This book looks at you how to analyze 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, Stefanie Molin shows how to use the pandas library to perform data wrangling to reshape, clean, and aggregate data. Later chapters cover conducting exploratory data analysis by calculating summary statistics and visualizing the data to find patterns.

Author: Stefanie Molin
Publisher: Packt Publishing
Date: April 2021
Pages: 788
ISBN: 978-1800563452
Print: 1800563450
Kindle: B08R67H7F5
Audience: Panda users
Level: Intermediate
Category: Data Science

• Understand how data analysts and scientists gather and analyze data
• Perform data analysis and data wrangling using Python
• Combine, group, and aggregate data from multiple sources
• Create data visualizations with pandas, matplotlib, and seaborn
• Apply machine learning algorithms to identify patterns and make predictions
• Use Python data science libraries to analyze real-world datasets
• Solve common data representation and analysis problems using pandas
• Build Python scripts, modules, and packages for reusable analysis code

For recommendations of Big Data books see Reading Your Way Into Big Data in our Programmer's Bookshelf section.

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