How to RANK in a Pandas DataFrame in Python

Ranking the rows in a Pandas DataFrame in Python can be done by using the rank() function. This function assigns a rank to each value in a column, based on their order.

First, you need to import the Pandas library and create a DataFrame. For example, you can create a DataFrame with some sample data.

import pandas as pd

data = {'product': ['Apple', 'Banana', 'Cherry', 'Date', 'Eggplant'],

              'price': [1.2, 2.3, 2.5, 1.7, 2.0]}

df = pd.DataFrame(data)

Next, you can use the rank() function to assign a rank to each value in the ‘price’ column, based on their order.

For example, to assign a rank to each value in the ‘price’ column, you can use the following code:

df['rank'] = df['price'].rank()

You can also use the rank() function with method parameter, it can take ‘average’,’min’,’max’,’first’,’dense’ methods to define the way of ranking

df['rank'] = df['price'].rank(method='min')

You can also use the ascending parameter in rank() function, to define if ranking should be in ascending or descending order

df['rank'] = df['price'].rank(ascending = False)

By using the rank() function, you can easily assign a rank to each value in a column of a Pandas DataFrame in Python. This can be useful for data analysis and can help you understand how different values compare to each other. It can also be used for competition analysis where you want to see how a particular value ranks with respect to other values in the dataset.

In this Learn through Codes example, you will learn: How to RANK in a Pandas DataFrame in Python.

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