Hands-On Data Analysis with Pandas: Efficiently perform data collection, wrangling, analysis, and visualization using Python

Get to grips with pandas-a versatile and high-performance Python library for data manipulation, analysis, and discovery

Key Features

• Perform efficient data analysis and manipulation tasks using pandas
• Apply pandas to different real-world domains using step-by-step demonstrations
• Get accustomed to using pandas as an effective data exploration tool

Book Description

Data analysis has become a necessary skill in a variety of positions 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 learn how 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.

What you will learn

• Understand how data analysts and scientists gather and analyze data
• Perform data analysis and data wrangling in Python
• Combine, group, and aggregate data from multiple sources
• Create data visualizations with pandas, matplotlib, and seaborn
• Apply machine learning (ML) algorithms to identify patterns and make predictions
• Use Python data science libraries to analyze real-world datasets
• Use pandas to solve common data representation and analysis problems
• Build Python scripts, modules, and packages for reusable analysis code

Who this book is for

This book is for data analysts, data science beginners, and Python developers who want to explore each stage of data analysis and scientific computing using a wide range of datasets. You will also find this book useful if you are a data scientist who is looking to implement pandas in machine learning. Working knowledge of Python programming language will be beneficial.

Table of Contents

1. Introduction to Data Analysis
2. Working with Pandas DataFrames
3. Data Wrangling with Pandas
4. Aggregating Pandas DataFrames
5. Data Visualization with Pandas and Matplotlib
6. Plotting with Seaborn and Customization Techniques
7. Financial Analysis with Pandas: Bitcoin and the Stock Market
8. Rule-based Anomaly Detection: Catching Hackers
9. Getting started with Machine Learning in Python
10. Making Better Predictions: Optimizing ML Models
11. ML Anomaly Detection: Catching Hackers, Part 2
12. The Road Ahead

Stefanie Molin is a data scientist and software engineer at Bloomberg LP in NYC, tackling tough problems in information security, particularly revolving around anomaly detection, building tools for gathering data, and knowledge sharing. She has extensive experience in data science, designing anomaly detection solutions, and utilizing machine learning in both R and Python in the AdTech and FinTech industries. She holds a B.S. in operations research from Columbia University's Fu Foundation School of Engineering and Applied Science, with minors in economics, and entrepreneurship and innovation. In her free time, she enjoys traveling the world, inventing new recipes, and learning new languages spoken among both people and computers.