to True to force re-projection and set to False to ignore CRS. False by default. Notes ----- 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. If the GeoDataFrame has a defined CRS, its definition will be included in the output unless it is equal to WGS84 (default GeoJSON CRS) or not possible to represent in the URN OGC format, or unless ``to_wgs84=True`` is specified. Examples -------- >>> from shapely.geometry import Point >>> d = {'col1': ['name1', 'name2'], 'geometry': [Point(1, 2), Point(2, 1)]} >>> gdf = geopandas.GeoDataFrame(d, crs="EPSG:3857") >>> gdf col1 geometry 0 name1 POINT (1.000 2.000) 1 name2 POINT (2.000 1.000) >>> gdf.to_json() '{"type": "FeatureCollection", "features": [{"id": "0", "type": "Feature", \ "properties": {"col1": "name1"}, "geometry": {"type": "Point", "coordinates": [1.0,\ 2.0]}}, {"id": "1", "type": "Feature", "properties": {"col1": "name2"}, "geometry"\ : {"type": "Point", "coordinates": [2.0, 1.0]}}], "crs": {"type": "name", "properti\ es": {"name": "urn:ogc:def:crs:EPSG::3857"}}}' Alternatively, you can write GeoJSON to file: >>> gdf.to_file(path, driver="GeoJSON") # doctest: +SKIP See also -------- GeoDataFrame.to_file : write GeoDataFrame to file """ if to_wgs84: if self.crs: df = self.to_crs(epsg=4326) else: raise ValueError( "CRS is not set. Cannot re-project to WGS84 (EPSG:4326)." ) else: df = self geo = df._to_geo(na=na, show_bbox=show_bbox, drop_id=drop_id) # if the geometry is not in WGS84, include CRS in the JSON if df.crs is not None and not df.crs.equals("epsg:4326"): auth_crsdef = self.crs.to_authority() allowed_authorities = ["EDCS", "EPSG", "OGC", "SI", "UCUM"] if auth_crsdef is None or auth_crsdef[0] not in allowed_authorities: warnings.warn( "GeoDataFrame's CRS is not representable in URN OGC " "format. Resulting JSON will contain no CRS information.", stacklevel=2, ) else: authority, code = auth_crsdef ogc_crs = f"urn:ogc:def:crs:{authority}::{code}" geo["crs"] = {"type": "name", "properties": {"name": ogc_crs}} return json.dumps(geo, **kwargs) @property 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 ``FeatureCollection``. This differs from `_to_geo()` only in that it is a property with default args instead of a method. CRS of the dataframe is not passed on to the output, unlike :meth:`~GeoDataFrame.to_json()`. Examples -------- >>> from shapely.geometry import Point >>> d = {'col1': ['name1', 'name2'], 'geometry': [Point(1, 2), Point(2, 1)]} >>> gdf = geopandas.GeoDataFrame(d, crs="EPSG:4326") >>> gdf col1 geometry 0 name1 POINT (1.00000 2.00000) 1 name2 POINT (2.00000 1.00000) >>> gdf.__geo_interface__ {'type': 'FeatureCollection', 'features': [{'id': '0', 'type': 'Feature', \ 'properties': {'col1': 'name1'}, 'geometry': {'type': 'Point', 'coordinates': (1.0\ , 2.0)}, 'bbox': (1.0, 2.0, 1.0, 2.0)}, {'id': '1', 'type': 'Feature', 'properties\ ': {'col1': 'name2'}, 'geometry': {'type': 'Point', 'coordinates': (2.0, 1.0)}, 'b\ box': (2.0, 1.0, 2.0, 1.0)}], 'bbox': (1.0, 1.0, 2.0, 2.0)} """ return self._to_geo(na="null", show_bbox=True, drop_id=False) def iterfeatures(self, na="null", show_bbox=False, drop_id=False): """ Returns an iterator that yields feature dictionaries that comply with __geo_interface__ Parameters ---------- na : str, optional Options are {'null', 'drop', 'keep'}, default 'null'. Indicates how to output missing (NaN) values in the GeoDataFrame - 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 show_bbox : bool, optional Include bbox (bounds) in the geojson. Default False. drop_id : bool, default: False Whether to retain the index of the GeoDataFrame as the id property in the generated GeoJSON. Default is False, but may want True if the index is just arbitrary row numbers. Examples -------- >>> from shapely.geometry import Point >>> d = {'col1': ['name1', 'name2'], 'geometry': [Point(1, 2), Point(2, 1)]} >>> gdf = geopandas.GeoDataFrame(d, crs="EPSG:4326") >>> gdf col1 geometry 0 name1 POINT (1.00000 2.00000) 1 name2 POINT (2.00000 1.00000) >>> feature = next(gdf.iterfeatures()) >>> feature {'id': '0', 'type': 'Feature', 'properties': {'col1': 'name1'}, 'geometry': {\ 'type': 'Point', 'coordinates': (1.0, 2.0)}} """ if na not in ["null", "drop", "keep"]: raise ValueError("Unknown na method {0}".format(na)) if self._geometry_column_name not in self: raise AttributeError( "No geometry data set (expected in" " column '%s')." % self._geometry_column_name ) ids = np.array(self.index, copy=False) geometries = np.array(self[self._geometry_column_name], copy=False) if not self.columns.is_unique: raise ValueError("GeoDataFrame cannot contain duplicated column names.") properties_cols = self.columns.drop(self._geometry_column_name) if len(properties_cols) > 0: # convert to object to get python scalars. properties_cols = self[properties_cols] properties = properties_cols.astype(object) na_mask = pd.isna(properties_cols).values if na == "null": properties[na_mask] = None for i, row in enumerate(properties.values): geom = geometries[i] if na == "drop": na_mask_row = na_mask[i] properties_items = { k: v for k, v, na in zip(properties_cols, row, na_mask_row) if not na } else: properties_items = dict(zip(properties_cols, row)) if drop_id: feature = {} else: feature = {"id": str(ids[i])} feature["type"] = "Feature" feature["properties"] = properties_items feature["geometry"] = mapping(geom) if geom else None if show_bbox: feature["bbox"] = geom.bounds if geom else None yield feature else: for fid, geom in zip(ids, geometries): if drop_id: feature = {} else: feature = {"id": str(fid)} feature["type"] = "Feature" feature["properties"] = {} feature["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_wkb(self, hex=False, **kwargs): """ Encode all geometry columns in the GeoDataFrame to WKB. Parameters ---------- hex : bool If true, export the WKB as a hexadecimal string. The default is to return a binary bytes object. kwargs Additional keyword args will be passed to :func:`shapely.to_wkb` if shapely >= 2 is installed or :func:`pygeos.to_wkb` if pygeos is installed. Returns ------- DataFrame geometry columns are encoded to WKB """ df = DataFrame(self.copy()) # Encode all geometry columns to WKB for col in df.columns[df.dtypes == "geometry"]: df[col] = to_wkb(df[col].values, hex=hex, **kwargs) return df def to_wkt(self, **kwargs): """ Encode all geometry columns in the GeoDataFrame to WKT. Parameters ---------- kwargs Keyword args will be passed to :func:`pygeos.to_wkt` if pygeos is installed. Returns ------- DataFrame geometry columns are encoded to WKT """ df = DataFrame(self.copy()) # Encode all geometry columns to WKT for col in df.columns[df.dtypes == "geometry"]: df[col] = to_wkt(df[col].values, **kwargs) return df def to_parquet( self, path, index=None, compression="snappy", schema_version=None, **kwargs ): """Write a GeoDataFrame to the Parquet format. Any geometry columns present are serialized to WKB format in the file. Requires 'pyarrow'. WARNING: this is an early implementation of Parquet file support and associated metadata, the specification for which continues to evolve. This is tracking version 0.4.0 of the GeoParquet specification at: https://github.com/opengeospatial/geoparquet This metadata specification does not yet make stability promises. As such, we do not yet recommend using this in a production setting unless you are able to rewrite your Parquet files. .. versionadded:: 0.8 Parameters ---------- path : str, path object index : bool, default None If ``True``, always include the dataframe's index(es) as columns in the file output. If ``False``, the index(es) will not be written to the file. If ``None``, the index(ex) will be included as columns in the file output except `RangeIndex` which is stored as metadata only. compression : {'snappy', 'gzip', 'brotli', None}, default 'snappy' Name of the compression to use. Use ``None`` for no compression. schema_version : {'0.1.0', '0.4.0', None} GeoParquet specification version; if not provided will default to latest supported version. kwargs Additional keyword arguments passed to :func:`pyarrow.parquet.write_table`. Examples -------- >>> gdf.to_parquet('data.parquet') # doctest: +SKIP See also -------- GeoDataFrame.to_feather : write GeoDataFrame to feather GeoDataFrame.to_file : write GeoDataFrame to file """ # Accept engine keyword for compatibility with pandas.DataFrame.to_parquet # The only engine currently supported by GeoPandas is pyarrow, so no # other engine should be specified. engine = kwargs.pop("engine", "auto") if engine not in ("auto", "pyarrow"): raise ValueError( f"GeoPandas only supports using pyarrow as the engine for " f"to_parquet: {engine!r} passed instead." ) from geopandas.io.arrow import _to_parquet _to_parquet( self, path, compression=compression, index=index, schema_version=schema_version, **kwargs, ) def to_feather( self, path, index=None, compression=None, schema_version=None, **kwargs ): """Write a GeoDataFrame to the Feather format. Any geometry columns present are serialized to WKB format in the file. Requires 'pyarrow' >= 0.17. WARNING: this is an early implementation of Parquet file support and associated metadata, the specification for which continues to evolve. This is tracking version 0.4.0 of the GeoParquet specification at: https://github.com/opengeospatial/geoparquet This metadata specification does not yet make stability promises. As such, we do not yet recommend using this in a production setting unless you are able to rewrite your Feather files. .. versionadded:: 0.8 Parameters ---------- path : str, path object index : bool, default None If ``True``, always include the dataframe's index(es) as columns in the file output. If ``False``, the index(es) will not be written to the file. If ``None``, the index(ex) will be included as columns in the file output except `RangeIndex` which is stored as metadata only. compression : {'zstd', 'lz4', 'uncompressed'}, optional Name of the compression to use. Use ``"uncompressed"`` for no compression. By default uses LZ4 if available, otherwise uncompressed. schema_version : {'0.1.0', '0.4.0', None} GeoParquet specification version; if not provided will default to latest supported version. kwargs Additional keyword arguments passed to to :func:`pyarrow.feather.write_feather`. Examples -------- >>> gdf.to_feather('data.feather') # doctest: +SKIP See also -------- GeoDataFrame.to_parquet : write GeoDataFrame to parquet GeoDataFrame.to_file : write GeoDataFrame to file """ from geopandas.io.arrow import _to_feather _to_feather( self, path, index=index, compression=compression, schema_version=schema_version, **kwargs, ) def to_file(self, filename, driver=None, schema=None, index=None, **kwargs): """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 # doctest: +SKIP Parameters ---------- filename : string File path or file handle to write to. The path may specify a GDAL VSI scheme. driver : string, default None The OGR format driver used to write the vector file. If not specified, it attempts to infer it from the file extension. If no extension is specified, it saves ESRI Shapefile to a folder. schema : dict, default None If specified, the schema dictionary is passed to Fiona to better control how the file is written. If None, GeoPandas will determine the schema based on each column's dtype. Not supported for the "pyogrio" engine. index : bool, default None If True, write index into one or more columns (for MultiIndex). Default None writes the index into one or more columns only if the index is named, is a MultiIndex, or has a non-integer data type. If False, no index is written. .. versionadded:: 0.7 Previously the index was not written. mode : string, default 'w' The write mode, 'w' to overwrite the existing file and 'a' to append. Not all drivers support appending. The drivers that support appending are listed in fiona.supported_drivers or https://github.com/Toblerity/Fiona/blob/master/fiona/drvsupport.py crs : pyproj.CRS, default None If specified, the CRS is passed to Fiona to better control how the file is written. If None, GeoPandas will determine the crs based on crs df attribute. The value can be anything accepted by :meth:`pyproj.CRS.from_user_input() `, such as an authority string (eg "EPSG:4326") or a WKT string. engine : str, "fiona" or "pyogrio" The underlying library that is used to write the file. Currently, the supported options are "fiona" and "pyogrio". Defaults to "fiona" if installed, otherwise tries "pyogrio". **kwargs : Keyword args to be passed to the engine, and can be used to write to multi-layer data, store data within archives (zip files), etc. In case of the "fiona" engine, the keyword arguments are passed to fiona.open`. For more information on possible keywords, type: ``import fiona; help(fiona.open)``. In case of the "pyogrio" engine, the keyword arguments are passed to `pyogrio.write_dataframe`. Notes ----- The format drivers will attempt to detect the encoding of your data, but may fail. In this case, the proper encoding can be specified explicitly by using the encoding keyword parameter, e.g. ``encoding='utf-8'``. See Also -------- GeoSeries.to_file GeoDataFrame.to_postgis : write GeoDataFrame to PostGIS database GeoDataFrame.to_parquet : write GeoDataFrame to parquet GeoDataFrame.to_feather : write GeoDataFrame to feather Examples -------- >>> gdf.to_file('dataframe.shp') # doctest: +SKIP >>> gdf.to_file('dataframe.gpkg', driver='GPKG', layer='name') # doctest: +SKIP >>> gdf.to_file('dataframe.geojson', driver='GeoJSON') # doctest: +SKIP With selected drivers you can also append to a file with `mode="a"`: >>> gdf.to_file('dataframe.shp', mode="a") # doctest: +SKIP Using the engine-specific keyword arguments it is possible to e.g. create a spatialite file with a custom layer name: >>> gdf.to_file( ... 'dataframe.sqlite', driver='SQLite', spatialite=True, layer='test' ... ) # doctest: +SKIP """ from geopandas.io.file import _to_file _to_file(self, filename, driver, schema, index, **kwargs) def set_crs(self, crs=None, epsg=None, inplace=False, allow_override=False): """ Set the Coordinate Reference System (CRS) of the ``GeoDataFrame``. If there are multiple geometry columns within the GeoDataFrame, only the CRS of the active geometry column is set. NOTE: The underlying geometries are not transformed to this CRS. To transform the geometries to a new CRS, use the ``to_crs`` method. Parameters ---------- crs : pyproj.CRS, optional if `epsg` is specified The value can be anything accepted by :meth:`pyproj.CRS.from_user_input() `, such as an authority string (eg "EPSG:4326") or a WKT string. epsg : int, optional if `crs` is specified EPSG code specifying the projection. inplace : bool, default False If True, the CRS of the GeoDataFrame will be changed in place (while still returning the result) instead of making a copy of the GeoDataFrame. allow_override : bool, default False If the the GeoDataFrame already has a CRS, allow to replace the existing CRS, even when both are not equal. Examples -------- >>> from shapely.geometry import Point >>> d = {'col1': ['name1', 'name2'], 'geometry': [Point(1, 2), Point(2, 1)]} >>> gdf = geopandas.GeoDataFrame(d) >>> gdf col1 geometry 0 name1 POINT (1.00000 2.00000) 1 name2 POINT (2.00000 1.00000) Setting CRS to a GeoDataFrame without one: >>> gdf.crs is None True >>> gdf = gdf.set_crs('epsg:3857') >>> gdf.crs # doctest: +SKIP Name: WGS 84 / Pseudo-Mercator Axis Info [cartesian]: - X[east]: Easting (metre) - Y[north]: Northing (metre) Area of Use: - name: World - 85°S to 85°N - bounds: (-180.0, -85.06, 180.0, 85.06) Coordinate Operation: - name: Popular Visualisation Pseudo-Mercator - method: Popular Visualisation Pseudo Mercator Datum: World Geodetic System 1984 - Ellipsoid: WGS 84 - Prime Meridian: Greenwich Overriding existing CRS: >>> gdf = gdf.set_crs(4326, allow_override=True) Without ``allow_override=True``, ``set_crs`` returns an error if you try to override CRS. See also -------- GeoDataFrame.to_crs : re-project to another CRS """ if not inplace: df = self.copy() else: df = self df.geometry = df.geometry.set_crs( crs=crs, epsg=epsg, allow_override=allow_override, inplace=True ) return df def to_crs(self, crs=None, epsg=None, inplace=False): """Transform geometries to a new coordinate reference system. Transform all geometries in an active geometry column to a different coordinate reference system. The ``crs`` attribute on the current GeoSeries must be set. Either ``crs`` or ``epsg`` may be specified for output. This method will transform all points in all objects. It has no notion of 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. Parameters ---------- crs : pyproj.CRS, optional if `epsg` is specified The value can be anything accepted by :meth:`pyproj.CRS.from_user_input() `, such as an authority string (eg "EPSG:4326") or a WKT string. epsg : int, optional if `crs` is specified EPSG code specifying output projection. inplace : bool, optional, default: False Whether to return a new GeoDataFrame or do the transformation in place. Returns ------- GeoDataFrame Examples -------- >>> from shapely.geometry import Point >>> d = {'col1': ['name1', 'name2'], 'geometry': [Point(1, 2), Point(2, 1)]} >>> gdf = geopandas.GeoDataFrame(d, crs=4326) >>> gdf col1 geometry 0 name1 POINT (1.00000 2.00000) 1 name2 POINT (2.00000 1.00000) >>> gdf.crs # doctest: +SKIP Name: WGS 84 Axis Info [ellipsoidal]: - Lat[north]: Geodetic latitude (degree) - Lon[east]: Geodetic longitude (degree) Area of Use: - name: World - bounds: (-180.0, -90.0, 180.0, 90.0) Datum: World Geodetic System 1984 - Ellipsoid: WGS 84 - Prime Meridian: Greenwich >>> gdf = gdf.to_crs(3857) >>> gdf col1 geometry 0 name1 POINT (111319.491 222684.209) 1 name2 POINT (222638.982 111325.143) >>> gdf.crs # doctest: +SKIP Name: WGS 84 / Pseudo-Mercator Axis Info [cartesian]: - X[east]: Easting (metre) - Y[north]: Northing (metre) Area of Use: - name: World - 85°S to 85°N - bounds: (-180.0, -85.06, 180.0, 85.06) Coordinate Operation: - name: Popular Visualisation Pseudo-Mercator - method: Popular Visualisation Pseudo Mercator Datum: World Geodetic System 1984 - Ellipsoid: WGS 84 - Prime Meridian: Greenwich See also -------- GeoDataFrame.set_crs : assign CRS without re-projection """ if inplace: df = self else: df = self.copy() geom = df.geometry.to_crs(crs=crs, epsg=epsg) df.geometry = geom if not inplace: return df def estimate_utm_crs(self, datum_name="WGS 84"): """Returns the estimated UTM CRS based on the bounds of the dataset. .. versionadded:: 0.9 Parameters ---------- datum_name : str, optional The name of the datum to use in the query. Default is WGS 84. Returns ------- pyproj.CRS Examples -------- >>> import geodatasets >>> df = geopandas.read_file( ... geodatasets.get_path("geoda.chicago_health") ... ) >>> df.estimate_utm_crs() # doctest: +SKIP Name: WGS 84 / UTM zone 16N Axis Info [cartesian]: - E[east]: Easting (metre) - N[north]: Northing (metre) Area of Use: - name: Between 90°W and 84°W, northern hemisphere between equator and 84°N... - bounds: (-90.0, 0.0, -84.0, 84.0) Coordinate Operation: - name: UTM zone 16N - method: Transverse Mercator Datum: World Geodetic System 1984 ensemble - Ellipsoid: WGS 84 - Prime Meridian: Greenwich """ return self.geometry.estimate_utm_crs(datum_name=datum_name) def __getitem__(self, key): """ If the result is a column containing only 'geometry', return a GeoSeries. If it's a DataFrame with any columns of GeometryDtype, return a GeoDataFrame. """ result = super().__getitem__(key) # Custom logic to avoid waiting for pandas GH51895 # result is not geometry dtype for multi-indexes if ( pd.api.types.is_scalar(key) and key == "" and isinstance(self.columns, pd.MultiIndex) and isinstance(result, Series) and not is_geometry_type(result) ): loc = self.columns.get_loc(key) # squeeze stops multilevel columns from returning a gdf result = self.iloc[:, loc].squeeze(axis="columns") geo_col = self._geometry_column_name if isinstance(result, Series) and isinstance(result.dtype, GeometryDtype): result.__class__ = GeoSeries elif isinstance(result, DataFrame): if (result.dtypes == "geometry").sum() > 0: result.__class__ = GeoDataFrame if geo_col in result: result._geometry_column_name = geo_col else: result.__class__ = DataFrame return result def _persist_old_default_geometry_colname(self): """Internal util to temporarily persist the default geometry column name of 'geometry' for backwards compatibility.""" # self.columns check required to avoid this warning in __init__ if self._geometry_column_name is None and "geometry" not in self.columns: msg = ( "You are adding a column named 'geometry' to a GeoDataFrame " "constructed without an active geometry column. Currently, " "this automatically sets the active geometry column to 'geometry' " "but in the future that will no longer happen. Instead, either " "provide geometry to the GeoDataFrame constructor " "(GeoDataFrame(... geometry=GeoSeries()) or use " "`set_geometry('geometry')` " "to explicitly set the active geometry column." ) warnings.warn(msg, category=FutureWarning, stacklevel=3) self._geometry_column_name = "geometry" def __setitem__(self, key, value): """ Overwritten to preserve CRS of GeometryArray in cases like df['geometry'] = [geom... for geom in df.geometry] """ if not pd.api.types.is_list_like(key) and ( key == self._geometry_column_name or key == "geometry" and self._geometry_column_name is None ): if pd.api.types.is_scalar(value) or isinstance(value, BaseGeometry): value = [value] * self.shape[0] try: if self._geometry_column_name is not None: crs = getattr(self, "crs", None) else: # don't use getattr, because a col "crs" might exist crs = None value = _ensure_geometry(value, crs=crs) if key == "geometry": self._persist_old_default_geometry_colname() except TypeError: warnings.warn( "Geometry column does not contain geometry.", stacklevel=2, ) super().__setitem__(key, value) # # Implement pandas methods # @doc(pd.DataFrame) def copy(self, deep=True): copied = super().copy(deep=deep) if type(copied) is pd.DataFrame: copied.__class__ = GeoDataFrame copied._geometry_column_name = self._geometry_column_name return copied def merge(self, *args, **kwargs): r"""Merge two ``GeoDataFrame`` objects with a database-style join. Returns a ``GeoDataFrame`` if a geometry column is present; otherwise, returns a pandas ``DataFrame``. Returns ------- GeoDataFrame or DataFrame Notes ----- The extra arguments ``*args`` and keyword arguments ``**kwargs`` are passed to DataFrame.merge. See https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas\ .DataFrame.merge.html for more details. """ 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.crs = self.crs result._geometry_column_name = geo_col elif isinstance(result, DataFrame) and geo_col not in result: result.__class__ = DataFrame return result @doc(pd.DataFrame) def apply(self, func, axis=0, raw=False, result_type=None, args=(), **kwargs): result = super().apply( func, axis=axis, raw=raw, result_type=result_type, args=args, **kwargs ) # pandas <1.4 re-attach last geometry col if lost if ( not compat.PANDAS_GE_14 and isinstance(result, GeoDataFrame) and result._geometry_column_name is None ): result._geometry_column_name = self._geometry_column_name # Reconstruct gdf if it was lost by apply if ( isinstance(result, DataFrame) and self._geometry_column_name in result.columns ): # axis=1 apply will split GeometryDType to object, try and cast back try: result = result.set_geometry(self._geometry_column_name) except TypeError: pass else: if self.crs is not None and result.crs is None: result.set_crs(self.crs, inplace=True) elif isinstance(result, Series) and result.dtype == "object": # Try reconstruct series GeometryDtype if lost by apply # If all none and object dtype assert list of nones is more likely # intended than list of null geometry. if not result.isna().all(): try: # not enough info about func to preserve CRS result = _ensure_geometry(result) except (TypeError, shapely.errors.GeometryTypeError): pass return result @property def _constructor(self): return _geodataframe_constructor_with_fallback @property def _constructor_sliced(self): def _geodataframe_constructor_sliced(*args, **kwargs): """ A specialized (Geo)Series constructor which can fall back to a Series if a certain operation does not produce geometries: - We only return a GeoSeries if the data is actually of geometry dtype (and so we don't try to convert geometry objects such as the normal GeoSeries(..) constructor does with `_ensure_geometry`). - When we get here from obtaining a row or column from a GeoDataFrame, the goal is to only return a GeoSeries for a geometry column, and not return a GeoSeries for a row that happened to come from a DataFrame with only geometry dtype columns (and thus could have a geometry dtype). Therefore, we don't return a GeoSeries if we are sure we are in a row selection case (by checking the identity of the index) """ srs = pd.Series(*args, **kwargs) is_row_proxy = srs.index is self.columns if is_geometry_type(srs) and not is_row_proxy: srs = GeoSeries(srs) return srs return _geodataframe_constructor_sliced def __finalize__(self, other, method=None, **kwargs): """propagate metadata from other to self""" self = super().__finalize__(other, method=method, **kwargs) # 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)) if (self.columns == self._geometry_column_name).sum() > 1: raise ValueError( "Concat operation has resulted in multiple columns using " f"the geometry column name '{self._geometry_column_name}'.\n" f"Please ensure this column from the first DataFrame is not " f"repeated." ) elif method == "unstack": # unstack adds multiindex columns and reshapes data. # it never makes sense to retain geometry column self._geometry_column_name = None self._crs = None return self def dissolve( self, by=None, aggfunc="first", as_index=True, level=None, sort=True, observed=False, dropna=True, **kwargs, ): """ 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`. Parameters ---------- by : str or list-like, default None Column(s) whose values define the groups to be dissolved. If None, the entire GeoDataFrame is considered as a single group. If a list-like object is provided, the values in the list are treated as categorical labels, and polygons will be combined based on the equality of these categorical labels. aggfunc : function or string, default "first" Aggregation function for manipulation of data associated with each group. Passed to pandas `groupby.agg` method. Accepted combinations are: - function - string function name - list of functions and/or function names, e.g. [np.sum, 'mean'] - dict of axis labels -> functions, function names or list of such. as_index : boolean, default True If true, groupby columns become index of result. level : int or str or sequence of int or sequence of str, default None If the axis is a MultiIndex (hierarchical), group by a particular level or levels. .. versionadded:: 0.9.0 sort : bool, default True Sort group keys. Get better performance by turning this off. Note this does not influence the order of observations within each group. Groupby preserves the order of rows within each group. .. versionadded:: 0.9.0 observed : bool, default False This only applies if any of the groupers are Categoricals. If True: only show observed values for categorical groupers. If False: show all values for categorical groupers. .. versionadded:: 0.9.0 dropna : bool, default True If True, and if group keys contain NA values, NA values together with row/column will be dropped. If False, NA values will also be treated as the key in groups. .. versionadded:: 0.9.0 **kwargs : Keyword arguments to be passed to the pandas `DataFrameGroupby.agg` method which is used by `dissolve`. In particular, `numeric_only` may be supplied, which will be required in pandas 2.0 for certain aggfuncs. .. versionadded:: 0.13.0 Returns ------- GeoDataFrame Examples -------- >>> from shapely.geometry import Point >>> d = { ... "col1": ["name1", "name2", "name1"], ... "geometry": [Point(1, 2), Point(2, 1), Point(0, 1)], ... } >>> gdf = geopandas.GeoDataFrame(d, crs=4326) >>> gdf col1 geometry 0 name1 POINT (1.00000 2.00000) 1 name2 POINT (2.00000 1.00000) 2 name1 POINT (0.00000 1.00000) >>> dissolved = gdf.dissolve('col1') >>> dissolved # doctest: +SKIP geometry col1 name1 MULTIPOINT (0.00000 1.00000, 1.00000 2.00000) name2 POINT (2.00000 1.00000) See also -------- GeoDataFrame.explode : explode multi-part geometries into single geometries """ if by is None and level is None: by = np.zeros(len(self), dtype="int64") groupby_kwargs = { "by": by, "level": level, "sort": sort, "observed": observed, "dropna": dropna, } # Process non-spatial component data = self.drop(labels=self.geometry.name, axis=1) with warnings.catch_warnings(record=True) as record: aggregated_data = data.groupby(**groupby_kwargs).agg(aggfunc, **kwargs) for w in record: if str(w.message).startswith("The default value of numeric_only"): msg = ( f"The default value of numeric_only in aggfunc='{aggfunc}' " "within pandas.DataFrameGroupBy.agg used in dissolve is " "deprecated. In pandas 2.0, numeric_only will default to False. " "Either specify numeric_only as additional argument in dissolve() " "or select only columns which should be valid for the function." ) warnings.warn(msg, FutureWarning, stacklevel=2) else: # Only want to capture specific warning, # other warnings from pandas should be passed through # TODO this is not an ideal approach warnings.showwarning( w.message, w.category, w.filename, w.lineno, w.file, w.line ) aggregated_data.columns = aggregated_data.columns.to_flat_index() # Process spatial component def merge_geometries(block): merged_geom = block.unary_union return merged_geom g = self.groupby(group_keys=False, **groupby_kwargs)[self.geometry.name].agg( merge_geometries ) # Aggregate aggregated_geometry = GeoDataFrame(g, geometry=self.geometry.name, crs=self.crs) # Recombine aggregated = aggregated_geometry.join(aggregated_data) # Reset if requested if not as_index: aggregated = aggregated.reset_index() return aggregated # overrides the pandas native explode method to break up features geometrically def explode(self, column=None, ignore_index=False, index_parts=None, **kwargs): """ Explode multi-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. Parameters ---------- column : string, default None Column to explode. In the case of a geometry column, multi-part geometries are converted to single-part. If None, the active geometry column is used. ignore_index : bool, default False If True, the resulting index will be labelled 0, 1, …, n - 1, ignoring `index_parts`. index_parts : boolean, default True If True, the resulting index will be a multi-index (original index with an additional level indicating the multiple geometries: a new zero-based index for each single part geometry per multi-part geometry). Returns ------- GeoDataFrame Exploded geodataframe with each single geometry as a separate entry in the geodataframe. Examples -------- >>> from shapely.geometry import MultiPoint >>> d = { ... "col1": ["name1", "name2"], ... "geometry": [ ... MultiPoint([(1, 2), (3, 4)]), ... MultiPoint([(2, 1), (0, 0)]), ... ], ... } >>> gdf = geopandas.GeoDataFrame(d, crs=4326) >>> gdf col1 geometry 0 name1 MULTIPOINT (1.00000 2.00000, 3.00000 4.00000) 1 name2 MULTIPOINT (2.00000 1.00000, 0.00000 0.00000) >>> exploded = gdf.explode(index_parts=True) >>> exploded col1 geometry 0 0 name1 POINT (1.00000 2.00000) 1 name1 POINT (3.00000 4.00000) 1 0 name2 POINT (2.00000 1.00000) 1 name2 POINT (0.00000 0.00000) >>> exploded = gdf.explode(index_parts=False) >>> exploded col1 geometry 0 name1 POINT (1.00000 2.00000) 0 name1 POINT (3.00000 4.00000) 1 name2 POINT (2.00000 1.00000) 1 name2 POINT (0.00000 0.00000) >>> exploded = gdf.explode(ignore_index=True) >>> exploded col1 geometry 0 name1 POINT (1.00000 2.00000) 1 name1 POINT (3.00000 4.00000) 2 name2 POINT (2.00000 1.00000) 3 name2 POINT (0.00000 0.00000) See also -------- GeoDataFrame.dissolve : dissolve geometries into a single observation. """ # If no column is specified then default to the active geometry column if column is None: column = self.geometry.name # If the specified column is not a geometry dtype use pandas explode if not isinstance(self[column].dtype, GeometryDtype): return super().explode(column, ignore_index=ignore_index, **kwargs) if index_parts is None: if not ignore_index: warnings.warn( "Currently, index_parts defaults to True, but in the future, " "it will default to False to be consistent with Pandas. " "Use `index_parts=True` to keep the current behavior and " "True/False to silence the warning.", FutureWarning, stacklevel=2, ) index_parts = True exploded_geom = self.geometry.reset_index(drop=True).explode(index_parts=True) df = self.drop(self._geometry_column_name, axis=1).take( exploded_geom.index.droplevel(-1) ) df[exploded_geom.name] = exploded_geom.values df = df.set_geometry(self._geometry_column_name).__finalize__(self) if ignore_index: df.reset_index(inplace=True, drop=True) elif index_parts: # reset to MultiIndex, otherwise df index is only first level of # exploded GeoSeries index. df = df.set_index( exploded_geom.index.droplevel( list(range(exploded_geom.index.nlevels - 1)) ), append=True, ) return df # overrides the pandas astype method to ensure the correct return type def astype(self, dtype, copy=True, errors="raise", **kwargs): """ Cast a pandas object to a specified dtype ``dtype``. Returns a GeoDataFrame when the geometry column is kept as geometries, otherwise returns a pandas DataFrame. See the pandas.DataFrame.astype docstring for more details. Returns ------- GeoDataFrame or DataFrame """ df = super().astype(dtype, copy=copy, errors=errors, **kwargs) try: geoms = df[self._geometry_column_name] if is_geometry_type(geoms): return geopandas.GeoDataFrame(df, geometry=self._geometry_column_name) except KeyError: pass # if the geometry column is converted to non-geometries or did not exist # do not return a GeoDataFrame return pd.DataFrame(df) def convert_dtypes(self, *args, **kwargs): """ Convert columns to best possible dtypes using dtypes supporting ``pd.NA``. Always returns a GeoDataFrame as no conversions are applied to the geometry column. See the pandas.DataFrame.convert_dtypes docstring for more details. Returns ------- GeoDataFrame """ # Overridden to fix GH1870, that return type is not preserved always # (and where it was, geometry col was not) return GeoDataFrame( super().convert_dtypes(*args, **kwargs), geometry=self.geometry.name, crs=self.crs, ) def to_postgis( self, name, con, schema=None, if_exists="fail", index=False, index_label=None, chunksize=None, dtype=None, ): """ Upload GeoDataFrame into PostGIS database. This method requires SQLAlchemy and GeoAlchemy2, and a PostgreSQL Python driver (e.g. psycopg2) to be installed. It is also possible to use :meth:`~GeoDataFrame.to_file` to write to a database. Especially for file geodatabases like GeoPackage or SpatiaLite this can be easier. Parameters ---------- name : str Name of the target table. con : sqlalchemy.engine.Connection or sqlalchemy.engine.Engine Active connection to the PostGIS database. if_exists : {'fail', 'replace', 'append'}, default 'fail' How to behave if the table already exists: - fail: Raise a ValueError. - replace: Drop the table before inserting new values. - append: Insert new values to the existing table. schema : string, optional Specify the schema. If None, use default schema: 'public'. index : bool, default False Write DataFrame index as a column. Uses *index_label* as the column name in the table. index_label : string or sequence, default None Column label for index column(s). If None is given (default) and index is True, then the index names are used. chunksize : int, optional Rows will be written in batches of this size at a time. By default, all rows will be written at once. dtype : dict of column name to SQL type, default None Specifying the datatype for columns. The keys should be the column names and the values should be the SQLAlchemy types. Examples -------- >>> from sqlalchemy import create_engine >>> engine = create_engine("postgresql://myusername:mypassword@myhost:5432\ /mydatabase") # doctest: +SKIP >>> gdf.to_postgis("my_table", engine) # doctest: +SKIP See also -------- GeoDataFrame.to_file : write GeoDataFrame to file read_postgis : read PostGIS database to GeoDataFrame """ geopandas.io.sql._write_postgis( self, name, con, schema, if_exists, index, index_label, chunksize, dtype ) # # Implement standard operators for GeoSeries # def __xor__(self, other): """Implement ^ operator as for builtin set type""" warnings.warn( "'^' operator will be deprecated. Use the 'symmetric_difference' " "method instead.", FutureWarning, stacklevel=2, ) return self.geometry.symmetric_difference(other) def __or__(self, other): """Implement | operator as for builtin set type""" warnings.warn( "'|' operator will be deprecated. Use the 'union' method instead.", FutureWarning, stacklevel=2, ) return self.geometry.union(other) def __and__(self, other): """Implement & operator as for builtin set type""" warnings.warn( "'&' operator will be deprecated. Use the 'intersection' method instead.", FutureWarning, stacklevel=2, ) return self.geometry.intersection(other) def __sub__(self, other): """Implement - operator as for builtin set type""" warnings.warn( "'-' operator will be deprecated. Use the 'difference' method instead.", FutureWarning, stacklevel=2, ) return self.geometry.difference(other) plot = CachedAccessor("plot", geopandas.plotting.GeoplotAccessor) @doc(_explore) def explore(self, *args, **kwargs): return _explore(self, *args, **kwargs) def sjoin(self, df, *args, **kwargs): """Spatial join of two GeoDataFrames. See the User Guide page :doc:`../../user_guide/mergingdata` for details. Parameters ---------- df : GeoDataFrame how : string, default 'inner' The type of join: * 'left': use keys from left_df; retain only left_df geometry column * 'right': use keys from right_df; retain only right_df geometry column * 'inner': use intersection of keys from both dfs; retain only left_df geometry column predicate : string, default 'intersects' Binary predicate. Valid values are determined by the spatial index used. You can check the valid values in left_df or right_df as ``left_df.sindex.valid_query_predicates`` or ``right_df.sindex.valid_query_predicates`` lsuffix : string, default 'left' Suffix to apply to overlapping column names (left GeoDataFrame). rsuffix : string, default 'right' Suffix to apply to overlapping column names (right GeoDataFrame). Examples -------- >>> import geodatasets >>> chicago = geopandas.read_file( ... geodatasets.get_path("geoda.chicago_commpop") ... ) >>> groceries = geopandas.read_file( ... geodatasets.get_path("geoda.groceries") ... ).to_crs(chicago.crs) >>> chicago.head() # doctest: +SKIP community ... geometry 0 DOUGLAS ... MULTIPOLYGON (((-87.60914 41.84469, -87.60915 ... 1 OAKLAND ... MULTIPOLYGON (((-87.59215 41.81693, -87.59231 ... 2 FULLER PARK ... MULTIPOLYGON (((-87.62880 41.80189, -87.62879 ... 3 GRAND BOULEVARD ... MULTIPOLYGON (((-87.60671 41.81681, -87.60670 ... 4 KENWOOD ... MULTIPOLYGON (((-87.59215 41.81693, -87.59215 ... [5 rows x 9 columns] >>> groceries.head() # doctest: +SKIP OBJECTID Ycoord ... Category geometry 0 16 41.973266 ... NaN MULTIPOINT (-87.65661 41.97321) 1 18 41.696367 ... NaN MULTIPOINT (-87.68136 41.69713) 2 22 41.868634 ... NaN MULTIPOINT (-87.63918 41.86847) 3 23 41.877590 ... new MULTIPOINT (-87.65495 41.87783) 4 27 41.737696 ... NaN MULTIPOINT (-87.62715 41.73623) [5 rows x 8 columns] >>> groceries_w_communities = groceries.sjoin(chicago) >>> groceries_w_communities[["OBJECTID", "community", "geometry"]].head() OBJECTID community geometry 0 16 UPTOWN MULTIPOINT (-87.65661 41.97321) 87 365 UPTOWN MULTIPOINT (-87.65465 41.96138) 90 373 UPTOWN MULTIPOINT (-87.65598 41.96297) 140 582 UPTOWN MULTIPOINT (-87.67417 41.96977) 1 18 MORGAN PARK MULTIPOINT (-87.68136 41.69713) Notes ----- Every operation in GeoPandas is planar, i.e. the potential third dimension is not taken into account. See also -------- GeoDataFrame.sjoin_nearest : nearest neighbor join sjoin : equivalent top-level function """ return geopandas.sjoin(left_df=self, right_df=df, *args, **kwargs) # noqa: B026 def sjoin_nearest( self, right, how="inner", max_distance=None, lsuffix="left", rsuffix="right", distance_col=None, exclusive=False, ): """ Spatial join of two GeoDataFrames based on the distance between their geometries. Results will include multiple output records for a single input record where there are multiple equidistant nearest or intersected neighbors. See the User Guide page https://geopandas.readthedocs.io/en/latest/docs/user_guide/mergingdata.html for more details. Parameters ---------- right : GeoDataFrame how : string, default 'inner' The type of join: * 'left': use keys from left_df; retain only left_df geometry column * 'right': use keys from right_df; retain only right_df geometry column * 'inner': use intersection of keys from both dfs; retain only left_df geometry column max_distance : float, default None Maximum distance within which to query for nearest geometry. Must be greater than 0. The max_distance used to search for nearest items in the tree may have a significant impact on performance by reducing the number of input geometries that are evaluated for nearest items in the tree. lsuffix : string, default 'left' Suffix to apply to overlapping column names (left GeoDataFrame). rsuffix : string, default 'right' Suffix to apply to overlapping column names (right GeoDataFrame). distance_col : string, default None If set, save the distances computed between matching geometries under a column of this name in the joined GeoDataFrame. exclusive : bool, optional, default False If True, the nearest geometries that are equal to the input geometry will not be returned, default False. Requires Shapely >= 2.0 Examples -------- >>> import geodatasets >>> groceries = geopandas.read_file( ... geodatasets.get_path("geoda.groceries") ... ) >>> chicago = geopandas.read_file( ... geodatasets.get_path("geoda.chicago_health") ... ).to_crs(groceries.crs) >>> chicago.head() # doctest: +SKIP ComAreaID ... geometry 0 35 ... POLYGON ((-87.60914 41.84469, -87.60915 41.844... 1 36 ... POLYGON ((-87.59215 41.81693, -87.59231 41.816... 2 37 ... POLYGON ((-87.62880 41.80189, -87.62879 41.801... 3 38 ... POLYGON ((-87.60671 41.81681, -87.60670 41.816... 4 39 ... POLYGON ((-87.59215 41.81693, -87.59215 41.816... [5 rows x 87 columns] >>> groceries.head() # doctest: +SKIP OBJECTID Ycoord ... Category geometry 0 16 41.973266 ... NaN MULTIPOINT (-87.65661 41.97321) 1 18 41.696367 ... NaN MULTIPOINT (-87.68136 41.69713) 2 22 41.868634 ... NaN MULTIPOINT (-87.63918 41.86847) 3 23 41.877590 ... new MULTIPOINT (-87.65495 41.87783) 4 27 41.737696 ... NaN MULTIPOINT (-87.62715 41.73623) [5 rows x 8 columns] >>> groceries_w_communities = groceries.sjoin_nearest(chicago) >>> groceries_w_communities[["Chain", "community", "geometry"]].head(2) Chain community geometry 0 VIET HOA PLAZA UPTOWN MULTIPOINT (1168268.672 1933554.350) 87 JEWEL OSCO UPTOWN MULTIPOINT (1168837.980 1929246.962) To include the distances: >>> groceries_w_communities = groceries.sjoin_nearest(chicago, \ distance_col="distances") >>> groceries_w_communities[["Chain", "community", \ "distances"]].head(2) # doctest: +SKIP Chain community distances 0 VIET HOA PLAZA UPTOWN 0.0 87 JEWEL OSCO UPTOWN 0.0 In the following example, we get multiple groceries for Uptown because all results are equidistant (in this case zero because they intersect). In fact, we get 4 results in total: >>> chicago_w_groceries = groceries.sjoin_nearest(chicago, \ distance_col="distances", how="right") >>> uptown_results = \ chicago_w_groceries[chicago_w_groceries["community"] == "UPTOWN"] >>> uptown_results[["Chain", "community"]] # doctest: +SKIP Chain community 30 VIET HOA PLAZA UPTOWN 30 JEWEL OSCO UPTOWN 30 TARGET UPTOWN 30 Mariano's UPTOWN See also -------- GeoDataFrame.sjoin : binary predicate joins sjoin_nearest : equivalent top-level function Notes ----- Since this join relies on distances, results will be inaccurate if your geometries are in a geographic CRS. Every operation in GeoPandas is planar, i.e. the potential third dimension is not taken into account. """ return geopandas.sjoin_nearest( self, right, how=how, max_distance=max_distance, lsuffix=lsuffix, rsuffix=rsuffix, distance_col=distance_col, exclusive=exclusive, ) def clip(self, mask, keep_geom_type=False): """Clip points, lines, or polygon geometries to the mask extent. Both layers must be in the same Coordinate Reference System (CRS). The GeoDataFrame will be clipped to the full extent of the ``mask`` object. If there are multiple polygons in mask, data from the GeoDataFrame will be clipped to the total boundary of all polygons in mask. Parameters ---------- mask : GeoDataFrame, GeoSeries, (Multi)Polygon, list-like Polygon vector layer used to clip the GeoDataFrame. The mask's geometry is dissolved into one geometric feature and intersected with GeoDataFrame. If the mask is list-like with four elements ``(minx, miny, maxx, maxy)``, ``clip`` will use a faster rectangle clipping (:meth:`~GeoSeries.clip_by_rect`), possibly leading to slightly different results. keep_geom_type : boolean, default False If True, return only geometries of original type in case of intersection resulting in multiple geometry types or GeometryCollections. If False, return all resulting geometries (potentially mixed types). Returns ------- GeoDataFrame Vector data (points, lines, polygons) from the GeoDataFrame clipped to polygon boundary from mask. See also -------- clip : equivalent top-level function Examples -------- Clip points (grocery stores) with polygons (the Near West Side community): >>> import geodatasets >>> chicago = geopandas.read_file( ... geodatasets.get_path("geoda.chicago_health") ... ) >>> near_west_side = chicago[chicago["community"] == "NEAR WEST SIDE"] >>> groceries = geopandas.read_file( ... geodatasets.get_path("geoda.groceries") ... ).to_crs(chicago.crs) >>> groceries.shape (148, 8) >>> nws_groceries = groceries.clip(near_west_side) >>> nws_groceries.shape (7, 8) """ return geopandas.clip(self, mask=mask, keep_geom_type=keep_geom_type) def overlay(self, right, how="intersection", keep_geom_type=None, make_valid=True): """Perform spatial overlay between GeoDataFrames. Currently only supports data GeoDataFrames with uniform geometry types, i.e. containing only (Multi)Polygons, or only (Multi)Points, or a combination of (Multi)LineString and LinearRing shapes. Implements several methods that are all effectively subsets of the union. See the User Guide page :doc:`../../user_guide/set_operations` for details. Parameters ---------- right : GeoDataFrame how : string Method of spatial overlay: 'intersection', 'union', 'identity', 'symmetric_difference' or 'difference'. keep_geom_type : bool If True, return only geometries of the same geometry type the GeoDataFrame has, if False, return all resulting geometries. Default is None, which will set keep_geom_type to True but warn upon dropping geometries. make_valid : bool, default True If True, any invalid input geometries are corrected with a call to `buffer(0)`, if False, a `ValueError` is raised if any input geometries are invalid. Returns ------- df : GeoDataFrame GeoDataFrame with new set of polygons and attributes resulting from the overlay Examples -------- >>> from shapely.geometry import Polygon >>> polys1 = geopandas.GeoSeries([Polygon([(0,0), (2,0), (2,2), (0,2)]), ... Polygon([(2,2), (4,2), (4,4), (2,4)])]) >>> polys2 = geopandas.GeoSeries([Polygon([(1,1), (3,1), (3,3), (1,3)]), ... Polygon([(3,3), (5,3), (5,5), (3,5)])]) >>> df1 = geopandas.GeoDataFrame({'geometry': polys1, 'df1_data':[1,2]}) >>> df2 = geopandas.GeoDataFrame({'geometry': polys2, 'df2_data':[1,2]}) >>> df1.overlay(df2, how='union') df1_data df2_data geometry 0 1.0 1.0 POLYGON ((2.00000 2.00000, 2.00000 1.00000, 1.... 1 2.0 1.0 POLYGON ((2.00000 2.00000, 2.00000 3.00000, 3.... 2 2.0 2.0 POLYGON ((4.00000 4.00000, 4.00000 3.00000, 3.... 3 1.0 NaN POLYGON ((2.00000 0.00000, 0.00000 0.00000, 0.... 4 2.0 NaN MULTIPOLYGON (((3.00000 4.00000, 3.00000 3.000... 5 NaN 1.0 MULTIPOLYGON (((2.00000 3.00000, 2.00000 2.000... 6 NaN 2.0 POLYGON ((3.00000 5.00000, 5.00000 5.00000, 5.... >>> df1.overlay(df2, how='intersection') df1_data df2_data geometry 0 1 1 POLYGON ((2.00000 2.00000, 2.00000 1.00000, 1.... 1 2 1 POLYGON ((2.00000 2.00000, 2.00000 3.00000, 3.... 2 2 2 POLYGON ((4.00000 4.00000, 4.00000 3.00000, 3.... >>> df1.overlay(df2, how='symmetric_difference') df1_data df2_data geometry 0 1.0 NaN POLYGON ((2.00000 0.00000, 0.00000 0.00000, 0.... 1 2.0 NaN MULTIPOLYGON (((3.00000 4.00000, 3.00000 3.000... 2 NaN 1.0 MULTIPOLYGON (((2.00000 3.00000, 2.00000 2.000... 3 NaN 2.0 POLYGON ((3.00000 5.00000, 5.00000 5.00000, 5.... >>> df1.overlay(df2, how='difference') geometry df1_data 0 POLYGON ((2.00000 0.00000, 0.00000 0.00000, 0.... 1 1 MULTIPOLYGON (((3.00000 4.00000, 3.00000 3.000... 2 >>> df1.overlay(df2, how='identity') df1_data df2_data geometry 0 1.0 1.0 POLYGON ((2.00000 2.00000, 2.00000 1.00000, 1.... 1 2.0 1.0 POLYGON ((2.00000 2.00000, 2.00000 3.00000, 3.... 2 2.0 2.0 POLYGON ((4.00000 4.00000, 4.00000 3.00000, 3.... 3 1.0 NaN POLYGON ((2.00000 0.00000, 0.00000 0.00000, 0.... 4 2.0 NaN MULTIPOLYGON (((3.00000 4.00000, 3.00000 3.000... See also -------- GeoDataFrame.sjoin : spatial join overlay : equivalent top-level function Notes ----- Every operation in GeoPandas is planar, i.e. the potential third dimension is not taken into account. """ return geopandas.overlay( self, right, how=how, keep_geom_type=keep_geom_type, make_valid=make_valid ) 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) DataFrame.set_geometry = _dataframe_set_geometry