{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After you have followed the instructions PDF, use this file to test that Jupyter is working on your machine and that the `datascience` library was installed correctly. Simpy select the cell below, then press the run button. If it runs without errors, your installation was successful."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from datascience import *\n",
    "import numpy as np\n",
    "\n",
    "test = Table().with_column('Numbers',np.arange(1,10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's see how to use Jupyter: We will add a cell, type code in it and run it.\n",
    "\n",
    "Add a new cell by clicking the \"+\" button above and typing in the word `test`. This is an actual line of code. If you click the Run button for your new cell, you shall be retrieving what is inside `test`. The output will be a Table column titled \"Numbers\" and having 9 rows from 1 to 9."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The cell type may be chosen from the drop down menu above. It has the options: \"Code\", \"Markdown\", \"Raw\" and \"Heading\". We will mainly use the first two; \"Code\" is to write code and \"Markdown\" is to write text. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, notice that it is not important where your cells are, it is only important that you run them in the order that you want. But a good readable document will have the code written in the sequence they are meant to run. If you would like to move around cells, use the arrow buttons above. You can also cut, copy and paste cells using the corresponding  buttons above."
   ]
  }
 ],
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