pyspark.pandas.groupby.GroupBy.tail

GroupBy.tail(n: int = 5) → FrameLike[source]

Return last n rows of each group.

Similar to .apply(lambda x: x.tail(n)), but it returns a subset of rows from the original DataFrame with original index and order preserved (as_index flag is ignored).

Does not work for negative values of n.

Returns
DataFrame or Series

Examples

>>> df = ps.DataFrame({'a': [1, 1, 1, 1, 2, 2, 2, 3, 3, 3],
...                    'b': [2, 3, 1, 4, 6, 9, 8, 10, 7, 5],
...                    'c': [3, 5, 2, 5, 1, 2, 6, 4, 3, 6]},
...                   columns=['a', 'b', 'c'],
...                   index=[7, 2, 3, 1, 3, 4, 9, 10, 5, 6])
>>> df
    a   b  c
7   1   2  3
2   1   3  5
3   1   1  2
1   1   4  5
3   2   6  1
4   2   9  2
9   2   8  6
10  3  10  4
5   3   7  3
6   3   5  6

>>> df.groupby('a').tail(2).sort_index()
   a  b  c
1  1  4  5
3  1  1  2
4  2  9  2
5  3  7  3
6  3  5  6
9  2  8  6

>>> df.groupby('a')['b'].tail(2).sort_index()
1    4
3    1
4    9
5    7
6    5
9    8
Name: b, dtype: int64

Supports Groupby positional indexing Since pandas on Spark 3.4 (with pandas 1.4+):

>>> df = ps.DataFrame([["g", "g0"],
...                   ["g", "g1"],
...                   ["g", "g2"],
...                   ["g", "g3"],
...                   ["h", "h0"],
...                   ["h", "h1"]], columns=["A", "B"])
>>> df.groupby("A").tail(-1) 
   A   B
3  g  g3
2  g  g2
1  g  g1
5  h  h1
pyspark.pandas.groupby.GroupBy.shift pyspark.pandas.groupby.DataFrameGroupBy.describe