Ë
|
nñúhÁQ ã óè d dl mZ d dlmZ d dlmZmZ d dlmZ er,d dl m
|
Z
|
m Z d dl m Z d dlmZ d dlmZ d d lmZ d d
|
lZd d lmZmZ dd Zdd ZddZ d ddZ d d dZ d d dZ d d!dZ d" d#dZ d$d%dZ!d&d'dZ" G d de#« Z$ e$« Z%y
|
)(é )Ú annotations)Úcontextmanager)Ú TYPE_CHECKINGÚAny)Ú_get_plot_backend)Ú GeneratorÚMapping)ÚAxes)ÚColormap)ÚFigure)ÚTableN)Ú DataFrameÚSeriesc óF t d« } |j d| |ddd|¤S )ar
|
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
|
|
Examples
|
--------
|
|
.. plot::
|
:context: close-figs
|
|
>>> import matplotlib.pyplot as plt
|
>>> df = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
|
>>> fix, ax = plt.subplots()
|
>>> ax.axis('off')
|
(0.0, 1.0, 0.0, 1.0)
|
>>> table = pd.plotting.table(ax, df, loc='center',
|
... cellLoc='center', colWidths=list([.2, .2]))
|
Ú
|
matplotlibN)ÚaxÚdataÚ rowLabelsÚ colLabels© )r Útable)r r ÚkwargsÚ plot_backends úHH:\Change_password\venv_build\Lib\site-packages\pandas/plotting/_misc.pyr r s: ô@ % \Ó2LØ <× Ñ ð Ø D D°Dñ Ø<Bñ ð ó c ó: t d« } | j « y)aª
|
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.
|
|
Examples
|
--------
|
.. plot::
|
:context: close-figs
|
|
The following line is done automatically by pandas so
|
the plot can be rendered:
|
|
>>> pd.plotting.register_matplotlib_converters()
|
|
>>> df = pd.DataFrame({'ts': pd.period_range('2020', periods=2, freq='M'),
|
... 'y': [1, 2]
|
... })
|
>>> plot = df.plot.line(x='ts', y='y')
|
|
Unsetting the register manually an error will be raised:
|
|
>>> pd.set_option("plotting.matplotlib.register_converters",
|
... False) # doctest: +SKIP
|
>>> df.plot.line(x='ts', y='y') # doctest: +SKIP
|
Traceback (most recent call last):
|
TypeError: float() argument must be a string or a real number, not 'Period'
|
r N)r Úregister©r s r r r C s ôR % \Ó2LØ×ÑÕr c ó: t d« } | j « y)a9
|
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.
|
|
Examples
|
--------
|
.. plot::
|
:context: close-figs
|
|
The following line is done automatically by pandas so
|
the plot can be rendered:
|
|
>>> pd.plotting.register_matplotlib_converters()
|
|
>>> df = pd.DataFrame({'ts': pd.period_range('2020', periods=2, freq='M'),
|
... 'y': [1, 2]
|
... })
|
>>> plot = df.plot.line(x='ts', y='y')
|
|
Unsetting the register manually an error will be raised:
|
|
>>> pd.set_option("plotting.matplotlib.register_converters",
|
... False) # doctest: +SKIP
|
>>> df.plot.line(x='ts', y='y') # doctest: +SKIP
|
Traceback (most recent call last):
|
TypeError: float() argument must be a string or a real number, not 'Period'
|
r N)r Ú
|
deregisterr s r r r p s ôN % \Ó2LØ×ÑÕ r c
|
óR t d« } | j d| ||||||||| d
|
|
|
¤S )a
|
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([[<Axes: xlabel='A', ylabel='A'>, <Axes: xlabel='B', ylabel='A'>,
|
<Axes: xlabel='C', ylabel='A'>, <Axes: xlabel='D', ylabel='A'>],
|
[<Axes: xlabel='A', ylabel='B'>, <Axes: xlabel='B', ylabel='B'>,
|
<Axes: xlabel='C', ylabel='B'>, <Axes: xlabel='D', ylabel='B'>],
|
[<Axes: xlabel='A', ylabel='C'>, <Axes: xlabel='B', ylabel='C'>,
|
<Axes: xlabel='C', ylabel='C'>, <Axes: xlabel='D', ylabel='C'>],
|
[<Axes: xlabel='A', ylabel='D'>, <Axes: xlabel='B', ylabel='D'>,
|
<Axes: xlabel='C', ylabel='D'>, <Axes: xlabel='D', ylabel='D'>]],
|
dtype=object)
|
r )
|
ÚframeÚalphaÚfigsizer ÚgridÚdiagonalÚmarkerÚ density_kwdsÚ hist_kwdsÚ range_paddingr )r Úscatter_matrix) r" r# r$ r r% r& r' r( r) r* r r s r r+ r+ sQ ô@ % \Ó2LØ &<× &Ñ &ð ØØØØ Ø ØØØ!ØØ#ñ ð ñ ð r c óH t d« } |j d| ||||d|¤S )al
|
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') # doctest: +SKIP
|
r )r" Ú class_columnr ÚcolorÚcolormapr )r Úradviz)r" r- r r. r/ Úkwdsr s r r0 r0 ë sB ôX % \Ó2LØ <× Ñ ð ØØ!Ø ØØñ ð ñ ð r c
|
óJ t d« } |j d| |||||d|¤S )a
|
Generate a matplotlib plot for visualizing 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') # doctest: +SKIP
|
r )r" r- r Úsamplesr. r/ r )r Úandrews_curves)r" r- r r3 r. r/ r r s r r4 r4 B sE ôr % \Ó2LØ &<× &Ñ &ð ØØ!Ø ØØØñ ð ñ ð r c óF t d« } |j d| |||d|¤S )aA
|
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) # doctest: +SKIP
|
<Figure size 640x480 with 6 Axes>
|
r )ÚseriesÚfigÚsizer3 r )r Úbootstrap_plot)r6 r7 r8 r3 r1 r s r r9 r9 s: ôl % \Ó2LØ &<× &Ñ &ð Ø3 T°7ñ Ø>Bñ ð r c óT t d« } | j d| ||||||||| |
|
d | ¤S )a¤
|
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')
|
... ) # doctest: +SKIP
|
r ) r" r- Úcolsr r. Ú use_columnsÚxticksr/ ÚaxvlinesÚ axvlines_kwdsÚ sort_labelsr )r Úparallel_coordinates) r" r- r; r r. r< r= r/ r> r? r@ r r s r rA rA Ã sT ôz % \Ó2LØ ,<× ,Ñ ,ð ØØ!Ø Ø ØØ ØØØØ#Ø ñ ð ñ ð r c óD t d« } |j d| ||d|¤S )a
|
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() # doctest: +SKIP
|
|
A lag plot with ``lag=1`` returns
|
|
.. plot::
|
:context: close-figs
|
|
>>> pd.plotting.lag_plot(s, lag=1)
|
<Axes: xlabel='y(t)', ylabel='y(t + 1)'>
|
r )r6 Úlagr r )r Úlag_plot)r6 rC r r1 r s r rD rD s. ôR % \Ó2LØ <× Ñ Ð G¨°C¸BÑ GÀ$Ñ GÐGr c óB t d« } |j d| |d|¤S )aì
|
Autocorrelation plot for time series.
|
|
Parameters
|
----------
|
series : Series
|
The time series to visualize.
|
ax : Matplotlib axis object, optional
|
The matplotlib axis object to use.
|
**kwargs
|
Options to pass to matplotlib plotting method.
|
|
Returns
|
-------
|
matplotlib.axes.Axes
|
|
Examples
|
--------
|
The horizontal lines in the plot correspond to 95% and 99% confidence bands.
|
|
The dashed line is 99% confidence band.
|
|
.. plot::
|
:context: close-figs
|
|
>>> spacing = np.linspace(-9 * np.pi, 9 * np.pi, num=1000)
|
>>> s = pd.Series(0.7 * np.random.rand(1000) + 0.3 * np.sin(spacing))
|
>>> pd.plotting.autocorrelation_plot(s) # doctest: +SKIP
|
r )r6 r r )r Úautocorrelation_plot)r6 r r r s r rF rF > s+ ô< % \Ó2LØ ,<× ,Ñ ,Ð L°F¸rÑ LÀVÑ LÐLr c ó e Zd ZdZddiZdgZd d fd Z fdZd fd Zd fd Z d fd Z
|
dd Z d
|
Z e dd « Z xZS )Ú_Optionsa
|
Stores pandas plotting options.
|
|
Allows for parameter aliasing so you can just use parameter names that are
|
the same as the plot function parameters, but is stored in a canonical
|
format that makes it easy to breakdown into groups later.
|
|
Examples
|
--------
|
|
.. plot::
|
:context: close-figs
|
|
>>> np.random.seed(42)
|
>>> df = pd.DataFrame({'A': np.random.randn(10),
|
... 'B': np.random.randn(10)},
|
... index=pd.date_range("1/1/2000",
|
... freq='4MS', periods=10))
|
>>> with pd.plotting.plot_params.use("x_compat", True):
|
... _ = df["A"].plot(color="r")
|
... _ = df["B"].plot(color="g")
|
Úx_compatú xaxis.compatc ó4 || _ t | dd« y )NrJ F)Ú _deprecatedÚsuperÚ __setitem__)ÚselfÚ
|
deprecatedÚ __class__s r Ú__init__z_Options.__init__| s ø Ø%ÔÜ ÑN¨EÕ2r c óh | j |« }|| vrt | d« t | |« S )Nz& is not a valid pandas plotting option)Ú_get_canonical_keyÚ
|
ValueErrorrM Ú __getitem__©rO ÚkeyrQ s r rV z_Options.__getitem__ s= ø Ø×%Ñ% cÓ*Ø d?Ü uÐ$JÐ KÓLÐ LÜwÑ" 3Ó'Ð'r c óH | j |« }t | ||« y ©N)rT rM rN )rO rX ÚvaluerQ s r rN z_Options.__setitem__ s" ø Ø×%Ñ% cÓ*Ü ÑC Õ'r c ó~ | j |« }|| j v rt d| « t | |« y )Nz Cannot remove default parameter )rT Ú _DEFAULT_KEYSrU rM Ú __delitem__rW s r r^ z_Options.__delitem__ sA ø Ø×%Ñ% cÓ*Ø $×$Ñ$Ñ $Ü Ð ?À¸uÐ EÓFÐ FÜ ÑCÕ r c óD | j |« }t | |« S rZ )rT rM Ú __contains__rW s r r` z_Options.__contains__ s# ø Ø×%Ñ% cÓ*ÜwÑ# CÓ(Ð(r c ó$ | j « y)zk
|
Reset the option store to its initial state
|
|
Returns
|
-------
|
None
|
N)rR )rO s r Úresetz_Options.reset s ð r c ó: | j j ||« S rZ )Ú_ALIASESÚget)rO rX s r rT z_Options._get_canonical_key s Ø}}× Ñ cÓ*Ð*r c # óH K | | } || |< | || |< y# || |< w xY ww)zg
|
Temporarily set a parameter value using the with statement.
|
Aliasing allowed.
|
Nr )rO rX r[ Ú old_values r Úusez _Options.use¢ s6 è ø ð I ð "Ø DIØJà!DIø DIüs " " ")F)rP ÚboolÚreturnÚNone©rj rk )rj ri )rj z Generator[_Options, None, None])Ú__name__Ú
|
__module__Ú __qualname__Ú__doc__rd r] rR rV rN r^ r` rb rT r rh Ú __classcell__)rQ s @r rH rH ` sS ø ñð0 NÐ+HØ#Ð$Mö3ô(õ (õ!õ )ó ò+ð ò
|
"ó ô
|
"r rH )r r
|
r zDataFrame | Seriesrj r rl ) g à?NNFÚhistú.NNg©?)r" r r# Úfloatr$ ztuple[float, float] | Noner ú Axes | Noner% ri r& Ústrr' rv r( úMapping[str, Any] | Noner) rw r* rt rj z
|
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|
)NéÈ NN)r" r r- rv r ru r3 Úintr. rx r/ ry rj r
|
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|
r6 r r7 z Figure | Noner8 r{ r3 r{ rj r ) NNNFNNTNF)r" r r- rv r; zlist[str] | Noner ru r. rx r< ri r= zlist | tuple | Noner/ ry r> ri r? rw r@ ri rj r
|
)é N)r6 r rC r{ r ru rj r
|
rZ )r6 r r ru rj r
|
)&Ú
|
__future__r Ú
|
contextlibr Útypingr r Úpandas.plotting._corer Úcollections.abcr r Úmatplotlib.axesr
|
Úmatplotlib.colorsr Úmatplotlib.figurer Úmatplotlib.tabler ÚnumpyÚnpÚpandasr r r r r r+ r0 r4 r9 rA rD rF ÚdictrH Ú plot_paramsr r r ú<module>r s ðÝ "å %÷õ
|
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|
%Ý*Ý(Ý&Û÷ó #óL* óZ( ðZ Ø*.ØØØØØ-1Ø*.Ø ðMØ ðMà ðMð (ðMð ð Mð
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