nodebook_demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Nodebook \n",
    "Repeatable jupyter notebooks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Notebook configuration\n",
    "\n",
    "`#pragma nodebook off` instructs the cell to run in the global environment, this is required for ipython magics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "node_exists": false,
    "node_name": "A5DD3DDCC9474F27B9FF8E7E88B10652",
    "node_parent": 0,
    "run_control": {
     "state": "n"
    }
   },
   "outputs": [],
   "source": [
    "#pragma nodebook off\n",
    "# (optional) inline matplotlib figures\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "node_exists": false,
    "node_name": "0B7F6A76C7834459B0790F244E4F44C4"
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "Jupyter.utils.load_extensions('nodebook/nodebookext')"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#pragma nodebook off\n",
    "# here we load the nodebook extension and initialize the nodebook\n",
    "%load_ext nodebook.ipython\n",
    "%nodebook memory test_nb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example 1\n",
    "### Chain of execution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ordinary notebook behavior"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true,
    "node_exists": false,
    "node_name": "F1A1FED924194E9EA016979D3B23BEA5"
   },
   "outputs": [],
   "source": [
    "#pragma nodebook off\n",
    "x = (10+4) % 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "node_exists": false,
    "node_name": "FF84458F275649F389C48E8F41DEB536"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#pragma nodebook off\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true,
    "node_exists": false,
    "node_name": "2A50187EBDA5452A984EDF8E7F90501C"
   },
   "outputs": [],
   "source": [
    "#pragma nodebook off\n",
    "x += 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "node_exists": false,
    "node_name": "6E357C9D129E4E869A76889BD8EE8AFD"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#pragma nodebook off\n",
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nodebook behavior"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "node_exists": true,
    "node_name": "EB910D7B09F2443081D464E1B36C9392",
    "node_parent": 7,
    "run_control": {
     "state": "n"
    }
   },
   "outputs": [],
   "source": [
    "x = 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "node_exists": true,
    "node_name": "4B8AB43B29EC4524AE9DBBA8485F9B23",
    "node_parent": 8,
    "run_control": {
     "state": "n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "node_exists": true,
    "node_name": "17C98397C28445D68406D9AA38FFFDB4",
    "node_parent": 9,
    "run_control": {
     "state": "n"
    }
   },
   "outputs": [],
   "source": [
    "x += 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "node_exists": true,
    "node_name": "6E21FE582C4643F6BDA61DB040294A5C",
    "node_parent": 10,
    "run_control": {
     "state": "n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example 2\n",
    "### Inspecting intermediate data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "node_exists": true,
    "node_name": "0E269658505D4EE5914DD1CD33236788"
   },
   "outputs": [],
   "source": [
    "# first we'll load some common imports\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "node_exists": true,
    "node_name": "1846537E19A44D308AF5E23F0620B54D",
    "node_parent": 16,
    "run_control": {
     "state": "n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    x\n",
       "0  -9\n",
       "1  -8\n",
       "2  -7\n",
       "3  -6\n",
       "4  -5\n",
       "5  -4\n",
       "6  -3\n",
       "7  -2\n",
       "8  -1\n",
       "9   0\n",
       "10  1\n",
       "11  2\n",
       "12  3\n",
       "13  4\n",
       "14  5\n",
       "15  6\n",
       "16  7\n",
       "17  8\n",
       "18  9"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# define a dataframe\n",
    "df = pd.DataFrame({'x':np.arange(-9,10)})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "node_exists": true,
    "node_name": "0423E3B5977F4518A3F9EF885313A3C5",
    "node_parent": 17,
    "run_control": {
     "state": "n"
    }
   },
   "outputs": [],
   "source": [
    "# transform the data\n",
    "df **= 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "node_exists": true,
    "node_name": "13F16C6E3B3D4E4687B5CDD12B06076E",
    "node_parent": 19,
    "run_control": {
     "state": "n"
    }
   },
   "outputs": [],
   "source": [
    "# additional transformation\n",
    "df = -df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "node_exists": true,
    "node_name": "44138EFE05A3418099F3E6FB697B2A28"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1124f18d0>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KyAbb7dwmz80RkWwR2S4ik+yP2v5W7Gz8pL95QiIRXQKsjqOUsoivtxf3nj+Q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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1124d8cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# inspect the data\n",
    "# try moving this cell up!\n",
    "df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "node_exists": true,
    "node_name": "DCFFAB46E4064FAD88DE491CD466A0F2",
    "node_parent": 21,
    "run_control": {
     "state": "n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   y  z\n",
       "0  0  1\n",
       "1  1  1\n",
       "2  2  1\n",
       "3  3  1\n",
       "4  4  1\n",
       "5  5  1\n",
       "6  6  1\n",
       "7  7  1\n",
       "8  8  1\n",
       "9  9  1"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# overwrite df with something else\n",
    "df = pd.DataFrame({'y':np.arange(10),'z':1})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "node_exists": false,
    "node_name": "174D7423CAC546B3881D1DE8A50BC0A4"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}