Examples

import numpy as np
import pandas as pd

df = pd.DataFrame(data={'Animal': ['fox', 'Kangaroo', 'deer',
                                   'spider', 'snake'],
                        'Number_legs': [4, 2, 4, 8, np.nan]})
df

	Animal	Number_legs
0	fox	4.0
1	Kangaroo	2.0
2	deer	4.0
3	spider	8.0
4	snake	NaN

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

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	fox	4.0	2.5	3.0	2.5	0.625
1	Kangaroo	2.0	1.0	1.0	1.0	0.250
2	deer	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