Python Data Analysis,2nd Edition
by Armando Fandango,Ivan Idris
Paperback: 330 pages
Publisher: Packt Publishing; 2nd Revised edition edition (30 Mar. 2017)
Language: English
ISBN-10: 1787127486
ISBN-13: 9781787127487
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.
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
主要特征
使用Python 3.5库查找，操作和分析数据
使用干净，高效的Python代码执行高级，高性能线性代数和数学计算
一个易于理解的指南，其实际示例经常用于现实世界的数据分析项目。
图书说明
数据分析技术从小量和大量的数据中产生有用的见解。 Python拥有强大的图书馆，已经成为进行各种数据分析和预测建模任务的流行平台。
使用本书，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来学习数据操作，例如聚合，连接，附加，清理和处理缺失的值。本书介绍如何从各种数据源（如SQL和NoSQL，CSV fies和HDF5）中存储和检索数据。我们学习如何使用可视化库可视化数据，以及高级主题，如信号处理，时间序列，文本数据分析，机器学习和社交媒体分析。
这本书涵盖了大量的Python模块，如matplotlib，statsmodels，scikit-learn和NLTK。它还涵盖使用Python与外部环境，如R，Fortran，C / C ++和Boost库。
你会学到什么
在各种平台上安装开源Python模块，如NumPy，SciPy，Pandas，stasmodels，scikit-learn，theano，keras和tensorflow
准备和清理您的数据，并将其用于探索性分析
使用熊猫管理您的数据
从RDBMS，NoSQL和分布式文件系统（如HDFS和HDF5）检索和存储数据
使用开源库（如matplotlib，景观和图形）可视化您的数据
了解各种机器学习方法，如监督，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
Armando Fandango是Epic工程咨询集团的首席数据科学家，负责与国防和政府机构有关的机密项目。 Armando是一位技术精湛的技术人员，拥有全球初创公司和大型公司的实践能力和高级执行水平的经验。他的工作包括FinTech，证券交易所，银行，生物信息学，基因组学，AdTech，基础设施，交通运输，能源，人力资源和娱乐等多种行业。
Armando在预测分析，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等项目中工作了十多年。他的研究兴趣跨越机器学习，深度学习和科学计算。
目录
第1章Python库入门
第二章Numpy阵列
第3章熊猫入门
第四章统计与线性代数
第5章检索，处理和存储数据
第六章数据可视化
信号处理和时间序列
第8章使用数据库
第九章分析文本数据和社交媒体
第10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
性能调优，分析和并发
第13章主要概念
第14章有用的功能
第十五章在线资源