MAINT: regularize notebook titles

notebooks/01.02-Shell-Keyboard-Shortcuts.ipynb

@@ -23,8 +23,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Keyboard Shortcuts in the IPython Shell\n",
-    "\n",
+    "# Keyboard Shortcuts in the IPython Shell"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
     "If you spend any amount of time on the computer, you've probably found a use for keyboard shortcuts in your workflow.\n",
     "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",
     "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

@@ -23,8 +23,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# IPython Magic Commands\n",
-    "\n",
+    "# IPython Magic Commands"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
     "The previous two sections showed how IPython lets you use and explore Python efficiently and interactively.\n",
     "Here we'll begin discussing some of the enhancements that IPython adds on top of the normal Python syntax.\n",
     "These are known in IPython as *magic commands*, and are prefixed by the ``%`` character.\n",

notebooks/01.04-Input-Output-History.ipynb

@@ -23,8 +23,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Input and Output History\n",
-    "\n",
+    "# Input and Output History"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
     "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",
     "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",
     "We'll explore those here."

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

@@ -23,8 +23,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# IPython and Shell Commands\n",
-    "\n",
+    "# IPython and Shell Commands"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
     "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",
     "IPython bridges this gap, and gives you a syntax for executing shell commands directly from within the IPython terminal.\n",
     "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

@@ -23,8 +23,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Errors and Debugging\n",
-    "\n",
+    "# Errors and Debugging"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
     "Code development and data analysis always require a bit of trial and error, and IPython contains tools to streamline this process.\n",
     "This section will briefly cover some options for controlling Python's exception reporting, followed by exploring tools for debugging errors in code."
    ]

notebooks/01.07-Timing-and-Profiling.ipynb

@@ -23,8 +23,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Profiling and Timing Code\n",
-    "\n",
+    "# Profiling and Timing Code"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
     "In the process of developing code and creating data processing pipelines, there are often trade-offs you can make between various implementations.\n",
     "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",
     "\n",

notebooks/01.08-More-IPython-Resources.ipynb

@@ -23,8 +23,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# More IPython Resources\n",
-    "\n",
+    "# More IPython Resources"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
     "In this chapter, we've just scratched the surface of using IPython to enable data science tasks.\n",
     "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."
    ]

notebooks/02.00-Introduction-to-NumPy.ipynb

@@ -2,7 +2,10 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "<!--BOOK_INFORMATION-->\n",
     "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
@@ -13,22 +16,31 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "<!--NAVIGATION-->\n",
     "< [More IPython Resources](01.08-More-IPython-Resources.ipynb) | [Contents](Index.ipynb) | [Understanding Data Types in Python](02.01-Understanding-Data-Types.ipynb) >"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
-    "# Introduction to NumPy\n"
+    "# Introduction to NumPy"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "This chapter, along with chapter 3, outlines techniques for effectively loading, storing, and manipulating in-memory data in Python.\n",
     "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",
@@ -56,7 +68,9 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [
     {
@@ -77,7 +91,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "For the pieces of the package discussed here, I'd recommend NumPy version 1.8 or later.\n",
     "By convention, you'll find that most people in the SciPy/PyData world will import NumPy using ``np`` as an alias:"
@@ -87,7 +104,9 @@
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -96,14 +115,20 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "Throughout this chapter, and indeed the rest of the book, you'll find that this is the way we will import and use NumPy."
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "## Reminder about Built In Documentation\n",
     "\n",
@@ -126,7 +151,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "<!--NAVIGATION-->\n",
     "< [More IPython Resources](01.08-More-IPython-Resources.ipynb) | [Contents](Index.ipynb) | [Understanding Data Types in Python](02.01-Understanding-Data-Types.ipynb) >"