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.
Index to direct ranking. For Series this parameter is unused and defaults to 0.
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 âminâ, but rank always increases by 1 between groups.
For DataFrame objects, rank only numeric columns if set to True.
Changed in version 2.0.0: The default value of numeric_only is now False.
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.
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 âcatâ and âdogâ 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
RetroSearch is an open source project built by @garambo | Open a GitHub Issue
Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo
HTML:
3.2
| Encoding:
UTF-8
| Version:
0.7.4