CODEWITHSUNDEEP
pandas
panda
panda

Reputation: 625

How to make two NaN as NaN after the operation instead of making it zero?

I have the following data frame

import pandas as pd
import numpy as np
d = {
    'ID':[1,2,3],
    'W1':[5,6,7],
    'W2':[9, np.nan,10],
    'w3':[11,np.nan,np.nan]
}
df = pd.DataFrame(data = d)
df


  ID    W1  W2   w3
0   1   5   9.0    11.0
1   2   6   NaN     NaN
2   3   7   10.0    NaN

I am doing the following operations

df['Sum1'] = (df[['W1','W2']]).sum(axis = 1)/2
df['Sum2'] = (df[['W2','w3']]).sum(axis = 1)/2


    ID  W1  W2      w3  Sum1    Sum2
0   1   5   9.0    11.0 7.0     10.0
1   2   6   NaN     NaN 3.0     0.0
2   3   7   10.0    NaN 8.5     5.0

How to make Sum2 of ID "2" as NaN instead of 0 after doing the above operations??

Upvotes: 1

Views: 203

Answers (1)

jezrael
jezrael

Reputation: 862761

Add parameter min_count=1 to DataFrame.sum:

min_count : int, default 0
The required number of valid values to perform the operation. If fewer than min_count non-NA values are present the result will be NA.

New in version 0.22.0: Added with the default being 0. This means the sum of an all-NA or empty Series is 0, and the product of an all-NA or empty Series is 1.

df['Sum1'] = (df[['W1','W2']]).sum(axis = 1, min_count=1)/2
df['Sum2'] = (df[['W2','w3']]).sum(axis = 1, min_count=1)/2

print (df)
   ID  W1    W2    w3  Sum1  Sum2
0   1   5   9.0  11.0   7.0  10.0
1   2   6   NaN   NaN   3.0   NaN
2   3   7  10.0   NaN   8.5   5.0

But is seems you need means - then it working like need:

df['Sum1'] = (df[['W1','W2']]).mean(axis = 1)
df['Sum2'] = (df[['W2','w3']]).mean(axis = 1)

print (df)
   ID  W1    W2    w3  Sum1  Sum2
0   1   5   9.0  11.0   7.0  10.0
1   2   6   NaN   NaN   6.0   NaN
2   3   7  10.0   NaN   8.5  10.0

Upvotes: 2

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