Solutions for each chapter's exercises can be found at https://github.com/stefmolin/Hands-On-Data-Analysis-with-Pandas/tree/master/solutions in their respective folders.
Hands-On Data Analysis with Pandas
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Hands-On Data Analysis with Pandas
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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)
Preface
Introduction to Data Analysis
Working with Pandas DataFrames
Section 2: Using Pandas for Data Analysis
Data Wrangling with Pandas
Aggregating Pandas DataFrames
Visualizing Data with Pandas and Matplotlib
Plotting with Seaborn and Customization Techniques
Section 3: Applications - Real-World Analyses Using Pandas
Financial Analysis - Bitcoin and the Stock Market
Rule-Based Anomaly Detection
Section 4: Introduction to Machine Learning with Scikit-Learn
Getting Started with Machine Learning in Python
Making Better Predictions - Optimizing Models
Machine Learning Anomaly Detection
Section 5: Additional Resources
The Road Ahead
Solutions
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