python - pandas: Problems with merging multiple data frames to get desired MultiIndex -
i have defined 3 simple data frames follows:
dates = [dt.datetime(2015,1,1),dt.datetime(2015,1,2)] column_names = ['a','b'] data_open = pd.dataframe([[1,2],[3,4]], index=dates, columns=column_names) data_close = pd.dataframe([[5,6],[7,8]], index=dates, columns=column_names) data_volume = pd.dataframe([[9,10],[11,12]], index=dates, columns=column_names) when printed follows:
print data_open b 2015-01-01 1 2 2015-01-02 3 4 print data_close b 2015-01-01 5 6 2015-01-02 7 8 print data_volume b 2015-01-01 9 10 2015-01-02 11 12 what looking way merge 3 of them , single dataframe multiindex this:
i new pandas , looked in documentation , not find looking for.
you can use concat specify keys , swaplevel , sort_index:
df = pd.concat([data_open, data_close, data_volume], keys=['open', 'close', 'volume']) df = df.swaplevel(0,1).sort_index() print df b 2015-01-01 close 5 6 open 1 2 volume 9 10 2015-01-02 close 7 8 open 3 4 volume 11 12 and if want sort column a:
df = df.groupby(level=0).apply(lambda x: x.sort_values('a')) df.index = df.index.droplevel(0) print df b 2015-01-01 open 1 2 close 5 6 volume 9 10 2015-01-02 open 3 4 close 7 8 volume 11 12
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