graphics.correlation.plot_corr()

statsmodels.graphics.correlation.plot_corr

statsmodels.graphics.correlation.plot_corr(dcorr, xnames=None, ynames=None, title=None, normcolor=False, ax=None, cmap='RdYlBu_r') [source]

Plot correlation of many variables in a tight color grid.

Parameters:

dcorr : ndarray Correlation matrix, square 2-D array. xnames : list of str, optional Labels for the horizontal axis. If not given (None), then the matplotlib defaults (integers) are used. If it is an empty list, [], then no ticks and labels are added. ynames : list of str, optional Labels for the vertical axis. Works the same way as xnames. If not given, the same names as for xnames are re-used. title : str, optional The figure title. If None, the default (?Correlation Matrix?) is used. If title='', then no title is added. normcolor : bool or tuple of scalars, optional If False (default), then the color coding range corresponds to the range of dcorr. If True, then the color range is normalized to (-1, 1). If this is a tuple of two numbers, then they define the range for the color bar. ax : Matplotlib AxesSubplot instance, optional If ax is None, then a figure is created. If an axis instance is given, then only the main plot but not the colorbar is created. cmap : str or Matplotlib Colormap instance, optional The colormap for the plot. Can be any valid Matplotlib Colormap instance or name.

Returns:

fig : Matplotlib figure instance If ax is None, the created figure. Otherwise the figure to which ax is connected.

Examples

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>>> import numpy as np >>> import matplotlib.pyplot as plt >>> import statsmodels.api as sm

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>>> hie_data = sm.datasets.randhie.load_pandas() >>> corr_matrix = np.corrcoef(hie_data.data.T) >>> sm.graphics.plot_corr(corr_matrix, xnames=hie_data.names) >>> plt.show()