Pandas Get Most Frequent Value

[Linux Hint]

While working with datasets for analysis and data manipulation, developers frequently need to determine the most frequent value within a dataset. For this purpose, a powerful data manipulation library in Python called “Pandas” is used. It provides the most efficient methods for extracting the most frequent value from a DataFrame or Series.

In this post, we will demonstrate the methods for extracting the most frequent value using Pandas.

How to Get the Most Frequent Value in Pandas?

For getting the most frequent value in pandas, use the following methods:

• mod() method
• value_counts() method with idxmax() method

Method 1: Get the Most Frequent Value Using “mod()” Method

To find the values that are used the most frequently in data frames using Pandas, utilize the panda’s series “mod()” method. It returns a series containing the most frequent values in the original series, as there can be multiple values with the same highest frequency.

Example

In this example, first, we will create a data frame using the “DataFrame()” method of the “pandas” library and then print it on the console:

import pandas

data = pandas.DataFrame({

'Color': ['Red', 'Blue', 'Green', 'Yellow', 'White', 'Brown', 'Black'],

'Alpha_num': ['1', '2', '3', '4', '5', '6', '7']})

print(data)

Output

Now, call the “mod()” method on the array “Alpha_num” of the data frame to get the repetitive values in an array:

print(data['Alpha_num'].mode())

The below-given output shows that the “1” and “2” are the most frequent values in the array “Alpha_num”:

Method 2: Get Most Frequent Value Using “value_counts()” Method With “idxmax()” Method

Use the “value_counts()” method followed by “idxmax()” method. The “value_counts()” method counts the occurrences of each unique value, and the “idxmax()” method returns the value with the highest count.

Example

Here, we will call the “value_counts()” method with the “idxmax()” method on the array “Alpha_num” of the data frame to get the repetitive values in the array. The “value_counts()” method will first get the frequency count of each unique value and then, retrieve the most frequent value using the “idxmax()” method:

print(data['Alpha_num'].value_counts().idxmax())

The given output indicates that the “1” is the most frequent value on the array “Alpha_num”:

You can also update the values at any index of the array in a data frame with the help of the “at” attribute:

data.at[2, 'Alpha_num'] = '6'

print(data)

As you can see that the value of “Green” at index “2” has been successfully updated from “3” to “6”:

That was all about the pandas getting the most frequent value in Python.

Conclusion

To get the most frequent value in pandas, use the panda series “mod()” method or the “value_counts()” method followed by the “idxmax()” method. The first approach panda series “mod()” method is useful for getting the series of most frequent values. In this post, we demonstrated the methods for extracting the most frequent value using Pandas.

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