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

Visualizing Data with Pandas and Matplotlib

So far, we have been working with data strictly in a tabular format. However, the human brain excels at picking out visual patterns; hence, our natural next step is learning how to visualize our data. Visualizations make it much easier to spot aberrations in our data and explain our findings to others. However, we should not reserve data visualizations exclusively for those we present our conclusions to, as visualizations will be crucial in helping us understand our data quicker and more completely in our exploratory data analysis.

There are numerous types of visualizations that go way beyond what we may have seen in the past. In this chapter, we will cover the most common plot types, such as line plots, histograms, scatter plots, and bar plots, along with several other plot types that build upon these. We won't be covering pie...