{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploring the GIPHY.com Metadata\n",
    "\n",
    "This notebook demonstrates an intermediate approach to exploring the GIPHY.com data set produced by the Library of Congress. GIPHY.com is a site that has been archived by the Library of Congress in its Web Cultures archive. The data explored here is available from the LC Labs experiments page located here: https://labs.loc.gov/experiments/webarchive-datasets/. \n",
    "\n",
    "As an intermediate approach, this notebook uses functions as reuseable blocks of code. These functions could potentially be used outside of this notebook to help you explore the data further. \n",
    "\n",
    "If you're looking for more of a beginner's approach, check out the Meme Generator notebook, which explores the memegenerator.net data set, also produced by the Library of Congress.\n",
    "\n",
    "This notebook is divided into the following sections:\n",
    "\n",
    "1. Setting up the Environment\n",
    "2. Defining the Functions\n",
    "3. Running the Functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setting up the Environment\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Module Imports\n",
    "\n",
    "Below are all the `import` statements we'll need in order perform some basic computations over the GIPHY data set. They are broken out into sections based on their use in this notebook. You will need to install the following Python modules:\n",
    "\n",
    "* matplotlib\n",
    "* tqdm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reading, Writing, and Getting\n",
    "\n",
    "The `import` statements below are for modules that we'll use to get and read in the `giphy.csv`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "import os\n",
    "import zipfile"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Processing\n",
    "\n",
    "The following `import` statements will help in the processing, sorting, and counting of the information from the data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "from datetime import datetime\n",
    "import operator\n",
    "from tqdm import tqdm\n",
    "from urllib.request import urlretrieve\n",
    "from time import sleep"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing the Results\n",
    "\n",
    "The following `import` statements allow for us to visual the processes we'll be running."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "from random import choice"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Defining the Functions\n",
    "\n",
    "Below are four functions that we'll use in our exploration of the GIPHY data set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## get_giphy_data_set\n",
    "\n",
    "This function checks to see if the giphy data set is present. The path it checks and the location to which it eventually downloads and unzips the data set can be modified, if desired. But by default, the function checks for a folder called `giphy-dataset` in the current working directory and for a file called `giphy.csv` within that folder.\n",
    "\n",
    "If the folder and file are present, nothing is done because the data set is already downloaded and extracted. If it's not, then the function navigations to the data set's downloadable URL, and saves the file as `giphy-dataset.zip` in the current working directory.\n",
    "\n",
    "Then, the function unzips the folder in the current working directory to create a folder called `giphy-dataset`, which contains the dataset file: `giphy.csv`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_giphy_data_set():\n",
    "    if not os.path.exists('giphy-dataset/giphy.csv'):\n",
    "        urlretrieve('https://s3.us-east-2.amazonaws.com/lclabspublicdata/giphy-dataset.zip', 'giphy-dataset.zip')\n",
    "        z = zipfile.ZipFile('giphy-dataset.zip', 'r')\n",
    "        z.extractall('./')\n",
    "        z.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## load_giphy_data_set\n",
    "\n",
    "As the name implies, this function loads, or reads in, the data set as a list of lists--where each row in the data set is a list of each cell in that row. This list of lists is stored as a variable and return as the output of the function.\n",
    "\n",
    "This function utilizes prior knowledge of the data set, which may elude first-time users. For instance:\n",
    "The data set is read in under a `utf16` encoding.\n",
    "\n",
    "Additionally, you'll see that a delimiter is set to the value equivalent of a tab. That is because the rows' cells are tab, not comma, separated. This again, was due to certain constraints where creating and saving the original data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_giphy_data_set():\n",
    "    data = []\n",
    "    if os.path.exists('giphy-dataset/giphy.csv'):\n",
    "        with open('giphy-dataset/giphy.csv', 'r+', encoding='utf16') as f:\n",
    "            for row in csv.DictReader(f, delimiter='\\t'):\n",
    "                data.append(dict(row))\n",
    "    return data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## get_import_dates\n",
    "\n",
    "The following function will grab all the values from the `Import Date` column of the data set and turn the string `m/dd/yyyy` into a [`datetime` object](https://docs.python.org/3.7/library/datetime.html) that can be grouped and sorted chronologically. \n",
    "\n",
    "The function takes the data set as a parameter and will return a dictionary of dates and the frequency of their occurrence. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_import_dates(data):\n",
    "    results = []\n",
    "    for row in data:\n",
    "        # The Import Date field is structured like so: m/dd/yyyy H:M\n",
    "        # We're not interested in the hours or minutes, so we split the string\n",
    "        # on the empty space, creating a list: ['m/dd/yyyy', 'H:M'].\n",
    "        # However, we only want the first item in the list, so we specify that\n",
    "        # by adding [0] after the split.\n",
    "        string_date = row['Import Date'].split()[0]\n",
    "        # We don't want the entries that are listed as 'NA'\n",
    "        # and since 1970 is a default date, we don't want to \n",
    "        # count those either.\n",
    "        # All other dates are added.\n",
    "        if string_date != 'NA' and '1970'not in string_date:\n",
    "            date = datetime.strptime(string_date, '%m/%d/%Y')\n",
    "            results.append(date)\n",
    "    dates = dict(Counter(results))\n",
    "    dates = sorted(dates.items(), key=operator.itemgetter(0))\n",
    "    results = []\n",
    "    # we only want to include the dates that have more than 35 GIFs associated with them.\n",
    "    # Otherwise, the tail of the data gets very long. In fact, doing this limits\n",
    "    # the results to just 44 dates.\n",
    "    for date_pair in dates:\n",
    "        if date_pair[1] > 35:\n",
    "            results.append(date_pair)\n",
    "    return results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## create_bar_chart_from_distribution\n",
    "\n",
    "The following function creates a bar chart from a distribution object, that is, a list of tuples wherein the first item is a value and the second item in the tuple is the number of times that value occurred in the distribution. \n",
    "\n",
    "It takes an optional parameter of title for the chart."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_bar_chart_from_distributions(dist, title=None):\n",
    "    # This is bit of code in the brackets, is just a fancy way\n",
    "    # of creating a list with a for loop. It's called a list comprehension.\n",
    "    # All it is doing is looping through the tuples in the dist variable\n",
    "    # and assigning the first item in that tuple to the list, which is then\n",
    "    # stored in the variable x_values\n",
    "    x_values = [pair[0] for pair in dist]\n",
    "    y_pos = np.arange(len(x_values))\n",
    "    # Similar to x_values, except the second value from the tuple is stored\n",
    "    # in the list\n",
    "    freq = [pair[1] for pair in dist]\n",
    "    # Assigns all the colors from the Matplotlib's XKCD's color dictionary to a list\n",
    "    colors = list(mpl.colors.XKCD_COLORS.values())\n",
    "    # We're using another list comprehension to randomly choose a color for each item in x_values\n",
    "    colors = [choice(colors) for i in x_values]\n",
    "    plt.bar(y_pos, freq, align='center', alpha=.5, color=colors)\n",
    "    plt.xticks(y_pos, x_values, fontsize=35, rotation='vertical')\n",
    "    plt.yticks(fontsize=35)\n",
    "    plt.ylabel('Frequency', fontsize=35)\n",
    "    if title:\n",
    "        plt.title(title, fontsize=35)   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Running the Functions\n",
    "\n",
    "Now that we've defined the functions, we can run them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1) Get the Data Set\n",
    "Call the `get_giphy_data_set` function that will download and extract the data set for us"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "get_giphy_data_set()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2) Load the Data Set\n",
    "Now that the data set is downloded and extracted, call the `load_giphy_data_set` function that reads in and returns the GIPHY data set as a list of dictionary objects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "giphy_data_set = load_giphy_data_set()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preview of the `giphy_data_set` content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'GIF ID': '1000eGIbYHercI',\n",
       "  'Web Archive Link': 'http://webarchive.loc.gov/all/20150318155641/http://media.giphy.com/media/1000eGIbYHercI/giphy.gif',\n",
       "  'MD5 Hash': 'b9a248bc783052ba1ccf29c5a9ac9ed1',\n",
       "  'File Size (In Bytes)': '1508448',\n",
       "  'GIPHY Title': 'slipper GIF',\n",
       "  'Import Date': '9/21/2013 3:37',\n",
       "  'Trending Date': '1/1/1970 0:00'},\n",
       " {'GIF ID': '1000fHsBSKSL6w',\n",
       "  'Web Archive Link': 'http://webarchive.loc.gov/all/20150318155641/http://media.giphy.com/media/1000fHsBSKSL6w/giphy.gif',\n",
       "  'MD5 Hash': '91d00a6dd66f7fa8e6d132601a9778e1',\n",
       "  'File Size (In Bytes)': '547019',\n",
       "  'GIPHY Title': 'swag hustling GIF',\n",
       "  'Import Date': '6/22/2015 5:35',\n",
       "  'Trending Date': '1/12/2016 23:45'}]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "giphy_data_set[0:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import_dates = get_import_dates(giphy_data_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3) Chart the Import Dates\n",
    "We'll chart the Import Dates collected, counted, sorted, and stored in the `import_dates` variable. We'll pass that variable into the `create_bar_chart_from_distributions`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 3456x2304 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = (48.0, 32.0)\n",
    "create_bar_chart_from_distributions(import_dates, title='GIFs By Upload Date')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4) Smallest & Largest\n",
    "\n",
    "Just for fun let's see what the smallest GIF is, bytes wise. We can sort our `giphy_data_set` list using the `File Size (In Bytes)` dictionary key:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NA - [20Tx6RZNskcxi.gif] is 158 bytes\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<img src=\"http://webarchive.loc.gov/all/20150318155641/https://media.giphy.com/media/20Tx6RZNskcxi/giphy.gif\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "\n",
    "giphy_data_set = sorted(giphy_data_set, key=lambda gif: int(gif['File Size (In Bytes)']))\n",
    "s = giphy_data_set[0]\n",
    "\n",
    "print('{} - [{}.gif] is {} bytes'.format(s['GIPHY Title'], s['GIF ID'], s['File Size (In Bytes)']))\n",
    "Image(url=s['Web Archive Link'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "... and the largest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "flaming guitar GIF - [3AvZg1V7GsuAM.gif] is 120784008 bytes\n"
     ]
    }
   ],
   "source": [
    "l = giphy_data_set[-1]\n",
    "\n",
    "print('{} - [{}.gif] is {} bytes'.format(l['GIPHY Title'], l['GIF ID'], l['File Size (In Bytes)']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Yeah, that's a 115 MB GIF so we're not displaying it here in the Notebook! But you can check it out [here](http://webarchive.loc.gov/all/20150318155641/https://media.giphy.com/media/3AvZg1V7GsuAM/giphy.gif)\n",
    "\n",
    "\n",
    "### 5) Search the Titles\n",
    "\n",
    "We can also create a text box here in the notebook to let you search through the GIF titles and display them. Type a word into the text box and you should see some results pop up. If you aren't running the notebook yourself you probably won't see the input box, but the word 'book' was used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "007c55bb7d9447fbadfa02806441f7e4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Text(value='book')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "supreme court book GIF\n",
      "http://webarchive.loc.gov/all/20150318155641/https://media.giphy.com/media/12vuErPc7GgUmc/giphy.gif\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<img src=\"http://webarchive.loc.gov/all/20150318155641/https://media.giphy.com/media/12vuErPc7GgUmc/giphy.gif\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stare down super bowl GIF by Disney's The Jungle Book\n",
      "http://webarchive.loc.gov/all/20150318155641/https://media.giphy.com/media/3o6gbaRCPEmINyv088/giphy.gif\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<img src=\"http://webarchive.loc.gov/all/20150318155641/https://media.giphy.com/media/3o6gbaRCPEmINyv088/giphy.gif\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "thats not what happened in the book GIF by Look Human\n",
      "http://webarchive.loc.gov/all/20150318155641/https://media.giphy.com/media/3o7TKyf2G4drLNOsUw/giphy.gif\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<img src=\"http://webarchive.loc.gov/all/20150318155641/https://media.giphy.com/media/3o7TKyf2G4drLNOsUw/giphy.gif\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "colors book GIF\n",
      "http://webarchive.loc.gov/all/20150318155641/https://media.giphy.com/media/3o7qE4Glxfb2oTuoWk/giphy.gif\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<img src=\"http://webarchive.loc.gov/all/20150318155641/https://media.giphy.com/media/3o7qE4Glxfb2oTuoWk/giphy.gif\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "jungle book snake GIF\n",
      "http://webarchive.loc.gov/all/20150318155641/http://media.giphy.com/media/2lbhL8dSGMh8I/giphy.gif\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<img src=\"http://webarchive.loc.gov/all/20150318155641/http://media.giphy.com/media/2lbhL8dSGMh8I/giphy.gif\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "super bowl disney GIF by Disney's The Jungle Book\n",
      "http://webarchive.loc.gov/all/20150318155641/https://media.giphy.com/media/3o6gb2FAisWxzLGVna/giphy.gif\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<img src=\"http://webarchive.loc.gov/all/20150318155641/https://media.giphy.com/media/3o6gb2FAisWxzLGVna/giphy.gif\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "super bowl disney GIF by Disney's The Jungle Book\n",
      "http://webarchive.loc.gov/all/20150318155641/https://media.giphy.com/media/3o6gb7NIowzNCp7Qsg/giphy.gif\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<img src=\"http://webarchive.loc.gov/all/20150318155641/https://media.giphy.com/media/3o6gb7NIowzNCp7Qsg/giphy.gif\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "super bowl disney GIF by Disney's The Jungle Book\n",
      "http://webarchive.loc.gov/all/20150318155641/https://media.giphy.com/media/3o6gb19H5BFEB4cjZK/giphy.gif\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<img src=\"http://webarchive.loc.gov/all/20150318155641/https://media.giphy.com/media/3o6gb19H5BFEB4cjZK/giphy.gif\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import re\n",
    "from ipywidgets import widgets\n",
    "from IPython.display import clear_output\n",
    "\n",
    "text = widgets.Text()\n",
    "display(text)\n",
    "\n",
    "def handle_text(sender):\n",
    "    clear_output()\n",
    "    display(text)\n",
    "    p = re.compile(r'\\b' + text.value + r'\\b', re.IGNORECASE)\n",
    "    for gif in giphy_data_set:\n",
    "        if p.search(gif['GIPHY Title']):\n",
    "            print(gif['GIPHY Title'])\n",
    "            print(gif['Web Archive Link'])\n",
    "            display(Image(url=gif['Web Archive Link']))\n",
    "            \n",
    "text.on_submit(handle_text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6) Hydrate the Dataset\n",
    "\n",
    "Finally we will download all the GIFs to your workstation for you to use. There are 10,972 of them, and we will pause a little bit between image downloads so as not to overload LC's webarchive server, so it'll take a while (probably about 6 hours).\n",
    "\n",
    "First lets make a directory to put the GIFs (`files/gifs`):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "gifs_dir = os.path.join('giphy-dataset/gifs')\n",
    "if not os.path.isdir(gifs_dir):\n",
    "    os.mkdir(gifs_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now lets iterate through our dataset and download each one. They will be downloaded to the `giphy-dataset/gifs` directory. We use [tqdm](https://github.com/tqdm/tqdm) to provide a progress bar so you know something is happening."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10972/10972 [00:19<00:00, 89102.24gif/s]"
     ]
    }
   ],
   "source": [
    "progress = tqdm(total=len(giphy_data_set), unit='gif')\n",
    "\n",
    "for gif in giphy_data_set:\n",
    "    progress.update(1)\n",
    "    \n",
    "    gif_id = gif['GIF ID']\n",
    "    gif_url = gif['Web Archive Link']\n",
    "    \n",
    "    filename = os.path.join(gifs_dir, '{}.gif'.format(gif_id))\n",
    "    if os.path.isfile(filename):\n",
    "        continue\n",
    "        \n",
    "    urlretrieve(gif_url, os.path.join(gifs_dir, '{}.gif'.format(gif_id)))\n",
    "    sleep(.5)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "We hope that this notebook has helped you understand and explore this dataset! As you continue to explore, we'd love to hear about anything interesting and cool that you make. You can contact us by leaving a comment on the Library of Congress' The Signal blog (http://blogs.loc.gov/thesignal/) or through our contact form at https://www.loc.gov/programs/web-archiving/about-this-program/contact-us/. \n",
    "\n",
    "You can read more about how Library of Congress staff analyzed this data set in this post on The Signal blog: http://blogs.loc.gov/thesignal/2018/10/data-mining-memes-in-the-digital-culture-web-archive/. Have fun exploring these memes in more detail!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Contributors:**\n",
    "- Chase Dooley\n",
    "- Jesse Johnston\n",
    "- Aly DesRochers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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