from collections import defaultdict import param try: import panel as pn panel_available = True except: panel_available = False from ..converter import HoloViewsConverter from ..util import is_list_like, process_dynamic_args # Color palette for examples: https://www.color-hex.com/color-palette/1018056 # light green: #55a194 # Dark green: #3b7067 # Blue: #1e85f7 # Orange: #f8b014 # Red: #f16a6f class hvPlotBase: """ Internal base class. Concrete subclasses must implement plotting methods (e.g. `line`, `scatter`, `image`). A plotting method must call `self` which will effectively create a HoloViewsConverter and call it to return a HoloViews object. Concrete subclasses are meant to be mounted onto a datastructure property, e.g.: ``` _patch_plot = lambda self: hvPlotTabular(self) _patch_plot.__doc__ = hvPlotTabular.__call__.__doc__ plot_prop = property(_patch_plot) setattr(pd.DataFrame, 'hvplot', plot_prop) ``` """ __all__ = [] def __init__(self, data, custom_plots={}, **metadata): if "query" in metadata: data = data.query(metadata.pop("query")) if "sel" in metadata: data = data.sel(**metadata.pop("sel")) if "isel" in metadata: data = data.isel(**metadata.pop("isel")) self._data = data self._plots = custom_plots self._metadata = metadata def __call__(self, x=None, y=None, kind=None, **kwds): # Convert an array-like to a list x = list(x) if is_list_like(x) else x y = list(y) if is_list_like(y) else y if isinstance(kind, str) and kind not in self.__all__: raise NotImplementedError( f"kind='{kind}' for data of type {type(self._data)}" ) if isinstance(kind, str) and kind == "explorer": return self.explorer(x=x, y=y, **kwds) if panel_available: panel_args = ["widgets", "widget_location", "widget_layout", "widget_type"] panel_dict = {} for k in panel_args: if k in kwds: panel_dict[k] = kwds.pop(k) dynamic, arg_deps, arg_names = process_dynamic_args(x, y, kind, **kwds) if dynamic or arg_deps: @pn.depends(*arg_deps, **dynamic) def callback(*args, **dyn_kwds): xd = dyn_kwds.pop("x", x) yd = dyn_kwds.pop("y", y) kindd = dyn_kwds.pop("kind", kind) combined_kwds = dict(kwds, **dyn_kwds) fn_args = defaultdict(list) for name, arg in zip(arg_names, args): fn_args[(name, kwds[name])].append(arg) for (name, fn), args in fn_args.items(): combined_kwds[name] = fn(*args) plot = self._get_converter(xd, yd, kindd, **combined_kwds)(kindd, xd, yd) return pn.panel(plot, **panel_dict) return pn.panel(callback) if panel_dict: plot = self._get_converter(x, y, kind, **kwds)(kind, x, y) return pn.panel(plot, **panel_dict) return self._get_converter(x, y, kind, **kwds)(kind, x, y) def _get_converter(self, x=None, y=None, kind=None, **kwds): params = dict(self._metadata, **kwds) x = x or params.pop("x", None) y = y or params.pop("y", None) kind = kind or params.pop("kind", None) return HoloViewsConverter(self._data, x, y, kind=kind, **params) def __dir__(self): """ List default attributes and custom defined plots. """ dirs = super().__dir__() return sorted(list(dirs) + list(self._plots)) def __getattribute__(self, name): """ Custom getattribute to expose user defined subplots. """ plots = object.__getattribute__(self, "_plots") if name in plots: plot_opts = plots[name] if "kind" in plot_opts and name in HoloViewsConverter._kind_mapping: param.main.param.warning( "Custom options for existing plot types should not " "declare the 'kind' argument. The .{} plot method " "was unexpectedly customized with kind={!r}.".format(plot_opts["kind"], name) ) plot_opts["kind"] = name return hvPlotBase(self._data, **dict(self._metadata, **plot_opts)) return super().__getattribute__(name) def explorer(self, x=None, y=None, **kwds): """ The `explorer` plot allows you to interactively explore your data. Reference: https://hvplot.holoviz.org/user_guide/Explorer.html Parameters ---------- x : string, optional The coordinate variable along the x-axis y : string, optional The coordinate variable along the y-axis **kwds : optional Additional keywords arguments typically passed to hvplot's call. Returns ------- The corresponding explorer type based on data, e.g. hvplot.ui.hvDataFrameExplorer. Examples -------- .. code-block: import hvplot.pandas import pandas as pd df = pd.DataFrame( { "actual": [100, 150, 125, 140, 145, 135, 123], "forecast": [90, 160, 125, 150, 141, 141, 120], "numerical": [1.1, 1.9, 3.2, 3.8, 4.3, 5.0, 5.5], "date": pd.date_range("2022-01-03", "2022-01-09"), "string": ["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"], }, ) df.hvplot.explorer() """ from ..ui import explorer as ui_explorer return ui_explorer(self._data, x=x, y=y, **kwds) class hvPlotTabular(hvPlotBase): """ The plotting method: `df.hvplot(...)` creates a plot similarly to the familiar Pandas `df.plot` method. For more detailed options use a specific plotting method, e.g. `df.hvplot.line`. Reference: https://hvplot.holoviz.org/reference/index.html Parameters ---------- x : string, optional Field name(s) to draw x-positions from. If not specified, the index is used. y : string or list, optional Field name(s) to draw y-positions from. If not specified, all numerical fields are used. kind : string, optional The kind of plot to generate, e.g. 'area', 'bar', 'line', 'scatter' etc. To see the available plots run `print(df.hvplot.__all__)`. **kwds : optional Additional keywords arguments are documented in `hvplot.help('scatter')` or similar depending on the kind of plot. Returns ------- A Holoviews object. You can `print` the object to study its composition and run .. code-block:: import holoviews as hv hv.help(the_holoviews_object) to learn more about its parameters and options. Examples -------- .. code-block:: import pandas as pd import hvplot.pandas df = pd.DataFrame( { "actual": [100, 150, 125, 140, 145, 135, 123], "forecast": [90, 160, 125, 150, 141, 141, 120], "numerical": [1.1, 1.9, 3.2, 3.8, 4.3, 5.0, 5.5], "date": pd.date_range("2022-01-03", "2022-01-09"), "string": ["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"], }, ) line = df.hvplot.line( x="numerical", y=["actual", "forecast"], ylabel="value", legend="bottom", height=500, color=["steelblue", "teal"], alpha=0.7, line_width=5, ) line You can can add *markers* to a `line` plot by overlaying with a `scatter` plot. .. code-block:: markers = df.hvplot.scatter( x="numerical", y=["actual", "forecast"], color=["#f16a6f", "#1e85f7"], size=50 ) line * markers Please note that you can pass widgets or reactive functions as arguments instead of literal values, c.f. https://hvplot.holoviz.org/user_guide/Widgets.html. """ __all__ = [ "line", "step", "scatter", "area", "errorbars", "ohlc", "heatmap", "hexbin", "bivariate", "bar", "barh", "box", "violin", "hist", "kde", "density", "table", "dataset", "points", "vectorfield", "polygons", "paths", "labels", "explorer", ] def line(self, x=None, y=None, **kwds): """ The `line` plot connects the points with a continuous curve. Reference: https://hvplot.holoviz.org/reference/pandas/line.html Parameters ---------- x : string, optional Field name(s) to draw x-positions from. If not specified, the index is used. Can refer to continuous and categorical data. y : string or list, optional Field name(s) to draw y-positions from. If not specified, all numerical fields are used. by : string, optional A single column or list of columns to group by. All the subgroups are visualized. groupby: string, list, optional A single field or list of fields to group and filter by. Adds one or more widgets to select the subgroup(s) to visualize. color : str or array-like, optional. The color for each of the series. Possible values are: A single color string referred to by name, RGB or RGBA code, for instance 'red' or '#a98d19. A sequence of color strings referred to by name, RGB or RGBA code, which will be used for each series recursively. For instance ['green','yellow'] each field’s line will be filled in green or yellow, alternatively. If there is only a single series to be plotted, then only the first color from the color list will be used. **kwds : optional Additional keywords arguments are documented in `hvplot.help('line')`. Returns ------- A Holoviews object. You can `print` the object to study its composition and run .. code-block:: import holoviews as hv hv.help(the_holoviews_object) to learn more about its parameters and options. Examples -------- .. code-block:: import hvplot.pandas import pandas as pd df = pd.DataFrame( { "actual": [100, 150, 125, 140, 145, 135, 123], "forecast": [90, 160, 125, 150, 141, 141, 120], "numerical": [1.1, 1.9, 3.2, 3.8, 4.3, 5.0, 5.5], "date": pd.date_range("2022-01-03", "2022-01-09"), "string": ["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"], }, ) line = df.hvplot.line( x="numerical", y=["actual", "forecast"], ylabel="value", legend="bottom", height=500, color=["steelblue", "teal"], alpha=0.7, line_width=5, ) line You can can add *markers* to a `line` plot by overlaying with a `scatter` plot. .. code-block:: markers = df.hvplot.scatter( x="numerical", y=["actual", "forecast"], color=["steelblue", "teal"], size=50 ) line * markers Please note that you can pass widgets or reactive functions as arguments instead of literal values, c.f. https://hvplot.holoviz.org/user_guide/Widgets.html. References ---------- - Bokeh: https://docs.bokeh.org/en/latest/docs/reference/models/glyphs/line.html - HoloViews: https://holoviews.org/reference/elements/bokeh/Curve.html - Pandas: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.plot.line.html - Plotly: https://plotly.com/python/line-charts/ - Matplotlib: https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.plot.html - Seaborn: https://seaborn.pydata.org/generated/seaborn.lineplot.html - Wiki: https://en.wikipedia.org/wiki/Line_chart """ return self(x, y, kind="line", **kwds) def step(self, x=None, y=None, where="mid", **kwds): """ The `step` plot connects the points with piece-wise constant curves. The `step` plot can be used pretty much anytime the `line` plot might be used, and has many of the same options available. Reference: https://hvplot.holoviz.org/reference/pandas/step.html Parameters ---------- x : string, optional Field name(s) to draw x-positions from. If not specified, the index is used. Must refer to continuous data. Not categorical data. y : string or list, optional Field name(s) to draw y-positions from. If not specified, all numerical fields are used. by : string, optional A single field or list of fields to group by. All the subgroups are visualized. groupby: string, list, optional A single field or list of fields to group and filter by. Adds one or more widgets to select the subgroup(s) to visualize. where: string, optional The interpolation method. One of 'mid', 'pre', 'post'. Default is 'mid'. color : str or array-like, optional. The color for each of the series. Possible values are: A single color string referred to by name, RGB or RGBA code, for instance 'red' or '#a98d19. A sequence of color strings referred to by name, RGB or RGBA code, which will be used for each series recursively. For instance ['green','yellow'] each field’s line will be filled in green or yellow, alternatively. If there is only a single series to be plotted, then only the first color from the color list will be used. **kwds : optional Additional keywords arguments are documented in `hvplot.help('step')`. Returns ------- A Holoviews object. You can `print` the object to study its composition and run .. code-block:: import holoviews as hv hv.help(the_holoviews_object) to learn more about its parameters and options. Examples -------- .. code-block:: import hvplot.pandas import pandas as pd df = pd.DataFrame( { "actual": [100, 150, 125, 140, 145, 135, 123], "forecast": [90, 160, 125, 150, 141, 141, 120], "numerical": [1.1, 1.9, 3.2, 3.8, 4.3, 5.0, 5.5], "date": pd.date_range("2022-01-03", "2022-01-09"), "string": ["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"], }, ) step = df.hvplot.step( x="numerical", y=["actual", "forecast"], ylabel="value", legend="bottom", height=500, color=["#f16a6f", "#1e85f7"], line_width=5, ) step You can can add *markers* to a `step` plot by overlaying with a `scatter` plot. .. code-block:: markers = df.hvplot.scatter( x="numerical", y=["actual", "forecast"], color=["#f16a6f", "#1e85f7"], size=100 ) step * markers Please note that you can pass widgets or reactive functions as arguments instead of literal values, c.f. https://hvplot.holoviz.org/user_guide/Widgets.html. References ---------- - Bokeh: https://docs.bokeh.org/en/latest/docs/reference/models/glyphs/step.html - HoloViews: https://holoviews.org/gallery/demos/bokeh/step_chart.html - Pandas: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.plot.line.html (use `draw_style='step') - Plotly: https://plotly.com/python/line-charts/ (See the Interpolation Section) - Matplotlib: https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.step.html """ return self(x, y, kind="step", where=where, **kwds) def scatter(self, x=None, y=None, **kwds): """ The `scatter` plot visualizes your points as markers in 2D space. You can visualize one more dimension by using colors. The `scatter` plot is a good first way to plot data with non continuous axes. Reference: https://hvplot.holoviz.org/reference/pandas/scatter.html Parameters ---------- x : string, optional Field name(s) to draw x-positions from. If not specified, the index is used. Can refer to continuous and categorical data. y : string or list, optional Field name(s) to draw y-positions from. If not specified, all numerical fields are used. marker : string, optional The marker shape specified above can be any supported by matplotlib, e.g. s, d, o etc. See https://matplotlib.org/stable/api/markers_api.html. c : string, optional A color or a Field name to draw the color of the marker from s : int, optional, also available as 'size' The size of the marker by : string, optional A single field or list of fields to group by. All the subgroups are visualized. groupby: string, list, optional A single field or list of fields to group and filter by. Adds one or more widgets to select the subgroup(s) to visualize. scale: number, optional Scaling factor to apply to point scaling. logz : bool Whether to apply log scaling to the z-axis. Default is False. color : str or array-like, optional. The color for each of the series. Possible values are: A single color string referred to by name, RGB or RGBA code, for instance 'red' or '#a98d19. A sequence of color strings referred to by name, RGB or RGBA code, which will be used for each series recursively. For instance ['green','yellow'] each field’s line will be filled in green or yellow, alternatively. If there is only a single series to be plotted, then only the first color from the color list will be used. **kwds : optional Additional keywords arguments are documented in `hvplot.help('scatter')`. Returns ------- A Holoviews object. You can `print` the object to study its composition and run .. code-block:: import holoviews as hv hv.help(the_holoviews_object) to learn more about its parameters and options. Example ------- .. code-block:: import hvplot.pandas import pandas as pd df = pd.DataFrame( { "actual": [100, 150, 125, 140, 145, 135, 123], "forecast": [90, 160, 125, 150, 141, 141, 120], "numerical": [1.1, 1.9, 3.2, 3.8, 4.3, 5.0, 5.5], "date": pd.date_range("2022-01-03", "2022-01-09"), "string": ["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"], }, ) scatter = df.hvplot.scatter( x="numerical", y=["actual", "forecast"], ylabel="value", legend="bottom", height=500, color=["#f16a6f", "#1e85f7"], size=100, ) scatter You can overlay the `scatter` markers on for example a `line` plot .. code-block:: line = df.hvplot.line( x="numerical", y=["actual", "forecast"], color=["#f16a6f", "#1e85f7"], line_width=5 ) scatter * line References ---------- - Bokeh: https://docs.bokeh.org/en/latest/docs/user_guide/plotting.html#scatter-markers - HoloViews: https://holoviews.org/reference/elements/matplotlib/Scatter.html - Pandas: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.plot.scatter.html - Plotly: https://plotly.com/python/line-and-scatter/ - Matplotlib: https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.scatter.html - Seaborn: https://seaborn.pydata.org/generated/seaborn.scatterplot.html - Wiki: https://en.wikipedia.org/wiki/Scatter_plot """ return self(x, y, kind="scatter", **kwds) def area(self, x=None, y=None, y2=None, stacked=True, **kwds): """ The `area` plot can be used to color the area under a line or to color the space between two lines. Reference: https://hvplot.holoviz.org/reference/pandas/area.html Parameters ---------- x : string, optional Field name(s) to draw x-positions from. If not specified, the index is used. Can refer to continuous and categorical data. y : string, optional Field name to draw the first y-position from y2 : string, optional Field name to draw the second y-position from stacked : boolean, optional Whether to stack multiple areas. Default is False. **kwds : optional Additional keywords arguments are documented in `hvplot.help('area')`. Returns ------- A Holoviews object. You can `print` the object to study its composition and run .. code-block:: import holoviews as hv hv.help(the_holoviews_object) to learn more about its parameters and options. Example ------- .. code-block:: import hvplot.pandas import pandas as pd df = pd.DataFrame( { "actual": [100, 150, 125, 140, 145, 135, 123], "forecast": [90, 160, 125, 150, 141, 141, 120], "numerical": [1.1, 1.9, 3.2, 3.8, 4.3, 5.0, 5.5], "date": pd.date_range("2022-01-03", "2022-01-09"), "string": ["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"], }, ) df["min"] = df[["actual", "forecast"]].min(axis=1) df["max"] = df[["actual", "forecast"]].max(axis=1) area = df.hvplot.area( x="numerical", y="min", y2="max", ylabel="value", legend="bottom", height=500, color=["#55a194"], alpha=0.7, line_width=2, ylim=(0, 200), ) area References ---------- - Bokeh: https://docs.bokeh.org/en/latest/docs/user_guide/plotting.html#directed-areas - HoloViews: https://holoviews.org/reference/elements/matplotlib/Area.html - Pandas: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.plot.area.html - Plotly: https://plotly.com/python/filled-area-plots/ - Matplotlib: https://matplotlib.org/stable/gallery/lines_bars_and_markers/fill_between_demo.html - Wiki: https://en.wikipedia.org/wiki/Area_chart """ if "alpha" not in kwds and not stacked: kwds["alpha"] = 0.5 return self(x, y, y2=y2, kind="area", stacked=stacked, **kwds) def errorbars(self, x=None, y=None, yerr1=None, yerr2=None, **kwds): """ `errorbars` provide a visual indicator for the variability of the plotted data on a graph. They are usually overlaid with other plots such as `scatter` , `line` or `bar` plots to indicate the variability. Reference: https://hvplot.holoviz.org/reference/pandas/errorbars.html Parameters ---------- x : string, optional Field name to draw the x-position from. If not specified, the index is used. Can refer to continuous and categorical data. y : string, optional Field name to draw the y-position from yerr1 : string, optional Field name to draw symmetric / negative errors from yerr2 : string, optional Field name to draw positive errors from **kwds : optional Additional keywords arguments are documented in `hvplot.help('errorbars')`. Returns ------- A Holoviews object. You can `print` the object to study its composition and run .. code-block:: import holoviews as hv hv.help(the_holoviews_object) to learn more about its parameters and options. Example ------- .. code-block:: import hvplot.pandas import pandas as pd df = pd.DataFrame( { "actual": [100, 150, 125, 140, 145, 135, 123], "forecast": [90, 160, 125, 150, 141, 141, 120], "numerical": [1.1, 1.9, 3.2, 3.8, 4.3, 5.0, 5.5], "date": pd.date_range("2022-01-03", "2022-01-09"), "string": ["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"], }, ) df["min"] = df[["actual", "forecast"]].min(axis=1) df["max"] = df[["actual", "forecast"]].max(axis=1) df["mean"] = df[["actual", "forecast"]].mean(axis=1) df["yerr2"] = df["max"] - df["mean"] df["yerr1"] = df["mean"] - df["min"] errorbars = df.hvplot.errorbars( x="numerical", y="mean", yerr1="yerr1", yerr2="yerr2", legend="bottom", height=500, alpha=0.5, line_width=2, ) errorbars Normally you would overlay the `errorbars` on for example a `scatter` plot. .. code-block:: mean = df.hvplot.scatter(x="numerical", y=["mean"], color=["#55a194"], size=50) errorbars * mean References ---------- - Bokeh: https://docs.bokeh.org/en/latest/docs/user_guide/annotations.html#whiskers - HoloViews: https://holoviews.org/reference/elements/bokeh/ErrorBars.html - Matplotlib: https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.errorbar.html - Pandas: https://pandas.pydata.org/docs/user_guide/visualization.html#visualization-errorbars - Plotly: https://plotly.com/python/error-bars/ - Wikipedia: https://en.wikipedia.org/wiki/Error_bar """ return self(x, y, kind="errorbars", yerr1=yerr1, yerr2=yerr2, **kwds) def ohlc(self, x=None, y=None, **kwds): """ The `ohlc` plot visualizes the open, high, low and close prices of stocks and other assets. Reference: https://hvplot.holoviz.org/reference/pandas/ohlc.html Parameters ---------- x : string, optional Field name to draw x coordinates from. If not specified, the index is used. Normally refers to date values. y : list or tuple, optional Field names of the OHLC fields. Default is ["open", "high", "low", "close"] line_color : string, optional The line color. Default is black pos_color : string, optional The color indicating a positive change. Default is green. neg_color : string, optional The color indicating a negative change. Default is red. **kwds : optional Additional keywords arguments are documented in `hvplot.help('ohlc')`. Returns ------- A Holoviews object. You can `print` the object to study its composition and run .. code-block:: import holoviews as hv hv.help(the_holoviews_object) to learn more about its parameters and options. Example ------- .. code-block:: data = pd.DataFrame({ "open": [100, 101, 102], "high": [104, 105, 110], "low": [94, 97, 99], "close": [101, 99, 103], }, index=[pd.Timestamp("2022-08-01"), pd.Timestamp("2022-08-02"), pd.Timestamp("2022-08-03")]) ohlc = data.hvplot.ohlc(pos_color="#55a194", neg_color="#f16a6f") ohlc References ---------- - Bokeh: https://docs.bokeh.org/en/latest/docs/gallery/candlestick.html - Matplotlib: https://www.statology.org/matplotlib-python-candlestick-chart/ - Plotly: https://plotly.com/python/ohlc-charts/ - Wikipedia: https://en.wikipedia.org/wiki/Candlestick_chart """ return self(kind="ohlc", x=x, y=y, **kwds) def heatmap(self, x=None, y=None, C=None, colorbar=True, **kwds): """ `heatmap` visualises tabular data indexed by two key dimensions as a grid of colored values. This allows spotting correlations in multivariate data and provides a high-level overview of how the two variables are plotted. Reference: https://hvplot.holoviz.org/reference/pandas/heatmap.html Parameters ---------- x : string, optional Field name to draw x coordinates from. If not specified, the index is used. Can refer to continuous and categorical data. y : string Field name to draw y-positions from. Can refer to continuous and categorical data. C : string, optional Field to draw heatmap color from. If not specified a simple count will be used. colorbar: boolean, optional Whether to display a colorbar. Default is True. logz : bool Whether to apply log scaling to the z-axis. Default is False. reduce_function : function, optional Function to compute statistics for heatmap, for example `np.mean`. **kwds : optional Additional keywords arguments are documented in `hvplot.help('heatmap')`. Returns ------- A Holoviews object. You can `print` the object to study its composition and run .. code-block:: import holoviews as hv hv.help(the_holoviews_object) to learn more about its parameters and options. Example ------- .. code-block:: import hvplot.pandas import numpy as np from bokeh.sampledata import sea_surface_temperature as sst df = sst.sea_surface_temperature df.hvplot.heatmap( x="time.month", y="time.day", C="temperature", reduce_function=np.mean, height=500, width=500, colorbar=False, cmap="blues" ) References ---------- - Bokeh: https://docs.bokeh.org/en/latest/docs/gallery/categorical_heatmap.html - HoloViews: https://holoviews.org/reference/elements/bokeh/HeatMap.html - Matplotlib: https://matplotlib.org/stable/gallery/images_contours_and_fields/image_annotated_heatmap.html - Plotly: https://plotly.com/python/heatmaps/ - Wiki: https://en.wikipedia.org/wiki/Heat_map """ return self(x, y, kind="heatmap", C=C, colorbar=colorbar, **kwds) def hexbin(self, x=None, y=None, C=None, colorbar=True, **kwds): """ The `hexbin` plot uses hexagons to split the area into several parts and attribute a color to it. `hexbin` offers a straightforward method for plotting dense data. Reference: https://hvplot.holoviz.org/reference/pandas/hexbin.html Parameters ---------- x : string, optional Field name to draw x coordinates from. If not specified, the index is used. y : string Field name to draw y-positions from C : string, optional Field to draw hexbin color from. If not specified a simple count will be used. colorbar: boolean, optional Whether to display a colorbar. Default is True. reduce_function : function, optional Function to compute statistics for hexbins, for example `np.mean`. gridsize: int, optional The number of hexagons in the x-direction logz : bool Whether to apply log scaling to the z-axis. Default is False. min_count : number, optional The display threshold before a bin is shown, by default bins with a count of less than 1 are hidden **kwds : optional Additional keywords arguments are documented in `hvplot.help('hexbin')`. Returns ------- A Holoviews object. You can `print` the object to study its composition and run .. code-block:: import holoviews as hv hv.help(the_holoviews_object) to learn more about its parameters and options. Example ------- .. code-block:: import hvplot.pandas import pandas as pd import numpy as np n = 500 df = pd.DataFrame({ "x": 2 + 2 * np.random.standard_normal(n), "y": 2 + 2 * np.random.standard_normal(n), }) df.hvplot.hexbin("x", "y", clabel="Count", cmap="plasma_r", height=400, width=500) References ---------- - Bokeh: https://docs.bokeh.org/en/latest/docs/gallery/hexbin.html - HoloViews: https://holoviews.org/reference/elements/bokeh/HexTiles.html - Plotly: https://plotly.com/python/hexbin-mapbox/ - Wiki: https://think.design/services/data-visualization-data-design/hexbin/ """ return self(x, y, kind="hexbin", C=C, colorbar=colorbar, **kwds) def bivariate(self, x=None, y=None, colorbar=True, **kwds): """ A bivariate, density plot uses nested contours (or contours plus colors) to indicate regions of higher local density. `bivariate` plots can be a useful alternative to scatter plots, if your data are too dense to plot each point individually. Reference: https://hvplot.holoviz.org/reference/pandas/bivariate.html Parameters ---------- x : string, optional Field name to draw x-positions from. If not specified, the index is used. y : string, optional Field name to draw y-positions from colorbar: boolean Whether to display a colorbar bandwidth: int, optional The bandwidth of the kernel for the density estimate. Default is None. cut: Integer, Optional Draw the estimate to cut * bw from the extreme data points. Default is None. filled : bool, optional If True the the contours will be filled. Default is False. levels: int, optional The number of contour lines to draw. Default is 10. **kwds : optional Additional keywords arguments are documented in `hvplot.help('bivariate')`. Returns ------- A Holoviews object. You can `print` the object to study its composition and run .. code-block:: import holoviews as hv hv.help(the_holoviews_object) to learn more about its parameters and options. Examples -------- .. code-block:: import hvplot.pandas from bokeh.sampledata.autompg import autompg_clean as df bivariate = df.hvplot.bivariate("accel", "mpg", filled=True, cmap="blues") bivariate To get a better intuitive understanding of the `bivariate` plot, you can try overlaying the corresponding scatter plot. .. code-block:: scatter = df.hvplot.scatter("accel", "mpg") bivariate * scatter References ---------- - ggplot: https://bio304-class.github.io/bio304-fall2017/ggplot-bivariate.html - HoloViews: https://holoviews.org/reference/elements/bokeh/Bivariate.html - Plotly: https://plotly.com/python/2d-histogram-contour/ - Matplotlib: https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.contour.html - Seaborn: https://seaborn.pydata.org/generated/seaborn.kdeplot.html - Wiki: https://en.wikipedia.org/wiki/Bivariate_analysis """ return self(x, y, kind="bivariate", colorbar=colorbar, **kwds) def bar(self, x=None, y=None, **kwds): """ A vertical bar plot A `bar` plot represents categorical data with rectangular bars with heights proportional to the values that they represent. The x-axis represents the categories and the y axis represents the value scale. The bars are of equal width which allows for instant comparison of data. Reference: https://hvplot.holoviz.org/reference/pandas/bar.html Parameters ---------- x : string, optional Field name to draw x-positions from. If not specified, the index is used. y : string, optional Field name to draw y-positions from. If not specified, all numerical fields are used. stacked : bool, optional If True, creates a stacked bar plot. Default is False. color : str or array-like, optional. The color for each of the series. Possible values are: The name of the field to draw the colors from. The field can contain numerical values or strings representing colors. A single color string referred to by name, RGB or RGBA code, for instance 'red' or '#a98d19'. A sequence of color strings referred to by name, RGB or RGBA code, which will be used for each series recursively. For instance ['red', 'green','blue']. **kwds : optional Additional keywords arguments are documented in `hvplot.help('bar')`. Returns ------- A Holoviews object. You can `print` the object to study its composition and run .. code-block:: import holoviews as hv hv.help(the_holoviews_object) to learn more about its parameters and options. Example ------- .. code-block:: import hvplot.pandas import pandas as pd df = pd.DataFrame( { "actual": [100, 150, 125, 140, 145, 135, 123], "forecast": [90, 160, 125, 150, 141, 141, 120], "numerical": [1.1, 1.9, 3.2, 3.8, 4.3, 5.0, 5.5], "date": pd.date_range("2022-01-03", "2022-01-09"), "string": ["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"], }, ) bar = df.hvplot.bar(x="string", y="actual", color="#f16a6f", legend="bottom", xlabel="day", ylabel="value") bar You can overlay for example a line plot via .. code-block:: forecast_line = df.hvplot.line(x="string", y="forecast", color="#1e85f7", line_width=5, legend="bottom") forecast_markers = df.hvplot.scatter(x="string", y="forecast", color="#1e85f7", size=100, legend="bottom") bar * forecast_line * forecast_markers .. code-block:: df.hvplot.bar(stacked=True, rot=90, color=["#457278", "#615078"]) References ---------- - Bokeh: https://docs.bokeh.org/en/latest/docs/reference/models/glyphs/vbar.html - HoloViews: https://holoviews.org/reference/elements/bokeh/Bars.html - Matplotlib: https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.bar.html - Pandas: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.plot.bar.html - Plotly: https://plotly.com/python/bar-charts/ - Wiki: https://en.wikipedia.org/wiki/Bar_chart """ return self(x, y, kind="bar", **kwds) def barh(self, x=None, y=None, **kwds): """ A horizontal bar plot A `barh` plot represents categorical data with rectangular bars with heights proportional to the values that they represent. The y-axis of the chart plots categories and the x-axis represents the value scale. The bars are of equal width which allows for instant comparison of data. `barh` can be used on dataframes with regular Index or MultiIndex. Reference: https://hvplot.holoviz.org/reference/pandas/barh.html Parameters ---------- **kwds : optional Additional keywords arguments are documented in `hvplot.help('image')`. Returns ------- A Holoviews object. You can `print` the object to study its composition and run .. code-block:: import holoviews as hv hv.help(the_holoviews_object) to learn more about its parameters and options. Examples -------- .. code-block:: import hvplot.pandas import pandas as pd df = pd.DataFrame( { "speed": [0.1, 17.5, 40, 48, 52, 69, 88], "lifespan": [2, 8, 70, 1.5, 25, 12, 28], }, index=["snail", "pig", "elephant", "rabbit", "giraffe", "coyote", "horse"], ) df.hvplot.barh(color=["#457278", "#615078"]) You can stack the bars by setting `stacked=True` .. code-block:: df.hvplot.barh(stacked=True, color=["#457278", "#615078"]) References ---------- - Bokeh: https://docs.bokeh.org/en/latest/docs/reference/models/glyphs/hbar.html - HoloViews: https://holoviews.org/reference/elements/bokeh/Bars.html - Matplotlib: https://matplotlib.org/stable/gallery/lines_bars_and_markers/barh.html - Pandas: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.plot.barh.html - Plotly: https://plotly.com/python/horizontal-bar-charts/ - Wiki: https://en.wikipedia.org/wiki/Bar_chart """ return self(x, y, kind="barh", **kwds) def box(self, y=None, by=None, **kwds): """ The `box` plot gives you a visual idea about the *locality*, *spread* and *skewness* of numerical data through their quartiles. It is also known as *box and whiskers plot*. `box` plots are most useful when grouped by additional dimensions. Reference: https://hvplot.holoviz.org/reference/pandas/box.html Parameters ---------- y : string or sequence Field(s) in the *wide* data to compute distribution from. If none is provided all numerical fields will be used. by : string or sequence Field in the *long* data to group by. kwds : optional Additional keywords arguments are documented in `hvplot.help('box')`. Returns ------- A Holoviews object. You can `print` the object to study its composition and run .. code-block:: import holoviews as hv hv.help(the_holoviews_object) to learn more about its parameters and options. Example ------- Here is an example using *wide* data. .. code-block:: import hvplot.pandas import numpy as np import pandas as pd data = np.random.randn(25, 4) df = pd.DataFrame(data, columns=list('ABCD')) df.hvplot.box() Here is an example using *long* data and the `by` argument. .. code-block:: import hvplot.pandas # noqa import pandas as pd age_list = [8, 10, 12, 14, 72, 74, 76, 78, 20, 25, 30, 35, 60, 85] df = pd.DataFrame({"gender": list("MMMMMMMMFFFFFF"), "age": age_list}) df.hvplot.box(y='age', by='gender', height=400, width=400, legend=False, ylim=(0, None)) References ---------- - Bokeh: https://docs.bokeh.org/en/latest/docs/gallery/boxplot.html - HoloViews: https://holoviews.org/reference/elements/bokeh/BoxWhisker.html - Matplotlib: https://matplotlib.org/stable/plot_types/stats/boxplot_plot.html#sphx-glr-plot-types-stats-boxplot-plot-py - Pandas: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.boxplot.html - Plotly: https://plotly.com/python/box-plots/ - Wiki: https://en.wikipedia.org/wiki/Box_plot """ return self(kind="box", x=None, y=y, by=by, **dict(kwds, hover=False)) def violin(self, y=None, by=None, **kwds): """ `violin` plots are similar to `box` plots, but they provide a better sense of the distribution of data. Note that `violin` plots depend on the `scipy` library. Reference: https://hvplot.holoviz.org/reference/pandas/violin.html Parameters ---------- y : string or sequence Field(s) in the *wide* data to compute distribution from. If none is provided all numerical fields will be used. by : string or sequence Field in the *long* data to group by. kwds : optional Additional keywords arguments are documented in `hvplot.help('violin')`. Returns ------- A Holoviews object. You can `print` the object to study its composition and run .. code-block:: import holoviews as hv hv.help(the_holoviews_object) to learn more about its parameters and options. Examples -------- Here is an example using *wide* data. .. code-block:: import hvplot.pandas import numpy as np import pandas as pd data = np.random.randn(25, 4) df = pd.DataFrame(data, columns=list('ABCD')) df.hvplot.violin(ylim=(-5, 5)) Here is an example using *long* data and the `by` argument. .. code-block:: import hvplot.pandas # noqa import pandas as pd age_list = [8, 10, 12, 14, 72, 74, 76, 78, 20, 25, 30, 35, 60, 85] df = pd.DataFrame({"gender": list("MMMMMMMMFFFFFF"), "age": age_list}) df.hvplot.violin(y='age', by='gender', height=400, width=400, legend=False, ylim=(-100, 200)) References ---------- - Seaborn: https://seaborn.pydata.org/generated/seaborn.violinplot.html - HoloViews: https://holoviews.org/reference/elements/bokeh/Violin.html - Matplotlib: https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.violinplot.html - Plotly: https://plotly.com/python/violin/ - Wiki: https://en.wikipedia.org/wiki/Violin_plot """ return self(kind="violin", x=None, y=y, by=by, **dict(kwds, hover=False)) def hist(self, y=None, by=None, **kwds): """ A `histogram` displays an approximate representation of the distribution of continuous data. Reference: https://hvplot.holoviz.org/reference/pandas/hist.html Parameters ---------- y : string or sequence Field(s) in the *wide* data to compute the distribution(s) from. Please note the fields should contain continuous data. Not categorical. by : string or sequence Field(s) in the *long* data to group by. bins : int, optional The number of bins bin_range: tuple, optional The lower and upper range of the bins. Default is None. normed : bool, optional If True the distribution will sum to 1. Default is False. cumulative: bool, optional If True, then a histogram is computed where each bin gives the counts in that bin plus all bins for smaller values. The last bin gives the total number of datapoints. Default is False. alpha : float, optional An alpha value between 0.0 and 1.0 to better visualize multiple fields. Default is 1.0. kwds : optional Additional keywords arguments are documented in `hvplot.help('hist')`. Returns ------- A Holoviews object. You can `print` the object to study its composition and run .. code-block:: import holoviews as hv hv.help(the_holoviews_object) to learn more about its parameters and options. Examples -------- Lets display some *wide* data created by rolling two dices .. code-block:: import hvplot.pandas import numpy as np import pandas as pd df = pd.DataFrame(np.random.randint(1, 7, 6000), columns = ['one']) df['two'] = df['one'] + np.random.randint(1, 7, 6000) df.hvplot.hist(bins=12, alpha=0.5, color=["lightgreen", "pink"]) If you want to show the distribution of the values of a categorical column, you can use Pandas' method `value_counts` and `bar` as shown below .. code-block:: import hvplot.pandas import pandas as pd data = pd.DataFrame({ "library": ["bokeh", "plotly", "matplotlib", "bokeh", "matplotlib", "matplotlib"] }) data["library"].value_counts().hvplot.bar() References ---------- - Bokeh: https://docs.bokeh.org/en/latest/docs/gallery/histogram.html - HoloViews: https://holoviews.org/reference/elements/bokeh/Histogram.html - Pandas: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.plot.hist.html - Plotly: https://plotly.com/python/histograms/ - Matplotlib: https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.hist.html - Seaborn: https://seaborn.pydata.org/generated/seaborn.histplot.html - Wiki: https://en.wikipedia.org/wiki/Histogram """ return self(kind="hist", x=None, y=y, by=by, **kwds) def kde(self, y=None, by=None, **kwds): """ The Kernel density estimate (`kde`) plot shows the distribution and spread of the data. The `kde` and `density` plots are the same. Reference: https://hvplot.holoviz.org/reference/pandas/kde.html Parameters ---------- y : string or sequence Field(s) in the data to compute distribution on. If not specified all numerical fields are used. by : string or sequence Field(s) in the data to group by. bandwidth : float, optional The bandwidth of the kernel for the density estimate. Default is None. cut : Draw the estimate to cut * bw from the extreme data points. n_samples : int, optional Number of samples to compute the KDE over. Default is 100. filled : Whether the bivariate contours should be filled. Default is True. kwds : optional Additional keywords arguments are documented in `hvplot.help('kde')`. Returns ------- A Holoviews object. You can `print` the object to study its composition and run .. code-block:: import holoviews as hv hv.help(the_holoviews_object) to learn more about its parameters and options. Examples -------- Lets display a 'kde' plot from *wide* data .. code-block:: import hvplot.pandas import numpy as np import pandas as pd df = pd.DataFrame({ 'x': [1, 2, 2.5, 3, 3.5, 4, 5], 'y': [4, 4, 4.5, 5, 5.5, 6, 6], }) df.hvplot.kde(color=["orange", "green"]) Lets display a 'kde' plot from *long* data using the 'by' attribute .. code-block:: import hvplot.pandas # noqa import pandas as pd import numpy as np df = pd.DataFrame({ 'category': list('xxxxxxxyyyyyyy'), 'value': [1, 2, 2.5, 3, 3.5, 4, 5, 4, 4, 4.5, 5, 5.5, 6, 6], }) df.hvplot.kde(by='category', filled=False) References ---------- - HoloViews: https://holoviews.org/reference/elements/bokeh/Distribution.html - Pandas: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.plot.kde.html - Plotly: https://plotly.com/python/distplot/ - Seaborn: https://seaborn.pydata.org/generated/seaborn.kdeplot.html - Wiki: https://en.wikipedia.org/wiki/Kernel_density_estimation """ return self(kind="kde", x=None, y=y, by=by, **kwds) def density(self, y=None, by=None, **kwds): """ The Kernel density estimate (`density`) plot shows the distribution and spread of the data. The `kde` and `density` plots are the same. Reference: https://hvplot.holoviz.org/reference/pandas/kde.html Parameters ---------- y : string or sequence Field(s) in the data to compute distribution from. If not specified all numerical fields are used. by : string or sequence Field(s) in the data to group by. bandwidth : float, optional The bandwidth of the kernel for the density estimate. Default is None. cut : Draw the estimate to cut * bw from the extreme data points. n_samples : int, optional Number of samples to compute the KDE over. Default is 100. filled : Whether the bivariate contours should be filled. Default is True. kwds : optional Additional keywords arguments are documented in `hvplot.help('density')`. Returns ------- A Holoviews object. You can `print` the object to study its composition and run .. code-block:: import holoviews as hv hv.help(the_holoviews_object) to learn more about its parameters and options. Examples -------- Lets display a 'density' plot from *wide* data .. code-block:: import hvplot.pandas import numpy as np import pandas as pd df = pd.DataFrame({ 'x': [1, 2, 2.5, 3, 3.5, 4, 5], 'y': [4, 4, 4.5, 5, 5.5, 6, 6], }) df.hvplot.density(color=["orange", "green"]) Lets display a 'density' plot from *long* data using the 'by' attribute .. code-block:: import hvplot.pandas # noqa import pandas as pd import numpy as np df = pd.DataFrame({ 'category': list('xxxxxxxyyyyyyy'), 'value': [1, 2, 2.5, 3, 3.5, 4, 5, 4, 4, 4.5, 5, 5.5, 6, 6], }) df.hvplot.density(by='category', filled=False) References ---------- - HoloViews: https://holoviews.org/reference/elements/bokeh/Distribution.html - Pandas: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.plot.kde.html - Plotly: https://plotly.com/python/distplot/ - Seaborn: https://seaborn.pydata.org/generated/seaborn.kdeplot.html - Wiki: https://en.wikipedia.org/wiki/Kernel_density_estimation """ return self(kind="kde", x=None, y=y, by=by, **kwds) def table(self, columns=None, **kwds): """ Displays a 'table'. Reference: https://hvplot.holoviz.org/reference/pandas/table.html Parameters ---------- columns : string or sequence The field(s) to display as columns. sortable : bool, optional If True the columns are sortable. Default is False. selectable : bool, optional If True the cells are selectable. Default is False. # Todo: Describe how to use this **kwds : optional Additional keywords arguments are documented in `hvplot.help('table')`. Returns ------- A Holoviews object. You can `print` the object to study its composition and run .. code-block:: import holoviews as hv hv.help(the_holoviews_object) to learn more about its parameters and options. Example ------- .. code-block:: import hvplot.pandas from bokeh.sampledata.autompg import autompg_clean as df df.hvplot.table(columns=['origin', 'name', 'yr'], sortable=True, selectable=True) References ---------- - HoloViews: https://holoviews.org/reference/elements/bokeh/Table.html - Plotly: https://plotly.com/python/table/ - Matplotlib: https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.table.html """ return self(kind="table", **dict(kwds, columns=columns)) def dataset(self, columns=None, **kwds): """ The 'dataset' wraps a tabular or gridded dataset and can be further transformed and annotated via methods from HoloViews. Parameters ---------- **kwds : optional Additional keywords arguments are documented in `hvplot.help('dataset')`. Returns ------- A Holoviews object. You can `print` the object to study its composition and run .. code-block:: import holoviews as hv hv.help(the_holoviews_object) to learn more about its parameters and options. Example ------- .. code-block:: import hvplot.pandas import pandas as pd data = pd.DataFrame({"x": ['a', 'b', 'c'], "y": [1, 2, 3]}) data.hvplot.dataset() References ---------- - HoloViews Tabular: https://holoviews.org/getting_started/Tabular_Datasets.html - HoloViews Gridded: https://holoviews.org/getting_started/Gridded_Datasets.html """ return self(kind="dataset", **dict(kwds, columns=columns)) def points(self, x=None, y=None, **kwds): """ A `points` plot visualizes positions in a 2D space. This is useful for example for geographic plots. There is no assumption that 'y' depends on 'x'. This is different from a `scatter` plot which assumes that `y` depends `x`. Reference: https://hvplot.holoviz.org/reference/geopandas/points.html Parameters ---------- x : string, optional The coordinate variable along the x-axis. Default is the first numeric field. y : string, optional The coordinate variable along the y-axis. Default is the second numeric field. c : string, optional The dimension to color the points by s : int, optional, also available as 'size' The size of the marker marker : string, optional The marker shape specified above can be any supported by matplotlib, e.g. s, d, o etc. See https://matplotlib.org/stable/api/markers_api.html. scale: number, optional Scaling factor to apply to point scaling. logz : bool Whether to apply log scaling to the z-axis. Default is False. **kwds : optional Additional keywords arguments are documented in `hvplot.help('points')`. Returns ------- A Holoviews object. You can `print` the object to study its composition and run .. code-block:: import holoviews as hv hv.help(the_holoviews_object) to learn more about its parameters and options. Examples -------- .. code-block:: import hvplot.pandas import pandas as pd data = pd.DataFrame(dict(x=[49.9, 50.0, 50.1, 50.2], y=[50.2, 49.9, 50.0, 50.2])) plot = data.hvplot.points(color="green", size=100, marker="square") plot References ---------- - HoloViews: https://holoviews.org/reference/elements/bokeh/Points.html """ return self(x, y, kind="points", **kwds) def vectorfield(self, x=None, y=None, angle=None, mag=None, **kwds): """ `vectorfield visualizes vectors given by the (`x , `y`) starting point, a magnitude (`mag`) and an `angle` . A `vectorfield` plot is also known as a `quiver` plot. Reference: https://hvplot.holoviz.org/reference/xarray/vectorfield.html Parameters ---------- x : string Field name to draw x-positions from y : string Field name to draw y-positions from mag : string Magnitude angle : string Angle in radians. **kwds : optional Additional keywords arguments are documented in `hvplot.help('vectorfield')`. Returns ------- A Holoviews object. You can `print` the object to study its composition and run .. code-block:: import holoviews as hv hv.help(the_holoviews_object) to learn more about its parameters and options. Example ------- .. code-block:: import hvplot.pandas import numpy as np import pandas as pd data = pd.DataFrame( dict( x=[49.9, 50.0, 50.1, 50.2], y=[50.2, 49.9, 50.0, 50.2], angle=[2 * np.pi, np.pi, np.pi, np.pi], mag=[0.01, 0.02, -0.02, -0.01], ) ) data.hvplot.vectorfield(x="x", y="y", angle="angle", mag="mag") References ---------- - Bokeh: https://docs.bokeh.org/en/latest/docs/gallery/quiver.html - HoloViews: https://holoviews.org/reference/elements/bokeh/VectorField.html - Matplotlib: https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.quiver.html - Plotly: https://plotly.com/python/quiver-plots/ - Wiki: https://simple.wikipedia.org/wiki/Vector_field """ return self(x, y, angle=angle, mag=mag, kind="vectorfield", **kwds) def polygons(self, x=None, y=None, c=None, **kwds): """ Polygon plot for geopandas dataframes. Reference: https://hvplot.holoviz.org/reference/geopandas/polygons.html Parameters ---------- c : string, optional The dimension to color the polygons by logz : bool Enables logarithmic colormapping. Default is False. geo : bool, optional Whether the plot should be treated as geographic (and assume PlateCarree, i.e. lat/lon coordinates). **kwds : optional Additional keywords arguments are documented in `hvplot.help('polygons')`. Returns ------- A Holoviews object. You can `print` the object to study its composition and run .. code-block:: import holoviews as hv hv.help(the_holoviews_object) to learn more about its parameters and options. Examples -------- .. code-block:: import geopandas as gpd import hvplot.pandas countries = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres')) countries.hvplot.polygons(geo=True, c='pop_est', hover_cols='all') """ return self(x, y, c=c, kind="polygons", **kwds) def paths(self, x=None, y=None, c=None, **kwds): """ LineString and LineRing plot for geopandas dataframes. Parameters ---------- **kwds : optional Additional keywords arguments are documented in `hvplot.help('paths')`. Returns ------- A Holoviews object. You can `print` the object to study its composition and run .. code-block:: import holoviews as hv hv.help(the_holoviews_object) to learn more about its parameters and options. References ---------- - HoloViews: https://holoviews.org/reference/elements/bokeh/Path.html """ return self(x, y, c=c, kind="paths", **kwds) def labels(self, x=None, y=None, text=None, **kwds): """ Labels plot. `labels` are mostly useful when overlaid on top of other plots using the `*` operator. Reference: https://hvplot.holoviz.org/reference/pandas/labels.html Parameters ---------- x : string, optional The coordinate variable along the x-axis y : string, optional The coordinate variable along the y-axis text : string, optional The column to draw the text labels from; it's also possible to provide a template string containing the column names to automatically format the text, e.g. "{col1}, {col2}". **kwds : optional Additional keywords arguments are documented in `hvplot.help('labels')`. Returns ------- A Holoviews object. You can `print` the object to study its composition and run .. code-block:: import holoviews as hv hv.help(the_holoviews_object) to learn more about its parameters and options. Examples -------- .. code-block:: import hvplot.pandas import pandas as pd df = pd.DataFrame( {'City': ['Buenos Aires', 'Brasilia', 'Santiago', 'Bogota', 'Caracas'], 'Country': ['Argentina', 'Brazil', 'Chile', 'Colombia', 'Venezuela'], 'Latitude': [-34.58, -15.78, -33.45, 4.60, 10.48], 'Longitude': [-58.66, -47.91, -70.66, -74.08, -66.86], 'Color': ['blue', 'green', 'white', 'black', 'yellow']}) df.hvplot.points(x='Longitude', y='Latitude') * \ df.hvplot.labels(x='Longitude', y='Latitude', text='City', text_baseline="bottom") References ---------- - Bokeh: https://docs.bokeh.org/en/latest/docs/reference/models/glyphs/text.html - HoloViews: https://holoviews.org/reference/elements/bokeh/Labels.html - Matplotlib: https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.text.html#matplotlib.pyplot.text - Plotly: https://plotly.com/python/text-and-annotations/ """ return self(x, y, text=text, kind="labels", **kwds) class hvPlot(hvPlotTabular): """ The plotting method: `df.hvplot(...)` creates a plot similarly to the familiar Pandas `df.plot` method. For more detailed options use a specific plotting method, e.g. `df.hvplot.line`. Reference: https://hvplot.holoviz.org/reference/index.html Parameters ---------- x : string, optional Field name(s) to draw x-positions from. If not specified, the index is used. y : string or list, optional Field name(s) to draw y-positions from. If not specified, all numerical fields are used. kind : string, optional The kind of plot to generate, e.g. 'area', 'bar', 'line', 'scatter' etc. To see the available plots run `print(df.hvplot.__all__)`. **kwds : optional Additional keywords arguments are documented in `hvplot.help('scatter')` or similar depending on the kind of plot. Returns ------- A Holoviews object. You can `print` the object to study its composition and run `hv.help` on the object to learn more about its parameters and options. Examples -------- .. code-block:: import hvplot.pandas import pandas as pd df = pd.DataFrame( { "actual": [100, 150, 125, 140, 145, 135, 123], "forecast": [90, 160, 125, 150, 141, 141, 120], "numerical": [1.1, 1.9, 3.2, 3.8, 4.3, 5.0, 5.5], "date": pd.date_range("2022-01-03", "2022-01-09"), "string": ["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"], }, ) line = df.hvplot.line( x="numerical", y=["actual", "forecast"], ylabel="value", legend="bottom", height=500, color=["steelblue", "teal"], alpha=0.7, line_width=5, ) line You can can add *markers* to a `line` plot by overlaying with a `scatter` plot. .. code-block:: markers = df.hvplot.scatter( x="numerical", y=["actual", "forecast"], color=["#f16a6f", "#1e85f7"], size=50 ) line * markers Please note that you can pass widgets or reactive functions as arguments instead of literal values, c.f. https://hvplot.holoviz.org/user_guide/Widgets.html. """ __all__ = [ "line", "step", "scatter", "area", "errorbars", "heatmap", "hexbin", "bivariate", "bar", "barh", "box", "violin", "hist", "kde", "density", "table", "dataset", "points", "vectorfield", "polygons", "paths", "labels", "image", "rgb", "quadmesh", "contour", "contourf", "explorer", ] def image(self, x=None, y=None, z=None, colorbar=True, **kwds): """ Image plot You can use `image` to display for example geographic data with independent `latitude` and `longitude` fields and a third dependent field. Reference: https://hvplot.holoviz.org/reference/xarray/image.html Parameters ---------- x : string, optional The coordinate variable along the x-axis y : string, optional The coordinate variable along the y-axis z : string, optional The data variable to plot colorbar: boolean Whether to display a colorbar kwds : optional To see all the keyword arguments available, run `hvplot.help('image')`. Returns ------- A Holoviews object. You can `print` the object to study its composition and run .. code-block:: import holoviews as hv hv.help(the_holoviews_object) to learn more about its parameters and options. Example ------- .. code-block:: import hvplot.xarray import xarray as xr ds = xr.tutorial.open_dataset('air_temperature') ds.hvplot.image(x='lon', y='lat', z='air', groupby='time', cmap='kbc_r') References ---------- - Bokeh: https://docs.bokeh.org/en/latest/docs/gallery/image.html - HoloViews: https://holoviews.org/reference/elements/bokeh/Image.html - Matplotlib: https://matplotlib.org/stable/tutorials/introductory/images.html - Plotly: https://plotly.com/python/imshow/ """ return self(x, y, z=z, kind="image", colorbar=colorbar, **kwds) def rgb(self, x=None, y=None, z=None, bands=None, **kwds): """ RGB plot `rgb` can be used to display images that are distributed as three separate "channels" or "bands". Reference: https://hvplot.holoviz.org/reference/xarray/rgb.html Parameters ---------- x : string, optional The coordinate variable along the x-axis y : string, optional The coordinate variable along the y-axis bands : string, optional The coordinate variable to draw the RGB channels from z : string, optional The data variable to plot **kwds : optional Additional keywords arguments are documented in `hvplot.help('rgb')`. Returns ------- A Holoviews object. You can `print` the object to study its composition and run .. code-block:: import holoviews as hv hv.help(the_holoviews_object) to learn more about its parameters and options. References ---------- - Bokeh: https://docs.bokeh.org/en/latest/docs/reference/models/glyphs/image_rgba.html - HoloViews: https://holoviews.org/reference/elements/bokeh/RGB.html - Matplotlib: https://matplotlib.org/stable/tutorials/introductory/images.html - Plotly: https://plotly.com/python/imshow/ """ if bands is not None: kwds["bands"] = bands return self(x, y, z=z, kind="rgb", **kwds) def quadmesh(self, x=None, y=None, z=None, colorbar=True, **kwds): """ QuadMesh plot `quadmesh` allows you to plot values on an irregular grid by representing each value as a polygon. Reference: https://hvplot.holoviz.org/reference/xarray/quadmesh.html Parameters ---------- x : string, optional The coordinate variable along the x-axis y : string, optional The coordinate variable along the y-axis z : string, optional The data variable to plot colorbar: boolean Whether to display a colorbar **kwds : optional Additional keywords arguments are documented in `hvplot.help('quadmesh')`. Returns ------- A Holoviews object. You can `print` the object to study its composition and run .. code-block:: import holoviews as hv hv.help(the_holoviews_object) to learn more about its parameters and options. Examples -------- .. code-block:: import hvplot.xarray import xarray as xr ds = xr.tutorial.open_dataset('rasm') ds.Tair.hvplot.quadmesh(x='xc', y='yc', geo=True, widget_location='bottom') References ---------- - HoloViews: https://holoviews.org/reference/elements/bokeh/QuadMesh.html """ return self(x, y, z=z, kind="quadmesh", colorbar=colorbar, **kwds) def contour(self, x=None, y=None, z=None, colorbar=True, **kwds): """ Line contour plot Reference: https://hvplot.holoviz.org/reference/xarray/contour.html Parameters ---------- x : string, optional The coordinate variable along the x-axis y : string, optional The coordinate variable along the y-axis z : string, optional The data variable to plot levels: int, optional The number of contour levels colorbar: boolean Whether to display a colorbar **kwds : optional Additional keywords arguments are documented in `hvplot.help('contour')`. Returns ------- A Holoviews object. You can `print` the object to study its composition and run .. code-block:: import holoviews as hv hv.help(the_holoviews_object) to learn more about its parameters and options. Examples -------- .. code-block:: import hvplot.xarray import xarray as xr ds = xr.tutorial.open_dataset("air_temperature") ds.hvplot.contour( geo=True, tiles="EsriImagery", z="air", x="lon", y="lat", levels=20, clabel="T [K]", line_width=2, label="Mean Air temperature [K]", cmap="reds", ) References ---------- - HoloViews: https://holoviews.org/reference/elements/bokeh/Contours.html - Matplotlib: https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.contour.html - Plotly: https://plotly.com/python/contour-plots/ """ return self(x, y, z=z, kind="contour", colorbar=colorbar, **kwds) def contourf(self, x=None, y=None, z=None, colorbar=True, **kwds): """ Filled contour plot Reference. https://hvplot.holoviz.org/reference/xarray/contourf.html Parameters ---------- x : string, optional The coordinate variable along the x-axis y : string, optional The coordinate variable along the y-axis z : string, optional The data variable to plot levels: int, optional The number of contour levels colorbar: boolean Whether to display a colorbar **kwds : optional Additional keywords arguments are documented in `hvplot.help('contourf')`. Returns ------- A Holoviews object. You can `print` the object to study its composition and run .. code-block:: import holoviews as hv hv.help(the_holoviews_object) to learn more about its parameters and options. Examples .. code-block:: import hvplot.xarray import xarray as xr ds = xr.tutorial.open_dataset("air_temperature") ds.hvplot.contourf( geo=True, coastline=True, z="air", x="lon", y="lat", levels=20, clabel="T [K]", line_width=2, label="Mean Air temperature [K]", cmap="reds", ) References ---------- - HoloViews: https://holoviews.org/reference/elements/bokeh/Contours.html - Matplotlib: https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.contour.html - Plotly: https://plotly.com/python/contour-plots/ """ return self(x, y, z=z, kind="contourf", colorbar=colorbar, **kwds)