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

DataFrames hold tabular data. Databases hold tabular data. We can perform many of the same operations on DataFrames that we can on database tables. Here are the two tables we'll be using for examples:

Learn how to add rows and columns in a DataFrame.

Introduction

Series Deep Dive

DataFrames

Manipulating Data

Wrapping Up

Appendix

Adding Rows and Columns in a DataFrame