611 lines
18 KiB
Python
611 lines
18 KiB
Python
from __future__ import annotations
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from contextlib import contextmanager
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from typing import (
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TYPE_CHECKING,
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Iterator,
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)
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from pandas.plotting._core import _get_plot_backend
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if TYPE_CHECKING:
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from matplotlib.axes import Axes
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from matplotlib.figure import Figure
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import numpy as np
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from pandas import (
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DataFrame,
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Series,
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)
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def table(ax, data, rowLabels=None, colLabels=None, **kwargs):
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"""
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Helper function to convert DataFrame and Series to matplotlib.table.
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Parameters
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----------
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ax : Matplotlib axes object
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data : DataFrame or Series
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Data for table contents.
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**kwargs
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Keyword arguments to be passed to matplotlib.table.table.
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If `rowLabels` or `colLabels` is not specified, data index or column
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name will be used.
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Returns
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-------
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matplotlib table object
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"""
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plot_backend = _get_plot_backend("matplotlib")
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return plot_backend.table(
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ax=ax, data=data, rowLabels=None, colLabels=None, **kwargs
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)
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def register() -> None:
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"""
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Register pandas formatters and converters with matplotlib.
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This function modifies the global ``matplotlib.units.registry``
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dictionary. pandas adds custom converters for
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* pd.Timestamp
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* pd.Period
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* np.datetime64
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* datetime.datetime
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* datetime.date
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* datetime.time
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See Also
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--------
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deregister_matplotlib_converters : Remove pandas formatters and converters.
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"""
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plot_backend = _get_plot_backend("matplotlib")
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plot_backend.register()
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def deregister() -> None:
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"""
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Remove pandas formatters and converters.
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Removes the custom converters added by :func:`register`. This
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attempts to set the state of the registry back to the state before
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pandas registered its own units. Converters for pandas' own types like
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Timestamp and Period are removed completely. Converters for types
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pandas overwrites, like ``datetime.datetime``, are restored to their
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original value.
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See Also
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--------
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register_matplotlib_converters : Register pandas formatters and converters
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with matplotlib.
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"""
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plot_backend = _get_plot_backend("matplotlib")
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plot_backend.deregister()
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def scatter_matrix(
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frame: DataFrame,
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alpha: float = 0.5,
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figsize: tuple[float, float] | None = None,
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ax: Axes | None = None,
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grid: bool = False,
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diagonal: str = "hist",
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marker: str = ".",
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density_kwds=None,
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hist_kwds=None,
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range_padding: float = 0.05,
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**kwargs,
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) -> np.ndarray:
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"""
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Draw a matrix of scatter plots.
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Parameters
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----------
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frame : DataFrame
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alpha : float, optional
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Amount of transparency applied.
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figsize : (float,float), optional
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A tuple (width, height) in inches.
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ax : Matplotlib axis object, optional
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grid : bool, optional
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Setting this to True will show the grid.
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diagonal : {'hist', 'kde'}
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Pick between 'kde' and 'hist' for either Kernel Density Estimation or
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Histogram plot in the diagonal.
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marker : str, optional
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Matplotlib marker type, default '.'.
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density_kwds : keywords
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Keyword arguments to be passed to kernel density estimate plot.
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hist_kwds : keywords
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Keyword arguments to be passed to hist function.
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range_padding : float, default 0.05
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Relative extension of axis range in x and y with respect to
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(x_max - x_min) or (y_max - y_min).
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**kwargs
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Keyword arguments to be passed to scatter function.
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Returns
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-------
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numpy.ndarray
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A matrix of scatter plots.
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Examples
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--------
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.. plot::
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:context: close-figs
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>>> df = pd.DataFrame(np.random.randn(1000, 4), columns=['A','B','C','D'])
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>>> pd.plotting.scatter_matrix(df, alpha=0.2)
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array([[<AxesSubplot: xlabel='A', ylabel='A'>,
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<AxesSubplot: xlabel='B', ylabel='A'>,
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<AxesSubplot: xlabel='C', ylabel='A'>,
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<AxesSubplot: xlabel='D', ylabel='A'>],
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[<AxesSubplot: xlabel='A', ylabel='B'>,
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<AxesSubplot: xlabel='B', ylabel='B'>,
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<AxesSubplot: xlabel='C', ylabel='B'>,
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<AxesSubplot: xlabel='D', ylabel='B'>],
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[<AxesSubplot: xlabel='A', ylabel='C'>,
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<AxesSubplot: xlabel='B', ylabel='C'>,
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<AxesSubplot: xlabel='C', ylabel='C'>,
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<AxesSubplot: xlabel='D', ylabel='C'>],
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[<AxesSubplot: xlabel='A', ylabel='D'>,
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<AxesSubplot: xlabel='B', ylabel='D'>,
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<AxesSubplot: xlabel='C', ylabel='D'>,
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<AxesSubplot: xlabel='D', ylabel='D'>]], dtype=object)
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"""
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plot_backend = _get_plot_backend("matplotlib")
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return plot_backend.scatter_matrix(
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frame=frame,
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alpha=alpha,
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figsize=figsize,
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ax=ax,
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grid=grid,
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diagonal=diagonal,
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marker=marker,
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density_kwds=density_kwds,
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hist_kwds=hist_kwds,
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range_padding=range_padding,
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**kwargs,
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)
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def radviz(
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frame: DataFrame,
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class_column: str,
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ax: Axes | None = None,
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color: list[str] | tuple[str, ...] | None = None,
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colormap=None,
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**kwds,
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) -> Axes:
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"""
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Plot a multidimensional dataset in 2D.
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Each Series in the DataFrame is represented as a evenly distributed
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slice on a circle. Each data point is rendered in the circle according to
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the value on each Series. Highly correlated `Series` in the `DataFrame`
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are placed closer on the unit circle.
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RadViz allow to project a N-dimensional data set into a 2D space where the
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influence of each dimension can be interpreted as a balance between the
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influence of all dimensions.
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More info available at the `original article
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<https://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.135.889>`_
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describing RadViz.
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Parameters
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----------
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frame : `DataFrame`
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Object holding the data.
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class_column : str
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Column name containing the name of the data point category.
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ax : :class:`matplotlib.axes.Axes`, optional
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A plot instance to which to add the information.
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color : list[str] or tuple[str], optional
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Assign a color to each category. Example: ['blue', 'green'].
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colormap : str or :class:`matplotlib.colors.Colormap`, default None
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Colormap to select colors from. If string, load colormap with that
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name from matplotlib.
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**kwds
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Options to pass to matplotlib scatter plotting method.
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Returns
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-------
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class:`matplotlib.axes.Axes`
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See Also
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--------
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plotting.andrews_curves : Plot clustering visualization.
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Examples
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--------
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.. plot::
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:context: close-figs
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>>> df = pd.DataFrame(
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... {
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... 'SepalLength': [6.5, 7.7, 5.1, 5.8, 7.6, 5.0, 5.4, 4.6, 6.7, 4.6],
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... 'SepalWidth': [3.0, 3.8, 3.8, 2.7, 3.0, 2.3, 3.0, 3.2, 3.3, 3.6],
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... 'PetalLength': [5.5, 6.7, 1.9, 5.1, 6.6, 3.3, 4.5, 1.4, 5.7, 1.0],
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... 'PetalWidth': [1.8, 2.2, 0.4, 1.9, 2.1, 1.0, 1.5, 0.2, 2.1, 0.2],
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... 'Category': [
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... 'virginica',
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... 'virginica',
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... 'setosa',
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... 'virginica',
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... 'virginica',
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... 'versicolor',
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... 'versicolor',
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... 'setosa',
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... 'virginica',
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... 'setosa'
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... ]
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... }
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... )
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>>> pd.plotting.radviz(df, 'Category')
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<AxesSubplot: xlabel='y(t)', ylabel='y(t + 1)'>
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"""
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plot_backend = _get_plot_backend("matplotlib")
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return plot_backend.radviz(
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frame=frame,
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class_column=class_column,
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ax=ax,
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color=color,
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colormap=colormap,
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**kwds,
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)
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def andrews_curves(
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frame: DataFrame,
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class_column: str,
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ax: Axes | None = None,
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samples: int = 200,
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color: list[str] | tuple[str, ...] | None = None,
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colormap=None,
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**kwargs,
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) -> Axes:
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"""
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Generate a matplotlib plot for visualising clusters of multivariate data.
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Andrews curves have the functional form:
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f(t) = x_1/sqrt(2) + x_2 sin(t) + x_3 cos(t) +
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x_4 sin(2t) + x_5 cos(2t) + ...
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Where x coefficients correspond to the values of each dimension and t is
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linearly spaced between -pi and +pi. Each row of frame then corresponds to
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a single curve.
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Parameters
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----------
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frame : DataFrame
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Data to be plotted, preferably normalized to (0.0, 1.0).
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class_column : Name of the column containing class names
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ax : matplotlib axes object, default None
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samples : Number of points to plot in each curve
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color : list or tuple, optional
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Colors to use for the different classes.
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colormap : str or matplotlib colormap object, default None
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Colormap to select colors from. If string, load colormap with that name
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from matplotlib.
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**kwargs
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Options to pass to matplotlib plotting method.
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Returns
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-------
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class:`matplotlip.axis.Axes`
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Examples
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--------
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.. plot::
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:context: close-figs
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>>> df = pd.read_csv(
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... 'https://raw.github.com/pandas-dev/'
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... 'pandas/main/pandas/tests/io/data/csv/iris.csv'
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... )
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>>> pd.plotting.andrews_curves(df, 'Name')
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<AxesSubplot: title={'center': 'width'}>
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"""
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plot_backend = _get_plot_backend("matplotlib")
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return plot_backend.andrews_curves(
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frame=frame,
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class_column=class_column,
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ax=ax,
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samples=samples,
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color=color,
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colormap=colormap,
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**kwargs,
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)
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def bootstrap_plot(
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series: Series,
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fig: Figure | None = None,
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size: int = 50,
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samples: int = 500,
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**kwds,
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) -> Figure:
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"""
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Bootstrap plot on mean, median and mid-range statistics.
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The bootstrap plot is used to estimate the uncertainty of a statistic
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by relaying on random sampling with replacement [1]_. This function will
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generate bootstrapping plots for mean, median and mid-range statistics
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for the given number of samples of the given size.
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.. [1] "Bootstrapping (statistics)" in \
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https://en.wikipedia.org/wiki/Bootstrapping_%28statistics%29
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Parameters
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----------
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series : pandas.Series
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Series from where to get the samplings for the bootstrapping.
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fig : matplotlib.figure.Figure, default None
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If given, it will use the `fig` reference for plotting instead of
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creating a new one with default parameters.
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size : int, default 50
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Number of data points to consider during each sampling. It must be
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less than or equal to the length of the `series`.
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samples : int, default 500
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Number of times the bootstrap procedure is performed.
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**kwds
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Options to pass to matplotlib plotting method.
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Returns
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-------
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matplotlib.figure.Figure
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Matplotlib figure.
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See Also
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--------
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DataFrame.plot : Basic plotting for DataFrame objects.
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Series.plot : Basic plotting for Series objects.
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Examples
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--------
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This example draws a basic bootstrap plot for a Series.
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.. plot::
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:context: close-figs
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>>> s = pd.Series(np.random.uniform(size=100))
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>>> pd.plotting.bootstrap_plot(s)
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<Figure size 640x480 with 6 Axes>
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"""
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plot_backend = _get_plot_backend("matplotlib")
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return plot_backend.bootstrap_plot(
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series=series, fig=fig, size=size, samples=samples, **kwds
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)
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def parallel_coordinates(
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frame: DataFrame,
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class_column: str,
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cols: list[str] | None = None,
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ax: Axes | None = None,
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color: list[str] | tuple[str, ...] | None = None,
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use_columns: bool = False,
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xticks: list | tuple | None = None,
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colormap=None,
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axvlines: bool = True,
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axvlines_kwds=None,
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sort_labels: bool = False,
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**kwargs,
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) -> Axes:
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"""
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Parallel coordinates plotting.
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Parameters
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----------
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frame : DataFrame
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class_column : str
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Column name containing class names.
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cols : list, optional
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A list of column names to use.
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ax : matplotlib.axis, optional
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Matplotlib axis object.
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color : list or tuple, optional
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Colors to use for the different classes.
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use_columns : bool, optional
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If true, columns will be used as xticks.
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xticks : list or tuple, optional
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A list of values to use for xticks.
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colormap : str or matplotlib colormap, default None
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Colormap to use for line colors.
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axvlines : bool, optional
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If true, vertical lines will be added at each xtick.
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axvlines_kwds : keywords, optional
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Options to be passed to axvline method for vertical lines.
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sort_labels : bool, default False
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Sort class_column labels, useful when assigning colors.
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**kwargs
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Options to pass to matplotlib plotting method.
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Returns
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-------
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class:`matplotlib.axis.Axes`
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Examples
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--------
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.. plot::
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:context: close-figs
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>>> df = pd.read_csv(
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... 'https://raw.github.com/pandas-dev/'
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... 'pandas/main/pandas/tests/io/data/csv/iris.csv'
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... )
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>>> pd.plotting.parallel_coordinates(
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... df, 'Name', color=('#556270', '#4ECDC4', '#C7F464')
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... )
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<AxesSubplot: xlabel='y(t)', ylabel='y(t + 1)'>
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"""
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plot_backend = _get_plot_backend("matplotlib")
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return plot_backend.parallel_coordinates(
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frame=frame,
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class_column=class_column,
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cols=cols,
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ax=ax,
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color=color,
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use_columns=use_columns,
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xticks=xticks,
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colormap=colormap,
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axvlines=axvlines,
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axvlines_kwds=axvlines_kwds,
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sort_labels=sort_labels,
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**kwargs,
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)
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def lag_plot(series: Series, lag: int = 1, ax: Axes | None = None, **kwds) -> Axes:
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"""
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Lag plot for time series.
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Parameters
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----------
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series : Time series
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lag : lag of the scatter plot, default 1
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ax : Matplotlib axis object, optional
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**kwds
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Matplotlib scatter method keyword arguments.
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Returns
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-------
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class:`matplotlib.axis.Axes`
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Examples
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--------
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Lag plots are most commonly used to look for patterns in time series data.
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Given the following time series
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.. plot::
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:context: close-figs
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>>> np.random.seed(5)
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>>> x = np.cumsum(np.random.normal(loc=1, scale=5, size=50))
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>>> s = pd.Series(x)
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>>> s.plot()
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<AxesSubplot: xlabel='Midrange'>
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A lag plot with ``lag=1`` returns
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.. plot::
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:context: close-figs
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>>> pd.plotting.lag_plot(s, lag=1)
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<AxesSubplot: xlabel='y(t)', ylabel='y(t + 1)'>
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"""
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plot_backend = _get_plot_backend("matplotlib")
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return plot_backend.lag_plot(series=series, lag=lag, ax=ax, **kwds)
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def autocorrelation_plot(series: Series, ax: Axes | None = None, **kwargs) -> Axes:
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"""
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Autocorrelation plot for time series.
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Parameters
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----------
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series : Time series
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ax : Matplotlib axis object, optional
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**kwargs
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Options to pass to matplotlib plotting method.
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Returns
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-------
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class:`matplotlib.axis.Axes`
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Examples
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--------
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The horizontal lines in the plot correspond to 95% and 99% confidence bands.
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The dashed line is 99% confidence band.
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.. plot::
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:context: close-figs
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>>> spacing = np.linspace(-9 * np.pi, 9 * np.pi, num=1000)
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>>> s = pd.Series(0.7 * np.random.rand(1000) + 0.3 * np.sin(spacing))
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>>> pd.plotting.autocorrelation_plot(s)
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<AxesSubplot: title={'center': 'width'}, xlabel='Lag', ylabel='Autocorrelation'>
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"""
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plot_backend = _get_plot_backend("matplotlib")
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return plot_backend.autocorrelation_plot(series=series, ax=ax, **kwargs)
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class _Options(dict):
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"""
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Stores pandas plotting options.
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Allows for parameter aliasing so you can just use parameter names that are
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the same as the plot function parameters, but is stored in a canonical
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format that makes it easy to breakdown into groups later.
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"""
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# alias so the names are same as plotting method parameter names
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_ALIASES = {"x_compat": "xaxis.compat"}
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_DEFAULT_KEYS = ["xaxis.compat"]
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def __init__(self, deprecated: bool = False) -> None:
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self._deprecated = deprecated
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super().__setitem__("xaxis.compat", False)
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def __getitem__(self, key):
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key = self._get_canonical_key(key)
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if key not in self:
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raise ValueError(f"{key} is not a valid pandas plotting option")
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return super().__getitem__(key)
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def __setitem__(self, key, value) -> None:
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key = self._get_canonical_key(key)
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super().__setitem__(key, value)
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def __delitem__(self, key) -> None:
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key = self._get_canonical_key(key)
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if key in self._DEFAULT_KEYS:
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raise ValueError(f"Cannot remove default parameter {key}")
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super().__delitem__(key)
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def __contains__(self, key) -> bool:
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key = self._get_canonical_key(key)
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return super().__contains__(key)
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def reset(self) -> None:
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"""
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Reset the option store to its initial state
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Returns
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-------
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None
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"""
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# error: Cannot access "__init__" directly
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self.__init__() # type: ignore[misc]
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def _get_canonical_key(self, key):
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return self._ALIASES.get(key, key)
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@contextmanager
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def use(self, key, value) -> Iterator[_Options]:
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"""
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Temporarily set a parameter value using the with statement.
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Aliasing allowed.
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"""
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old_value = self[key]
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try:
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self[key] = value
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yield self
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finally:
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self[key] = old_value
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plot_params = _Options()
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