pyspark.pandas.Series.rank#

Series.rank(method='average', ascending=True, numeric_only=False, axis=0, na_option='keep', pct=False)[source]#

Compute numerical data ranks (1 through n) along axis. Equal values are assigned a rank that is the average of the ranks of those values.

Note

the current implementation of rank uses Spark’s Window without specifying partition specification. This leads to moving all data into a single partition in a single machine and could cause serious performance degradation. Avoid this method with very large datasets.

Parameters:
method{‘average’, ‘min’, ‘max’, ‘first’, ‘dense’}

  • average: average rank of group

  • min: lowest rank in group

  • max: highest rank in group

  • first: ranks assigned in order they appear in the array

  • dense: like ‘min’, but rank always increases by 1 between groups

ascendingboolean, default True

False for ranks by high (1) to low (N)

numeric_onlybool, default False

For DataFrame objects, rank only numeric columns if set to True.

Changed in version 4.0.0: The default value of numeric_only is now False.

axis{0 or ‘index’}, default 0

Parameter needed for compatibility with DataFrame.

na_option{‘keep’, ‘top’, ‘bottom’}, default ‘keep’

  • keep: leave NA values where they are

  • top: smallest rank if ascending

  • bottom: largest rank if ascending

Added in version 4.4.0.

pctbool, default False

Whether or not to display the returned rankings in percentile form.

Added in version 4.4.0.

Returns:
rankssame type as caller

Examples

>>> s = ps.Series([1, 2, 2, 3], name='A')
>>> s
0    1
1    2
2    2
3    3
Name: A, dtype: int64

>>> s.rank()
0    1.0
1    2.5
2    2.5
3    4.0
Name: A, dtype: float64

If method is set to ‘min’, it uses lowest rank in group.

>>> s.rank(method='min')
0    1.0
1    2.0
2    2.0
3    4.0
Name: A, dtype: float64

If method is set to ‘max’, it uses highest rank in group.

>>> s.rank(method='max')
0    1.0
1    3.0
2    3.0
3    4.0
Name: A, dtype: float64

If method is set to ‘first’, it is assigned rank in order without groups.

>>> s.rank(method='first')
0    1.0
1    2.0
2    3.0
3    4.0
Name: A, dtype: float64

If method is set to ‘dense’, it leaves no gaps in group.

>>> s.rank(method='dense')
0    1.0
1    2.0
2    2.0
3    3.0
Name: A, dtype: float64

If numeric_only is set to ‘True’, rank only numeric Series, return an empty Series otherwise.

>>> s = ps.Series(['a', 'b', 'c'], name='A', index=['x', 'y', 'z'])
>>> s
x    a
y    b
z    c
Name: A, dtype: object

With pct=True, ranks are expressed as percentiles.

>>> s = ps.Series([1, 2, 2, 3], name='A')
>>> s.rank(pct=True)
0    0.250
1    0.625
2    0.625
3    1.000
Name: A, dtype: float64

With na_option=’top’, NaN values are assigned the smallest rank.

>>> s = ps.Series([1, float('nan'), 2, 3], name='A')
>>> s.rank(na_option='top').sort_index()
0    2.0
1    1.0
2    3.0
3    4.0
Name: A, dtype: float64

With na_option=’bottom’, NaN values are assigned the largest rank.

>>> s.rank(na_option='bottom').sort_index()
0    1.0
1    4.0
2    2.0
3    3.0
Name: A, dtype: float64