619 lines
18 KiB
Python
619 lines
18 KiB
Python
|
from __future__ import annotations
|
||
|
|
|
||
|
|
from contextlib import contextmanager
|
||
|
|
from typing import (
|
||
|
|
TYPE_CHECKING,
|
||
|
|
Generator,
|
||
|
|
)
|
||
|
|
|
||
|
|
from pandas.plotting._core import _get_plot_backend
|
||
|
|
|
||
|
|
if TYPE_CHECKING:
|
||
|
|
from matplotlib.axes import Axes
|
||
|
|
from matplotlib.figure import Figure
|
||
|
|
import numpy as np
|
||
|
|
|
||
|
|
from pandas import (
|
||
|
|
DataFrame,
|
||
|
|
Series,
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
def table(ax, data, **kwargs):
|
||
|
|
"""
|
||
|
|
Helper function to convert DataFrame and Series to matplotlib.table.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
ax : Matplotlib axes object
|
||
|
|
data : DataFrame or Series
|
||
|
|
Data for table contents.
|
||
|
|
**kwargs
|
||
|
|
Keyword arguments to be passed to matplotlib.table.table.
|
||
|
|
If `rowLabels` or `colLabels` is not specified, data index or column
|
||
|
|
name will be used.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
matplotlib table object
|
||
|
|
"""
|
||
|
|
plot_backend = _get_plot_backend("matplotlib")
|
||
|
|
return plot_backend.table(
|
||
|
|
ax=ax, data=data, rowLabels=None, colLabels=None, **kwargs
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
def register() -> None:
|
||
|
|
"""
|
||
|
|
Register pandas formatters and converters with matplotlib.
|
||
|
|
|
||
|
|
This function modifies the global ``matplotlib.units.registry``
|
||
|
|
dictionary. pandas adds custom converters for
|
||
|
|
|
||
|
|
* pd.Timestamp
|
||
|
|
* pd.Period
|
||
|
|
* np.datetime64
|
||
|
|
* datetime.datetime
|
||
|
|
* datetime.date
|
||
|
|
* datetime.time
|
||
|
|
|
||
|
|
See Also
|
||
|
|
--------
|
||
|
|
deregister_matplotlib_converters : Remove pandas formatters and converters.
|
||
|
|
"""
|
||
|
|
plot_backend = _get_plot_backend("matplotlib")
|
||
|
|
plot_backend.register()
|
||
|
|
|
||
|
|
|
||
|
|
def deregister() -> None:
|
||
|
|
"""
|
||
|
|
Remove pandas formatters and converters.
|
||
|
|
|
||
|
|
Removes the custom converters added by :func:`register`. This
|
||
|
|
attempts to set the state of the registry back to the state before
|
||
|
|
pandas registered its own units. Converters for pandas' own types like
|
||
|
|
Timestamp and Period are removed completely. Converters for types
|
||
|
|
pandas overwrites, like ``datetime.datetime``, are restored to their
|
||
|
|
original value.
|
||
|
|
|
||
|
|
See Also
|
||
|
|
--------
|
||
|
|
register_matplotlib_converters : Register pandas formatters and converters
|
||
|
|
with matplotlib.
|
||
|
|
"""
|
||
|
|
plot_backend = _get_plot_backend("matplotlib")
|
||
|
|
plot_backend.deregister()
|
||
|
|
|
||
|
|
|
||
|
|
def scatter_matrix(
|
||
|
|
frame: DataFrame,
|
||
|
|
alpha: float = 0.5,
|
||
|
|
figsize: tuple[float, float] | None = None,
|
||
|
|
ax: Axes | None = None,
|
||
|
|
grid: bool = False,
|
||
|
|
diagonal: str = "hist",
|
||
|
|
marker: str = ".",
|
||
|
|
density_kwds=None,
|
||
|
|
hist_kwds=None,
|
||
|
|
range_padding: float = 0.05,
|
||
|
|
**kwargs,
|
||
|
|
) -> np.ndarray:
|
||
|
|
"""
|
||
|
|
Draw a matrix of scatter plots.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
frame : DataFrame
|
||
|
|
alpha : float, optional
|
||
|
|
Amount of transparency applied.
|
||
|
|
figsize : (float,float), optional
|
||
|
|
A tuple (width, height) in inches.
|
||
|
|
ax : Matplotlib axis object, optional
|
||
|
|
grid : bool, optional
|
||
|
|
Setting this to True will show the grid.
|
||
|
|
diagonal : {'hist', 'kde'}
|
||
|
|
Pick between 'kde' and 'hist' for either Kernel Density Estimation or
|
||
|
|
Histogram plot in the diagonal.
|
||
|
|
marker : str, optional
|
||
|
|
Matplotlib marker type, default '.'.
|
||
|
|
density_kwds : keywords
|
||
|
|
Keyword arguments to be passed to kernel density estimate plot.
|
||
|
|
hist_kwds : keywords
|
||
|
|
Keyword arguments to be passed to hist function.
|
||
|
|
range_padding : float, default 0.05
|
||
|
|
Relative extension of axis range in x and y with respect to
|
||
|
|
(x_max - x_min) or (y_max - y_min).
|
||
|
|
**kwargs
|
||
|
|
Keyword arguments to be passed to scatter function.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
numpy.ndarray
|
||
|
|
A matrix of scatter plots.
|
||
|
|
|
||
|
|
Examples
|
||
|
|
--------
|
||
|
|
|
||
|
|
.. plot::
|
||
|
|
:context: close-figs
|
||
|
|
|
||
|
|
>>> df = pd.DataFrame(np.random.randn(1000, 4), columns=['A','B','C','D'])
|
||
|
|
>>> pd.plotting.scatter_matrix(df, alpha=0.2)
|
||
|
|
array([[<AxesSubplot: xlabel='A', ylabel='A'>,
|
||
|
|
<AxesSubplot: xlabel='B', ylabel='A'>,
|
||
|
|
<AxesSubplot: xlabel='C', ylabel='A'>,
|
||
|
|
<AxesSubplot: xlabel='D', ylabel='A'>],
|
||
|
|
[<AxesSubplot: xlabel='A', ylabel='B'>,
|
||
|
|
<AxesSubplot: xlabel='B', ylabel='B'>,
|
||
|
|
<AxesSubplot: xlabel='C', ylabel='B'>,
|
||
|
|
<AxesSubplot: xlabel='D', ylabel='B'>],
|
||
|
|
[<AxesSubplot: xlabel='A', ylabel='C'>,
|
||
|
|
<AxesSubplot: xlabel='B', ylabel='C'>,
|
||
|
|
<AxesSubplot: xlabel='C', ylabel='C'>,
|
||
|
|
<AxesSubplot: xlabel='D', ylabel='C'>],
|
||
|
|
[<AxesSubplot: xlabel='A', ylabel='D'>,
|
||
|
|
<AxesSubplot: xlabel='B', ylabel='D'>,
|
||
|
|
<AxesSubplot: xlabel='C', ylabel='D'>,
|
||
|
|
<AxesSubplot: xlabel='D', ylabel='D'>]], dtype=object)
|
||
|
|
"""
|
||
|
|
plot_backend = _get_plot_backend("matplotlib")
|
||
|
|
return plot_backend.scatter_matrix(
|
||
|
|
frame=frame,
|
||
|
|
alpha=alpha,
|
||
|
|
figsize=figsize,
|
||
|
|
ax=ax,
|
||
|
|
grid=grid,
|
||
|
|
diagonal=diagonal,
|
||
|
|
marker=marker,
|
||
|
|
density_kwds=density_kwds,
|
||
|
|
hist_kwds=hist_kwds,
|
||
|
|
range_padding=range_padding,
|
||
|
|
**kwargs,
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
def radviz(
|
||
|
|
frame: DataFrame,
|
||
|
|
class_column: str,
|
||
|
|
ax: Axes | None = None,
|
||
|
|
color: list[str] | tuple[str, ...] | None = None,
|
||
|
|
colormap=None,
|
||
|
|
**kwds,
|
||
|
|
) -> Axes:
|
||
|
|
"""
|
||
|
|
Plot a multidimensional dataset in 2D.
|
||
|
|
|
||
|
|
Each Series in the DataFrame is represented as a evenly distributed
|
||
|
|
slice on a circle. Each data point is rendered in the circle according to
|
||
|
|
the value on each Series. Highly correlated `Series` in the `DataFrame`
|
||
|
|
are placed closer on the unit circle.
|
||
|
|
|
||
|
|
RadViz allow to project a N-dimensional data set into a 2D space where the
|
||
|
|
influence of each dimension can be interpreted as a balance between the
|
||
|
|
influence of all dimensions.
|
||
|
|
|
||
|
|
More info available at the `original article
|
||
|
|
<https://doi.org/10.1145/331770.331775>`_
|
||
|
|
describing RadViz.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
frame : `DataFrame`
|
||
|
|
Object holding the data.
|
||
|
|
class_column : str
|
||
|
|
Column name containing the name of the data point category.
|
||
|
|
ax : :class:`matplotlib.axes.Axes`, optional
|
||
|
|
A plot instance to which to add the information.
|
||
|
|
color : list[str] or tuple[str], optional
|
||
|
|
Assign a color to each category. Example: ['blue', 'green'].
|
||
|
|
colormap : str or :class:`matplotlib.colors.Colormap`, default None
|
||
|
|
Colormap to select colors from. If string, load colormap with that
|
||
|
|
name from matplotlib.
|
||
|
|
**kwds
|
||
|
|
Options to pass to matplotlib scatter plotting method.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
:class:`matplotlib.axes.Axes`
|
||
|
|
|
||
|
|
See Also
|
||
|
|
--------
|
||
|
|
pandas.plotting.andrews_curves : Plot clustering visualization.
|
||
|
|
|
||
|
|
Examples
|
||
|
|
--------
|
||
|
|
|
||
|
|
.. plot::
|
||
|
|
:context: close-figs
|
||
|
|
|
||
|
|
>>> df = pd.DataFrame(
|
||
|
|
... {
|
||
|
|
... 'SepalLength': [6.5, 7.7, 5.1, 5.8, 7.6, 5.0, 5.4, 4.6, 6.7, 4.6],
|
||
|
|
... 'SepalWidth': [3.0, 3.8, 3.8, 2.7, 3.0, 2.3, 3.0, 3.2, 3.3, 3.6],
|
||
|
|
... 'PetalLength': [5.5, 6.7, 1.9, 5.1, 6.6, 3.3, 4.5, 1.4, 5.7, 1.0],
|
||
|
|
... 'PetalWidth': [1.8, 2.2, 0.4, 1.9, 2.1, 1.0, 1.5, 0.2, 2.1, 0.2],
|
||
|
|
... 'Category': [
|
||
|
|
... 'virginica',
|
||
|
|
... 'virginica',
|
||
|
|
... 'setosa',
|
||
|
|
... 'virginica',
|
||
|
|
... 'virginica',
|
||
|
|
... 'versicolor',
|
||
|
|
... 'versicolor',
|
||
|
|
... 'setosa',
|
||
|
|
... 'virginica',
|
||
|
|
... 'setosa'
|
||
|
|
... ]
|
||
|
|
... }
|
||
|
|
... )
|
||
|
|
>>> pd.plotting.radviz(df, 'Category')
|
||
|
|
<AxesSubplot: xlabel='y(t)', ylabel='y(t + 1)'>
|
||
|
|
"""
|
||
|
|
plot_backend = _get_plot_backend("matplotlib")
|
||
|
|
return plot_backend.radviz(
|
||
|
|
frame=frame,
|
||
|
|
class_column=class_column,
|
||
|
|
ax=ax,
|
||
|
|
color=color,
|
||
|
|
colormap=colormap,
|
||
|
|
**kwds,
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
def andrews_curves(
|
||
|
|
frame: DataFrame,
|
||
|
|
class_column: str,
|
||
|
|
ax: Axes | None = None,
|
||
|
|
samples: int = 200,
|
||
|
|
color: list[str] | tuple[str, ...] | None = None,
|
||
|
|
colormap=None,
|
||
|
|
**kwargs,
|
||
|
|
) -> Axes:
|
||
|
|
"""
|
||
|
|
Generate a matplotlib plot for visualising clusters of multivariate data.
|
||
|
|
|
||
|
|
Andrews curves have the functional form:
|
||
|
|
|
||
|
|
.. math::
|
||
|
|
f(t) = \\frac{x_1}{\\sqrt{2}} + x_2 \\sin(t) + x_3 \\cos(t) +
|
||
|
|
x_4 \\sin(2t) + x_5 \\cos(2t) + \\cdots
|
||
|
|
|
||
|
|
Where :math:`x` coefficients correspond to the values of each dimension
|
||
|
|
and :math:`t` is linearly spaced between :math:`-\\pi` and :math:`+\\pi`.
|
||
|
|
Each row of frame then corresponds to a single curve.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
frame : DataFrame
|
||
|
|
Data to be plotted, preferably normalized to (0.0, 1.0).
|
||
|
|
class_column : label
|
||
|
|
Name of the column containing class names.
|
||
|
|
ax : axes object, default None
|
||
|
|
Axes to use.
|
||
|
|
samples : int
|
||
|
|
Number of points to plot in each curve.
|
||
|
|
color : str, list[str] or tuple[str], optional
|
||
|
|
Colors to use for the different classes. Colors can be strings
|
||
|
|
or 3-element floating point RGB values.
|
||
|
|
colormap : str or matplotlib colormap object, default None
|
||
|
|
Colormap to select colors from. If a string, load colormap with that
|
||
|
|
name from matplotlib.
|
||
|
|
**kwargs
|
||
|
|
Options to pass to matplotlib plotting method.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
:class:`matplotlib.axes.Axes`
|
||
|
|
|
||
|
|
Examples
|
||
|
|
--------
|
||
|
|
|
||
|
|
.. plot::
|
||
|
|
:context: close-figs
|
||
|
|
|
||
|
|
>>> df = pd.read_csv(
|
||
|
|
... 'https://raw.githubusercontent.com/pandas-dev/'
|
||
|
|
... 'pandas/main/pandas/tests/io/data/csv/iris.csv'
|
||
|
|
... )
|
||
|
|
>>> pd.plotting.andrews_curves(df, 'Name')
|
||
|
|
<AxesSubplot: title={'center': 'width'}>
|
||
|
|
"""
|
||
|
|
plot_backend = _get_plot_backend("matplotlib")
|
||
|
|
return plot_backend.andrews_curves(
|
||
|
|
frame=frame,
|
||
|
|
class_column=class_column,
|
||
|
|
ax=ax,
|
||
|
|
samples=samples,
|
||
|
|
color=color,
|
||
|
|
colormap=colormap,
|
||
|
|
**kwargs,
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
def bootstrap_plot(
|
||
|
|
series: Series,
|
||
|
|
fig: Figure | None = None,
|
||
|
|
size: int = 50,
|
||
|
|
samples: int = 500,
|
||
|
|
**kwds,
|
||
|
|
) -> Figure:
|
||
|
|
"""
|
||
|
|
Bootstrap plot on mean, median and mid-range statistics.
|
||
|
|
|
||
|
|
The bootstrap plot is used to estimate the uncertainty of a statistic
|
||
|
|
by relying on random sampling with replacement [1]_. This function will
|
||
|
|
generate bootstrapping plots for mean, median and mid-range statistics
|
||
|
|
for the given number of samples of the given size.
|
||
|
|
|
||
|
|
.. [1] "Bootstrapping (statistics)" in \
|
||
|
|
https://en.wikipedia.org/wiki/Bootstrapping_%28statistics%29
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
series : pandas.Series
|
||
|
|
Series from where to get the samplings for the bootstrapping.
|
||
|
|
fig : matplotlib.figure.Figure, default None
|
||
|
|
If given, it will use the `fig` reference for plotting instead of
|
||
|
|
creating a new one with default parameters.
|
||
|
|
size : int, default 50
|
||
|
|
Number of data points to consider during each sampling. It must be
|
||
|
|
less than or equal to the length of the `series`.
|
||
|
|
samples : int, default 500
|
||
|
|
Number of times the bootstrap procedure is performed.
|
||
|
|
**kwds
|
||
|
|
Options to pass to matplotlib plotting method.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
matplotlib.figure.Figure
|
||
|
|
Matplotlib figure.
|
||
|
|
|
||
|
|
See Also
|
||
|
|
--------
|
||
|
|
pandas.DataFrame.plot : Basic plotting for DataFrame objects.
|
||
|
|
pandas.Series.plot : Basic plotting for Series objects.
|
||
|
|
|
||
|
|
Examples
|
||
|
|
--------
|
||
|
|
This example draws a basic bootstrap plot for a Series.
|
||
|
|
|
||
|
|
.. plot::
|
||
|
|
:context: close-figs
|
||
|
|
|
||
|
|
>>> s = pd.Series(np.random.uniform(size=100))
|
||
|
|
>>> pd.plotting.bootstrap_plot(s)
|
||
|
|
<Figure size 640x480 with 6 Axes>
|
||
|
|
"""
|
||
|
|
plot_backend = _get_plot_backend("matplotlib")
|
||
|
|
return plot_backend.bootstrap_plot(
|
||
|
|
series=series, fig=fig, size=size, samples=samples, **kwds
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
def parallel_coordinates(
|
||
|
|
frame: DataFrame,
|
||
|
|
class_column: str,
|
||
|
|
cols: list[str] | None = None,
|
||
|
|
ax: Axes | None = None,
|
||
|
|
color: list[str] | tuple[str, ...] | None = None,
|
||
|
|
use_columns: bool = False,
|
||
|
|
xticks: list | tuple | None = None,
|
||
|
|
colormap=None,
|
||
|
|
axvlines: bool = True,
|
||
|
|
axvlines_kwds=None,
|
||
|
|
sort_labels: bool = False,
|
||
|
|
**kwargs,
|
||
|
|
) -> Axes:
|
||
|
|
"""
|
||
|
|
Parallel coordinates plotting.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
frame : DataFrame
|
||
|
|
class_column : str
|
||
|
|
Column name containing class names.
|
||
|
|
cols : list, optional
|
||
|
|
A list of column names to use.
|
||
|
|
ax : matplotlib.axis, optional
|
||
|
|
Matplotlib axis object.
|
||
|
|
color : list or tuple, optional
|
||
|
|
Colors to use for the different classes.
|
||
|
|
use_columns : bool, optional
|
||
|
|
If true, columns will be used as xticks.
|
||
|
|
xticks : list or tuple, optional
|
||
|
|
A list of values to use for xticks.
|
||
|
|
colormap : str or matplotlib colormap, default None
|
||
|
|
Colormap to use for line colors.
|
||
|
|
axvlines : bool, optional
|
||
|
|
If true, vertical lines will be added at each xtick.
|
||
|
|
axvlines_kwds : keywords, optional
|
||
|
|
Options to be passed to axvline method for vertical lines.
|
||
|
|
sort_labels : bool, default False
|
||
|
|
Sort class_column labels, useful when assigning colors.
|
||
|
|
**kwargs
|
||
|
|
Options to pass to matplotlib plotting method.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
matplotlib.axes.Axes
|
||
|
|
|
||
|
|
Examples
|
||
|
|
--------
|
||
|
|
|
||
|
|
.. plot::
|
||
|
|
:context: close-figs
|
||
|
|
|
||
|
|
>>> df = pd.read_csv(
|
||
|
|
... 'https://raw.githubusercontent.com/pandas-dev/'
|
||
|
|
... 'pandas/main/pandas/tests/io/data/csv/iris.csv'
|
||
|
|
... )
|
||
|
|
>>> pd.plotting.parallel_coordinates(
|
||
|
|
... df, 'Name', color=('#556270', '#4ECDC4', '#C7F464')
|
||
|
|
... )
|
||
|
|
<AxesSubplot: xlabel='y(t)', ylabel='y(t + 1)'>
|
||
|
|
"""
|
||
|
|
plot_backend = _get_plot_backend("matplotlib")
|
||
|
|
return plot_backend.parallel_coordinates(
|
||
|
|
frame=frame,
|
||
|
|
class_column=class_column,
|
||
|
|
cols=cols,
|
||
|
|
ax=ax,
|
||
|
|
color=color,
|
||
|
|
use_columns=use_columns,
|
||
|
|
xticks=xticks,
|
||
|
|
colormap=colormap,
|
||
|
|
axvlines=axvlines,
|
||
|
|
axvlines_kwds=axvlines_kwds,
|
||
|
|
sort_labels=sort_labels,
|
||
|
|
**kwargs,
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
def lag_plot(series: Series, lag: int = 1, ax: Axes | None = None, **kwds) -> Axes:
|
||
|
|
"""
|
||
|
|
Lag plot for time series.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
series : Series
|
||
|
|
The time series to visualize.
|
||
|
|
lag : int, default 1
|
||
|
|
Lag length of the scatter plot.
|
||
|
|
ax : Matplotlib axis object, optional
|
||
|
|
The matplotlib axis object to use.
|
||
|
|
**kwds
|
||
|
|
Matplotlib scatter method keyword arguments.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
matplotlib.axes.Axes
|
||
|
|
|
||
|
|
Examples
|
||
|
|
--------
|
||
|
|
Lag plots are most commonly used to look for patterns in time series data.
|
||
|
|
|
||
|
|
Given the following time series
|
||
|
|
|
||
|
|
.. plot::
|
||
|
|
:context: close-figs
|
||
|
|
|
||
|
|
>>> np.random.seed(5)
|
||
|
|
>>> x = np.cumsum(np.random.normal(loc=1, scale=5, size=50))
|
||
|
|
>>> s = pd.Series(x)
|
||
|
|
>>> s.plot()
|
||
|
|
<AxesSubplot: xlabel='Midrange'>
|
||
|
|
|
||
|
|
A lag plot with ``lag=1`` returns
|
||
|
|
|
||
|
|
.. plot::
|
||
|
|
:context: close-figs
|
||
|
|
|
||
|
|
>>> pd.plotting.lag_plot(s, lag=1)
|
||
|
|
<AxesSubplot: xlabel='y(t)', ylabel='y(t + 1)'>
|
||
|
|
"""
|
||
|
|
plot_backend = _get_plot_backend("matplotlib")
|
||
|
|
return plot_backend.lag_plot(series=series, lag=lag, ax=ax, **kwds)
|
||
|
|
|
||
|
|
|
||
|
|
def autocorrelation_plot(series: Series, ax: Axes | None = None, **kwargs) -> Axes:
|
||
|
|
"""
|
||
|
|
Autocorrelation plot for time series.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
series : Series
|
||
|
|
The time series to visualize.
|
||
|
|
ax : Matplotlib axis object, optional
|
||
|
|
The matplotlib axis object to use.
|
||
|
|
**kwargs
|
||
|
|
Options to pass to matplotlib plotting method.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
matplotlib.axes.Axes
|
||
|
|
|
||
|
|
Examples
|
||
|
|
--------
|
||
|
|
The horizontal lines in the plot correspond to 95% and 99% confidence bands.
|
||
|
|
|
||
|
|
The dashed line is 99% confidence band.
|
||
|
|
|
||
|
|
.. plot::
|
||
|
|
:context: close-figs
|
||
|
|
|
||
|
|
>>> spacing = np.linspace(-9 * np.pi, 9 * np.pi, num=1000)
|
||
|
|
>>> s = pd.Series(0.7 * np.random.rand(1000) + 0.3 * np.sin(spacing))
|
||
|
|
>>> pd.plotting.autocorrelation_plot(s)
|
||
|
|
<AxesSubplot: title={'center': 'width'}, xlabel='Lag', ylabel='Autocorrelation'>
|
||
|
|
"""
|
||
|
|
plot_backend = _get_plot_backend("matplotlib")
|
||
|
|
return plot_backend.autocorrelation_plot(series=series, ax=ax, **kwargs)
|
||
|
|
|
||
|
|
|
||
|
|
class _Options(dict):
|
||
|
|
"""
|
||
|
|
Stores pandas plotting options.
|
||
|
|
|
||
|
|
Allows for parameter aliasing so you can just use parameter names that are
|
||
|
|
the same as the plot function parameters, but is stored in a canonical
|
||
|
|
format that makes it easy to breakdown into groups later.
|
||
|
|
"""
|
||
|
|
|
||
|
|
# alias so the names are same as plotting method parameter names
|
||
|
|
_ALIASES = {"x_compat": "xaxis.compat"}
|
||
|
|
_DEFAULT_KEYS = ["xaxis.compat"]
|
||
|
|
|
||
|
|
def __init__(self, deprecated: bool = False) -> None:
|
||
|
|
self._deprecated = deprecated
|
||
|
|
super().__setitem__("xaxis.compat", False)
|
||
|
|
|
||
|
|
def __getitem__(self, key):
|
||
|
|
key = self._get_canonical_key(key)
|
||
|
|
if key not in self:
|
||
|
|
raise ValueError(f"{key} is not a valid pandas plotting option")
|
||
|
|
return super().__getitem__(key)
|
||
|
|
|
||
|
|
def __setitem__(self, key, value) -> None:
|
||
|
|
key = self._get_canonical_key(key)
|
||
|
|
super().__setitem__(key, value)
|
||
|
|
|
||
|
|
def __delitem__(self, key) -> None:
|
||
|
|
key = self._get_canonical_key(key)
|
||
|
|
if key in self._DEFAULT_KEYS:
|
||
|
|
raise ValueError(f"Cannot remove default parameter {key}")
|
||
|
|
super().__delitem__(key)
|
||
|
|
|
||
|
|
def __contains__(self, key) -> bool:
|
||
|
|
key = self._get_canonical_key(key)
|
||
|
|
return super().__contains__(key)
|
||
|
|
|
||
|
|
def reset(self) -> None:
|
||
|
|
"""
|
||
|
|
Reset the option store to its initial state
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
None
|
||
|
|
"""
|
||
|
|
# error: Cannot access "__init__" directly
|
||
|
|
self.__init__() # type: ignore[misc]
|
||
|
|
|
||
|
|
def _get_canonical_key(self, key):
|
||
|
|
return self._ALIASES.get(key, key)
|
||
|
|
|
||
|
|
@contextmanager
|
||
|
|
def use(self, key, value) -> Generator[_Options, None, None]:
|
||
|
|
"""
|
||
|
|
Temporarily set a parameter value using the with statement.
|
||
|
|
Aliasing allowed.
|
||
|
|
"""
|
||
|
|
old_value = self[key]
|
||
|
|
try:
|
||
|
|
self[key] = value
|
||
|
|
yield self
|
||
|
|
finally:
|
||
|
|
self[key] = old_value
|
||
|
|
|
||
|
|
|
||
|
|
plot_params = _Options()
|