CODEWITHSUNDEEP
pythonpandasdataframe
prashant
prashant

Reputation: 271

Convert Pandas dataframe values to percentage

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 %

Upvotes: 6

Views: 10344

Answers (5)

Gold79
Gold79

Reputation: 342

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 %

Upvotes: 1

ThePyGuy
ThePyGuy

Reputation: 18406

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 %

Upvotes: 2

timgeb
timgeb

Reputation: 78650

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 %

Upvotes: 8

Albo
Albo

Reputation: 1644

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 |

Upvotes: 3

Joe Thor
Joe Thor

Reputation: 1260

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%

Upvotes: 0

Related Questions