CODEWITHSUNDEEP
pythonpython-3.xpandaspivot-tablepandas-groupby
Ayush Kesarwani
Ayush Kesarwani

Reputation: 530

Convert Categorical data to numeric percentage in Pandas

I have a dataframe like this, all categorical values:

col1    col2
0   A   x
1   A   y
2   A   x
3   A   z
4   A   z
5   A   z
6   B   x
7   B   y
8   B   x

I want to group this by "col1" and get the percentage of time I get values of "col2" in separate columns. Like:

    col1    x    y       z
0   A     33.33  16.67   50.0
1   B     66.67  33.37   0.0

I tried pivot table that gives me only count of each values of a column but how to get it in percentage?

Thanks in advance.

Upvotes: 2

Views: 2065

Answers (1)

user3471881
user3471881

Reputation: 2724

You want to make a cross-tabulation of two factors (col1 and col2) with the frequency normalized over each row. To do this you can use pd.crosstab() with normalize set to index:

>> df = pd.DataFrame({'col1': list('aaaaaabbb'), 'col2': list('xyxzzzxyx')})
>> pd.crosstab(df['col1'], df['col2'], normalize='index') * 100
col2    x           y           z
col1            
a       33.333333   16.666667   50.0
b       66.666667   33.333333   0.0

If you want to use multiple factors, just call crosstab with a list of factors:

>> df['col3'] = list('112231345')
>> pd.crosstab([df['col1'], df['col3']], df['col2'], normalize='index') * 100
        col2    x           y           z
col1    col3            
a       1       33.333333   33.333333   33.333333
        2       50.000000   0.000000    50.000000
        3       0.000000    0.000000    100.000000
b       3       100.000000  0.000000    0.000000
        4       0.000000    100.000000  0.000000
        5       100.000000  0.000000    0.000000

If you want to round up, just call round:

>> round(pd.crosstab(df['col1'], df['col2'], normalize='index') * 100, 2)
col2    x       y       z
col1            
a       33.33   16.67   50.0
b       66.67   33.33   0.0

Upvotes: 3

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