Hands-On Data Analysis with Pandas

A Python data science handbook for data collection, wrangling, analysis, and visualization, 2nd Edition

; Ken Jee (Forord)

Get to grips with pandas - a fast, versatile, and high-performance Python library for data discovery, data manipulation, data preparation, and handling data for analytical tasks

Key Features

Perform efficient data analysis and manipulation tasks using pandas 1. Les mer
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Paperback
Legg i
Paperback
Legg i
Vår pris: 528,-

(Paperback) Fri frakt!
Leveringstid: Sendes innen 21 dager

Om boka

Get to grips with pandas - a fast, versatile, and high-performance Python library for 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
Become well-versed in using pandas as an effective data exploration tool

Book DescriptionData analysis has become an essential 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 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 forThis 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. You'll also find this book useful if you are a data scientist looking to implement pandas in your machine learning workflow. Working knowledge of the Python programming language will assist with understanding the key concepts covered in this book; however, a Python crash-course tutorial is provided in the code bundle for anyone who needs a refresher.

Fakta

Innholdsfortegnelse

Table of Contents

Introduction to Data Analysis
Working with Pandas DataFrames
Data Wrangling with Pandas
Aggregating Pandas DataFrames
Visualizing Data with Pandas and Matplotlib
Plotting with Seaborn and Customization Techniques
Financial Analysis - Bitcoin and the Stock Market
Rule-Based Anomaly Detection
Getting Started with Machine Learning in Python
Making Better Predictions - Optimizing Models
Machine Learning Anomaly Detection
The Road Ahead

Om forfatteren

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.