Python Data Analysis, 2nd Edition

Book Description

Key Features

• Find, manipulate, and analyze your data using the Python 3.5 libraries
• Perform advanced, high-performance linear algebra and mathematical calculations with clean and efficient Python code
• An easy-to-follow guide with realistic examples that are frequently used in real-world data analysis projects.

Book Description

Data analysis techniques generate useful insights from small and large volumes of data. Python, with its strong set of libraries, has become a popular platform to conduct various data analysis and predictive modeling tasks.

With this book, you will learn how to process and manipulate data with Python for complex analysis and modeling. We learn data manipulations such as aggregating, concatenating, appending, cleaning, and handling missing values, with NumPy and Pandas. The book covers how to store and retrieve data from various data sources such as SQL and NoSQL, CSV fies, and HDF5. We learn how to visualize data using visualization libraries, along with advanced topics such as signal processing, time series, textual data analysis, machine learning, and social media analysis.

The book covers a plethora of Python modules, such as matplotlib, statsmodels, scikit-learn, and NLTK. It also covers using Python with external environments such as R, Fortran, C/C++, and Boost libraries.

What you will learn

• Install open source Python modules such NumPy, SciPy, Pandas, stasmodels, scikit-learn,theano, keras, and tensorflow on various platforms
• Prepare and clean your data, and use it for exploratory analysis
• Manipulate your data with Pandas
• Retrieve and store your data from RDBMS, NoSQL, and distributed filesystems such as HDFS and HDF5
• Visualize your data with open source libraries such as matplotlib, bokeh, and plotly
• Learn about various machine learning methods such as supervised, unsupervised, probabilistic, and Bayesian
• Understand signal processing and time series data analysis
• Get to grips with graph processing and social network analysis

About the Author

Armando Fandango is Chief Data Scientist at Epic Engineering and Consulting Group, and works on confidential projects related to defense and government agencies. Armando is an accomplished technologist with hands-on capabilities and senior executive-level experience with startups and large companies globally. His work spans diverse industries including FinTech, stock exchanges, banking, bioinformatics, genomics, AdTech, infrastructure, transportation, energy, human resources, and entertainment.

Armando has worked for more than ten years in projects involving predictive analytics, data science, machine learning, big data, product engineering, high performance computing, and cloud infrastructures. His research interests spans machine learning, deep learning, and scientific computing.

Table of Contents

Chapter 1. Getting Started With Python Libraries
Chapter 2. Numpy Arrays
Chapter 3. The Pandas Primer
Chapter 4. Statistics And Linear Algebra
Chapter 5. Retrieving, Processing, And Storing Data
Chapter 6. Data Visualization
Chapter 7. Signal Processing And Time Series
Chapter 8. Working With Databases
Chapter 9. Analyzing Textual Data And Social Media
Chapter 10. Predictive Analytics And Machine Learning
Chapter 11. Environments Outside The Python Ecosystem And Cloud Computing
Chapter 12. Performance Tuning, Profiling, And Concurrency
Chapter 13. Key Concepts
Chapter 14. Useful Functions
Chapter 15. Online Resources

中文：

书名：Python Data Analysis, 2nd Edition

Key Features

• 使用Python3.5库查找、操作和分析数据
• 使用干净高效的Python代码执行高级、高性能的线性代数和数学计算
• 一个易于遵循的指南与现实世界中经常使用的数据分析项目的实际例子。

图书描述

数据分析技术从大量和少量的数据中产生有用的见解。凭借其强大的库集合，Python已成为执行各种数据分析和预测性建模任务的流行平台。

通过这本书，您将学习如何使用Python处理和操作数据，以进行复杂的分析和建模。我们使用NumPy和Pandas学习数据操作，如聚合、连接、追加、清理和处理缺失的值。这本书涵盖了如何存储和检索各种数据源的数据，如SQL和NoSQL、CSV文件和HDF5。我们将学习如何使用可视化库来可视化数据，以及信号处理、时间序列、文本数据分析、机器学习和社交媒体分析等高级主题。

这本书涵盖了大量的Python模块，如matplotlib、statsModels、SCRICKIT-LEARN和NLTK。它还介绍了如何在外部环境(如R、Fortran、C/C++和Boost库)中使用Python。

你将学到什么

• 在各种平台上安装开源的Python模块，如NumPy、SciPy、Pandas、StasModels、SCRICKIT-LEARN、Theano、Keras和TensorFlow
• 准备和清理数据，并将其用于探索性分析
• Manipulate your data with Pandas
• 从RDBMS、NoSQL以及HDFS和HDF5等分布式文件系统检索和存储数据
• Visualize your data with open source libraries such as matplotlib, bokeh, and plotly
• 了解各种机器学习方法，如有监督、无监督、概率和贝叶斯
• 了解信号处理和时间序列数据分析
• 掌握图形处理和社交网络分析

关于作者

阿曼多·范达戈。 是Epic Engineering and Consulting Group的首席数据科学家，从事与国防和政府机构相关的机密项目。Armando是一位成就卓著的技术专家，拥有动手能力和全球初创企业和大公司的高级管理人员级别的经验。他的工作涉及多个行业，包括金融科技、证券交易所、银行、生物信息学、基因组学、广告技术、基础设施、交通、能源、人力资源和娱乐。

Armando在涉及预测分析、数据科学、机器学习、大数据、产品工程、高性能计算和云基础设施的项目中工作了十多年。他的研究兴趣涵盖机器学习、深度学习和科学计算。

目录表

第1章：Python库入门
Chapter 2. Numpy Arrays
第三章熊猫入门读本
Chapter 4. Statistics And Linear Algebra
第5章.检索、处理和存储数据
第6章：数据可视化
第七章信号处理和时间序列
Chapter 8. Working With Databases
第九章：分析文本数据和社交媒体
第十章预测分析和机器学习
第11章.Python生态系统和云计算之外的环境
Chapter 12. Performance Tuning, Profiling, And Concurrency
第13章.主要概念
第十四章.有用的功能
第15章.在线资源