cudf.unstack#
- cudf.unstack(df, level, fill_value=None, sort: bool = True)[source]#
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 aDataframe
with specified level of the index pivoted to the column levels.If the index of
df
has single level, returns aSeries
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.
- sortbool, default True
Sort the level(s) in the resulting MultiIndex columns.
- 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