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