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B �5�g'> � @ s� d dl mZ d dlmZ d$dd�Zdd� Zdd � Zd%dd�Zd&dd�Zd'dd�Z d(dd�Z d)dd �Z d*d d �Z d+d d!�Z G d"d#� d#e�Ze� ZdS ),� )�contextmanager)�_get_plot_backendNc K s" t d�}|jf | |ddd�|��S )a� 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 � matplotlibN)�ax�data� rowLabels� colLabels)r �table)r r r r �kwargs� plot_backend� r �K/home/digitalm-up/venv/lib/python3.7/site-packages/pandas/plotting/_misc.pyr s r c 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 �register)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 � deregister)r r r r r 4 s r � �?F�hist�.皙�����?c K s. t d�} | jf | ||||||||| 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) r ) �frame�alpha�figsizer �grid�diagonal�marker� density_kwds� hist_kwds� range_padding)r �scatter_matrix) r r r r r r r r r r r r r r r r H s 7r c K s$ t d�}|jf | ||||d�|��S )am 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://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.135.889>`_ 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 -------- 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') r )r � class_columnr �color�colormap)r �radviz)r r r r r �kwdsr r r r r! � s Er! �� c K s& t d�}|jf | |||||d�|��S )aW Generate a matplotlib plot of Andrews curves, for visualising clusters of multivariate data. Andrews curves have the functional form: f(t) = x_1/sqrt(2) + x_2 sin(t) + x_3 cos(t) + x_4 sin(2t) + x_5 cos(2t) + ... Where x coefficients correspond to the values of each dimension and t is linearly spaced between -pi and +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 : Name of the column containing class names ax : matplotlib axes object, default None samples : Number of points to plot in each curve color : list or tuple, optional Colors to use for the different classes. colormap : str or matplotlib colormap object, default None Colormap to select colors from. If string, load colormap with that name from matplotlib. **kwargs Options to pass to matplotlib plotting method. Returns ------- class:`matplotlip.axis.Axes` Examples -------- .. plot:: :context: close-figs >>> df = pd.read_csv( ... 'https://raw.github.com/pandas-dev/' ... 'pandas/master/pandas/tests/io/data/csv/iris.csv' ... ) >>> pd.plotting.andrews_curves(df, 'Name') r )r r r �samplesr r )r �andrews_curves)r r r r$ r r r r r r r r% � s /r% �2 �� 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 relaying 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 -------- DataFrame.plot : Basic plotting for DataFrame objects. 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) r )�series�fig�sizer$ )r �bootstrap_plot)r( r) r* r$ r" r r r r r+ s /r+ Tc K s0 t d�} | jf | ||||||||| | 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 ------- class:`matplotlib.axis.Axes` Examples -------- .. plot:: :context: close-figs >>> df = pd.read_csv( ... 'https://raw.github.com/pandas-dev/' ... 'pandas/master/pandas/tests/io/data/csv/iris.csv' ... ) >>> pd.plotting.parallel_coordinates( ... df, 'Name', color=('#556270', '#4ECDC4', '#C7F464') ... ) r ) r r �colsr r � use_columns�xticksr �axvlines� axvlines_kwds� sort_labels)r �parallel_coordinates) r r r, r r r- r. r r/ r0 r1 r r r r r r2 O s =r2 � c K s t d�}|jf | ||d�|��S )a� Lag plot for time series. Parameters ---------- series : Time series lag : lag of the scatter plot, default 1 ax : Matplotlib axis object, optional **kwds Matplotlib scatter method keyword arguments. Returns ------- class:`matplotlib.axis.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() A lag plot with ``lag=1`` returns .. plot:: :context: close-figs >>> pd.plotting.lag_plot(s, lag=1) r )r( �lagr )r �lag_plot)r( r4 r r" r r r r r5 � s &r5 c K s t d�}|jf | |d�|��S )a� Autocorrelation plot for time series. Parameters ---------- series : Time series ax : Matplotlib axis object, optional **kwargs Options to pass to matplotlib plotting method. Returns ------- class:`matplotlib.axis.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) r )r( r )r �autocorrelation_plot)r( r r r r r r r6 � s r6 c s� e Zd ZdZddiZdgZd� fdd� Z� fdd�Z� fd d �Z� fd d �Z e d �� fdd� Z dd� Z dd� Z edd� �Z� ZS )�_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_compatz xaxis.compatFc s || _ t� �dd� d S )Nz xaxis.compatF)Z _deprecated�super� __setitem__)�self� deprecated)� __class__r r �__init__� s z_Options.__init__c s, | � |�}|| kr t|� d���t� �|�S )Nz& is not a valid pandas plotting option)�_get_canonical_key� ValueErrorr8 � __getitem__)r: �key)r< r r r@ � s z_Options.__getitem__c s | � |�}t� �||�S )N)r> r8 r9 )r: rA �value)r< r r r9 � s z_Options.__setitem__c s. | � |�}|| jkr"td|� ���t� �|�S )Nz Cannot remove default parameter )r> � _DEFAULT_KEYSr? r8 � __delitem__)r: rA )r< r r rD s z_Options.__delitem__)�returnc s | � |�}t� �|�S )N)r> r8 � __contains__)r: rA )r< r r rF s z_Options.__contains__c C s | � � dS )zk Reset the option store to its initial state Returns ------- None N)r= )r: r r r �reset s z_Options.resetc C s | j �||�S )N)�_ALIASES�get)r: rA r r r r> s z_Options._get_canonical_keyc c s* | | }z|| |< | V W d|| |< X dS )zg Temporarily set a parameter value using the with statement. Aliasing allowed. Nr )r: rA rB � old_valuer r r �use s z _Options.use)F)�__name__� __module__� __qualname__�__doc__rH rC r= r@ r9 rD �boolrF rG r> r rK � __classcell__r r )r< r r7 � s r7 )NN) r NNFr r NNr )NNN)Nr# NN)Nr& r' ) NNNFNNTNF)r3 N)N)� contextlibr Zpandas.plotting._corer r r r r r! r% r+ r2 r5 r6 �dictr7 Z plot_paramsr r r r �<module> s: = Q : 8 C * !A
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