Pandas DataFrame - value_counts() function

The Pandas DataFrame value_counts() function returns a Series containing counts of unique rows in the DataFrame.

Syntax

DataFrame.value_counts(subset=None, normalize=False, sort=True, 
                       ascending=False, dropna=True)

Parameters

`subset`	`Optional.` Specify columns to use when counting unique combinations, by default use all of the columns.
`normalize`	`Optional.` If set to True, returns proportions rather than frequencies of the unique values.
`sort`	`Optional.` A boolean value to specify to sort by frequencies or not. Default is True.
`ascending`	`Optional.` A boolean value to specify to sort in ascending order or not. Default is False.
`dropna`	`Optional.` A boolean value to specify whether to drop rows that contain NA values or not. Default is True.

Return Value

Returns a Series containing counts of unique rows in the DataFrame.

Example: using value_counts() on a DataFrame

In the example below, a DataFrame df is created. The value_counts() function is used to get the count of unique rows in this DataFrame.

import pandas as pd
import numpy as np

df = pd.DataFrame({
  "Age": [24, 27, 24, 27, 30],
  "Sex": ['M', 'F', 'M', 'F', 'M'],
  "City": ['London', 'London', 'Paris', 'London', 'Paris']},
  index= ["John", "Marry", "Jo", "Kim", "Huang"]
)

print("The DataFrame contains:")
print(df)

print("\ndf.value_counts() returns:")
print(df.value_counts())

print("\ndf.value_counts(sort=False) returns:")
print(df.value_counts(sort=False))

print("\ndf.value_counts(ascending=True) returns:")
print(df.value_counts(ascending=True))

print("\ndf.value_counts(normalize=True) returns:")
print(df.value_counts(normalize=True))

The output of the above code will be:

The DataFrame contains:
       Age Sex    City
John    24   M  London
Marry   27   F  London
Jo      24   M   Paris
Kim     27   F  London
Huang   30   M   Paris

df.value_counts() returns:
Age  Sex  City  
27   F    London    2
24   M    London    1
          Paris     1
30   M    Paris     1
dtype: int64

df.value_counts(sort=False) returns:
Age  Sex  City  
24   M    London    1
          Paris     1
27   F    London    2
30   M    Paris     1
dtype: int64

df.value_counts(ascending=True) returns:
Age  Sex  City  
24   M    London    1
          Paris     1
30   M    Paris     1
27   F    London    2
dtype: int64

df.value_counts(normalize=True) returns:
Age  Sex  City  
27   F    London    0.4
24   M    London    0.2
          Paris     0.2
30   M    Paris     0.2
dtype: float64

Example: using subset parameter

By using subset parameter, we can specify columns to use when counting unique combinations. Consider the example below:

import pandas as pd
import numpy as np

df = pd.DataFrame({
  "Age": [24, 27, 24, 27, 30],
  "Sex": ['M', 'F', 'M', 'F', 'M'],
  "City": ['London', 'London', 'Paris', 'London', 'Paris']},
  index= ["John", "Marry", "Jo", "Kim", "Huang"]
)

print("The DataFrame contains:")
print(df)

print("\ndf.value_counts() returns:")
print(df.value_counts())

print("\ndf.value_counts(subset=['Age', 'Sex']) returns:")
print(df.value_counts(subset=['Age', 'Sex']))

The output of the above code will be:

The DataFrame contains:
       Age Sex    City
John    24   M  London
Marry   27   F  London
Jo      24   M   Paris
Kim     27   F  London
Huang   30   M   Paris

df.value_counts() returns:
Age  Sex  City  
27   F    London    2
24   M    London    1
          Paris     1
30   M    Paris     1
dtype: int64

df.value_counts(subset=['Age', 'Sex']) returns:
Age  Sex
24   M      2
27   F      2
30   M      1
dtype: int64

❮ Pandas DataFrame - Functions