Pandas. Dataframe.unstack

Official documents: Pandas. Dataframe.unstack¶Dataframe. Unstack (Level=-1, fill_value=none) [source]¶

Pivot A level of the (necessarily hierarchical) index labels, returning a DATAFRAME has a new level of column labels WH OSE Inner-most level consists of the pivoted index labels. If The index is not a multiindex, the output would be a Series (the analogue's stack when the columns are not a multiindex (when there is only one row index, the result generates a series, which is detailed in the following example) the level involved'll automatically get sorted.

Parameters:	Level: int, string, or List of the default-1 (last level) Level (s) of the index to Unstack, can pass level name fill_value : Replace NaN with this value if the unstack produces Missing values
Returns:	unstacked : Dataframe or Series

Also dataframe.pivot pivot a table based on column values. Dataframe.stack Pivot A level of the column labels (inverse operation from Unstack).

Examples

>>> index = PD. Multiindex.from_tuples (' One ', ' a '), (' One ', ' B '),
...                                    (' Two ', ' a '), (' Two ', ' B ')])
>>> s = PD. Series (Np.arange (1.0, 5.0), Index=index)
>>> s
one  a   1.0
     B  2.0 two A   3.0
     B   4.0
Dtype:float64

>>> S.unstack (level=-1)
     a   b
one  1.0  2.0
Two  3.0  4.0

>>> S.unstack (level=0)
   one  two
a  1.0   3.0
B  2.0   4.0

>>> df = s.unstack (level=0)
>>> df.unstack ()
one  a  1.0
     B  2.0
Two  a  3.0
     B  4.0
Dtype:float64

The series returned here would have been: one     two
a  b       a  b
1  2       3  4

Such a dateframe, the same is to convert the row index to the column index, only so that the data is only one row, so automatically converted to series.