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


We use clustering to divide our data points into groups of similar points. The points in each group are more like their fellow group members than those of other groups. Clustering is commonly used for tasks such as recommendation systems (think of how Netflix recommends what to watch based on what other people who've watched similar things are watching) and market segmentation.

For example, say we work at an online retailer and want to segment our website users for more targeted marketing efforts; we can gather data on time spent on the site, page visits, products viewed, products purchased, and much more. Then, we can have an unsupervised clustering algorithm find groups of users with similar behavior; if we make three groups, we can come up with labels for each group according to its behavior:

Figure 9.17 – Clustering website users into three groups

Since we can use clustering for unsupervised learning, we will need to interpret...