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()