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

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