pyspark.pandas.groupby.GroupBy.head

GroupBy.head(n: int = 5) → FrameLike

Return first n rows of each group.

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, 4, 1, 3, 4, 9, 10, 5, 6])
>>> df
    a   b  c
7   1   2  3
2   1   3  5
4   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').head(2).sort_index()
    a   b  c
2   1   3  5
3   2   6  1
4   2   9  2
5   3   7  3
7   1   2  3
10  3  10  4

>>> df.groupby('a')['b'].head(2).sort_index()
2      3
3      6
4      9
5      7
7      2
10    10
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").head(-1) 
   A   B
0  g  g0
1  g  g1
2  g  g2
4  h  h0

pyspark.pandas.groupby.GroupBy.ffill pyspark.pandas.groupby.GroupBy.backfill