How do you fill-in missing values despite differences in index values? - arrays

Question

Here's my situation. I have a predicted values in the form of array (i.e. ([1,3,1,2,3,...3]) ) and a data frame column of missing NA's. Both array and column of data frame have the same dimensions. But, the indices don't match another.
For instance, the indices of predicted array are 0:100.
On the other hand, the indices of the column of NA's don't begin with 0, rather the first index where NA is observed in the dataFrame.
What's Pandas function will fill-in the first missing value with the first element of predicted array, second missing value with the second element, and so forth?

Answer 1

Assuming your missing data is represented in the DF as NaN/None values:
df = pd.DataFrame({'col1': [2,3,4,5,7,6,5], 'col2': [2,3,None,5,None,None,5],}) # Column 2 has missing values
pred_vals = [11, 22, 33] # Predicted values to be inserted in place of the missing values
print 'Original:'
print df
missing = df[pd.isnull(df['col2'])].index # Find indices of missing values
df.loc[missing, 'col2'] = pred_vals # Replace missing values
print '\nFilled:'
print df
Result:
Original:
col1 col2
0 2 2
1 3 3
2 4 NaN
3 5 5
4 7 NaN
5 6 NaN
6 5 5
Filled:
col1 col2
0 2 2
1 3 3
2 4 11
3 5 5
4 7 22
5 6 33
6 5 5