Hands-On Data Analysis with Pandas: A Python data science handbook for data collection, wrangling, analysis, and visualization

Molin, Stefanie

ISBN 10: 1800563450 / ISBN 13: 9781800563452

Edité par Packt Publishing (edition 2nd ed.), 2021

A propos de cet article

It's a preowned item in good condition and includes all the pages. It may have some general signs of wear and tear, such as markings, highlighting, slight damage to the cover, minimal wear to the binding, etc., but they will not affect the overall reading experience.

N° de réf. du vendeur 1800563450-11-1

Détails bibliographiques

• Titre: Hands-On Data Analysis with Pandas: A Python data science handbook for data collection, wrangling, analysis, and visualization
• Éditeur: Packt Publishing (edition 2nd ed.)
• Date de parution: 2021
• Langue: anglais
• ISBN à 10 chiffres: 1800563450
• ISBN à 13 chiffres: 9781800563452
• Reliure: Paperback
• Édition: 2nd ed.
• État de l'article: Good

A propos de ce titre

Synopsis

Get to grips with pandas by working with real datasets and master data discovery, data manipulation, data preparation, and handling data for analytical tasks

Key Features

• Perform efficient data analysis and manipulation tasks using pandas 1.x
• Apply pandas to different real-world domains with the help of step-by-step examples
• Make the most of pandas as an effective data exploration tool

Book Description

Extracting valuable business insights is no longer a 'nice-to-have', but an essential skill for anyone who handles data in their enterprise. Hands-On Data Analysis with Pandas is here to help beginners and those who are migrating their skills into data science get up to speed in no time.

This book will show you how to analyze your data, get started with machine learning, and work effectively with the 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 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.

This updated edition will equip you with the skills you need to use pandas 1.x to efficiently perform various data manipulation tasks, reliably reproduce analyses, and visualize your data for effective decision making - valuable knowledge that can be applied across multiple domains.

What you will learn

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

Who this book is for

This book is for data science beginners, data analysts, and Python developers who want to explore each stage of data analysis and scientific computing using a wide range of datasets. Data scientists looking to implement pandas in their machine learning workflow will also find plenty of valuable know-how as they progress.

You'll find it easier to follow along with this book if you have a working knowledge of the Python programming language, but a Python crash-course tutorial is provided in the code bundle for anyone who needs a refresher.

Table of Contents

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

À propos de l?auteur

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.