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

Chapter 1: Introduction to Data Analysis

Before we can begin our hands-on introduction to data analysis with pandas, we need to learn about the fundamentals of data analysis. Those who have ever looked at the documentation for a software library know how overwhelming it can be if you have no clue what you are looking for. Therefore, it is essential that we master not only the coding aspect but also the thought process and workflow required to analyze data, which will prove the most useful in augmenting our skill set in the future.

Much like the scientific method, data science has some common workflows that we can follow when we want to conduct an analysis and present the results. The backbone of this process is statistics, which gives us ways to describe our data, make predictions, and also draw conclusions about it. Since prior knowledge of statistics is not a prerequisite, this chapter will give us exposure to the statistical concepts we will use throughout this book, as well as areas for further exploration.

After covering the fundamentals, we will get our Python environment set up for the remainder of this book. Python is a powerful language, and its uses go way beyond data science: building web applications, software, and web scraping, to name a few. In order to work effectively across projects, we need to learn how to make virtual environments, which will isolate each project's dependencies. Finally, we will learn how to work with Jupyter Notebooks in order to follow along with the text.

The following topics will be covered in this chapter:

  • The fundamentals of data analysis
  • Statistical foundations
  • Setting up a virtual environment