Book Image

Hands-On Data Analysis with Pandas - Second Edition

By : Stefanie Molin
5 (1)
Book Image

Hands-On Data Analysis with Pandas - Second Edition

5 (1)
By: Stefanie Molin

Overview of this book

Extracting valuable business insights is no longer a ‘nice-to-have’, but an essential skill for anyone who handles data in their enterprise. Hands-On Data Analysis with Pandas is here to help beginners and those who are migrating their skills into data science get up to speed in no time. This book 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, you will learn how to use the pandas library to perform data wrangling to reshape, clean, and aggregate your data. Then, you will learn how 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. This updated edition will equip you with the skills you need to use pandas 1.x to efficiently perform various data manipulation tasks, reliably reproduce analyses, and visualize your data for effective decision making – valuable knowledge that can be applied across multiple domains.
Table of Contents (21 chapters)
Section 1: Getting Started with Pandas
Section 2: Using Pandas for Data Analysis
Section 3: Applications – Real-World Analyses Using Pandas
Section 4: Introduction to Machine Learning with Scikit-Learn
Section 5: Additional Resources


This chapter served as an introduction to machine learning in Python. We discussed the terminology that's commonly used to describe learning types and tasks. Then, we practiced EDA using the skills we learned throughout this book to get a feel for the wine and planet datasets. This gave us some ideas about what kinds of models we would want to build. A thorough exploration of the data is essential before attempting to build a model.

Next, we learned how to prepare our data for use in machine learning models and the importance of splitting the data into training and testing sets before modeling. In order to prepare our data efficiently, we used pipelines in scikit-learn to package up everything from our preprocessing through our model.

We used unsupervised k-means to cluster the planets using their semi-major axis and period; we also discussed how to use the elbow point method to find a good value for k. Then, we moved on to supervised learning and made a linear regression...