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

Feature engineering

When trying to improve performance, we may also consider ways to provide the best features (model inputs) to our model through the process of feature engineering. The Preprocessing data section in Chapter 9, Getting Started with Machine Learning in Python, introduced us to feature transformation when we scaled, encoded, and imputed our data. Unfortunately, feature transformation may mute some elements of our data that we want to use in our model, such as the unscaled value of the mean of a specific feature. For this situation, we can create a new feature with this value; this and other new features are added during feature construction (sometimes called feature creation).

Feature selection is the process of determining which features to train the model on. This can be done manually or through another process, such as machine learning. When looking to choose features for our model, we want features that have an impact on our dependent variable without unnecessarily...