statsmodels.graphics.correlation.plot_corr_grid¶

statsmodels.graphics.correlation.
plot_corr_grid
(dcorrs, titles=None, ncols=None, normcolor=False, xnames=None, ynames=None, fig=None, cmap='RdYlBu_r')[source]¶ Create a grid of correlation plots.
The individual correlation plots are assumed to all have the same variables, axis labels can be specified only once.
Parameters:  dcorrs (list or iterable of ndarrays) – List of correlation matrices.
 titles (list of str, optional) – List of titles for the subplots. By default no title are shown.
 ncols (int, optional) – Number of columns in the subplot grid. If not given, the number of columns is determined automatically.
 normcolor (bool or tuple, 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.
 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 reused.
 fig (Matplotlib figure instance, optional) – If given, this figure is simply returned. Otherwise a new figure 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 – If ax is None, the created figure. Otherwise the figure to which ax is connected.
Return type: Matplotlib figure instance
Examples
>>> import numpy as np >>> import matplotlib.pyplot as plt >>> import statsmodels.api as sm
In this example we just reuse the same correlation matrix several times. Of course in reality one would show a different correlation (measuring a another type of correlation, for example Pearson (linear) and Spearman, Kendall (nonlinear) correlations) for the same variables.
>>> hie_data = sm.datasets.randhie.load_pandas() >>> corr_matrix = np.corrcoef(hie_data.data.T) >>> sm.graphics.plot_corr_grid([corr_matrix] * 8, xnames=hie_data.names) >>> plt.show()