Pandas groupby two columns and plot

Question

I have a dataframe like this:

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline

df = pd.DataFrame({\'category\': lis         


        

Answer 1

Data

import numpy as np
import pandas as pd
df = pd.DataFrame({'category': list('XYZXY'),
                   'NotUsed': range(5,10),
                   'sex': list('mfmff')})

  category  NotUsed sex
0        X        5   m
1        Y        6   f
2        Z        7   m
3        X        8   f
4        Y        9   f

Using crosstab

pd.crosstab(df['category'],df['sex']).plot.bar()

Using groupby+unstack:

(df.groupby(['sex','category'])['B']
   .count().unstack('sex').plot.bar())

Using pivot_table:

pd.pivot_table(df, values = 'B', index = 'category',
               columns = 'sex',aggfunc ='count').plot.bar()

Using seaborn:

import seaborn as sns
sns.countplot(data=df,x='category',hue='sex')

or,
sns.catplot(data=df,kind='count',x='category',hue='sex')

output

Answer 2

IIUC,

df.groupby(['category','sex']).B.count().unstack().reset_index()\
.plot.bar(x = 'category', y = ['f', 'm'])

Edit: If you have multiple columns, you can use groupby, count and droplevel.

new_df = df.groupby(['category','sex']).count().unstack()
new_df.columns = new_df.columns.droplevel()
new_df.reset_index().plot.bar()

Answer 3

You can also use this

pd.pivot_table(df, values = 'B', index = 'category', columns = 'sex',
               aggfunc = lambda x: len(x)).plot.bar()

which results in exactly the same plot.