Mastering Python Data Analysis

Book Description

Become an expert at using Python for advanced statistical analysis of data using real-world examples

About This Book

• Clean, format, and explore data using graphical and numerical summaries
• Leverage the IPython environment to efficiently analyze data with Python
• Packed with easy-to-follow examples to develop advanced computational skills for the analysis of complex data

Who This Book Is For

If you are a competent Python developer who wants to take your data analysis skills to the next level by solving complex problems, then this advanced guide is for you. Familiarity with the basics of applying Python libraries to data sets is assumed.

What You Will Learn

• Read, sort, and map various data into Python and Pandas
• Recognise patterns so you can understand and explore data
• Use statistical models to discover patterns in data
• Review classical statistical inference using Python, Pandas, and SciPy
• Detect similarities and differences in data with clustering
• Clean your data to make it useful
• Work in Jupyter Notebook to produce publication ready figures to be included in reports

In Detail

Python, a multi-paradigm programming language, has become the language of choice for data scientists for data analysis, visualization, and machine learning. Ever imagined how to become an expert at effectively approaching data analysis problems, solving them, and extracting all of the available information from your data? Well, look no further, this is the book you want!

Through this comprehensive guide, you will explore data and present results and conclusions from statistical analysis in a meaningful way. You’ll be able to quickly and accurately perform the hands-on sorting, reduction, and subsequent analysis, and fully appreciate how data analysis methods can support business decision-making.

You’ll start off by learning about the tools available for data analysis in Python and will then explore the statistical models that are used to identify patterns in data. Gradually, you’ll move on to review statistical inference using Python, Pandas, and SciPy. After that, we’ll focus on performing regression using computational tools and you’ll get to understand the problem of identifying clusters in data in an algorithmic way. Finally, we delve into advanced techniques to quantify cause and effect using Bayesian methods and you’ll discover how to use Python’s tools for supervised machine learning.

Style and approach

This book takes a step-by-step approach to reading, processing, and analyzing data in Python using various methods and tools. Rich in examples, each topic connects to real-world examples and retrieves data directly online where possible. With this book, you are given the knowledge and tools to explore any data on your own, encouraging a curiosity befitting all data scientists.

Table of Contents

Chapter 1. Tools of the Trade
Chapter 2. Exploring Data
Chapter 3. Learning About Models
Chapter 4. Regression
Chapter 5. Clustering
Chapter 6. Bayesian Methods
Chapter 7. Supervised and Unsupervised Learning
Chapter 8. Time Series Analysis

中文：

书名：Mastering Python Data Analysis

成为使用Python进行高级数据统计分析的专家，使用真实世界的示例

About This Book

• 使用图形和数字摘要清理、格式化和浏览数据
• 利用IPython环境通过Python高效地分析数据
• 为开发复杂数据分析的高级计算技能提供了简单易懂的示例

这本书是为谁写的

如果您是一名称职的Python开发人员，希望通过解决复杂问题来提升您的数据分析技能，那么这本高级指南是为您准备的。假设您熟悉将Python库应用于数据集的基础知识。

What You Will Learn

• 读取、排序各种数据并将其映射到Python和Pandas中
• 识别模式，以便您能够理解和探索数据
• 使用统计模型发现数据中的模式
• 回顾使用Python、Pandas和SciPy进行的经典统计推断
• 通过聚类检测数据的相似性和差异性
• Clean your data to make it useful
• 在Jupyter Notebook中工作，以生成可供出版的数据，并将其包含在报告中

In Detail

作为一种多范例编程语言，Python已成为数据科学家用于数据分析、可视化和机器学习的首选语言。想过如何成为有效处理数据分析问题、解决问题并从数据中提取所有可用信息的专家吗？好了，不用再看了，这就是你要的书！

通过这本综合指南，您将探索数据，并以有意义的方式展示统计分析的结果和结论。您将能够快速、准确地执行实际操作的排序、简化和后续分析，并充分了解数据分析方法如何支持业务决策。

您将从了解可用于在Python中进行数据分析的工具开始，然后探索用于识别数据模式的统计模型。逐渐地，您将继续使用Python、Pandas和SciPy回顾统计推断。之后，我们将重点介绍使用计算工具执行回归，您将了解以算法方式识别数据中的簇的问题。最后，我们将深入研究使用贝叶斯方法量化因果关系的高级技术，您将发现如何使用Python的工具进行有监督的机器学习。

Style and approach

这本书采取了一步一步的方法，使用各种方法和工具在Python中读取、处理和分析数据。每个主题都有丰富的例子，与真实世界的例子相联系，并在可能的情况下直接在线检索数据。有了这本书，你就有了自己探索任何数据的知识和工具，鼓励了一种适合所有数据科学家的好奇心。

Table of Contents

Chapter 1. Tools of the Trade
第2章：探索数据
Chapter 3. Learning About Models
第四章回归
第5章.集群
第六章贝叶斯方法
Chapter 7. Supervised and Unsupervised Learning
Chapter 8. Time Series Analysis