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 as pd
df = pd.DataFrame({ 'Name' : [ 'Mukul' , 'Rohan' , 'Mukul' , 'Manoj' ,
'Kamal' , 'Rohan' , 'Robin' ],
'age' : [ 22 , 22 , 21 , 20 , 21 , 24 , 20 ]})
print (df)
df = df.groupby([ 'Name' ])[ 'age' ].count().reset_index(
name = 'Count' ).sort_values([ 'Count' ], ascending = False )
print (df)
|
Output:
Example 2: Program to sort data frame in ascending order according to the element frequency.
Python
import pandas as pd
df = pd.DataFrame({ 'Name' : [ 'Mukul' , 'Rohan' , 'Mukul' , 'Manoj' ,
'Kamal' , 'Rohan' , 'Robin' ],
'age' : [ 22 , 22 , 21 , 20 , 21 , 24 , 20 ]})
print (df)
df = df.groupby([ 'Name' ])[ 'age' ].count().reset_index(
name = 'Count' ).sort_values([ 'Count' ], ascending = True )
print (df)
|
Output:
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