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

Pandas data structures

Python has several data structures already, such as tuples, lists, and dictionaries. Pandas provides two main structures to facilitate working with data: Series and DataFrame. The Series and DataFrame data structures each contain another pandas data structure, which is very important to be aware of: Index. However, in order to understand the pandas data structures, we need to take a look at NumPy, which provides the n-dimensional arrays that pandas builds upon.

For the remainder of this book, we will refer to DataFrame objects as dataframes, Series objects as series, and Index objects as index, unless we are referring to the class itself.

The aforementioned data structures are created as Python classes; when we actually create one, they are referred to as objects or instances. This is an important distinction, since, as we will see, some actions can be...