Adding two pandas dataframes

Question

I have two dataframes, both indexed by timeseries. I need to add the elements together to form a new dataframe, but only if the index and column are the same. If the item does not exist in one of the dataframes then it should be treated as a zero.

I've tried using .add but this sums regardless of index and column. Also tried a simple combined_data = dataframe1 + dataframe2 but this give a NaN if both dataframes don't have the element.

Any suggestions?

Answer 1

How about x.add(y, fill_value=0)?

import pandas as pd  df1 = pd.DataFrame([(1,2),(3,4),(5,6)], columns=['a','b']) Out:     a  b 0  1  2 1  3  4 2  5  6  df2 = pd.DataFrame([(100,200),(300,400),(500,600)], columns=['a','b']) Out:       a    b 0  100  200 1  300  400 2  500  600  df_add = df1.add(df2, fill_value=0) Out:       a    b 0  101  202 1  303  404 2  505  606 

Answer 2

If I understand you correctly, you want something like:

(x.reindex_like(y).fillna(0) + y.fillna(0).fillna(0)) 

This will give the sum of the two dataframes. If a value is in one dataframe and not the other, the result at that position will be that existing value (look at B0 in X and B0 in Y and look at final output). If a value is missing in both dataframes, the result at that position will be zero (look at B1 in X and B1 in Y and look at final output).

>>> x    A   B   C 0  1   2 NaN 1  3 NaN   4 >>> y     A   B   C 0   8 NaN  88 1   2 NaN   5 2  10  11  12 >>> (x.reindex_like(y).fillna(0) + y.fillna(0).fillna(0))     A   B   C 0   9   2  88 1   5   0   9 2  10  11  12