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

We will be building a simulation package to generate the data for this chapter; it is on GitHub at https://github.com/stefmolin/login-attempt-simulator/tree/pandas_book. This package was installed from GitHub when we set up our environment back in Chapter 1, Introduction to Data Analysis; however, if we want, we can follow the instructions in Chapter 7, Financial Analysis – Bitcoin and the Stock Market, to install a version of the package that we can edit.

The repository for this chapter, which can be found at https://github.com/stefmolin/Hands-On-Data-Analysis-with-Pandas/tree/master/ch_08, has the notebook we will use for our actual analysis (anomaly_detection.ipynb), the data files we will be working with in the logs/ folder, the data used for the simulation in the user_data/ folder, and the simulate.py file, which contains a Python script that we can...