The Pandas Workshop: A comprehensive guide to using Python for data analysis with real-world case studies

Learn the fundamentals of data science with Python by analyzing real datasets and solving problems using pandas

Key Features

• Learn how to apply data retrieval, transformation, visualization, and modeling techniques using pandas
• Become highly efficient in unlocking deeper insights from your data, including databases, web data, and more
• Build your experience and confidence with hands-on exercises and activities

Book Description

The Pandas Workshop will teach you how to be more productive with data and generate real business insights to inform your decision-making. You will be guided through real-world data science problems and shown how to apply key techniques in the context of realistic examples and exercises. Engaging activities will then challenge you to apply your new skills in a way that prepares you for real data science projects.

You'll see how experienced data scientists tackle a wide range of problems using data analysis with pandas. Unlike other Python books, which focus on theory and spend too long on dry, technical explanations, this workshop is designed to quickly get you to write clean code and build your understanding through hands-on practice. As you work through this Python pandas book, you'll tackle various real-world scenarios, such as using an air quality dataset to understand the pattern of nitrogen dioxide emissions in a city, as well as analyzing transportation data to improve bus transportation services.

By the end of this data analytics book, you'll have the knowledge, skills, and confidence you need to solve your own challenging data science problems with pandas.

What you will learn

• Access and load data from different sources using pandas
• Work with a range of data types and structures to understand your data
• Perform data transformation to prepare it for analysis
• Use Matplotlib for data visualization to create a variety of plots
• Create data models to find relationships and test hypotheses
• Manipulate time-series data to perform date-time calculations
• Optimize your code to ensure more efficient business data analysis

Who this book is for

This data analysis book is for anyone with prior experience working with the Python programming language who wants to learn the fundamentals of data analysis with pandas. Previous knowledge of pandas is not necessary.

Table of Contents

1. An Introduction to pandas
2. Working with Data Structures
3. Data I/O
4. pandas Data Types
5. Data Selection – DataFrames
6. Data Selection – Series
7. Data Exploration and Transformation
8. Data Visualization
9. Data Modeling – Preprocessing
10. Data Modeling – Modeling Basics
11. Data Modeling – Regression Modeling
12. Using Time in pandas
13. Exploring Time Series
14. Applying pandas Data Processing for Case Studies

Blaine Bateman has more than 35 years of experience working with various industries from government R&D to startups to $1B public companies. His experience focuses on analytics including machine learning and forecasting. His hands-on abilities include Python and R coding, Keras/Tensorflow, and AWS & Azure machine learning services. As a machine learning consultant, he has developed and deployed actual ML models in industry.

Saikat Basak is a data scientist and a passionate programmer. Having worked with multiple industry leaders, he has a good understanding of problem areas that can potentially be solved using data. Apart from being a data guy, he is also a science geek and loves to explore new ideas in the frontiers of science and technology.

Thomas V. Joseph is a data science practitioner, researcher, trainer, mentor, and writer with more than 19 years of experience. He has extensive experience in solving business problems using machine learning toolsets across multiple industry segments.

William So is a Data Scientist with both a strong academic background and extensive professional experience. He is currently the Head of Data Science at Douugh and also a Lecturer for Master of Data Science and Innovation at the University of Technology Sydney. During his career, he successfully covered the end-end spectrum of data analytics from ML to Business Intelligence helping stakeholders derive valuable insights and achieve amazing results that benefits the business. William is a co-author of the "The Applied Artificial Intelligence Workshop" published by Packt.