jupyter basics

get_good_at_ipython.ipynb

 {
  "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true,
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import numpy, scipy, matplotlib.pyplot as plt, pandas, librosa"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -33,7 +19,7 @@
     }
    },
    "source": [
-    "Getting Good at IPython\n",
+    "Jupyter Basics\n",
     "======================="
    ]
   },
@@ -45,7 +31,7 @@
     }
    },
    "source": [
-    "You are looking at an **IPython Notebook**, an interactive Python shell inside of a web browser. With it, you can run individual Python commands and immediately view their output.  It's basically the Matlab Desktop or Mathematica Notebook for Python.\n",
+    "You are looking at a **Jupyter Notebook**, an interactive Python shell inside of a web browser. With it, you can run individual Python commands and immediately view their output.  It's basically the Matlab Desktop or Mathematica Notebook for Python.\n",
     "\n",
     "First, we recommend that you take the *User Interface Tour* in the Help Menu above."
    ]
@@ -69,16 +55,15 @@
     }
    },
    "source": [
-    "An IPython Notebook is comprised of **cells**. Cells are just small units of code or text. For example, the text that you are reading is inside a *Markdown* cell. (More on that later.)\n",
+    "A Jupyter Notebook is comprised of **cells**. Cells are just small units of code or text. For example, the text that you are reading is inside a *Markdown* cell. (More on that later.)\n",
     "\n",
     "*Code* cells allow you to edit, execute, and analyze small portions of Python code at a time. Here is a code cell:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {
-    "collapsed": false,
     "slideshow": {
      "slide_type": "subslide"
     }
@@ -90,7 +75,7 @@
        "3"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 1,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -118,7 +103,7 @@
     }
    },
    "source": [
-    "The IPython Notebook has two different keyboard input modes. \n",
+    "The Jupyter Notebook has two different keyboard input modes. \n",
     "\n",
     "In **Edit Mode**, you type code/text into a cell. Edit Mode is indicated by a *green* cell border. \n",
     "\n",
@@ -152,7 +137,7 @@
     }
    },
    "source": [
-    "Your code goes directly into an IPython notebook. To save your changes, click on the \"Save\" icon in the menu bar, or type **`s`** in command mode.\n",
+    "Your code goes directly into a Jupyter notebook. To save your changes, click on the \"Save\" icon in the menu bar, or type **`s`** in command mode.\n",
     "\n",
     "If this notebook is in a Git repo, use `git checkout -- <file>` to revert a saved edit."
    ]
@@ -187,7 +172,7 @@
     }
    },
    "source": [
-    "A cell may contain Python code or Markdown code. To convert any Python cell to a markdown cell, press **`m`**."
+    "A cell may contain Python code or Markdown code. To convert any Python cell to a Markdown cell, press **`m`**. To convert from a Markdown cell to a Python cell, press **`y`**."
    ]
   },
   {
@@ -198,7 +183,7 @@
     }
    },
    "source": [
-    "For headings, we recommend that you use IPython's keyboard shortcuts. To change the text in a cell to a level-3 header, simply press `3`. For similar commands, press **`h`** to view the Help menu."
+    "For headings, we recommend that you use Jupyter's keyboard shortcuts. To change the text in a cell to a level-3 header, simply press `3`. For similar commands, press **`h`** to view the Help menu."
    ]
   },
   {
@@ -264,7 +249,7 @@
     }
    },
    "source": [
-    "## Default Imports"
+    "## Imports"
    ]
   },
   {
@@ -275,80 +260,48 @@
     }
    },
    "source": [
-    "If you run IPython inside the [`stanford-mir` Vagrant box](https://vagrantcloud.com/stevetjoa/stanford-mir) used for this workshop, every IPython notebook will automatically have the following packages imported: "
+    "You may encounter the following imports while using this website:"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 2,
    "metadata": {
+    "collapsed": true,
     "slideshow": {
      "slide_type": "subslide"
     }
    },
+   "outputs": [],
    "source": [
-    "    import numpy\n",
-    "    import scipy\n",
-    "    import pandas\n",
-    "    import matplotlib\n",
-    "    import matplotlib.pyplot as plt\n",
-    "    import librosa"
+    "import numpy\n",
+    "import scipy\n",
+    "import pandas\n",
+    "import sklearn\n",
+    "import seaborn\n",
+    "import matplotlib\n",
+    "import matplotlib.pyplot as plt\n",
+    "import librosa\n",
+    "import librosa.display\n",
+    "import IPython.display as ipd"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
+   "metadata": {},
    "source": [
-    "Example:"
+    "You can also combine imports on one line:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {
-    "collapsed": false,
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x108759110>]"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXgAAAEACAYAAAC57G0KAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x108312bd0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.plot(scipy.hamming(101))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "notes"
-    }
+    "collapsed": true
    },
+   "outputs": [],
    "source": [
-    "To learn more about automatic imports, view the IPython configuration file at `~/.ipython/profile_default/ipython_config.py`."
+    "import numpy, scipy, pandas"
    ]
   },
   {
@@ -379,25 +332,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {
-    "collapsed": false,
     "slideshow": {
      "slide_type": "subslide"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<ufunc 'sin'>"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Press Tab at the end of the following line\n",
     "scipy.sin"
@@ -429,7 +370,6 @@
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {
-    "collapsed": false,
     "slideshow": {
      "slide_type": "subslide"
     }
@@ -499,7 +439,6 @@
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {
-    "collapsed": false,
     "slideshow": {
      "slide_type": "notes"
     }
@@ -539,7 +478,6 @@
   }
  ],
  "metadata": {
-  "celltoolbar": "Slideshow",
   "kernelspec": {
    "display_name": "Python 2",
    "language": "python",
@@ -555,9 +493,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
-   "version": "2.7.6"
+   "version": "2.7.13"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }

index.html

@@ -11814,8 +11814,8 @@ div#notebook {
 <li><a href="why_mir.html">What is MIR?</a> (<a href="why_mir.ipynb">ipynb</a>)</li>
 <li><a href="basic_mir.html">Overview of a Basic MIR System</a> (<a href="basic_mir.ipynb">ipynb</a>)</li>
 <li><a href="python_basics.html">Python Basics</a> (<a href="python_basics.ipynb">ipynb</a>)</li>
-<li><a href="get_good_at_ipython.html">Getting Good at IPython</a> (<a href="get_good_at_ipython.ipynb">ipynb</a>)</li>
-<li><a href="ipython_audio.html">Using Audio in IPython</a> (<a href="ipython_audio.ipynb">ipynb</a>)</li>
+<li><a href="get_good_at_ipython.html">Jupyter Basics</a> (<a href="get_good_at_ipython.ipynb">ipynb</a>)</li>
+<li><a href="ipython_audio.html">Jupyter Audio Basics</a> (<a href="ipython_audio.ipynb">ipynb</a>)</li>
 <li><a href="numpy_basics.html">NumPy and SciPy Basics</a> (<a href="numpy_basics.ipynb">ipynb</a>)</li>
 </ol>
 

index.ipynb

@@ -36,8 +36,8 @@
     "1.   [What is MIR?](why_mir.html) ([ipynb](why_mir.ipynb))\n",
     "1.   [Overview of a Basic MIR System](basic_mir.html) ([ipynb](basic_mir.ipynb))\n",
     "1.   [Python Basics](python_basics.html) ([ipynb](python_basics.ipynb))\n",
-    "1.   [Getting Good at IPython](get_good_at_ipython.html) ([ipynb](get_good_at_ipython.ipynb))\n",
-    "1.   [Using Audio in IPython](ipython_audio.html) ([ipynb](ipython_audio.ipynb))\n",
+    "1.   [Jupyter Basics](get_good_at_ipython.html) ([ipynb](get_good_at_ipython.ipynb))\n",
+    "1.   [Jupyter Audio Basics](ipython_audio.html) ([ipynb](ipython_audio.ipynb))\n",
     "1.   [NumPy and SciPy Basics](numpy_basics.html) ([ipynb](numpy_basics.ipynb))"
    ]
   },