import numpy as np
import pandas as pd
df = pd.DataFrame(np.random.rand(10, 5))
|
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
|
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)
|
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 |