I have 2 df one is
df1 = {'col_1': [3, 2, 1, 0], 'col_2': ['a', 'b', 'c', 'd']} df2 = {'col_1': [3, 2, 1, 3]} I want the result as follows
df3 = {'col_1': [3, 2, 1, 3], 'col_2': ['a', 'b', 'c', 'a']} The column 2 of the new df is the same as the column 2 of the df1 depending on the value of the df1.
Add the new column by mapping the values from df1 after setting its first column as index:
df3 = df2.copy() df3['col_2'] = df2['col_1'].map(df1.set_index('col_1')['col_2']) output:
col_1 col_2 0 3 a 1 2 b 2 1 c 3 3 a You can do it with merge after converting the dicts to df with pd.DataFrame():
output = pd.DataFrame(df2) output = output.merge(pd.DataFrame(df1),on='col_1',how='left') Or in a one-liner:
output = pd.DataFrame(df2).merge(pd.DataFrame(df1),on='col_1',how='left') Outputs:
col_1 col_2 0 3 a 1 2 b 2 1 c 3 3 a This could be a simple way of doing it.
# use df1 to create a lookup dictionary lookup = df1.set_index("col_1").to_dict()["col_2"] # look up each value from df2's "col_1" in the lookup dict df2["col_2"] = df2["col_1"].apply(lambda d: lookup[d]) 
