coursera python.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Final Project - Word Cloud"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this project, you'll create a \"word cloud\" from a text by writing a script.  This script needs to process the text, remove punctuation, ignore case and words that do not contain all alphabets, count the frequencies, and ignore uninteresting or irrelevant words.  A dictionary is the output of the `calculate_frequencies` function.  The `wordcloud` module will then generate the image from your dictionary."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the input text of your script, you will need to provide a file that contains text only.  For the text itself, you can copy and paste the contents of a website you like.  Or you can use a site like [Project Gutenberg](https://www.gutenberg.org/) to find books that are available online.  You could see what word clouds you can get from famous books, like a Shakespeare play or a novel by Jane Austen. Save this as a .txt file somewhere on your computer.\n",
    "<br><br>\n",
    "Now you will need to upload your input file here so that your script will be able to process it.  To do the upload, you will need an uploader widget.  Run the following cell to perform all the installs and imports for your word cloud script and uploader widget.  It may take a minute for all of this to run and there will be a lot of output messages. But, be patient. Once you get the following final line of output, the code is done executing. Then you can continue on with the rest of the instructions for this notebook.\n",
    "<br><br>\n",
    "**Enabling notebook extension fileupload/extension...**\n",
    "<br>\n",
    "**- Validating: <font color =green>OK</font>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: wordcloud in /opt/conda/lib/python3.6/site-packages (1.8.1)\n",
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      "Installing /opt/conda/lib/python3.6/site-packages/fileupload/static -> fileupload\n",
      "Up to date: /home/jovyan/.local/share/jupyter/nbextensions/fileupload/extension.js\n",
      "Up to date: /home/jovyan/.local/share/jupyter/nbextensions/fileupload/widget.js\n",
      "Up to date: /home/jovyan/.local/share/jupyter/nbextensions/fileupload/fileupload/widget.js\n",
      "- Validating: \u001b[32mOK\u001b[0m\n",
      "\n",
      "    To initialize this nbextension in the browser every time the notebook (or other app) loads:\n",
      "    \n",
      "          jupyter nbextension enable fileupload --user --py\n",
      "    \n",
      "Enabling notebook extension fileupload/extension...\n",
      "      - Validating: \u001b[32mOK\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "# Here are all the installs and imports you will need for your word cloud script and uploader widget\n",
    "\n",
    "!pip install wordcloud\n",
    "!pip install fileupload\n",
    "!pip install ipywidgets\n",
    "!jupyter nbextension install --py --user fileupload\n",
    "!jupyter nbextension enable --py fileupload\n",
    "\n",
    "import wordcloud\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from IPython.display import display\n",
    "import fileupload\n",
    "import io\n",
    "import sys"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Whew! That was a lot. All of the installs and imports for your word cloud script and uploader widget have been completed. \n",
    "<br><br>\n",
    "**IMPORTANT!** If this was your first time running the above cell containing the installs and imports, you will need save this notebook now. Then under the File menu above,  select Close and Halt. When the notebook has completely shut down, reopen it. This is the only way the necessary changes will take affect.\n",
    "<br><br>\n",
    "To upload your text file, run the following cell that contains all the code for a custom uploader widget. Once you run this cell, a \"Browse\" button should appear below it. Click this button and navigate the window to locate your saved text file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3f612205a6234ce38815ff258900c27e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FileUploadWidget(label='Browse', _dom_classes=('widget_item', 'btn-group'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Uploaded `text.txt` (7.93 kB)\n"
     ]
    }
   ],
   "source": [
    "# This is the uploader widget\n",
    "\n",
    "def _upload():\n",
    "\n",
    "    _upload_widget = fileupload.FileUploadWidget()\n",
    "\n",
    "    def _cb(change):\n",
    "        global file_contents\n",
    "        decoded = io.StringIO(change['owner'].data.decode('utf-8'))\n",
    "        filename = change['owner'].filename\n",
    "        print('Uploaded `{}` ({:.2f} kB)'.format(\n",
    "            filename, len(decoded.read()) / 2 **10))\n",
    "        file_contents = decoded.getvalue()\n",
    "\n",
    "    _upload_widget.observe(_cb, names='data')\n",
    "    display(_upload_widget)\n",
    "\n",
    "_upload()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The uploader widget saved the contents of your uploaded file into a string object named *file_contents* that your word cloud script can process. This was a lot of preliminary work, but you are now ready to begin your script. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Write a function in the cell below that iterates through the words in *file_contents*, removes punctuation, and counts the frequency of each word.  Oh, and be sure to make it ignore word case, words that do not contain all alphabets and boring words like \"and\" or \"the\".  Then use it in the `generate_from_frequencies` function to generate your very own word cloud!\n",
    "<br><br>\n",
    "**Hint:** Try storing the results of your iteration in a dictionary before passing them into wordcloud via the `generate_from_frequencies` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_frequencies(file_contents):\n",
    "    # Here is a list of punctuations and uninteresting words you can use to process your text\n",
    "    punctuations = '''!()-[]{};:'\"\\,<>./?@#$%^&*_~'''\n",
    "    uninteresting_words = [\"the\", \"a\", \"to\", \"if\", \"is\", \"it\", \"of\", \"and\", \"or\", \"an\", \"as\", \"i\", \"me\", \"my\", \\\n",
    "    \"we\", \"our\", \"ours\", \"you\", \"your\", \"yours\", \"he\", \"she\", \"him\", \"his\", \"her\", \"hers\", \"its\", \"they\", \"them\", \\\n",
    "    \"their\", \"what\", \"which\", \"who\", \"whom\", \"this\", \"that\", \"am\", \"are\", \"was\", \"were\", \"be\", \"been\", \"being\", \\\n",
    "    \"have\", \"has\", \"had\", \"do\", \"does\", \"did\", \"but\", \"at\", \"by\", \"with\", \"from\", \"here\", \"when\", \"where\", \"how\", \\\n",
    "    \"all\", \"any\", \"both\", \"each\", \"few\", \"more\", \"some\", \"such\", \"no\", \"nor\", \"too\", \"very\", \"can\", \"will\", \"just\"]\n",
    "    \n",
    "    # LEARNER CODE START HERE \n",
    "    words = file_contents.lower()\n",
    "    for i in punctuations:\n",
    "        words = words.replace(i, \" \")\n",
    "    words = words.split()\n",
    "    dic = {}\n",
    "    for i in words:\n",
    "        if (i not in uninteresting_words) and (len(i) > 1):\n",
    "            if i not in dic.keys():\n",
    "                dic[i] = 1\n",
    "            else:\n",
    "                dic[i] += 1\n",
    "\n",
    "    new_dict = {key:val for key, val in dic.items() if val > 2}\n",
    "    \n",
    "\n",
    "    #wordcloud\n",
    "    cloud = wordcloud.WordCloud()\n",
    "    cloud.generate_from_frequencies(new_dict)\n",
    "    return cloud.to_array()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'people': 10,\n",
       " 'among': 5,\n",
       " 'powers': 5,\n",
       " 'laws': 9,\n",
       " 'mankind': 3,\n",
       " 'should': 4,\n",
       " 'hold': 3,\n",
       " 'these': 13,\n",
       " 'rights': 3,\n",
       " 'governments': 3,\n",
       " 'consent': 3,\n",
       " 'government': 6,\n",
       " 'right': 7,\n",
       " 'new': 4,\n",
       " 'on': 8,\n",
       " 'most': 5,\n",
       " 'long': 3,\n",
       " 'themselves': 3,\n",
       " 'abolishing': 3,\n",
       " 'usurpations': 3,\n",
       " 'absolute': 3,\n",
       " 'colonies': 4,\n",
       " 'repeated': 3,\n",
       " 'states': 7,\n",
       " 'world': 3,\n",
       " 'refused': 3,\n",
       " 'assent': 4,\n",
       " 'pass': 3,\n",
       " 'other': 3,\n",
       " 'large': 4,\n",
       " 'time': 5,\n",
       " 'others': 3,\n",
       " 'without': 3,\n",
       " 'justice': 3,\n",
       " 'us': 11,\n",
       " 'peace': 3,\n",
       " 'independent': 4,\n",
       " 'power': 3,\n",
       " 'seas': 3,\n",
       " 'free': 4,\n",
       " 'war': 3}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "punctuations = ''' !()-[]{};:’'\"\\,<>./?@#$%^&*_~'''\n",
    "uninteresting_words = [\"the\", \"a\", \"to\", \"if\", \"is\", \"it\",\"for\", \"of\", \"and\",\"in\", \"or\", \"an\", \"as\", \"i\", \"me\", \"my\",\n",
    "                       \"we\", \"our\", \"ours\", \"you\", \"your\", \"yours\", \"he\", \"she\", \"him\", \"his\", \"her\", \"hers\", \"its\",\n",
    "                       \"they\", \"them\", \"their\", \"what\", \"which\", \"who\", \"whom\", \"this\", \"that\", \"am\", \"are\", \"was\", \n",
    "                       \"were\", \"be\", \"been\", \"being\",\"have\", \"has\", \"had\", \"do\", \"does\", \"did\", \"but\", \"at\", \"by\",\n",
    "                       \"with\", \"from\", \"here\", \"when\", \"where\", \"how\",\"all\", \"any\", \"both\", \"each\", \"few\", \"more\",\n",
    "                       \"some\", \"such\", \"no\", \"nor\", \"too\", \"very\", \"can\", \"will\", \"just\"]\n",
    "words = file_contents.lower()\n",
    "for i in punctuations:\n",
    "    words = words.replace(i, \" \")\n",
    "words = words.split()\n",
    "dic = {}\n",
    "for i in words:\n",
    "    if (i not in uninteresting_words) and (len(i) > 1):\n",
    "        if i not in dic.keys():\n",
    "            dic[i] = 1\n",
    "        else:\n",
    "            dic[i] += 1\n",
    "\n",
    "new_dict = {key:val for key, val in dic.items() if val > 2}\n",
    "new_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you have done everything correctly, your word cloud image should appear after running the cell below.  Fingers crossed!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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": [
    "# Display your wordcloud image\n",
    "\n",
    "myimage = calculate_frequencies(file_contents)\n",
    "plt.imshow(myimage, interpolation = 'nearest')\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If your word cloud image did not appear, go back and rework your `calculate_frequencies` function until you get the desired output.  Definitely check that you passed your frequecy count dictionary into the `generate_from_frequencies` function of `wordcloud`. Once you have correctly displayed your word cloud image, you are all done with this project. Nice work!"
   ]
  }
 ],
 "metadata": {
  "coursera": {
   "course_slug": "python-crash-course",
   "graded_item_id": "Z5d28",
   "launcher_item_id": "eSjyd"
  },
  "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": "3.6.7"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {},
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}