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

Understanding data wrangling

Like any professional field, data analysis is filled with buzzwords, and it can often be difficult for newcomers to understand the lingo—the topic of this chapter is no exception. When we perform data wrangling, we are taking our input data from its original state and putting it in a format where we can perform meaningful analysis on it. Data manipulation is another way to refer to this process. There is no set list of operations; the only goal is that the data post-wrangling is more useful to us than when we started. In practice, there are three common tasks involved in the data wrangling process:

  • Data cleaning
  • Data transformation
  • Data enrichment

It should be noted that there is no inherent order to these tasks, and it is highly probable that we will perform each many times throughout the data wrangling process. This idea brings up an interesting conundrum: if we need to wrangle our data to prepare it for our analysis, isn...