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## Pandas Group by Quantile

Python is the most commonly used for developing software among programmers. It provides multiple built-in functions/methods inside the different modules for multitasking, such as calculating the desired nth number of quartiles between two points and many more. For this purpose, the “groupby.quantile()” function can be used.

This blog will discuss:

## What is a Quantile Group in Python?

In Python, the quantile indicates the particular part of the dataset that defines how many values are above and below a specific limit in a distribution. In Python, the quantiles follow the generic concept of quantile group. To perform this task, it takes input in the form of an array with the number and as a result, generates the value at the “nth” quantile.

## What is the “groupby.quantile()” Function in Python?

The “groupby.quantile()” function is utilized for calculating the quartile by the group in Python. This functionality can be utilized provided by importing the “pandas” module. It is commonly utilized for data analysis. To do so, first, it will divide each row in a provided DataFrame into equal groups sized on a particular column’s values. Then, it finds the aggregated values for each divided group.

Now, move ahead and check out the provided example for a better understanding!

Example

First, import the “pandas” module. Then, create a new “courses” DataFrame with values. After that, use the DataFrame() method and then, call the “groupby.quantile()” function along with the required parameters. For instance, we have provided the DataFrame column name as “Name” and quantile values as “0.25”:

import pandas as pd

courses = {'Name': ['Maria', 'Maria', 'Maria',
'Alex', 'Alex', 'Alex',
'David', 'David', 'David'],
'Marks': [1, 5, 6, 9, 2, 9, 9, 3, 5]
}
df = pd.DataFrame(courses)
print(df.groupby('Name').quantile(0.25))

As you can see, we have successfully retrieved the 25th quantile of the group from the newly created DataFrame with respect to all column elements: If you want to get the 5th quantile of the group, then simply place the “0.50” inside the “groupby.quantile()” function as a parameter:

print(df.groupby('Name').quantile(0.50))

Output You can also get the 75th quantile of the group from the desired DataFrame by using the “0.75” value and passing it as an argument to the “groupby.quantile()” function:

print(df.groupby('Name').quantile(0.75)) That’s all! We have briefly explained the “groupby.quantile()” function in Python.

## Conclusion

The “groupby.quantile()” function can be utilized for calculating the quartile by the group in Python. It can be utilized by importing the “pandas” module for data analysis. This article demonstrated about the “groupby.quantile()” function in Python in detail.

Source: linuxhint.com