Convert Pandas dataframe values to percentage

user_7396126 0
Viewed 2368

I have a dataframe df I want to calculate the percentage based on the column total.

Suppose I have:

df = pd.DataFrame({
    'ID': range(1, 4),
    'col1': [10, 5, 10],
    'col2': [15, 10, 15],
    'col3': [10, 15, 15],
    'total': [35, 30, 40]
})

print(df)

     ID  col1 col2 col3 total
0     1    10   15   10    35
1     2    5    10   15    30
2     3    10   15   15    40

I want to get:

     ID       col1       col2       col3      total
0     1    28.57 %    42.85 %    28.57 %      100 %
1     2    16.66 %    33.33 %       50 %      100 %
2     3       25 %      37.5%     37.5 %      100 %
pandas dataframe python
5 Answers

Use

>>> df.iloc[:, 1:] = df.iloc[:, 1:].div(df['total'], axis=0).mul(100).round(2).astype(str).add(' %')
>>> df 
   ID     col1     col2     col3    total
0   1  28.57 %  42.86 %  28.57 %  100.0 %
1   2  16.67 %  33.33 %   50.0 %  100.0 %
2   3   25.0 %   37.5 %   37.5 %  100.0 %
user_36200030

You can use:

import pandas as pd
df = pd.DataFrame({
    'ID': range(1, 4),
    'col1': [10, 5, 10],
    'col2': [15, 10, 15],
    'col3': [10, 15, 15],
    'total': [35, 30, 40]
})

idx = ['col1', 'col2', 'col3', 'total']

df[idx] = df[idx].apply(lambda x: x / x['total'], axis=1)
df

which gives you:

|    |   ID |     col1 |     col2 |     col3 |   total |
|---:|-----:|---------:|---------:|---------:|--------:|
|  0 |    1 | 0.285714 | 0.428571 | 0.285714 |       1 |
|  1 |    2 | 0.166667 | 0.333333 | 0.5      |       1 |
|  2 |    3 | 0.25     | 0.375    | 0.375    |       1 |
user_85978640

You can use div or divide, and perform division on bulk column and assign them back, and in case if you don't know the index of the column, you can just use the column labels.

cols = ['col1', 'col2', 'col3', 'total']

df[cols] = df[cols].divide(df['total'], axis=0)*100

OUTPUT:

   ID       col1       col2       col3  total
0   1  28.571429  42.857143  28.571429  100.0
1   2  16.666667  33.333333  50.000000  100.0
2   3  25.000000  37.500000  37.500000  100.0

If you want the values to round and to be represented as string with % sign, you can just use round and convert it to string and add the % sign

df[cols] = (df[cols].divide(df['total'], axis=0)*100).round(2).astype(str) + ' %'

OUTPUT:

   ID     col1     col2     col3    total
0   1  28.57 %  42.86 %  28.57 %  100.0 %
1   2  16.67 %  33.33 %   50.0 %  100.0 %
2   3   25.0 %   37.5 %   37.5 %  100.0 %
user_91363480 
df = pd.DataFrame({
    'ID': range(1, 4),
    'col1': [10, 5, 10],
    'col2': [15, 10, 15],
    'col3': [10, 15, 15],
    'total': [35, 30, 40]
})

cols = ['col1', 'col2', 'col3', 'total']
for col in cols:
    df[col] = (df[col]/df['total']*100).round(2).astype(str) + ' %'

Output

    ID  col1    col2    col3    total
0   1   28.57 % 42.86 % 28.57 % 100.0 %
1   2   16.67 % 33.33 % 50.0 %  100.0 %
2   3   25.0 %  37.5 %  37.5 %  100.0 %
user_160795390 
import pandas as pd 

df = pd.DataFrame({
    'ID': range(1, 4),
    'col1': [10, 5, 10],
    'col2': [15, 10, 15],
    'col3': [10, 15, 15],
    'total': [35, 30, 40]
})

df['col1'] = (df['col1']/df['total']).mul(100).round(2).astype(str).add('%')
df['col2'] = (df['col2']/df['total']).mul(100).round(2).astype(str).add('%')
df['col3'] = (df['col3']/df['total']).mul(100).round(2).astype(str).add('%')
df['total'] = (df['total']/df['total']).mul(100).round(2).astype(str).add('%')


print(df)

prints

   ID    col1    col2    col3   total
0   1  28.57%  42.86%  28.57%  100.0%
1   2  16.67%  33.33%   50.0%  100.0%
2   3   25.0%   37.5%   37.5%  100.0%
user_43523170
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