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Hands-On Data Analysis with Pandas

Hands-On Data Analysis with Pandas

By : Stefanie Molin
4.7 (11)
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Hands-On Data Analysis with Pandas

Hands-On Data Analysis with Pandas

4.7 (11)
By: Stefanie Molin

Overview of this book

Data analysis has become a necessary skill in a variety of domains where knowing how to work with data and extract insights can generate significant value. Hands-On Data Analysis with Pandas will show you how to analyze your data, get started with machine learning, and work effectively with 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 powerful pandas library to perform data wrangling to reshape, clean, and aggregate your data. Then, you will be able 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. By the end of this book, you will be equipped with the skills you need to use pandas to ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets.
Table of Contents (21 chapters)
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1
Section 1: Getting Started with Pandas
4
Section 2: Using Pandas for Data Analysis
9
Section 3: Applications - Real-World Analyses Using Pandas
12
Section 4: Introduction to Machine Learning with Scikit-Learn
16
Section 5: Additional Resources
18
Solutions

Financial Analysis - Bitcoin and the Stock Market

It's time to switch gears and work on an application. In this chapter, we will explore a financial application by performing an analysis of bitcoin and the stock market. This chapter builds upon everything we have learned so far—we will extract data from the Internet with pandas; perform some exploratory data analysis; create visualizations with pandas, seaborn, and matplotlib; calculate important metrics for analyzing the performance of financial instruments using pandas; and get a taste of building some models. Note that we are not trying to learn financial analysis here, but rather walk through an introduction of how the skills we have learned in this book can be applied to financial analysis.

This chapter is also a departure from the standard workflow in this book. Up until this point, we have been working with...

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