I have a list of dataframes named merged_dfs that I am looping through to get the correlation and plot subplots of heatmap correlation matrix using seaborn.

I want to customize the colorbar tick labels, but I am having trouble figuring out how to do it with my example.

Currently, my colorbar scale values from top to bottom are

[1,0.5,0,-0.5,-1]

I want to keep these values, but change the tick labels to be

[1,0.5,0,0.5,1] 

for my diverging color bar.

Here is the code and my attempt:

fig, ax = plt.subplots(nrows=6, ncols=2, figsize=(20,20))
for i, (title,merging) in enumerate (zip(new_name_data,merged_dfs)):
    graph = merging.corr()
    colormap = sns.diverging_palette(250, 250, as_cmap=True)
    a = sns.heatmap(graph.abs(), cmap=colormap, vmin=-1,vmax=1,center=0,annot = graph, ax=ax.flat[i])
    cbar = fig.colorbar(a)
    cbar.set_ticklabels(["1","0.5","0","0.5","1"])
fig.delaxes(ax[5,1])
        
plt.show()
plt.close()

I keep getting this error:

AttributeError: 'AxesSubplot' object has no attribute 'get_array'

CodePudding user response：

Several things are going wrong:

• fig.colorbar(...) would create a new colorbar, by default appended to the last subplot that was created.
• sns.heatmap returns an ax (indicates a subplot). This is very different to matplotlib functions, e.g. plt.imshow(), which would return the graphical element that was plotted.
• You can suppress the heatmap's colorbar (cbar=False), and then create it newly with the parameters you want.
• fig.colorbar(...) needs a parameter ax=... when the figure contains more than one subplot.
• Instead of creating a new colorbar, you can add the colorbar parameters to sns.heatmap via cbar_kws=.... The colorbar itself can be found via ax.collections[0].colobar. (ax.collections[0] is where matplotlib stored the graphical object that contains the heatmap.)
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