How to make a pandas crosstab with percentages?

Question

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

df = pd.DataFrame({\'A\' : [\         


        

Answer 1

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)

Answer 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.)

Answer 3

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.

Answer 4

Normalizing the index will simply work out. Use parameter, normalize = "index" in pd.crosstab().

Answer 5

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.

Answer 6

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