Learn the best practices and efficient techniques to manipulate and reshape data using pandas.

Machine Learning Essentials

This module 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 module, we’ll learn about the best practices and efficient techniques for working with data. We’ll encounter numerous examples of real-world data analysis problems and will gain hands-on experience with the pandas library to solve them effectively. This module will also cover performance considerations and provide insights on common pitfalls to avoid when using pandas. The module 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 our code. At the end of this module, we’ll be able to effectively handle data using pandas.

Effective Data Manipulation with pandas

* The Series object is a one-dimensional data structure. It can hold numerical data, time data, strings, or arbitrary Python objects. If we're dealing with numeric data, using pandas rather than a Python list will benefit us.
* As compared to Python lists, pandas is faster, consumes less memory, and comes with built-in methods that are very useful to manipulate the data. Also, the index abstraction allows us to access values by position or label. 
A Series can have empty values and has some similarities to NumPy arrays. It's the primary workhorse of pandas; mastering it will pay dividends.


See the advantages of Series and the pandas library.

Introduction

Series Deep Dive

DataFrames

Manipulating Data

Conclusion

Summary