Pandas histogram df.hist() group by

Question

How to plot a histogram with pandas DataFrame.hist() using group by? I have a data frame with 5 columns: "A", "B", "C", "D" and "Group"

There are two Groups classes: "yes" and "no"

Using:

df.hist() 

I get the hist for each of the 4 columns.

Now I would like to get the same 4 graphs but with blue bars (group="yes") and red bars (group = "no").

I tried this withouth success:

df.hist(by = "group")

Answer 1

This is not the most flexible workaround but will work for your question specifically.

def sephist(col):
    yes = df[df['group'] == 'yes'][col]
    no = df[df['group'] == 'no'][col]
    return yes, no

for num, alpha in enumerate('abcd'):
    plt.subplot(2, 2, num)
    plt.hist(sephist(alpha)[0], bins=25, alpha=0.5, label='yes', color='b')
    plt.hist(sephist(alpha)[1], bins=25, alpha=0.5, label='no', color='r')
    plt.legend(loc='upper right')
    plt.title(alpha)
plt.tight_layout(pad=0.4, w_pad=0.5, h_pad=1.0)

You could make this more generic by:

• adding a df and by parameter to sephist: def sephist(df, by, col)
• making the subplots loop more flexible: for num, alpha in enumerate(df.columns)

Because the first argument to matplotlib.pyplot.hist can take

either a single array or a sequency of arrays which are not required to be of the same length

...an alternattive would be:

for num, alpha in enumerate('abcd'):
    plt.subplot(2, 2, num)
    plt.hist((sephist(alpha)[0], sephist(alpha)[1]), bins=25, alpha=0.5, label=['yes', 'no'], color=['r', 'b'])
    plt.legend(loc='upper right')
    plt.title(alpha)
plt.tight_layout(pad=0.4, w_pad=0.5, h_pad=1.0)