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

Chapter materials

The materials for this chapter can be found at https://github.com/stefmolin/Hands-On-Data-Analysis-with-Pandas/tree/master/ch_11. In this chapter, we will be revisiting attempted login data; however, the simulate.py script has been updated to allow for additional command-line arguments. We won't be running the simulations, but be sure to take a look at the script and to check out the 0-simulating_the_data.ipynb notebook for the process that was followed to generate the data files and create the database for this chapter. The user_data/ directory contains files used for this simulation, but we won't be using them directly in this chapter.

The merge_logs.py file contains the Python code to merge the logs from each of the individual simulations, and run_simulations.sh contains a Bash script for running the entire process; these are provided for completeness...