02A-Git-Jupyter.ipynb

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   "source": [
    "# Essential Tools: Git and Jupyter "
   ]
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    "%matplotlib inline\n",
    "%config InlineBackend.figure_format='retina'\n",
    "%run -i common.py\n",
    "%run -i ln_preamble.py\n",
    "from importlib import reload\n",
    "from datetime import datetime"
   ]
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   },
   "source": [
    "## Critical Course Tools"
   ]
  },
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   "source": [
    "This course focuses on developing practical skills in working with data and providing students with a hands-on understanding of classical data analysis techniques. \n",
    "\n",
    "This will be a coding-intensive course. \n",
    "\n",
    "Instruction in this course will use [Python](https://www.python.org/), since it allows for fast prototyping and is supported by a great variety of scientific (and, specifically, data related) libraries."
   ]
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   "source": [
    "The materials of this course can be found under [this GitHub account](https://github.com/mcrovella/DS701-Tools-for-Data-Science). \n",
    "\n",
    "Both the lectures and the homeworks of this course are in the format of [Jupyter notebooks](https://jupyter.org)."
   ]
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   "source": [
    "__Today's Agenda__ is a quick review of essential tools you will need for this course.\n",
    "\n",
    "We are __not__ providing a comprehensive introduction to these tools.\n",
    "\n",
    "They are all __extensively__ documented online and you will need to familiarize with online resources to learn what you need.  \n",
    "\n",
    "Stack overflow, etc, is also your friend -- but as a reference, not as a way to solve homework problems!"
   ]
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   "source": [
    "__The key tools you will need__ are:\n",
    "1. Git\n",
    "2. Jupyter\n",
    "3. Pandas "
   ]
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   "source": [
    "## Python"
   ]
  },
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   "metadata": {},
   "source": [
    "We will not be teaching you python in this course.  \n",
    "\n",
    "You are assumed to be proficient in python.\n",
    "\n",
    "If you need to refresh your memory, there is a quick review of Python in the online lecture notes."
   ]
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   "source": [
    "There are many ways to install Python.  \n",
    "\n",
    "The biggest challenge in installing Python is configuring the many different packages that can be used.\n",
    "\n",
    "For that reason, I recommend the free **Anaconda Scientific Python** distribution, which you can find via Google. This Python distribution contains most of the packages that we will be using throughout the course. It also includes an easy-to-use but powerful package install/update system, **`conda`**.\n",
    "\n",
    "Note that we are using Python 3 (get the latest version, which should be 3.9 or later).  "
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   "source": [
    "## Git"
   ]
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    "One of the goals of this course is make you familiar with the modern workflow of code-versioning and collaboration. \n",
    "\n",
    "\n",
    "\n",
    "If you don't have it already, download **git** from [here](http://git-scm.com/downloads). \n",
    "\n",
    "If you don't already have one, you __must__ also create an account on GitHub.\n",
    "\n",
    "You can find extensive documentation on how to use **git** on the [Help Pages of Github](https://docs.github.com/en/get-started/quickstart), on [Atlassian](https://www.atlassian.com/git/tutorials/setting-up-a-repository), and many other sites."
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   "source": [
    "## Working with Git"
   ]
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   "source": [
    "### Configuration"
   ]
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    "The first time we use *git* on a new machine, we need to configure our name and email\n",
    "\n",
    "```\n",
    "$ git config --global user.name \"Mark Crovella\"\n",
    "$ git config --global user.mail \"crovella@bu.edu\"\n",
    "```\n",
    "Use the email that you used for your GitHub account."
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   "source": [
    "### Creating a Repository"
   ]
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    "def showGitTerm(title=\"Command Line\"):\n",
    "    return showTerm(EDITORTERM, title, \"100%\", 1000)\n",
    "showGitTerm()"
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    "After installing Git, we can configure our first repository. First, let's create a new directory.\n",
    "\n",
    "```\n",
    "$ mkdir thoughts\n",
    "$ cd thoughts\n",
    "```\n",
    "\n",
    "Now, we can create a *git* repository in this directory.\n",
    "\n",
    "```\n",
    "$ git init\n",
    "```\n",
    "\n",
    "We can check that everything is set up correctly by asking *git* to tell us the status of our project.\n",
    "\n",
    "```\n",
    "$ git status\n",
    "On branch master\n",
    "\n",
    "No commits yet\n",
    "\n",
    "nothing to commit (create/copy files and use \"git add\" to track)\n",
    "```"
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   "source": [
    "Now, create a file named ```science.txt```, edit it with your favorite text editor and add the following lines\n",
    "\n",
    "```\n",
    "Starting to think about data\n",
    "```"
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   "source": [
    "If we check the status of our repository again, *git* tells us that there is a new file:\n",
    "```\n",
    "$ git status\n",
    "On branch master\n",
    "\n",
    "No commits yet\n",
    "\n",
    "Untracked files:\n",
    "  (use \"git add <file>...\" to include in what will be committed)\n",
    "\n",
    "        science.txt\n",
    "\n",
    "nothing added to commit but untracked files present (use \"git add\" to track)\n",
    "```"
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    "To give you precise control about what changes are included in each revision, *git* has a special staging area.\n",
    "\n",
    "In the staging area it keeps track of things that you have added to the current change set but not yet committed. \n",
    "\n",
    "```git add``` puts things in this area, and ```git commit``` then copies them to long-term storage (as a commit):\n",
    "\n",
    "![Alt text](./figs/git-staging-area.png)"
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    "The \"untracked files\" message means that there's a file in the directory that *git* isn't keeping track of. We can tell *git* that it should do so using ```git add```:\n",
    "\n",
    "```\n",
    "$ git add science.txt\n",
    "```\n",
    "\n",
    "and then check that the file is now being tracked:\n",
    "\n",
    "```\n",
    "$ git status\n",
    "On branch master\n",
    "\n",
    "No commits yet\n",
    "\n",
    "Changes to be committed:\n",
    "  (use \"git rm --cached <file>...\" to unstage)\n",
    "\n",
    "        new file:   science.txt\n",
    "\n",
    "```"
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    "*git* now knows that it's supposed to keep track of ```science.txt```, but it hasn't yet recorded any changes for posterity as a commit. \n",
    "\n",
    "To get it to do that, we need to run one more command:\n",
    "\n",
    "```\n",
    "$ git commit -m \"Preparing for science\"\n",
    "[master (root-commit) f516d22] Preparing for science\n",
    " 1 file changed, 1 insertion(+)\n",
    " create mode 100644 science.txt\n",
    "```"
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    "When we run ```git commit```, *git* takes everything we have told it to save by using ```git add``` and stores a copy permanently inside the special ```.git``` directory. \n",
    "\n",
    "This permanent copy is called a **revision** and its short identifier is *f516d22*. (Your revision will have another identifier.)"
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    "We use the -m flag (for \"message\") to record a comment that will help us remember later on what we did and why. \n",
    "\n",
    "If we just run ```git commit``` without the ```-m``` option, *git* will launch an editor such as ```vim``` (or whatever other editor we configured at the start) so that we can write a longer message. "
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    "If we run git status now:\n",
    "```\n",
    "$ git status\n",
    "On branch master\n",
    "nothing to commit, working tree clean\n",
    "```"
   ]
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   "source": [
    "it tells us everything is up to date. If we want to know what we've done recently, we can ask *git* to show us the project's history using ```git log```:\n",
    "\n",
    "```\n",
    "$ git log\n",
    "Author: Mark Crovella <crovella@bu.edu>\n",
    "Date:   Sun Jan 25 12:48:44 2015 -0500\n",
    "\n",
    "    Preparing for science\n",
    "```"
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   "source": [
    "### Changing a file"
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   "source": [
    "Now, suppose that we edit the file:\n",
    "\n",
    "```\n",
    "Starting to think about data\n",
    "I need to attend CS506\n",
    "```"
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    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Now if we run ```git status```, *git* will tell us that a file that it is tracking has been modified:\n",
    "\n",
    "```\n",
    "$ git status\n",
    "On branch master\n",
    "Changes not staged for commit:\n",
    "  (use \"git add <file>...\" to update what will be committed)\n",
    "  (use \"git checkout -- <file>...\" to discard changes in working directory)\n",
    "\n",
    "        modified:   science.txt\n",
    "\n",
    "no changes added to commit (use \"git add\" and/or \"git commit -a\")\n",
    "```\n",
    "\n",
    "The last line is the key phrase: *\"no changes added to commit\"*. \n",
    "\n",
    "We have changed this file, but we haven't told *git* we will want to save those changes (which we do with ```git add```) much less actually saved them. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_style": "split",
    "nbpresent": {
     "id": "defb543e-d5d5-4ae2-ac99-b057d6648f96"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Let's double-check our work using ```git diff```, which shows us the differences between the current state of the file and the most recently saved version:\n",
    "\n",
    "```\n",
    "$ git diff\n",
    "diff --git a/science.txt b/science.txt\n",
    "index 0ac4b7b..c5b1b05 100644\n",
    "--- a/science.txt\n",
    "+++ b/science.txt\n",
    "@@ -1 +1,2 @@\n",
    " Starting to think about data\n",
    "+I need to attend CS506\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "cell_style": "split",
    "hide_input": true,
    "scrolled": false,
    "tags": [
     "remove-cell"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<b>Command Line</b>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"100%\"\n",
       "            height=\"1000\"\n",
       "            src=\"/terminals/1\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x7f8630095220>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "showGitTerm()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_style": "split",
    "nbpresent": {
     "id": "a40271c6-d186-4179-b573-fec299bc9d8e"
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     "slide_type": "fragment"
    }
   },
   "source": [
    "OK, we are happy with that, so let's commit our change:\n",
    "```\n",
    "$ git commit -m \"Related course\"\n",
    "On branch master\n",
    "Changes not staged for commit:\n",
    "        modified:   science.txt\n",
    "\n",
    "no changes added to commit\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_style": "split",
    "nbpresent": {
     "id": "b603b598-e15d-40c7-9836-55b4e8905fd4"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "*Whoops!* *Git* won't commit the file because we didn't use ```git add``` first. Let's fix that:\n",
    "```\n",
    "$ git add science.txt\n",
    "$ git commit -m \"Related course\"\n",
    "[master 1bd7277] Related course\n",
    " 1 file changed, 1 insertion(+)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_style": "split",
    "nbpresent": {
     "id": "154a475c-394b-4796-93f0-9287ceca948e"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "*Git* insists that we add files to the set we want to commit before actually committing anything because we may not want to commit everything at once. \n",
    "\n",
    "For example, suppose we're adding a few citations to our project. We might want to commit those additions, and the corresponding addition to the bibliography, but not commit the work we're doing on the analysis (which we haven't finished yet)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "0a85a94c-1749-405d-a381-3ccf78fd824c"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Recovering old versions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "cell_style": "split",
    "hide_input": true,
    "scrolled": true,
    "tags": [
     "remove-cell"
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   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<b>Command Line</b>"
      ],
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       "<IPython.core.display.HTML object>"
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       "\n",
       "        <iframe\n",
       "            width=\"100%\"\n",
       "            height=\"1000\"\n",
       "            src=\"/terminals/1\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x7f86300955e0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "showGitTerm()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_style": "split",
    "nbpresent": {
     "id": "9712b523-d0b0-4d82-a2c8-9a16b133032c"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "We can save changes to files and see what we have changed. How can we restore older versions however? Let's suppose we accidentally overwrite the file:\n",
    "\n",
    "```\n",
    "$ cat science.txt\n",
    "Despair! Nothing works\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_style": "split",
    "nbpresent": {
     "id": "309e9219-c681-447f-b151-f60402182084"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Now, ```git status``` tells us that the file has been changed, but those changes haven't been staged:\n",
    "\n",
    "```\n",
    "$ git status\n",
    "On branch master\n",
    "Changes not staged for commit:\n",
    "  (use \"git add <file>...\" to update what will be committed)\n",
    "  (use \"git checkout -- <file>...\" to discard changes in working directory)\n",
    "\n",
    "        modified:   science.txt\n",
    "\n",
    "no changes added to commit (use \"git add\" and/or \"git commit -a\")\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_style": "split",
    "nbpresent": {
     "id": "3305017a-ba7c-4099-911a-7d9327bb3420"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "We can put things back the way they were by using ```git checkout```:\n",
    "\n",
    "```\n",
    "$ git checkout HEAD science.txt\n",
    "$ cat science.txt\n",
    "Starting to think about data\n",
    "I need to attend CS506\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "63469f5c-77a5-491d-a3bb-6bcc28aca70d"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## GitHub"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "0b741034-531d-4538-bc1f-b573a4eab34d"
    }
   },
   "source": [
    "Systems like *git* allow us to move work between any two repositories.\n",
    "\n",
    "In practice, though, it's easiest to use one copy as a central hub, and to keep it on the web rather than on someone's laptop. Most programmers use hosting services like [GitHub](https://github.com/) to hold those master copies. \n",
    "\n",
    "For the purpose of our course, we will be using [GitHub](https://github.com/) to host the course material. \n",
    "\n",
    "You will also submit your homeworks through this platform. \n",
    "\n",
    "Next, we will cover how you can clone the course's repository and how to submit your solutions to the homework. \n",
    "\n",
    "For more information on how to create your own repository on GitHub and upload code to it, please see the tutorial by [Software Carpentry](http://swcarpentry.github.io/git-novice/)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<img src=\"./figs/github-workflow.jpg\" alt=\"Drawing\" width=\"500px\">\n",
    "\n",
    "This workflow shows you the essential new commands:\n",
    "\n",
    "* ``git pull``\n",
    "* ``git push``\n",
    "\n",
    "And when you first copy a remote repository, you will use:\n",
    "\n",
    "* ``git clone``"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "04460419-9b64-4fd9-a1e1-3dd29c00105a"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Course repositories"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "4b173db4-8824-4065-ba0c-ab406fb8f544"
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "The material of the course is hosted on GitHub, under [this account](https://github.com/mcrovella/CS506-Computational-Tools-for-Data-Science)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "559240af-1ae4-4f7b-b47b-4967bca0fe30"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "In order to download a copy of the lectures and run them locally on your computer, you need to clone the lecture repository. To do that:\n",
    "\n",
    "1. Create a new folder for the course.\n",
    "```\n",
    "$ mkdir cs506\n",
    "$ cd cs506\n",
    "```\n",
    "2. Copy the clone url from the [repository's website](https://github.com/mcrovella/CS506-Computational-Tools-for-Data-Science).\n",
    "3. Clone the repository from *git*.\n",
    "```\n",
    "$ git clone https://github.com/mcrovella/CS506-Computational-Tools-for-Data-Science.git\n",
    "```\n",
    "\n",
    "You should now have a directory with the course material.\n",
    "\n",
    "To update the repository and download the **new material**, type\n",
    "```\n",
    "$ git pull\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "6c03a8ae-289c-41f0-9469-6002b0c1fc9c"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Create a private homework repository"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "9725ece4-64ba-4afb-be06-fba207bc94e8"
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "Each assignment will have its own private repository, on GitHub shared only by you and the instructors.  \n",
    "\n",
    "The last commit time on the repo will be used as the submission time."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "d8eb6fe6-100b-4b06-a69b-6b21f9e892c9"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Jupyter Notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "You should get used to developing your code _incrementally._\n",
    "\n",
    "If you typically are writing files that you then run from the command line, e.g., `python code.py`, then you are not getting one of the main benefits of working in python."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "c32ab510-2293-4222-986a-189b33a0730a"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Instead you should __interact with the interpreter.__  \n",
    "\n",
    "An ideal way to do this is to use `Jupyter notebook`.   This puts a browser front-end on `ipython`.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "f8e65780-5034-4b28-b506-d5f22e28f571"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "In fact, these slides are actually just a Jupyter notebook.\n",
    "\n",
    "To run the Jupyter Notebook server from the command line, type ```jupyter notebook```. Your web browser will open and load the environment."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "8bc5dd73-54f6-443d-97fd-249446d1c111"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "In the notebook, you can type and run code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "nbpresent": {
     "id": "c82fac99-51ab-41f4-af0d-b272bee62cdc"
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "whatever!\n"
     ]
    }
   ],
   "source": [
    "print(\"whatever!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Ideal Use of Jupyter Notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "The Jupyter Notebook environment is beneficially used as follows:\n",
    "* Documentation Cell(s)\n",
    "* Computation Cell(s)\n",
    "* Visualization Cell(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "For example, let's say you are studying correlation in stock prices.\n",
    "\n",
    "First, you document what you are doing:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "We are interested in asking \"how similar\" are two stock prices.\n",
    "\n",
    "The tool for this purpose is __correlation__, ie, normalized covariance:\n",
    "\n",
    "$$\\rho(X,Y) = \\frac{E\\left[(X-\\mu_X)(Y-\\mu_Y)\\right]}{\\sigma_X \\sigma_Y}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Then, you perform the computation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TSLA</th>\n",
       "      <th>YELP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TSLA</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.137742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YELP</th>\n",
       "      <td>0.137742</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          TSLA      YELP\n",
       "TSLA  1.000000  0.137742\n",
       "YELP  0.137742  1.000000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import yfinance as yf\n",
    "\n",
    "stocks = ['TSLA', 'YELP']\n",
    "df = pd.DataFrame()\n",
    "for s in stocks:\n",
    "    df[s] = pd.DataFrame(yf.download(s,start='2014-01-01',end='2014-12-31', progress = False))['Close']\n",
    "\n",
    "rets = df.pct_change(30)\n",
    "\n",
    "df.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Then, you visualize the results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 259,
       "width": 387
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rets[['TSLA', 'YELP']].plot(lw=2)\n",
    "plt.legend(loc='best');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "As you use the Jupyter notebook, this will become a natural cycle:\n",
    "    \n",
    "* Document\n",
    "* Compute\n",
    "* Visualize"
   ]
  }
 ],
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