xorbits.pandas.DataFrame.rank#

DataFrame.rank(axis: Union[int, Literal['index', 'columns', 'rows']] = 0, method: Literal['average', 'min', 'max', 'first', 'dense'] = 'average', numeric_only: bool = False, na_option: Literal['keep', 'top', 'bottom'] = 'keep', ascending: bool = True, pct: bool = False) → None#

Compute numerical data ranks (1 through n) along axis.

By default, equal values are assigned a rank that is the average of the ranks of those values.

Parameters

  • axis ({0 or 'index', 1 or 'columns'}, default 0) – Index to direct ranking. For Series this parameter is unused and defaults to 0.

  • method ({'average', 'min', 'max', 'first', 'dense'}, default 'average') –

    How to rank the group of records that have the same value (i.e. ties):

    • average: average rank of the group

    • min: lowest rank in the group

    • max: highest rank in the group

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

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

  • numeric_only (bool, default False) –

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

    Changed in version 2.0.0(pandas): The default value of numeric_only is now False.

  • na_option ({'keep', 'top', 'bottom'}, default 'keep') –

    How to rank NaN values:

    • keep: assign NaN rank to NaN values

    • top: assign lowest rank to NaN values

    • bottom: assign highest rank to NaN values

  • ascending (bool, default True) – Whether or not the elements should be ranked in ascending order.

  • pct (bool, default False) – Whether or not to display the returned rankings in percentile form.

Returns

Return a Series or DataFrame with data ranks as values.

Return type

same type as caller

See also

core.groupby.DataFrameGroupBy.rank

Rank of values within each group.

core.groupby.SeriesGroupBy.rank

Rank of values within each group.

Examples

>>> df = pd.DataFrame(data={'Animal': ['cat', 'penguin', 'dog',  
...                                    'spider', 'snake'],
...                         'Number_legs': [4, 2, 4, 8, np.nan]})
>>> df  
    Animal  Number_legs
0      cat          4.0
1  penguin          2.0
2      dog          4.0
3   spider          8.0
4    snake          NaN

Ties are assigned the mean of the ranks (by default) for the group.

>>> s = pd.Series(range(5), index=list("abcde"))  
>>> s["d"] = s["b"]  
>>> s.rank()  
a    1.0
b    2.5
c    4.0
d    2.5
e    5.0
dtype: float64

The following example shows how the method behaves with the above parameters:

  • default_rank: this is the default behaviour obtained without using any parameter.

  • max_rank: setting method = 'max' the records that have the same values are ranked using the highest rank (e.g.: since ‘cat’ and ‘dog’ are both in the 2nd and 3rd position, rank 3 is assigned.)

  • NA_bottom: choosing na_option = 'bottom', if there are records with NaN values they are placed at the bottom of the ranking.

  • pct_rank: when setting pct = True, the ranking is expressed as percentile rank.

>>> df['default_rank'] = df['Number_legs'].rank()  
>>> df['max_rank'] = df['Number_legs'].rank(method='max')  
>>> df['NA_bottom'] = df['Number_legs'].rank(na_option='bottom')  
>>> df['pct_rank'] = df['Number_legs'].rank(pct=True)  
>>> df  
    Animal  Number_legs  default_rank  max_rank  NA_bottom  pct_rank
0      cat          4.0           2.5       3.0        2.5     0.625
1  penguin          2.0           1.0       1.0        1.0     0.250
2      dog          4.0           2.5       3.0        2.5     0.625
3   spider          8.0           4.0       4.0        4.0     1.000
4    snake          NaN           NaN       NaN        5.0       NaN

Warning

This method has not been implemented yet. Xorbits will try to execute it with pandas.

This docstring was copied from pandas.core.frame.DataFrame.