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Commit e3a2257

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MAINT: regularize notebook titles
1 parent de0cc6b commit e3a2257

21 files changed

+2758
-758
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‎notebooks/01.02-Shell-Keyboard-Shortcuts.ipynb‎

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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Keyboard Shortcuts in the IPython Shell\n",
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"\n",
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"# Keyboard Shortcuts in the IPython Shell"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"If you spend any amount of time on the computer, you've probably found a use for keyboard shortcuts in your workflow.\n",
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"Most familiar perhaps are the Cmd-C and Cmd-V (or Ctrl-C and Ctrl-V) for copying and pasting in a wide variety of programs and systems.\n",
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"Power-users tend to go even further: popular text editors like Emacs, Vim, and others provide users an incredible range of operations through intricate combinations of keystrokes.\n",

‎notebooks/01.03-Magic-Commands.ipynb‎

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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# IPython Magic Commands\n",
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"\n",
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"# IPython Magic Commands"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"The previous two sections showed how IPython lets you use and explore Python efficiently and interactively.\n",
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"Here we'll begin discussing some of the enhancements that IPython adds on top of the normal Python syntax.\n",
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"These are known in IPython as *magic commands*, and are prefixed by the ``%`` character.\n",

‎notebooks/01.04-Input-Output-History.ipynb‎

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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Input and Output History\n",
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"\n",
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"# Input and Output History"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Previously we saw that the IPython shell allows you to access previous commands with the up and down arrow keys, or equivalently the Ctrl-p/Ctrl-n shortcuts.\n",
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"Additionally, in both the shell and the notebook, IPython exposes several ways to obtain the output of previous commands, as well as string versions of the commands themselves.\n",
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"We'll explore those here."

‎notebooks/01.05-IPython-And-Shell-Commands.ipynb‎

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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# IPython and Shell Commands\n",
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"\n",
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"# IPython and Shell Commands"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"When working interactively with the standard Python interpreter, one of the frustrations is the need to switch between multiple windows to access Python tools and system command-line tools.\n",
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"IPython bridges this gap, and gives you a syntax for executing shell commands directly from within the IPython terminal.\n",
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"The magic happens with the exclamation point: anything appearing after ``!`` on a line will be executed not by the Python kernel, but by the system command-line.\n",

‎notebooks/01.06-Errors-and-Debugging.ipynb‎

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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Errors and Debugging\n",
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"\n",
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"# Errors and Debugging"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Code development and data analysis always require a bit of trial and error, and IPython contains tools to streamline this process.\n",
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"This section will briefly cover some options for controlling Python's exception reporting, followed by exploring tools for debugging errors in code."
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]

‎notebooks/01.07-Timing-and-Profiling.ipynb‎

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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Profiling and Timing Code\n",
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"\n",
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"# Profiling and Timing Code"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"In the process of developing code and creating data processing pipelines, there are often trade-offs you can make between various implementations.\n",
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"Early in developing your algorithm, it can be counterproductive to worry about such things. As Donald Knuth famously quipped, \"We should forget about small efficiencies, say about 97% of the time: premature optimization is the root of all evil.\"\n",
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"\n",

‎notebooks/01.08-More-IPython-Resources.ipynb‎

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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# More IPython Resources\n",
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"\n",
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"# More IPython Resources"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"In this chapter, we've just scratched the surface of using IPython to enable data science tasks.\n",
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"Much more information is available both in print and on the Web, and here we'll list some other resources that you may find helpful."
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]

‎notebooks/02.00-Introduction-to-NumPy.ipynb‎

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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"metadata": {
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"deletable": true,
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"editable": true
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},
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"source": [
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"<!--BOOK_INFORMATION-->\n",
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"<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"metadata": {
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"deletable": true,
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},
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"source": [
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"<!--NAVIGATION-->\n",
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"< [More IPython Resources](01.08-More-IPython-Resources.ipynb) | [Contents](Index.ipynb) | [Understanding Data Types in Python](02.01-Understanding-Data-Types.ipynb) >"
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]
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{
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"cell_type": "markdown",
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"metadata": {},
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"metadata": {
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"deletable": true,
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},
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"source": [
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"# Introduction to NumPy\n"
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"# Introduction to NumPy"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"deletable": true,
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},
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"source": [
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"This chapter, along with chapter 3, outlines techniques for effectively loading, storing, and manipulating in-memory data in Python.\n",
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"The topic is very broad: datasets can come from a wide range of sources and a wide range of formats, including be collections of documents, collections of images, collections of sound clips, collections of numerical measurements, or nearly anything else.\n",
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"collapsed": false
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"collapsed": false,
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"deletable": true,
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"editable": true
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"outputs": [
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{
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"metadata": {
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"deletable": true,
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"editable": true
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},
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"source": [
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"For the pieces of the package discussed here, I'd recommend NumPy version 1.8 or later.\n",
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"By convention, you'll find that most people in the SciPy/PyData world will import NumPy using ``np`` as an alias:"
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"collapsed": false
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"editable": true
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"outputs": [],
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"source": [
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"metadata": {
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"deletable": true,
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"editable": true
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},
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"source": [
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"Throughout this chapter, and indeed the rest of the book, you'll find that this is the way we will import and use NumPy."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"metadata": {
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"deletable": true,
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},
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"source": [
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"## Reminder about Built In Documentation\n",
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"\n",
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"metadata": {
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"deletable": true,
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"editable": true
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},
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"source": [
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"<!--NAVIGATION-->\n",
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"< [More IPython Resources](01.08-More-IPython-Resources.ipynb) | [Contents](Index.ipynb) | [Understanding Data Types in Python](02.01-Understanding-Data-Types.ipynb) >"

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