How to make a pandas crosstab with percentages?

Given a dataframe with different categorical variables, how do I return a cross-tabulation with percentages instead of frequencies?

df = pd.DataFrame({'A' : ['one', 'one', 'two', 'three'] * 6,
                   'B' : ['A', 'B', 'C'] * 8,
                   'C' : ['foo', 'foo', 'foo', 'bar', 'bar', 'bar'] * 4,
                   'D' : np.random.randn(24),
                   'E' : np.random.randn(24)})


pd.crosstab(df.A,df.B)


B       A    B    C
A               
one     4    4    4
three   2    2    2
two     2    2    2

Using the margins option in crosstab to compute row and column totals gets us close enough to think that it should be possible using an aggfunc or groupby, but my meager brain can't think it through.

B       A     B    C
A               
one     .33  .33  .33
three   .33  .33  .33
two     .33  .33  .33

Solution 1:

From Pandas 0.18.1 onwards, there's a normalize option:

In [1]: pd.crosstab(df.A,df.B, normalize='index')
Out[1]:

B              A           B           C
A           
one     0.333333    0.333333    0.333333
three   0.333333    0.333333    0.333333
two     0.333333    0.333333    0.333333

Where you can normalise across either all, index (rows), or columns.

More details are available in the documentation.

Solution 2:

pd.crosstab(df.A, df.B).apply(lambda r: r/r.sum(), axis=1)

Basically you just have the function that does row/row.sum(), and you use apply with axis=1 to apply it by row.

(If doing this in Python 2, you should use from __future__ import division to make sure division always returns a float.)

Solution 3:

We can show it as percentages by multiplying by 100:

pd.crosstab(df.A,df.B, normalize='index')\
    .round(4)*100

B          A      B      C
A                         
one    33.33  33.33  33.33
three  33.33  33.33  33.33
two    33.33  33.33  33.33

Where I've rounded for convenience.

Solution 4:

If you're looking for a percentage of the total, you can divide by the len of the df instead of the row sum:

pd.crosstab(df.A, df.B).apply(lambda r: r/len(df), axis=1)

Solution 5:

Another option is to use div rather than apply:

In [11]: res = pd.crosstab(df.A, df.B)

Divide by the sum over the index:

In [12]: res.sum(axis=1)
Out[12]: 
A
one      12
three     6
two       6
dtype: int64

Similar to above, you need to do something about integer division (I use astype('float')):

In [13]: res.astype('float').div(res.sum(axis=1), axis=0)
Out[13]: 
B             A         B         C
A                                  
one    0.333333  0.333333  0.333333
three  0.333333  0.333333  0.333333
two    0.333333  0.333333  0.333333