Sort Dataframe according to row frequency in Pandas

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: