Python for data analysis data wrangling with Pandas, NumPy, and IPython
This second edition offers instructions for manipulating, processing, cleaning, and crunching datasets in Python
| Main Author: | |
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| Format: | eBook |
| Language: | English |
| Published: |
Sebastopol, CA
O'Reilly Media
2017
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| Edition: | Second edition |
| Subjects: | |
| Online Access: | |
| Collection: | O'Reilly - Collection details see MPG.ReNa |
Table of Contents:
- Preliminaries
- Python language basics, IPython, and Jupyter notebook
- Built-in data structures, functions, and files
- NumPy basics : arrays and vectorized computation
- Getting started with pandas
- Data loading, storage, and file formats
- Data cleaning and preparation
- Data wrangling : join, combine, and reshape
- Plotting and visualization
- Data aggregation and group operations
- Time series
- Advanced pandas
- Introduction to modeling libraries in Python
- Data analysis examples
- Advanced NumPy
- More on the IPython system
- Copyright; Table of Contents; Preface; Section 1. New for the Second Edition; Section 2. Conventions Used in This Book; Section 3. Using Code Examples; Section 4. O'Reilly Safari; Section 5. How to Contact Us; Section 6. Acknowledgments; In Memoriam: John D. Hunter (1968-2012); Acknowledgments for the Second Edition (2017); Acknowledgments for the First Edition (2012); Chapter 1. Preliminaries; 1.1 What Is This Book About?; What Kinds of Data?; 1.2 Why Python for Data Analysis?; Python as Glue; Solving the "Two-Language" Problem; Why Not Python?; 1.3 Essential Python Libraries; NumPy; pandas
- Errors and Exception Handling3.3 Files and the Operating System; Bytes and Unicode with Files; 3.4 Conclusion; Chapter 4. NumPy Basics: Arrays and Vectorized Computation; 4.1 The NumPy ndarray: A Multidimensional Array Object; Creating ndarrays; Data Types for ndarrays; Arithmetic with NumPy Arrays; Basic Indexing and Slicing; Boolean Indexing; Fancy Indexing; Transposing Arrays and Swapping Axes; 4.2 Universal Functions: Fast Element-Wise Array Functions; 4.3 Array-Oriented Programming with Arrays; Expressing Conditional Logic as Array Operations; Mathematical and Statistical Methods
- MatplotlibIPython and Jupyter; SciPy; scikit-learn; statsmodels; 1.4 Installation and Setup; Windows; Apple (OS X, macOS); GNU/Linux; Installing or Updating Python Packages; Python 2 and Python 3; Integrated Development Environments (IDEs) and Text Editors; 1.5 Community and Conferences; 1.6 Navigating This Book; Code Examples; Data for Examples; Import Conventions; Jargon; Chapter 2. Python Language Basics, IPython, and Jupyter Notebooks; 2.1 The Python Interpreter; 2.2 IPython Basics; Running the IPython Shell; Running the Jupyter Notebook; Tab Completion; Introspection
- Methods for Boolean ArraysSorting; Unique and Other Set Logic; 4.4 File Input and Output with Arrays; 4.5 Linear Algebra; 4.6 Pseudorandom Number Generation; 4.7 Example: Random Walks; Simulating Many Random Walks at Once; 4.8 Conclusion; Chapter 5. Getting Started with pandas; 5.1 Introduction to pandas Data Structures; Series; DataFrame; Index Objects; 5.2 Essential Functionality; Reindexing; Dropping Entries from an Axis; Indexing, Selection, and Filtering; Integer Indexes; Arithmetic and Data Alignment; Function Application and Mapping; Sorting and Ranking
- The %run CommandExecuting Code from the Clipboard; Terminal Keyboard Shortcuts; About Magic Commands; Matplotlib Integration; 2.3 Python Language Basics; Language Semantics; Scalar Types; Control Flow; Chapter 3. Built-in Data Structures, Functions, and Files; 3.1 Data Structures and Sequences; Tuple; List; Built-in Sequence Functions; dict; set; List, Set, and Dict Comprehensions; 3.2 Functions; Namespaces, Scope, and Local Functions; Returning Multiple Values; Functions Are Objects; Anonymous (Lambda) Functions; Currying: Partial Argument Application; Generators