rank(self: 'NDFrameT', axis=0, method: 'str' = 'average', numeric_only: 'bool_t | None | lib.NoDefault' = <no_default>, na_option: 'str' = 'keep', ascending: 'bool_t' = True, pct: 'bool_t' = False) -> 'NDFrameT'
By default, equal values are assigned a rank that is the average of the ranks of those values.
Index to direct ranking.
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.
For DataFrame objects, rank only numeric columns if set to True.
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
Whether or not the elements should be ranked in ascending order.
Whether or not to display the returned rankings in percentile form.
Return a Series or DataFrame with data ranks as values.
Compute numerical data ranks (1 through n) along axis.
core.groupby.GroupBy.rank
Rank of values within each group.
>>> 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
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()See :
... 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
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