Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython

Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython

by Wes McKinney

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Product Details

ISBN-13: 9781449319793
Publisher: O'Reilly Media, Incorporated
Publication date: 10/29/2012
Pages: 466
Sales rank: 437,693
Product dimensions: 7.00(w) x 9.10(h) x 0.90(d)

About the Author

Wes McKinney is the main author of pandas, the popular open sourcePython library for data analysis. Wes is an active speaker andparticipant in the Python and open source communities. He worked as aquantitative analyst at AQR Capital Management and Python consultantbefore founding DataPad, a data analytics company, in 2013. Hegraduated from MIT with an S.B. in Mathematics.

Table of Contents

Preface;
Conventions Used in This Book;
Using Code Examples;
Safari® Books Online;
How to Contact Us;
Chapter 1: Preliminaries;
1.1 What Is This Book About?;
1.2 Why Python for Data Analysis?;
1.3 Essential Python Libraries;
1.4 Installation and Setup;
1.5 Community and Conferences;
1.6 Navigating This Book;
1.7 Acknowledgements;
Chapter 2: Introductory Examples;
2.1 1.usa.gov data from bit.ly;
2.2 MovieLens 1M Data Set;
2.3 US Baby Names 1880-2010;
2.4 Conclusions and The Path Ahead;
Chapter 3: IPython: An Interactive Computing and Development Environment;
3.1 IPython Basics;
3.2 Using the Command History;
3.3 Interacting with the Operating System;
3.4 Software Development Tools;
3.5 IPython HTML Notebook;
3.6 Tips for Productive Code Development Using IPython;
3.7 Advanced IPython Features;
3.8 Credits;
Chapter 4: NumPy Basics: Arrays and Vectorized Computation;
4.1 The NumPy ndarray: A Multidimensional Array Object;
4.2 Universal Functions: Fast Element-wise Array Functions;
4.3 Data Processing Using Arrays;
4.4 File Input and Output with Arrays;
4.5 Linear Algebra;
4.6 Random Number Generation;
4.7 Example: Random Walks;
Chapter 5: Getting Started with pandas;
5.1 Introduction to pandas Data Structures;
5.2 Essential Functionality;
5.3 Summarizing and Computing Descriptive Statistics;
5.4 Handling Missing Data;
5.5 Hierarchical Indexing;
5.6 Other pandas Topics;
Chapter 6: Data Loading, Storage, and File Formats;
6.1 Reading and Writing Data in Text Format;
6.2 Binary Data Formats;
6.3 Interacting with HTML and Web APIs;
6.4 Interacting with Databases;
Chapter 7: Data Wrangling: Clean, Transform, Merge, Reshape;
7.1 Combining and Merging Data Sets;
7.2 Reshaping and Pivoting;
7.3 Data Transformation;
7.4 String Manipulation;
7.5 Example: USDA Food Database;
Chapter 8: Plotting and Visualization;
8.1 A Brief matplotlib API Primer;
8.2 Plotting Functions in pandas;
8.3 Plotting Maps: Visualizing Haiti Earthquake Crisis Data;
8.4 Python Visualization Tool Ecosystem;
Chapter 9: Data Aggregation and Group Operations;
9.1 GroupBy Mechanics;
9.2 Data Aggregation;
9.3 Group-wise Operations and Transformations;
9.4 Pivot Tables and Cross-Tabulation;
9.5 Example: 2012 Federal Election Commission Database;
Chapter 10: Time Series;
10.1 Date and Time Data Types and Tools;
10.2 Time Series Basics;
10.3 Date Ranges, Frequencies, and Shifting;
10.4 Time Zone Handling;
10.5 Periods and Period Arithmetic;
10.6 Resampling and Frequency Conversion;
10.7 Time Series Plotting;
10.8 Moving Window Functions;
10.9 Performance and Memory Usage Notes;
Chapter 11: Financial and Economic Data Applications;
11.1 Data Munging Topics;
11.2 Group Transforms and Analysis;
11.3 More Example Applications;
Chapter 12: Advanced NumPy;
12.1 ndarray Object Internals;
12.2 Advanced Array Manipulation;
12.3 Broadcasting;
12.4 Advanced ufunc Usage;
12.5 Structured and Record Arrays;
12.6 More About Sorting;
12.7 NumPy Matrix Class;
12.8 Advanced Array Input and Output;
12.9 Performance Tips;
Python Language Essentials;
The Python Interpreter;
The Basics;
Data Structures and Sequences;
Functions;
Files and the operating system;
Colophon;

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