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


In this chapter, we reviewed various techniques we can employ to improve model performance. We learned how to use grid search to find the best hyperparameters in a search space, and how to tune our model using the scoring metric of our choosing with GridSearchCV. This means we don't have to accept the default in the score() method of our model and can customize it to our needs.

In our discussion of feature engineering, we learned how to reduce the dimensionality of our data using techniques such as PCA and feature selection. We saw how to use the PolynomialFeatures class to add interaction terms to models with categorical and numerical features. Then, we learned how to use the FeatureUnion class to augment our training data with transformed features. In addition, we saw how decision trees can help us understand which features in the data contribute most to the classification or regression task at hand, using feature importances. This helped us see the importance of...