The Journey.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "d86ed395",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import all libraries\n",
    "import glob\n",
    "import os\n",
    "import time\n",
    "import pandas as pd\n",
    "import datetime as dt\n",
    "import pathlib\n",
    "import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "from pathlib import Path\n",
    "import qgrid\n",
    "import numpy as np\n",
    "import warnings\n",
    "import warnings\n",
    "import mpl_axes_aligner\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "92daa55c",
   "metadata": {},
   "outputs": [],
   "source": [
    "dir_name = '/Users/brendan/Library/Mobile Documents/com~apple~CloudDocs/Desktop/Data Science'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5fd78a3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# get list of all filies only in the given diretory\n",
    "list_of_files = filter(os.path.isfile, glob.glob(dir_name + '/**/*'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a7df4248",
   "metadata": {},
   "outputs": [],
   "source": [
    "# sort list of files based on last modification time\n",
    "list_of_files = sorted(list_of_files, key=os.path.getmtime)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "94cd314c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11/18/2020 :: 01:55:44  --> /Users/brendan/Library/Mobile Documents/com~apple~CloudDocs/Desktop/Data Science/Bokeh/widgets.py\n",
      "11/18/2020 :: 23:28:45  --> /Users/brendan/Library/Mobile Documents/com~apple~CloudDocs/Desktop/Data Science/Bokeh/widgets_interactive.py\n",
      "11/19/2020 :: 00:13:59  --> /Users/brendan/Library/Mobile Documents/com~apple~CloudDocs/Desktop/Data Science/Bokeh/labels.py\n",
      "11/22/2020 :: 20:16:56  --> /Users/brendan/Library/Mobile Documents/com~apple~CloudDocs/Desktop/Data Science/Bokeh/exercise_8.py\n",
      "11/22/2020 :: 20:39:04  --> /Users/brendan/Library/Mobile Documents/com~apple~CloudDocs/Desktop/Data Science/Bokeh/labels_radio_buttons.py\n",
      "11/27/2020 :: 23:57:27  --> /Users/brendan/Library/Mobile Documents/com~apple~CloudDocs/Desktop/Data Science/Bokeh/filtering_glyphs.py\n",
      "12/05/2020 :: 19:49:52  --> /Users/brendan/Library/Mobile Documents/com~apple~CloudDocs/Desktop/Data Science/Bokeh/bitcoin_streaming_webscrapping.py\n",
      "12/05/2020 :: 20:26:15  --> /Users/brendan/Library/Mobile Documents/com~apple~CloudDocs/Desktop/Data Science/Bokeh/random_graphing.py\n",
      "12/07/2020 :: 03:30:00  --> /Users/brendan/Library/Mobile Documents/com~apple~CloudDocs/Desktop/Data Science/insty/11788939_10101414218421757_535490599_n.mp4\n",
      "07/04/2021 :: 01:45:52  --> /Users/brendan/Library/Mobile Documents/com~apple~CloudDocs/Desktop/Data Science/Bokeh/bokeh_static.py\n",
      "07/04/2021 :: 01:53:14  --> /Users/brendan/Library/Mobile Documents/com~apple~CloudDocs/Desktop/Data Science/Bokeh/bokeh_static.ipynb\n",
      "07/17/2021 :: 19:49:51  --> /Users/brendan/Library/Mobile Documents/com~apple~CloudDocs/Desktop/Data Science/Python/Untitled.ipynb\n"
     ]
    }
   ],
   "source": [
    "# iterate over sorted list of files and print file path\n",
    "# along with last modidicaiton date time\n",
    "for file_path in list_of_files:\n",
    "    time_stamp_str = time.strftime('%m/%d/%Y :: %H:%M:%S',\n",
    "                                  time.gmtime(os.path.getmtime(file_path)))\n",
    "    print(time_stamp_str, ' -->', file_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "51ea9049",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'07/17/2021 :: 19:49:51'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_stamp_str"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ced7d1c0",
   "metadata": {},
   "source": [
    "## try 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f87c0976",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/brendan/Library/Mobile Documents/com~apple~CloudDocs/Desktop/Data Science'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f90575ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "filelist = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "16d23340",
   "metadata": {},
   "outputs": [],
   "source": [
    "for root, dirs, files in os.walk(dir_name):\n",
    "    for file in files:\n",
    "        # append the file name to the list\n",
    "        filelist.append(os.path.join(root,file))\n",
    "    \n",
    "# print all the file names\n",
    "#for name in filelist:\n",
    "    #print(name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bf225e2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#type(name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7a7c7c6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(filelist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "625aefdb",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(filelist, columns=['FileName'])\n",
    "df.to_csv('files.csv',index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdbe7ef9",
   "metadata": {},
   "source": [
    "# sept 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "edf7a1eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "fname = pathlib.Path(dir_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "21a1a88a",
   "metadata": {},
   "outputs": [],
   "source": [
    "assert fname.exists(), f'No such file: {fname}'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9e833a35",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "os.stat_result(st_mode=16877, st_ino=287493, st_dev=16777230, st_nlink=15, st_uid=501, st_gid=20, st_size=480, st_atime=1631747261, st_mtime=1631747261, st_ctime=1631747261)\n"
     ]
    }
   ],
   "source": [
    "print(fname.stat())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6120cfac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-09-15 19:07:41\n"
     ]
    }
   ],
   "source": [
    "mtime = datetime.datetime.fromtimestamp(fname.stat().st_mtime)\n",
    "print(mtime)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6705d93f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "python_graph.png\n",
      "files.csv\n",
      "jupyter_graph.png\n"
     ]
    }
   ],
   "source": [
    "\n",
    "root = dir_name\n",
    "for item in os.listdir(root):\n",
    "    if not item.startswith('.') and os.path.isfile(os.path.join(root, item)):\n",
    "        print(item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6da6f817",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['/Users/brendan/Library/Mobile Documents/com~apple~CloudDocs/Desktop/Data Science/Bokeh',\n",
       " '/Users/brendan/Library/Mobile Documents/com~apple~CloudDocs/Desktop/Data Science/Udemy',\n",
       " '/Users/brendan/Library/Mobile Documents/com~apple~CloudDocs/Desktop/Data Science/Tableau',\n",
       " '/Users/brendan/Library/Mobile Documents/com~apple~CloudDocs/Desktop/Data Science/Python',\n",
       " '/Users/brendan/Library/Mobile Documents/com~apple~CloudDocs/Desktop/Data Science/insty',\n",
       " '/Users/brendan/Library/Mobile Documents/com~apple~CloudDocs/Desktop/Data Science/13F',\n",
       " '/Users/brendan/Library/Mobile Documents/com~apple~CloudDocs/Desktop/Data Science/python_graph.png',\n",
       " '/Users/brendan/Library/Mobile Documents/com~apple~CloudDocs/Desktop/Data Science/files.csv',\n",
       " '/Users/brendan/Library/Mobile Documents/com~apple~CloudDocs/Desktop/Data Science/Udemy-BT',\n",
       " '/Users/brendan/Library/Mobile Documents/com~apple~CloudDocs/Desktop/Data Science/jupyter_graph.png']"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filesglob = glob.glob('/Users/brendan/Library/Mobile Documents/com~apple~CloudDocs/Desktop/Data Science/[!.DS_Store]*')\n",
    "filesglob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0ff10cd8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<generator object Path.glob at 0x7fb9929515f0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = Path('/Users/brendan/Library/Mobile Documents/com~apple~CloudDocs/Desktop/Data Science'\n",
    ").glob('')\n",
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "7749d0f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def getListOfFiles(dirName):\n",
    "    # create a list of file and sub directories \n",
    "    # names in the given directory \n",
    "    listOfFile = os.listdir(dirName)\n",
    "    allFiles = list()\n",
    "    # Iterate over all the entries\n",
    "    for entry in listOfFile:\n",
    "        # Create full path\n",
    "        fullPath = os.path.join(dirName, entry)\n",
    "        # If entry is a directory then get the list of files in this directory \n",
    "        if os.path.isdir(fullPath):\n",
    "            allFiles = allFiles + getListOfFiles(fullPath)\n",
    "        else:\n",
    "            allFiles.append(fullPath)\n",
    "       \n",
    "                \n",
    "    return allFiles\n",
    "\n",
    "dirName = dir_name;\n",
    "# Get the list of all files in directory tree at given path\n",
    "listOfFiles = getListOfFiles(dirName)\n",
    "#listOfFiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b82961f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "d_ext_desc = {'csv':'CSV file',\n",
    "              'css' : 'CSS Styling',\n",
    "              'db':'Thumbnail',\n",
    "              'doc':'Microsoft Word Document',\n",
    "              'docx':'Microsoft Word Document',\n",
    "              'GIF':'GIF Image file',\n",
    "              'html':'HTML file',\n",
    "              'ico':'Icon Image file',\n",
    "              'ipynb': 'Jupyter Notebook',\n",
    "              '.ipynb':'Jupyter',\n",
    "              'ipynb.icloud': 'Jupyter',\n",
    "              'html': 'HTML',\n",
    "              'jpg':'JPG Image file',\n",
    "              'JPEG':'JPEG Image file',\n",
    "              'js': 'Javascript',\n",
    "              'json':'JSON file',\n",
    "              'lnk':'Shortcut file',\n",
    "              'msg':'Microsoft Outlook Message file',\n",
    "              'pdf':'PDF file',\n",
    "              'pkl':'Pickle (python) file',\n",
    "              'png':'PNG Image file',\n",
    "              'ppt':'Microsoft Powerpoint file',\n",
    "              'pptx':'Microsoft Powerpoint file',\n",
    "              'pst':'Microsoft Outlook Data file',\n",
    "              'py':'Python file',\n",
    "              'pyc':'Python file (compiled)',\n",
    "              'rtf':'Rich Text Format',\n",
    "              'svg':'SVG Image file',\n",
    "              'txt':'Text document',\n",
    "              'url':'Hyperlink',\n",
    "              'vsd':'Microsoft Visio file',\n",
    "              'xls':'Microsoft Excel file',\n",
    "              'xlsb':'Microsoft Excel file',\n",
    "              'xlsm':'Microsoft Excel (Macro-enabled) file',\n",
    "              'xlsx':'Microsoft Excel file',\n",
    "              'yml':'Requirements file (python)',\n",
    "              'zip':'ZIP file'}\n",
    "\n",
    "def ext_desc(ext):\n",
    "    try:\n",
    "        desc = d_ext_desc[ext]\n",
    "    except KeyError:\n",
    "        desc = ''\n",
    "    else:\n",
    "        pass\n",
    "    return desc\n",
    "\n",
    "\n",
    "def generate_index(path=None, max=0):\n",
    "    # stops generating index whenever there are more than 500 records, to test if the script works\n",
    "    # use 'max=0' to generate the full index\n",
    "    \n",
    "    path = dir_name\n",
    "\n",
    "    df = pd.DataFrame(columns=['File','File Type','Folder Location','Date','Path'])\n",
    "    for root, _ , files in os.walk(path):\n",
    "        files = [f for f in files if not f.startswith('~') and f!='Thumbs.db']\n",
    "        paths = [os.path.join(root, f) for f in files]\n",
    "        exts = [os.path.splitext(f)[1][1:].lower() for f in files]\n",
    "        filetypes = [ext_desc(ext) for ext in exts]\n",
    "        file_links = ['=HYPERLINK(\"{}\",\"link\")'.format(p) if len(p) < 256 else '' for p in paths]\n",
    "        folders = [os.path.dirname(p) for p in paths]\n",
    "        time_stamp_str = [time.strftime('%m/%d/%Y :: %H:%M:%S',\n",
    "                                  time.gmtime(os.path.getmtime(p))) for p in paths]\n",
    "        df1 = pd.DataFrame({'File': files,\n",
    "                            'File Type': filetypes,\n",
    "                            'Folder Location': folders,\n",
    "                            'Date': time_stamp_str,\n",
    "                            'Path': paths})\n",
    "        df = df.append(df1)\n",
    "        if max and (df.shape[0]>max):\n",
    "            break\n",
    "    df = df.reset_index(drop=True)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2e96fe7e",
   "metadata": {},
   "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>File</th>\n",
       "      <th>File Type</th>\n",
       "      <th>Folder Location</th>\n",
       "      <th>Date</th>\n",
       "      <th>Path</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>.DS_Store</td>\n",
       "      <td></td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>09/15/2021 :: 01:08:47</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>python_graph.png</td>\n",
       "      <td>PNG Image file</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>09/15/2021 :: 23:07:37</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>files.csv</td>\n",
       "      <td>CSV file</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>09/15/2021 :: 23:07:33</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>jupyter_graph.png</td>\n",
       "      <td>PNG Image file</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>09/15/2021 :: 23:07:36</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>.simple_bokeh.html.icloud</td>\n",
       "      <td></td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>11/18/2020 :: 01:55:53</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        File       File Type  \\\n",
       "0                  .DS_Store                   \n",
       "1           python_graph.png  PNG Image file   \n",
       "2                  files.csv        CSV file   \n",
       "3          jupyter_graph.png  PNG Image file   \n",
       "4  .simple_bokeh.html.icloud                   \n",
       "\n",
       "                                     Folder Location                    Date  \\\n",
       "0  /Users/brendan/Library/Mobile Documents/com~ap...  09/15/2021 :: 01:08:47   \n",
       "1  /Users/brendan/Library/Mobile Documents/com~ap...  09/15/2021 :: 23:07:37   \n",
       "2  /Users/brendan/Library/Mobile Documents/com~ap...  09/15/2021 :: 23:07:33   \n",
       "3  /Users/brendan/Library/Mobile Documents/com~ap...  09/15/2021 :: 23:07:36   \n",
       "4  /Users/brendan/Library/Mobile Documents/com~ap...  11/18/2020 :: 01:55:53   \n",
       "\n",
       "                                                Path  \n",
       "0  /Users/brendan/Library/Mobile Documents/com~ap...  \n",
       "1  /Users/brendan/Library/Mobile Documents/com~ap...  \n",
       "2  /Users/brendan/Library/Mobile Documents/com~ap...  \n",
       "3  /Users/brendan/Library/Mobile Documents/com~ap...  \n",
       "4  /Users/brendan/Library/Mobile Documents/com~ap...  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_df = generate_index(dir_name)\n",
    "raw_df['File']=raw_df['File'].str.replace('.ipynb.icloud', '.ipynb')\n",
    "raw_df.loc[raw_df['File'].str.contains('ipynb'), 'File Type'] = 'Jupyter Notebook'\n",
    "raw_df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b9e7b955",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15479, 5)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9f9b7c2f",
   "metadata": {},
   "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>File</th>\n",
       "      <th>File Type</th>\n",
       "      <th>Folder Location</th>\n",
       "      <th>Date</th>\n",
       "      <th>Path</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>.DS_Store</td>\n",
       "      <td></td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>09/15/2021 :: 01:08:47</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>python_graph.png</td>\n",
       "      <td>PNG Image file</td>\n",
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       "      <td>09/15/2021 :: 23:07:37</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
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       "      <th>2</th>\n",
       "      <td>files.csv</td>\n",
       "      <td>CSV file</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>09/15/2021 :: 23:07:33</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>jupyter_graph.png</td>\n",
       "      <td>PNG Image file</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>09/15/2021 :: 23:07:36</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>.simple_bokeh.html.icloud</td>\n",
       "      <td></td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>11/18/2020 :: 01:55:53</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>labels.py</td>\n",
       "      <td>Python file</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>11/19/2020 :: 00:13:59</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>exercise_8.py</td>\n",
       "      <td>Python file</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>11/22/2020 :: 20:16:56</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
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    "raw_df['File']=raw_df['File'].str.replace('.ipynb.icloud', '.ipynb')\n",
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    "raw_df.head(7)"
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   "execution_count": 25,
   "id": "9b7b42db",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "08/26/2018 :: 00:52:52    1576\n",
       "09/21/2019 :: 02:56:04     536\n",
       "08/10/2018 :: 02:01:48     411\n",
       "09/21/2019 :: 02:56:02     390\n",
       "                          ... \n",
       "07/26/2020 :: 21:29:34       1\n",
       "07/26/2020 :: 21:25:25       1\n",
       "07/26/2020 :: 21:24:21       1\n",
       "07/25/2018 :: 20:35:43       1\n",
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   "cell_type": "code",
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   "cell_type": "code",
   "execution_count": 28,
   "id": "a4bf6e6b",
   "metadata": {},
   "outputs": [
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       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
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       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
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       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
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       "3  /Users/brendan/Library/Mobile Documents/com~ap...  \n",
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   "source": [
    "\n",
    "qgrid_widget_10_26_85  = qgrid.show_grid(raw_df, show_toolbar = True)\n",
    "df_10_26_1985_filtered = qgrid_widget_10_26_85.get_changed_df()\n",
    "df_10_26_1985_filtered.head()\n"
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  {
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   "execution_count": 29,
   "id": "71ba6e80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
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       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
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       "      <td>.simple_bokeh.html.icloud</td>\n",
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       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
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       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
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       "      <th>5</th>\n",
       "      <td>labels.py</td>\n",
       "      <td>Python file</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>11/19/2020 :: 00:13:59</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>exercise_8.py</td>\n",
       "      <td>Python file</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>11/22/2020 :: 20:16:56</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>.DS_Store</td>\n",
       "      <td></td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>09/04/2021 :: 15:11:01</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>widgets_interactive.py</td>\n",
       "      <td>Python file</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>11/18/2020 :: 23:28:45</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>filtering_glyphs.py</td>\n",
       "      <td>Python file</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>11/27/2020 :: 23:57:27</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>widgets.py</td>\n",
       "      <td>Python file</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>11/18/2020 :: 01:55:44</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>.Untitled.ipynb</td>\n",
       "      <td>Jupyter Notebook</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>12/05/2020 :: 20:01:10</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>bokeh_static.ipynb</td>\n",
       "      <td>Jupyter Notebook</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>07/04/2021 :: 01:53:14</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>bitcoin_streaming_webscrapping.py</td>\n",
       "      <td>Python file</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>12/05/2020 :: 19:49:52</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>bokeh_static.py</td>\n",
       "      <td>Python file</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>07/04/2021 :: 01:45:52</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>labels_radio_buttons.py</td>\n",
       "      <td>Python file</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>11/22/2020 :: 20:39:04</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>random_graphing.py</td>\n",
       "      <td>Python file</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>12/05/2020 :: 20:26:15</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>.random_graphing.cpython-37.pyc.icloud</td>\n",
       "      <td></td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>12/05/2020 :: 20:12:25</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>bokeh_static-checkpoint.ipynb</td>\n",
       "      <td>Jupyter Notebook</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>07/04/2021 :: 01:53:14</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>.Untitled-checkpoint.ipynb</td>\n",
       "      <td>Jupyter Notebook</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>12/05/2020 :: 19:53:08</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>settings.json</td>\n",
       "      <td>JSON file</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>11/22/2020 :: 20:53:32</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>.DS_Store</td>\n",
       "      <td></td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>09/06/2021 :: 00:46:16</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>.05 Matplotlib Lecture.ipynb</td>\n",
       "      <td>Jupyter Notebook</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>12/27/2020 :: 14:23:29</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>.08 Plotly Lecture.ipynb</td>\n",
       "      <td>Jupyter Notebook</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>12/27/2020 :: 22:04:05</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>.14 KNN Lecture.ipynb</td>\n",
       "      <td>Jupyter Notebook</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>12/29/2020 :: 20:21:42</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>.01 Python Crash Course Lecture.ipynb</td>\n",
       "      <td>Jupyter Notebook</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>12/26/2020 :: 02:04:50</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>.02 Numpy Lecture.ipynb</td>\n",
       "      <td>Jupyter Notebook</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>12/26/2020 :: 03:02:42</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>.13 Logistic Regression Lecture.ipynb</td>\n",
       "      <td>Jupyter Notebook</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>12/29/2020 :: 17:52:06</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>.07 Pandas Visualization lecture.ipynb</td>\n",
       "      <td>Jupyter Notebook</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>01/01/2021 :: 04:38:43</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>.11 Linear Regression lecture.ipynb</td>\n",
       "      <td>Jupyter Notebook</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>12/28/2020 :: 21:42:48</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>.15 Decision Trees lecture.ipynb</td>\n",
       "      <td>Jupyter Notebook</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>12/30/2020 :: 01:28:02</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>.16 Support Vector Machines lecture.ipynb</td>\n",
       "      <td>Jupyter Notebook</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>12/30/2020 :: 15:54:15</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>.06 Seaborn Lecture.ipynb</td>\n",
       "      <td>Jupyter Notebook</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>12/29/2020 :: 18:14:15</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>.03 Pandas Lecture.ipynb</td>\n",
       "      <td>Jupyter Notebook</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>12/27/2020 :: 15:27:59</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Bias Variance Trade-Off.pdf</td>\n",
       "      <td>PDF file</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>12/21/2016 :: 05:37:49</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Intro to Logistic Regression.pdf</td>\n",
       "      <td>PDF file</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>12/21/2016 :: 05:56:21</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Principal Component Analysis.pdf</td>\n",
       "      <td>PDF file</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>12/21/2016 :: 05:35:32</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Intro to Natural Language Processing.pdf</td>\n",
       "      <td>PDF file</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>12/21/2016 :: 05:34:12</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Intro to Machine Learning.pdf</td>\n",
       "      <td>PDF file</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>12/21/2016 :: 05:30:30</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Intro to K Nearest Neighbors.pdf</td>\n",
       "      <td>PDF file</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>12/21/2016 :: 05:37:12</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Intro to Tree Methods.pdf</td>\n",
       "      <td>PDF file</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>12/21/2016 :: 05:33:31</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Python - Intro to Linear Regression.pdf</td>\n",
       "      <td>PDF file</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>12/21/2016 :: 05:32:38</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Intro to K Means Clustering.pdf</td>\n",
       "      <td>PDF file</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>12/21/2016 :: 05:36:36</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Intro to Neural Nets.pdf</td>\n",
       "      <td>PDF file</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>12/21/2016 :: 05:36:09</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>.10  Finance Project.ipynb</td>\n",
       "      <td>Jupyter Notebook</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>12/30/2020 :: 14:24:25</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>.16 Support Vector Machines Project.ipynb</td>\n",
       "      <td>Jupyter Notebook</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>12/30/2020 :: 16:04:45</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>.11 Linear Regression Project.ipynb</td>\n",
       "      <td>Jupyter Notebook</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>12/28/2020 :: 21:57:56</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>.01-Python Crash Course Project.ipynb</td>\n",
       "      <td>Jupyter Notebook</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>08/30/2018 :: 18:54:18</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>.13 Logistic Regression Project.ipynb</td>\n",
       "      <td>Jupyter Notebook</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>12/29/2020 :: 18:30:40</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>.02 Numpy Project.ipynb</td>\n",
       "      <td>Jupyter Notebook</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>08/30/2018 :: 18:54:18</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
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       "22               .05 Matplotlib Lecture.ipynb  Jupyter Notebook   \n",
       "23                   .08 Plotly Lecture.ipynb  Jupyter Notebook   \n",
       "24                      .14 KNN Lecture.ipynb  Jupyter Notebook   \n",
       "25      .01 Python Crash Course Lecture.ipynb  Jupyter Notebook   \n",
       "26                    .02 Numpy Lecture.ipynb  Jupyter Notebook   \n",
       "27      .13 Logistic Regression Lecture.ipynb  Jupyter Notebook   \n",
       "28     .07 Pandas Visualization lecture.ipynb  Jupyter Notebook   \n",
       "29        .11 Linear Regression lecture.ipynb  Jupyter Notebook   \n",
       "30           .15 Decision Trees lecture.ipynb  Jupyter Notebook   \n",
       "31  .16 Support Vector Machines lecture.ipynb  Jupyter Notebook   \n",
       "32                  .06 Seaborn Lecture.ipynb  Jupyter Notebook   \n",
       "33                   .03 Pandas Lecture.ipynb  Jupyter Notebook   \n",
       "34                Bias Variance Trade-Off.pdf          PDF file   \n",
       "35           Intro to Logistic Regression.pdf          PDF file   \n",
       "36           Principal Component Analysis.pdf          PDF file   \n",
       "37   Intro to Natural Language Processing.pdf          PDF file   \n",
       "38              Intro to Machine Learning.pdf          PDF file   \n",
       "39           Intro to K Nearest Neighbors.pdf          PDF file   \n",
       "40                  Intro to Tree Methods.pdf          PDF file   \n",
       "41    Python - Intro to Linear Regression.pdf          PDF file   \n",
       "42            Intro to K Means Clustering.pdf          PDF file   \n",
       "43                   Intro to Neural Nets.pdf          PDF file   \n",
       "44                 .10  Finance Project.ipynb  Jupyter Notebook   \n",
       "45  .16 Support Vector Machines Project.ipynb  Jupyter Notebook   \n",
       "46        .11 Linear Regression Project.ipynb  Jupyter Notebook   \n",
       "47      .01-Python Crash Course Project.ipynb  Jupyter Notebook   \n",
       "48      .13 Logistic Regression Project.ipynb  Jupyter Notebook   \n",
       "49                    .02 Numpy Project.ipynb  Jupyter Notebook   \n",
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       "31  /Users/brendan/Library/Mobile Documents/com~ap...  \n",
       "32  /Users/brendan/Library/Mobile Documents/com~ap...  \n",
       "33  /Users/brendan/Library/Mobile Documents/com~ap...  \n",
       "34  /Users/brendan/Library/Mobile Documents/com~ap...  \n",
       "35  /Users/brendan/Library/Mobile Documents/com~ap...  \n",
       "36  /Users/brendan/Library/Mobile Documents/com~ap...  \n",
       "37  /Users/brendan/Library/Mobile Documents/com~ap...  \n",
       "38  /Users/brendan/Library/Mobile Documents/com~ap...  \n",
       "39  /Users/brendan/Library/Mobile Documents/com~ap...  \n",
       "40  /Users/brendan/Library/Mobile Documents/com~ap...  \n",
       "41  /Users/brendan/Library/Mobile Documents/com~ap...  \n",
       "42  /Users/brendan/Library/Mobile Documents/com~ap...  \n",
       "43  /Users/brendan/Library/Mobile Documents/com~ap...  \n",
       "44  /Users/brendan/Library/Mobile Documents/com~ap...  \n",
       "45  /Users/brendan/Library/Mobile Documents/com~ap...  \n",
       "46  /Users/brendan/Library/Mobile Documents/com~ap...  \n",
       "47  /Users/brendan/Library/Mobile Documents/com~ap...  \n",
       "48  /Users/brendan/Library/Mobile Documents/com~ap...  \n",
       "49  /Users/brendan/Library/Mobile Documents/com~ap...  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# filter for all files with the data that matches '10/26/1985 :: 08:15:00'\n",
    "df_10_26_1985_filtered_vs = raw_df.loc[(raw_df['File Type'] == 'Python file') & \n",
    "                                       (raw_df['Date'] == '10/26/1985 :: 08:15:00')]\n",
    "df_10_26_1985_filtered_jupyter = raw_df.loc[(raw_df['File Type'] == 'Jupyter Notebook') \n",
    "                                             & (raw_df['Date'] == '10/26/1985 :: 08:15:00')]\n",
    "df_10_26_1985_filtered.head(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "bf0d1281",
   "metadata": {},
   "outputs": [
    {
     "data": {
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   "source": [
    "df_10_26_1985_filtered_jupyter = raw_df.loc[(raw_df['File Type'] == 'Jupyter Notebook') & (raw_df['Date'] == '10/26/1985 :: 08:15:00')]\n",
    "df_10_26_1985_filtered_jupyter"
   ]
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  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "41b1d411",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 147 entries, 8309 to 14291\n",
      "Data columns (total 5 columns):\n",
      " #   Column           Non-Null Count  Dtype \n",
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      " 2   Folder Location  147 non-null    object\n",
      " 3   Date             147 non-null    object\n",
      " 4   Path             147 non-null    object\n",
      "dtypes: object(5)\n",
      "memory usage: 6.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df_10_26_1985_filtered_vs.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "0db7f038",
   "metadata": {},
   "outputs": [
    {
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     "execution_count": 32,
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   "source": [
    "# filter for all files with the data that matches '08/26/2018 :: 00:52:52'\n",
    "df_8_26_2018_filtered_vs = raw_df.loc[(raw_df['File Type'] == 'Python file') & \n",
    "                                       (raw_df['Date'] == '08/26/2018 :: 00:52:52')]\n",
    "df_8_26_2018_filtered_jupyter = raw_df.loc[(raw_df['File Type'] == 'Jupyter Notebook') \n",
    "                                             & (raw_df['Date'] == '08/26/2018 :: 00:52:52')]\n",
    "df_8_26_2018_filtered_vs.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "4be715ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
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     "execution_count": 33,
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   "source": [
    "df_8_26_2018_filtered_jupyter.head()"
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  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "62a14b49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'08/10/2018 :: 02:01:48'"
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     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "'08/10/2018 :: 02:01:48'"
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  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "5ec4082e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>5893</th>\n",
       "      <td>formatted_name.py</td>\n",
       "      <td>Python file</td>\n",
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       "      <td>08/10/2018 :: 02:01:48</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
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       "    <tr>\n",
       "      <th>5894</th>\n",
       "      <td>person.py</td>\n",
       "      <td>Python file</td>\n",
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       "      <td>08/10/2018 :: 02:01:48</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
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       "    <tr>\n",
       "      <th>5895</th>\n",
       "      <td>printing_models.py</td>\n",
       "      <td>Python file</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>08/10/2018 :: 02:01:48</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5896</th>\n",
       "      <td>pizza.py</td>\n",
       "      <td>Python file</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>08/10/2018 :: 02:01:48</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5897</th>\n",
       "      <td>pets.py</td>\n",
       "      <td>Python file</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>08/10/2018 :: 02:01:48</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "                    File    File Type  \\\n",
       "5893   formatted_name.py  Python file   \n",
       "5894           person.py  Python file   \n",
       "5895  printing_models.py  Python file   \n",
       "5896            pizza.py  Python file   \n",
       "5897             pets.py  Python file   \n",
       "\n",
       "                                        Folder Location  \\\n",
       "5893  /Users/brendan/Library/Mobile Documents/com~ap...   \n",
       "5894  /Users/brendan/Library/Mobile Documents/com~ap...   \n",
       "5895  /Users/brendan/Library/Mobile Documents/com~ap...   \n",
       "5896  /Users/brendan/Library/Mobile Documents/com~ap...   \n",
       "5897  /Users/brendan/Library/Mobile Documents/com~ap...   \n",
       "\n",
       "                        Date  \\\n",
       "5893  08/10/2018 :: 02:01:48   \n",
       "5894  08/10/2018 :: 02:01:48   \n",
       "5895  08/10/2018 :: 02:01:48   \n",
       "5896  08/10/2018 :: 02:01:48   \n",
       "5897  08/10/2018 :: 02:01:48   \n",
       "\n",
       "                                                   Path  \n",
       "5893  /Users/brendan/Library/Mobile Documents/com~ap...  \n",
       "5894  /Users/brendan/Library/Mobile Documents/com~ap...  \n",
       "5895  /Users/brendan/Library/Mobile Documents/com~ap...  \n",
       "5896  /Users/brendan/Library/Mobile Documents/com~ap...  \n",
       "5897  /Users/brendan/Library/Mobile Documents/com~ap...  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# filter for all files with the data that matches '08/10/2018 :: 02:01:48'\n",
    "df_8_10_2018_filtered_vs = raw_df.loc[(raw_df['File Type'] == 'Python file') & \n",
    "                                       (raw_df['Date'] == '08/10/2018 :: 02:01:48')]\n",
    "df_8_10_2018_filtered_jupyter = raw_df.loc[(raw_df['File Type'] == 'Jupyter Notebook') \n",
    "                                             & (raw_df['Date'] == '08/10/2018 :: 02:01:48')]\n",
    "\n",
    "df_8_10_2018_filtered_vs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "bc93a17f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 215 entries, 5893 to 6300\n",
      "Data columns (total 5 columns):\n",
      " #   Column           Non-Null Count  Dtype \n",
      "---  ------           --------------  ----- \n",
      " 0   File             215 non-null    object\n",
      " 1   File Type        215 non-null    object\n",
      " 2   Folder Location  215 non-null    object\n",
      " 3   Date             215 non-null    object\n",
      " 4   Path             215 non-null    object\n",
      "dtypes: object(5)\n",
      "memory usage: 10.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df_8_10_2018_filtered_vs.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "fd516e4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'09/21/2019 :: 02:56:04'"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'09/21/2019 :: 02:56:04'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "64a8da45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "Empty DataFrame\n",
       "Columns: [File, File Type, Folder Location, Date, Path]\n",
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     "execution_count": 38,
     "metadata": {},
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   ],
   "source": [
    "# filter for all files with the data that matches '09/21/2019 :: 02:56:02'\n",
    "df_9_21_2019_filtered_vs = raw_df.loc[(raw_df['File Type'] == 'Python file') & \n",
    "                                       (raw_df['Date'] == '09/21/2019 :: 02:56:02')]\n",
    "df_9_21_2019_filtered_jupyter = raw_df.loc[(raw_df['File Type'] == 'Jupyter Notebook') \n",
    "                                             & (raw_df['Date'] == '09/21/2019 :: 02:56:02')]\n",
    "\n",
    "df_9_21_2019_filtered_vs.head()\n",
    "df_9_21_2019_filtered_jupyter.head()"
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  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "7ec05976",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2 entries, 4969 to 6718\n",
      "Data columns (total 5 columns):\n",
      " #   Column           Non-Null Count  Dtype \n",
      "---  ------           --------------  ----- \n",
      " 0   File             2 non-null      object\n",
      " 1   File Type        2 non-null      object\n",
      " 2   Folder Location  2 non-null      object\n",
      " 3   Date             2 non-null      object\n",
      " 4   Path             2 non-null      object\n",
      "dtypes: object(5)\n",
      "memory usage: 96.0+ bytes\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 0 entries\n",
      "Data columns (total 5 columns):\n",
      " #   Column           Non-Null Count  Dtype \n",
      "---  ------           --------------  ----- \n",
      " 0   File             0 non-null      object\n",
      " 1   File Type        0 non-null      object\n",
      " 2   Folder Location  0 non-null      object\n",
      " 3   Date             0 non-null      object\n",
      " 4   Path             0 non-null      object\n",
      "dtypes: object(5)\n",
      "memory usage: 0.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "df_9_21_2019_filtered_vs.info()\n",
    "df_9_21_2019_filtered_jupyter.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7c9efc53",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "22de167c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 938 entries, 5 to 8878\n",
      "Data columns (total 5 columns):\n",
      " #   Column           Non-Null Count  Dtype \n",
      "---  ------           --------------  ----- \n",
      " 0   File             938 non-null    object\n",
      " 1   File Type        938 non-null    object\n",
      " 2   Folder Location  938 non-null    object\n",
      " 3   Date             938 non-null    object\n",
      " 4   Path             938 non-null    object\n",
      "dtypes: object(5)\n",
      "memory usage: 44.0+ KB\n"
     ]
    }
   ],
   "source": [
    "raw_df_filtered = raw_df.loc[(raw_df['Date']!='10/26/1985 :: 08:15:00')]\n",
    "raw_df_filtered2 = raw_df_filtered.loc[(raw_df_filtered['Date']!='09/21/2019 :: 02:56:04')]\n",
    "raw_df_filtered3 = raw_df_filtered2.loc[(raw_df_filtered2['Date']!='08/26/2018 :: 00:52:52')]\n",
    "raw_df_filtered_vs = raw_df_filtered3[(raw_df_filtered3['File Type'] == 'Python file')]\n",
    "raw_df_filtered_vs.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "ff8ec4fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 548 entries, 11 to 7448\n",
      "Data columns (total 5 columns):\n",
      " #   Column           Non-Null Count  Dtype \n",
      "---  ------           --------------  ----- \n",
      " 0   File             548 non-null    object\n",
      " 1   File Type        548 non-null    object\n",
      " 2   Folder Location  548 non-null    object\n",
      " 3   Date             548 non-null    object\n",
      " 4   Path             548 non-null    object\n",
      "dtypes: object(5)\n",
      "memory usage: 25.7+ KB\n"
     ]
    }
   ],
   "source": [
    "raw_df_filtered_jupyter = raw_df_filtered3[(raw_df_filtered3['File Type'] == 'Jupyter Notebook')]\n",
    "raw_df_filtered_jupyter.info()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "08aeff99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(File Type\n",
       " Jupyter Notebook    548\n",
       " dtype: int64,\n",
       " File Type\n",
       " Python file    938\n",
       " dtype: int64)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_df_filtered_jupyter.value_counts('File Type'), raw_df_filtered_vs.value_counts('File Type')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "77bb2bbb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "File Type\n",
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       "Javascript                 794\n",
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       "JSON file                  529\n",
       "JPG Image file             473\n",
       "PNG Image file             290\n",
       "Text document               61\n",
       "PDF file                    40\n",
       "SVG Image file              36\n",
       "Python file (compiled)      14\n",
       "HTML                        10\n",
       "Thumbnail                    7\n",
       "CSS Styling                  6\n",
       "Icon Image file              4\n",
       "CSV file                     3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_df_filtered3.value_counts('File Type')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "433434ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "08/10/2018 :: 02:01:48    411\n",
       "09/21/2019 :: 02:56:02    390\n",
       "08/26/2018 :: 00:56:42    220\n",
       "08/21/2018 :: 18:21:08    162\n",
       "11/14/2020 :: 21:38:22    150\n",
       "                         ... \n",
       "07/26/2020 :: 21:29:34      1\n",
       "07/26/2020 :: 21:25:25      1\n",
       "07/26/2020 :: 21:24:21      1\n",
       "07/25/2018 :: 20:35:43      1\n",
       "12/31/2020 :: 22:09:58      1\n",
       "Length: 2209, dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_df_filtered3.value_counts('Date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "01c0be90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f7e9a04a97644858b4ee299819cf15db",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "QgridWidget(grid_options={'fullWidthRows': True, 'syncColumnCellResize': True, 'forceFitColumns': True, 'defau…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "qgrid_widget = qgrid.show_grid(raw_df_filtered3, show_toolbar = True)\n",
    "qgrid_widget"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "08d83ef1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(raw_df_filtered3['Date'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88a2ae0a",
   "metadata": {},
   "source": [
    "## File type = .py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "af8fb848",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(File Type\n",
       " Jupyter Notebook    548\n",
       " dtype: int64,\n",
       " File Type\n",
       " Python file    938\n",
       " dtype: int64)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the 'Date' variable in each dataframe needs to be converted to datetime\n",
    "raw_df_filtered_vs_ = raw_df_filtered_vs.copy()\n",
    "raw_df_filtered_jupyter_ = raw_df_filtered_jupyter.copy()\n",
    "\n",
    "raw_df_filtered_vs_['Date'] = pd.to_datetime(raw_df_filtered_vs_['Date'], format = '%m/%d/%Y :: %H:%M:%S')\n",
    "raw_df_filtered_jupyter_ ['Date']= pd.to_datetime(raw_df_filtered_jupyter_['Date'], format = '%m/%d/%Y :: %H:%M:%S')\n",
    "\n",
    "raw_df_filtered_jupyter_.value_counts('File Type'), raw_df_filtered_vs_.value_counts('File Type')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "605a315b",
   "metadata": {},
   "outputs": [
    {
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       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>2015-03-30 13:51:24</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
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       "      <th>6358</th>\n",
       "      <td>allMyCats2.py</td>\n",
       "      <td>Python file</td>\n",
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       "      <td>2015-03-30 13:51:24</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
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       "      <td>allMyCats1.py</td>\n",
       "      <td>Python file</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
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       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
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       "      <th>6350</th>\n",
       "      <td>characterCount.py</td>\n",
       "      <td>Python file</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>2015-03-30 13:51:24</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
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       "    <tr>\n",
       "      <th>6401</th>\n",
       "      <td>buggyAddingProgram.py</td>\n",
       "      <td>Python file</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>2015-03-30 13:51:24</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
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       "      <th>...</th>\n",
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       "      <th>430</th>\n",
       "      <td>data.py</td>\n",
       "      <td>Python file</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>2021-07-17 19:10:12</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
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       "    <tr>\n",
       "      <th>426</th>\n",
       "      <td>ui.py</td>\n",
       "      <td>Python file</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>2021-07-17 19:17:25</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429</th>\n",
       "      <td>main.py</td>\n",
       "      <td>Python file</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>2021-07-17 19:18:22</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6641</th>\n",
       "      <td>Untitled-checkpoint.ipynb</td>\n",
       "      <td>Jupyter Notebook</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>2021-07-17 19:47:48</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4922</th>\n",
       "      <td>Untitled.ipynb</td>\n",
       "      <td>Jupyter Notebook</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>2021-07-17 19:49:51</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "    </tr>\n",
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       "<p>1486 rows × 5 columns</p>\n",
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      "text/plain": [
       "                           File         File Type  \\\n",
       "6370            errorExample.py       Python file   \n",
       "6358              allMyCats2.py       Python file   \n",
       "6399              allMyCats1.py       Python file   \n",
       "6350          characterCount.py       Python file   \n",
       "6401      buggyAddingProgram.py       Python file   \n",
       "...                         ...               ...   \n",
       "430                     data.py       Python file   \n",
       "426                       ui.py       Python file   \n",
       "429                     main.py       Python file   \n",
       "6641  Untitled-checkpoint.ipynb  Jupyter Notebook   \n",
       "4922             Untitled.ipynb  Jupyter Notebook   \n",
       "\n",
       "                                        Folder Location                Date  \\\n",
       "6370  /Users/brendan/Library/Mobile Documents/com~ap... 2015-03-30 13:51:24   \n",
       "6358  /Users/brendan/Library/Mobile Documents/com~ap... 2015-03-30 13:51:24   \n",
       "6399  /Users/brendan/Library/Mobile Documents/com~ap... 2015-03-30 13:51:24   \n",
       "6350  /Users/brendan/Library/Mobile Documents/com~ap... 2015-03-30 13:51:24   \n",
       "6401  /Users/brendan/Library/Mobile Documents/com~ap... 2015-03-30 13:51:24   \n",
       "...                                                 ...                 ...   \n",
       "430   /Users/brendan/Library/Mobile Documents/com~ap... 2021-07-17 19:10:12   \n",
       "426   /Users/brendan/Library/Mobile Documents/com~ap... 2021-07-17 19:17:25   \n",
       "429   /Users/brendan/Library/Mobile Documents/com~ap... 2021-07-17 19:18:22   \n",
       "6641  /Users/brendan/Library/Mobile Documents/com~ap... 2021-07-17 19:47:48   \n",
       "4922  /Users/brendan/Library/Mobile Documents/com~ap... 2021-07-17 19:49:51   \n",
       "\n",
       "                                                   Path  \n",
       "6370  /Users/brendan/Library/Mobile Documents/com~ap...  \n",
       "6358  /Users/brendan/Library/Mobile Documents/com~ap...  \n",
       "6399  /Users/brendan/Library/Mobile Documents/com~ap...  \n",
       "6350  /Users/brendan/Library/Mobile Documents/com~ap...  \n",
       "6401  /Users/brendan/Library/Mobile Documents/com~ap...  \n",
       "...                                                 ...  \n",
       "430   /Users/brendan/Library/Mobile Documents/com~ap...  \n",
       "426   /Users/brendan/Library/Mobile Documents/com~ap...  \n",
       "429   /Users/brendan/Library/Mobile Documents/com~ap...  \n",
       "6641  /Users/brendan/Library/Mobile Documents/com~ap...  \n",
       "4922  /Users/brendan/Library/Mobile Documents/com~ap...  \n",
       "\n",
       "[1486 rows x 5 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_python = pd.concat([raw_df_filtered_vs_, raw_df_filtered_jupyter_])\n",
    "df_python_ = df_python.copy()\n",
    "df_python_['Date'] = pd.to_datetime(df_python[\"Date\"], format = '%m/%d/%Y :: %H:%M:%S')\n",
    "df_python_.sort_values(by=['Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "b2eaae6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2018-08-10 02:01:48    215\n",
       "2018-08-21 18:21:08     90\n",
       "2018-08-21 18:19:06     82\n",
       "2018-08-30 18:54:18     69\n",
       "2018-08-21 19:05:30     26\n",
       "                      ... \n",
       "2019-12-27 21:12:30      1\n",
       "2019-12-27 21:07:19      1\n",
       "2019-12-27 18:13:33      1\n",
       "2019-12-27 18:02:35      1\n",
       "2021-07-17 19:49:51      1\n",
       "Length: 618, dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_python_.value_counts('Date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "b0fc97bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "566d3f7422c24160a65a11c1303860ba",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "QgridWidget(grid_options={'fullWidthRows': True, 'syncColumnCellResize': True, 'forceFitColumns': True, 'defau…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_python_widget = qgrid.show_grid(df_python_, show_toolbar = True)\n",
    "df_python_widget"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "021512f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_python_vs = df_python_[df_python_['Date']>'2019-08-31 00:00:00']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6c64e00",
   "metadata": {},
   "source": [
    "# File type = .ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "a361fccf",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>.Untitled.ipynb</td>\n",
       "      <td>Jupyter Notebook</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>12/05/2020 :: 20:01:10</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>bokeh_static.ipynb</td>\n",
       "      <td>Jupyter Notebook</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>07/04/2021 :: 01:53:14</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>bokeh_static-checkpoint.ipynb</td>\n",
       "      <td>Jupyter Notebook</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>07/04/2021 :: 01:53:14</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>.Untitled-checkpoint.ipynb</td>\n",
       "      <td>Jupyter Notebook</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>12/05/2020 :: 19:53:08</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>.05 Matplotlib Lecture.ipynb</td>\n",
       "      <td>Jupyter Notebook</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>12/27/2020 :: 14:23:29</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7436</th>\n",
       "      <td>.Matrix_capital.ipynb</td>\n",
       "      <td>Jupyter Notebook</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>08/08/2020 :: 04:22:56</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7445</th>\n",
       "      <td>.Coatue-checkpoint.ipynb</td>\n",
       "      <td>Jupyter Notebook</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>08/08/2020 :: 23:02:20</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7446</th>\n",
       "      <td>.Tiger Global-checkpoint.ipynb</td>\n",
       "      <td>Jupyter Notebook</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>08/16/2020 :: 01:25:32</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7447</th>\n",
       "      <td>.Matrix_capital-checkpoint.ipynb</td>\n",
       "      <td>Jupyter Notebook</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>08/06/2020 :: 00:57:09</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7448</th>\n",
       "      <td>.whale_rock-checkpoint.ipynb</td>\n",
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       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>08/06/2020 :: 01:24:15</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
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       "  </tbody>\n",
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       "<p>548 rows × 5 columns</p>\n",
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       "                                  File         File Type  \\\n",
       "11                     .Untitled.ipynb  Jupyter Notebook   \n",
       "12                  bokeh_static.ipynb  Jupyter Notebook   \n",
       "18       bokeh_static-checkpoint.ipynb  Jupyter Notebook   \n",
       "19          .Untitled-checkpoint.ipynb  Jupyter Notebook   \n",
       "22        .05 Matplotlib Lecture.ipynb  Jupyter Notebook   \n",
       "...                                ...               ...   \n",
       "7436             .Matrix_capital.ipynb  Jupyter Notebook   \n",
       "7445          .Coatue-checkpoint.ipynb  Jupyter Notebook   \n",
       "7446    .Tiger Global-checkpoint.ipynb  Jupyter Notebook   \n",
       "7447  .Matrix_capital-checkpoint.ipynb  Jupyter Notebook   \n",
       "7448      .whale_rock-checkpoint.ipynb  Jupyter Notebook   \n",
       "\n",
       "                                        Folder Location  \\\n",
       "11    /Users/brendan/Library/Mobile Documents/com~ap...   \n",
       "12    /Users/brendan/Library/Mobile Documents/com~ap...   \n",
       "18    /Users/brendan/Library/Mobile Documents/com~ap...   \n",
       "19    /Users/brendan/Library/Mobile Documents/com~ap...   \n",
       "22    /Users/brendan/Library/Mobile Documents/com~ap...   \n",
       "...                                                 ...   \n",
       "7436  /Users/brendan/Library/Mobile Documents/com~ap...   \n",
       "7445  /Users/brendan/Library/Mobile Documents/com~ap...   \n",
       "7446  /Users/brendan/Library/Mobile Documents/com~ap...   \n",
       "7447  /Users/brendan/Library/Mobile Documents/com~ap...   \n",
       "7448  /Users/brendan/Library/Mobile Documents/com~ap...   \n",
       "\n",
       "                        Date  \\\n",
       "11    12/05/2020 :: 20:01:10   \n",
       "12    07/04/2021 :: 01:53:14   \n",
       "18    07/04/2021 :: 01:53:14   \n",
       "19    12/05/2020 :: 19:53:08   \n",
       "22    12/27/2020 :: 14:23:29   \n",
       "...                      ...   \n",
       "7436  08/08/2020 :: 04:22:56   \n",
       "7445  08/08/2020 :: 23:02:20   \n",
       "7446  08/16/2020 :: 01:25:32   \n",
       "7447  08/06/2020 :: 00:57:09   \n",
       "7448  08/06/2020 :: 01:24:15   \n",
       "\n",
       "                                                   Path  \n",
       "11    /Users/brendan/Library/Mobile Documents/com~ap...  \n",
       "12    /Users/brendan/Library/Mobile Documents/com~ap...  \n",
       "18    /Users/brendan/Library/Mobile Documents/com~ap...  \n",
       "19    /Users/brendan/Library/Mobile Documents/com~ap...  \n",
       "22    /Users/brendan/Library/Mobile Documents/com~ap...  \n",
       "...                                                 ...  \n",
       "7436  /Users/brendan/Library/Mobile Documents/com~ap...  \n",
       "7445  /Users/brendan/Library/Mobile Documents/com~ap...  \n",
       "7446  /Users/brendan/Library/Mobile Documents/com~ap...  \n",
       "7447  /Users/brendan/Library/Mobile Documents/com~ap...  \n",
       "7448  /Users/brendan/Library/Mobile Documents/com~ap...  \n",
       "\n",
       "[548 rows x 5 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_python_jupyter = raw_df_filtered3[raw_df_filtered3['File Type'] == 'Jupyter Notebook']\n",
    "df_python_jupyter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "7363e7de",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>.01-NLP (Natural Language Processing) with Pyt...</td>\n",
       "      <td>Jupyter Notebook</td>\n",
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       "      <td>.01-Matplotlib Concepts Lecture.ipynb</td>\n",
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       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
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       "      <td>.04-Advanced Matplotlib Concepts.ipynb</td>\n",
       "      <td>Jupyter Notebook</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>2018-08-30 18:54:18</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>.03-Matplotlib Exercises - Solutions.ipynb</td>\n",
       "      <td>Jupyter Notebook</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>2018-08-30 18:54:18</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>.03-NLP Project - Solutions.ipynb</td>\n",
       "      <td>Jupyter Notebook</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>2018-08-30 18:54:18</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>goodreads.ipynb</td>\n",
       "      <td>Jupyter Notebook</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>2021-07-15 07:05:08</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
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       "    <tr>\n",
       "      <th>424</th>\n",
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       "      <td>2021-07-15 11:44:57</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
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       "    <tr>\n",
       "      <th>421</th>\n",
       "      <td>ISS_sky.ipynb</td>\n",
       "      <td>Jupyter Notebook</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>2021-07-15 11:44:57</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6641</th>\n",
       "      <td>Untitled-checkpoint.ipynb</td>\n",
       "      <td>Jupyter Notebook</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>2021-07-17 19:47:48</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
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       "    <tr>\n",
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       "      <td>Untitled.ipynb</td>\n",
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       "      <td>2021-07-17 19:49:51</td>\n",
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      ],
      "text/plain": [
       "                                                   File         File Type  \\\n",
       "100   .01-NLP (Natural Language Processing) with Pyt...  Jupyter Notebook   \n",
       "106               .01-Matplotlib Concepts Lecture.ipynb  Jupyter Notebook   \n",
       "105              .04-Advanced Matplotlib Concepts.ipynb  Jupyter Notebook   \n",
       "104          .03-Matplotlib Exercises - Solutions.ipynb  Jupyter Notebook   \n",
       "101                   .03-NLP Project - Solutions.ipynb  Jupyter Notebook   \n",
       "...                                                 ...               ...   \n",
       "5528                                    goodreads.ipynb  Jupyter Notebook   \n",
       "424                            ISS_sky-checkpoint.ipynb  Jupyter Notebook   \n",
       "421                                       ISS_sky.ipynb  Jupyter Notebook   \n",
       "6641                          Untitled-checkpoint.ipynb  Jupyter Notebook   \n",
       "4922                                     Untitled.ipynb  Jupyter Notebook   \n",
       "\n",
       "                                        Folder Location                Date  \\\n",
       "100   /Users/brendan/Library/Mobile Documents/com~ap... 2018-08-30 18:54:18   \n",
       "106   /Users/brendan/Library/Mobile Documents/com~ap... 2018-08-30 18:54:18   \n",
       "105   /Users/brendan/Library/Mobile Documents/com~ap... 2018-08-30 18:54:18   \n",
       "104   /Users/brendan/Library/Mobile Documents/com~ap... 2018-08-30 18:54:18   \n",
       "101   /Users/brendan/Library/Mobile Documents/com~ap... 2018-08-30 18:54:18   \n",
       "...                                                 ...                 ...   \n",
       "5528  /Users/brendan/Library/Mobile Documents/com~ap... 2021-07-15 07:05:08   \n",
       "424   /Users/brendan/Library/Mobile Documents/com~ap... 2021-07-15 11:44:57   \n",
       "421   /Users/brendan/Library/Mobile Documents/com~ap... 2021-07-15 11:44:57   \n",
       "6641  /Users/brendan/Library/Mobile Documents/com~ap... 2021-07-17 19:47:48   \n",
       "4922  /Users/brendan/Library/Mobile Documents/com~ap... 2021-07-17 19:49:51   \n",
       "\n",
       "                                                   Path  \n",
       "100   /Users/brendan/Library/Mobile Documents/com~ap...  \n",
       "106   /Users/brendan/Library/Mobile Documents/com~ap...  \n",
       "105   /Users/brendan/Library/Mobile Documents/com~ap...  \n",
       "104   /Users/brendan/Library/Mobile Documents/com~ap...  \n",
       "101   /Users/brendan/Library/Mobile Documents/com~ap...  \n",
       "...                                                 ...  \n",
       "5528  /Users/brendan/Library/Mobile Documents/com~ap...  \n",
       "424   /Users/brendan/Library/Mobile Documents/com~ap...  \n",
       "421   /Users/brendan/Library/Mobile Documents/com~ap...  \n",
       "6641  /Users/brendan/Library/Mobile Documents/com~ap...  \n",
       "4922  /Users/brendan/Library/Mobile Documents/com~ap...  \n",
       "\n",
       "[548 rows x 5 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_python_jupyter_ = df_python_jupyter.copy()\n",
    "df_python_jupyter_['Date'] = pd.to_datetime(df_python_jupyter_[\"Date\"], format = '%m/%d/%Y :: %H:%M:%S')\n",
    "df_python_jupyter_.sort_values(by=['Date'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8ce7c7e",
   "metadata": {},
   "source": [
    "## File type = ipynb.cloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "66377dd6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_python_jupyter_ = df_python_jupyter_[df_python_jupyter_['Date']>'2019-08-31 00:00:00']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "565d75d5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "4bfec3a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fb971cf4850>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "plt.style.use('fivethirtyeight')\n",
    "plt.title(\"Python Files over time\")\n",
    "plt.xlabel(\"Date\")\n",
    "plt.ylabel('# of Python files on VS Code or Jupyter')\n",
    "plt.legend(['Visual Studio', 'Jupyter'], loc='upper right')\n",
    "s = df_python_vs['Date'].value_counts().sort_index()\n",
    "j = df_python_jupyter_['Date'].value_counts().sort_index()\n",
    "plt.plot(s, label='Visual Studio Code')\n",
    "plt.plot(j, label='Jupyter Notebook')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f5324edd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "c24755ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Jupyter Notebook    469\n",
       " Name: File Type, dtype: int64,\n",
       " Jupyter Notebook    469\n",
       " Python file         264\n",
       " Name: File Type, dtype: int64)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_python_jupyter_['File Type'].value_counts(), df_python_vs['File Type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "6eb56826",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2019-12-31 02:54:21    4\n",
       "2019-12-30 23:40:50    2\n",
       "2020-12-30 01:28:02    2\n",
       "2020-03-07 22:31:56    2\n",
       "2020-12-05 16:17:47    2\n",
       "                      ..\n",
       "2020-12-28 22:40:24    1\n",
       "2020-10-24 17:13:34    1\n",
       "2021-07-15 07:04:49    1\n",
       "2019-09-08 16:55:10    1\n",
       "2021-07-17 19:47:48    1\n",
       "Name: Date, Length: 301, dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_python_jupyter_['Date'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "701abc6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2020-07-28 22:10:22    4\n",
       "2019-12-31 02:54:21    4\n",
       "2020-07-28 21:59:55    4\n",
       "2019-12-27 17:11:21    4\n",
       "2019-12-27 16:52:45    4\n",
       "                      ..\n",
       "2020-10-10 14:38:25    1\n",
       "2019-12-26 17:36:20    1\n",
       "2019-10-17 16:34:16    1\n",
       "2020-10-17 17:01:55    1\n",
       "2019-09-23 20:20:32    1\n",
       "Name: Date, Length: 527, dtype: int64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_python_vs['Date'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "0a4fb3ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                          6624\n",
       "Javascript                5261\n",
       "Python file               1085\n",
       "JSON file                  635\n",
       "Jupyter Notebook           548\n",
       "JPG Image file             502\n",
       "SVG Image file             298\n",
       "PNG Image file             290\n",
       "Text document              152\n",
       "PDF file                    40\n",
       "Python file (compiled)      14\n",
       "HTML                        10\n",
       "Thumbnail                    7\n",
       "CSS Styling                  6\n",
       "Icon Image file              4\n",
       "CSV file                     3\n",
       "Name: File Type, dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_df['File Type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "68e40e51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10/26/1985 :: 08:15:00    5449\n",
       "08/26/2018 :: 00:52:52    1576\n",
       "09/21/2019 :: 02:56:04     536\n",
       "08/10/2018 :: 02:01:48     411\n",
       "09/21/2019 :: 02:56:02     390\n",
       "                          ... \n",
       "08/25/2015 :: 04:42:00       1\n",
       "04/28/2002 :: 11:43:03       1\n",
       "12/29/2019 :: 22:10:14       1\n",
       "10/04/2018 :: 18:45:35       1\n",
       "11/07/2019 :: 03:23:04       1\n",
       "Name: Date, Length: 2212, dtype: int64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_df['Date'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a50bbff",
   "metadata": {},
   "source": [
    "## Sorting by dates"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5df19f85",
   "metadata": {},
   "source": [
    "### For Python-Jupyter first"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "c570d7bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>File</th>\n",
       "      <th>File Type</th>\n",
       "      <th>Folder Location</th>\n",
       "      <th>Date</th>\n",
       "      <th>Path</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>.Untitled.ipynb</td>\n",
       "      <td>Jupyter Notebook</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>2020-12-05 20:01:10</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>bokeh_static.ipynb</td>\n",
       "      <td>Jupyter Notebook</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>2021-07-04 01:53:14</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>bokeh_static-checkpoint.ipynb</td>\n",
       "      <td>Jupyter Notebook</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>2021-07-04 01:53:14</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>.Untitled-checkpoint.ipynb</td>\n",
       "      <td>Jupyter Notebook</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>2020-12-05 19:53:08</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>.05 Matplotlib Lecture.ipynb</td>\n",
       "      <td>Jupyter Notebook</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "      <td>2020-12-27 14:23:29</td>\n",
       "      <td>/Users/brendan/Library/Mobile Documents/com~ap...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             File         File Type  \\\n",
       "11                .Untitled.ipynb  Jupyter Notebook   \n",
       "12             bokeh_static.ipynb  Jupyter Notebook   \n",
       "18  bokeh_static-checkpoint.ipynb  Jupyter Notebook   \n",
       "19     .Untitled-checkpoint.ipynb  Jupyter Notebook   \n",
       "22   .05 Matplotlib Lecture.ipynb  Jupyter Notebook   \n",
       "\n",
       "                                      Folder Location                Date  \\\n",
       "11  /Users/brendan/Library/Mobile Documents/com~ap... 2020-12-05 20:01:10   \n",
       "12  /Users/brendan/Library/Mobile Documents/com~ap... 2021-07-04 01:53:14   \n",
       "18  /Users/brendan/Library/Mobile Documents/com~ap... 2021-07-04 01:53:14   \n",
       "19  /Users/brendan/Library/Mobile Documents/com~ap... 2020-12-05 19:53:08   \n",
       "22  /Users/brendan/Library/Mobile Documents/com~ap... 2020-12-27 14:23:29   \n",
       "\n",
       "                                                 Path  \n",
       "11  /Users/brendan/Library/Mobile Documents/com~ap...  \n",
       "12  /Users/brendan/Library/Mobile Documents/com~ap...  \n",
       "18  /Users/brendan/Library/Mobile Documents/com~ap...  \n",
       "19  /Users/brendan/Library/Mobile Documents/com~ap...  \n",
       "22  /Users/brendan/Library/Mobile Documents/com~ap...  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_df_filtered_vs_.head()\n",
    "raw_df_filtered_jupyter_.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "9c727ce5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>.Untitled.ipynb</td>\n",
       "      <td>Jupyter Notebook</td>\n",
       "      <td>2020-12-05 20:01:10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>bokeh_static.ipynb</td>\n",
       "      <td>Jupyter Notebook</td>\n",
       "      <td>2021-07-04 01:53:14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>bokeh_static-checkpoint.ipynb</td>\n",
       "      <td>Jupyter Notebook</td>\n",
       "      <td>2021-07-04 01:53:14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>.Untitled-checkpoint.ipynb</td>\n",
       "      <td>Jupyter Notebook</td>\n",
       "      <td>2020-12-05 19:53:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>.05 Matplotlib Lecture.ipynb</td>\n",
       "      <td>Jupyter Notebook</td>\n",
       "      <td>2020-12-27 14:23:29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7436</th>\n",
       "      <td>.Matrix_capital.ipynb</td>\n",
       "      <td>Jupyter Notebook</td>\n",
       "      <td>2020-08-08 04:22:56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7445</th>\n",
       "      <td>.Coatue-checkpoint.ipynb</td>\n",
       "      <td>Jupyter Notebook</td>\n",
       "      <td>2020-08-08 23:02:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7446</th>\n",
       "      <td>.Tiger Global-checkpoint.ipynb</td>\n",
       "      <td>Jupyter Notebook</td>\n",
       "      <td>2020-08-16 01:25:32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7447</th>\n",
       "      <td>.Matrix_capital-checkpoint.ipynb</td>\n",
       "      <td>Jupyter Notebook</td>\n",
       "      <td>2020-08-06 00:57:09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7448</th>\n",
       "      <td>.whale_rock-checkpoint.ipynb</td>\n",
       "      <td>Jupyter Notebook</td>\n",
       "      <td>2020-08-06 01:24:15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>548 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  File         File Type                Date\n",
       "11                     .Untitled.ipynb  Jupyter Notebook 2020-12-05 20:01:10\n",
       "12                  bokeh_static.ipynb  Jupyter Notebook 2021-07-04 01:53:14\n",
       "18       bokeh_static-checkpoint.ipynb  Jupyter Notebook 2021-07-04 01:53:14\n",
       "19          .Untitled-checkpoint.ipynb  Jupyter Notebook 2020-12-05 19:53:08\n",
       "22        .05 Matplotlib Lecture.ipynb  Jupyter Notebook 2020-12-27 14:23:29\n",
       "...                                ...               ...                 ...\n",
       "7436             .Matrix_capital.ipynb  Jupyter Notebook 2020-08-08 04:22:56\n",
       "7445          .Coatue-checkpoint.ipynb  Jupyter Notebook 2020-08-08 23:02:20\n",
       "7446    .Tiger Global-checkpoint.ipynb  Jupyter Notebook 2020-08-16 01:25:32\n",
       "7447  .Matrix_capital-checkpoint.ipynb  Jupyter Notebook 2020-08-06 00:57:09\n",
       "7448      .whale_rock-checkpoint.ipynb  Jupyter Notebook 2020-08-06 01:24:15\n",
       "\n",
       "[548 rows x 3 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_python_jupyter_dates = raw_df_filtered_jupyter_[['File', 'File Type', 'Date']]\n",
    "df_python_vs_dates = raw_df_filtered_vs_[['File', 'File Type', 'Date']]\n",
    "df_python_jupyter_dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "fde82941",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>File</th>\n",
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       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>.01-NLP (Natural Language Processing) with Pyt...</td>\n",
       "      <td>Jupyter Notebook</td>\n",
       "      <td>2018-08-30 18:54:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>.01-Matplotlib Concepts Lecture.ipynb</td>\n",
       "      <td>Jupyter Notebook</td>\n",
       "      <td>2018-08-30 18:54:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>.04-Advanced Matplotlib Concepts.ipynb</td>\n",
       "      <td>Jupyter Notebook</td>\n",
       "      <td>2018-08-30 18:54:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>.03-Matplotlib Exercises - Solutions.ipynb</td>\n",
       "      <td>Jupyter Notebook</td>\n",
       "      <td>2018-08-30 18:54:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>.03-NLP Project - Solutions.ipynb</td>\n",
       "      <td>Jupyter Notebook</td>\n",
       "      <td>2018-08-30 18:54:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>5528</th>\n",
       "      <td>goodreads.ipynb</td>\n",
       "      <td>Jupyter Notebook</td>\n",
       "      <td>2021-07-15 07:05:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>424</th>\n",
       "      <td>ISS_sky-checkpoint.ipynb</td>\n",
       "      <td>Jupyter Notebook</td>\n",
       "      <td>2021-07-15 11:44:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>421</th>\n",
       "      <td>ISS_sky.ipynb</td>\n",
       "      <td>Jupyter Notebook</td>\n",
       "      <td>2021-07-15 11:44:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6641</th>\n",
       "      <td>Untitled-checkpoint.ipynb</td>\n",
       "      <td>Jupyter Notebook</td>\n",
       "      <td>2021-07-17 19:47:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4922</th>\n",
       "      <td>Untitled.ipynb</td>\n",
       "      <td>Jupyter Notebook</td>\n",
       "      <td>2021-07-17 19:49:51</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>548 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   File         File Type  \\\n",
       "100   .01-NLP (Natural Language Processing) with Pyt...  Jupyter Notebook   \n",
       "106               .01-Matplotlib Concepts Lecture.ipynb  Jupyter Notebook   \n",
       "105              .04-Advanced Matplotlib Concepts.ipynb  Jupyter Notebook   \n",
       "104          .03-Matplotlib Exercises - Solutions.ipynb  Jupyter Notebook   \n",
       "101                   .03-NLP Project - Solutions.ipynb  Jupyter Notebook   \n",
       "...                                                 ...               ...   \n",
       "5528                                    goodreads.ipynb  Jupyter Notebook   \n",
       "424                            ISS_sky-checkpoint.ipynb  Jupyter Notebook   \n",
       "421                                       ISS_sky.ipynb  Jupyter Notebook   \n",
       "6641                          Untitled-checkpoint.ipynb  Jupyter Notebook   \n",
       "4922                                     Untitled.ipynb  Jupyter Notebook   \n",
       "\n",
       "                    Date  \n",
       "100  2018-08-30 18:54:18  \n",
       "106  2018-08-30 18:54:18  \n",
       "105  2018-08-30 18:54:18  \n",
       "104  2018-08-30 18:54:18  \n",
       "101  2018-08-30 18:54:18  \n",
       "...                  ...  \n",
       "5528 2021-07-15 07:05:08  \n",
       "424  2021-07-15 11:44:57  \n",
       "421  2021-07-15 11:44:57  \n",
       "6641 2021-07-17 19:47:48  \n",
       "4922 2021-07-17 19:49:51  \n",
       "\n",
       "[548 rows x 3 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_python_jupyter_dates_sorted = df_python_jupyter_dates.sort_values(by=['Date'])\n",
    "df_python_vs_dates_sorted = df_python_vs_dates.sort_values(by=['Date'])\n",
    "\n",
    "df_python_vs_dates_sorted\n",
    "df_python_jupyter_dates_sorted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "a831be1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>File</th>\n",
       "      <th>File Type</th>\n",
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       "    <tr>\n",
       "      <th>Date</th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-03-30 13:51:24</th>\n",
       "      <td>characterCount.py</td>\n",
       "      <td>Python file</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-30 13:51:24</th>\n",
       "      <td>birthdays.py</td>\n",
       "      <td>Python file</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-30 13:51:24</th>\n",
       "      <td>errorExample.py</td>\n",
       "      <td>Python file</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-30 13:51:24</th>\n",
       "      <td>buggyAddingProgram.py</td>\n",
       "      <td>Python file</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-30 13:51:24</th>\n",
       "      <td>coinFlip.py</td>\n",
       "      <td>Python file</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-10 13:35:28</th>\n",
       "      <td>quotes_app.py</td>\n",
       "      <td>Python file</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-11 17:38:28</th>\n",
       "      <td>main.py</td>\n",
       "      <td>Python file</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-17 19:10:12</th>\n",
       "      <td>data.py</td>\n",
       "      <td>Python file</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-17 19:17:25</th>\n",
       "      <td>ui.py</td>\n",
       "      <td>Python file</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-17 19:18:22</th>\n",
       "      <td>main.py</td>\n",
       "      <td>Python file</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>938 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      File    File Type\n",
       "Date                                                   \n",
       "2015-03-30 13:51:24      characterCount.py  Python file\n",
       "2015-03-30 13:51:24           birthdays.py  Python file\n",
       "2015-03-30 13:51:24        errorExample.py  Python file\n",
       "2015-03-30 13:51:24  buggyAddingProgram.py  Python file\n",
       "2015-03-30 13:51:24            coinFlip.py  Python file\n",
       "...                                    ...          ...\n",
       "2021-07-10 13:35:28          quotes_app.py  Python file\n",
       "2021-07-11 17:38:28                main.py  Python file\n",
       "2021-07-17 19:10:12                data.py  Python file\n",
       "2021-07-17 19:17:25                  ui.py  Python file\n",
       "2021-07-17 19:18:22                main.py  Python file\n",
       "\n",
       "[938 rows x 2 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_python_jupyter_dates_sorted.set_index('Date')\n",
    "df_python_vs_dates_sorted.set_index('Date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e537cce9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "53c55240",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Date</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>.01-NLP (Natural Language Processing) with Pyt...</td>\n",
       "      <td>Jupyter Notebook</td>\n",
       "      <td>2018-08-30 18:54:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>.01-Matplotlib Concepts Lecture.ipynb</td>\n",
       "      <td>Jupyter Notebook</td>\n",
       "      <td>2018-08-30 18:54:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>.04-Advanced Matplotlib Concepts.ipynb</td>\n",
       "      <td>Jupyter Notebook</td>\n",
       "      <td>2018-08-30 18:54:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>.03-Matplotlib Exercises - Solutions.ipynb</td>\n",
       "      <td>Jupyter Notebook</td>\n",
       "      <td>2018-08-30 18:54:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>.03-NLP Project - Solutions.ipynb</td>\n",
       "      <td>Jupyter Notebook</td>\n",
       "      <td>2018-08-30 18:54:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <th>5528</th>\n",
       "      <td>goodreads.ipynb</td>\n",
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       "      <th>424</th>\n",
       "      <td>ISS_sky-checkpoint.ipynb</td>\n",
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       "      <td>2021-07-15 11:44:57</td>\n",
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       "    <tr>\n",
       "      <th>421</th>\n",
       "      <td>ISS_sky.ipynb</td>\n",
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       "      <td>2021-07-15 11:44:57</td>\n",
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       "      <th>6641</th>\n",
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       "      <td>2021-07-17 19:47:48</td>\n",
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       "      <th>4922</th>\n",
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       "      <td>2021-07-17 19:49:51</td>\n",
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       "<p>548 rows × 3 columns</p>\n",
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       "                                                   File         File Type  \\\n",
       "100   .01-NLP (Natural Language Processing) with Pyt...  Jupyter Notebook   \n",
       "106               .01-Matplotlib Concepts Lecture.ipynb  Jupyter Notebook   \n",
       "105              .04-Advanced Matplotlib Concepts.ipynb  Jupyter Notebook   \n",
       "104          .03-Matplotlib Exercises - Solutions.ipynb  Jupyter Notebook   \n",
       "101                   .03-NLP Project - Solutions.ipynb  Jupyter Notebook   \n",
       "...                                                 ...               ...   \n",
       "5528                                    goodreads.ipynb  Jupyter Notebook   \n",
       "424                            ISS_sky-checkpoint.ipynb  Jupyter Notebook   \n",
       "421                                       ISS_sky.ipynb  Jupyter Notebook   \n",
       "6641                          Untitled-checkpoint.ipynb  Jupyter Notebook   \n",
       "4922                                     Untitled.ipynb  Jupyter Notebook   \n",
       "\n",
       "                    Date  \n",
       "100  2018-08-30 18:54:18  \n",
       "106  2018-08-30 18:54:18  \n",
       "105  2018-08-30 18:54:18  \n",
       "104  2018-08-30 18:54:18  \n",
       "101  2018-08-30 18:54:18  \n",
       "...                  ...  \n",
       "5528 2021-07-15 07:05:08  \n",
       "424  2021-07-15 11:44:57  \n",
       "421  2021-07-15 11:44:57  \n",
       "6641 2021-07-17 19:47:48  \n",
       "4922 2021-07-17 19:49:51  \n",
       "\n",
       "[548 rows x 3 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_python_vs_dates_sorted\n",
    "df_python_jupyter_dates_sorted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "fbf61ca5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# First, reformat the Date variable so it reads month/day/Year\n",
    "df_python_jupyter_dates_sorted['Date'] = pd.to_datetime(df_python_jupyter_dates_sorted['Date'].dt.strftime('%m/%d/%Y'))\n",
    "df_python_vs_dates_sorted['Date'] = pd.to_datetime(df_python_vs_dates_sorted['Date'].dt.strftime('%m/%d/%Y'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "56457e43",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Then call value_counts and convert this to a new DataFrame\n",
    "\n",
    "df_python_vs_dates_sorted['Date'] = pd.to_datetime(df_python_vs_dates_sorted['Date'].dt.strftime('%m/%d/%Y'))\n",
    "df_python_vs_dates_sorted_toframe = df_python_vs_dates_sorted['Date'].value_counts().to_frame()\n",
    "\n",
    "df_python_jupyter_dates_sorted['Date'] = pd.to_datetime(df_python_jupyter_dates_sorted['Date'].dt.strftime('%m/%d/%Y'))\n",
    "df_python_jupyter_dates_sorted_toframe = df_python_jupyter_dates_sorted['Date'].value_counts().to_frame()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "bd6c2678",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Then I reset the index and rename the labels\n",
    "df_jupyter_raw = df_python_jupyter_dates_sorted_toframe.reset_index()\n",
    "df_vs_raw = df_python_vs_dates_sorted_toframe.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "c269a528",
   "metadata": {},
   "outputs": [],
   "source": [
    "# And rename the column labels\n",
    "df_jupyter = df_jupyter_raw.rename(columns={'index':'Date', 'Date':'Files'})\n",
    "df_jupyter_output = df_jupyter.sort_values(by='Date')\n",
    "\n",
    "df_vs = df_vs_raw.rename(columns={'index':'Date', 'Date':'Files'})\n",
    "df_vs_output = df_vs.sort_values(by='Date')\n",
    "df_vs_output_ = df_vs_output[df_vs_output['Date'] > '2018-08-27']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "d56a03e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>2019-09-02</td>\n",
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      ],
      "text/plain": [
       "          Date  Files\n",
       "0   2018-08-30     69\n",
       "7   2018-11-14     10\n",
       "105 2019-09-01      1\n",
       "46  2019-09-02      3\n",
       "27  2019-09-07      5"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# DataFrame to be used for visualization\n",
    "df_jupyter_output.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "d7f75a71",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "         Date  Files\n",
       "15 2018-09-27     10\n",
       "7  2018-10-04     14\n",
       "10 2018-10-05     12\n",
       "34 2019-09-21      2\n",
       "33 2019-10-17      2"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_vs_output_.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "65ab211a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>105</th>\n",
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       "      <th>46</th>\n",
       "      <td>2019-09-02</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2019-09-07</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date  Files\n",
       "0   2018-08-30     69\n",
       "7   2018-11-14     10\n",
       "105 2019-09-01      1\n",
       "46  2019-09-02      3\n",
       "27  2019-09-07      5"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_python_jupyter_dates_sorted['Date'] = pd.to_datetime(df_python_jupyter_dates_sorted['Date'].dt.strftime('%m/%d/%Y'))\n",
    "df_python_jupyter_dates_sorted.head(10)\n",
    "\n",
    "df_jupyter_dates_frame = df_python_jupyter_dates_sorted['Date'].value_counts().to_frame()\n",
    "df_jupyter_raw = df_jupyter_dates_frame.reset_index()\n",
    "df_jupyter_raw\n",
    "\n",
    "df_jupyter = df_jupyter_raw.rename(columns={'index':'Date', 'Date':'Files'})\n",
    "df_jupyter_output = df_jupyter.sort_values(by='Date')\n",
    "df_jupyter_output.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "364b2753",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((119, 2), (55, 2))"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_jupyter_output.shape, df_vs_output.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "a3efc6d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>15</th>\n",
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       "      <td>10</td>\n",
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       "      <th>7</th>\n",
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       "      <td>14</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2018-10-05</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2019-09-21</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2019-10-17</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date  Files\n",
       "15 2018-09-27     10\n",
       "7  2018-10-04     14\n",
       "10 2018-10-05     12\n",
       "34 2019-09-21      2\n",
       "33 2019-10-17      2"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_jupyter_output.head()\n",
    "df_vs_output_.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "220a9d00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Graph Jupyter files\n",
    "fig = plt.figure(figsize=(15,10))\n",
    "ax = plt.subplot(111)\n",
    "ax.bar(df_jupyter_output['Date'], df_jupyter_output['Files'], width=7, ec='black')\n",
    "ax.xaxis_date()\n",
    "\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('# of Files uploaded')\n",
    "plt.title('# of Python Files Uploaded (Jupyter Notebook) during the Pandemic')\n",
    "plt.savefig('jupyter_graph.png')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "f1081d60",
   "metadata": {},
   "outputs": [],
   "source": [
    "# \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "4f089b68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Combine the two plots\n",
    "plt.style.use('fivethirtyeight')\n",
    "fig = plt.figure(figsize=(15,10))\n",
    "ax = plt.subplot(111)\n",
    "ax.bar(df_vs_output_['Date'], df_vs_output_['Files'], width=7, ec='black', color='red')\n",
    "ax.bar(df_jupyter_output['Date'], df_jupyter_output['Files'], width=7, ec='black', color='blue')\n",
    "ax.xaxis_date()\n",
    "\n",
    "\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('# of Files uploaded')\n",
    "plt.title('# of Python Files Uploaded (Visual Studio Code & Jupyter Notebook) during the Pandemic')\n",
    "plt.savefig('python_graph.png')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "005a8d6d",
   "metadata": {},
   "source": [
    "## Bring in COVID dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "e8474c62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "110899    2020-01-22\n",
       "110900    2020-01-23\n",
       "110901    2020-01-24\n",
       "110902    2020-01-25\n",
       "110903    2020-01-26\n",
       "             ...    \n",
       "111497    2021-09-11\n",
       "111498    2021-09-12\n",
       "111499    2021-09-13\n",
       "111500    2021-09-14\n",
       "111501    2021-09-15\n",
       "Name: date, Length: 603, dtype: object"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cov_df_raw_usa_new_cases = cov_df_raw_usa[['date', 'new_cases']]\n",
    "cov_df_raw_usa_new_cases['date']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "d6a23e96",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cov_df_raw_usa_new_cases['date'] = pd.to_datetime(cov_df_raw_usa_new_cases['date'], format='%Y-%m-%d')\n",
    "\n",
    "x = cov_df_raw_usa_new_cases['date']\n",
    "y = cov_df_raw_usa_new_cases['new_cases']\n",
    "\n",
    "plt.bar(x,y, color='blue')\n",
    "\n",
    "\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('# of new cases')\n",
    "plt.title('Covid new case data')\n",
    "#plt.savefig('covid_graph.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "cce99133",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x1080 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Combine the two plots and COVID dataset\n",
    "plt.style.use('fivethirtyeight')\n",
    "fig = plt.figure(figsize=(25,15))\n",
    "ax = plt.subplot(111)\n",
    "ax.bar(df_vs_output_['Date'], df_vs_output_['Files'], width=7, ec='black', color='red')\n",
    "ax.bar(df_jupyter_output['Date'], df_jupyter_output['Files'], width=7, ec='black', color='blue')\n",
    "ax.xaxis_date()\n",
    "\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('# of Files uploaded')\n",
    "plt.title('# of Python Files Uploaded (Visual Studio Code & Jupyter Notebook) during the Pandemic')\n",
    "plt.savefig('python_graph.png')\n",
    "\n",
    "# twin objecty\n",
    "ax2 = ax.twinx()\n",
    "ax2.plot(cov_df_raw_usa_new_cases['date'],cov_df_raw_usa_new_cases['new_cases'])\n",
    "ax2.xaxis_date()\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "44ddeabb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "5a6e6701",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1296x864 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Combine the two plots and COVID dataset\n",
    "\n",
    "fig = plt.figure(figsize=(18,12))\n",
    "ax = plt.subplot(111)\n",
    "\n",
    "df_vs_output__ = df_vs_output_[df_vs_output_['Date'] > '2020-03-12']\n",
    "df_jupyter_output_ = df_jupyter_output[df_jupyter_output['Date'] > '2020-03-12']\n",
    "\n",
    "\n",
    "ax.bar(df_vs_output__['Date'], df_vs_output__['Files'], width=7, ec='black', color='red')\n",
    "ax.bar(df_jupyter_output_['Date'], df_jupyter_output_['Files'], width=7, ec='black', color='blue')\n",
    "ax.xaxis_date()\n",
    "ax.axhline(0)\n",
    "\n",
    "plt.xlabel('Date', fontsize=18)\n",
    "plt.ylabel('# of Files uploaded', fontsize=18)\n",
    "plt.title('How did the COVID case count in the United States impact the # of Python files I created?', fontsize=18)\n",
    "plt.savefig('python_covid.png')\n",
    "\n",
    "# twin objecty\n",
    "ax2 = ax.twinx()\n",
    "ax2.plot(cov_df_raw_usa_new_cases['date'],cov_df_raw_usa_new_cases['new_cases'])\n",
    "ax2.xaxis_date()\n",
    "\n",
    "\n",
    "\n",
    "plt.tight_layout()\n",
    "mpl_axes_aligner.align.yaxes(ax, 0, ax2, 0, 0.1)\n",
    "plt.savefig('python_covid.png')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4d8f01c2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cc605a08",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "228e3e71",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ffc4a4ff",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "b6664d3f",
   "metadata": {},
   "source": [
    "## Count the files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2ab8c4cb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2114c765",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Visual Studio Code - count # of files pre and post 3/12\n",
    "df_vs_output_.head()\n",
    "df_vs_output_pre = df_vs_output_.copy()\n",
    "df_vs_output_pre = df_vs_output_pre[df_vs_output_pre['Date'] <='2020-03-12'] # files pre-COVID\n",
    "df_vs_output_pre.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a2ec2745",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_vs_output_post = df_vs_output_.copy()\n",
    "df_vs_output_post = df_vs_output_post[df_vs_output_post['Date'] > '2020-03-12']\n",
    "df_vs_output_post.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "00c33cf3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Jupyter Notebook - count # of files pre and post 3/12\n",
    "df_jupyter.head()\n",
    "df_jupyter_output_pre = df_jupyter_output.copy()\n",
    "df_jupyter_output_pre = df_jupyter_output_pre[df_jupyter_output_pre['Date'] <='2020-03-12'] # files pre-COVID\n",
    "df_jupyter_output_pre.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f35e6227",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Jupyter Notebook - count # of files pre and post 3/12\n",
    "df_jupyter.head()\n",
    "df_jupyter_output_post = df_jupyter_output.copy()\n",
    "df_jupyter_output_post = df_jupyter_output_post[df_jupyter_output_post['Date'] >'2020-03-12'] # files pre-COVID\n",
    "df_jupyter_output_post.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c71cb45f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 4 dataframes, 1 each for pre and post '3/12/2020' for VS Code and Jupyter Notebook\n",
    "df_jupyter_output_pre_files = df_jupyter_output_pre['Files'].sum()\n",
    "df_jupyter_output_post_files = df_jupyter_output_post['Files'].sum()\n",
    "\n",
    "df_vs_output_pre_files = df_vs_output_pre['Files'].sum()\n",
    "df_vs_output_post_files = df_vs_output_post['Files'].sum()\n",
    "\n",
    "print('Jupyter Files, pre 3/12/20210: ', df_jupyter_output_pre_files)\n",
    "print('Jupyter Files, post 3/12/2020: ', df_jupyter_output_post_files)\n",
    "print('VS Code Files, pre 3/12/20210: ', df_vs_output_pre_files)\n",
    "print('VS Code Files, post 3/12/2020: ', df_vs_output_post_files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "51696aa8",
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_df_filtered_vs_.head()\n",
    "raw_df_filtered_jupyter.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "11720efd",
   "metadata": {},
   "outputs": [],
   "source": [
    "filenames_vs = raw_df_filtered_vs_['Path'].astype(str).to_list()\n",
    "filenames_jupyter = raw_df_filtered_jupyter['Path'].astype(str).to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "23dad8a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "len(filenames_vs), len(filenames_jupyter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c5e0e93c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# of lines for VS code\n",
    "line_count = 0\n",
    "for file in filenames_vs:\n",
    "    file_open = open(file)\n",
    "    for line in file:\n",
    "        line_count += 1\n",
    "    file_open.close()\n",
    "print(line_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a8a57849",
   "metadata": {},
   "outputs": [],
   "source": [
    "# of lines for jupyter\n",
    "line_count = 0\n",
    "for file in filenames_jupyter:\n",
    "    file_open = open(file)\n",
    "    for line in file:\n",
    "        line_count += 1\n",
    "    file_open.close()\n",
    "print(line_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "871db844",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c3c53859",
   "metadata": {},
   "outputs": [],
   "source": [
    "# of words for VS\n",
    "\n",
    "number_of_words = 0\n",
    "for file in filenames_vs:\n",
    "    file_open = open(file)\n",
    "    for line in file:\n",
    "        words = line.split()\n",
    "        number_of_words += len(words)\n",
    "    file_open.close()\n",
    "print(number_of_words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "07f6252f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# of words for Jupyter\n",
    "\n",
    "number_of_words = 0\n",
    "for file in filenames_jupyter:\n",
    "    file_open = open(file)\n",
    "    for line in file:\n",
    "        words = line.split()\n",
    "        number_of_words += len(words)\n",
    "    file_open.close()\n",
    "print(number_of_words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "111c0f1a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "e1410b54",
   "metadata": {},
   "source": [
    "## Analysis by days of the week"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7989a16f",
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_df_filtered_vs_.shape\n",
    "raw_df_filtered_jupyter.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0bd7edae",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "871da526",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "03365a90",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1cf3f793",
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_df_filtered_vs_.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8f5138a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_df_filtered_vs_2= raw_df_filtered_vs_[raw_df_filtered_vs_['Date'] >'2018-08-11 00:00:00']\n",
    "raw_df_filtered_vs_2\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ab83a0a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_df_filtered_vs_2['Date'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "92112238",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Jupyter\n",
    "raw_df_jupyter_date = raw_df_filtered_jupyter[['File', 'Date']]\n",
    "raw_df_jupyter_date['Date'] = pd.to_datetime(raw_df_jupyter_date[\"Date\"], format = '%m/%d/%Y :: %H:%M:%S')\n",
    "\n",
    "raw_df_jupyter_date['Day'] = raw_df_jupyter_date['Date'].dt.day_name()\n",
    "raw_df_jupyter_date.style.set_caption(\"Jupyter Notebook\")\n",
    "\n",
    "raw_df_jupyter_date.groupby(['Day']).count()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6ea0c713",
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_df_filtered_vs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "62f6d8aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# VS\n",
    "raw_df_filtered_vs_3 = raw_df_filtered_vs_2.loc[(raw_df_filtered_vs['Date'] >= '08/22/2019 :: 0:0:0')]\n",
    "raw_df_filtered_vs_3.shape\n",
    "raw_df_filtered_vs_3['Date'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8203f7dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# VS\n",
    "raw_df_vs_date = raw_df_filtered_vs_3[['File', 'Date']]\n",
    "raw_df_vs_date['Date'] = pd.to_datetime(raw_df_vs_date[\"Date\"], format = '%m/%d/%Y :: %H:%M:%S')\n",
    "\n",
    "raw_df_vs_date['Day'] = raw_df_vs_date['Date'].dt.day_name()\n",
    "raw_df_vs_date.style.set_caption(\"VS Code\")\n",
    "\n",
    "raw_df_vs_date.groupby(['Day']).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c9ea4371",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "c980a033",
   "metadata": {},
   "source": [
    "## Pull in Covid data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "3796e62d",
   "metadata": {},
   "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>iso_code</th>\n",
       "      <th>continent</th>\n",
       "      <th>location</th>\n",
       "      <th>date</th>\n",
       "      <th>total_cases</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>new_cases_smoothed</th>\n",
       "      <th>total_deaths</th>\n",
       "      <th>new_deaths</th>\n",
       "      <th>new_deaths_smoothed</th>\n",
       "      <th>...</th>\n",
       "      <th>extreme_poverty</th>\n",
       "      <th>cardiovasc_death_rate</th>\n",
       "      <th>diabetes_prevalence</th>\n",
       "      <th>female_smokers</th>\n",
       "      <th>male_smokers</th>\n",
       "      <th>handwashing_facilities</th>\n",
       "      <th>hospital_beds_per_thousand</th>\n",
       "      <th>life_expectancy</th>\n",
       "      <th>human_development_index</th>\n",
       "      <th>excess_mortality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-02-24</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>597.029</td>\n",
       "      <td>9.59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.746</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "      <td>0.511</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-02-25</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>597.029</td>\n",
       "      <td>9.59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.746</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "      <td>0.511</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-02-26</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>597.029</td>\n",
       "      <td>9.59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.746</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "      <td>0.511</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>597.029</td>\n",
       "      <td>9.59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.746</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "      <td>0.511</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-02-28</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>597.029</td>\n",
       "      <td>9.59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.746</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "      <td>0.511</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 62 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  iso_code continent     location        date  total_cases  new_cases  \\\n",
       "0      AFG      Asia  Afghanistan  2020-02-24          5.0        5.0   \n",
       "1      AFG      Asia  Afghanistan  2020-02-25          5.0        0.0   \n",
       "2      AFG      Asia  Afghanistan  2020-02-26          5.0        0.0   \n",
       "3      AFG      Asia  Afghanistan  2020-02-27          5.0        0.0   \n",
       "4      AFG      Asia  Afghanistan  2020-02-28          5.0        0.0   \n",
       "\n",
       "   new_cases_smoothed  total_deaths  new_deaths  new_deaths_smoothed  ...  \\\n",
       "0                 NaN           NaN         NaN                  NaN  ...   \n",
       "1                 NaN           NaN         NaN                  NaN  ...   \n",
       "2                 NaN           NaN         NaN                  NaN  ...   \n",
       "3                 NaN           NaN         NaN                  NaN  ...   \n",
       "4                 NaN           NaN         NaN                  NaN  ...   \n",
       "\n",
       "   extreme_poverty  cardiovasc_death_rate  diabetes_prevalence  \\\n",
       "0              NaN                597.029                 9.59   \n",
       "1              NaN                597.029                 9.59   \n",
       "2              NaN                597.029                 9.59   \n",
       "3              NaN                597.029                 9.59   \n",
       "4              NaN                597.029                 9.59   \n",
       "\n",
       "   female_smokers  male_smokers  handwashing_facilities  \\\n",
       "0             NaN           NaN                  37.746   \n",
       "1             NaN           NaN                  37.746   \n",
       "2             NaN           NaN                  37.746   \n",
       "3             NaN           NaN                  37.746   \n",
       "4             NaN           NaN                  37.746   \n",
       "\n",
       "   hospital_beds_per_thousand  life_expectancy  human_development_index  \\\n",
       "0                         0.5            64.83                    0.511   \n",
       "1                         0.5            64.83                    0.511   \n",
       "2                         0.5            64.83                    0.511   \n",
       "3                         0.5            64.83                    0.511   \n",
       "4                         0.5            64.83                    0.511   \n",
       "\n",
       "   excess_mortality  \n",
       "0               NaN  \n",
       "1               NaN  \n",
       "2               NaN  \n",
       "3               NaN  \n",
       "4               NaN  \n",
       "\n",
       "[5 rows x 62 columns]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_df_raw = pd.read_csv('owid-covid-data.csv')\n",
    "covid_df_raw.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "145007ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "# explore the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "cb5a4b98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Africa           29731\n",
       "Europe           27481\n",
       "Asia             27296\n",
       "North America    14989\n",
       "South America     6896\n",
       "Oceania           5275\n",
       "Name: continent, dtype: int64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_df_raw['continent'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "77bbd486",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "United States    603\n",
       "Name: location, dtype: int64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_df_raw[covid_df_raw['location'] == 'United States']['location'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "1857b19c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>iso_code</th>\n",
       "      <th>continent</th>\n",
       "      <th>location</th>\n",
       "      <th>date</th>\n",
       "      <th>total_cases</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>new_cases_smoothed</th>\n",
       "      <th>total_deaths</th>\n",
       "      <th>new_deaths</th>\n",
       "      <th>new_deaths_smoothed</th>\n",
       "      <th>...</th>\n",
       "      <th>extreme_poverty</th>\n",
       "      <th>cardiovasc_death_rate</th>\n",
       "      <th>diabetes_prevalence</th>\n",
       "      <th>female_smokers</th>\n",
       "      <th>male_smokers</th>\n",
       "      <th>handwashing_facilities</th>\n",
       "      <th>hospital_beds_per_thousand</th>\n",
       "      <th>life_expectancy</th>\n",
       "      <th>human_development_index</th>\n",
       "      <th>excess_mortality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>110939</th>\n",
       "      <td>USA</td>\n",
       "      <td>North America</td>\n",
       "      <td>United States</td>\n",
       "      <td>2020-03-02</td>\n",
       "      <td>55.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>5.571</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.857</td>\n",
       "      <td>...</td>\n",
       "      <td>1.2</td>\n",
       "      <td>151.089</td>\n",
       "      <td>10.79</td>\n",
       "      <td>19.1</td>\n",
       "      <td>24.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.77</td>\n",
       "      <td>78.86</td>\n",
       "      <td>0.926</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110940</th>\n",
       "      <td>USA</td>\n",
       "      <td>North America</td>\n",
       "      <td>United States</td>\n",
       "      <td>2020-03-03</td>\n",
       "      <td>74.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>8.286</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.2</td>\n",
       "      <td>151.089</td>\n",
       "      <td>10.79</td>\n",
       "      <td>19.1</td>\n",
       "      <td>24.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.77</td>\n",
       "      <td>78.86</td>\n",
       "      <td>0.926</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110941</th>\n",
       "      <td>USA</td>\n",
       "      <td>North America</td>\n",
       "      <td>United States</td>\n",
       "      <td>2020-03-04</td>\n",
       "      <td>107.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>13.000</td>\n",
       "      <td>11.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.571</td>\n",
       "      <td>...</td>\n",
       "      <td>1.2</td>\n",
       "      <td>151.089</td>\n",
       "      <td>10.79</td>\n",
       "      <td>19.1</td>\n",
       "      <td>24.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.77</td>\n",
       "      <td>78.86</td>\n",
       "      <td>0.926</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110942</th>\n",
       "      <td>USA</td>\n",
       "      <td>North America</td>\n",
       "      <td>United States</td>\n",
       "      <td>2020-03-05</td>\n",
       "      <td>184.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>23.857</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.714</td>\n",
       "      <td>...</td>\n",
       "      <td>1.2</td>\n",
       "      <td>151.089</td>\n",
       "      <td>10.79</td>\n",
       "      <td>19.1</td>\n",
       "      <td>24.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.77</td>\n",
       "      <td>78.86</td>\n",
       "      <td>0.926</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110943</th>\n",
       "      <td>USA</td>\n",
       "      <td>North America</td>\n",
       "      <td>United States</td>\n",
       "      <td>2020-03-06</td>\n",
       "      <td>237.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>31.429</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.2</td>\n",
       "      <td>151.089</td>\n",
       "      <td>10.79</td>\n",
       "      <td>19.1</td>\n",
       "      <td>24.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.77</td>\n",
       "      <td>78.86</td>\n",
       "      <td>0.926</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111497</th>\n",
       "      <td>USA</td>\n",
       "      <td>North America</td>\n",
       "      <td>United States</td>\n",
       "      <td>2021-09-11</td>\n",
       "      <td>41021592.0</td>\n",
       "      <td>63965.0</td>\n",
       "      <td>146195.571</td>\n",
       "      <td>659691.0</td>\n",
       "      <td>699.0</td>\n",
       "      <td>1626.286</td>\n",
       "      <td>...</td>\n",
       "      <td>1.2</td>\n",
       "      <td>151.089</td>\n",
       "      <td>10.79</td>\n",
       "      <td>19.1</td>\n",
       "      <td>24.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.77</td>\n",
       "      <td>78.86</td>\n",
       "      <td>0.926</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111498</th>\n",
       "      <td>USA</td>\n",
       "      <td>North America</td>\n",
       "      <td>United States</td>\n",
       "      <td>2021-09-12</td>\n",
       "      <td>41057176.0</td>\n",
       "      <td>35584.0</td>\n",
       "      <td>145551.857</td>\n",
       "      <td>659970.0</td>\n",
       "      <td>279.0</td>\n",
       "      <td>1614.000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.2</td>\n",
       "      <td>151.089</td>\n",
       "      <td>10.79</td>\n",
       "      <td>19.1</td>\n",
       "      <td>24.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.77</td>\n",
       "      <td>78.86</td>\n",
       "      <td>0.926</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111499</th>\n",
       "      <td>USA</td>\n",
       "      <td>North America</td>\n",
       "      <td>United States</td>\n",
       "      <td>2021-09-13</td>\n",
       "      <td>41317328.0</td>\n",
       "      <td>260152.0</td>\n",
       "      <td>172001.571</td>\n",
       "      <td>662106.0</td>\n",
       "      <td>2136.0</td>\n",
       "      <td>1843.286</td>\n",
       "      <td>...</td>\n",
       "      <td>1.2</td>\n",
       "      <td>151.089</td>\n",
       "      <td>10.79</td>\n",
       "      <td>19.1</td>\n",
       "      <td>24.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.77</td>\n",
       "      <td>78.86</td>\n",
       "      <td>0.926</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111500</th>\n",
       "      <td>USA</td>\n",
       "      <td>North America</td>\n",
       "      <td>United States</td>\n",
       "      <td>2021-09-14</td>\n",
       "      <td>41461662.0</td>\n",
       "      <td>144334.0</td>\n",
       "      <td>153146.714</td>\n",
       "      <td>663929.0</td>\n",
       "      <td>1823.0</td>\n",
       "      <td>1805.000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.2</td>\n",
       "      <td>151.089</td>\n",
       "      <td>10.79</td>\n",
       "      <td>19.1</td>\n",
       "      <td>24.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.77</td>\n",
       "      <td>78.86</td>\n",
       "      <td>0.926</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111501</th>\n",
       "      <td>USA</td>\n",
       "      <td>North America</td>\n",
       "      <td>United States</td>\n",
       "      <td>2021-09-15</td>\n",
       "      <td>41627946.0</td>\n",
       "      <td>166284.0</td>\n",
       "      <td>150968.286</td>\n",
       "      <td>666607.0</td>\n",
       "      <td>2678.0</td>\n",
       "      <td>1880.429</td>\n",
       "      <td>...</td>\n",
       "      <td>1.2</td>\n",
       "      <td>151.089</td>\n",
       "      <td>10.79</td>\n",
       "      <td>19.1</td>\n",
       "      <td>24.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.77</td>\n",
       "      <td>78.86</td>\n",
       "      <td>0.926</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>563 rows × 62 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       iso_code      continent       location        date  total_cases  \\\n",
       "110939      USA  North America  United States  2020-03-02         55.0   \n",
       "110940      USA  North America  United States  2020-03-03         74.0   \n",
       "110941      USA  North America  United States  2020-03-04        107.0   \n",
       "110942      USA  North America  United States  2020-03-05        184.0   \n",
       "110943      USA  North America  United States  2020-03-06        237.0   \n",
       "...         ...            ...            ...         ...          ...   \n",
       "111497      USA  North America  United States  2021-09-11   41021592.0   \n",
       "111498      USA  North America  United States  2021-09-12   41057176.0   \n",
       "111499      USA  North America  United States  2021-09-13   41317328.0   \n",
       "111500      USA  North America  United States  2021-09-14   41461662.0   \n",
       "111501      USA  North America  United States  2021-09-15   41627946.0   \n",
       "\n",
       "        new_cases  new_cases_smoothed  total_deaths  new_deaths  \\\n",
       "110939       23.0               5.571           6.0         5.0   \n",
       "110940       19.0               8.286           7.0         1.0   \n",
       "110941       33.0              13.000          11.0         4.0   \n",
       "110942       77.0              23.857          12.0         1.0   \n",
       "110943       53.0              31.429          14.0         2.0   \n",
       "...           ...                 ...           ...         ...   \n",
       "111497    63965.0          146195.571      659691.0       699.0   \n",
       "111498    35584.0          145551.857      659970.0       279.0   \n",
       "111499   260152.0          172001.571      662106.0      2136.0   \n",
       "111500   144334.0          153146.714      663929.0      1823.0   \n",
       "111501   166284.0          150968.286      666607.0      2678.0   \n",
       "\n",
       "        new_deaths_smoothed  ...  extreme_poverty  cardiovasc_death_rate  \\\n",
       "110939                0.857  ...              1.2                151.089   \n",
       "110940                1.000  ...              1.2                151.089   \n",
       "110941                1.571  ...              1.2                151.089   \n",
       "110942                1.714  ...              1.2                151.089   \n",
       "110943                2.000  ...              1.2                151.089   \n",
       "...                     ...  ...              ...                    ...   \n",
       "111497             1626.286  ...              1.2                151.089   \n",
       "111498             1614.000  ...              1.2                151.089   \n",
       "111499             1843.286  ...              1.2                151.089   \n",
       "111500             1805.000  ...              1.2                151.089   \n",
       "111501             1880.429  ...              1.2                151.089   \n",
       "\n",
       "        diabetes_prevalence  female_smokers  male_smokers  \\\n",
       "110939                10.79            19.1          24.6   \n",
       "110940                10.79            19.1          24.6   \n",
       "110941                10.79            19.1          24.6   \n",
       "110942                10.79            19.1          24.6   \n",
       "110943                10.79            19.1          24.6   \n",
       "...                     ...             ...           ...   \n",
       "111497                10.79            19.1          24.6   \n",
       "111498                10.79            19.1          24.6   \n",
       "111499                10.79            19.1          24.6   \n",
       "111500                10.79            19.1          24.6   \n",
       "111501                10.79            19.1          24.6   \n",
       "\n",
       "        handwashing_facilities  hospital_beds_per_thousand  life_expectancy  \\\n",
       "110939                     NaN                        2.77            78.86   \n",
       "110940                     NaN                        2.77            78.86   \n",
       "110941                     NaN                        2.77            78.86   \n",
       "110942                     NaN                        2.77            78.86   \n",
       "110943                     NaN                        2.77            78.86   \n",
       "...                        ...                         ...              ...   \n",
       "111497                     NaN                        2.77            78.86   \n",
       "111498                     NaN                        2.77            78.86   \n",
       "111499                     NaN                        2.77            78.86   \n",
       "111500                     NaN                        2.77            78.86   \n",
       "111501                     NaN                        2.77            78.86   \n",
       "\n",
       "        human_development_index  excess_mortality  \n",
       "110939                    0.926               NaN  \n",
       "110940                    0.926               NaN  \n",
       "110941                    0.926               NaN  \n",
       "110942                    0.926               NaN  \n",
       "110943                    0.926               NaN  \n",
       "...                         ...               ...  \n",
       "111497                    0.926               NaN  \n",
       "111498                    0.926               NaN  \n",
       "111499                    0.926               NaN  \n",
       "111500                    0.926               NaN  \n",
       "111501                    0.926               NaN  \n",
       "\n",
       "[563 rows x 62 columns]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cov_df_raw_usa = covid_df_raw[covid_df_raw['location'] == 'United States']\n",
    "cov_df_raw_usa_ = cov_df_raw_usa[cov_df_raw_usa['date'] > '2020-03-01']\n",
    "cov_df_raw_usa_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1a237811",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "d8b19759",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>File</th>\n",
       "      <th>File Type</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>.01-NLP (Natural Language Processing) with Pyt...</td>\n",
       "      <td>Jupyter Notebook</td>\n",
       "      <td>2018-08-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>.01-Matplotlib Concepts Lecture.ipynb</td>\n",
       "      <td>Jupyter Notebook</td>\n",
       "      <td>2018-08-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>.04-Advanced Matplotlib Concepts.ipynb</td>\n",
       "      <td>Jupyter Notebook</td>\n",
       "      <td>2018-08-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>.03-Matplotlib Exercises - Solutions.ipynb</td>\n",
       "      <td>Jupyter Notebook</td>\n",
       "      <td>2018-08-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>.03-NLP Project - Solutions.ipynb</td>\n",
       "      <td>Jupyter Notebook</td>\n",
       "      <td>2018-08-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5528</th>\n",
       "      <td>goodreads.ipynb</td>\n",
       "      <td>Jupyter Notebook</td>\n",
       "      <td>2021-07-15</td>\n",
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       "    <tr>\n",
       "      <th>424</th>\n",
       "      <td>ISS_sky-checkpoint.ipynb</td>\n",
       "      <td>Jupyter Notebook</td>\n",
       "      <td>2021-07-15</td>\n",
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       "    <tr>\n",
       "      <th>421</th>\n",
       "      <td>ISS_sky.ipynb</td>\n",
       "      <td>Jupyter Notebook</td>\n",
       "      <td>2021-07-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6641</th>\n",
       "      <td>Untitled-checkpoint.ipynb</td>\n",
       "      <td>Jupyter Notebook</td>\n",
       "      <td>2021-07-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4922</th>\n",
       "      <td>Untitled.ipynb</td>\n",
       "      <td>Jupyter Notebook</td>\n",
       "      <td>2021-07-17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>548 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   File         File Type  \\\n",
       "100   .01-NLP (Natural Language Processing) with Pyt...  Jupyter Notebook   \n",
       "106               .01-Matplotlib Concepts Lecture.ipynb  Jupyter Notebook   \n",
       "105              .04-Advanced Matplotlib Concepts.ipynb  Jupyter Notebook   \n",
       "104          .03-Matplotlib Exercises - Solutions.ipynb  Jupyter Notebook   \n",
       "101                   .03-NLP Project - Solutions.ipynb  Jupyter Notebook   \n",
       "...                                                 ...               ...   \n",
       "5528                                    goodreads.ipynb  Jupyter Notebook   \n",
       "424                            ISS_sky-checkpoint.ipynb  Jupyter Notebook   \n",
       "421                                       ISS_sky.ipynb  Jupyter Notebook   \n",
       "6641                          Untitled-checkpoint.ipynb  Jupyter Notebook   \n",
       "4922                                     Untitled.ipynb  Jupyter Notebook   \n",
       "\n",
       "           Date  \n",
       "100  2018-08-30  \n",
       "106  2018-08-30  \n",
       "105  2018-08-30  \n",
       "104  2018-08-30  \n",
       "101  2018-08-30  \n",
       "...         ...  \n",
       "5528 2021-07-15  \n",
       "424  2021-07-15  \n",
       "421  2021-07-15  \n",
       "6641 2021-07-17  \n",
       "4922 2021-07-17  \n",
       "\n",
       "[548 rows x 3 columns]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.getcwd()\n",
    "os.chdir('/Users/brendan/Library/Mobile Documents/com~apple~CloudDocs/Desktop/Data Science')\n",
    "df_python_jupyter_dates_sorted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "83bdc566",
   "metadata": {},
   "outputs": [],
   "source": [
    "# store the filenames as"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "36049e05",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "949dd1a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir('/Users/brendan/Library/Mobile Documents/com~apple~CloudDocs/Desktop/Data Science')\n",
    "os.getcwd()\n",
    "filenames = df_python_jupyter_dates_sorted['Path'].astype(str).to_list()\n",
    "line_count = 0\n",
    "for file in filenames:\n",
    "    file_open = open(file)\n",
    "    for line in file:\n",
    "        line_count += 1\n",
    "    file_open.close()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6027af02",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e031af7c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "72ccad6f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a75d46fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Graph VS files\n",
    "fig = plt.figure(figsize=(15,10))\n",
    "ax = plt.subplot(111)\n",
    "ax.bar(df_vs_output_['Date'], df_vs_output_['Files'], width=7, ec='black', color='red')\n",
    "ax.xaxis_date()\n",
    "\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('# of Files uploaded')\n",
    "plt.title('# of Python Files Uploaded (Visual Studio Code) during the Pandemic')\n",
    "plt.savefig('vs_graph.png')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f6c1bce8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1210b593",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ecd9eb00",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure(figsize=(15,10))\n",
    "ax = plt.subplot(111)\n",
    "ax.bar(df_jupyter_output['Date'], df_jupyter_output['Files'], width=7, ec='black')\n",
    "ax.xaxis_date()\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('# of Files uploaded')\n",
    "plt.title('# of Python Files Uploaded to Jupyter Notebook during the Pandemic')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "184cba2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "    # split the date by date and time\n",
    "# then count frequyency by day"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3f84364",
   "metadata": {},
   "source": [
    "### For Python - VS Code next"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "48565ae6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_python_vs_dates = df_python_vs[['File', 'File Type', 'Date']]\n",
    "df_python_vs_dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9baf4b70",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_python_vs_dates_sorted = df_python_vs_dates.sort_values(by=['Date'])\n",
    "df_python_vs_dates_sorted['Date'] = pd.to_datetime(df_python_vs_dates_sorted['Date'].dt.strftime('%m/%d/%Y'))\n",
    "df_python_vs_dates_sorted.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f399b2a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_python_vs_dates_sorted['Date'] = pd.to_datetime(df_python_vs_dates_sorted['Date'].dt.strftime('%m/%d/%Y'))\n",
    "df_python_vs_dates_sorted.head(10)\n",
    "\n",
    "df_vs_dates_frame = df_python_vs_dates_sorted['Date'].value_counts().to_frame()\n",
    "#df_jupyter_dates_frame_sorted = df_jupyter_dates_frame.sort_values(by='Date')\n",
    "\n",
    "#df_vs = df_vs_dates_frame.rename(columns={'index':'Date', 'Date':'Files'})\n",
    "#df_vs_output = df_vs.sort_values(by='Date')\n",
    "df_vs_raw = df_vs_dates_frame.reset_index()\n",
    "df_vs_raw\n",
    "\n",
    "df_vs = df_vs_raw.rename(columns={'index':'Date', 'Date':'Files'})\n",
    "df_vs_output = df_vs.sort_values(by='Date')\n",
    "df_vs_output.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "de82eaab",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure(figsize=(15,10))\n",
    "ax = plt.subplot(111)\n",
    "ax.bar(df_vs_output['Date'], df_vs_output['Files'], width=7, ec='black', color='red')\n",
    "ax.xaxis_date()\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('# of Files uploaded')\n",
    "plt.title('# of Python Files Uploaded to Visual Studio during the Pandemic')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3c7dd350",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure(figsize=(10,7))\n",
    "ax = plt.subplot(111)\n",
    "ax.bar(df_jupyter_output['Date'], df_jupyter_output['Files'], width=7, ec='black')\n",
    "ax.bar(df_vs_output['Date'], df_vs_output['Files'], width=7, ec='black', color='red')\n",
    "ax.xaxis_date()\n",
    "\n",
    "ax.xaxis.set_major_locator(mdates.MonthLocator(interval=1))\n",
    "plt.xlabel('Date')\n",
    "plt.xticks(rotation=45, fontsize=8)\n",
    "plt.ylabel('# of Files uploaded')\n",
    "plt.title('# of Python Files Uploaded (Daily) during the Pandemic')\n",
    "plt.legend(['Jupyter', 'Visual Code'])\n",
    "plt.savefig('python_files.jpg')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c6f989b9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f30bf8d7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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