plot histogram for many columns quickly using groupby function of pandas dataframe

Question

I have a pandas dataframe, which looks like Here one of column is named label, that can take only two possible values 0 or 1.

I would like to make histogram for label 1 and for label 0 separately one top of other, like

I am able to make this for one of the column (named "MA_CL05") like:

temp = infile.groupby('label')
for k, v in temp:
  if k == 1:
    v.MA_CL05.hist(label='1',figsize=(15,15),bins=25,alpha=1.0,histtype = 'step',lw=4)
  if k == 0:
    v.MA_CL05.hist(label='0',figsize=(15,15),bins=25,alpha=1.0,histtype = 'step',lw=4)
plt.legend(loc=1, prop={'size': 51})
plt.show()

I can copy and past this patch for all of 20 columns and it will be fine. But, is there any easy way to plot this histogram of type (2) in one go?

nope.... I want separate histogram for each attribute. But, in dataframe there are two labels 0 and 1. So, for each attribute, we have two histogram one for 0 and another for 1. I want these two histograms in one plot. — ramkrishna
– ramkrishna, Commented Nov 6, 2017 at 4:35

ImportanceOfBeingErnest · Accepted Answer · 2017-11-06 11:20:18Z

1

You can add another loop, looping about the columns of the dataframe and specifying the axes to plot to.

fig, axes = plt.subplots(4,5)

for col,ax in zip(infile.columns[2:],axes.flatten()):
    temp = infile.groupby('label')
    for k, v in temp:
        v[col].hist(label=str(k),bins=25,alpha=1.0,histtype = 'step',lw=4, ax=ax)

plt.legend(loc=1, prop={'size': 51})
plt.show()

answered Nov 6, 2017 at 11:20

ImportanceOfBeingErnest

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