cudf.DataFrame.unstack#

DataFrame.unstack(level=-1, fill_value=None)#

Pivot one or more levels of the (necessarily hierarchical) index labels.

Pivots the specified levels of the index labels of df to the innermost levels of the columns labels of the result.

If the index of df has multiple levels, returns a Dataframe with specified level of the index pivoted to the column levels.
If the index of df has single level, returns a Series with all column levels pivoted to the index levels.

Parameters:

dfDataFrame
levellevel name or index, list-like: Integer, name or list of such, specifying one or more levels of the index to pivot
fill_value: Non-functional argument provided for compatibility with Pandas.

Returns:

Series or DataFrame

Examples

>>> df = cudf.DataFrame()
>>> df['a'] = [1, 1, 1, 2, 2]
>>> df['b'] = [1, 2, 3, 1, 2]
>>> df['c'] = [5, 6, 7, 8, 9]
>>> df['d'] = ['a', 'b', 'a', 'd', 'e']
>>> df = df.set_index(['a', 'b', 'd'])
>>> df
       c
a b d
1 1 a  5
  2 b  6
  3 a  7
2 1 d  8
  2 e  9

Unstacking level ‘a’:

>>> df.unstack('a')
        c
a       1     2
b d
1 a     5  <NA>
  d  <NA>     8
2 b     6  <NA>
  e  <NA>     9
3 a     7  <NA>

Unstacking level ‘d’ :

>>> df.unstack('d')
        c
d       a     b     d     e
a b
1 1     5  <NA>  <NA>  <NA>
  2  <NA>     6  <NA>  <NA>
  3     7  <NA>  <NA>  <NA>
2 1  <NA>  <NA>     8  <NA>
  2  <NA>  <NA>  <NA>     9

Unstacking multiple levels:

>>> df.unstack(['b', 'd'])
      c
b     1           2           3
d     a     d     b     e     a
a
1     5  <NA>     6  <NA>     7
2  <NA>     8  <NA>     9  <NA>

Unstacking single level index dataframe:

>>> df = cudf.DataFrame({('c', 1): [1, 2, 3], ('c', 2):[9, 8, 7]})
>>> df.unstack()
c  1  0    1
      1    2
      2    3
   2  0    9
      1    8
      2    7
dtype: int64