Weighted Average - omit data if missing from value or weight

Question

I have code like this

>>> import pandas as pd
>>> import numpy as np
>>> 
>>> df1 = pd.DataFrame({'value':[10,20,np.nan,40],
...                         'weight':[1,np.nan,3,4]}) 
>>> df1
   value  weight
0   10.0     1.0
1   20.0     NaN
2    NaN     3.0
3   40.0     4.0
>>> (df1["value"] * df1["weight"]).sum() / df1["weight"].sum()
21.25

I want to omit data from calculation if values or weight is missing . i.e I want weighted average like like (10*1 + 40*4) /(1+4) = 34

Please help if this is possible using single expression in pandas.

Answer 1

You can filter first with boolean indexing, mask is created by notnull and all for check all True values per row:

df1 = df1[df1.notnull().all(axis=1)]
print (df1)
   value  weight
0   10.0     1.0
3   40.0     4.0

df2 = (df1["value"] * df1["weight"]).sum() / df1["weight"].sum()
print (df2)
34.0

Or check both columns separately:

df1 = df1[df1["value"].notnull() & df1["weight"].notnull()]
print (df1)
   value  weight
0   10.0     1.0
3   40.0     4.0

Simplier solution with dropna:

df1 = df1.dropna()
print (df1)
   value  weight
0   10.0     1.0
3   40.0     4.0

Or if is necessary specify columns:

df1 = df1.dropna(subset=['value','weight'])
print (df1)
   value  weight
0   10.0     1.0
3   40.0     4.0