Book Image

Hands-On Data Analysis with Pandas

By : Stefanie Molin
Book Image

Hands-On Data Analysis with Pandas

By: Stefanie Molin

Overview of this book

Data analysis has become a necessary skill in a variety of domains where knowing how to work with data and extract insights can generate significant value. Hands-On Data Analysis with Pandas will show you how to analyze your data, get started with machine learning, and work effectively with 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 powerful pandas library to perform data wrangling to reshape, clean, and aggregate your data. Then, you will be able 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. By the end of this book, you will be equipped with the skills you need to use pandas to ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets.
Table of Contents (21 chapters)
Free Chapter
1
Section 1: Getting Started with Pandas
4
Section 2: Using Pandas for Data Analysis
9
Section 3: Applications - Real-World Analyses Using Pandas
12
Section 4: Introduction to Machine Learning with Scikit-Learn
16
Section 5: Additional Resources
18
Solutions

Data Wrangling with Pandas

In the previous chapter, we learned about the pandas data structures, how to bring our collected data into DataFrame objects, and various ways to inspect, summarize, filter, select, and work with DataFrame objects. Now that we are well-versed in the initial data collection and inspection stage, we can begin our foray into the world of data wrangling.

As mentioned in Chapter 1, Introduction to Data Analysis, preparing data for analysis is often the largest portion of the job time-wise for those working with data, and often the least enjoyable. On the bright side, pandas is well-equipped to help with these tasks, and, by mastering the skills presented in this book, we will be able to get to the more interesting parts sooner.

It should be noted that data wrangling isn't something we do merely once in our analysis; it is highly likely that we will do...