Find percentile stats of a given column

Question

I have a pandas data frame my_df, where I can find the mean(), median(), mode() of a given column:

my_df['field_A'].mean()
my_df['field_A'].median()
my_df['field_A'].mode()

I am wondering is it possible to find more detailed stats such as 90 percentile? Thanks!

Answer 1

• 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

Answer 2

assume series s

s = pd.Series(np.arange(100))

Get quantiles for [.1, .2, .3, .4, .5, .6, .7, .8, .9]

s.quantile(np.linspace(.1, 1, 9, 0))

0.1     9.9
0.2    19.8
0.3    29.7
0.4    39.6
0.5    49.5
0.6    59.4
0.7    69.3
0.8    79.2
0.9    89.1
dtype: float64

OR

s.quantile(np.linspace(.1, 1, 9, 0), 'lower')

0.1     9
0.2    19
0.3    29
0.4    39
0.5    49
0.6    59
0.7    69
0.8    79
0.9    89
dtype: int32

Answer 3

I figured out below would work:

my_df.dropna().quantile([0.0, .9])

Answer 4

You can even give multiple columns with null values and get multiple quantile values (I use 95 percentile for outlier treatment)

my_df[['field_A','field_B']].dropna().quantile([0.0, .5, .90, .95])

Answer 5

a very easy and efficient way is to call the describe function on the particular column

df['field_A'].describe()

this will give you the mean ,max ,median and the 75th percentile

Answer 6

Describe will give you quartiles, if you want percentiles, you can do something like

 df['YOUR_COLUMN_HERE'].describe(percentiles=[.1, .2, .3, .4, .5, .6 , .7, .8, .9, 1])