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

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

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

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