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 forxnames
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 whichax
is connected.Examples
>>> import numpy as np >>> import matplotlib.pyplot as plt >>> import statsmodels.api as sm
>>> 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()
Please login to continue.