U
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¬ý°dQJ ã @ s\ d dl mZ d dlmZ d dlmZmZ d dlmZ erhd dl m
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Z
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d dl m Z d dl Zd dlmZmZ d d
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Zd d d dZd d ddZdAddddddddd d d d ZdBdddd d!d"d#d$ZdCdddd&d d!d'd(d)ZdDd,d-d&d&d.d/d0d1ZdEddd3dd dd4ddd!d5
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d6d7ZdFd,d&dd!d9d:d;ZdGd,dd!d<d=d>ZG d?d@ d@e Z e Z dS )Hé )Ú annotations)Úcontextmanager)Ú TYPE_CHECKINGÚ Generator)Ú_get_plot_backend)ÚAxes)ÚFigureN)Ú DataFrameÚSeriesc K s" t d}|jf | |ddd|S )a¹
|
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
|
Ú
|
matplotlibN)ÚaxÚdataZ rowLabelsZ colLabels)r Útable)r r ÚkwargsÚ plot_backend© r úLd:\z\workplace\vscode\pyvenv\venv\Lib\site-packages\pandas/plotting/_misc.pyr s ÿÿr ÚNone©Úreturnc C s t d} | ¡ dS )a
|
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.
|
r N)r Úregister©r r r r r . s r c C s t d} | ¡ dS )a-
|
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.
|
r N)r Ú
|
deregisterr r r r r D s r ç à?FÚhistÚ.ç©?r Úfloatztuple[float, float] | Nonez Axes | NoneÚboolÚstrz
|
np.ndarray) ÚframeÚalphaÚfigsizer ÚgridÚdiagonalÚmarkerÚ range_paddingr c
|
K s. t d} | jf | ||||||||| d
|
|
|
S )aB
|
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)
|
r )
|
r r! r" r r# r$ r% Ú density_kwdsÚ hist_kwdsr& )r Úscatter_matrix) r r! r" r r# r$ r% r' r( r& r r r r r r) X s Gö õr) z"list[str] | tuple[str, ...] | Noner )r Ú class_columnr Úcolorr c K s$ t d}|jf | ||||d|S )a
|
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)'>
|
r )r r* r r+ Úcolormap)r Úradviz)r r* r r+ r, Úkwdsr r r r r- ¯ s Mûúr- éÈ Úint)r r* r Úsamplesr+ r c K s& t d}|jf | |||||d|S )a9
|
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'}>
|
r )r r* r r1 r+ r, )r Úandrews_curves)r r* r r1 r+ r, r r r r r r2 s :úùr2 é2 éô r
|
z Figure | Noner )ÚseriesÚfigÚsizer1 r c K s" t d}|jf | |||d|S )a/
|
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>
|
r )r5 r6 r7 r1 )r Úbootstrap_plot)r5 r6 r7 r1 r. r r r r r8 M s 6 ÿÿr8 Tzlist[str] | Nonezlist | tuple | None)
|
r r* Úcolsr r+ Ú use_columnsÚxticksÚaxvlinesÚ sort_labelsr c K s0 t d} | jf | ||||||||| |
|
d | S )aÊ
|
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)'>
|
r ) r r* r9 r r+ r: r; r, r< Ú axvlines_kwdsr= )r Úparallel_coordinates) r r* r9 r r+ r: r; r, r< r> r= r r r r r r? s >õ ôr? é )r5 Úlagr r c K s t d}|jf | ||d|S )aµ
|
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)'>
|
r )r5 rA r )r Úlag_plot)r5 rA r r. r r r r rB Ø s *rB )r5 r r c K s t d}|jf | |d|S )a3
|
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'>
|
r )r5 r )r Úautocorrelation_plot)r5 r r r r r r rC s rC c s¢ e Zd ZdZddiZdgZdddd fdd Z fd
|
d Zdd fd d Zdd fdd Z dd fdd Z
|
dd ddZ dd Z e dd ddZ ZS )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.
|
Zx_compatú xaxis.compatFr r )Ú
|
deprecatedr c s || _ t dd¡ d S )NrE F)Z _deprecatedÚsuperÚ __setitem__)ÚselfrF ©Ú __class__r r Ú__init__6 s z_Options.__init__c s, | |¡}|| kr t| dt |¡S )Nz& is not a valid pandas plotting option)Ú_get_canonical_keyÚ
|
ValueErrorrG Ú __getitem__©rI ÚkeyrJ r r rO : s
|
z_Options.__getitem__r c s | |¡}t ||¡ d S ©N)rM rG rH )rI rQ ÚvaluerJ r r rH @ s
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z_Options.__setitem__c s2 | |¡}|| jkr"td| t |¡ d S )Nz Cannot remove default parameter )rM Ú _DEFAULT_KEYSrN rG Ú __delitem__rP rJ r r rU D s
|
|
z_Options.__delitem__c s | |¡}t |¡S rR )rM rG Ú __contains__rP rJ r r rV J s
|
z_Options.__contains__c C s | ¡ dS )zk
|
Reset the option store to its initial state
|
|
Returns
|
-------
|
None
|
N)rL )rI r r r ÚresetN s z_Options.resetc C s | j ||¡S rR )Ú_ALIASESÚgetrP r r r rM Y s z_Options._get_canonical_keyz Generator[_Options, None, None]c c s* | | }z|| |< | V W 5 || |< X dS )zg
|
Temporarily set a parameter value using the with statement.
|
Aliasing allowed.
|
Nr )rI rQ rS Ú old_valuer r r Úuse\ s
|
|
z _Options.use)F)Ú__name__Ú
|
__module__Ú __qualname__Ú__doc__rX rT rL rO rH rU rV rW rM r r[ Ú __classcell__r r rJ r rD ) s rD ) r NNFr r NNr )NNN)Nr/ NN)Nr3 r4 ) NNNFNNTNF)r@ N)N) Ú
|
__future__r Ú
|
contextlibr Útypingr r Zpandas.plotting._corer Zmatplotlib.axesr Zmatplotlib.figurer ÚnumpyÚnpZpandasr r
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r r r r) r- r2 r8 r? rB rC ÚdictrD Z plot_paramsr r r r Ú<module> s` ö Z û[ úH ü? õ"O.#A
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