Python, Pandas: Replacing Values In One Df By Same-index Values From Another Df

I have two data frames with exactly the same index: the first one: 0 1 2 2 0.011765 0.490196 0.015686 2 0.011765 0.490196 0.015686 2 0.007843

Solution 1:

Use the index of the second dataframe to slice the first one and then assign.

df1.loc[df2.index] = df2

Solution 2:

You can use merge empty dataframe df1 with df2 by indexes:

print pd.merge(df1[[]], df2, left_index=True, right_index=True)
          01200.0558520.1181380.05238600.0558520.1181380.05238600.0558520.1181380.05238600.0558520.1181380.05238620.0963940.6356410.06852420.0963940.6356410.06852420.0963940.6356410.06852420.0963940.6356410.06852420.0963940.6356410.068524

Or join:

print df1[[]].join(df2)
          01200.0558520.1181380.05238600.0558520.1181380.05238600.0558520.1181380.05238600.0558520.1181380.05238620.0963940.6356410.06852420.0963940.6356410.06852420.0963940.6356410.06852420.0963940.6356410.06852420.0963940.6356410.068524

If you need preserved index ordering use merge with reset_index, merge on column index and then set_index:

df = pd.merge(df1[[]].reset_index(), df2.reset_index(), on='index').set_index('index')
df.index.name = None 
print df

          01220.0963940.6356410.06852420.0963940.6356410.06852420.0963940.6356410.06852420.0963940.6356410.06852420.0963940.6356410.06852400.0558520.1181380.05238600.0558520.1181380.05238600.0558520.1181380.05238600.0558520.1181380.052386