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1from __future__ import annotations
3from contextlib import contextmanager
4from typing import (
5 TYPE_CHECKING,
6 Any,
7)
9from pandas.util._decorators import set_module
11from pandas.plotting._core import _get_plot_backend
13if TYPE_CHECKING:
14 from collections.abc import (
15 Generator,
16 Mapping,
17 )
19 from matplotlib.axes import Axes
20 from matplotlib.colors import Colormap
21 from matplotlib.figure import Figure
22 from matplotlib.table import Table
23 import numpy as np
25 from pandas import (
26 DataFrame,
27 Series,
28 )
31@set_module("pandas.plotting")
32def table(ax: Axes, data: DataFrame | Series, **kwargs) -> Table:
33 """
34 Helper function to convert DataFrame and Series to matplotlib.table.
36 This method provides an easy way to visualize tabular data within a Matplotlib
37 figure. It automatically extracts index and column labels from the DataFrame
38 or Series, unless explicitly specified. This function is particularly useful
39 when displaying summary tables alongside other plots or when creating static
40 reports. It utilizes the `matplotlib.pyplot.table` backend and allows
41 customization through various styling options available in Matplotlib.
43 Parameters
44 ----------
45 ax : Matplotlib axes object
46 The axes on which to draw the table.
47 data : DataFrame or Series
48 Data for table contents.
49 **kwargs
50 Keyword arguments to be passed to matplotlib.table.table.
51 If `rowLabels` or `colLabels` is not specified, data index or column
52 names will be used.
54 Returns
55 -------
56 matplotlib table object
57 The created table as a matplotlib Table object.
59 See Also
60 --------
61 DataFrame.plot : Make plots of DataFrame using matplotlib.
62 matplotlib.pyplot.table : Create a table from data in a Matplotlib plot.
64 Examples
65 --------
67 .. plot::
68 :context: close-figs
70 >>> import matplotlib.pyplot as plt
71 >>> df = pd.DataFrame({"A": [1, 2], "B": [3, 4]})
72 >>> fig, ax = plt.subplots()
73 >>> ax.axis("off")
74 (np.float64(0.0), np.float64(1.0), np.float64(0.0), np.float64(1.0))
75 >>> table = pd.plotting.table(
76 ... ax, df, loc="center", cellLoc="center", colWidths=[0.2, 0.2]
77 ... )
78 """
79 plot_backend = _get_plot_backend("matplotlib")
80 return plot_backend.table(
81 ax=ax, data=data, rowLabels=None, colLabels=None, **kwargs
82 )
85@set_module("pandas.plotting")
86def register() -> None:
87 """
88 Register pandas formatters and converters with matplotlib.
90 This function modifies the global ``matplotlib.units.registry``
91 dictionary. pandas adds custom converters for
93 * pd.Timestamp
94 * pd.Period
95 * np.datetime64
96 * datetime.datetime
97 * datetime.date
98 * datetime.time
100 See Also
101 --------
102 deregister_matplotlib_converters : Remove pandas formatters and converters.
104 Examples
105 --------
106 .. plot::
107 :context: close-figs
109 The following line is done automatically by pandas so
110 the plot can be rendered:
112 >>> pd.plotting.register_matplotlib_converters()
114 >>> df = pd.DataFrame(
115 ... {"ts": pd.period_range("2020", periods=2, freq="M"), "y": [1, 2]}
116 ... )
117 >>> plot = df.plot.line(x="ts", y="y")
119 Unsetting the register manually an error will be raised:
121 >>> pd.set_option(
122 ... "plotting.matplotlib.register_converters", False
123 ... ) # doctest: +SKIP
124 >>> df.plot.line(x="ts", y="y") # doctest: +SKIP
125 Traceback (most recent call last):
126 TypeError: float() argument must be a string or a real number, not 'Period'
127 """
128 plot_backend = _get_plot_backend("matplotlib")
129 plot_backend.register()
132@set_module("pandas.plotting")
133def deregister() -> None:
134 """
135 Remove pandas formatters and converters.
137 Removes the custom converters added by :func:`register`. This
138 attempts to set the state of the registry back to the state before
139 pandas registered its own units. Converters for pandas' own types like
140 Timestamp and Period are removed completely. Converters for types
141 pandas overwrites, like ``datetime.datetime``, are restored to their
142 original value.
144 See Also
145 --------
146 register_matplotlib_converters : Register pandas formatters and converters
147 with matplotlib.
149 Examples
150 --------
151 .. plot::
152 :context: close-figs
154 The following line is done automatically by pandas so
155 the plot can be rendered:
157 >>> pd.plotting.register_matplotlib_converters()
159 >>> df = pd.DataFrame(
160 ... {"ts": pd.period_range("2020", periods=2, freq="M"), "y": [1, 2]}
161 ... )
162 >>> plot = df.plot.line(x="ts", y="y")
164 Unsetting the register manually an error will be raised:
166 >>> pd.set_option(
167 ... "plotting.matplotlib.register_converters", False
168 ... ) # doctest: +SKIP
169 >>> df.plot.line(x="ts", y="y") # doctest: +SKIP
170 Traceback (most recent call last):
171 TypeError: float() argument must be a string or a real number, not 'Period'
172 """
173 plot_backend = _get_plot_backend("matplotlib")
174 plot_backend.deregister()
177@set_module("pandas.plotting")
178def scatter_matrix(
179 frame: DataFrame,
180 alpha: float = 0.5,
181 figsize: tuple[float, float] | None = None,
182 ax: Axes | None = None,
183 grid: bool = False,
184 diagonal: str = "hist",
185 marker: str = ".",
186 density_kwds: Mapping[str, Any] | None = None,
187 hist_kwds: Mapping[str, Any] | None = None,
188 range_padding: float = 0.05,
189 **kwargs,
190) -> np.ndarray:
191 """
192 Draw a matrix of scatter plots.
194 Each pair of numeric columns in the DataFrame is plotted against each other,
195 resulting in a matrix of scatter plots. The diagonal plots can display either
196 histograms or Kernel Density Estimation (KDE) plots for each variable.
198 Parameters
199 ----------
200 frame : DataFrame
201 The data to be plotted.
202 alpha : float, optional
203 Amount of transparency applied.
204 figsize : (float,float), optional
205 A tuple (width, height) in inches.
206 ax : Matplotlib axis object, optional
207 An existing Matplotlib axis object for the plots. If None, a new axis is
208 created.
209 grid : bool, optional
210 Setting this to True will show the grid.
211 diagonal : {'hist', 'kde'}
212 Pick between 'kde' and 'hist' for either Kernel Density Estimation or
213 Histogram plot in the diagonal.
214 marker : str, optional
215 Matplotlib marker type, default '.'.
216 density_kwds : keywords
217 Keyword arguments to be passed to kernel density estimate plot.
218 hist_kwds : keywords
219 Keyword arguments to be passed to hist function.
220 range_padding : float, default 0.05
221 Relative extension of axis range in x and y with respect to
222 (x_max - x_min) or (y_max - y_min).
223 **kwargs
224 Keyword arguments to be passed to scatter function.
226 Returns
227 -------
228 numpy.ndarray
229 A matrix of scatter plots.
231 See Also
232 --------
233 plotting.parallel_coordinates : Plots parallel coordinates for multivariate data.
234 plotting.andrews_curves : Generates Andrews curves for visualizing clusters of
235 multivariate data.
236 plotting.radviz : Creates a RadViz visualization.
237 plotting.bootstrap_plot : Visualizes uncertainty in data via bootstrap sampling.
239 Examples
240 --------
242 .. plot::
243 :context: close-figs
245 >>> data = np.random.default_rng(42).standard_normal((1000, 4))
246 >>> df = pd.DataFrame(data, columns=["A", "B", "C", "D"])
247 >>> pd.plotting.scatter_matrix(df, alpha=0.2)
248 array([[<Axes: xlabel='A', ylabel='A'>, <Axes: xlabel='B', ylabel='A'>,
249 <Axes: xlabel='C', ylabel='A'>, <Axes: xlabel='D', ylabel='A'>],
250 [<Axes: xlabel='A', ylabel='B'>, <Axes: xlabel='B', ylabel='B'>,
251 <Axes: xlabel='C', ylabel='B'>, <Axes: xlabel='D', ylabel='B'>],
252 [<Axes: xlabel='A', ylabel='C'>, <Axes: xlabel='B', ylabel='C'>,
253 <Axes: xlabel='C', ylabel='C'>, <Axes: xlabel='D', ylabel='C'>],
254 [<Axes: xlabel='A', ylabel='D'>, <Axes: xlabel='B', ylabel='D'>,
255 <Axes: xlabel='C', ylabel='D'>, <Axes: xlabel='D', ylabel='D'>]],
256 dtype=object)
257 """
258 plot_backend = _get_plot_backend("matplotlib")
259 return plot_backend.scatter_matrix(
260 frame=frame,
261 alpha=alpha,
262 figsize=figsize,
263 ax=ax,
264 grid=grid,
265 diagonal=diagonal,
266 marker=marker,
267 density_kwds=density_kwds,
268 hist_kwds=hist_kwds,
269 range_padding=range_padding,
270 **kwargs,
271 )
274@set_module("pandas.plotting")
275def radviz(
276 frame: DataFrame,
277 class_column: str,
278 ax: Axes | None = None,
279 color: list[str] | tuple[str, ...] | None = None,
280 colormap: Colormap | str | None = None,
281 **kwds,
282) -> Axes:
283 """
284 Plot a multidimensional dataset in 2D.
286 Each Series in the DataFrame is represented as an evenly distributed
287 slice on a circle. Each data point is rendered in the circle according to
288 the value on each Series. Highly correlated `Series` in the `DataFrame`
289 are placed closer on the unit circle.
291 RadViz allows you to project an N-dimensional data set into a 2D space
292 where the influence of each dimension can be interpreted as a balance
293 between the influence of all dimensions.
295 More info available at the `original article
296 <https://doi.org/10.1145/331770.331775>`_
297 describing RadViz.
299 Parameters
300 ----------
301 frame : `DataFrame`
302 Object holding the data.
303 class_column : str
304 Column name containing the name of the data point category.
305 ax : :class:`matplotlib.axes.Axes`, optional
306 A plot instance to which to add the information.
307 color : list[str] or tuple[str], optional
308 Assign a color to each category. Example: ['blue', 'green'].
309 colormap : str or :class:`matplotlib.colors.Colormap`, default None
310 Colormap to select colors from. If string, load colormap with that
311 name from matplotlib.
312 **kwds
313 Options to pass to matplotlib scatter plotting method.
315 Returns
316 -------
317 :class:`matplotlib.axes.Axes`
318 The Axes object from Matplotlib.
320 See Also
321 --------
322 plotting.andrews_curves : Plot clustering visualization.
324 Examples
325 --------
327 .. plot::
328 :context: close-figs
330 >>> df = pd.DataFrame(
331 ... {
332 ... "SepalLength": [6.5, 7.7, 5.1, 5.8, 7.6, 5.0, 5.4, 4.6, 6.7, 4.6],
333 ... "SepalWidth": [3.0, 3.8, 3.8, 2.7, 3.0, 2.3, 3.0, 3.2, 3.3, 3.6],
334 ... "PetalLength": [5.5, 6.7, 1.9, 5.1, 6.6, 3.3, 4.5, 1.4, 5.7, 1.0],
335 ... "PetalWidth": [1.8, 2.2, 0.4, 1.9, 2.1, 1.0, 1.5, 0.2, 2.1, 0.2],
336 ... "Category": [
337 ... "virginica",
338 ... "virginica",
339 ... "setosa",
340 ... "virginica",
341 ... "virginica",
342 ... "versicolor",
343 ... "versicolor",
344 ... "setosa",
345 ... "virginica",
346 ... "setosa",
347 ... ],
348 ... }
349 ... )
350 >>> pd.plotting.radviz(df, "Category") # doctest: +SKIP
351 """
352 plot_backend = _get_plot_backend("matplotlib")
353 return plot_backend.radviz(
354 frame=frame,
355 class_column=class_column,
356 ax=ax,
357 color=color,
358 colormap=colormap,
359 **kwds,
360 )
363@set_module("pandas.plotting")
364def andrews_curves(
365 frame: DataFrame,
366 class_column: str,
367 ax: Axes | None = None,
368 samples: int = 200,
369 color: list[str] | tuple[str, ...] | None = None,
370 colormap: Colormap | str | None = None,
371 **kwargs,
372) -> Axes:
373 """
374 Generate a matplotlib plot for visualizing clusters of multivariate data.
376 Andrews curves have the functional form:
378 .. math::
379 f(t) = \\frac{x_1}{\\sqrt{2}} + x_2 \\sin(t) + x_3 \\cos(t) +
380 x_4 \\sin(2t) + x_5 \\cos(2t) + \\cdots
382 Where :math:`x` coefficients correspond to the values of each dimension
383 and :math:`t` is linearly spaced between :math:`-\\pi` and :math:`+\\pi`.
384 Each row of frame then corresponds to a single curve.
386 Parameters
387 ----------
388 frame : DataFrame
389 Data to be plotted, preferably normalized to (0.0, 1.0).
390 class_column : label
391 Name of the column containing class names.
392 ax : axes object, default None
393 Axes to use.
394 samples : int
395 Number of points to plot in each curve.
396 color : str, list[str] or tuple[str], optional
397 Colors to use for the different classes. Colors can be strings
398 or 3-element floating point RGB values.
399 colormap : str or matplotlib colormap object, default None
400 Colormap to select colors from. If a string, load colormap with that
401 name from matplotlib.
402 **kwargs
403 Options to pass to matplotlib plotting method.
405 Returns
406 -------
407 :class:`matplotlib.axes.Axes`
408 The matplotlib Axes object with the plot.
410 See Also
411 --------
412 plotting.parallel_coordinates : Plot parallel coordinates chart.
413 DataFrame.plot : Make plots of Series or DataFrame.
415 Examples
416 --------
418 .. plot::
419 :context: close-figs
421 >>> df = pd.DataFrame(
422 ... {
423 ... "SepalLength": [6.5, 7.7, 5.1, 5.8, 7.6, 5.0, 5.4, 4.6, 6.7, 4.6],
424 ... "SepalWidth": [3.0, 3.8, 3.8, 2.7, 3.0, 2.3, 3.0, 3.2, 3.3, 3.6],
425 ... "PetalLength": [5.5, 6.7, 1.9, 5.1, 6.6, 3.3, 4.5, 1.4, 5.7, 1.0],
426 ... "PetalWidth": [1.8, 2.2, 0.4, 1.9, 2.1, 1.0, 1.5, 0.2, 2.1, 0.2],
427 ... "Name": [
428 ... "virginica",
429 ... "virginica",
430 ... "setosa",
431 ... "virginica",
432 ... "virginica",
433 ... "versicolor",
434 ... "versicolor",
435 ... "setosa",
436 ... "virginica",
437 ... "setosa",
438 ... ],
439 ... }
440 ... )
441 >>> pd.plotting.andrews_curves(df, "Name") # doctest: +SKIP
442 """
443 plot_backend = _get_plot_backend("matplotlib")
444 return plot_backend.andrews_curves(
445 frame=frame,
446 class_column=class_column,
447 ax=ax,
448 samples=samples,
449 color=color,
450 colormap=colormap,
451 **kwargs,
452 )
455@set_module("pandas.plotting")
456def bootstrap_plot(
457 series: Series,
458 fig: Figure | None = None,
459 size: int = 50,
460 samples: int = 500,
461 **kwds,
462) -> Figure:
463 """
464 Bootstrap plot on mean, median and mid-range statistics.
466 The bootstrap plot is used to estimate the uncertainty of a statistic
467 by relying on random sampling with replacement [1]_. This function will
468 generate bootstrapping plots for mean, median and mid-range statistics
469 for the given number of samples of the given size.
471 .. [1] "Bootstrapping (statistics)" in \
472 https://en.wikipedia.org/wiki/Bootstrapping_%28statistics%29
474 Parameters
475 ----------
476 series : pandas.Series
477 Series from where to get the samplings for the bootstrapping.
478 fig : matplotlib.figure.Figure, default None
479 If given, it will use the `fig` reference for plotting instead of
480 creating a new one with default parameters.
481 size : int, default 50
482 Number of data points to consider during each sampling. It must be
483 less than or equal to the length of the `series`.
484 samples : int, default 500
485 Number of times the bootstrap procedure is performed.
486 **kwds
487 Options to pass to matplotlib plotting method.
489 Returns
490 -------
491 matplotlib.figure.Figure
492 Matplotlib figure.
494 See Also
495 --------
496 DataFrame.plot : Basic plotting for DataFrame objects.
497 Series.plot : Basic plotting for Series objects.
499 Examples
500 --------
501 This example draws a basic bootstrap plot for a Series.
503 .. plot::
504 :context: close-figs
506 >>> rng = np.random.default_rng(42)
507 >>> s = pd.Series(rng.uniform(size=100))
508 >>> pd.plotting.bootstrap_plot(s) # doctest: +SKIP
509 <Figure size 640x480 with 6 Axes>
510 """
511 plot_backend = _get_plot_backend("matplotlib")
512 return plot_backend.bootstrap_plot(
513 series=series, fig=fig, size=size, samples=samples, **kwds
514 )
517@set_module("pandas.plotting")
518def parallel_coordinates(
519 frame: DataFrame,
520 class_column: str,
521 cols: list[str] | None = None,
522 ax: Axes | None = None,
523 color: list[str] | tuple[str, ...] | None = None,
524 use_columns: bool = False,
525 xticks: list[float] | tuple[float, ...] | None = None,
526 colormap: Colormap | str | None = None,
527 axvlines: bool = True,
528 axvlines_kwds: Mapping[str, Any] | None = None,
529 sort_labels: bool = False,
530 **kwargs,
531) -> Axes:
532 """
533 Parallel coordinates plotting.
535 This visualization technique represents each observation as a polyline
536 connecting values across vertical axes, one for each feature. Lines are
537 colored by class membership, making it useful for identifying clusters
538 and separability in multivariate data.
540 Parameters
541 ----------
542 frame : DataFrame
543 The DataFrame to be plotted.
544 class_column : str
545 Column name containing class names.
546 cols : list, optional
547 A list of column names to use.
548 ax : matplotlib.axis, optional
549 Matplotlib axis object.
550 color : list or tuple, optional
551 Colors to use for the different classes.
552 use_columns : bool, optional
553 If true, columns will be used as xticks.
554 xticks : list or tuple, optional
555 A list of values to use for xticks.
556 colormap : str or matplotlib colormap, default None
557 Colormap to use for line colors.
558 axvlines : bool, optional
559 If true, vertical lines will be added at each xtick.
560 axvlines_kwds : keywords, optional
561 Options to be passed to axvline method for vertical lines.
562 sort_labels : bool, default False
563 Sort class_column labels, useful when assigning colors.
564 **kwargs
565 Options to pass to matplotlib plotting method.
567 Returns
568 -------
569 matplotlib.axes.Axes
570 The matplotlib axes containing the parallel coordinates plot.
572 See Also
573 --------
574 plotting.andrews_curves : Generate a matplotlib plot for visualizing clusters
575 of multivariate data.
576 plotting.radviz : Plot a multidimensional dataset in 2D.
578 Examples
579 --------
581 .. plot::
582 :context: close-figs
584 >>> df = pd.DataFrame(
585 ... {
586 ... "SepalLength": [6.5, 7.7, 5.1, 5.8, 7.6, 5.0, 5.4, 4.6, 6.7, 4.6],
587 ... "SepalWidth": [3.0, 3.8, 3.8, 2.7, 3.0, 2.3, 3.0, 3.2, 3.3, 3.6],
588 ... "PetalLength": [5.5, 6.7, 1.9, 5.1, 6.6, 3.3, 4.5, 1.4, 5.7, 1.0],
589 ... "PetalWidth": [1.8, 2.2, 0.4, 1.9, 2.1, 1.0, 1.5, 0.2, 2.1, 0.2],
590 ... "Name": [
591 ... "virginica",
592 ... "virginica",
593 ... "setosa",
594 ... "virginica",
595 ... "virginica",
596 ... "versicolor",
597 ... "versicolor",
598 ... "setosa",
599 ... "virginica",
600 ... "setosa",
601 ... ],
602 ... }
603 ... )
604 >>> pd.plotting.parallel_coordinates(
605 ... df, "Name", color=("#556270", "#4ECDC4", "#C7F464")
606 ... ) # doctest: +SKIP
607 """
608 plot_backend = _get_plot_backend("matplotlib")
609 return plot_backend.parallel_coordinates(
610 frame=frame,
611 class_column=class_column,
612 cols=cols,
613 ax=ax,
614 color=color,
615 use_columns=use_columns,
616 xticks=xticks,
617 colormap=colormap,
618 axvlines=axvlines,
619 axvlines_kwds=axvlines_kwds,
620 sort_labels=sort_labels,
621 **kwargs,
622 )
625@set_module("pandas.plotting")
626def lag_plot(series: Series, lag: int = 1, ax: Axes | None = None, **kwds) -> Axes:
627 """
628 Lag plot for time series.
630 A lag plot is a scatter plot of a time series against a lag of itself. It helps
631 in visualizing the temporal dependence between observations by plotting the values
632 at time `t` on the x-axis and the values at time `t + lag` on the y-axis.
634 Parameters
635 ----------
636 series : Series
637 The time series to visualize.
638 lag : int, default 1
639 Lag length of the scatter plot.
640 ax : Matplotlib axis object, optional
641 The matplotlib axis object to use.
642 **kwds
643 Matplotlib scatter method keyword arguments.
645 Returns
646 -------
647 matplotlib.axes.Axes
648 The matplotlib Axes object containing the lag plot.
650 See Also
651 --------
652 plotting.autocorrelation_plot : Autocorrelation plot for time series.
653 matplotlib.pyplot.scatter : A scatter plot of y vs. x with varying marker size
654 and/or color in Matplotlib.
656 Examples
657 --------
658 Lag plots are most commonly used to look for patterns in time series data.
660 Given the following time series
662 .. plot::
663 :context: close-figs
665 >>> rng = np.random.default_rng(42)
666 >>> x = np.cumsum(rng.normal(loc=1, scale=5, size=50))
667 >>> s = pd.Series(x)
668 >>> s.plot() # doctest: +SKIP
670 A lag plot with ``lag=1`` returns
672 .. plot::
673 :context: close-figs
675 >>> _ = pd.plotting.lag_plot(s, lag=1)
676 """
677 plot_backend = _get_plot_backend("matplotlib")
678 return plot_backend.lag_plot(series=series, lag=lag, ax=ax, **kwds)
681@set_module("pandas.plotting")
682def autocorrelation_plot(series: Series, ax: Axes | None = None, **kwargs) -> Axes:
683 """
684 Autocorrelation plot for time series.
686 This method generates an autocorrelation plot for a given time series,
687 which helps to identify any periodic structure or correlation within the
688 data across various lags. It shows the correlation of a time series with a
689 delayed copy of itself as a function of delay. Autocorrelation plots are useful for
690 checking randomness in a data set. If the data are random, the autocorrelations
691 should be near zero for any and all time-lag separations. If the data are not
692 random, then one or more of the autocorrelations will be significantly
693 non-zero.
695 Parameters
696 ----------
697 series : Series
698 The time series to visualize.
699 ax : Matplotlib axis object, optional
700 The matplotlib axis object to use.
701 **kwargs
702 Options to pass to matplotlib plotting method.
704 Returns
705 -------
706 matplotlib.axes.Axes
707 The matplotlib axes containing the autocorrelation plot.
709 See Also
710 --------
711 Series.autocorr : Compute the lag-N autocorrelation for a Series.
712 plotting.lag_plot : Lag plot for time series.
714 Examples
715 --------
716 The horizontal lines in the plot correspond to 95% and 99% confidence bands.
718 The dashed line is 99% confidence band.
720 .. plot::
721 :context: close-figs
723 >>> spacing = np.linspace(-9 * np.pi, 9 * np.pi, num=1000)
724 >>> rng = np.random.default_rng(42)
725 >>> s = pd.Series(0.7 * rng.random(1000) + 0.3 * np.sin(spacing))
726 >>> pd.plotting.autocorrelation_plot(s) # doctest: +SKIP
727 """
728 plot_backend = _get_plot_backend("matplotlib")
729 return plot_backend.autocorrelation_plot(series=series, ax=ax, **kwargs)
732class _Options(dict[str, Any]):
733 """
734 Stores pandas plotting options.
736 Allows for parameter aliasing so you can just use parameter names that are
737 the same as the plot function parameters, but is stored in a canonical
738 format that makes it easy to breakdown into groups later.
740 See Also
741 --------
742 plotting.register_matplotlib_converters : Register pandas formatters and
743 converters with matplotlib.
744 plotting.bootstrap_plot : Bootstrap plot on mean, median and mid-range statistics.
745 plotting.autocorrelation_plot : Autocorrelation plot for time series.
746 plotting.lag_plot : Lag plot for time series.
748 Examples
749 --------
751 .. plot::
752 :context: close-figs
754 >>> rng = np.random.default_rng(42)
755 >>> df = pd.DataFrame(
756 ... {"A": rng.standard_normal(10), "B": rng.standard_normal(10)},
757 ... index=pd.date_range("1/1/2000", freq="4MS", periods=10),
758 ... )
759 >>> with pd.plotting.plot_params.use("x_compat", True):
760 ... _ = df["A"].plot(color="r")
761 ... _ = df["B"].plot(color="g")
762 """
764 # alias so the names are same as plotting method parameter names
765 _ALIASES = {"x_compat": "xaxis.compat"}
766 _DEFAULT_KEYS = ["xaxis.compat"]
768 def __init__(self) -> None:
769 super().__setitem__("xaxis.compat", False)
771 def __getitem__(self, key):
772 key = self._get_canonical_key(key)
773 if key not in self:
774 raise ValueError(f"{key} is not a valid pandas plotting option")
775 return super().__getitem__(key)
777 def __setitem__(self, key, value) -> None:
778 key = self._get_canonical_key(key)
779 super().__setitem__(key, value)
781 def __delitem__(self, key) -> None:
782 key = self._get_canonical_key(key)
783 if key in self._DEFAULT_KEYS:
784 raise ValueError(f"Cannot remove default parameter {key}")
785 super().__delitem__(key)
787 def __contains__(self, key) -> bool:
788 key = self._get_canonical_key(key)
789 return super().__contains__(key)
791 def reset(self) -> None:
792 """
793 Reset the option store to its initial state
795 Returns
796 -------
797 None
798 """
799 # error: Cannot access "__init__" directly
800 self.__init__() # type: ignore[misc]
802 def _get_canonical_key(self, key: str) -> str:
803 return self._ALIASES.get(key, key)
805 @contextmanager
806 def use(self, key, value) -> Generator[_Options]:
807 """
808 Temporarily set a parameter value using the with statement.
809 Aliasing allowed.
810 """
811 old_value = self[key]
812 try:
813 self[key] = value
814 yield self
815 finally:
816 self[key] = old_value
819plot_params = _Options()
820plot_params.__module__ = "pandas.plotting"