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


With the planets dataset, we want to predict the length of the year, which is a numeric value, so we will turn to regression. As mentioned at the beginning of this chapter, regression is a technique for modeling the strength and magnitude of the relationship between independent variables (our X data)—often called regressors—and the dependent variable (our y data) that we want to predict.

Linear regression

Scikit-learn provides many algorithms that can handle regression tasks, ranging from decision trees to linear regression, spread across modules according to the various algorithm classes. However, typically, the best starting point is a linear regression, which can be found in the linear_model module. In simple linear regression, we fit our data to a line of the following form:

Here, epsilon (ε) is the error term and betas (β) are coefficients.

Important note

The coefficients we get from our model are those...