You can do this using rank, where pct=True option displays ranks in percentile form.

In [1551]: v = pd.Series([0,2,4,2,10,8,6,1])
In [1556]: v.rank(pct=True)                 
Out[1556]: 
0    0.1250
1    0.4375
2    0.6250
3    0.4375
4    1.0000
5    0.8750
6    0.7500
7    0.2500
dtype: float64

• You can use the pandas.DataFrame.quantile() function.
  • If you look at the API for quantile(), you will see it takes an argument for how to do interpolation. If you want a quantile that falls between two positions in your data:
    • 'linear', 'lower', 'higher', 'midpoint', or 'nearest'.
    • By default, it performs linear interpolation.
    • These interpolation methods are discussed in the Wikipedia article for percentile

import pandas as pd
import numpy as np

# sample data 
np.random.seed(2023)  # for reproducibility
data = {'Category': np.random.choice(['hot', 'cold'], size=(10,)),
        'field_A': np.random.randint(0, 100, size=(10,)),
        'field_B': np.random.randint(0, 100, size=(10,))}
df = pd.DataFrame(data)

df.field_A.mean()  # Same as df['field_A'].mean()
# 51.1

df.field_A.median() 
# 50.0

# You can call `quantile(i)` to get the i'th quantile,
# where `i` should be a fractional number.

df.field_A.quantile(0.1)  # 10th percentile
# 15.6

df.field_A.quantile(0.5)  # same as median
# 50.0

df.field_A.quantile(0.9)  # 90th percentile
# 88.8

df.groupby('Category').field_A.quantile(0.1)
#Category
#cold    28.8
#hot      8.6
#Name: field_A, dtype: float64

df

  Category  field_A  field_B
0     cold       96       58
1     cold       22       28
2      hot       17       81
3     cold       53       71
4     cold       47       63
5      hot       77       48
6     cold       39       32
7      hot       69       29
8      hot       88       49
9      hot        3       49