Mastering Adding & Removing Rows/Columns in Python Pandas

Table of Contents

1. Introduction
2. Adding Columns to Data Frames
3. Removing Columns from Data Frames
4. Adding Rows to Data Frames
5. Removing Rows from Data Frames
6. Conclusion

Introduction

In this tutorial, we will learn how to add and remove columns and rows from data frames using Python's pandas library. Data frames are a popular data structure in pandas that allow us to organize and manipulate data efficiently. We will explore different methods to add and remove columns and rows, and also discuss scenarios where these operations are useful. So let's get started!

Adding Columns to Data Frames

Adding columns to a data frame is a straightforward process. We can create a new column and assign it a series of values that we want the column to have. For example, we can combine the first name and last name columns into a single column and name it "full name". We can use the string concatenation method to achieve this. It's important to note that we need to use brackets instead of dot notation when assigning a column to a data frame to avoid conflicts with object attributes.

Removing Columns from Data Frames

Removing columns from a data frame is as easy as using the drop() method. We can specify the columns we want to remove by passing in their names or indexes. The drop method returns a new data frame without the specified columns. If we want to make the changes permanent, we can set the inplace argument to True.

Adding Rows to Data Frames

There are two ways to add rows to a data frame. Firstly, we can add a single row of new data using the append() method. We need to pass the values for the new row as a dictionary or a series. Secondly, we can concatenate two data frames together to create a new data frame with combined rows. We can use the append() method and set the ignore_index argument to True to ensure proper indexing.

Removing Rows from Data Frames

Removing rows from a data frame can be done by specifying the indexes of the rows we want to delete. We can use the drop() method and pass in the indexes as a list or using a conditional. If we want to use a conditional, we need to filter the rows based on a specific condition and then use the resulting indexes to remove the rows.

Conclusion

In this tutorial, we have covered the basics of adding and removing columns and rows from data frames in pandas. We have explored different methods and scenarios where these operations can be useful. By mastering these operations, you will be able to efficiently manipulate and organize your data.