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  • Book Overview & Buying IPython Notebook Essentials
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IPython Notebook Essentials

IPython Notebook Essentials

By : Martins
3.7 (3)
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IPython Notebook Essentials

IPython Notebook Essentials

3.7 (3)
By: Martins
Buy this Book

Overview of this book

If you are a professional, student, or educator who wants to learn to use IPython Notebook as a tool for technical and scientific computing, visualization, and data analysis, this is the book for you. This book will prove valuable for anyone that needs to do computations in an agile environment.
Table of Contents (10 chapters)
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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Conventions
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Reader feedback
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Customer support
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1
1. A Tour of the IPython Notebook
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1. A Tour of the IPython Notebook
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Getting started with Anaconda or Wakari
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Running the notebook
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Example – the coffee cooling problem
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Exercises
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Summary
2
2. The Notebook Interface
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2. The Notebook Interface
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Editing and navigating a notebook
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IPython magics
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Interacting with the operating system
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Running scripts, loading data, and saving data
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The rich display system
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Summary
3
3. Graphics with matplotlib
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3. Graphics with matplotlib
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The plot function
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Three-dimensional plots
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Animations
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Summary
4
4. Handling Data with pandas
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4. Handling Data with pandas
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The Series class
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The DataFrame class
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Computational and graphics tools
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An example with a realistic dataset
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Summary
5
5. Advanced Computing with SciPy, Numba, and NumbaPro
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5. Advanced Computing with SciPy, Numba, and NumbaPro
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Overview of SciPy
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Advanced mathematical algorithms with SciPy
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Accelerating computations with Numba and NumbaPro
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Summary
6
A. IPython Notebook Reference Card
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Starting the notebook
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Keyboard shortcuts
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Importing modules
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Getting help
7
B. A Brief Review of Python
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Introduction
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Basic types, expressions, and variables and their assignment
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Sequence types
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Dictionaries
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Control structures
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Summary
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C. NumPy Arrays
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Introduction
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Array creation and member access
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Indexing and Slicing
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Index
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Index

The DataFrame class

The DataFrame class is used to represent two-dimensional data. To illustrate its use, let's create a DataFrame class containing student data as follows:

grades = DataFrame(
    [['Alice',  80., 92., 84,],
     ['Bob',    78., NaN, 86,],
     ['Samaly', 75., 78., 88.]],
    index = [17005, 17035, 17028],
    columns = ['Name', 'Test 1', 'Test 2', 'Final']
    )

This code demonstrates one of the most straightforward ways to construct a DataFrame class. In the preceding case, the data can be specified as any two-dimensional Python data structure, such as a list of lists (as shown in the example) or a NumPy array. The index option sets the row names, which are integers representing student IDs here. Likewise, the columns option sets the column names. Both the index and column arguments can be given as any one-dimensional Python structure, such as lists, NumPy arrays, or a Series object.

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IPython Notebook Essentials
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