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Adding a solution for when you want to take the second element from your pandas dataframe index, which is a tuple, and move it into its own column First, assign a column with the default value ('other' in the example in the op), and then replace values in this new column using a list of (condition, replacement value) tuples. Not sure if there is a shorter way to do this but this way works:

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Pandas offers a versatile toolkit for creating new columns from string slices of existing ones We can use case_when method to create a new column using a switch statement For straightforward fixed slices, the vectorized.str[] accessor is highly efficient.

In this article, we’ll explore different ways to create a new column in a pandas dataframe based on existing columns

This is a common task in data analysis when you need to transform or categorize your data. The most efficient way to create a new column that slices strings from an existing column is to use the str accessor and apply a slice directly This method is vectorized and significantly faster. One common operation is extracting substrings from existing string columns and creating a new column based on these slices

In this article, we will explore how to accomplish this using pandas. To create a new column, use the [] brackets with the new column name at the left side of the assignment This means all values in the given column are multiplied by the value 1.882 at once You do not need to use a loop to iterate each of the rows!

First, let’s create an example dataframe that we’ll reference throughout the article in order to demonstrate a few concepts and showcase how to create new columns based on values from existing ones.

Imagine having a dataframe containing two columns “a” and “b” and you want to create a new column “c” which is a summation of “a” and “b” The article will guide you through various methods to achieve this.