Add a portion of a dataframe to another dataframe-CodePudding

Suppose to have two dataframes, df1 and df2, with equal number of columns, but different number of rows, e.g:

df1 = pd.DataFrame([(1,2),(3,4),(5,6),(7,8),(9,10),(11,12)], columns=['a','b'])

       a   b
   1   1   2
   2   3   4
   3   5   6
   4   7   8
   5   9   10
   6   11  12

df2 = pd.DataFrame([(100,200),(300,400),(500,600)], columns=['a','b'])

        a     b
   1   100   200
   2   300   400
   3   500   600

I would like to add df2 to the df1 tail (df1.loc[df2.shape[0]:]), thus obtaining:

        a     b
   1    1     2
   2    3     4
   3    5     6
   4   107   208
   5   309   410
   6   511   612

Any idea? Thanks!

CodePudding user response：

If there is more rows in df1 like in df2 rows is possible use DataFrame.iloc with convert values to numpy array for avoid alignment (different indices create NaNs):

df1.iloc[-df2.shape[0]:]  = df2.to_numpy()
print (df1)
     a    b
0    1    2
1    3    4
2    5    6
3  107  208
4  309  410
5  511  612

For general solution working with any number of rows with unique indices in both Dataframe with rename and DataFrame.add:

df = df1.add(df2.rename(dict(zip(df2.index[::-1], df1.index[::-1]))), fill_value=0)
print (df)

       a      b
0    1.0    2.0
1    3.0    4.0
2    5.0    6.0
3  107.0  208.0
4  309.0  410.0
5  511.0  612.0

CodePudding user response：

How about:

idx = df1.shape[0] - df2.shape[0]
df1[idx:] = df1[idx:].values   df2.values

Output:

     a    b
0    1    2
1    3    4
2    5    6
3  107  208
4  309  410
5  511  612