Python Data Analysis

Book Description

Dive deeper into data analysis with the flexibility of Python and learn how its extensive range of scientific and mathematical libraries can be used to solve some of the toughest challenges in data analysis. Build your confidence and expertise and develop valuable skills in high demand in a world driven by Big Data with this expert data analysis book.

This data science tutorial will help you learn how to effectively retrieve, clean, manipulate, and visualize data and establish a successful data analysis workflow. Apply the impressive functionality of Python’s data mining tools and scientific and numerical libraries to a range of the most important tasks within data analysis and data science, and develop strategies and ideas to take control your own data analysis projects. Get to grips with statistical analysis using NumPy and SciPy, visualize data with Matplotlib, and uncover sophisticated insights through predictive analytics and machine learning with SciKit-Learn. You will also learn how to use the tools needed to work with databases and find out how Python can be used to analyze textual and social media data, as you work through this essential data science tutorial.

Python Data Analysis makes the difficult challenges of data analysis easier, offering you practical guidance that doesn’t reduce the complexity of the tasks and technology at hand but instead makes them much more manageable.

• Learn how to find, manipulate, and analyze data using Python
• Perform advanced, high performance linear algebra and mathematical calculations with clean and efficient Python code
• Explore predictive analytics and machine learning using SciKit-Learn with this Python machine learning tutorial
• Learn cluster and regression analysis
• Gain insights into textual data and social media with NLTK
• Create effective visualizations to present your data using Matplotlib
• Do more with your databases and explore the relationship between MongoDB and PyMongo
• Discover techniques and tricks for performance tuning and concurrency

Table of Contents

Chapter 1: Getting Started with Python Libraries
Chapter 2: NumPy Arrays
Chapter 3: Statistics and Linear Algebra
Chapter 4: pandas Primer
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
Appendix A: Key Concepts
Appendix B: Useful Functions
Appendix C: Online Resources

中文：

书名：Python数据分析

利用Python的灵活性更深入地研究数据分析，并了解如何使用其广泛的科学和数学库来解决数据分析中的一些最棘手的挑战。使用这本专家数据分析书籍，在大数据驱动的世界中建立您的信心和专业知识，并在高需求中发展有价值的技能。

本数据科学教程将帮助您学习如何有效地检索、清理、操作和可视化数据，并建立一个成功的数据分析工作流程。在数据分析和数据科学的一系列最重要的任务中，应用Python数据挖掘工具以及科学和数值库的令人印象深刻的功能，并制定策略和想法来控制您自己的数据分析项目。使用NumPy和SciPy掌握统计分析，使用Matplotlib可视化数据，并使用SciKit-Learn通过预测分析和机器学习发现复杂的见解。您还将学习如何使用使用数据库所需的工具，并了解如何使用Python来分析文本和社交媒体数据，因为您将学习这一基本的数据科学教程。

Python数据分析使数据分析的困难挑战变得更容易，它为您提供了实用的指导，不仅没有降低手头任务和技术的复杂性，反而使它们更易于管理。

• 了解如何使用Python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
• 使用SciKit探索预测分析和机器学习-通过此Python机器学习教程学习
• 学习聚类和回归分析
• 借助NLTK深入了解文本数据和社交媒体
• 使用Matplotlib创建有效的可视化来呈现数据
• 更好地利用您的数据库，并探索MongoDB和PyMongo之间的关系
• 了解性能调优和并发的技术和诀窍

目录表

第1章：Python库入门
第2章：NumPy数组
第三章：统计学与线性代数
第四章：熊猫入门
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
第8章：使用数据库
第9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
第12章：性能调优、性能分析和并发性
附录A：主要概念
附录B：有用的功能
Appendix C: Online Resources