I want to use applymap method with a little bit complex function in the dataset below.
value1 value2 value3 value4 value5 people
147 119 69 92 106 533.0
31 20 12 14 26 103.0
37 22 24 18 19 120.0
10 13 7 13 10 53.0
38 48 18 30 27 161.0
401 409 168 354 338 1670.0
109 92 55 82 69 407.0
5 9 7 11 9 41.0
44 36 21 48 28 177.0
59 40 19 38 27 183.0
8 9 1 7 10 35.0
People column represents sum of the value columns. I want to replace the value numbers with percentages of them. For example: In first row value1 is 147 and sum of the values in first row is 533. I want to replace 147 with (147/533)*100
I think it looks like this. but i couldn't make it work.
df.loc[:, 'value1':'value5'] = df.loc[:, 'value1':'value5'].applymap(lambda x: (x / df['people'])*100)
Function applymap is used for process each value of DataFrame elemenwise.
Better is use vectorized solution with DataFrame.div:
df.loc[:, 'value1':'value5'] = df.loc[:, 'value1':'value5'].div(df['people'], axis=0) * 100
print (df)
value1 value2 value3 value4 value5 people
0 27.579737 22.326454 12.945591 17.260788 19.887430 533.0
1 30.097087 19.417476 11.650485 13.592233 25.242718 103.0
2 30.833333 18.333333 20.000000 15.000000 15.833333 120.0
3 18.867925 24.528302 13.207547 24.528302 18.867925 53.0
4 23.602484 29.813665 11.180124 18.633540 16.770186 161.0
5 24.011976 24.491018 10.059880 21.197605 20.239521 1670.0
6 26.781327 22.604423 13.513514 20.147420 16.953317 407.0
7 12.195122 21.951220 17.073171 26.829268 21.951220 41.0
8 24.858757 20.338983 11.864407 27.118644 15.819209 177.0
9 32.240437 21.857923 10.382514 20.765027 14.754098 183.0
10 22.857143 25.714286 2.857143 20.000000 28.571429 35.0
Another numpy solution with broadcasting:
df.loc[:, 'value1':'value5'] = (df.loc[:, 'value1':'value5'].values /
df['people'].values[:, None] * 100)
print (df)
value1 value2 value3 value4 value5 people
0 27.579737 22.326454 12.945591 17.260788 19.887430 533.0
1 30.097087 19.417476 11.650485 13.592233 25.242718 103.0
2 30.833333 18.333333 20.000000 15.000000 15.833333 120.0
3 18.867925 24.528302 13.207547 24.528302 18.867925 53.0
4 23.602484 29.813665 11.180124 18.633540 16.770186 161.0
5 24.011976 24.491018 10.059880 21.197605 20.239521 1670.0
6 26.781327 22.604423 13.513514 20.147420 16.953317 407.0
7 12.195122 21.951220 17.073171 26.829268 21.951220 41.0
8 24.858757 20.338983 11.864407 27.118644 15.819209 177.0
9 32.240437 21.857923 10.382514 20.765027 14.754098 183.0
10 22.857143 25.714286 2.857143 20.000000 28.571429 35.0
If want something similar like applymap is possible use apply, but solutions above are faster:
df.loc[:, 'value1':'value5'] = )df.loc[:, 'value1':'value5']
.apply(lambda x: (x / df['people'])*100))
print (df)
value1 value2 value3 value4 value5 people
0 27.579737 22.326454 12.945591 17.260788 19.887430 533.0
1 30.097087 19.417476 11.650485 13.592233 25.242718 103.0
2 30.833333 18.333333 20.000000 15.000000 15.833333 120.0
3 18.867925 24.528302 13.207547 24.528302 18.867925 53.0
4 23.602484 29.813665 11.180124 18.633540 16.770186 161.0
5 24.011976 24.491018 10.059880 21.197605 20.239521 1670.0
6 26.781327 22.604423 13.513514 20.147420 16.953317 407.0
7 12.195122 21.951220 17.073171 26.829268 21.951220 41.0
8 24.858757 20.338983 11.864407 27.118644 15.819209 177.0
9 32.240437 21.857923 10.382514 20.765027 14.754098 183.0
10 22.857143 25.714286 2.857143 20.000000 28.571429 35.0