Lecture-1-Introduction-to-Jupyter-Notebooks.ipynb

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   "source": [
    "# Introduction to Jupyter Notebooks\n",
    "\n",
    "based on Jessica B. Hamrick's excellent [Introduction to the IPython notebook](https://github.com/jhamrick/nbgrader-demo/blob/master/instructor/source/ps1/1%20-%20Introduction%20to%20the%20IPython%20notebook.ipynb) \n",
    "\n",
    "In this class we will be using Jupyter Notebooks for intertactively learning how run GWAS analysis so this notebook is a short introduction to the features and functions of Jupyter notebooks\n",
    "\n",
    "What are Juputer notebooks ?\n",
    "Juputer notebooks provide an interactive coding environment to quickly execute code, get feedback, and make notes directly in your browser.\n",
    "\n",
    "\n",
    "## Installation & Usage\n",
    "\n",
    "Notebooks can be easily installed either via pip or conda:\n",
    "\n",
    "- pip: `pip install jupyter`\n",
    "- conda: `conda install jupyter`\n",
    "\n",
    "To start the interface just open a terminal and execute the following command:\n",
    "\n",
    "    jupyter notebook\n",
    "    \n",
    "## Notebook cells\n",
    "\n",
    "The notebook consists of a series of cells. There are 4 cell types:\n",
    "\n",
    "1. **Markdown —** This is where you add the documentation by putting text formatted using Markdown. The output is displayed in place of the cell when it is run. This text is in what is called a \"Markdown cell\". Here is a link to the [Markdown cheatsheet](https://github.com/adam-p/markdown-here/wiki/Markdown-Cheatsheet)\n",
    "\n",
    "2. **Code —** This is the cell where we write our python code that will be computed by the ipython kernel and the output is displayed under the cell. Here is an example of a code cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello World\n"
     ]
    }
   ],
   "source": [
    "# Code cell and it's output below\n",
    "print('Hello World')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. **Raw NBConvert —** This is another tool to convert your jupyter notebook into another file format like PDF, HTML, etc.\n",
    "4. **Heading —** This is the same as writing a heading(Line starting with #) in Markdown. This is deprecated. Simply use Markdown."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can tell what the type of a cell is by selecting the cell, and looking at the toolbar at the top of the page. For example if you select this cell you can see that it is of type \"Markdown\"\n",
    "\n",
    "![](images/cell_type.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Command and edit mode\n",
    "\n",
    "In the notebook, there are two modes: edit mode and command mode. By default the notebook begins in command mode. In order to edit a cell, you need to be in edit mode.\n",
    "\n",
    "<div class=\"alert alert-info\"><b>When you are in command mode</b>, you can press <b>enter</b> to switch to edit mode. The outline of the cell you currently have selected will turn green, and a cursor will appear.</div>\n",
    "\n",
    "<div class=\"alert alert-info\"><b>When you are in edit mode</b>, you can press <b>escape</b> to switch to command mode. The outline of the cell you currently have selected will turn gray, and the cursor will disappear.</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Markdown cells\n",
    "\n",
    "For example, a markdown cell might look like this in **command mode** (Note: the following few cells are not actually cells -- they are images and just look like cells! This is for demonstration purposes only.)\n",
    "\n",
    "![](images/command-mode-markdown-rendered.png)\n",
    "\n",
    "Then, when you press enter, it will change to **edit mode**:\n",
    "\n",
    "![](images/edit-mode-markdown.png)\n",
    "\n",
    "Now, when we press escape, it will change back to **command mode**:\n",
    "\n",
    "![](images/command-mode-markdown-unrendered.png)\n",
    "\n",
    "However, you'll notice that the cell no longer looks like it did originally. This is because IPython will only *render* the markdown when you tell it to. To do this, we need to \"run\" the cell by pressing **`Ctrl-Enter`**, and then it will go back to looking like it did originally:\n",
    "\n",
    "![](images/command-mode-markdown-rendered.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Code cells\n",
    "\n",
    "For code cells, it is pretty much the same thing. This is what a code cell looks like in command mode (again, the next few cells LOOK like cells, but are just images):\n",
    "\n",
    "![](images/command-mode-outline.png)\n",
    "\n",
    "If we press enter, it will change to **edit mode**:\n",
    "\n",
    "![](images/edit-mode-outline.png)\n",
    "\n",
    "And pressing escape will also go back to **command mode**:\n",
    "\n",
    "![](images/command-mode-outline.png)\n",
    "\n",
    "If we were to press **`Ctrl-Enter`** like we did for the markdown cell, this would actually *run* the code in the code cell:\n",
    "\n",
    "![](images/code-cell-run.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Executing cells\n",
    "\n",
    "Code cells can contain any valid Python code in them. When you run the cell, the code is executed and any output is displayed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\">\n",
    "You can execute cells with <b><code>Ctrl-Enter</code></b> (which will keep the cell selected), or <b><code>Shift-Enter</code></b> (which will select the next cell).\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Try running the following cell and see what it prints out:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"Printing cumulative sum from 1-10:\")\n",
    "total = 0\n",
    "for i in range(1, 11):\n",
    "    total += i\n",
    "    print(\"Sum of 1 to \" + str(i) + \" is: \" + str(total))\n",
    "print(\"Done printing numbers.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You'll notice that the output beneath the cell corresponds to the `print` statements in the code. Here is another example, which only prints out the final sum:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "total = 0\n",
    "for i in range(1, 11):\n",
    "    total += i\n",
    "print(total)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another way to print something out is to have that thing be the last line in the cell. For example, we could rewrite our example above to be:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "total = 0\n",
    "for i in range(1, 11):\n",
    "    total += i\n",
    "total"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, this *will not work* unless the thing to be displayed is on the last line. For example, if we wanted to print the total sum and then a message after that, this will not do what we want (it will only print \"Done computing total.\", and not the total sum itself)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "total = 0\n",
    "for i in range(1, 11):\n",
    "    total += i\n",
    "total\n",
    "print(\"Done computing total.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The IPython kernel\n",
    "\n",
    "When you first start a notebook, you are also starting what is called a *kernel*. This is a special program that runs in the background and executes Python code. Whenever you run a code cell, you are telling the kernel to execute the code that is in the cell, and to print the output (if any).\n",
    "\n",
    "Just like if you were typing code at the Python interpreter, you need to make sure your variables are declared before you can use them. What will happen when you run the following cell? Try it and see:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The issue is that the variable `a` does not exist. Modify the cell above so that `a` is declared first (for example, you could set the value of `a` to 1 -- or pick whatever value you want). Once you have modified the above cell, you should be able to run the following cell (if you haven't modified the above cell, you'll get the same error!):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"The value of 'a' is: \" + str(a))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Running the above cell should work, because `a` has now been declared. To see what variables have been declared, you can use the `%whos` command:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%whos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you ran the summing examples from the previous section, you'll notice that `total` and `i` are listed under the `%whos` command. That is because when we ran the code for those examples, they also modified the kernel state.\n",
    "\n",
    "(Note that commands beginning with a percent (%) or double percent (%%) are special IPython commands called *magics*. They will **only** work in IPython.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Restarting the kernel\n",
    "\n",
    "It is generally a good idea to periodically restart the kernel and start fresh, because you may be using some variables that you declared at some point, but at a later point deleted that declaration. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-danger\">\n",
    "Your code should <b>always</b> be able to work if you run every cell in the notebook, in order, starting from a new kernel. </div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To test that your code can do this, first restart the kernel by clicking the restart button:\n",
    "\n",
    "![](images/restart-kernel-button.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, run all cells in the notebook in order by choosing **Cell$\\rightarrow$Run All** from the menu above."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\">\n",
    "There are many keyboard shortcuts for the notebook. To see a full list of these, go to <b>Help$\\rightarrow$Keyboard Shortcuts</b> or pres the <b>\"H\"</b> on the keyboard.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\">To learn a little more about what things are what in the IPython Notebook, check out the user interface tour, which you can access by going to <b>Help$\\rightarrow$User Interface Tour</b>.</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ohter useful features\n",
    "\n",
    "### Inline plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "# This line configures matplotlib(plotting library) to show figures embedded in the notebook, \n",
    "# instead of opening a new window for each figure. It also imports numpy \n",
    "# and matplotlib.pyplot in their common namespaces, np and plt\n",
    "%pylab inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.linspace(0, 5, 10)\n",
    "y = x ** 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(x, y, 'r')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.title('This be the quadratic function')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Latex code\n",
    "\n",
    "The Markdown cells easily allow you to easily write equations using LaTeX.\n",
    "You should be able to find all of the symbols you need on [this](http://web.ift.uib.no/Teori/KURS/WRK/TeX/symALL.html) page. For more examples check out [Jessica B. Hamrick's Latex notebook](https://github.com/jhamrick/nbgrader-demo/blob/master/instructor/source/ps1/2%20-%20Markdown%20and%20LaTeX%20Cheatsheet.ipynb)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Example of complex LaTeX formular:\n",
    "\n",
    "$\\displaystyle \\int_a^b\\frac{\\mathrm{d}^2x}{\\mathrm{d}t^2} + 2\\zeta\\omega_0\\frac{\\mathrm{d}x}{\\mathrm{d}t} + \\omega^2_0 x = 0$\n",
    "\n",
    "(This isn't an image. Enter the cell in edit mode to see it)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Magics\n",
    "\n",
    "Jupyter notebook code cells can contain special commands which are not valid Python code but affect the behavior of the notebook. These special commands are called magic commands. This magic commands are tied to corresponding ipython kernel that is running/powering the notebook. To get a list of available magic commands run:\n",
    "\n",
    "    %quickref"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/json": {
       "cell": {
        "!": "OSMagics",
        "HTML": "Other",
        "SVG": "Other",
        "bash": "Other",
        "capture": "ExecutionMagics",
        "debug": "ExecutionMagics",
        "file": "Other",
        "html": "DisplayMagics",
        "javascript": "DisplayMagics",
        "js": "DisplayMagics",
        "latex": "DisplayMagics",
        "markdown": "DisplayMagics",
        "perl": "Other",
        "prun": "ExecutionMagics",
        "pypy": "Other",
        "python": "Other",
        "python2": "Other",
        "python3": "Other",
        "ruby": "Other",
        "script": "ScriptMagics",
        "sh": "Other",
        "svg": "DisplayMagics",
        "sx": "OSMagics",
        "system": "OSMagics",
        "time": "ExecutionMagics",
        "timeit": "ExecutionMagics",
        "writefile": "OSMagics"
       },
       "line": {
        "alias": "OSMagics",
        "alias_magic": "BasicMagics",
        "autoawait": "AsyncMagics",
        "autocall": "AutoMagics",
        "automagic": "AutoMagics",
        "autosave": "KernelMagics",
        "bookmark": "OSMagics",
        "cat": "Other",
        "cd": "OSMagics",
        "clear": "KernelMagics",
        "colors": "BasicMagics",
        "conda": "PackagingMagics",
        "config": "ConfigMagics",
        "connect_info": "KernelMagics",
        "cp": "Other",
        "debug": "ExecutionMagics",
        "dhist": "OSMagics",
        "dirs": "OSMagics",
        "doctest_mode": "BasicMagics",
        "ed": "Other",
        "edit": "KernelMagics",
        "env": "OSMagics",
        "gui": "BasicMagics",
        "hist": "Other",
        "history": "HistoryMagics",
        "killbgscripts": "ScriptMagics",
        "ldir": "Other",
        "less": "KernelMagics",
        "lf": "Other",
        "lk": "Other",
        "ll": "Other",
        "load": "CodeMagics",
        "load_ext": "ExtensionMagics",
        "loadpy": "CodeMagics",
        "logoff": "LoggingMagics",
        "logon": "LoggingMagics",
        "logstart": "LoggingMagics",
        "logstate": "LoggingMagics",
        "logstop": "LoggingMagics",
        "ls": "Other",
        "lsmagic": "BasicMagics",
        "lx": "Other",
        "macro": "ExecutionMagics",
        "magic": "BasicMagics",
        "man": "KernelMagics",
        "matplotlib": "PylabMagics",
        "mkdir": "Other",
        "more": "KernelMagics",
        "mv": "Other",
        "notebook": "BasicMagics",
        "page": "BasicMagics",
        "pastebin": "CodeMagics",
        "pdb": "ExecutionMagics",
        "pdef": "NamespaceMagics",
        "pdoc": "NamespaceMagics",
        "pfile": "NamespaceMagics",
        "pinfo": "NamespaceMagics",
        "pinfo2": "NamespaceMagics",
        "pip": "PackagingMagics",
        "popd": "OSMagics",
        "pprint": "BasicMagics",
        "precision": "BasicMagics",
        "prun": "ExecutionMagics",
        "psearch": "NamespaceMagics",
        "psource": "NamespaceMagics",
        "pushd": "OSMagics",
        "pwd": "OSMagics",
        "pycat": "OSMagics",
        "pylab": "PylabMagics",
        "qtconsole": "KernelMagics",
        "quickref": "BasicMagics",
        "recall": "HistoryMagics",
        "rehashx": "OSMagics",
        "reload_ext": "ExtensionMagics",
        "rep": "Other",
        "rerun": "HistoryMagics",
        "reset": "NamespaceMagics",
        "reset_selective": "NamespaceMagics",
        "rm": "Other",
        "rmdir": "Other",
        "run": "ExecutionMagics",
        "save": "CodeMagics",
        "sc": "OSMagics",
        "set_env": "OSMagics",
        "store": "StoreMagics",
        "sx": "OSMagics",
        "system": "OSMagics",
        "tb": "ExecutionMagics",
        "time": "ExecutionMagics",
        "timeit": "ExecutionMagics",
        "unalias": "OSMagics",
        "unload_ext": "ExtensionMagics",
        "who": "NamespaceMagics",
        "who_ls": "NamespaceMagics",
        "whos": "NamespaceMagics",
        "xdel": "NamespaceMagics",
        "xmode": "BasicMagics"
       }
      },
      "text/plain": [
       "Available line magics:\n",
       "%alias  %alias_magic  %autoawait  %autocall  %automagic  %autosave  %bookmark  %cat  %cd  %clear  %colors  %conda  %config  %connect_info  %cp  %debug  %dhist  %dirs  %doctest_mode  %ed  %edit  %env  %gui  %hist  %history  %killbgscripts  %ldir  %less  %lf  %lk  %ll  %load  %load_ext  %loadpy  %logoff  %logon  %logstart  %logstate  %logstop  %ls  %lsmagic  %lx  %macro  %magic  %man  %matplotlib  %mkdir  %more  %mv  %notebook  %page  %pastebin  %pdb  %pdef  %pdoc  %pfile  %pinfo  %pinfo2  %pip  %popd  %pprint  %precision  %prun  %psearch  %psource  %pushd  %pwd  %pycat  %pylab  %qtconsole  %quickref  %recall  %rehashx  %reload_ext  %rep  %rerun  %reset  %reset_selective  %rm  %rmdir  %run  %save  %sc  %set_env  %store  %sx  %system  %tb  %time  %timeit  %unalias  %unload_ext  %who  %who_ls  %whos  %xdel  %xmode\n",
       "\n",
       "Available cell magics:\n",
       "%%!  %%HTML  %%SVG  %%bash  %%capture  %%debug  %%file  %%html  %%javascript  %%js  %%latex  %%markdown  %%perl  %%prun  %%pypy  %%python  %%python2  %%python3  %%ruby  %%script  %%sh  %%svg  %%sx  %%system  %%time  %%timeit  %%writefile\n",
       "\n",
       "Automagic is ON, % prefix IS NOT needed for line magics."
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%lsmagic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Time code\n",
    "\n",
    "Using the Magics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sorting an unsorted list:\n",
      "CPU times: user 30.2 ms, sys: 426 µs, total: 30.6 ms\n",
      "Wall time: 30.8 ms\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "L = [random.random() for i in range(100000)]\n",
    "print(\"sorting an unsorted list:\")\n",
    "%time L.sort()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sorting an already sorted list:\n",
      "CPU times: user 2.98 ms, sys: 7 µs, total: 2.98 ms\n",
      "Wall time: 3 ms\n"
     ]
    }
   ],
   "source": [
    "print(\"sorting an already sorted list:\")\n",
    "%time L.sort()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Shell commands\n",
    "\n",
    "It is also possible to run shell commands directly inside a cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lecture-0-Setup-of-Python-Environment.ipynb\r\n",
      "Lecture-1-Introduction to Jupyter Notebooks.ipynb\r\n",
      "Lecture-1-Python-Data-Science-Guide.ipynb\r\n",
      "Lecture-2-Linear-Regression.ipynb\r\n",
      "Lecture-3-Linear-Mixed-Model.ipynb\r\n",
      "Lecture-4-Multi-Trait-Linear-Mixed-Model.ipynb\r\n",
      "Lecture-5-SNPHeritability.ipynb\r\n",
      "Lecture-6-BroadSenseHeritability.ipynb\r\n",
      "Lecture-7-importing-data.ipynb\r\n",
      "README.md\r\n",
      "\u001b[34mdata\u001b[m\u001b[m/\r\n",
      "environment.yml\r\n",
      "gwas-dockerfile\r\n",
      "\u001b[34mimages\u001b[m\u001b[m/\r\n",
      "index.ipynb\r\n",
      "install.R\r\n",
      "runtime.txt\r\n"
     ]
    }
   ],
   "source": [
    "ls"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Excercices \n",
    "\n",
    "Here are some excercises to test your knowledge"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "Implement the function <code>hello</code> and make sure the test cells runs without any errors. You will need to delete the line with `raise NotImplementedError`, write your own solution, and then re-run the cell before running the test cell. Each time you change code in a cell, you  will need to re-run that cell before running any other cells that depend on it!\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "def hello(name):\n",
    "    \"\"\"Returns a message containing \"Hello, <name>!\", \n",
    "    where <name> is passed in as an argument.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    name : string\n",
    "        The name of the person to say hello to\n",
    "        \n",
    "    Returns\n",
    "    -------\n",
    "    the message containing \"Hello, <name>!\"\n",
    "    \n",
    "    \"\"\"\n",
    "    ### BEGIN SOLUTION\n",
    "    raise NotImplementedError()\n",
    "    ### END SOLUTION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# try running your hello function with your own name and see what \n",
    "# it returns\n",
    "hello(\"YOUR NAME HERE\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nose.tools import assert_equal\n",
    "assert_equal(hello(\"Jessica\"), \"Hello, Jessica!\")\n",
    "assert_equal(hello(\"jessica\"), \"Hello, jessica!\")\n",
    "assert_equal(hello(\"Tom\"), \"Hello, Tom!\")\n",
    "assert_equal(hello(\"123\"), \"Hello, 123!\")\n",
    "\n",
    "print(\"Success!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Resources:\n",
    "\n",
    "Dataquest Jupyter tutorial: https://www.dataquest.io/blog/jupyter-notebook-tutorial/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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