["python","pandas","dataframe"]
How to merge a Series and DataFrame

If you came here looking for information on how to merge a DataFrame and Series on the index, please look at this answer. The OP's original intention was to ask how to assign series elements as columns to another DataFrame. If you are interested in knowing the answer to this, look at the accepted answer by EdChum. Best I can come up with is df = pd.DataFrame({'a':[1, 2], 'b':[3, 4]}) # see EDIT below s = pd.Series({'s1':5, 's2':6}) for name in s.index: df[name] = s[name] a b s1 s2 0 1 3 5 6 1 2 4 5 6 Can anybody suggest better syntax / faster method? My attempts: df.merge(s) AttributeError: 'Series' object has no attribute 'columns' and df.join(s) ValueError: Other Series must have a name EDIT The first two answers posted highlighted a problem with my question, so please use the following to construct df: df = pd.DataFrame({'a':[np.nan, 2, 3], 'b':[4, 5, 6]}, index=[3, 5, 6]) with the final result a b s1 s2 3 NaN 4 5 6 5 2 5 5 6 6 3 6 5 6
You could construct a dataframe from the series and then merge with the dataframe. So you specify the data as the values but multiply them by the length, set the columns to the index and set params for left_index and right_index to True: In [27]: df.merge(pd.DataFrame(data = [s.values] * len(s), columns = s.index), left_index=True, right_index=True) Out[27]: a b s1 s2 0 1 3 5 6 1 2 4 5 6 EDIT for the situation where you want the index of your constructed df from the series to use the index of the df then you can do the following: df.merge(pd.DataFrame(data = [s.values] * len(df), columns = s.index, index=df.index), left_index=True, right_index=True) This assumes that the indices match the length.