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:¶

dcorrndarray

Correlation matrix, square 2-D array.

xnameslist[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.

ynameslist[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.

titlestr, optional

The figure title. If None, the default (‘Correlation Matrix’) is used. If title='', then no title is added.

normcolorbool 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.

axAxesSubplot, 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.

cmapstr or Matplotlib Colormap instance, optional

The colormap for the plot. Can be any valid Matplotlib Colormap instance or name.

Returns:¶

Figure

If ax is None, the created figure. Otherwise the figure to which ax is connected.

Examples

>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> import statsmodels.graphics.api as smg

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

(Source code, png, hires.png, pdf)