English | ISBN: 9781800563452 | 749 pages | 2021 | True AZW | 38 MB

Get to grips with pandas - a versatile and high-performance library for manipulating, processing, cleaning, and crunching datasets in Python

Key Features
Perform efficient data analysis and manipulation tasks using pandas 1.

x
Implement pandas in different real-world domains with the help of step-by-step demonstrations
Become well versed in using pandas as an effective data exploration tool
Book Description
pandas is a powerful and popular library synonymous with Python data science that makes data wrangling and visualization easy by enabling you to work efficiently with tabular data. This second edition will help you get well-versed with the new features in pandas 1.x and enhance your data analysis skills for extracting significant insights and value from data.

Hands-On Data Analysis with Pandas 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, the book shows you how to use the powerful pandas library to perform data wrangling to reshape, clean, and aggregate your data. As you advance, you'll learn how to conduct exploratory data analysis by calculating summary statistics and visualizing the data to find patterns. You'll also 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 data analysis book, you'll 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 domains.

What you will learn
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



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