pandas combining 2 dataframe and filling all rows
Question:
i have 2 dataframe
df1: df2:
A B C D X Y Z
1 2 3 4 p2 p3 p4
5 6 7 8
9 10 11 12
How can i concat the 2 dataframe such that the output is
A B C D X Y Z
1 2 3 4 p2 p3 p4
5 6 7 8 p2 p3 p4
9 10 11 12 p2 p3 p4
Currently if i use concat only the first row will be applied with the df2 values but other rows are filled with NaN.
Answers:
Use a cross merge:
out = df1.merge(df2, how='cross')
output:
A B C D X Y Z
0 1 2 3 4 p2 p3 p4
1 5 6 7 8 p2 p3 p4
2 9 10 11 12 p2 p3 p4
You will however lose the original index of df1. If it is important to keep use:
out = df1.merge(df2, how='cross').set_axis(df1.index)
# or
out = df1.reset_index().merge(df2, how='cross').set_index('index')
i have 2 dataframe
df1: df2:
A B C D X Y Z
1 2 3 4 p2 p3 p4
5 6 7 8
9 10 11 12
How can i concat the 2 dataframe such that the output is
A B C D X Y Z
1 2 3 4 p2 p3 p4
5 6 7 8 p2 p3 p4
9 10 11 12 p2 p3 p4
Currently if i use concat only the first row will be applied with the df2 values but other rows are filled with NaN.
Use a cross merge:
out = df1.merge(df2, how='cross')
output:
A B C D X Y Z
0 1 2 3 4 p2 p3 p4
1 5 6 7 8 p2 p3 p4
2 9 10 11 12 p2 p3 p4
You will however lose the original index of df1. If it is important to keep use:
out = df1.merge(df2, how='cross').set_axis(df1.index)
# or
out = df1.reset_index().merge(df2, how='cross').set_index('index')