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Sort Dataframe according to row frequency in Pandas
  • Last Updated : 24 Feb, 2021

In this article, we will discuss how to use count() and sort_values() in pandas. So the count in pandas counts the frequency of elements in the dataframe column and then sort sorts the dataframe according to element frequency.

  • count(): This method will show you the number of values for each column in your DataFrame.
  • sort_values(): This method helps us to sort our dataframe. In this method, we pass the column and our data frame is sorted according to this column.

Example 1: Program to sort data frame in descending order according to the element frequency.

Python




# import pandas
import pandas as pd
  
# create dataframe
df = pd.DataFrame({'Name': ['Mukul', 'Rohan', 'Mukul', 'Manoj',
                            'Kamal', 'Rohan', 'Robin'],
                     
                   'age': [22, 22, 21, 20, 21, 24, 20]})
  
# print dataframe
print(df)
  
# use count() and sort()
df = df.groupby(['Name'])['age'].count().reset_index(
  name='Count').sort_values(['Count'], ascending=False)
  
# print dataframe
print(df)


Output:



Example 2: Program to sort data frame in ascending order according to the element frequency.

Python




# import pandas
import pandas as pd
  
# create dataframe
df = pd.DataFrame({'Name': ['Mukul', 'Rohan', 'Mukul', 'Manoj',
                            'Kamal', 'Rohan', 'Robin'],
                     
                   'age': [22, 22, 21, 20, 21, 24, 20]})
  
# print dataframe
print(df)
  
# use count() and sort()
df = df.groupby(['Name'])['age'].count().reset_index(
  name='Count').sort_values(['Count'], ascending=True)
  
# print dataframe
print(df)


Output:


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