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

Restructuring the data

Data isn't always given to us in the format that's most convenient for our analysis. Therefore, we need to be able to restructure data into both wide and long formats, depending on the analysis we want to perform. For many analyses, we will want wide format data so that we can look at the summary statistics easily and share our results in that format.

However, this isn't always as black and white as going from long format to wide format or vice versa. Consider the following data from the Exercises section:

ticker date open high low close volume
0 AAPL 2018-01-02 166.9271 169.0264 166.0442 168.9872 25555934
0 AMZN 2018-01-02 1172.0000 1190.0000 1170.5100 1189.0100 2694494
0 FB 2018-01-02 177.6800 181.5800 177.5500 181.4200 18151903
0 GOOG 2018-01-02 1048.3400 1066.9400 1045.2300 1065.0000 1237564
0 NFLX 2018-01-02 196.1000...