Source code for

from __future__ import absolute_import, division

import sys
import warnings

from collections import defaultdict

import numpy as np

from ..dimension import dimension_name
from ..util import isscalar, unique_iterator, pd, unique_array
from .interface import DataError, Interface
from .multipath import MultiInterface, ensure_ring
from .pandas import PandasInterface

[docs]class SpatialPandasInterface(MultiInterface): types = () datatype = 'spatialpandas' multi = True
[docs] @classmethod def loaded(cls): return 'spatialpandas' in sys.modules
[docs] @classmethod def applies(cls, obj): if not cls.loaded(): return False from spatialpandas import GeoDataFrame, GeoSeries is_sdf = isinstance(obj, (GeoDataFrame, GeoSeries)) if 'geopandas' in sys.modules and not 'geoviews' in sys.modules: import geopandas as gpd is_sdf |= isinstance(obj, (gpd.GeoDataFrame, gpd.GeoSeries)) return is_sdf
@classmethod def geo_column(cls, data): from spatialpandas import GeoSeries col = 'geometry' if col in data and isinstance(data[col], GeoSeries): return col cols = [c for c in data.columns if isinstance(data[c], GeoSeries)] if not cols: raise ValueError('No geometry column found in spatialpandas.GeoDataFrame, ' 'use the PandasInterface instead.') return cols[0] @classmethod def init(cls, eltype, data, kdims, vdims): import pandas as pd from spatialpandas import GeoDataFrame, GeoSeries if kdims is None: kdims = eltype.kdims if vdims is None: vdims = eltype.vdims if isinstance(data, GeoSeries): data = data.to_frame() if 'geopandas' in sys.modules: import geopandas as gpd if isinstance(data, gpd.GeoSeries): data = data.to_frame() if isinstance(data, gpd.GeoDataFrame): data = GeoDataFrame(data) if isinstance(data, list): if 'shapely' in sys.modules: data = from_shapely(data) if isinstance(data, list): data = from_multi(eltype, data, kdims, vdims) elif not isinstance(data, GeoDataFrame): raise ValueError("SpatialPandasInterface only support spatialpandas DataFrames.") elif 'geometry' not in data: cls.geo_column(data) index_names = data.index.names if isinstance(data, pd.DataFrame) else [] if index_names == [None]: index_names = ['index'] for kd in kdims+vdims: kd = dimension_name(kd) if kd in data.columns: continue if any(kd == ('index' if name is None else name) for name in index_names): data = data.reset_index() break return data, {'kdims': kdims, 'vdims': vdims}, {} @classmethod def validate(cls, dataset, vdims=True): dim_types = 'key' if vdims else 'all' geom_dims = cls.geom_dims(dataset) if len(geom_dims) != 2: raise DataError('Expected %s instance to declare two key ' 'dimensions corresponding to the geometry ' 'coordinates but %d dimensions were found ' 'which did not refer to any columns.' % (type(dataset).__name__, len(geom_dims)), cls) not_found = [ for d in dataset.dimensions(dim_types) if d not in geom_dims and not in] if not_found: raise DataError("Supplied data does not contain specified " "dimensions, the following dimensions were " "not found: %s" % repr(not_found), cls) @classmethod def dtype(cls, dataset, dimension): dim = dataset.get_dimension(dimension, strict=True) if dim in cls.geom_dims(dataset): col = cls.geo_column( return[col].values.numpy_dtype return[].dtype @classmethod def has_holes(cls, dataset): from spatialpandas.geometry import ( MultiPolygonDtype, PolygonDtype, Polygon, MultiPolygon ) col = cls.geo_column( series =[col] if not isinstance(series.dtype, (MultiPolygonDtype, PolygonDtype)): return False for geom in series: if isinstance(geom, Polygon) and len( > 1: return True elif isinstance(geom, MultiPolygon): for p in if len(p) > 1: return True return False @classmethod def holes(cls, dataset): holes = [] if not len( return holes col = cls.geo_column( series =[col] return [geom_to_holes(geom) for geom in series]
[docs] @classmethod def select(cls, dataset, selection_mask=None, **selection): xdim, ydim = cls.geom_dims(dataset) selection.pop(, None) selection.pop(, None) df = if not selection: return df elif selection_mask is None: selection_mask = cls.select_mask(dataset, selection) indexed = cls.indexed(dataset, selection) df = df.iloc[selection_mask] if indexed and len(df) == 1 and len(dataset.vdims) == 1: return df[dataset.vdims[0].name].iloc[0] return df
[docs] @classmethod def select_mask(cls, dataset, selection): mask = np.ones(len(, dtype=np.bool) for dim, k in selection.items(): if isinstance(k, tuple): k = slice(*k) arr =[dim].values if isinstance(k, slice): with warnings.catch_warnings(): warnings.filterwarnings('ignore', r'invalid value encountered') if k.start is not None: mask &= k.start <= arr if k.stop is not None: mask &= arr < k.stop elif isinstance(k, (set, list)): iter_slcs = [] for ik in k: with warnings.catch_warnings(): warnings.filterwarnings('ignore', r'invalid value encountered') iter_slcs.append(arr == ik) mask &= np.logical_or.reduce(iter_slcs) elif callable(k): mask &= k(arr) else: index_mask = arr == k if dataset.ndims == 1 and np.sum(index_mask) == 0: data_index = np.argmin(np.abs(arr - k)) mask = np.zeros(len(dataset), dtype=np.bool) mask[data_index] = True else: mask &= index_mask return mask
@classmethod def geom_dims(cls, dataset): return [d for d in dataset.kdims + dataset.vdims if not in] @classmethod def dimension_type(cls, dataset, dim): dim = dataset.get_dimension(dim) col = cls.geo_column( if dim in cls.geom_dims(dataset) and len( arr = geom_to_array([col].iloc[0]) ds = dataset.clone(arr, datatype=cls.subtypes, vdims=[]) return ds.interface.dimension_type(ds, dim) else: return cls.dtype(dataset, dim).type
[docs] @classmethod def isscalar(cls, dataset, dim, per_geom=False): """ Tests if dimension is scalar in each subpath. """ dim = dataset.get_dimension(dim) if (dim in cls.geom_dims(dataset)): return False elif per_geom: return all(isscalar(v) or len(list(unique_array(v))) == 1 for v in[]) dim = dataset.get_dimension(dim) return len([].unique()) == 1
@classmethod def range(cls, dataset, dim): dim = dataset.get_dimension(dim) geom_dims = cls.geom_dims(dataset) if dim in geom_dims: col = cls.geo_column( idx = geom_dims.index(dim) bounds =[col].total_bounds if idx == 0: return (bounds[0], bounds[2]) else: return (bounds[1], bounds[3]) else: return Interface.range(dataset, dim) @classmethod def groupby(cls, dataset, dimensions, container_type, group_type, **kwargs): geo_dims = cls.geom_dims(dataset) if any(d in geo_dims for d in dimensions): raise DataError("SpatialPandasInterface does not allow grouping " "by geometry dimension.", cls) return PandasInterface.groupby(dataset, dimensions, container_type, group_type, **kwargs) @classmethod def aggregate(cls, columns, dimensions, function, **kwargs): raise NotImplementedError @classmethod def sample(cls, columns, samples=[]): raise NotImplementedError @classmethod def reindex(cls, dataset, kdims=None, vdims=None): return
[docs] @classmethod def shape(cls, dataset): return (cls.length(dataset), len(dataset.dimensions()))
@classmethod def sort(cls, dataset, by=[], reverse=False): geo_dims = cls.geom_dims(dataset) if any(d in geo_dims for d in by): raise DataError("SpatialPandasInterface does not allow sorting " "by geometry dimension.", cls) return PandasInterface.sort(dataset, by, reverse)
[docs] @classmethod def length(cls, dataset): from spatialpandas.geometry import MultiPointDtype, Point col_name = cls.geo_column( column =[col_name] geom_type = cls.geom_type(dataset) if not isinstance(column.dtype, MultiPointDtype) and geom_type != 'Point': return PandasInterface.length(dataset) length = 0 for i, geom in enumerate(column): if isinstance(geom, Point): length += 1 else: length += (len(geom.buffer_values)//2) return length
@classmethod def nonzero(cls, dataset): return bool(cls.length(dataset)) @classmethod def redim(cls, dataset, dimensions): return PandasInterface.redim(dataset, dimensions) @classmethod def add_dimension(cls, dataset, dimension, dim_pos, values, vdim): data = geom_col = cls.geo_column( if dim_pos >= list(data.columns).index(geom_col): dim_pos -= 1 if not in data: data.insert(dim_pos,, values) return data @classmethod def iloc(cls, dataset, index): from spatialpandas import GeoSeries from spatialpandas.geometry import MultiPointDtype rows, cols = index geom_dims = cls.geom_dims(dataset) geom_col = cls.geo_column( scalar = False columns = list( if isinstance(cols, slice): cols = [ for d in dataset.dimensions()][cols] elif np.isscalar(cols): scalar = np.isscalar(rows) cols = [dataset.get_dimension(cols).name] else: cols = [dataset.get_dimension(d).name for d in index[1]] if not all(d in cols for d in geom_dims): raise DataError("Cannot index a dimension which is part of the " "geometry column of a spatialpandas DataFrame.", cls) cols = list(unique_iterator([ columns.index(geom_col) if c in geom_dims else columns.index(c) for c in cols ])) if not isinstance([geom_col].dtype, MultiPointDtype): if scalar: return[rows[0], cols[0]] elif isscalar(rows): rows = [rows] return[rows, cols] geoms =[geom_col] count = 0 new_geoms, indexes = [], [] for i, geom in enumerate(geoms): length = int(len(geom.buffer_values)/2) if np.isscalar(rows): if count <= rows < (count+length): idx = (rows-count)*2 data = geom.buffer_values[idx:idx+2] new_geoms.append(type(geom)(data)) indexes.append(i) break elif isinstance(rows, slice): if rows.start is not None and rows.start > (count+length): continue elif rows.stop is not None and rows.stop < count: break start = None if rows.start is None else max(rows.start - count, 0)*2 stop = None if rows.stop is None else min(rows.stop - count, length)*2 if rows.step is not None: dataset.param.warning(".iloc step slicing currently not supported for" "the multi-tabular data format.") sliced = geom.buffer_values[start:stop] if len(sliced): indexes.append(i) new_geoms.append(type(geom)(sliced)) else: sub_rows = [v for r in rows for v in ((r-count)*2, (r-count)*2+1) if count <= r < (count+length)] if sub_rows: indexes.append(i) idxs = np.array(sub_rows, dtype=int) new_geoms.append(type(geom)(geom.buffer_values[idxs])) count += length new =[indexes].copy() new[geom_col] = GeoSeries(new_geoms) return new
[docs] @classmethod def values(cls, dataset, dimension, expanded=True, flat=True, compute=True, keep_index=False): dimension = dataset.get_dimension(dimension) geom_dims = dataset.interface.geom_dims(dataset) data = isgeom = (dimension in geom_dims) geom_col = cls.geo_column( is_points = cls.geom_type(dataset) == 'Point' if isgeom and keep_index: return data[geom_col] elif not isgeom: return get_value_array(data, dimension, expanded, keep_index, geom_col, is_points) elif not len(data): return np.array([]) geom_type = cls.geom_type(dataset) index = geom_dims.index(dimension) return geom_array_to_array(data[geom_col].values, index, expanded, geom_type)
[docs] @classmethod def split(cls, dataset, start, end, datatype, **kwargs): from spatialpandas import GeoDataFrame, GeoSeries from ...element import Polygons objs = [] if not len( return [] xdim, ydim = cls.geom_dims(dataset) value_dims = [dim for dim in dataset.kdims+dataset.vdims if dim not in (xdim, ydim)] row =[0] col = cls.geo_column( geom_type = cls.geom_type(dataset) if datatype is not None: arr = geom_to_array(row[col], geom_type=geom_type) d = {(, arr} d.update({ row[] for dim in value_dims}) ds = dataset.clone(d, datatype=['dictionary']) holes = cls.holes(dataset) if cls.has_holes(dataset) else None for i, row in if datatype is None: gdf = GeoDataFrame({c: GeoSeries([row[c]]) if c == 'geometry' else [row[c]] for c in}) objs.append(dataset.clone(gdf)) continue geom = row[col] gt = geom_type or get_geom_type(, col) arr = geom_to_array(geom, geom_type=gt) d = { arr[:, 0], arr[:, 1]} d.update({ row[] for dim in value_dims}) if datatype in ('dictionary', 'columns'): if holes is not None: d[Polygons._hole_key] = holes[i] d['geom_type'] = gt objs.append(d) continue = d if datatype == 'array': obj = ds.array(**kwargs) elif datatype == 'dataframe': obj = ds.dframe(**kwargs) else: raise ValueError("%s datatype not support" % datatype) objs.append(obj) return objs
[docs]def get_geom_type(gdf, col): """Return the HoloViews geometry type string for the geometry column. Args: gdf: The GeoDataFrame to get the geometry from col: The geometry column Returns: A string representing the type of geometry """ from spatialpandas.geometry import ( PointDtype, MultiPointDtype, LineDtype, MultiLineDtype, PolygonDtype, MultiPolygonDtype, RingDtype ) column = gdf[col] if isinstance(column.dtype, (PointDtype, MultiPointDtype)): return 'Point' elif isinstance(column.dtype, (LineDtype, MultiLineDtype)): return 'Line' elif isinstance(column.dtype, (PolygonDtype, MultiPolygonDtype)): return 'Polygon' elif isinstance(column.dtype, RingDtype): return 'Ring'
[docs]def geom_to_array(geom, index=None, multi=False, geom_type=None): """Converts spatialpandas geometry to an array. Args: geom: spatialpandas geometry index: The column index to return multi: Whether to concatenate multiple arrays or not Returns: Array or list of arrays. """ from spatialpandas.geometry import ( Point, Polygon, Line, Ring, MultiPolygon, MultiPoint ) if isinstance(geom, Point): if index is None: return np.array([[geom.x, geom.y]]) arrays = [np.array([geom.y if index else geom.x])] elif isinstance(geom, (Polygon, Line, Ring)): exterior =[0] if isinstance(geom, Polygon) else arr = np.array(exterior.as_py()).reshape(-1, 2) if isinstance(geom, (Polygon, Ring)): arr = ensure_ring(arr) arrays = [arr if index is None else arr[:, index]] elif isinstance(geom, MultiPoint): if index is None: arrays = [np.array(geom.buffer_values).reshape(-1, 2)] else: arrays = [np.array(geom.buffer_values[index::2])] else: arrays = [] for g in exterior = g[0] if isinstance(geom, MultiPolygon) else g arr = np.array(exterior.as_py()).reshape(-1, 2) if isinstance(geom, MultiPolygon): arr = ensure_ring(arr) arrays.append(arr if index is None else arr[:, index]) if geom_type != 'Point': arrays.append([[np.nan, np.nan]] if index is None else [np.nan]) if geom_type != 'Point': arrays = arrays[:-1] if multi: return arrays elif len(arrays) == 1: return arrays[0] else: return np.concatenate(arrays)
[docs]def geom_array_to_array(geom_array, index, expand=False, geom_type=None): """Converts spatialpandas extension arrays to a flattened array. Args: geom: spatialpandas geometry index: The column index to return Returns: Flattened array """ from spatialpandas.geometry import PointArray, MultiPointArray if isinstance(geom_array, PointArray): return geom_array.y if index else geom_array.x arrays = [] multi_point = isinstance(geom_array, MultiPointArray) or geom_type == 'Point' for geom in geom_array: array = geom_to_array(geom, index, multi=expand, geom_type=geom_type) if expand: arrays.extend(array) if not multi_point: arrays.append([np.nan]) else: arrays.append(array) if expand: if not multi_point: arrays = arrays[:-1] return np.concatenate(arrays) if arrays else np.array([]) else: array = np.empty(len(arrays), dtype=object) array[:] = arrays return array
def geom_length(geom): from spatialpandas.geometry import Polygon, Ring, MultiPolygon, MultiLine if isinstance(geom, Polygon): offset = 0 exterior =[0] if exterior[0] != exterior[-2] or exterior[1] != exterior[-1]: offset = 1 return len(exterior)//2 + offset elif isinstance(geom, (MultiPolygon, MultiLine)): length = 0 for g in offset = 0 if isinstance(geom, MultiLine): exterior = g else: exterior = g[0] if exterior[0] != exterior[-2] or exterior[1] != exterior[-1]: offset = 1 length += (len(exterior)//2 + 1) + offset return length-1 if length else 0 else: offset = 0 exterior = geom.buffer_values if isinstance(geom, Ring) and (exterior[0] != exterior[-2] or exterior[1] != exterior[-1]): offset = 1 return len(exterior)//2
[docs]def get_value_array(data, dimension, expanded, keep_index, geom_col, is_points, geom_length=geom_length): """Returns an array of values from a GeoDataFrame. Args: data: GeoDataFrame dimension: The dimension to get the values from expanded: Whether to expand the value array keep_index: Whether to return a Series geom_col: The column in the data that contains the geometries is_points: Whether the geometries are points geom_length: The function used to compute the length of each geometry Returns: An array containing the values along a dimension """ column = data[] if keep_index: return column all_scalar = True arrays, scalars = [], [] for i, geom in enumerate(data[geom_col]): length = geom_length(geom) val = column.iloc[i] scalar = isscalar(val) if scalar: val = np.array([val]) if not scalar and len(unique_array(val)) == 1: val = val[:1] scalar = True all_scalar &= scalar scalars.append(scalar) if not expanded or not scalar: arrays.append(val) elif scalar: arrays.append(np.full(length, val)) if expanded and not is_points and not i == (len(data[geom_col])-1): arrays.append(np.array([np.NaN])) if not len(data): return np.array([]) if expanded: return np.concatenate(arrays) if len(arrays) > 1 else arrays[0] elif (all_scalar and arrays): return np.array([a[0] for a in arrays]) else: array = np.empty(len(arrays), dtype=object) array[:] = [a[0] if s else a for s, a in zip(scalars, arrays)] return array
[docs]def geom_to_holes(geom): """Extracts holes from spatialpandas Polygon geometries. Args: geom: spatialpandas geometry Returns: List of arrays representing holes """ from spatialpandas.geometry import Polygon, MultiPolygon if isinstance(geom, Polygon): holes = [] for i, hole in enumerate( if i == 0: continue hole = ensure_ring(np.array(hole.as_py()).reshape(-1, 2)) holes.append(hole) return [holes] elif isinstance(geom, MultiPolygon): holes = [] for poly in poly_holes = [] for i, hole in enumerate(poly): if i == 0: continue arr = ensure_ring(np.array(hole.as_py()).reshape(-1, 2)) poly_holes.append(arr) holes.append(poly_holes) return holes elif 'Multi' in type(geom).__name__: return [[]]*len(geom) else: return [[]]
[docs]def to_spatialpandas(data, xdim, ydim, columns=[], geom='point'): """Converts list of dictionary format geometries to spatialpandas line geometries. Args: data: List of dictionaries representing individual geometries xdim: Name of x-coordinates column ydim: Name of y-coordinates column columns: List of columns to add geom: The type of geometry Returns: A spatialpandas.GeoDataFrame version of the data """ from spatialpandas import GeoSeries, GeoDataFrame from spatialpandas.geometry import ( Point, Line, Polygon, Ring, LineArray, PolygonArray, PointArray, MultiLineArray, MultiPolygonArray, MultiPointArray, RingArray ) from ...element import Polygons poly = any(Polygons._hole_key in d for d in data) or geom == 'Polygon' if poly: geom_type = Polygon single_array, multi_array = PolygonArray, MultiPolygonArray elif geom == 'Line': geom_type = Line single_array, multi_array = LineArray, MultiLineArray elif geom == 'Ring': geom_type = Ring single_array, multi_array = RingArray, MultiLineArray else: geom_type = Point single_array, multi_array = PointArray, MultiPointArray array_type = None hole_arrays, geom_arrays = [], [] for geom in data: geom = dict(geom) if xdim not in geom or ydim not in geom: raise ValueError('Could not find geometry dimensions') xs, ys = geom.pop(xdim), geom.pop(ydim) xscalar, yscalar = isscalar(xs), isscalar(ys) if xscalar and yscalar: xs, ys = np.array([xs]), np.array([ys]) elif xscalar: xs = np.full_like(ys, xs) elif yscalar: ys = np.full_like(xs, ys) geom_array = np.column_stack([xs, ys]) if geom_type in (Polygon, Ring): geom_array = ensure_ring(geom_array) splits = np.where(np.isnan(geom_array[:, :2].astype('float')).sum(axis=1))[0] split_geoms = np.split(geom_array, splits+1) if len(splits) else [geom_array] split_holes = geom.pop(Polygons._hole_key, None) if split_holes is not None: if len(split_holes) != len(split_geoms): raise DataError('Polygons with holes containing multi-geometries ' 'must declare a list of holes for each geometry.', SpatialPandasInterface) else: split_holes = [[ensure_ring(np.asarray(h)) for h in hs] for hs in split_holes] geom_arrays.append(split_geoms) hole_arrays.append(split_holes) if geom_type is Point: if len(splits) > 1 or any(len(g) > 1 for g in split_geoms): array_type = multi_array elif array_type is None: array_type = single_array elif len(splits): array_type = multi_array elif array_type is None: array_type = single_array converted = defaultdict(list) for geom, arrays, holes in zip(data, geom_arrays, hole_arrays): parts = [] for i, g in enumerate(arrays): if i != (len(arrays)-1): g = g[:-1] if len(g) < (3 if poly else 2) and geom_type is not Point: continue if poly: parts.append([]) subparts = parts[-1] else: subparts = parts subparts.append(g[:, :2]) if poly and holes is not None: subparts += [np.array(h) for h in holes[i]] for c, v in geom.items(): converted[c].append(v) if array_type is PointArray: parts = parts[0].flatten() elif array_type is MultiPointArray: parts = np.concatenate([sp.flatten() for sp in parts]) elif array_type is multi_array: parts = [[ssp.flatten() for ssp in sp] if poly else sp.flatten() for sp in parts] else: parts = [np.asarray(sp).flatten() for sp in parts[0]] if poly else parts[0].flatten() converted['geometry'].append(parts) if converted: geometries = converted['geometry'] if array_type is PointArray: geometries = np.concatenate(geometries) geom_array = array_type(geometries) if poly: geom_array = geom_array.oriented() converted['geometry'] = GeoSeries(geom_array) else: converted['geometry'] = GeoSeries(single_array([])) return GeoDataFrame(converted, columns=['geometry']+columns)
[docs]def to_geom_dict(eltype, data, kdims, vdims, interface=None): """Converts data from any list format to a dictionary based format. Args: eltype: Element type to convert data: The original data kdims: The declared key dimensions vdims: The declared value dimensions Returns: A list of dictionaries containing geometry coordinates and values. """ from . import Dataset xname, yname = ( for kd in kdims[:2]) if isinstance(data, dict): data = {k: v if isscalar(v) else np.asarray(v) for k, v in data.items()} return data new_el = Dataset(data, kdims, vdims) if new_el.interface is interface: return new_dict = {} for d in new_el.dimensions(): if d in (xname, yname): scalar = False else: scalar = new_el.interface.isscalar(new_el, d) vals = new_el.dimension_values(d, not scalar) new_dict[] = vals[0] if scalar else vals return new_dict
[docs]def from_multi(eltype, data, kdims, vdims): """Converts list formats into spatialpandas.GeoDataFrame. Args: eltype: Element type to convert data: The original data kdims: The declared key dimensions vdims: The declared value dimensions Returns: A GeoDataFrame containing in the list based format. """ from spatialpandas import GeoDataFrame xname, yname = ( for kd in kdims[:2]) new_data, types, geom_types = [], [], [] for d in data: types.append(type(d)) new_dict = to_geom_dict(eltype, d, kdims, vdims, SpatialPandasInterface) if 'geom_type' in new_dict and new_dict['geom_type'] not in geom_types: geom_types.append(new_dict['geom_type']) new_data.append(new_dict) if not isinstance(new_data[-1], dict): types[-1] = type(new_data[-1]) if len(set(types)) > 1: raise DataError('Mixed types not supported') if new_data and types[0] is GeoDataFrame: data = pd.concat(new_data) else: columns = [ for d in kdims+vdims if d not in (xname, yname)] if len(geom_types) == 1: geom = geom_types[0] else: geom = SpatialPandasInterface.geom_type(eltype) data = to_spatialpandas(new_data, xname, yname, columns, geom) return data
[docs]def from_shapely(data): """Converts shapely based data formats to spatialpandas.GeoDataFrame. Args: data: A list of shapely objects or dictionaries containing shapely objects Returns: A GeoDataFrame containing the shapely geometry data. """ from spatialpandas import GeoDataFrame, GeoSeries from shapely.geometry.base import BaseGeometry if not data: pass elif all(isinstance(d, BaseGeometry) for d in data): data = GeoSeries(data).to_frame() elif all(isinstance(d, dict) and 'geometry' in d and isinstance(d['geometry'], BaseGeometry) for d in data): new_data = {col: [] for col in data[0]} for d in data: for col, val in d.items(): new_data[col].append(val if isscalar(val) or isinstance(val, BaseGeometry) else np.asarray(val)) new_data['geometry'] = GeoSeries(new_data['geometry']) data = GeoDataFrame(new_data) return data