Book Image

Hands-On Data Analysis with Pandas - Second Edition

By : Stefanie Molin
5 (1)
Book Image

Hands-On Data Analysis with Pandas - Second Edition

5 (1)
By: Stefanie Molin

Overview of this book

Extracting valuable business insights is no longer a ‘nice-to-have’, but an essential skill for anyone who handles data in their enterprise. Hands-On Data Analysis with Pandas is here to help beginners and those who are migrating their skills into data science get up to speed in no time. This book will show you how to analyze your data, get started with machine learning, and work effectively with the Python libraries often used for data science, such as pandas, NumPy, matplotlib, seaborn, and scikit-learn. Using real-world datasets, you will learn how to use the pandas library to perform data wrangling to reshape, clean, and aggregate your data. Then, you will learn how to conduct exploratory data analysis by calculating summary statistics and visualizing the data to find patterns. In the concluding chapters, you will explore some applications of anomaly detection, regression, clustering, and classification using scikit-learn to make predictions based on past data. This updated edition will equip you with the skills you need to use pandas 1.x to efficiently perform various data manipulation tasks, reliably reproduce analyses, and visualize your data for effective decision making – valuable knowledge that can be applied across multiple domains.
Table of Contents (21 chapters)
Section 1: Getting Started with Pandas
Section 2: Using Pandas for Data Analysis
Section 3: Applications – Real-World Analyses Using Pandas
Section 4: Introduction to Machine Learning with Scikit-Learn
Section 5: Additional Resources

Section 4: Introduction to Machine Learning with Scikit-Learn

Up to this point in the book, we have focused on data analysis tasks using pandas, but there is so much more data science we can do with Python. These next three chapters will serve as an introduction to machine learning in Python with scikit-learn—that's not to say that we will be abandoning everything we have worked on so far, though. As we have seen, pandas is an essential tool for quickly exploring, cleaning, visualizing, and analyzing data—all of which still need to be done before attempting any machine learning. We won't go into any theory; instead, we will show how machine learning tasks, such as clustering, classification, and regression, can be easily implemented in Python.

This section comprises the following chapters:

  • Chapter 9, Getting Started with Machine Learning in Python
  • Chapter 10, Making Better Predictions – Optimizing Models
  • Chapter 11, Machine Learning...