Pandas Merge DataFrame Columns With Same Name But Different Rows

Question

I would like to merge two dataframes. Both have the same column names, but different numbers of rows.

The values from the smaller dataframe should then replace the values from the other dataframe

So far I tried using pd.merge

pd.merge(df1, df2, how='left', on='NodeID)

But I do not know how to tell the merge command to use the values from the right dataframe for the columnes 'X' and 'Y'.

df1 = pd.DataFrame(data={'NodeID': [1, 2, 3, 4, 5], 'X': [0, 0, 0, 0, 0], 'Y': [0, 0, 0, 0, 0]})
df2 = pd.DataFrame(data={'NodeID': [2, 4], 'X': [1, 1], 'Y': [1, 1]})

The result should then look like this:

df3 = pd.DataFrame(data={'NodeID': [1, 2, 3, 4, 5], 'X': [0, 1, 0, 1, 0], 'Y':[0, 1, 0, 1, 0]})

df2.set_index('NodeID').combine_first(df1.set_index('NodeID')).reset_index() — anky
– anky, Commented Aug 29, 2019 at 15:13

BENY · Accepted Answer · 2019-08-29 15:14:35Z

2

This is can be done with concat and drop_duplicates

pd.concat([df2,df1]).drop_duplicates('NodeID').sort_values('NodeID')
Out[763]: 
   NodeID  X  Y
0       1  0  0
0       2  1  1
2       3  0  0
1       4  1  1
4       5  0  0

answered Aug 29, 2019 at 15:14

BENY

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