Sum-Of-All

Sat 17 May 2025

title: "Sum of All on NA" author: "Rj" date: 2019-04-22 description: "-" type: technical_note draft: false

import numpy as np
import pandas as pd

df = pd.DataFrame(np.random.rand(10, 5))

df
0 1 2 3 4
0 0.051358 0.465768 0.998450 0.131171 0.997680
1 0.237126 0.703764 0.055986 0.120710 0.489311
2 0.364137 0.449693 0.803241 0.905241 0.271881
3 0.620432 0.877218 0.282262 0.633186 0.568208
4 0.070414 0.098475 0.811134 0.125991 0.547385
5 0.227223 0.293223 0.299701 0.216877 0.109066
6 0.806406 0.615722 0.780990 0.354237 0.075826
7 0.474298 0.062300 0.292121 0.201447 0.662283
8 0.935634 0.699771 0.814459 0.444021 0.131643
9 0.601526 0.696834 0.386316 0.795693 0.012131 
# Apply some NA on the existing DF

df.iloc[0:3, 0:4] = np.nan

df
0 1 2 3 4
0 NaN NaN NaN NaN 0.997680
1 NaN NaN NaN NaN 0.489311
2 NaN NaN NaN NaN 0.271881
3 0.620432 0.877218 0.282262 0.633186 0.568208
4 0.070414 0.098475 0.811134 0.125991 0.547385
5 0.227223 0.293223 0.299701 0.216877 0.109066
6 0.806406 0.615722 0.780990 0.354237 0.075826
7 0.474298 0.062300 0.292121 0.201447 0.662283
8 0.935634 0.699771 0.814459 0.444021 0.131643
9 0.601526 0.696834 0.386316 0.795693 0.012131 
df.loc[:, 'test'] = df.iloc[:, 2:].sum(axis=1)

df
0 1 2 3 4 test
0 NaN NaN NaN NaN 0.997680 2.993039
1 NaN NaN NaN NaN 0.489311 1.467933
2 NaN NaN NaN NaN 0.271881 0.815643
3 0.620432 0.877218 0.282262 0.633186 0.568208 5.328186
4 0.070414 0.098475 0.811134 0.125991 0.547385 4.552009
5 0.227223 0.293223 0.299701 0.216877 0.109066 2.170157
6 0.806406 0.615722 0.780990 0.354237 0.075826 4.248881
7 0.474298 0.062300 0.292121 0.201447 0.662283 3.529852
8 0.935634 0.699771 0.814459 0.444021 0.131643 4.870143
9 0.601526 0.696834 0.386316 0.795693 0.012131 4.279256

Score: 5

Category: data-wrangling