google mobility trend pdf parsing

parse.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from collections import defaultdict\n",
    "from tqdm.auto import tqdm\n",
    "import glob\n",
    "import os\n",
    "import sys\n",
    "\n",
    "# pip3 install --user PyMuPDF\n",
    "import fitz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bash\n",
    "# https://www.google.com/covid19/mobility/\n",
    "mkdir -p pdfs/\n",
    "cd pdfs/\n",
    "\n",
    "states=\"Alabama Alaska Arizona Arkansas California Colorado Connecticut Delaware Florida Georgia Hawaii Idaho Illinois Indiana Iowa Kansas Kentucky Louisiana Maine Maryland Massachusetts Michigan Minnesota Mississippi Missouri Montana Nebraska Nevada New_Hampshire New_Jersey New_Mexico New_York North_Carolina North_Dakota Ohio Oklahoma Oregon Pennsylvania Rhode_Island South_Carolina South_Dakota Tennessee Texas Utah Vermont Virginia Washington West_Virginia Wisconsin Wyoming\"\n",
    "date=\"2020-03-29\"\n",
    "for state in $states ; do\n",
    "    curl -s -O https://www.gstatic.com/covid19/mobility/${date}_US_${state}_Mobility_Report_en.pdf\n",
    "done\n",
    "# rename the full US pdf slightly to conform to similar naming convention as the individual states\n",
    "curl -s -o ${date}_US_US_Mobility_Report_en.pdf https://www.gstatic.com/covid19/mobility/${date}_US_Mobility_Report_en.pdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def parse_stream(stream):\n",
    "    data_raw = []\n",
    "    data_transformed = []\n",
    "    rotparams = None\n",
    "    npatches = 0\n",
    "    for line in stream.splitlines():\n",
    "        if line.endswith(\" cm\"):\n",
    "            # page 146 of https://www.adobe.com/content/dam/acom/en/devnet/pdf/pdfs/pdf_reference_archives/PDFReference.pdf\n",
    "            rotparams = list(map(float,line.split()[:-1]))\n",
    "        elif line.endswith(\" l\"):\n",
    "            x,y = list(map(float,line.split()[:2]))\n",
    "            a,b,c,d,e,f = rotparams\n",
    "            xp = a*x+c*y+e\n",
    "            yp = b*x+d*y+f\n",
    "            data_transformed.append([xp,yp])\n",
    "            data_raw.append([x,y])\n",
    "        elif line.endswith(\" m\"):\n",
    "            npatches += 1\n",
    "        else:\n",
    "            pass\n",
    "    data_raw = np.array(data_raw)\n",
    "    basex, basey = data_raw[-1]\n",
    "    good = False\n",
    "    if basex == 0.:\n",
    "        data_raw[:,1] = basey - data_raw[:,1]\n",
    "        data_raw[:,1] *= 100/60.\n",
    "        data_raw = data_raw[data_raw[:,1]!=0.]\n",
    "        if npatches == 1: good = True\n",
    "    return dict(data=np.array(data_raw), npatches=npatches, good=good)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def parse_page(doc, ipage, verbose=False):\n",
    "    categories = [\n",
    "        \"Retail & recreation\",\n",
    "        \"Grocery & pharmacy\",\n",
    "        \"Parks\",\n",
    "        \"Transit stations\",\n",
    "        \"Workplace\",\n",
    "        \"Residential\",\n",
    "    ]\n",
    "\n",
    "    counties = []\n",
    "    curr_county = None\n",
    "    curr_category = None\n",
    "    data = defaultdict(lambda: defaultdict(list))\n",
    "    pagetext = doc.getPageText(ipage)\n",
    "    lines = pagetext.splitlines()\n",
    "    tickdates = list(filter(lambda x:len(x.split())==3, set(lines[-10:])))\n",
    "    for line in lines:\n",
    "        # don't need these lines at all\n",
    "        if (\"* Not enough data\") in line: continue\n",
    "        if (\"needs a significant volume of data\") in line: continue\n",
    "\n",
    "        # if we encountered a category, add to dict, otherwise\n",
    "        # push all seen lines into the existing dict entry\n",
    "        if any(line.startswith(c) for c in categories):\n",
    "            curr_category = line\n",
    "        elif curr_category:\n",
    "            data[curr_county][curr_category].append(line)\n",
    "\n",
    "        # If it doesn't match anything, then it's a county name\n",
    "        if (all(c not in line for c in categories)\n",
    "            and (\"compared to baseline\" not in line)\n",
    "            and (\"Not enough data\" not in line)\n",
    "           ):\n",
    "            # saw both counties already\n",
    "            if len(data.keys()) == 2: break\n",
    "            counties.append(line)\n",
    "            curr_county = line\n",
    "\n",
    "    newdata = {}\n",
    "    for county in data:\n",
    "        newdata[county] = {}\n",
    "        for category in data[county]:\n",
    "            # if the category text ends with a space, then there was a star/asterisk there\n",
    "            # indicating lack of data. we skip these.\n",
    "            if category.endswith(\" \"): continue\n",
    "            temp = [x for x in data[county][category] if \"compared to baseline\" in x]\n",
    "            if not temp: continue\n",
    "            percent = int(temp[0].split()[0].replace(\"%\",\"\"))\n",
    "            newdata[county][category.strip()] = percent\n",
    "    data = newdata\n",
    "\n",
    "    tomatch = []\n",
    "    for county in counties:\n",
    "        for category in categories:\n",
    "            if category in data[county]:\n",
    "                tomatch.append([county,category,data[county][category]])\n",
    "    if verbose:\n",
    "        print(len(tomatch))\n",
    "        print(data)\n",
    "    \n",
    "    goodplots = []\n",
    "    xrefs = sorted(doc.getPageXObjectList(ipage), key=lambda x:int(x[1].replace(\"X\",\"\")))\n",
    "    for i,xref in enumerate(xrefs):\n",
    "        stream = doc.xrefStream(xref[0]).decode()\n",
    "        info = parse_stream(stream)\n",
    "        if not info[\"good\"]: continue\n",
    "        goodplots.append(info)\n",
    "    if verbose:\n",
    "        print(len(goodplots))\n",
    "    \n",
    "    ret = []\n",
    "    \n",
    "    if len(tomatch) != len(goodplots):\n",
    "        return ret\n",
    "    \n",
    "    \n",
    "    for m,g in zip(tomatch,goodplots):\n",
    "        xs = g[\"data\"][:,0]\n",
    "        ys = g[\"data\"][:,1]\n",
    "        maxys = ys[np.where(xs==xs.max())[0]]\n",
    "        maxy = maxys[np.argmax(np.abs(maxys))]\n",
    "        \n",
    "        \n",
    "        # parsed the tick date labels as text. find the min/max (first/last)\n",
    "        # and make evenly spaced dates, one per day, to assign to x values between\n",
    "        # 0 and 200 (the width of the plots).\n",
    "        ts = list(map(lambda x: pd.Timestamp(x.split(None,1)[-1] + \", 2020\"), tickdates))\n",
    "        low, high = min(ts), max(ts)\n",
    "        dr = list(map(lambda x:str(x).split()[0], pd.date_range(low, high, freq=\"D\")))\n",
    "        lutpairs = list(zip(np.linspace(0,200,len(dr)),dr))\n",
    "\n",
    "        dates = []\n",
    "        values = []\n",
    "        asort = xs.argsort()\n",
    "        xs = xs[asort]\n",
    "        ys = ys[asort]\n",
    "        for x,y in zip(xs,ys):\n",
    "            date = min(lutpairs, key=lambda v:abs(v[0]-x))[1]\n",
    "            dates.append(date)\n",
    "            values.append(round(y,3))\n",
    "\n",
    "        ret.append(dict(\n",
    "            county=m[0],category=m[1],change=m[2],\n",
    "            values=values,\n",
    "            dates=dates,\n",
    "            changecalc=maxy,\n",
    "        ))\n",
    "    return ret\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "!mkdir -p data/\n",
    "\n",
    "def parse_state(state):\n",
    "    doc = fitz.Document(f\"pdfs/2020-03-29_US_{state}_Mobility_Report_en.pdf\")\n",
    "    data = []\n",
    "    for i in range(2,doc.pageCount-1):\n",
    "        for entry in parse_page(doc, i):\n",
    "            entry[\"state\"] = state\n",
    "            entry[\"page\"] = i\n",
    "            data.append(entry)\n",
    "    outname = f\"data/{state}.json.gz\"\n",
    "    df = pd.DataFrame(data)\n",
    "    ncounties = df['county'].nunique()\n",
    "    print(f\"Parsed {len(df)} plots for {ncounties} counties in {state}\")\n",
    "    df = df[[\"state\",\"county\",\"category\",\"change\",\"changecalc\",\"dates\", \"values\",\"page\"]]\n",
    "    return df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parsed 207 plots for 47 counties in New_York\n"
     ]
    },
    {
     "data": {
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       "\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>state</th>\n",
       "      <th>county</th>\n",
       "      <th>category</th>\n",
       "      <th>change</th>\n",
       "      <th>changecalc</th>\n",
       "      <th>dates</th>\n",
       "      <th>values</th>\n",
       "      <th>page</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>New_York</td>\n",
       "      <td>Albany County</td>\n",
       "      <td>Retail &amp; recreation</td>\n",
       "      <td>-59</td>\n",
       "      <td>-61.825713</td>\n",
       "      <td>[2020-02-16, 2020-02-17, 2020-02-18, 2020-02-1...</td>\n",
       "      <td>[6.817, 11.943, -7.269, 4.15, 2.239, 0.712, 9....</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>New_York</td>\n",
       "      <td>Albany County</td>\n",
       "      <td>Grocery &amp; pharmacy</td>\n",
       "      <td>-30</td>\n",
       "      <td>-31.368892</td>\n",
       "      <td>[2020-02-16, 2020-02-17, 2020-02-18, 2020-02-1...</td>\n",
       "      <td>[-5.853, 1.91, -7.346, -3.023, -3.381, -5.249,...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>New_York</td>\n",
       "      <td>Albany County</td>\n",
       "      <td>Parks</td>\n",
       "      <td>-15</td>\n",
       "      <td>-15.432097</td>\n",
       "      <td>[2020-02-16, 2020-02-17, 2020-02-18, 2020-02-1...</td>\n",
       "      <td>[8.681, -23.521, -34.124, -22.363, -8.464, -9....</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>New_York</td>\n",
       "      <td>Albany County</td>\n",
       "      <td>Transit stations</td>\n",
       "      <td>-64</td>\n",
       "      <td>-66.587550</td>\n",
       "      <td>[2020-02-16, 2020-02-17, 2020-02-18, 2020-02-1...</td>\n",
       "      <td>[-0.659, -7.722, 0.17, 1.691, -2.326, -4.777, ...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>New_York</td>\n",
       "      <td>Albany County</td>\n",
       "      <td>Workplace</td>\n",
       "      <td>-36</td>\n",
       "      <td>-37.739322</td>\n",
       "      <td>[2020-02-16, 2020-02-17, 2020-02-18, 2020-02-1...</td>\n",
       "      <td>[1.994, -42.578, -9.492, -8.561, -7.879, -9.87...</td>\n",
       "      <td>2</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>New_York</td>\n",
       "      <td>Wyoming County</td>\n",
       "      <td>Grocery &amp; pharmacy</td>\n",
       "      <td>-4</td>\n",
       "      <td>-4.585953</td>\n",
       "      <td>[2020-02-16, 2020-02-17, 2020-02-18, 2020-02-1...</td>\n",
       "      <td>[1.965, 4.735, -11.184, -2.706, 1.947, -2.713,...</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>New_York</td>\n",
       "      <td>Wyoming County</td>\n",
       "      <td>Workplace</td>\n",
       "      <td>-21</td>\n",
       "      <td>-21.802323</td>\n",
       "      <td>[2020-02-16, 2020-02-17, 2020-02-18, 2020-02-1...</td>\n",
       "      <td>[6.056, -24.547, -14.925, -11.539, -8.733, -9....</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>New_York</td>\n",
       "      <td>Yates County</td>\n",
       "      <td>Retail &amp; recreation</td>\n",
       "      <td>-34</td>\n",
       "      <td>-35.919545</td>\n",
       "      <td>[2020-02-16, 2020-02-17, 2020-02-18, 2020-02-1...</td>\n",
       "      <td>[43.103, 3.561, -1.844, 8.648, 5.977, 4.365, 2...</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>New_York</td>\n",
       "      <td>Yates County</td>\n",
       "      <td>Grocery &amp; pharmacy</td>\n",
       "      <td>-13</td>\n",
       "      <td>-13.973580</td>\n",
       "      <td>[2020-02-16, 2020-02-17, 2020-02-18, 2020-02-1...</td>\n",
       "      <td>[6.352, -8.198, -3.931, -0.965, -3.823, -5.928...</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>New_York</td>\n",
       "      <td>Yates County</td>\n",
       "      <td>Workplace</td>\n",
       "      <td>-31</td>\n",
       "      <td>-32.793210</td>\n",
       "      <td>[2020-02-16, 2020-02-17, 2020-02-18, 2020-02-1...</td>\n",
       "      <td>[-3.858, -27.282, -13.929, -14.881, -12.868, -...</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>207 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        state          county             category  change  changecalc  \\\n",
       "0    New_York   Albany County  Retail & recreation     -59  -61.825713   \n",
       "1    New_York   Albany County   Grocery & pharmacy     -30  -31.368892   \n",
       "2    New_York   Albany County                Parks     -15  -15.432097   \n",
       "3    New_York   Albany County     Transit stations     -64  -66.587550   \n",
       "4    New_York   Albany County            Workplace     -36  -37.739322   \n",
       "..        ...             ...                  ...     ...         ...   \n",
       "202  New_York  Wyoming County   Grocery & pharmacy      -4   -4.585953   \n",
       "203  New_York  Wyoming County            Workplace     -21  -21.802323   \n",
       "204  New_York    Yates County  Retail & recreation     -34  -35.919545   \n",
       "205  New_York    Yates County   Grocery & pharmacy     -13  -13.973580   \n",
       "206  New_York    Yates County            Workplace     -31  -32.793210   \n",
       "\n",
       "                                                 dates  \\\n",
       "0    [2020-02-16, 2020-02-17, 2020-02-18, 2020-02-1...   \n",
       "1    [2020-02-16, 2020-02-17, 2020-02-18, 2020-02-1...   \n",
       "2    [2020-02-16, 2020-02-17, 2020-02-18, 2020-02-1...   \n",
       "3    [2020-02-16, 2020-02-17, 2020-02-18, 2020-02-1...   \n",
       "4    [2020-02-16, 2020-02-17, 2020-02-18, 2020-02-1...   \n",
       "..                                                 ...   \n",
       "202  [2020-02-16, 2020-02-17, 2020-02-18, 2020-02-1...   \n",
       "203  [2020-02-16, 2020-02-17, 2020-02-18, 2020-02-1...   \n",
       "204  [2020-02-16, 2020-02-17, 2020-02-18, 2020-02-1...   \n",
       "205  [2020-02-16, 2020-02-17, 2020-02-18, 2020-02-1...   \n",
       "206  [2020-02-16, 2020-02-17, 2020-02-18, 2020-02-1...   \n",
       "\n",
       "                                                values  page  \n",
       "0    [6.817, 11.943, -7.269, 4.15, 2.239, 0.712, 9....     2  \n",
       "1    [-5.853, 1.91, -7.346, -3.023, -3.381, -5.249,...     2  \n",
       "2    [8.681, -23.521, -34.124, -22.363, -8.464, -9....     2  \n",
       "3    [-0.659, -7.722, 0.17, 1.691, -2.326, -4.777, ...     2  \n",
       "4    [1.994, -42.578, -9.492, -8.561, -7.879, -9.87...     2  \n",
       "..                                                 ...   ...  \n",
       "202  [1.965, 4.735, -11.184, -2.706, 1.947, -2.713,...    31  \n",
       "203  [6.056, -24.547, -14.925, -11.539, -8.733, -9....    31  \n",
       "204  [43.103, 3.561, -1.844, 8.648, 5.977, 4.365, 2...    32  \n",
       "205  [6.352, -8.198, -3.931, -0.965, -3.823, -5.928...    32  \n",
       "206  [-3.858, -27.282, -13.929, -14.881, -12.868, -...    32  \n",
       "\n",
       "[207 rows x 8 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parse_state(\"New_York\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parsed 306 plots for 51 counties in US\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>county</th>\n",
       "      <th>category</th>\n",
       "      <th>change</th>\n",
       "      <th>changecalc</th>\n",
       "      <th>dates</th>\n",
       "      <th>values</th>\n",
       "      <th>page</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Retail &amp; recreation</td>\n",
       "      <td>-41</td>\n",
       "      <td>-42.436015</td>\n",
       "      <td>[2020-02-16, 2020-02-17, 2020-02-18, 2020-02-1...</td>\n",
       "      <td>[-0.284, 3.589, -4.107, 3.814, -6.916, 3.289, ...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Grocery &amp; pharmacy</td>\n",
       "      <td>-13</td>\n",
       "      <td>-14.002120</td>\n",
       "      <td>[2020-02-16, 2020-02-17, 2020-02-18, 2020-02-1...</td>\n",
       "      <td>[-2.265, -0.266, -3.379, 1.154, -5.9, -0.154, ...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Parks</td>\n",
       "      <td>19</td>\n",
       "      <td>19.841270</td>\n",
       "      <td>[2020-02-16, 2020-02-17, 2020-02-18, 2020-02-1...</td>\n",
       "      <td>[-7.096, 17.869, -11.458, 6.701, -26.479, 12.1...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Transit stations</td>\n",
       "      <td>-30</td>\n",
       "      <td>-31.353645</td>\n",
       "      <td>[2020-02-16, 2020-02-17, 2020-02-18, 2020-02-1...</td>\n",
       "      <td>[2.915, 7.12, -1.241, 4.388, -0.888, 9.356, 13...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Workplace</td>\n",
       "      <td>-32</td>\n",
       "      <td>-32.935372</td>\n",
       "      <td>[2020-02-16, 2020-02-17, 2020-02-18, 2020-02-1...</td>\n",
       "      <td>[-1.485, -17.445, 0.882, 1.178, -0.464, 1.269,...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  state   county             category  change  changecalc  \\\n",
       "0    US  Alabama  Retail & recreation     -41  -42.436015   \n",
       "1    US  Alabama   Grocery & pharmacy     -13  -14.002120   \n",
       "2    US  Alabama                Parks      19   19.841270   \n",
       "3    US  Alabama     Transit stations     -30  -31.353645   \n",
       "4    US  Alabama            Workplace     -32  -32.935372   \n",
       "\n",
       "                                               dates  \\\n",
       "0  [2020-02-16, 2020-02-17, 2020-02-18, 2020-02-1...   \n",
       "1  [2020-02-16, 2020-02-17, 2020-02-18, 2020-02-1...   \n",
       "2  [2020-02-16, 2020-02-17, 2020-02-18, 2020-02-1...   \n",
       "3  [2020-02-16, 2020-02-17, 2020-02-18, 2020-02-1...   \n",
       "4  [2020-02-16, 2020-02-17, 2020-02-18, 2020-02-1...   \n",
       "\n",
       "                                              values  page  \n",
       "0  [-0.284, 3.589, -4.107, 3.814, -6.916, 3.289, ...     2  \n",
       "1  [-2.265, -0.266, -3.379, 1.154, -5.9, -0.154, ...     2  \n",
       "2  [-7.096, 17.869, -11.458, 6.701, -26.479, 12.1...     2  \n",
       "3  [2.915, 7.12, -1.241, 4.388, -0.888, 9.356, 13...     2  \n",
       "4  [-1.485, -17.445, 0.882, 1.178, -0.464, 1.269,...     2  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# US will be included in the dataframe as a \"state\", and the states are \"counties\"\n",
    "parse_state(\"US\").head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parsed 228 plots for 70 counties in Illinois\n",
      "Parsed 119 plots for 31 counties in South_Carolina\n",
      "Parsed 60 plots for 11 counties in Massachusetts\n",
      "Parsed 329 plots for 99 counties in Georgia\n",
      "Parsed 154 plots for 51 counties in Mississippi\n",
      "Parsed 187 plots for 69 counties in Kentucky\n",
      "Parsed 283 plots for 93 counties in Virginia\n",
      "Parsed 107 plots for 25 counties in Oregon\n",
      "Parsed 152 plots for 46 counties in Louisiana\n",
      "Parsed 171 plots for 51 counties in Alabama\n",
      "Parsed 283 plots for 73 counties in Ohio\n",
      "Parsed 131 plots for 37 counties in Colorado\n",
      "Parsed 260 plots for 51 counties in Florida\n",
      "Parsed 173 plots for 48 counties in Michigan\n",
      "Parsed 207 plots for 47 counties in New_York\n",
      "Parsed 99 plots for 35 counties in Kansas\n",
      "Parsed 8 plots for 2 counties in Rhode_Island\n",
      "Parsed 86 plots for 20 counties in Maryland\n",
      "Parsed 152 plots for 50 counties in Minnesota\n",
      "Parsed 16 plots for 3 counties in Delaware\n",
      "Parsed 518 plots for 148 counties in Texas\n",
      "Parsed 136 plots for 44 counties in Oklahoma\n",
      "Parsed 28 plots for 9 counties in North_Dakota\n",
      "Parsed 40 plots for 12 counties in Vermont\n",
      "Parsed 31 plots for 8 counties in New_Hampshire\n",
      "Parsed 224 plots for 63 counties in Tennessee\n",
      "Parsed 66 plots for 13 counties in Arizona\n",
      "Parsed 306 plots for 51 counties in US\n",
      "Parsed 43 plots for 8 counties in Connecticut\n",
      "Parsed 93 plots for 32 counties in West_Virginia\n",
      "Parsed 10 plots for 3 counties in Alaska\n",
      "Parsed 119 plots for 49 counties in Iowa\n",
      "Parsed 209 plots for 69 counties in Missouri\n",
      "Parsed 226 plots for 43 counties in California\n",
      "Parsed 60 plots for 18 counties in Idaho\n",
      "Parsed 42 plots for 10 counties in Utah\n",
      "Parsed 144 plots for 50 counties in Arkansas\n",
      "Parsed 256 plots for 69 counties in North_Carolina\n",
      "Parsed 165 plots for 47 counties in Wisconsin\n",
      "Parsed 51 plots for 14 counties in Maine\n",
      "Parsed 56 plots for 17 counties in Montana\n",
      "Parsed 64 plots for 24 counties in Nebraska\n",
      "Parsed 23 plots for 4 counties in Hawaii\n",
      "Parsed 44 plots for 13 counties in Wyoming\n",
      "Parsed 28 plots for 8 counties in Nevada\n",
      "Parsed 214 plots for 64 counties in Indiana\n",
      "Parsed 239 plots for 56 counties in Pennsylvania\n",
      "Parsed 87 plots for 15 counties in New_Jersey\n",
      "Parsed 82 plots for 22 counties in New_Mexico\n",
      "Parsed 84 plots for 19 counties in Washington\n",
      "Parsed 43 plots for 15 counties in South_Dakota\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3f11176f10df4473bc3c9626b4990f98",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "import glob\n",
    "states = [x.split(\"_US_\",1)[1].split(\"_Mobility\",1)[0] for x in glob.glob(\"pdfs/*.pdf\")]\n",
    "dfs = []\n",
    "for state in states:\n",
    "    dfs.append(parse_state(state))\n",
    "df = pd.concat(dfs).reset_index(drop=True)\n",
    "data = []\n",
    "for i,row in tqdm(df.iterrows()):\n",
    "    # do a little clean up and unstack the dates/values as separate rows\n",
    "    dorig = dict()\n",
    "    dorig[\"state\"] = row[\"state\"].replace(\"_\",\" \")\n",
    "    dorig[\"county\"] = row[\"county\"]\n",
    "    dorig[\"category\"] = row[\"category\"].replace(\" & \",\"/\").replace(\" \",\"\").lower()\n",
    "    dorig[\"page\"] = row[\"page\"]\n",
    "    dorig[\"change\"] = row[\"change\"]\n",
    "    dorig[\"changecalc\"] = row[\"changecalc\"]\n",
    "    for x,y in zip(row[\"dates\"],row[\"values\"]):\n",
    "        d = dorig.copy()\n",
    "        d[\"date\"] = x\n",
    "        d[\"value\"] = y\n",
    "        data.append(d)\n",
    "df = pd.DataFrame(data)\n",
    "df.to_json(\"data/data.json.gz\", orient=\"records\", indent=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfc = pd.read_json(\"data/data.json.gz\")\n",
    "dfc[\"date\"] = pd.to_datetime(dfc[\"date\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "294044"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dfc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "retail/recreation    77318\n",
       "grocery/pharmacy     74859\n",
       "workplace            67077\n",
       "transitstations      30982\n",
       "residential          25172\n",
       "parks                18636\n",
       "Name: category, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfc[\"category\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'workplace for New York counties')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 310,
       "width": 377
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "state = \"New York\"\n",
    "category = \"workplace\"\n",
    "dfc[(dfc[\"state\"]==state) & (dfc[\"category\"]==category)].set_index(\"date\").groupby(\"county\")[\"value\"].plot(\n",
    "    ax=ax,color=\"gray\",\n",
    "    alpha=0.5,\n",
    ");\n",
    "ax.set_title(f\"{category} for {state} counties\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}