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.
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Then, you will learn how to conduct exploratory data analysis by calculating summary statistics and visualizing the data to find patterns.
Using real-world datasets, you will learn how to use the pandas library to perform data wrangling to reshape, clean, and aggregate your 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. Available tags:ĭata analysis has become an essential skill in a variety of domains where knowing how to work with data and extract insights can generate significant value. This repository contains git tags for the materials as they were at time of publishing. This is the code repository for my book Hands-On Data Analysis with Pandas, published by Packt on J( 1st edition) and Ap( 2nd edition). Hands-On Data Analysis with Pandas – Second Edition