Python Data Analysis: Perform data collection, data processing, wrangling, visualization, and model building using Python, 3rd Edition

Book Description

Understand data analysis pipelines using machine learning algorithms and techniques with this practical guide

Key Features

• Prepare and clean your data to use it for exploratory analysis, data manipulation, and data wrangling
• Discover supervised, unsupervised, probabilistic, and Bayesian machine learning methods
• Get to grips with graph processing and sentiment analysis

Book Description

Data analysis enables you to generate value from small and big data by discovering new patterns and trends, and Python is one of the most popular tools for analyzing a wide variety of data. With this book, you’ll get up and running using Python for data analysis by exploring the different phases and methodologies used in data analysis and learning how to use modern libraries from the Python ecosystem to create efficient data pipelines.

Starting with the essential statistical and data analysis fundamentals using Python, you’ll perform complex data analysis and modeling, data manipulation, data cleaning, and data visualization using easy-to-follow examples. You’ll then understand how to conduct time series analysis and signal processing using ARMA models. As you advance, you’ll get to grips with smart processing and data analytics using machine learning algorithms such as regression, classification, Principal Component Analysis (PCA), and clustering. In the concluding chapters, you’ll work on real-world examples to analyze textual and image data using natural language processing (NLP) and image analytics techniques, respectively. Finally, the book will demonstrate parallel computing using Dask.

By the end of this data analysis book, you’ll be equipped with the skills you need to prepare data for analysis and create meaningful data visualizations for forecasting values from data.

What you will learn

• Explore data science and its various process models
• Perform data manipulation using NumPy and pandas for aggregating, cleaning, and handling missing values
• Create interactive visualizations using Matplotlib, Seaborn, and Bokeh
• Retrieve, process, and store data in a wide range of formats
• Understand data preprocessing and feature engineering using pandas and scikit-learn
• Perform time series analysis and signal processing using sunspot cycle data
• Analyze textual data and image data to perform advanced analysis
• Get up to speed with parallel computing using Dask

Who this book is for

This book is for data analysts, business analysts, statisticians, and data scientists looking to learn how to use Python for data analysis. Students and academic faculties will also find this book useful for learning and teaching Python data analysis using a hands-on approach. A basic understanding of math and working knowledge of the Python programming language will help you get started with this book.

Table of Contents

1. Getting Started with Python Libraries
2. NumPy and Pandas
3. Statistics
4. Linear Algebra
5. Data Visualization
6. Retrieving, Processing, and Storing Data
7. Cleaning Messy Data
8. Signal Processing and Time Series
9. Supervised Learning – Regression Analysis
10. Supervised Learning – Classification Techniques
11. Unsupervised Learning – PCA and Clustering
12. Analyzing Textual Data
13. Analyzing Image Data
14. Parallel Computing using Dask

中文：

书名：Python Data Analysis: Perform data collection, data processing, wrangling, visualization, and model building using Python, 3rd Edition

通过本实用指南了解使用机器学习算法和技术的数据分析管道

主要特点

• 准备和清理数据，以便将其用于探索性分析、数据操作和数据争论
• 发现有监督、无监督、概率和贝叶斯机器学习方法
• 掌握图形处理和情感分析

图书描述

数据分析使您能够通过发现新的模式和趋势，从大大小小的数据中产生价值，而Python是用于分析各种数据的最流行的工具之一。在这本书中，您将通过探索数据分析中使用的不同阶段和方法，并学习如何使用来自Python生态系统的现代库来创建高效的数据管道，从而开始使用Python进行数据分析。

从使用Python的基本统计和数据分析基础开始，您将使用简单易懂的示例执行复杂的数据分析和建模、数据操作、数据清理和数据可视化。然后，您将了解如何使用ARMA模型进行时间序列分析和信号处理。随着学习的深入，您将掌握使用机器学习算法(如回归、分类、主成分分析(PCA)和聚类)进行智能处理和数据分析。在最后几章中，你将使用真实世界的例子，分别使用自然语言处理(NLP)和图像分析技术分析文本和图像数据。最后，本书将演示使用DASK进行并行计算。

读完这本数据分析书后，您将掌握准备用于分析的数据和创建有意义的数据可视化以从数据中预测值所需的技能。

你将学到什么

• 探索数据科学及其各种过程模型
• 使用NumPy和Pandas执行数据操作，以聚合、清理和处理缺失的值
• 使用Matplotlib、Seborn和Bokeh创建交互式可视化
• 以多种格式检索、处理和存储数据
• 了解使用PANAS和SCRICKIT进行数据预处理和特征工程-了解
• Perform time series analysis and signal processing using sunspot cycle data
• 分析文本数据和图像数据以执行高级分析
• Get up to speed with parallel computing using Dask

这本书是为谁而写的

本书面向希望学习如何使用Python进行数据分析的数据分析师、业务分析师、统计学家和数据科学家。学生和学术人员也会发现这本书对使用实际操作方法学习和教授Python数据分析很有用。对数学的基本理解和对Python编程语言的应用知识将帮助您开始阅读这本书。

目录表

1. Python库入门
2. NumPy和Pandas
3. Statistics
4. 线性代数
5. Data Visualization
6. 检索、处理和存储数据
7. Cleaning Messy Data
8. Signal Processing and Time Series
9. 监督学习–回归分析
10. 监督学习–分类技术
11. 无监督学习–主元分析和聚类
12. 分析文本数据
13. 分析图像数据
14. 使用DASK的并行计算