pandas.tar.gz

pandas

jupyter

jupyter-display

Python

pandas-with-sqlite3

jupyter-live

The course is a comprehensive guide to using pandas for data analysis. It covers a wide range of topics related to data manipulation, including filtering, merging, grouping, pivoting, and reshaping data.

In this course, you’ll learn about the best practices and efficient techniques for working with data. You’ll be provided with numerous examples of real-world data analysis problems and gain hands-on experience with the pandas library to solve them effectively. This course will also cover performance considerations and provide insights on common pitfalls to avoid when using pandas.

The course is suitable for both beginners and experienced users of pandas. It includes detailed explanations of pandas concepts and functions, as well as tips and tricks for optimizing your code. By the time you’re done with this course, you’ll be able to effectively handle data using pandas.

Effective Data Manipulation with pandas

## If-else with pandas

We're going to see one more piece of code that illustrates what we consider a shortcoming of pandas. If we wanted to keep the top five makes and use `Top10` for the remainder of the top ten makes, with "Other" for the rest, there is no built-in pandas method to do that. We could use the following function in combination with `.apply`:


# If-else with pandas

We're going to see one more piece of code that illustrates what we consider a shortcoming of pandas. If we wanted to keep the top five makes and use `Top10` for the remainder of the top ten makes, with "Other" for the rest, there is no built-in pandas method to do that. We could use the following function in combination with `.apply`:


Learn how to perform conditional operations in pandas.

Introduction

Series Deep Dive

DataFrames

Manipulating Data

Wrapping Up

Appendix

Conditional Methods

If-else with pandas