CODEWITHSUNDEEP
pythonpandas
ba_ul
ba_ul

Reputation: 2209

Combine_first and null values in Pandas

df1:

     0    1
0   nan 3.00
1 -4.00  nan
2   nan 7.00

df2:

      0   1    2
1 -42.00 nan 8.00
2  -5.00 nan 4.00

df3 = df1.combine_first(df2)

df3:

      0    1    2
0   nan 3.00  nan
1 -4.00  nan 8.00
2 -5.00 7.00 4.00

This is what I'd like df3 to be:

      0    1    2
0   nan 3.00  nan
1 -4.00  nan 8.00
2   nan 7.00 4.00

(The difference is in df3.ix[2:2,0:0])

That is, if the column and index are the same for any cell in both df1 and df2, I'd like df1's value to prevail, even if that value is nan. combine_first does that, except when the value in df1 is nan.

Upvotes: 1

Views: 1685

Answers (1)

chrisb
chrisb

Reputation: 52276

Here's a bit of a hacky way to do it. First, align df2 with df1, which creates a frame indexed with the union of df1/df2, filled with df2's values. Then assign back df1's values.

In [325]: df3, _ = df2.align(df1)

In [327]: df3.loc[df1.index, df1.columns] = df1

In [328]: df3
Out[328]: 
    0   1   2
0 NaN   3 NaN
1  -4 NaN   8
2 NaN   7   4

Upvotes: 1

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