load-dataset-1.ipynb

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 "metadata": {
  "language_info": {
   "name": "python",
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   }
  },
  "orig_nbformat": 2,
  "file_extension": ".py",
  "mimetype": "text/x-python",
  "name": "python",
  "npconvert_exporter": "python",
  "pygments_lexer": "ipython3",
  "version": 3
 },
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from datetime import datetime\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dataset 1 is job listings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "job_listings = pd.read_csv('Datasets/job_listings.csv', dtype={\"title\":str, \"company_name\":str, \"city\":str, \"region\":str, \"zip\":str, \"created\":str, \"last_checked\":str, \"last_updated\":str, \"delete_date\":str, \"onet_occupation_code\":str, \"SOC_occupation_code\":str, \"url\":str, \"stock_exchange_country\":str, \"stock_exchange_name\":str, \"stock_ticker\":str, \"ticker_end_date\":str, \"ticker_start_date\":str})\n",
    "job_listings[\"created\"] = job_listings.created.apply(lambda x: datetime.fromisoformat(x))\n",
    "job_listings[\"delete_date\"] = job_listings.delete_date.apply(lambda x: datetime.fromisoformat(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# not using this\n",
    "onet_code_legend = pd.read_csv('Datasets/Code Legends/onet_code_legend.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "soc_code_legend = pd.read_csv('Datasets/Code Legends/soc_code_legend.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "job_listings[\"created_year\"] = [x.year for x in job_listings.created]\n",
    "job_listings[\"created_month\"] = [x.month for x in job_listings.created]\n",
    "job_listings[\"deleted_year\"] = [x.year for x in job_listings.delete_date]\n",
    "job_listings[\"deleted_month\"] = [x.month for x in job_listings.delete_date]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [],
   "source": [
    "jobcountsList = []\n",
    "datesList = []\n",
    "datesList2 = []\n",
    "monthmap = {\"1\":\"Jan\", \"2\":\"Feb\", \"3\":\"Mar\", \"4\":\"Apr\", \"5\":\"May\", \"6\":\"Jun\", \"7\":\"Jul\", \"8\":\"Aug\", \"9\":\"Sep\", \"10\":\"Oct\", \"11\":\"Nov\", \"12\":\"Dec\"}\n",
    "years = []\n",
    "for year in range (2011, 2020, 1):\n",
    "    years.append(year)\n",
    "    jobcounts = []\n",
    "    dates = []   \n",
    "    dates2 = [] \n",
    "    for month in range(1,13,1):\n",
    "        # this = job_listings[job_listings.created.apply(lambda x:  x.year <= year and  x.month <= month) & job_listings.delete_date.apply(lambda x:  x.year >= year and  x.month >= month)]\n",
    "        this = job_listings\n",
    "\n",
    "        this = this[this.created_year <= year]\n",
    "        this = this[this.created_month <= month]\n",
    "        this = this[this.deleted_year >= year]\n",
    "        this = this[this.deleted_month >= month]\n",
    "        # job_listings.deleted_year >= year & job_listings.deleted_month >= month]\n",
    "        jobcounts.append(this[\"title\"].count())\n",
    "        dates2.append(\"{} {}\".format(monthmap[str(month)],\"'\"+str(year).lstrip(\"20\")))\n",
    "        dates.append(\"{}\".format(monthmap[str(month)]))\n",
    "    jobcountsList.append(jobcounts)\n",
    "    datesList.append(dates)\n",
    "    datesList2.append(dates2)    \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": "<Figure size 1440x1080 with 9 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot by year\n",
    "# plt.figure(figsize=(20,10))\n",
    "fig, axs = plt.subplots(nrows=3, ncols=3, figsize=(20,15))\n",
    "fig.suptitle('Monthly Change in Jobs Available Within Each Year')\n",
    "for ax,dates, jobcounts, year in zip(axs.flat, datesList, jobcountsList, years):\n",
    "    ax.bar(dates,jobcounts)\n",
    "    ax.set_title(str(year))\n",
    "\n",
    "for ax in axs.flat:\n",
    "    ax.label_outer()\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": "<Figure size 1080x1080 with 12 Axes>"
     },
     "metadata": {
      "needs_background": "light"
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     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot by month\n",
    "# plt.figure(figsize=(20,10))\n",
    "fig, axs = plt.subplots(nrows=4, ncols=3, figsize=(15,15))\n",
    "fig.suptitle('Change in Jobs Available for Each Month Across Years')\n",
    "\n",
    "datesListnp = np.array(datesList2)\n",
    "\n",
    "# datesListnp = np.array(map(lambda x : x.split()[1], datesListnp))\n",
    "jobcountsListnp = np.array(jobcountsList)\n",
    "# for i, year in enumerate(years):\n",
    "\n",
    "for ax,dates, jobcounts in zip(axs.flat, datesListnp.T, jobcountsListnp.T):\n",
    "    newdates = [date.split()[1] for date in dates]\n",
    "    mth = dates[0].split()[0]\n",
    "    ax.bar(newdates,jobcounts)\n",
    "    ax.set_title(str(mth))\n",
    "\n",
    "# for ax in axs.flat:\n",
    "#     ax.label_outer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [],
   "source": [
    "job_listings[\"place\"] = jobs_listings[\"city\"] + \", \" + job_listings[\"region\"]\n",
    "job_listings_place = job_listings.dropna(subset=[\"place\"])\n",
    "# between 2011 to 2020 only\n",
    "job_listings_place"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [],
   "source": [
    "# filter for our timeframe\n",
    "job_listings_place = job_listings_place[job_listings_place.created_year >= 2011]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [],
   "source": [
    "top6 = job_listings_place.groupby(\"place\").count().reset_index().sort_values(\"title\", ascending=False).head(6)[\"place\"].values.tolist()\n",
    "job_listings_place_top6 = job_listings_place[job_listings_place.place.isin(top6)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
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      "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></th>\n      <th>title</th>\n      <th>company_name</th>\n      <th>city</th>\n      <th>region</th>\n      <th>zip</th>\n      <th>created</th>\n      <th>last_checked</th>\n      <th>last_updated</th>\n      <th>delete_date</th>\n      <th>onet_occupation_code</th>\n      <th>SOC_occupation_code</th>\n      <th>url</th>\n      <th>stock_exchange_country</th>\n      <th>stock_exchange_name</th>\n      <th>stock_ticker</th>\n      <th>ticker_end_date</th>\n      <th>ticker_start_date</th>\n      <th>created_month</th>\n      <th>deleted_year</th>\n      <th>deleted_month</th>\n    </tr>\n    <tr>\n      <th>place</th>\n      <th>created_year</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th rowspan=\"9\" valign=\"top\">Bristol, England</th>\n      <th>2011</th>\n      <td>498</td>\n      <td>498</td>\n      <td>498</td>\n      <td>498</td>\n      <td>498</td>\n      <td>498</td>\n      <td>498</td>\n      <td>0</td>\n      <td>498</td>\n      <td>498</td>\n      <td>498</td>\n      <td>498</td>\n      <td>498</td>\n      <td>498</td>\n      <td>498</td>\n      <td>2</td>\n      <td>231</td>\n      <td>498</td>\n      <td>498</td>\n      <td>498</td>\n    </tr>\n    <tr>\n      <th>2012</th>\n      <td>1155</td>\n      <td>1155</td>\n      <td>1155</td>\n      <td>1155</td>\n      <td>1155</td>\n      <td>1155</td>\n      <td>1155</td>\n      <td>0</td>\n      <td>1155</td>\n      <td>1155</td>\n      <td>1155</td>\n      <td>1155</td>\n      <td>1155</td>\n      <td>1155</td>\n      <td>1155</td>\n      <td>0</td>\n      <td>462</td>\n      <td>1155</td>\n      <td>1155</td>\n      <td>1155</td>\n    </tr>\n    <tr>\n      <th>2013</th>\n      <td>1622</td>\n      <td>1622</td>\n      <td>1622</td>\n      <td>1622</td>\n      <td>1622</td>\n      <td>1622</td>\n      <td>1622</td>\n      <td>0</td>\n      <td>1622</td>\n      <td>1622</td>\n      <td>1622</td>\n      <td>1622</td>\n      <td>1622</td>\n      <td>1622</td>\n      <td>1622</td>\n      <td>24</td>\n      <td>782</td>\n      <td>1622</td>\n      <td>1622</td>\n      <td>1622</td>\n    </tr>\n    <tr>\n      <th>2014</th>\n      <td>3759</td>\n      <td>3759</td>\n      <td>3759</td>\n      <td>3759</td>\n      <td>3759</td>\n      <td>3759</td>\n      <td>3759</td>\n      <td>0</td>\n      <td>3759</td>\n      <td>3759</td>\n      <td>3759</td>\n      <td>3759</td>\n      <td>3759</td>\n      <td>3759</td>\n      <td>3759</td>\n      <td>31</td>\n      <td>1829</td>\n      <td>3759</td>\n      <td>3759</td>\n      <td>3759</td>\n    </tr>\n    <tr>\n      <th>2015</th>\n      <td>3900</td>\n      <td>3900</td>\n      <td>3900</td>\n      <td>3900</td>\n      <td>3900</td>\n      <td>3900</td>\n      <td>3900</td>\n      <td>2</td>\n      <td>3900</td>\n      <td>3900</td>\n      <td>3900</td>\n      <td>3900</td>\n      <td>3900</td>\n      <td>3900</td>\n      <td>3900</td>\n      <td>51</td>\n      <td>1830</td>\n      <td>3900</td>\n      <td>3900</td>\n      <td>3900</td>\n    </tr>\n    <tr>\n      <th>2016</th>\n      <td>3000</td>\n      <td>3000</td>\n      <td>3000</td>\n      <td>3000</td>\n      <td>3000</td>\n      <td>3000</td>\n      <td>3000</td>\n      <td>19</td>\n      <td>3000</td>\n      <td>3000</td>\n      <td>3000</td>\n      <td>3000</td>\n      <td>3000</td>\n      <td>3000</td>\n      <td>3000</td>\n      <td>24</td>\n      <td>1247</td>\n      <td>3000</td>\n      <td>3000</td>\n      <td>3000</td>\n    </tr>\n    <tr>\n      <th>2017</th>\n      <td>5145</td>\n      <td>5145</td>\n      <td>5145</td>\n      <td>5145</td>\n      <td>5145</td>\n      <td>5145</td>\n      <td>5145</td>\n      <td>783</td>\n      <td>5145</td>\n      <td>5145</td>\n      <td>5145</td>\n      <td>5145</td>\n      <td>5145</td>\n      <td>5145</td>\n      <td>5145</td>\n      <td>92</td>\n      <td>2227</td>\n      <td>5145</td>\n      <td>5145</td>\n      <td>5145</td>\n    </tr>\n    <tr>\n      <th>2018</th>\n      <td>7133</td>\n      <td>7133</td>\n      <td>7133</td>\n      <td>7133</td>\n      <td>7133</td>\n      <td>7133</td>\n      <td>7133</td>\n      <td>710</td>\n      <td>7133</td>\n      <td>7133</td>\n      <td>7133</td>\n      <td>7133</td>\n      <td>7133</td>\n      <td>7133</td>\n      <td>7133</td>\n      <td>126</td>\n      <td>3341</td>\n      <td>7133</td>\n      <td>7133</td>\n      <td>7133</td>\n    </tr>\n    <tr>\n      <th>2019</th>\n      <td>4726</td>\n      <td>4726</td>\n      <td>4726</td>\n      <td>4726</td>\n      <td>4726</td>\n      <td>4726</td>\n      <td>4726</td>\n      <td>626</td>\n      <td>4726</td>\n      <td>4726</td>\n      <td>4726</td>\n      <td>4726</td>\n      <td>4726</td>\n      <td>4726</td>\n      <td>4726</td>\n      <td>83</td>\n      <td>1941</td>\n      <td>4726</td>\n      <td>4726</td>\n      <td>4726</td>\n    </tr>\n    <tr>\n      <th rowspan=\"9\" valign=\"top\">Cambridge, England</th>\n      <th>2011</th>\n      <td>478</td>\n      <td>478</td>\n      <td>478</td>\n      <td>478</td>\n      <td>0</td>\n      <td>478</td>\n      <td>478</td>\n      <td>0</td>\n      <td>478</td>\n      <td>478</td>\n      <td>478</td>\n      <td>478</td>\n      <td>478</td>\n      <td>478</td>\n      <td>478</td>\n      <td>2</td>\n      <td>213</td>\n      <td>478</td>\n      <td>478</td>\n      <td>478</td>\n    </tr>\n    <tr>\n      <th>2012</th>\n      <td>911</td>\n      <td>911</td>\n      <td>911</td>\n      <td>911</td>\n      <td>0</td>\n      <td>911</td>\n      <td>911</td>\n      <td>0</td>\n      <td>911</td>\n      <td>911</td>\n      <td>911</td>\n      <td>911</td>\n      <td>911</td>\n      <td>911</td>\n      <td>911</td>\n      <td>10</td>\n      <td>377</td>\n      <td>911</td>\n      <td>911</td>\n      <td>911</td>\n    </tr>\n    <tr>\n      <th>2013</th>\n      <td>1115</td>\n      <td>1115</td>\n      <td>1115</td>\n      <td>1115</td>\n      <td>0</td>\n      <td>1115</td>\n      <td>1115</td>\n      <td>0</td>\n      <td>1115</td>\n      <td>1115</td>\n      <td>1115</td>\n      <td>1115</td>\n      <td>1115</td>\n      <td>1115</td>\n      <td>1115</td>\n      <td>16</td>\n      <td>630</td>\n      <td>1115</td>\n      <td>1115</td>\n      <td>1115</td>\n    </tr>\n    <tr>\n      <th>2014</th>\n      <td>8470</td>\n      <td>8470</td>\n      <td>8470</td>\n      <td>8470</td>\n      <td>5</td>\n      <td>8470</td>\n      <td>8470</td>\n      <td>3</td>\n      <td>8470</td>\n      <td>8470</td>\n      <td>8470</td>\n      <td>8470</td>\n      <td>8470</td>\n      <td>8470</td>\n      <td>8470</td>\n      <td>27</td>\n      <td>1461</td>\n      <td>8470</td>\n      <td>8470</td>\n      <td>8470</td>\n    </tr>\n    <tr>\n      <th>2015</th>\n      <td>3132</td>\n      <td>3132</td>\n      <td>3132</td>\n      <td>3132</td>\n      <td>0</td>\n      <td>3132</td>\n      <td>3132</td>\n      <td>1</td>\n      <td>3132</td>\n      <td>3132</td>\n      <td>3132</td>\n      <td>3132</td>\n      <td>3132</td>\n      <td>3132</td>\n      <td>3132</td>\n      <td>20</td>\n      <td>1525</td>\n      <td>3132</td>\n      <td>3132</td>\n      <td>3132</td>\n    </tr>\n    <tr>\n      <th>2016</th>\n      <td>2676</td>\n      <td>2676</td>\n      <td>2676</td>\n      <td>2676</td>\n      <td>0</td>\n      <td>2676</td>\n      <td>2676</td>\n      <td>46</td>\n      <td>2676</td>\n      <td>2676</td>\n      <td>2676</td>\n      <td>2676</td>\n      <td>2676</td>\n      <td>2676</td>\n      <td>2676</td>\n      <td>16</td>\n      <td>1429</td>\n      <td>2676</td>\n      <td>2676</td>\n      <td>2676</td>\n    </tr>\n    <tr>\n      <th>2017</th>\n      <td>4807</td>\n      <td>4807</td>\n      <td>4807</td>\n      <td>4807</td>\n      <td>0</td>\n      <td>4807</td>\n      <td>4807</td>\n      <td>791</td>\n      <td>4807</td>\n      <td>4807</td>\n      <td>4807</td>\n      <td>4807</td>\n      <td>4807</td>\n      <td>4807</td>\n      <td>4807</td>\n      <td>40</td>\n      <td>3053</td>\n      <td>4807</td>\n      <td>4807</td>\n      <td>4807</td>\n    </tr>\n    <tr>\n      <th>2018</th>\n      <td>4994</td>\n      <td>4994</td>\n      <td>4994</td>\n      <td>4994</td>\n      <td>0</td>\n      <td>4994</td>\n      <td>4994</td>\n      <td>861</td>\n      <td>4994</td>\n      <td>4994</td>\n      <td>4994</td>\n      <td>4994</td>\n      <td>4994</td>\n      <td>4994</td>\n      <td>4994</td>\n      <td>37</td>\n      <td>2503</td>\n      <td>4994</td>\n      <td>4994</td>\n      <td>4994</td>\n    </tr>\n    <tr>\n      <th>2019</th>\n      <td>3326</td>\n      <td>3326</td>\n      <td>3326</td>\n      <td>3326</td>\n      <td>0</td>\n      <td>3326</td>\n      <td>3326</td>\n      <td>737</td>\n      <td>3326</td>\n      <td>3326</td>\n      <td>3326</td>\n      <td>3326</td>\n      <td>3326</td>\n      <td>3326</td>\n      <td>3326</td>\n      <td>20</td>\n      <td>1417</td>\n      <td>3326</td>\n      <td>3326</td>\n      <td>3326</td>\n    </tr>\n    <tr>\n      <th rowspan=\"9\" valign=\"top\">Edinburgh, Scotland</th>\n      <th>2011</th>\n      <td>870</td>\n      <td>870</td>\n      <td>870</td>\n      <td>870</td>\n      <td>0</td>\n      <td>870</td>\n      <td>870</td>\n      <td>0</td>\n      <td>870</td>\n      <td>870</td>\n      <td>870</td>\n      <td>870</td>\n      <td>870</td>\n      <td>870</td>\n      <td>870</td>\n      <td>9</td>\n      <td>668</td>\n      <td>870</td>\n      <td>870</td>\n      <td>870</td>\n    </tr>\n    <tr>\n      <th>2012</th>\n      <td>1756</td>\n      <td>1756</td>\n      <td>1756</td>\n      <td>1756</td>\n      <td>0</td>\n      <td>1756</td>\n      <td>1756</td>\n      <td>0</td>\n      <td>1756</td>\n      <td>1756</td>\n      <td>1756</td>\n      <td>1756</td>\n      <td>1756</td>\n      <td>1756</td>\n      <td>1756</td>\n      <td>60</td>\n      <td>1172</td>\n      <td>1756</td>\n      <td>1756</td>\n      <td>1756</td>\n    </tr>\n    <tr>\n      <th>2013</th>\n      <td>1501</td>\n      <td>1501</td>\n      <td>1501</td>\n      <td>1501</td>\n      <td>0</td>\n      <td>1501</td>\n      <td>1501</td>\n      <td>0</td>\n      <td>1501</td>\n      <td>1501</td>\n      <td>1501</td>\n      <td>1501</td>\n      <td>1501</td>\n      <td>1501</td>\n      <td>1501</td>\n      <td>72</td>\n      <td>691</td>\n      <td>1501</td>\n      <td>1501</td>\n      <td>1501</td>\n    </tr>\n    <tr>\n      <th>2014</th>\n      <td>3249</td>\n      <td>3249</td>\n      <td>3249</td>\n      <td>3249</td>\n      <td>0</td>\n      <td>3249</td>\n      <td>3249</td>\n      <td>3</td>\n      <td>3249</td>\n      <td>3249</td>\n      <td>3249</td>\n      <td>3249</td>\n      <td>3249</td>\n      <td>3249</td>\n      <td>3249</td>\n      <td>142</td>\n      <td>1572</td>\n      <td>3249</td>\n      <td>3249</td>\n      <td>3249</td>\n    </tr>\n    <tr>\n      <th>2015</th>\n      <td>4126</td>\n      <td>4126</td>\n      <td>4126</td>\n      <td>4126</td>\n      <td>0</td>\n      <td>4126</td>\n      <td>4126</td>\n      <td>1</td>\n      <td>4126</td>\n      <td>4126</td>\n      <td>4126</td>\n      <td>4126</td>\n      <td>4126</td>\n      <td>4126</td>\n      <td>4126</td>\n      <td>94</td>\n      <td>1894</td>\n      <td>4126</td>\n      <td>4126</td>\n      <td>4126</td>\n    </tr>\n    <tr>\n      <th>2016</th>\n      <td>3290</td>\n      <td>3290</td>\n      <td>3290</td>\n      <td>3290</td>\n      <td>0</td>\n      <td>3290</td>\n      <td>3290</td>\n      <td>25</td>\n      <td>3290</td>\n      <td>3290</td>\n      <td>3290</td>\n      <td>3290</td>\n      <td>3290</td>\n      <td>3290</td>\n      <td>3290</td>\n      <td>51</td>\n      <td>1656</td>\n      <td>3290</td>\n      <td>3290</td>\n      <td>3290</td>\n    </tr>\n    <tr>\n      <th>2017</th>\n      <td>5258</td>\n      <td>5258</td>\n      <td>5258</td>\n      <td>5258</td>\n      <td>0</td>\n      <td>5258</td>\n      <td>5258</td>\n      <td>764</td>\n      <td>5258</td>\n      <td>5258</td>\n      <td>5258</td>\n      <td>5258</td>\n      <td>5258</td>\n      <td>5258</td>\n      <td>5258</td>\n      <td>524</td>\n      <td>2546</td>\n      <td>5258</td>\n      <td>5258</td>\n      <td>5258</td>\n    </tr>\n    <tr>\n      <th>2018</th>\n      <td>6396</td>\n      <td>6396</td>\n      <td>6396</td>\n      <td>6396</td>\n      <td>0</td>\n      <td>6396</td>\n      <td>6396</td>\n      <td>689</td>\n      <td>6396</td>\n      <td>6396</td>\n      <td>6396</td>\n      <td>6396</td>\n      <td>6396</td>\n      <td>6396</td>\n      <td>6396</td>\n      <td>704</td>\n      <td>2765</td>\n      <td>6396</td>\n      <td>6396</td>\n      <td>6396</td>\n    </tr>\n    <tr>\n      <th>2019</th>\n      <td>4357</td>\n      <td>4357</td>\n      <td>4357</td>\n      <td>4357</td>\n      <td>0</td>\n      <td>4357</td>\n      <td>4357</td>\n      <td>606</td>\n      <td>4357</td>\n      <td>4357</td>\n      <td>4357</td>\n      <td>4357</td>\n      <td>4357</td>\n      <td>4357</td>\n      <td>4357</td>\n      <td>452</td>\n      <td>1705</td>\n      <td>4357</td>\n      <td>4357</td>\n      <td>4357</td>\n    </tr>\n    <tr>\n      <th rowspan=\"9\" valign=\"top\">London, England</th>\n      <th>2011</th>\n      <td>8247</td>\n      <td>8247</td>\n      <td>8247</td>\n      <td>8247</td>\n      <td>7933</td>\n      <td>8247</td>\n      <td>8247</td>\n      <td>0</td>\n      <td>8247</td>\n      <td>8247</td>\n      <td>8247</td>\n      <td>8247</td>\n      <td>8247</td>\n      <td>8247</td>\n      <td>8247</td>\n      <td>274</td>\n      <td>7053</td>\n      <td>8247</td>\n      <td>8247</td>\n      <td>8247</td>\n    </tr>\n    <tr>\n      <th>2012</th>\n      <td>12513</td>\n      <td>12513</td>\n      <td>12513</td>\n      <td>12513</td>\n      <td>12366</td>\n      <td>12513</td>\n      <td>12513</td>\n      <td>0</td>\n      <td>12513</td>\n      <td>12513</td>\n      <td>12513</td>\n      <td>12513</td>\n      <td>12513</td>\n      <td>12513</td>\n      <td>12513</td>\n      <td>204</td>\n      <td>8393</td>\n      <td>12513</td>\n      <td>12513</td>\n      <td>12513</td>\n    </tr>\n    <tr>\n      <th>2013</th>\n      <td>21832</td>\n      <td>21832</td>\n      <td>21832</td>\n      <td>21832</td>\n      <td>21350</td>\n      <td>21832</td>\n      <td>21832</td>\n      <td>0</td>\n      <td>21832</td>\n      <td>21832</td>\n      <td>21832</td>\n      <td>21832</td>\n      <td>21832</td>\n      <td>21832</td>\n      <td>21832</td>\n      <td>349</td>\n      <td>14724</td>\n      <td>21832</td>\n      <td>21832</td>\n      <td>21832</td>\n    </tr>\n    <tr>\n      <th>2014</th>\n      <td>40970</td>\n      <td>40970</td>\n      <td>40970</td>\n      <td>40970</td>\n      <td>40311</td>\n      <td>40970</td>\n      <td>40970</td>\n      <td>8</td>\n      <td>40970</td>\n      <td>40970</td>\n      <td>40970</td>\n      <td>40970</td>\n      <td>40970</td>\n      <td>40970</td>\n      <td>40970</td>\n      <td>684</td>\n      <td>25947</td>\n      <td>40970</td>\n      <td>40970</td>\n      <td>40970</td>\n    </tr>\n    <tr>\n      <th>2015</th>\n      <td>45919</td>\n      <td>45919</td>\n      <td>45919</td>\n      <td>45919</td>\n      <td>44919</td>\n      <td>45919</td>\n      <td>45919</td>\n      <td>103</td>\n      <td>45919</td>\n      <td>45919</td>\n      <td>45919</td>\n      <td>45919</td>\n      <td>45919</td>\n      <td>45919</td>\n      <td>45919</td>\n      <td>1009</td>\n      <td>28501</td>\n      <td>45919</td>\n      <td>45919</td>\n      <td>45919</td>\n    </tr>\n    <tr>\n      <th>2016</th>\n      <td>42850</td>\n      <td>42850</td>\n      <td>42850</td>\n      <td>42850</td>\n      <td>41715</td>\n      <td>42850</td>\n      <td>42850</td>\n      <td>464</td>\n      <td>42850</td>\n      <td>42850</td>\n      <td>42850</td>\n      <td>42849</td>\n      <td>42850</td>\n      <td>42850</td>\n      <td>42850</td>\n      <td>1482</td>\n      <td>29843</td>\n      <td>42850</td>\n      <td>42850</td>\n      <td>42850</td>\n    </tr>\n    <tr>\n      <th>2017</th>\n      <td>83464</td>\n      <td>83464</td>\n      <td>83464</td>\n      <td>83464</td>\n      <td>82279</td>\n      <td>83464</td>\n      <td>83464</td>\n      <td>13196</td>\n      <td>83464</td>\n      <td>83464</td>\n      <td>83464</td>\n      <td>83457</td>\n      <td>83464</td>\n      <td>83464</td>\n      <td>83464</td>\n      <td>3285</td>\n      <td>55252</td>\n      <td>83464</td>\n      <td>83464</td>\n      <td>83464</td>\n    </tr>\n    <tr>\n      <th>2018</th>\n      <td>91738</td>\n      <td>91738</td>\n      <td>91738</td>\n      <td>91738</td>\n      <td>90122</td>\n      <td>91738</td>\n      <td>91738</td>\n      <td>11393</td>\n      <td>91738</td>\n      <td>91738</td>\n      <td>91738</td>\n      <td>91738</td>\n      <td>91738</td>\n      <td>91738</td>\n      <td>91738</td>\n      <td>2617</td>\n      <td>61831</td>\n      <td>91738</td>\n      <td>91738</td>\n      <td>91738</td>\n    </tr>\n    <tr>\n      <th>2019</th>\n      <td>50711</td>\n      <td>50711</td>\n      <td>50711</td>\n      <td>50711</td>\n      <td>49655</td>\n      <td>50711</td>\n      <td>50711</td>\n      <td>8800</td>\n      <td>50711</td>\n      <td>50711</td>\n      <td>50711</td>\n      <td>50711</td>\n      <td>50711</td>\n      <td>50711</td>\n      <td>50711</td>\n      <td>1378</td>\n      <td>30616</td>\n      <td>50711</td>\n      <td>50711</td>\n      <td>50711</td>\n    </tr>\n    <tr>\n      <th rowspan=\"9\" valign=\"top\">Manchester, England</th>\n      <th>2011</th>\n      <td>647</td>\n      <td>647</td>\n      <td>647</td>\n      <td>647</td>\n      <td>647</td>\n      <td>647</td>\n      <td>647</td>\n      <td>0</td>\n      <td>647</td>\n      <td>647</td>\n      <td>647</td>\n      <td>647</td>\n      <td>647</td>\n      <td>647</td>\n      <td>647</td>\n      <td>1</td>\n      <td>405</td>\n      <td>647</td>\n      <td>647</td>\n      <td>647</td>\n    </tr>\n    <tr>\n      <th>2012</th>\n      <td>1745</td>\n      <td>1745</td>\n      <td>1745</td>\n      <td>1745</td>\n      <td>1745</td>\n      <td>1745</td>\n      <td>1745</td>\n      <td>0</td>\n      <td>1745</td>\n      <td>1745</td>\n      <td>1745</td>\n      <td>1745</td>\n      <td>1745</td>\n      <td>1745</td>\n      <td>1745</td>\n      <td>5</td>\n      <td>1284</td>\n      <td>1745</td>\n      <td>1745</td>\n      <td>1745</td>\n    </tr>\n    <tr>\n      <th>2013</th>\n      <td>1640</td>\n      <td>1640</td>\n      <td>1640</td>\n      <td>1640</td>\n      <td>1640</td>\n      <td>1640</td>\n      <td>1640</td>\n      <td>0</td>\n      <td>1640</td>\n      <td>1640</td>\n      <td>1640</td>\n      <td>1640</td>\n      <td>1640</td>\n      <td>1640</td>\n      <td>1640</td>\n      <td>18</td>\n      <td>935</td>\n      <td>1640</td>\n      <td>1640</td>\n      <td>1640</td>\n    </tr>\n    <tr>\n      <th>2014</th>\n      <td>3669</td>\n      <td>3669</td>\n      <td>3669</td>\n      <td>3669</td>\n      <td>3669</td>\n      <td>3669</td>\n      <td>3669</td>\n      <td>0</td>\n      <td>3669</td>\n      <td>3669</td>\n      <td>3669</td>\n      <td>3669</td>\n      <td>3669</td>\n      <td>3669</td>\n      <td>3669</td>\n      <td>45</td>\n      <td>2200</td>\n      <td>3669</td>\n      <td>3669</td>\n      <td>3669</td>\n    </tr>\n    <tr>\n      <th>2015</th>\n      <td>4368</td>\n      <td>4368</td>\n      <td>4368</td>\n      <td>4368</td>\n      <td>4368</td>\n      <td>4368</td>\n      <td>4368</td>\n      <td>37</td>\n      <td>4368</td>\n      <td>4368</td>\n      <td>4368</td>\n      <td>4368</td>\n      <td>4368</td>\n      <td>4368</td>\n      <td>4368</td>\n      <td>103</td>\n      <td>2875</td>\n      <td>4368</td>\n      <td>4368</td>\n      <td>4368</td>\n    </tr>\n    <tr>\n      <th>2016</th>\n      <td>3957</td>\n      <td>3957</td>\n      <td>3957</td>\n      <td>3957</td>\n      <td>3957</td>\n      <td>3957</td>\n      <td>3957</td>\n      <td>58</td>\n      <td>3957</td>\n      <td>3957</td>\n      <td>3957</td>\n      <td>3957</td>\n      <td>3957</td>\n      <td>3957</td>\n      <td>3957</td>\n      <td>124</td>\n      <td>2599</td>\n      <td>3957</td>\n      <td>3957</td>\n      <td>3957</td>\n    </tr>\n    <tr>\n      <th>2017</th>\n      <td>6850</td>\n      <td>6850</td>\n      <td>6850</td>\n      <td>6850</td>\n      <td>6850</td>\n      <td>6850</td>\n      <td>6850</td>\n      <td>1173</td>\n      <td>6850</td>\n      <td>6850</td>\n      <td>6850</td>\n      <td>6850</td>\n      <td>6850</td>\n      <td>6850</td>\n      <td>6850</td>\n      <td>476</td>\n      <td>3801</td>\n      <td>6850</td>\n      <td>6850</td>\n      <td>6850</td>\n    </tr>\n    <tr>\n      <th>2018</th>\n      <td>8413</td>\n      <td>8413</td>\n      <td>8413</td>\n      <td>8413</td>\n      <td>8413</td>\n      <td>8413</td>\n      <td>8413</td>\n      <td>1170</td>\n      <td>8413</td>\n      <td>8413</td>\n      <td>8413</td>\n      <td>8413</td>\n      <td>8413</td>\n      <td>8413</td>\n      <td>8413</td>\n      <td>416</td>\n      <td>5293</td>\n      <td>8413</td>\n      <td>8413</td>\n      <td>8413</td>\n    </tr>\n    <tr>\n      <th>2019</th>\n      <td>5980</td>\n      <td>5980</td>\n      <td>5980</td>\n      <td>5980</td>\n      <td>5980</td>\n      <td>5980</td>\n      <td>5980</td>\n      <td>890</td>\n      <td>5980</td>\n      <td>5980</td>\n      <td>5980</td>\n      <td>5980</td>\n      <td>5980</td>\n      <td>5980</td>\n      <td>5980</td>\n      <td>341</td>\n      <td>3409</td>\n      <td>5980</td>\n      <td>5980</td>\n      <td>5980</td>\n    </tr>\n    <tr>\n      <th rowspan=\"5\" valign=\"top\">Oakdale, Wales</th>\n      <th>2014</th>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2016</th>\n      <td>345</td>\n      <td>345</td>\n      <td>345</td>\n      <td>345</td>\n      <td>0</td>\n      <td>345</td>\n      <td>345</td>\n      <td>0</td>\n      <td>345</td>\n      <td>345</td>\n      <td>345</td>\n      <td>345</td>\n      <td>345</td>\n      <td>345</td>\n      <td>345</td>\n      <td>0</td>\n      <td>345</td>\n      <td>345</td>\n      <td>345</td>\n      <td>345</td>\n    </tr>\n    <tr>\n      <th>2017</th>\n      <td>17450</td>\n      <td>17450</td>\n      <td>17450</td>\n      <td>17450</td>\n      <td>0</td>\n      <td>17450</td>\n      <td>17450</td>\n      <td>0</td>\n      <td>17450</td>\n      <td>17450</td>\n      <td>17450</td>\n      <td>17450</td>\n      <td>17450</td>\n      <td>17450</td>\n      <td>17450</td>\n      <td>0</td>\n      <td>17449</td>\n      <td>17450</td>\n      <td>17450</td>\n      <td>17450</td>\n    </tr>\n    <tr>\n      <th>2018</th>\n      <td>19238</td>\n      <td>19238</td>\n      <td>19238</td>\n      <td>19238</td>\n      <td>0</td>\n      <td>19238</td>\n      <td>19238</td>\n      <td>0</td>\n      <td>19238</td>\n      <td>19238</td>\n      <td>19238</td>\n      <td>19238</td>\n      <td>19238</td>\n      <td>19238</td>\n      <td>19238</td>\n      <td>0</td>\n      <td>19238</td>\n      <td>19238</td>\n      <td>19238</td>\n      <td>19238</td>\n    </tr>\n    <tr>\n      <th>2019</th>\n      <td>11927</td>\n      <td>11927</td>\n      <td>11927</td>\n      <td>11927</td>\n      <td>0</td>\n      <td>11927</td>\n      <td>11927</td>\n      <td>0</td>\n      <td>11927</td>\n      <td>11927</td>\n      <td>11927</td>\n      <td>11927</td>\n      <td>11927</td>\n      <td>11927</td>\n      <td>11927</td>\n      <td>0</td>\n      <td>11927</td>\n      <td>11927</td>\n      <td>11927</td>\n      <td>11927</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
      "text/plain": "        3249   \n                    2015                 4126                  4126   \n                    2016                 3290                  3290   \n                    2017                 5258                  5258   \n                    2018                 6396                  6396   \n                    2019                 4357                  4357   \nLondon, England     2011                 8247                  8247   \n                    2012                12513                 12513   \n                    2013                21832                 21832   \n                    2014                40970                 40970   \n                    2015                45919                 45919   \n                    2016                42850                 42850   \n                    2017                83464                 83464   \n                    2018                91738                 91738   \n                    2019                50711                 50711   \nManchester, England 2011                  647                   647   \n                    2012                 1745                  1745   \n                    2013                 1640                  1640   \n                    2014                 3669                  3669   \n                    2015                 4368                  4368   \n                    2016                 3957                  3957   \n                    2017                 6850                  6850   \n                    2018                 8413                  8413   \n                    2019                 5980                  5980   \nOakdale, Wales      2014                    1                     1   \n                    2016                  345                   345   \n                    2017                17450                 17450   \n                    2018                19238                 19238   \n                    2019                11927                 11927   \n\n                                  SOC_occupation_code    url  \\\nplace               created_year                               \nBristol, England    2011                          498    498   \n                    2012                         1155   1155   \n                    2013                         1622   1622   \n                    2014                         3759   3759   \n                    2015                         3900   3900   \n                    2016                         3000   3000   \n                    2017                         5145   5145   \n                    2018                         7133   7133   \n                    2019                         4726   4726   \nCambridge, England  2011                          478    478   \n                    2012                          911    911   \n                    2013                         1115   1115   \n                    2014                         8470   8470   \n                    2015                         3132   3132   \n                    2016                         2676   2676   \n                    2017                         4807   4807   \n                    2018                         4994   4994   \n                    2019                         3326   3326   \nEdinburgh, Scotland 2011                          870    870   \n                    2012                         1756   1756   \n                    2013                         1501   1501   \n                    2014                         3249   3249   \n                    2015                         4126   4126   \n                    2016                         3290   3290   \n                    2017                         5258   5258   \n                    2018                         6396   6396   \n                    2019                         4357   4357   \nLondon, England     2011                         8247   8247   \n                    2012                        12513  12513   \n                    2013                        21832  21832   \n                    2014                        40970  40970   \n                    2015                        45919  45919   \n                    2016                        42850  42849   \n                    2017                        83464  83457   \n                    2018                        91738  91738   \n                    2019                        50711  50711   \nManchester, England 2011                          647    647   \n                    2012                         1745   1745   \n                    2013                         1640   1640   \n                    2014                         3669   3669   \n                    2015                         4368   4368   \n                    2016                         3957   3957   \n                    2017                         6850   6850   \n                    2018                         8413   8413   \n                    2019                         5980   5980   \nOakdale, Wales      2014                            1      1   \n                    2016                          345    345   \n                    2017                        17450  17450   \n                    2018                        19238  19238   \n                    2019                        11927  11927   \n\n                                  stock_exchange_country  stock_exchange_name  \\\nplace               created_year                                                \nBristol, England    2011                             498                  498   \n                    2012                            1155                 1155   \n                    2013                            1622                 1622   \n                    2014                            3759                 3759   \n                    2015                            3900                 3900   \n                    2016                            3000                 3000   \n                    2017                            5145                 5145   \n                    2018                            7133                 7133   \n                    2019                            4726                 4726   \nCambridge, England  2011                             478                  478   \n                    2012                             911                  911   \n                    2013                            1115                 1115   \n                    2014                            8470                 8470   \n                    2015                            3132                 3132   \n                    2016                            2676                 2676   \n                    2017                            4807                 4807   \n                    2018                            4994                 4994   \n                    2019                            3326                 3326   \nEdinburgh, Scotland 2011                             870                  870   \n                    2012                            1756                 1756   \n                    2013                            1501                 1501   \n                    2014                            3249                 3249   \n                    2015                            4126                 4126   \n                    2016                            3290                 3290   \n                    2017                            5258                 5258   \n                    2018                            6396                 6396   \n                    2019                            4357                 4357   \nLondon, England     2011                            8247                 8247   \n                    2012                           12513                12513   \n                    2013                           21832                21832   \n                    2014                           40970                40970   \n                    2015                           45919                45919   \n                    2016                           42850                42850   \n                    2017                           83464                83464   \n                    2018                           91738                91738   \n                    2019                           50711                50711   \nManchester, England 2011                             647                  647   \n                    2012                            1745                 1745   \n                    2013                            1640                 1640   \n                    2014                            3669                 3669   \n                    2015                            4368                 4368   \n                    2016                            3957                 3957   \n                    2017                            6850                 6850   \n                    2018                            8413                 8413   \n                    2019                            5980                 5980   \nOakdale, Wales      2014                               1                    1   \n                    2016                             345                  345   \n                    2017                           17450                17450   \n                    2018                           19238                19238   \n                    2019                           11927                11927   \n\n                                  stock_ticker  ticker_end_date  \\\nplace               created_year                                  \nBristol, England    2011                   498                2   \n                    2012                  1155                0   \n                    2013                  1622               24   \n                    2014                  3759               31   \n                    2015                  3900               51   \n                    2016                  3000               24   \n                    2017                  5145               92   \n                    2018                  7133              126   \n                    2019                  4726               83   \nCambridge, England  2011                   478                2   \n                    2012                   911               10   \n                    2013                  1115               16   \n                    2014                  8470               27   \n                    2015                  3132               20   \n                    2016                  2676               16   \n                    2017                  4807               40   \n                    2018                  4994               37   \n                    2019                  3326               20   \nEdinburgh, Scotland 2011                   870                9   \n                    2012                  1756               60   \n                    2013                  1501               72   \n                    2014                  3249              142   \n                    2015                  4126               94   \n                    2016                  3290               51   \n                    2017                  5258              524   \n                    2018                  6396              704   \n                    2019                  4357              452   \nLondon, England     2011                  8247              274   \n                    2012                 12513              204   \n                    2013                 21832              349   \n                    2014                 40970              684   \n                    2015                 45919             1009   \n                    2016                 42850             1482   \n                    2017                 83464             3285   \n                    2018                 91738             2617   \n                    2019                 50711             1378   \nManchester, England 2011                   647                1   \n                    2012                  1745                5   \n                    2013                  1640               18   \n                    2014                  3669               45   \n                    2015                  4368              103   \n                    2016                  3957              124   \n                    2017                  6850              476   \n                    2018                  8413              416   \n                    2019                  5980              341   \nOakdale, Wales      2014                     1                0   \n                    2016                   345                0   \n                    2017                 17450                0   \n                    2018                 19238                0   \n                    2019                 11927                0   \n\n                                  ticker_start_date  created_month  \\\nplace               created_year                                     \nBristol, England    2011                        231            498   \n                    2012                        462           1155   \n                    2013                        782           1622   \n                    2014                       1829           3759   \n                    2015                       1830           3900   \n                    2016                       1247           3000   \n                    2017                       2227           5145   \n                    2018                       3341           7133   \n                    2019                       1941           4726   \nCambridge, England  2011                        213            478   \n                    2012                        377            911   \n                    2013                        630           1115   \n                    2014                       1461           8470   \n                    2015                       1525           3132   \n                    2016                       1429           2676   \n                    2017                       3053           4807   \n                    2018                       2503           4994   \n                    2019                       1417           3326   \nEdinburgh, Scotland 2011                        668            870   \n                    2012                       1172           1756   \n                    2013                        691           1501   \n                    2014                       1572           3249   \n                    2015                       1894           4126   \n                    2016                       1656           3290   \n                    2017                       2546           5258   \n                    2018                       2765           6396   \n                    2019                       1705           4357   \nLondon, England     2011                       7053           8247   \n                    2012                       8393          12513   \n                    2013                      14724          21832   \n                    2014                      25947          40970   \n                    2015                      28501          45919   \n                    2016                      29843          42850   \n                    2017                      55252          83464   \n                    2018                      61831          91738   \n                    2019                      30616          50711   \nManchester, England 2011                        405            647   \n                    2012                       1284           1745   \n                    2013                        935           1640   \n                    2014                       2200           3669   \n                    2015                       2875           4368   \n                    2016                       2599           3957   \n                    2017                       3801           6850   \n                    2018                       5293           8413   \n                    2019                       3409           5980   \nOakdale, Wales      2014                          0              1   \n                    2016                        345            345   \n                    2017                      17449          17450   \n                    2018                      19238          19238   \n                    2019                      11927          11927   \n\n                                  deleted_year  deleted_month  \nplace               created_year                               \nBristol, England    2011                   498            498  \n                    2012                  1155           1155  \n                    2013                  1622           1622  \n                    2014                  3759           3759  \n                    2015                  3900           3900  \n                    2016                  3000           3000  \n                    2017                  5145           5145  \n                    2018                  7133           7133  \n                    2019                  4726           4726  \nCambridge, England  2011                   478            478  \n                    2012                   911            911  \n                    2013                  1115           1115  \n                    2014                  8470           8470  \n                    2015                  3132           3132  \n                    2016                  2676           2676  \n                    2017                  4807           4807  \n                    2018                  4994           4994  \n                    2019                  3326           3326  \nEdinburgh, Scotland 2011                   870            870  \n                    2012                  1756           1756  \n                    2013                  1501           1501  \n                    2014                  3249           3249  \n                    2015                  4126           4126  \n                    2016                  3290           3290  \n                    2017                  5258           5258  \n                    2018                  6396           6396  \n                    2019                  4357           4357  \nLondon, England     2011                  8247           8247  \n                    2012                 12513          12513  \n                    2013                 21832          21832  \n                    2014                 40970          40970  \n                    2015                 45919          45919  \n                    2016                 42850          42850  \n                    2017                 83464          83464  \n                    2018                 91738          91738  \n                    2019                 50711          50711  \nManchester, England 2011                   647            647  \n                    2012                  1745           1745  \n                    2013                  1640           1640  \n                    2014                  3669           3669  \n                    2015                  4368           4368  \n                    2016                  3957           3957  \n                    2017                  6850           6850  \n                    2018                  8413           8413  \n                    2019                  5980           5980  \nOakdale, Wales      2014                     1              1  \n                    2016                   345            345  \n                    2017                 17450          17450  \n                    2018                 19238          19238  \n                    2019                 11927          11927  "
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "job_listings_place_top6.groupby([\"place\",\"created_year\"]).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [],
   "source": [
    "jobcountsList2 = []\n",
    "yearsList = []\n",
    "for place in top6:\n",
    "    this = job_listings_place_top6[job_listings_place_top6.place == place]\n",
    "    jobcounts = []\n",
    "    years = []\n",
    "    for year in range (2011, 2020, 1):\n",
    "        years.append(str(year))\n",
    "        this = job_listings_place_top6[job_listings_place_top6.place == place]\n",
    "        this = this[this.created_year <= year]\n",
    "        this = this[this.deleted_year >= year]\n",
    "        jobcounts.append(this[\"title\"].count())\n",
    "    jobcountsList2.append(jobcounts)\n",
    "    yearsList.append(years)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": "<Figure size 1440x1080 with 6 Axes>"
     },
     "metadata": {
      "needs_background": "light"
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     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot by place\n",
    "# plt.figure(figsize=(20,10))\n",
    "fig, axs = plt.subplots(nrows=3, ncols=2, figsize=(20,15))\n",
    "fig.suptitle('Yearly Change in Jobs Available for Specific Cities')\n",
    "for ax,years, jobcounts,place in zip(axs.flat, yearsList, jobcountsList2,top6):\n",
    "    ax.bar(years,jobcounts)\n",
    "    ax.set_title(place)\n",
    "\n",
    "for ax in axs.flat:\n",
    "    ax.label_outer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}