import warnings import numpy as np import pandas as pd from pandas.plotting import PlotAccessor import geopandas from packaging.version import Version from ._decorator import doc def deprecated(new, warning_type=FutureWarning): """Helper to provide deprecation warning.""" def old(*args, **kwargs): warnings.warn( "{} is intended for internal ".format(new.__name__[1:]) + "use only, and will be deprecated.", warning_type, stacklevel=2, ) new(*args, **kwargs) return old def _flatten_multi_geoms(geoms, prefix="Multi"): """ Returns Series like geoms and index, except that any Multi geometries are split into their components and indices are repeated for all component in the same Multi geometry. Maintains 1:1 matching of geometry to value. Prefix specifies type of geometry to be flatten. 'Multi' for MultiPoint and similar, "Geom" for GeometryCollection. Returns ------- components : list of geometry component_index : index array indices are repeated for all components in the same Multi geometry """ components, component_index = [], [] if not geoms.geom_type.str.startswith(prefix).any(): return geoms, np.arange(len(geoms)) for ix, geom in enumerate(geoms): if geom is not None and geom.geom_type.startswith(prefix) and not geom.is_empty: for poly in geom.geoms: components.append(poly) component_index.append(ix) else: components.append(geom) component_index.append(ix) return components, np.array(component_index) def _expand_kwargs(kwargs, multiindex): """ Most arguments to the plot functions must be a (single) value, or a sequence of values. This function checks each key-value pair in 'kwargs' and expands it (in place) to the correct length/formats with help of 'multiindex', unless the value appears to already be a valid (single) value for the key. """ import matplotlib from matplotlib.colors import is_color_like from typing import Iterable mpl = Version(matplotlib.__version__) if mpl >= Version("3.4"): # alpha is supported as array argument with matplotlib 3.4+ scalar_kwargs = ["marker", "path_effects"] else: scalar_kwargs = ["marker", "alpha", "path_effects"] for att, value in kwargs.items(): if "color" in att: # color(s), edgecolor(s), facecolor(s) if is_color_like(value): continue elif "linestyle" in att: # linestyle(s) # A single linestyle can be 2-tuple of a number and an iterable. if ( isinstance(value, tuple) and len(value) == 2 and isinstance(value[1], Iterable) ): continue elif att in scalar_kwargs: # For these attributes, only a single value is allowed, so never expand. continue if pd.api.types.is_list_like(value): kwargs[att] = np.take(value, multiindex, axis=0) def _PolygonPatch(polygon, **kwargs): """Constructs a matplotlib patch from a Polygon geometry The `kwargs` are those supported by the matplotlib.patches.PathPatch class constructor. Returns an instance of matplotlib.patches.PathPatch. Example (using Shapely Point and a matplotlib axes):: b = shapely.geometry.Point(0, 0).buffer(1.0) patch = _PolygonPatch(b, fc='blue', ec='blue', alpha=0.5) ax.add_patch(patch) GeoPandas originally relied on the descartes package by Sean Gillies (BSD license, https://pypi.org/project/descartes) for PolygonPatch, but this dependency was removed in favor of the below matplotlib code. """ from matplotlib.patches import PathPatch from matplotlib.path import Path path = Path.make_compound_path( Path(np.asarray(polygon.exterior.coords)[:, :2]), *[Path(np.asarray(ring.coords)[:, :2]) for ring in polygon.interiors], ) return PathPatch(path, **kwargs) def _plot_polygon_collection( ax, geoms, values=None, color=None, cmap=None, vmin=None, vmax=None, **kwargs ): """ Plots a collection of Polygon and MultiPolygon geometries to `ax` Parameters ---------- ax : matplotlib.axes.Axes where shapes will be plotted geoms : a sequence of `N` Polygons and/or MultiPolygons (can be mixed) values : a sequence of `N` values, optional Values will be mapped to colors using vmin/vmax/cmap. They should have 1:1 correspondence with the geometries (not their components). Otherwise follows `color` / `facecolor` kwargs. edgecolor : single color or sequence of `N` colors Color for the edge of the polygons facecolor : single color or sequence of `N` colors Color to fill the polygons. Cannot be used together with `values`. color : single color or sequence of `N` colors Sets both `edgecolor` and `facecolor` **kwargs Additional keyword arguments passed to the collection Returns ------- collection : matplotlib.collections.Collection that was plotted """ from matplotlib.collections import PatchCollection geoms, multiindex = _flatten_multi_geoms(geoms) if values is not None: values = np.take(values, multiindex, axis=0) # PatchCollection does not accept some kwargs. kwargs = { att: value for att, value in kwargs.items() if att not in ["markersize", "marker"] } # Add to kwargs for easier checking below. if color is not None: kwargs["color"] = color _expand_kwargs(kwargs, multiindex) collection = PatchCollection( [_PolygonPatch(poly) for poly in geoms if not poly.is_empty], **kwargs ) if values is not None: collection.set_array(np.asarray(values)) collection.set_cmap(cmap) if "norm" not in kwargs: collection.set_clim(vmin, vmax) ax.add_collection(collection, autolim=True) ax.autoscale_view() return collection plot_polygon_collection = deprecated(_plot_polygon_collection) def _plot_linestring_collection( ax, geoms, values=None, color=None, cmap=None, vmin=None, vmax=None, **kwargs ): """ Plots a collection of LineString and MultiLineString geometries to `ax` Parameters ---------- ax : matplotlib.axes.Axes where shapes will be plotted geoms : a sequence of `N` LineStrings and/or MultiLineStrings (can be mixed) values : a sequence of `N` values, optional Values will be mapped to colors using vmin/vmax/cmap. They should have 1:1 correspondence with the geometries (not their components). color : single color or sequence of `N` colors Cannot be used together with `values`. Returns ------- collection : matplotlib.collections.Collection that was plotted """ from matplotlib.collections import LineCollection geoms, multiindex = _flatten_multi_geoms(geoms) if values is not None: values = np.take(values, multiindex, axis=0) # LineCollection does not accept some kwargs. kwargs = { att: value for att, value in kwargs.items() if att not in ["markersize", "marker"] } # Add to kwargs for easier checking below. if color is not None: kwargs["color"] = color _expand_kwargs(kwargs, multiindex) segments = [np.array(linestring.coords)[:, :2] for linestring in geoms] collection = LineCollection(segments, **kwargs) if values is not None: collection.set_array(np.asarray(values)) collection.set_cmap(cmap) if "norm" not in kwargs: collection.set_clim(vmin, vmax) ax.add_collection(collection, autolim=True) ax.autoscale_view() return collection plot_linestring_collection = deprecated(_plot_linestring_collection) def _plot_point_collection( ax, geoms, values=None, color=None, cmap=None, vmin=None, vmax=None, marker="o", markersize=None, **kwargs, ): """ Plots a collection of Point and MultiPoint geometries to `ax` Parameters ---------- ax : matplotlib.axes.Axes where shapes will be plotted geoms : sequence of `N` Points or MultiPoints values : a sequence of `N` values, optional Values mapped to colors using vmin, vmax, and cmap. Cannot be specified together with `color`. markersize : scalar or array-like, optional Size of the markers. Note that under the hood ``scatter`` is used, so the specified value will be proportional to the area of the marker (size in points^2). Returns ------- collection : matplotlib.collections.Collection that was plotted """ if values is not None and color is not None: raise ValueError("Can only specify one of 'values' and 'color' kwargs") geoms, multiindex = _flatten_multi_geoms(geoms) # values are expanded below as kwargs["c"] x = [p.x if not p.is_empty else None for p in geoms] y = [p.y if not p.is_empty else None for p in geoms] # matplotlib 1.4 does not support c=None, and < 2.0 does not support s=None if values is not None: kwargs["c"] = values if markersize is not None: kwargs["s"] = markersize # Add to kwargs for easier checking below. if color is not None: kwargs["color"] = color if marker is not None: kwargs["marker"] = marker _expand_kwargs(kwargs, multiindex) if "norm" not in kwargs: collection = ax.scatter(x, y, vmin=vmin, vmax=vmax, cmap=cmap, **kwargs) else: collection = ax.scatter(x, y, cmap=cmap, **kwargs) return collection plot_point_collection = deprecated(_plot_point_collection) def plot_series( s, cmap=None, color=None, ax=None, figsize=None, aspect="auto", **style_kwds ): """ Plot a GeoSeries. Generate a plot of a GeoSeries geometry with matplotlib. Parameters ---------- s : Series The GeoSeries to be plotted. Currently Polygon, MultiPolygon, LineString, MultiLineString, Point and MultiPoint geometries can be plotted. cmap : str (default None) The name of a colormap recognized by matplotlib. Any colormap will work, but categorical colormaps are generally recommended. Examples of useful discrete colormaps include: tab10, tab20, Accent, Dark2, Paired, Pastel1, Set1, Set2 color : str, np.array, pd.Series, List (default None) If specified, all objects will be colored uniformly. ax : matplotlib.pyplot.Artist (default None) axes on which to draw the plot figsize : pair of floats (default None) Size of the resulting matplotlib.figure.Figure. If the argument ax is given explicitly, figsize is ignored. aspect : 'auto', 'equal', None or float (default 'auto') Set aspect of axis. If 'auto', the default aspect for map plots is 'equal'; if however data are not projected (coordinates are long/lat), the aspect is by default set to 1/cos(s_y * pi/180) with s_y the y coordinate of the middle of the GeoSeries (the mean of the y range of bounding box) so that a long/lat square appears square in the middle of the plot. This implies an Equirectangular projection. If None, the aspect of `ax` won't be changed. It can also be set manually (float) as the ratio of y-unit to x-unit. **style_kwds : dict Color options to be passed on to the actual plot function, such as ``edgecolor``, ``facecolor``, ``linewidth``, ``markersize``, ``alpha``. Returns ------- ax : matplotlib axes instance """ if "colormap" in style_kwds: warnings.warn( "'colormap' is deprecated, please use 'cmap' instead " "(for consistency with matplotlib)", FutureWarning, ) cmap = style_kwds.pop("colormap") if "axes" in style_kwds: warnings.warn( "'axes' is deprecated, please use 'ax' instead " "(for consistency with pandas)", FutureWarning, ) ax = style_kwds.pop("axes") try: import matplotlib.pyplot as plt except ImportError: raise ImportError( "The matplotlib package is required for plotting in geopandas. " "You can install it using 'conda install -c conda-forge matplotlib' or " "'pip install matplotlib'." ) if ax is None: fig, ax = plt.subplots(figsize=figsize) if aspect == "auto": if s.crs and s.crs.is_geographic: bounds = s.total_bounds y_coord = np.mean([bounds[1], bounds[3]]) ax.set_aspect(1 / np.cos(y_coord * np.pi / 180)) # formula ported from R package sp # https://github.com/edzer/sp/blob/master/R/mapasp.R else: ax.set_aspect("equal") elif aspect is not None: ax.set_aspect(aspect) if s.empty: warnings.warn( "The GeoSeries you are attempting to plot is " "empty. Nothing has been displayed.", UserWarning, ) return ax if s.is_empty.all(): warnings.warn( "The GeoSeries you are attempting to plot is " "composed of empty geometries. Nothing has been displayed.", UserWarning, ) return ax # have colors been given for all geometries? color_given = pd.api.types.is_list_like(color) and len(color) == len(s) # if cmap is specified, create range of colors based on cmap values = None if cmap is not None: values = np.arange(len(s)) if hasattr(cmap, "N"): values = values % cmap.N style_kwds["vmin"] = style_kwds.get("vmin", values.min()) style_kwds["vmax"] = style_kwds.get("vmax", values.max()) # decompose GeometryCollections geoms, multiindex = _flatten_multi_geoms(s.geometry, prefix="Geom") values = np.take(values, multiindex, axis=0) if cmap else None # ensure indexes are consistent if color_given and isinstance(color, pd.Series): color = color.reindex(s.index) expl_color = np.take(color, multiindex, axis=0) if color_given else color expl_series = geopandas.GeoSeries(geoms) geom_types = expl_series.geom_type poly_idx = np.asarray((geom_types == "Polygon") | (geom_types == "MultiPolygon")) line_idx = np.asarray( (geom_types == "LineString") | (geom_types == "MultiLineString") | (geom_types == "LinearRing") ) point_idx = np.asarray((geom_types == "Point") | (geom_types == "MultiPoint")) # plot all Polygons and all MultiPolygon components in the same collection polys = expl_series[poly_idx] if not polys.empty: # color overrides both face and edgecolor. As we want people to be # able to use edgecolor as well, pass color to facecolor facecolor = style_kwds.pop("facecolor", None) color_ = expl_color[poly_idx] if color_given else color if color is not None: facecolor = color_ values_ = values[poly_idx] if cmap else None _plot_polygon_collection( ax, polys, values_, facecolor=facecolor, cmap=cmap, **style_kwds ) # plot all LineStrings and MultiLineString components in same collection lines = expl_series[line_idx] if not lines.empty: values_ = values[line_idx] if cmap else None color_ = expl_color[line_idx] if color_given else color _plot_linestring_collection( ax, lines, values_, color=color_, cmap=cmap, **style_kwds ) # plot all Points in the same collection points = expl_series[point_idx] if not points.empty: values_ = values[point_idx] if cmap else None color_ = expl_color[point_idx] if color_given else color _plot_point_collection( ax, points, values_, color=color_, cmap=cmap, **style_kwds ) plt.draw() return ax def plot_dataframe( df, column=None, cmap=None, color=None, ax=None, cax=None, categorical=False, legend=False, scheme=None, k=5, vmin=None, vmax=None, markersize=None, figsize=None, legend_kwds=None, categories=None, classification_kwds=None, missing_kwds=None, aspect="auto", **style_kwds, ): """ Plot a GeoDataFrame. Generate a plot of a GeoDataFrame with matplotlib. If a column is specified, the plot coloring will be based on values in that column. Parameters ---------- column : str, np.array, pd.Series (default None) The name of the dataframe column, np.array, or pd.Series to be plotted. If np.array or pd.Series are used then it must have same length as dataframe. Values are used to color the plot. Ignored if `color` is also set. kind: str The kind of plots to produce: - 'geo': Map (default) Pandas Kinds - 'line' : line plot - 'bar' : vertical bar plot - 'barh' : horizontal bar plot - 'hist' : histogram - 'box' : BoxPlot - 'kde' : Kernel Density Estimation plot - 'density' : same as 'kde' - 'area' : area plot - 'pie' : pie plot - 'scatter' : scatter plot - 'hexbin' : hexbin plot. cmap : str (default None) The name of a colormap recognized by matplotlib. color : str, np.array, pd.Series (default None) If specified, all objects will be colored uniformly. ax : matplotlib.pyplot.Artist (default None) axes on which to draw the plot cax : matplotlib.pyplot Artist (default None) axes on which to draw the legend in case of color map. categorical : bool (default False) If False, cmap will reflect numerical values of the column being plotted. For non-numerical columns, this will be set to True. legend : bool (default False) Plot a legend. Ignored if no `column` is given, or if `color` is given. scheme : str (default None) Name of a choropleth classification scheme (requires mapclassify). A mapclassify.MapClassifier object will be used under the hood. Supported are all schemes provided by mapclassify (e.g. 'BoxPlot', 'EqualInterval', 'FisherJenks', 'FisherJenksSampled', 'HeadTailBreaks', 'JenksCaspall', 'JenksCaspallForced', 'JenksCaspallSampled', 'MaxP', 'MaximumBreaks', 'NaturalBreaks', 'Quantiles', 'Percentiles', 'StdMean', 'UserDefined'). Arguments can be passed in classification_kwds. k : int (default 5) Number of classes (ignored if scheme is None) vmin : None or float (default None) Minimum value of cmap. If None, the minimum data value in the column to be plotted is used. vmax : None or float (default None) Maximum value of cmap. If None, the maximum data value in the column to be plotted is used. markersize : str or float or sequence (default None) Only applies to point geometries within a frame. If a str, will use the values in the column of the frame specified by markersize to set the size of markers. Otherwise can be a value to apply to all points, or a sequence of the same length as the number of points. figsize : tuple of integers (default None) Size of the resulting matplotlib.figure.Figure. If the argument axes is given explicitly, figsize is ignored. legend_kwds : dict (default None) Keyword arguments to pass to :func:`matplotlib.pyplot.legend` or :func:`matplotlib.pyplot.colorbar`. Additional accepted keywords when `scheme` is specified: fmt : string A formatting specification for the bin edges of the classes in the legend. For example, to have no decimals: ``{"fmt": "{:.0f}"}``. labels : list-like A list of legend labels to override the auto-generated labels. Needs to have the same number of elements as the number of classes (`k`). interval : boolean (default False) An option to control brackets from mapclassify legend. If True, open/closed interval brackets are shown in the legend. categories : list-like Ordered list-like object of categories to be used for categorical plot. classification_kwds : dict (default None) Keyword arguments to pass to mapclassify missing_kwds : dict (default None) Keyword arguments specifying color options (as style_kwds) to be passed on to geometries with missing values in addition to or overwriting other style kwds. If None, geometries with missing values are not plotted. aspect : 'auto', 'equal', None or float (default 'auto') Set aspect of axis. If 'auto', the default aspect for map plots is 'equal'; if however data are not projected (coordinates are long/lat), the aspect is by default set to 1/cos(df_y * pi/180) with df_y the y coordinate of the middle of the GeoDataFrame (the mean of the y range of bounding box) so that a long/lat square appears square in the middle of the plot. This implies an Equirectangular projection. If None, the aspect of `ax` won't be changed. It can also be set manually (float) as the ratio of y-unit to x-unit. **style_kwds : dict Style options to be passed on to the actual plot function, such as ``edgecolor``, ``facecolor``, ``linewidth``, ``markersize``, ``alpha``. Returns ------- ax : matplotlib axes instance Examples -------- >>> df = geopandas.read_file(geopandas.datasets.get_path("naturalearth_lowres")) >>> df.head() # doctest: +SKIP pop_est continent name iso_a3 \ gdp_md_est geometry 0 920938 Oceania Fiji FJI 8374.0 MULTIPOLY\ GON (((180.00000 -16.06713, 180.00000... 1 53950935 Africa Tanzania TZA 150600.0 POLYGON (\ (33.90371 -0.95000, 34.07262 -1.05982... 2 603253 Africa W. Sahara ESH 906.5 POLYGON (\ (-8.66559 27.65643, -8.66512 27.58948... 3 35623680 North America Canada CAN 1674000.0 MULTIPOLY\ GON (((-122.84000 49.00000, -122.9742... 4 326625791 North America United States of America USA 18560000.0 MULTIPOLY\ GON (((-122.84000 49.00000, -120.0000... >>> df.plot("pop_est", cmap="Blues") # doctest: +SKIP See the User Guide page :doc:`../../user_guide/mapping` for details. """ if "colormap" in style_kwds: warnings.warn( "'colormap' is deprecated, please use 'cmap' instead " "(for consistency with matplotlib)", FutureWarning, ) cmap = style_kwds.pop("colormap") if "axes" in style_kwds: warnings.warn( "'axes' is deprecated, please use 'ax' instead " "(for consistency with pandas)", FutureWarning, ) ax = style_kwds.pop("axes") if column is not None and color is not None: warnings.warn( "Only specify one of 'column' or 'color'. Using 'color'.", UserWarning ) column = None try: import matplotlib.pyplot as plt except ImportError: raise ImportError( "The matplotlib package is required for plotting in geopandas. " "You can install it using 'conda install -c conda-forge matplotlib' or " "'pip install matplotlib'." ) if ax is None: if cax is not None: raise ValueError("'ax' can not be None if 'cax' is not.") fig, ax = plt.subplots(figsize=figsize) if aspect == "auto": if df.crs and df.crs.is_geographic: bounds = df.total_bounds y_coord = np.mean([bounds[1], bounds[3]]) ax.set_aspect(1 / np.cos(y_coord * np.pi / 180)) # formula ported from R package sp # https://github.com/edzer/sp/blob/master/R/mapasp.R else: ax.set_aspect("equal") elif aspect is not None: ax.set_aspect(aspect) # GH 1555 # if legend_kwds set, copy so we don't update it in place if legend_kwds is not None: legend_kwds = legend_kwds.copy() if df.empty: warnings.warn( "The GeoDataFrame you are attempting to plot is " "empty. Nothing has been displayed.", UserWarning, ) return ax if isinstance(markersize, str): markersize = df[markersize].values if column is None: return plot_series( df.geometry, cmap=cmap, color=color, ax=ax, figsize=figsize, markersize=markersize, aspect=aspect, **style_kwds, ) # To accept pd.Series and np.arrays as column if isinstance(column, (np.ndarray, pd.Series)): if column.shape[0] != df.shape[0]: raise ValueError( "The dataframe and given column have different number of rows." ) else: values = column # Make sure index of a Series matches index of df if isinstance(values, pd.Series): values = values.reindex(df.index) else: values = df[column] if pd.api.types.is_categorical_dtype(values.dtype): if categories is not None: raise ValueError( "Cannot specify 'categories' when column has categorical dtype" ) categorical = True elif ( pd.api.types.is_object_dtype(values.dtype) or pd.api.types.is_bool_dtype(values.dtype) or pd.api.types.is_string_dtype(values.dtype) or categories ): categorical = True nan_idx = np.asarray(pd.isna(values), dtype="bool") if scheme is not None: mc_err = ( "The 'mapclassify' package (>= 2.4.0) is " "required to use the 'scheme' keyword." ) try: import mapclassify except ImportError: raise ImportError(mc_err) if Version(mapclassify.__version__) < Version("2.4.0"): raise ImportError(mc_err) if classification_kwds is None: classification_kwds = {} if "k" not in classification_kwds: classification_kwds["k"] = k binning = mapclassify.classify( np.asarray(values[~nan_idx]), scheme, **classification_kwds ) # set categorical to True for creating the legend categorical = True if legend_kwds is not None and "labels" in legend_kwds: if len(legend_kwds["labels"]) != binning.k: raise ValueError( "Number of labels must match number of bins, " "received {} labels for {} bins".format( len(legend_kwds["labels"]), binning.k ) ) else: labels = list(legend_kwds.pop("labels")) else: fmt = "{:.2f}" if legend_kwds is not None and "fmt" in legend_kwds: fmt = legend_kwds.pop("fmt") labels = binning.get_legend_classes(fmt) if legend_kwds is not None: show_interval = legend_kwds.pop("interval", False) else: show_interval = False if not show_interval: labels = [c[1:-1] for c in labels] values = pd.Categorical( [np.nan] * len(values), categories=binning.bins, ordered=True ) values[~nan_idx] = pd.Categorical.from_codes( binning.yb, categories=binning.bins, ordered=True ) if cmap is None: cmap = "viridis" # Define `values` as a Series if categorical: if cmap is None: cmap = "tab10" cat = pd.Categorical(values, categories=categories) categories = list(cat.categories) # values missing in the Categorical but not in original values missing = list(np.unique(values[~nan_idx & cat.isna()])) if missing: raise ValueError( "Column contains values not listed in categories. " "Missing categories: {}.".format(missing) ) values = cat.codes[~nan_idx] vmin = 0 if vmin is None else vmin vmax = len(categories) - 1 if vmax is None else vmax # fill values with placeholder where were NaNs originally to map them properly # (after removing them in categorical or scheme) if categorical: for n in np.where(nan_idx)[0]: values = np.insert(values, n, values[0]) mn = values[~np.isnan(values)].min() if vmin is None else vmin mx = values[~np.isnan(values)].max() if vmax is None else vmax # decompose GeometryCollections geoms, multiindex = _flatten_multi_geoms(df.geometry, prefix="Geom") values = np.take(values, multiindex, axis=0) nan_idx = np.take(nan_idx, multiindex, axis=0) expl_series = geopandas.GeoSeries(geoms) geom_types = expl_series.geom_type poly_idx = np.asarray((geom_types == "Polygon") | (geom_types == "MultiPolygon")) line_idx = np.asarray( (geom_types == "LineString") | (geom_types == "MultiLineString") | (geom_types == "LinearRing") ) point_idx = np.asarray((geom_types == "Point") | (geom_types == "MultiPoint")) # plot all Polygons and all MultiPolygon components in the same collection polys = expl_series[poly_idx & np.invert(nan_idx)] subset = values[poly_idx & np.invert(nan_idx)] if not polys.empty: _plot_polygon_collection( ax, polys, subset, vmin=mn, vmax=mx, cmap=cmap, **style_kwds ) # plot all LineStrings and MultiLineString components in same collection lines = expl_series[line_idx & np.invert(nan_idx)] subset = values[line_idx & np.invert(nan_idx)] if not lines.empty: _plot_linestring_collection( ax, lines, subset, vmin=mn, vmax=mx, cmap=cmap, **style_kwds ) # plot all Points in the same collection points = expl_series[point_idx & np.invert(nan_idx)] subset = values[point_idx & np.invert(nan_idx)] if not points.empty: if isinstance(markersize, np.ndarray): markersize = np.take(markersize, multiindex, axis=0) markersize = markersize[point_idx & np.invert(nan_idx)] _plot_point_collection( ax, points, subset, vmin=mn, vmax=mx, markersize=markersize, cmap=cmap, **style_kwds, ) missing_data = not expl_series[nan_idx].empty if missing_kwds is not None and missing_data: if color: if "color" not in missing_kwds: missing_kwds["color"] = color merged_kwds = style_kwds.copy() merged_kwds.update(missing_kwds) plot_series(expl_series[nan_idx], ax=ax, **merged_kwds) if legend and not color: if legend_kwds is None: legend_kwds = {} if "fmt" in legend_kwds: legend_kwds.pop("fmt") from matplotlib.lines import Line2D from matplotlib.colors import Normalize from matplotlib import cm norm = style_kwds.get("norm", None) if not norm: norm = Normalize(vmin=mn, vmax=mx) n_cmap = cm.ScalarMappable(norm=norm, cmap=cmap) if categorical: if scheme is not None: categories = labels patches = [] for value, cat in enumerate(categories): patches.append( Line2D( [0], [0], linestyle="none", marker="o", alpha=style_kwds.get("alpha", 1), markersize=10, markerfacecolor=n_cmap.to_rgba(value), markeredgewidth=0, ) ) if missing_kwds is not None and missing_data: if "color" in merged_kwds: merged_kwds["facecolor"] = merged_kwds["color"] patches.append( Line2D( [0], [0], linestyle="none", marker="o", alpha=merged_kwds.get("alpha", 1), markersize=10, markerfacecolor=merged_kwds.get("facecolor", None), markeredgecolor=merged_kwds.get("edgecolor", None), markeredgewidth=merged_kwds.get( "linewidth", 1 if merged_kwds.get("edgecolor", False) else 0 ), ) ) categories.append(merged_kwds.get("label", "NaN")) legend_kwds.setdefault("numpoints", 1) legend_kwds.setdefault("loc", "best") ax.legend(patches, categories, **legend_kwds) else: if cax is not None: legend_kwds.setdefault("cax", cax) else: legend_kwds.setdefault("ax", ax) n_cmap.set_array(np.array([])) ax.get_figure().colorbar(n_cmap, **legend_kwds) plt.draw() return ax @doc(plot_dataframe) class GeoplotAccessor(PlotAccessor): _pandas_kinds = PlotAccessor._all_kinds def __call__(self, *args, **kwargs): data = self._parent.copy() kind = kwargs.pop("kind", "geo") if kind == "geo": return plot_dataframe(data, *args, **kwargs) if kind in self._pandas_kinds: # Access pandas plots return PlotAccessor(data)(kind=kind, **kwargs) else: # raise error raise ValueError(f"{kind} is not a valid plot kind") def geo(self, *args, **kwargs): return self(kind="geo", *args, **kwargs)