02 - DataScienceUnemployment.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "47db90e8",
   "metadata": {},
   "source": [
    "# 1.0 Introduction\n",
    "\n",
    "In this notebook we will complement our previous analysis of the evolution in the percentage of data scientists with the evolution of unemployment. Since data science is being regarded as a promising profession, it was expected to see a rise in number of data scientists. With the tests and plots done in the last notebook we verified that wasn't the case. The opposite happended. We will continue to investigate our initial hypothesis by comparing the unemployment rate between data scientists and other developers. Might be the case that even though we have a decrease in the percentage of data scientists, we still have a high interest of employers in contracting data scientists, if that is the case it is expected for the data scientists to have a smaller unemployement rate compared to other developers.\n",
    "\n",
    "For this notebook we will adopt a simpler approach and won't make any statistical test. Only graphical analysis will be enough.\n",
    "\n",
    "## 1.1 Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cda035a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import os\n",
    "    \n",
    "sns.set(context='notebook', style='whitegrid')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e24bd4f",
   "metadata": {},
   "source": [
    "## 1.2 Loading data\n",
    "\n",
    "Looking at the schema of each year we identified that the variable regarding the employment status is **EmploymentStatus** for 2017 survey and **Employment** for all the other surveys. Combined with the already identified variable regarding the developer type, we will load the data from all the years (2017-2020) into a datatframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "676e9f43",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_data(year):\n",
    "    \"\"\"This function is used the load the data based on the year of the survey.\n",
    "    Based on how the data is structed in this project this function loads\n",
    "    the data with variables name conditioned by the year, and tidy then\n",
    "    into a dataframe with data from the employemnt and devtype variables.\n",
    "    The respective year of the survey is also added as a column.\n",
    "    \"\"\"\n",
    "    \n",
    "    # setting up column names according to the survey year\n",
    "    employment = \"EmploymentStatus\" if year == 2017 else \"Employment\"\n",
    "    devtype = \"DeveloperType\" if year == 2017 else \"DevType\"\n",
    "    \n",
    "    # setting the file path of the dataset\n",
    "    file = str(year) + \"_data.zip\"\n",
    "    file_path = os.path.join(\"data\", file)\n",
    "    \n",
    "    # loading the data and adding the year as a column\n",
    "    cols = [employment, devtype]\n",
    "    df = pd.read_csv(file_path, usecols=cols)[cols]\n",
    "    df.columns = ['employment', 'devtype']\n",
    "    df['year'] = year\n",
    "    df.dropna(inplace=True)\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2cc914b5",
   "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>employment</th>\n",
       "      <th>devtype</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>Other</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>Mobile developer; Graphics programming; Deskto...</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>Web developer</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>Web developer; Mobile developer</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Independent contractor, freelancer, or self-em...</td>\n",
       "      <td>Mobile developer; Desktop applications developer</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          employment  \\\n",
       "0                                 Employed full-time   \n",
       "1                                 Employed full-time   \n",
       "2                                 Employed full-time   \n",
       "3                                 Employed full-time   \n",
       "4  Independent contractor, freelancer, or self-em...   \n",
       "\n",
       "                                             devtype  year  \n",
       "0                                              Other  2017  \n",
       "1  Mobile developer; Graphics programming; Deskto...  2017  \n",
       "2                                      Web developer  2017  \n",
       "3                    Web developer; Mobile developer  2017  \n",
       "4   Mobile developer; Desktop applications developer  2017  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# loading and concatenating the data for all the selected years\n",
    "data = [load_data(year) for year in range(2017, 2021)]\n",
    "df = pd.concat(data, ignore_index=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acbf878f",
   "metadata": {},
   "source": [
    "# 2.0 Differences between Data Scientists and Other Developers\n",
    "\n",
    "## 2.1 Identifying Data Scientists\n",
    "\n",
    "Here we are replicating the figure generated in another notebook. This is just a process to validate our data wrangling in this notebok that is a little bit different from the other one. From the figure bellow we can see that with different steps we achieved the same result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "50321c79",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# identifying data scientists\n",
    "df['data_science'] = df['devtype'].str.contains('Data scientist|Machine learning')\n",
    "\n",
    "# ploting the evol of the share of data scientists\n",
    "ax = df.groupby('year')['data_science'].mean().plot(figsize=(12, 6), marker='.', markersize=12)\n",
    "plt.ylim(0, 0.2)\n",
    "plt.xticks([2017, 2018, 2019, 2020])\n",
    "ax.yaxis.set_major_formatter(lambda x, p: f'{x:.0%}')\n",
    "plt.xlabel('Survey Years')\n",
    "plt.ylabel('Percentage of Repondents')\n",
    "#plt.title('Percentage of Data Scientists Developers')\n",
    "plt.savefig('figs/ds_evol.png', bbox_inches='tight', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c79096fc",
   "metadata": {},
   "source": [
    "Here we also have not so many good news for Data Scients. For all the years, the group of data scientists developers had a greater unemployment rate compared to others developers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "714df4b8",
   "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>data_science</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>0.012204</td>\n",
       "      <td>0.018915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>0.059030</td>\n",
       "      <td>0.082759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>0.065442</td>\n",
       "      <td>0.083282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>0.037834</td>\n",
       "      <td>0.056921</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "data_science     False     True \n",
       "year                            \n",
       "2017          0.012204  0.018915\n",
       "2018          0.059030  0.082759\n",
       "2019          0.065442  0.083282\n",
       "2020          0.037834  0.056921"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df.groupby(['data_science', 'year'])\n",
    " ['employment'].value_counts(normalize=True)\n",
    " [:,:, \"Not employed, but looking for work\"].\n",
    " rename('percent').reset_index().\n",
    " pivot(index=\"year\", values=\"percent\", columns=\"data_science\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3ceafe09",
   "metadata": {},
   "outputs": [],
   "source": [
    "employment_distribution = (df.groupby(['data_science', 'year'])['employment'].\n",
    "                           value_counts(normalize=True).rename('percent').reset_index())\n",
    "unemployed_select = employment_distribution['employment'] == \"Not employed, but looking for work\"\n",
    "unemployed_evol = (employment_distribution.loc[unemployed_select].\n",
    "                   pivot(index='year', values='percent', columns='data_science'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c51aa3aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WTZs2sWPHDrZv387UqVNZsWIFM2fOZNeuXbz77rvA5bPX33///VWfu23bNrKysvjxxx8ByMjI4Pjx4wA0bNgQd3d3ACpWrEhycjJubm7XfV8dOnRg8ODBdOrUiW7dulGnTp2rSnjDhg157733iImJoX379owZM+Y2v4J3TiVbRERE5DZ5NGhf4Nnmcx+N+nNU5Gr2nv6UG/5aoWfKycnh9OnTVK9enXXr1vHhhx8yYsQI+vXrR2JiIhaL5arXR0dHM3z4cIYNG0bbtm3x9/fn8OHDV70mKSmJmTNn8uKLL9K2bVvatm3L6NGjad26NQkJCdjbX10pT548SdWqVfM/NpvNTJ06lXr16gGXR0+8vLxYtmwZTk5O+a8zGAzX5PurCRMmcOTIETZu3Mi4ceN44oknaNy4cf7zVapUYdWqVWzevJlffvmFr776ilWrVmEwGG79C3mHNJMtIiIiUoR8OgzFYO901WMGeyd8Ogwt9H2ZzWY++ugjQkNDqVSpEtu3byc8PJz+/fvj7+/Prl27MJlMABiNRvLy8jhw4ACVK1fmX//6F6GhoWzatCn/NVd4eHiwfv16Fi9enP/YuXPn8PPzw8vLi7CwMFauXAlcLtiPPPLIVcW2efPmzJs3D4DY2Fj69OlDdHT0Dd+Hvb09eXl5Vz2Wl5dH165d8fHxYdSoUURERHD48GHs7e3z886ePZuPPvqI8PBwJk6cSEJCAqmpqbf/Bb0DOpMtIiIiUoQ8QtoCkPjLHPJS4rH39MOnw9D8x+9UbGxs/q3yzGYzderUYdq0acDlCxufe+45Vq9ejaOjI/fcc0/+eEWnTp2IiIhg/vz5zJs3jx49euDo6EiDBg3yRzmuMBqNfP7550yePJkPPvgAZ2dnAgIC+PTTTzEajTz11FNMmDCBPn36YG9vzzvvvHNVyX7iiSeYNGkSvXr1wmQyMW7cOCpVqkRkZOR135Ofnx/lypVj+PDhvP3228Dl4v3UU0/x4IMP4uzsjKenJ1OmTMHX15eyZcsyfPhwPvnkE5599ll69+6Nvb09TzzxBJ6enoXydb5VBss/nZMvgbKzszl48CAhISFX/frBGqKioq76lYWIXJ+OFZGbo2OleDp8+PB1Z5bFdtLT0284x11Y/v7fvaDOqXEREREREZFCppItIiIiIlLIVLJFRERERAqZSraIiIjILbrLLmmTAtzOf2+VbBEREZFb4OzsTHx8vIp2KWGxWIiPj8fZ2fmWPk+38BMRERG5BRUqVOD8+fPExcXZOor8KScnB0dHxyLbvrOzMxUqVLilz1HJFhEREbkFDg4OV61mKLYXFRVFaGiorWNcReMiIiIiIiKFTCVbRERERKSQqWSLiIiIiBQylWwRERERkUKmki0iIiIiUshUskVERERECplKtoiIiIhIIVPJFhEREREpZCrZIiIiIiKFTCVbRERERKSQaVl1ERGRYib14CYSf5mDd8olzm3zx6fDUDxC2to6lojcApVsEbEaFQeRyywWC1jMYLFg+fPfVz5O/W0rCWu+wpKXgwHIS7nEpRWfAuh4ESlBVLJFxCpSD27i0opPseRlqzjcpL8Wsf+VsSvF7G/Pmf98jr+Wtsuvufyc5W+F7urt/b3sXbONK382m7FwvefMl5/7y+NX57yZ527xvV557koe89W5r30PN8h93dde3h5cJ7fZ/LdtWP73tb/Bc3/f/i3/v5CXTeIvc3SsiJQgKtkiYhWJv8zBkpd91WOWvGzif/oSc1bGdUvK3wvWP5fC65U0y/+KH397zvwP5eqa7RZQPK967m+5/148+Yfn/lY8Sx8DGAxgZ4fBYHf5zwYDGOwwXPXn6z13+bEbP3f1NjAYMNj9+Vo7I3ZXnuNvz91w31f2d719/+05uz+3wXVea3d1fgwGEtZ+c92vTl7KJTLPHcK5Yt0/tycixZlKtohYRV5K/HUfN2elEf/TF7ewpb8VqBuWob8Wl9spaX8rSkb7a4vY30vUNdu5tojd2nN/y2n399ddLm6XH7/OcwYDBoPxFsvq30uu3eV95H+tr/de/15kb/K9/jX3lX0IKTtXkJdy6TrPGIie9QqOAZXxDAvHvV4b7BydrZ5PRG6OSraIWIXR3RtTWuK1j3v4UuHhd29Qjq8+46ciJqWBT4eh+aNVVxjsnfDr/jBYLKTsWsWllZ+SsH4WHqGd8GzcDQefsjZMLCLXo5ItIkUuNyEac27ONY8b7J3w7Tgco5uXDVKJFE9X5q4Tf5lDbsolHDyvvkjYI7QT2eePkBy5iuRdK0jesQzX6o3wDAvHJTj08g+mImJzKtkiUqRyk2K5MGcSBjs7vDsMJTXqp+sWBxH5H4+QtniEtCUqKorGjRtf9ZzBYMC5Yh2cK9YhLyWelD0/k7rnZy5+9wYOvuXwDOuOR/322Dm72Si9iIBKtogUobyUS0TPmYglJ4ugoZNwKlsVn5b9rlscROTW2Xv64dvufnxa9Sf9yK8kR64kfs1XJGyYi0f99ng27o5jmYq2jilSKtmkZC9ZsoTPP/8cgLZt2/LCCy8wZcoUNm/eTJ06dZg6dSoAK1euJDExkaFDh9oipojcgbzUBC7MnogpM41yQybiVLaqrSOJ3LUM9g64h7TBPaQN2RdOkBy1itS960iJWo1Llfp4hoXjWiMMg53R1lFFSg2rD25lZmby5ptvMmvWLJYsWUJkZCSrV69m8+bNLF++nOTkZI4cOUJubi4LFy5k0KBB1o4oIncoLy2J6DmTMKUnETT4ZZzKVbd1JJFSw6lcdQJ6P0mlJz/Dp/1QchKiifnhHX6fOYakbYswZaTaOqJIqWD1M9kmkwmz2UxmZiaurq7k5eXh6+uLyWQiLy+PrKwsHBwcmDdvHgMGDMDeXhMtIiWJKSOF6LmvkpdyibL3T8C5fE1bRxIplYxuXvi06od3iwgyju0iOXIVCb/MJnHzfNzqtsarSThOZYNtHVPkrmX1Buvu7s6///1vwsPDcXZ2pmnTpjRp0oT+/fvTr18/2rRpQ2BgIFu3buWzzz6zdjwRuQOmzFSi575GXuJFyg56EZdKdW0dSaTUM9gZcavdHLfazcmJPUdy1CrSDmwkbf96nCrUwiusB261m2EwOtg6qshdxWC5vBSa1Rw5coTx48fz5Zdf4uHhwXPPPUeDBg0YOXJk/mumT59O69atSUhIYOHChVSsWJEXX3wRO7uCp1uys7M5ePBgUb4FEbme3Cw8ds3DmBpLWqOB5JXRGTKR4sqQm4XjH/txOheFMSMRs5M72RUbkl2hIRZnd1vHEylRQkJCcHJyuuZxq5/J3rJlCy1atMDPzw+Afv36MXfu3PySHRMTw9mzZ3nmmWfo1q0bS5cu5c0332Tbtm20bt36pvdzozdclHTHBCmtzNmZRM97jey0OAIHPo9bjbB/fL2OFZGbU7THSissFjOZJ/eSHLkSuxObcTm1Hbc6zfEKC8epfC0t/iQlhi2+rxR0YtfqJbt27dpMnTqVjIwMXFxcWL9+PfXr189/fsaMGYwZMwaA3Nxc7OzssLOzIzs7+0abFBEbMudkcfH7N8m+cILAfs8VWLBFpPgwGOxwrd4I1+qNyE2IJjlqNWn71pN+aAuOZYPxCgvHrW4r7Byse9JK5G5g9ZLdunVrfvvtN/r164eDgwP169fn0UcfBeDYsWMA1KhRA4ARI0YQERFB+fLladOmjbWjikgBzLnZXFwwmazzRwm492ncajezdSQRuU0OvkH4d3kQ33b3k3ZgE8lRq4hb/jHx677F454/l2/3CrB1TJESw+oz2UXtyql7jYuIFC1zXg4xCyaTeWo/ZSKeuqWVG3WsiNwcWx4rFouFrLMHSY5cRcaxXQC41gjDq0kPnCuHaJREihVbjosUm5lsESn5LKZcYn98l8xT+yjTa4yWRhe5CxkMBlyq1MelSn3ykuNI2b2GlL1ryTi2Ewf/CniFheNevx12ji62jipSLKlki8gtsZjyiFk0nYwTUfiHj8IjtKOtI4lIEbP3KoNvh6F4txlI+m9bSd61ikurvyD+lzl4NOhwefl2v3K2jilSrKhki8hNs5hNxC75gIyjO/Dr+jCejbraOpKIWJGdvSMeDTrgXr892ReOkxK5ipSon0jZtQKX4IZ4hYXjUr0hBoPVF5QWKXZUskXkpljMJuKWzSD98DZ8Oz2AV5Meto4kIjZiMBhwLl8T5/I18e00gtQ9a0nZvYaL89/C3jsQz7DueDToiNFF99yW0kslW0QKZLGYiVvxKWkHN+HTfijezfvYOpKIFBP27j74tBmId8u+pB/dQUrkKhLWfkPixu9wD2mLV1g4jgGVbR1TxOpUskXkH1ksFi6t+oK0/evxbnMfPq362TqSiBRDBqM97nVb4V63FdkXT5MSeXn59tQ9P+NcqR6eYeG41WqKwc5o66giVqGSLSI3ZLFYiF/zFal71uDdsi8+be6zdSQRKQGcylalTK/R+HYaTuredaRE/UTswncxevjh2agrng27YHTzsnVMkSKlki0i12WxWEhY9y0pkSvxatYbn/ZDdV9cEbklRhcPvFvci1ez3mSc2E1K5CoSN84jccsC3Ou2wjOsB87lqts6pkiRUMkWkWtYLBYSN8wlecdSPMPC8e30gAq2iNw2g50Rt5pNcKvZhJxL50mJWk3q/l9IO7ARp3I18AwLx71OSwz2DraOKlJodI8dEblG0pYFJG1biEfDLvh1fUgFW0QKjaN/Bfy7jaTyU1/g1/VhzNnpxC39kHMzRpGwYR55KfG2jihSKHQmW0SukrRtIYmbvse9QQf8wx/V/W5FpEjYObni1aQHnmHhZJ7eT0rkKpK2/kjStoW41WqGZ5NwnCvW1Q/5UmKpZItIvqQdy0j4ZQ7u9dpQpufjKtgiUuQMBgOuwaG4BoeSmxRDStRPpO5dR/qR7TgGVL48SlKvDXaOzraOKnJLVLJFBIDkyFUkrP0vbnVaUKbPk7rNlohYnYN3IH6dRuDTdhBphzaTsmsVl1Z+SsL6WXiEdsKzcTccfMraOqbITVHJFhFSdq8h/qf/w7VmEwIinlbBFhGbsnNwwvOezniEdiL7/BGSI1eRvGsFyTuW4Vq9EZ5h4bgEh+q3bVKsqWSLlHKp+9ZzadXnuFRrRGDfsRiM+mtBRIoHg8GAc8U6OFesQ15KPCl7fiZ1z89c/O4NHHzL/bl8ewfsnFxtHVXkGvpuKlKKpR3cTNzymbhUbUDggHG6fZaIFFv2nn74trsfn1b9ST/yK8mRK4lf8xUJG+biUb89nmHhOPpXsHVMkXwq2SKlVNrhbcQu/RDnyvUIHPgCdvaOto4kIlIgg70D7iFtcA9pQ/aFEyRHrfpzVcnVuFSpj2dYD1xrNNbYm9icSrZIKZR+dCexi9/HqXxNyt43HjsHJ1tHEhG5ZU7lqhNQ7klMHUeQsncdKbt/IuaHKdh7lcGzUTc87umM0dXD1jGllFLJFillMk5EEbNwGk5lgwm6/yXsHF1sHUlE5I4Y3bzwadUP7xYRZBzbdfluSb/MJnHzfNzqtsarSThOZYNtHVNKGZVskVIk49ReYn6YimNAZcoOflkXC4nIXcVgZ8StdnPcajcnJ/YcyVGrSDuwkbT963GqUAuvsB641W6GwajrT6ToqWSLlBKZZw4Qs2AKDn7lCRryMkZnN1tHEhEpMo4BlSgTPgrfDsNI2/8LyZGriF08HaO7D54Nu+LRsAv2Hj62jil3MZVskVIg6/fDXJz/NvY+gQQNeQWji2YURaR0MDq74dW0F55NepB5ci/JkStJ3Pw9iVt/xK1Oc7zCwnEqX0vLt0uhU8kWuctl/XGM6O/exN7Tj6AhEzG6edk6koiI1RkMdrhWb4Rr9UbkJkSTHLWatH3rST+0BceywXiFheNWt5UuBJdCc1NLJaWlpQGwd+9eFi9eTG5ubpGGEpHCkR19kovzXsfo5kXQkEnYu+tXoyIiDr5B+Hd5kEpPfY5/90exmHKJW/4x5z4aRfz6WeQmx9o6otwFCjyT/cEHH3Du3DnGjh3L6NGjqV69Ort27eLNN9+0Rj4RuU3ZMWeInvsads7ulBs6CXtPP1tHEhEpVuwcXfBs3A2PRl3JOnvw8vLtvy4l+deluNYIw6tJD5wrh2iURG5LgSV748aNzJkzh++//56ePXvy0ksv0b9/f2tkE5HblBN3jui5r2JwdCZo2CTsvcrYOpKISLFlMBhwqVIflyr1yUuOI2X3GlL2riXj2E4c/CvgFRaOe/12uuWp3JKbGhdxcXFh27ZtNG/eHICcnJwiDSUity8n/g+i57yKwc5IuaGTcPAOtHUkEZESw96rDL4dhlLpyc8o0/sJDPZOXFr9BWc/fJRLa74iJ/6CrSNKCVHgmWwfHx8mTZrEwYMH+eCDD3j33XcJCAiwRjYRuUW5CdFEz54EQNDQSTj4Btk2kIhICWVn74hHgw64129P9oXjpESuIiXqJ1J2rcAluCFeYeG4VG+IwXBT5yulFCqwZE+ZMoX58+fz2Wef4eLigsFg4J133rFGNhG5BblJsVyYMwmLKZdyw17D0b+CrSOJiJR4BoMB5/I1cS5fE99OI0jds5aU3Wu4OP8t7L0D8QzrjkeDjhhd3G0dVYqZAn/8mj17NqNHj6Z+/foAjB07lk8++aTIg4nIzctLuUT07IlYcrIIGjIRx4BKto4kInLXsXf3wafNQCo98QkBfZ/F3sOXhLXfcO6jR4lb+Sk5sWdtHVGKkRueyf7www9JSUlh5cqV+bfwA8jNzWX9+vVMmDDBKgFF5J/lpSZwYfZETFlplBsyEaeyVW0dSUTkrmYw2uNetxXudVuRffE0KZGXl29P3fMzzpXq4RkWjlutphjsjLaOKjZ0w5IdGhrKgQMHsLOzw9vbO/9xo9HIRx99ZI1sIlKAvLQkoudMxJSeRNCQiTiVq27rSCIipYpT2aqU6TUa307DSd27jpSon4hd+C5GDz88G3XFs2EXLQJWSt2wZLdr14527drRtm1bGjRoYM1MInITTOnJRM+dRF5KPEGDX8a5fE1bRxIRKbWMLh54t7gXr2a9yTixm5TIVSRunEfilgW4122FZ1gPnHUipFQp8MJHV1dX/vOf/5CUlITFYsl//NNPPy3SYCJyY6bMVKLnvkZeYgxlB72Ic8U6to4kIiKAwc6IW80muNVsQs6l86RErSZ1/y+kHdiIU7kaeIaF416nJQZ7B1tHlSJWYMl+/vnnadSoEU2aNNGKRyLFgCkrnei5r5MTf56y9/0Hlyr1bR1JRESuw9G/Av7dRuLbfgip+zeQErWKuKUfkrDuGzzu6YJno65ajfcuVmDJzs3N1UWOIsWEOTuTi9+9QU7sWcoOeB7X4HtsHUlERApg5+SKV5MeeIaFk3l6PymRq0ja+iNJ2xbiVrsZnmHhOFesq5OZd5kCS3blypWJjY3VAjQiNmbOyeLi92+SHX2SwH5jca3R2NaRRETkFhgMBlyDQ3ENDiU3KYaUqJ9I3buO9MPbcQyofHmUpF4b7BydbR1VCkGBJdtsNtOrVy/q1auHk5NT/uOayRaxHnNuNhfnv03W+aME9H0Gt1rNbB1JRETugIN3IH6dRuDTdhBphzaTsmsVl1Z+SsL6WXiEdsKzcTccfMraOqbcgQJLdpcuXejSpYs1sojIdZjzcoj5YQpZZw8REPFv3Ou0tHUkEREpJHYOTnje0xmP0E5knz9C8q6VJO9cTvKOZbhWb4RnWDguwaFavr0EKrBk9+3bl4sXL3L06FFat25NbGwsQUFB1sgmUupZTLnE/vgumaf2UabXGNxD2tg6koiIFAGDwYBzxTo4V6xDXko8KXvWkLrnZy5+9wYOvuX+XL69A3ZOrraOKjepwJK9ceNGJk6ciJ2dHd999x09evRg6tSpdO7c2Rr5REotiymPmIXvkXEiCv/wUXiEdrR1JBGxkg1Rv/PtqsPEJWZSZlU8I8Lr0L5xRVvHEiux9/TDt91gfFoNIP3IryRHriR+zVckbJiLR/32eIaF4+hfwdYxpQAF/u7ho48+Yv78+Xh6ehIQEMDcuXP58MMPrZFNpNSymE3ELnmfjGM78ev6MJ6Nuto6kohYyYao35mxYB9xiZkAxCVmMmPBPjZE/W7jZGJtBnsH3EPaUP5fb1P+wSm41W5O6t51nP/s30TPmUT60Z1YzCZbx5QbuKkLH/96Z5E6deroFjMiRchiNhG3bAbph7fj2/kBvJr0sHUkESkCObkmElKyiE/OIiE5i/iULBJSsli59TTZuVcXp+xcE9+uOqyz2aWYU7nqBJR7ElPHEaTsXUfK7p+I+WEK9l5l8GzcHY/QThhdPWwdU/6iwJLt4uLChQsX8ot1ZGTkVXcZEZHCY7GYiVvxCWkHN+HTfijezfrYOpKI3CKT2UJSatb/CnTKnyU6+cpjmSSkZJGakXvN5zrY25GbZ77uduMSM8nKzsPZqcBv3XIXM7p54dOqH94tIsg4tovkyFUkrJ9F4qbvca/XGs+wcJzKBts6pnATJXvs2LE89NBDxMXFMWjQIM6cOcNHH31kjWwipYrFYuHSqs9J2/8LPm0G4dOqn60jichfWCwW0jJz/1KYM4n/+5no5CySUrMwW67+XDsDeHs44+vlTFk/N+oG++Hn6YyflzO+ni74el3+s7uLAw+/+XP+qMjfPfj6Grq3qEKv1lXx83KxwruW4spgZ8StdnPcajcnJ/YcyVGrSDuwkdR963GqUAuvsB641W6Gwajl222lwJLdqFEj5s+fz549ezCbzYSGhuLr62uNbCKlhsViIX7Nl6Tu+Rnvlv3wbjPQ1pFESpWsnLyrRzeunIH+y5nnhOQscq5zltnD1QFfT2f8vFyoUtYzvzD7ev7v397uThiNN3cLthHhdZixYN9VIyNODkb6tq/OuZgUFv5ynEUbTtDmnvJEtK1G9YrehfVlkBLKMaASZcJH4dthGGn7fyE5chWxi6djdPfBs2FXPBp2wd7Dx9YxS50bluxdu3Zd9bGr6+Vbxpw8eZKTJ0/SpEmTok0mUkpYLBYS1n1DSuQqvJr1waf9EF33IFJI8kxmElOySUjJvGp84+qzz5mkZ+Vd87mODkb8/izMNSv54Oflcrk4ezrnF2kfT2ecHIyFmvnK3HX+3UV8XK66u8jF+HSWbTnFzzvOsmH3eeoF+3Fvu2o0qVsWo53+7ijNjM5ueDXthWeTHmSe3Ety5EoSN39P4tYfcavTHK+wcJzK19L3GCu5Ycl+7bXXAMjMzOTChQvUqFEDo9HIsWPHqFatGkuWLLFaSJG7lcViIXHDXJJ3LMMzrAe+nUboLz+Rm2CxWEhJz8kvzH+fd77yeHJaNpa/jW4Y7Qz4/FmWKwS4E1rdH9+/nXn283LB1dneZsdj+8YVad+4IlFRUTRu3Piq58r6ufFIRH2GdK3NzzvPsnTzKd78eidBfm70aRtMpyaVcNHcdqlmMNjhWr0RrtUbkZsQTXLUatL2rSf90BYcywbjFRaOW91W2DnoGruidMOjcNmyZQA8/fTTvPPOOzRq1AiAQ4cOaUl1kUKStHkBSdsW4tGwK35dH1LBFgEysnKvKsp/nXf+X5HOJs907eiGl7tjfkmuVt77f8X5LyXay80Ju7vgjK+biwP3tqtO79bBbD8YzeINJ/ls0QFmrz5C9+aV6dU6GH9vzW2Xdg6+Qfh3eRDfdveTdmATyVGriFv+MfHrvsXjnj+Xb/cKKHhDcssK/FH39OnT+QUboF69epw9e7ZIQ4mUBolbF5K4+XvcG3TEP/wRFWy56+XmmUn868hGSuZVBfrK45nZ145uuDjZ55fkKxcN+no54+fpkn/22cfTCQf7wh3dKAmMRjtah5andWh5jpxJYPHGkyzacILFG0/SKrQc97arRo2Kmsct7ewcXfBs3A2PRl3JOnuQ5MhVJP+6lORfl+JaIwyvJj1wrhyi70WFqMCS7ezszMKFC4mIiMBisbBgwQI8PT2tkU3krpW0YymJG+bgHtKWMj0fw2C4uQuiRIojs9lCcnr2P96uLiEli+S0nGs+195o+N9Fg0GeNK4dgO9fZp59PS//4+qsOyTcjNpVfBlfxZeYhAyWbT7Fmh1n2bTnD+oF+xHRNpim9YI0t13KGQwGXKrUx6VKffKS40jZvYaUvWvJOLYTB/8KeIWF416/HXaO+i3InSqwZL/11ls899xzTJgwAYPBQL169Zg2bZo1sonclZJ3rSRh7Te41WlBmd5PYLArfWfepGSwWCxkZOXlF+WrSvRf/p2YkoXpb/esMxjA290JXy9n/L1dqFXZ928zz5f/7enmqDNnRSDQ15WRESEM6VaLNTvOsWzzSd767y7K+rnSp001OjfV3LaAvVcZfDsMxbvNQNJ/20ryrlVcWv0F8b/MwaNBBzwbd8fRr5ytY5ZYBR5h1apVY9GiRSQlJWEwGPDy8rJGLpG7UsruNcSv+RLXmk0JiHhaBVts5karDf69UGfnXLtks5uLQ/5dNhpU9798Bw7Pv849u+Dt4YT9Td6yToqOq7MD97arRu/WVfn14EUWbzzB54sPMGf1Ybo1r0Kv1sGU8dEZy9LOzt4RjwYdcK/fnuwLx0mJXEVK1E+k7FqBS3BDvMLCcaneUL91vUUFluwTJ07w5ZdfkpSUhOUvl2jr4keRW5O6bz2XVn2Ga/XGBPZ9FoNRZ5Gk8F1vtcErRbqg1QYd7e3+HNNwoXoF72vPPHs54+vhrBUHSyCj0Y5WoeVoFVqOI2cTWLLxJIs3Xf6ndYNyRLSrRs1Kmtsu7QwGA87la+Jcvia+nUaQumctKbvXcHH+W9h7B+IZ1h2PBh0xurjbOmqJUODflM8//zyNGjWiSZMm+pWeyG1KPbiJuOUzcQkOJaD/cxjsNV8qt+a6qw3+bWyjoNUG/f5cbbBesN+fFw064+vlkn8W2t3FQX/PlwK1K/tSe4QvsQkZLNvy59z23j+oU8WXe9tVo1mI5rYF7N198GkzEO+WfUk/uoOUyFUkrP2GxI3f4R7SFq+wcBwDKts6ZrFWYMnOzc1lwoQJ1sgicldKO7yNuKUf4Vy5HoEDXsDO3tHWkaSYubPVBh3zzzZXDfL830WD+RcPuuDl7qTSJNcI8HXl4T4hDO5ai7U7z7F08yne/mYXgb6u9GkTTOemlXTBqWAw2uNetxXudVuRffE0KZF/Lt++52ecK9XDs0k4bjWbavzxOgos2ZUrVyY2NpaAAN1DUeRWpR/dQeyi6ThXqEXZ+/6jG/+XMney2qCToxG/P+eba1XyveZuG1f+7FjIqw1K6ePq7ECfttXo2TqYHQejWbzxJF8sOcicn47QtVllercOJsDX1dYxpRhwKluVMr1G49tpOKl715ES9ROxP76L0cMPz0Zd8WzYBaObrt27osCSbTab6dWrF/Xq1cPJ6X8F4U5mstevX8+MGTPIyMigdevWTJgwgWeffZZjx47RoUMHxo4dC8AXX3xBzZo1adeu3W3vS8RWMo5HEbPwPZzKVafsoJewc3S2dSQpJIWy2qDX1asN+v15z+crFw/acrVBKZ2MdgZaNihHywblOHYukSUbT7J08ymWbj5Fy/pB3NuuGrUq+9o6phQDRhcPvFvci1ez3mSc2E1K5CoSN84jccsC3Ou2wjOsB87lqts6ps0VWLK7dOlCly5dCm2Hv//+OxMnTmTBggX4+fnxwAMP8M0335Camsry5cvp3bs3jz76KCaTib179/LII48U2r5FrCXj1F4u/vgOjgGVKXv/BOycdPV+SXFltcHr3q4uv0TfeLXBK0X5yoWDf1+q29PN8a5YbVDubjUr+TBueBgPJGawfMtp1vx6hi37LlCnii8RbavRPKQsRt09ptQz2Blxq9kEt5pNyLl0npSo1aTu/4W0AxtxKlcDz7Bw3Ou0LLXXIRVYsvv27csff/zBzp07ycvLo2nTplSufPuD7j///DM9evSgbNmyAEyfPp3z58+zfv168vLyyM3NxWg0MnPmTEaNGnXb+xGxlcwzB4hZMAVHvwoEDXkZo7ObrSMJd7baoKuzfX5hrhfsl1+Y/zr77OPhjIO9SofcXQJ8XHmodz3u71KTtbvOsWzzKSZ/u4sAX1d6tw6mazPNbctljv4V8O82Et/2Q0jdv4GUqFXELf2QhHXf4HFPFzwbdcXe08/WMa2qwJK9efNmxo0bR+PGjTGZTLzzzju8/fbbdO7c+bZ2ePbsWRwcHHj44YeJi4ujQ4cOPP3009SqVYt+/foxePBgEhISiI+Pp0GDBre1DxFbyTz3Gxfnv429TyBBQydidPGwdaS73t9XG7ze7epuvNqgXX5RrlLu8mqD+XPPf1406OvprEU7pNRzdXagT5tq9GwVzM5D0SzZdIovlx5k7k9H6Na8Mr1aBxOouW0B7Jxc8WrSA8+wcDJP7yclchVJW38kadtC3Go3wzMsHOeKdUvFOJzBYvn7xODVBgwYwOTJk6le/fJszfHjxxk3bhyLFy++rR1OmDCBPXv2MGvWLFxdXRk9ejS9evWiX79++a8ZP348jz/+OFu2bGHTpk2EhoYyevTom9p+dnY2Bw8evK1sInfCmPQHHrvmYXb2ILXpUCxOuo/o3+0/nc66fSkkZ5jwcjXSKdSTBlWvf6bfYrGQnWshJdNEaqaJ1Iw//51pIiXDRGqmmdRME2mZpmtuWQfg7myHh6sRD5fL/3i6GP/ysR0eLkZcnexKxV/0IkXhj/gcth9J5dC5TADqVHShRS13KpbRBd5yNbuMJJx+343j+b3Y5WaR5xFAdqXG5JQLAWPJ/01ISEjIVdctXnFTt/C7UrABatSogcl07QpgN8vf358WLVrg63v54olOnTqxf//+/JJ98OBB3N3d8fPzY968eSxbtoyRI0dy+vRpqlatetP7udEbLkpRUVE0btzYqvuU4iH7wgmif3kfO09fyg1/HXsPXRz0dxuifmdF5D6ycy///ZGcYWLZrmRy7LwJ9HO7pdUG/bycKePnTO0rs85X7vf858c+Hk6aF5W7QnH+vtIY6NMV4hIzWbH1FKt/Pctv5+KoVdmHe9tVo0VIkI5D+YtOmHOzSTu0mZRdq7A/tAqPk5vwCO2EZ+NuOPiUvaOt2+JYKejEboEl29nZmQMHDlC/fn0ADhw4gIvL7V/E1aFDB1544QVSUlJwc3Nj8+bNdOrUKf/5mTNn8sYbb+SvLmkwGLCzsyM7O/u29ylSlLIvniZ63uvYObtTbtirKtg38O2qw/kF+4rcPDPLt54GLq82eGXOuUYF778s0f2/iwZ9PJ1wdtTohkhxUsbHhX/1qsegLrVYt+scSzedYsq3kQT4uNC7TTBdmlbGzaXkn62UO2fn4ITnPZ3xCO1E9vkjJO9aSfLO5STvWIZr9UZ4NumBS9UGd83y7QV+txo3bhyPPfYYlStXxmKxcObMGT744IPb3mFoaCgjR45kyJAh5Obm0qpVK/r37w/Axo0bqVevXv5Z7latWtGzZ09CQkKoXbv2be9TpKjkxJ4jet5rGBydCRo2CXtPf1tHKrbiEjNv+Ny818Nx02qDIiWai5M9vVoHE96yKjsPXWTJppN8ufQQc386SpdmlejdOpiyfroQXP5cvr1iHZwr1iEvJZ6UPWtI3fMzF+e9joNvuT+Xb++AnVPJnvMvcCYbICkpiX379mGxWAgNDcXHx8ca2W7LlVP3GheRopZz6TzRs18Bg5Fyw1/DwTfI1pGKJbPZwo+/HOfblYev+3wZHxe+mtDVyqlESoaS/n3lxO9JLNl0ks17/8BisdC8fhD3tq1O7So++qFarmLJyyX9yK8kR64k+49jGByd8ajfHs+wcBz9KxT4+bYcF7ntmWyTycSKFSvYsmULRqORhISEqy5SFCmNchMuED1nEmAgaOhEFewbSE7L5r15u9l9JJZalbw5HZ1CTu7/7i/t5GBkRHgdGyYUkaJUvaI3Y4c25oGedVmx9TSrtp9h2/5oalXyIaJtNVo20Ny2XGawd8A9pA3uIW3IvnCC5KhVf64quRqXKvXxDOuBa43GJWr59gJL9htvvMGJEyeIiIjAYrHwww8/cPbsWZ555hlr5BMpdnKTYrgwexIWs4lyw169qZ+wS6PfTsfzzqxIUtJzGN2/Ad1bVGHj7vN8u+owcYmZlPFxYUR4Hdo3rmjrqCJSxPy9XXigZ13u61yT9bvOsWTzKd6ZHUkZHxd6tQqma/PKuGtuW/7kVK46AeWexNRxBCl715Gy+ydifpiCvVcZPBt3xyO0E0bX4n+L3ALHRbp27cqKFStwcLj8P392djZ9+vThp59+skrAW6VxESlKeclxXJj1CubsDIKGvYpTYBVbRyp2zGYLizac4NtVhwn0ceWFEWFUq+B91Wt0rIjcnLv1WDGbLez67SJLNp3iwMlLuDgZ6dK0Mr3baG5brmUxm8g4tovkyFVknT2Iwd4R93qt8QwLJ+fSeRJ/mUNuyiUcPP3x6TAUj5C2Vsl1x+MiPj4+mEym/JJtMBjw9PQs/KQixVxeSjwX5kzCnJVG0NBJKtjXkZKew/R5u4k8HEOr0HI8dd89Wg1ORK5hZ2egWUgQzUKCOHH+8tz2iq2nWb7lFM1CgohoW426VX01ty3An8u3126OW+3m5MSeIzlqFWkHNpK6bz0YDGCxYADyUi5xacWnAFYr2v+kwJIdHBzMkCFD6NevH0ajkZUrV+Lj48PXX38NwIMPPljkIUVsLS8tkei5kzClJxM05BWcgqrZOlKxc+RMAlNmRZKUms1jfevTo1VVfYMUkQJVr+DN2CGN+deVue1tZ9h+IJoaFb2JaFuNVqHlsNfctvzJMaASZcJH4dthGL9//DjmrPSrnrfkZZP4y5ySUbIBatWqxaFDhwCoUOHy/OmxY8eKLpVIMWJKTyZ6ziTyUhIIGjwB5/I1bR2pWLFYLCzeeJJvVvyGv7cLU59sQ/WK3raOJSIljJ+XCyN61OW+TjVZH/U7Szae5N05Ufx3+SF6twmma/MqmtuWfEZnN8xZGdd9Li8l3spprq/Akv32228DkJKSojERKXVMmalEz32NvKRYyt7/Es4VdSeMv0rNyOH9eXvY+dtFWtQP4qlBDfVNUETuiLOTPT1aVqV78ypEHolhycaTfL38N+atOUrnppXo06YaQf6a2xaw9/QjL+XSdR8vDgr8/cvp06fp2bMnPXv2JCYmhvDwcE6ePGmNbCI2ZcpKJ3ru6+TG/0HgwPG4VA6xdaRi5cjZBP793gZ2H43hkXtD+M8DTVSwRaTQ2NkZaFq3LG8+3ooPnm1PywblWL39DKMmr+XNr3dw6FQ8N7HUh9zFfDoMxWB/9QWHBnsnfDoMtVGiqxVYsl9//XVefPFF/Pz8CAwMZNiwYbzyyivWyCZiM+bsDC7Oe52c2LMEDhiHa3CorSMVG1fGQ8bP2ILBYGDKE23o06aa5q9FpMgEl/fimcGN+L+XujCwU00OnYpn/MdbePaDTWzYfZ48k7ngjchdxyOkLf49H8Pe0x8LYO/pj3/Px4rFPDbcxLhIUlISrVq1YurUqQAMHTqU+fPnF3kwEVsx52Ry8fu3yL54isB+z+Fa/e67fdbtSsvI4f3v9rDj0EWa1SvL0/c3xN3V0daxRKSU8PNyYXh4HQZ2qsEvkb+zZNMppv05t92rdTDdm1fW30mljEdIWzxC2hbL213e1IWP2dnZ+Wep4uLiMJv1E6Pcncy52Vyc/zZZ548S0PcZ3Go1tXWkYuPYuUSmzIokPimTkREh9GkTrLPXImITzo72hLesSrfmVYg6EsOSTZcvvv7u56N0blKJPm2CKVfG3dYxpZQrsGQPGTKEhx9+mPj4eKZNm8aKFSsYOXKkNbKJWJU5L4eYBVPIOvsbARH/xr1OS1tHKhYsFgvLtpzi62WH8PF0ZsoTralV2dfWsUREsLMz0KRuWZrULcvpC8ks2XSSn349w8ptp2latywR7aoREuynEwJiEwWW7AEDBlC5cmU2bNhAXl4er732Gq1bt7ZGNhGrseTlEvPDVDJP76NMrzG4h7SxdaRiIS0zlw+/38P2A9E0rVuWpwc3xEO/ihWRYqhqOS+evr8RI3rUZeXW06zcdoYdhy5SrYIXEW2r0Tq0PA72ut+2WM9NjYs0adKEJk2a5H/8/fffM2jQoCILJWJNFlMeMYumkXlyN/7ho/AI7WjrSMXCid+TmDJrF3GJmTzUux73ttPFjSJS/Pl6OjMsvA4DO9f8c277JO/N3c1/l/9Gr9ZV6d6iik4WiFXc8Ee6zZs307p1a3r37s358+cB2L9/P/369WP69OlWCyhSlCxmE7GL3yfj2C78uo3Es1FXW0eyOYvFwootpxj30WbyTBYmj2lN3/bVVbBFpERxcjDSvUUVPh7XkYkjm1Mp0INvVx7mwdfX8MmP+/gjLs3WEeUud8Mz2e+88w4vv/wy58+f59NPP6VatWpMmzaNvn378uWXX1ozo0iRsJhNxC39iPQj2/Ht/C+8wsJtHcnm0jNz+WjBXrbuu0BYnUCeGdwITzed8RGRksvOzkBYnUDC6gRy+kIySzedYs2Oc6zafoYmdcpyb7tqhFTT3LYUvhuWbLPZTLdu3QBo164dO3fuZNasWTRs2NBq4USKisViJm7FJ6Qd2oxvh6F4N+tt60g2d+J8Eu98G0lMYgb/6lmXvu2rY2enbzoicveoWs6Lf9/fkBE96rBy2+ULJF/85CLB5byIaFeNNvdoblsKzw1LtqPj/85eGQwGvv76a8qXL2+VUCJFyWIxc2nlZ6Tt/wWftoPwbtnP1pFsymKxsGr7Gb5YfBAvd0feerwV9YKLx5K0IiJFwcfTmaHdazOgUw02RJ1nyaYTTJ+3m29WHKJnq2C6t6ii3+LJHbupCx99fHxUsOWuYLFYiP/pS1L3rsW7ZT+8Ww+0dSSbysjKZcaCfWze+weNagfw7OBGeLk7FfyJIiJ3AScHI92aV6Zrs0rsPhrLko0nmbXqMN+vPUansIr0aRtMhQAPW8eUEuqGJTsrK4vffvsNi8Vy1Z+vqFevnlUCihQWi8VCwrpvSIlajVfzPvi0H1KqZ/BOX0hm8je7uJiQwYgedejfoYbGQ0SkVDIYDDSuHUjj2oGcjU5hyaaTrN11eW47rE4g97arRoPq/qX6e4bcuhuW7OzsbJ544on8j//6Z4PBwLp164o2mUghslgsJG6YQ/KOZXg26YFvxxGl9i9Li8XCT7+e5fPFB/Bw1XiIiMhfVQ7y5KlBDRneow6r/pzbnvDpNqqW8ySibTXaNiyPg73R1jGlBLhhyV6/fr01c4gUqcTN80natgiPhl3x6/JQqS3Ymdl5fLxgHxv3nKdhzTI8O6Qx3h4aDxER+TsfD2eGdKvNgI412LD7PIs3nuT97/bwzYrf6Nm6Kt2bV9F4nfyjm5rJFinJErf+SNLm+bg36Ih/+COltmCfiU5h8je7iL6UxrDw2gzsWFPjISIiBXB0MNK1WWW6NK3EnmNxLNl4ktmrjjD/52N0bFKJPm2CqRiouW25lkq23NWSfl1K4oa5uIe0pUzPxzAYSt+tmSwWCz/vPMdnC/fj5uLAG4+1on51f1vHEhEpUQwGA41qBdCoVgBnL6awdNMp1u06x+orc9ttq9Gghua25X9uWLJzcnKuuo2fSEmTvGslCeu+wa1OS8r0fgKDXembocvMzmPmj/vYEHWe0Br+jB3aGB8PZ1vHEhEp0SqX9eTJ++5heHgdVm0/w8qtp5nw2TaqBF2e227XSHPb8g/Lqg8dOhSAqVOnWi2MSGFJ2b2G+DVf4lqrGQER/y6VBftsdArPvr+RjbvPM6RbbV59tKUKtohIIfL2cGJw11p8OaEL/x50DxaLhQ++38NDb/zMdz8fJTkt29YRxYZueCY7Pj6eTz/9lOXLl+Pvf+2vlh988MEiDSZyu1L2ruPSqs9wrd6YwL7PYDCWvqmotTvP8cnC/bg62/P6qJaE1ihj60giInctRwcjnZtWplOTSuw7HsfijSeZs/oIC9Yeo0NYRfq0CaZSWU9bxxQru2H7eP3111mxYgVZWVkcO3bMmplEblvqgY1cWvEJLsGhBPR/DoPRwdaRrCorO49PFu5nfeTvNKjuz3NDG+PjqbPXIiLWYDAYuKdmAPfUDOD3mFSWbDrJL5G/89OvZ2lUO4B721bjnpplNLddStywZLdq1YpWrVrx5Zdf8vDDD1szk8htSfttK3HLZuBcJYTAAS9gZ1+6rik4dzGFyd9Gcj42lfu71OL+rrUw6u4hIiI2UTHQgycG/m9ue8XW07zy+XYql/X4c267Ao4OpW+UsTQp8Pfo999/P5MmTWLTpk3k5eXRqlUrXnrpJdzd3a2RT+SmpB/ZQezi93GuUIuyA8dj51C67l26PvJ3Zv64DxdHe157tAX31AywdSQREQG83J24v0st+neozsbdf7Bk00k+nL+Xb1cepkerqvRoqftt360KLNmTJ0/GZDLx8ccfYzKZmDt3Lq+//jpTpkyxRj6RAqUfjyRm0Xs4latB2UEvYedYesYjsnLy+HzRAX7eeY6Qan48N7Qxfl4uto4lIiJ/42BvpHPTSnRqUpH9xy+xeNNJ5v50hAXrjtGhcUX6tA2msua27yoFlux9+/axdOnS/I/feOMNevbsWaShRG5Wxsk9xPw4FafAygTd/xJ2TqWnYP4ek8qUb3dxLiaV+zrXZEjXWhiNpe8+4CIiJYnBYCC0ZhlCa5bh95hUlm4+xfpd51iz4yyNagUQ0a4aDTW3fVcosGSbTCbMZjN2dpe/eZvNZoxGzRCJ7WWeOUDMD+/g6F+RsoNfxs7ZzdaRrGZD1O98/MM+HB2MTBrZgka1NR4iIlLSVAz0YMyAUIZ1r83qX8+wYstpJn6+nUp/zm2319x2iVZgyW7RogVPP/00gwcPBmDevHk0a9asyIOJ/JPMc79xcf7b2PuUJWjIKxhdSseSttm5Jr5YfICffj1LvWA/xg3TeIiISEnn5e7EoM616Ne+Opv3/sHijSf5aP5evl35Gz1aViW8ZRWtc1ACFViyx48fzyeffMJ7772H2WymdevWjB492hrZRK4r6/xRLn7/Jvae/gQNmYjRtXTMsP0Rl8bkb3ZxJjqFAR1rMKx7bY2HiIjcRRzsjXQMq0SHxhU5cPISizeeZN6ao/yw/jjtG1Ugom01KgeVju95d4MCS7a9vT1PPvkkTz75pDXyiPyj7AsniP7uDYxu3gQNnYS9u7etI1nFpj3nmbFgL/ZGIxNHNiesTqCtI4mISBExGAw0qF6GBtXLcD728tz2ul2/8/POc9xTswz3tqtGo1oBmtsu5krfUnhSYmVfPE30vNcwurhTbtir2Hv42jpSkcvJNfF/Sw6yavsZ6lTxZdywMMr4aDxERKS0qBDgwej+oQzrXoeffj3D8i2nmPTFr1QM9CCibTDtG1fESXPbxZJKtpQIObHniJ77KgZHF4KGvoq9p7+tIxW5C5fSmPJNJKcuJNO/Q3WGhdfBXuMhIiKlkqebIwM71eTedpfntpdsPMmMBfsu32+7ZVV6tNLcdnGjki3FXs6l80TPnYTB6EC5Ya/i4H3330ljy74/+PD7vdgbDbz8cDOa1i1r60giIlIMONjb0TGsIh0aV+DgyXgWbzzJ92v/MrfdrhpVNLddLBRYstPT03n33Xc5deoUH3zwAe+99x4vvPACbm6l53ZpYju5CReInjMJMBA0bBIOPnd32czNM/Hl0kOs2Hqa2pV9GDc8jAAfV1vHEhGRYsZgMFC/uj/1q/vzR1waSzedZF3k76zddY57apQh4s+5bTs7zW3bSoG/e37jjTfw9PQkPj4eJycn0tLSeOWVV6yRTUq53KQYLsyehMVsImjoRBz9yts6UpGKvpTOuI82s2Lrae5tV423x7RWwRYRkQKVL+PO4/1D+frlrozoUYdzMam8+n+/MmbqelZvP0N2rsnWEUulAkv24cOHeeaZZ7C3t8fFxYV3332Xw4cPWyOblGJ5yXFEz56IJS+boCETcSxTydaRitTW/Rd4evoGLsZnMOHBpjzcJ0Tz1yIicks8XC/Pbf/fS10YO6QRTo5GPv5hHw++tobZqw6TmJJl64ilSoHjIldWerzCZDJd85hIYcpLiefC7ImYs9IJGvoqToFVbB2pyOTmmfhq2SGWbzlNzUrePD+8CYG+OnstIiK3z8HejvaNK9KuUQUOnbo8tz1/3TF+/OU4bRtW4N521ahazsvWMe96BZbsJk2aMHXqVLKysti8eTNz5szRio9SZPLSEomeMwlTRgpBQ17BKSjY1pGKzMX4dKbMiuTE70n0aRvMv3rWw8FeP8CKiEjhMBgMhFTzJ6SaPxcupbFs0yl+3nWO9ZG/06C6P/e2q0bj2oGa2y4iBZbs5557js8//xwPDw+mT59OmzZttOKjFAlTejLRcyaRl5pA0OCXcS5f09aRisz2Axf44Ls9ALz4rya0qF/OxolERORuVs7fnVH9GjC0e21W/3qW5VtO8dqXOyhfxp2ItsF0CKuIs6NuOleYCvxqOjg4MGbMGMaMGWONPFJKmTJSiZ77KnlJsZS9/yWcK9a2daQikZtn5r8rDrF00ymqV/TmheFhlPXTnXpERMQ63F0dGdCxBve2q8aWfRdYsvEEM3/cz6xVhwlvWZWerari66n7bReGAkt2x44dr1q202Aw4OLiQo0aNRg/fjwBAXf/PYulaJmy0ome9xq58RcIvO8/uFQOsXWkIhGTkME7s3Zx7FwSvdsE82CvujjYa5UuERGxPnujHe0bVaBdw/L8djqBxRtPsGDdMRb+Obcd0bYaweU1t30nCizZnTt3Jj09naFDh2JnZ8cPP/xAeno6tWrV4pVXXuHTTz+1Rk65S5mzM7g473VyYs9RduDzuAaH2jpSkdhxMJrp3+3BYrEw/oEmtGqg8RAREbE9g8FAvWA/6gX7EX0pnaWbT7J25//mtiPaViOsjua2b0eBJTsyMpKFCxfmfzxhwgQGDBjA22+/zY8//lik4eTuZs7JJPq7N8m+eIrAfs/hWr2xrSMVujyTmW9W/MbijSepVsGLF4Y3Ichf4yEiIlL8BPm7MapvA4Z2r8OaX8+wbPMpXv9qB+XLuNGnbTU6Nq6Is5Pmtm/WTa34mJaWhru7OwBpaWlkZek+i3JnzLnZXJz/Ntl/HCOg77O41Wpq60iFLjYxg3dmRXL0bCI9W1Xl4T71NB4iIiLFnruLA/061KBP22ps23+BxRtP8smP+5m18jDhLavQs1VV/LxcbB2z2CuwZPfv35/77ruP7t27Y7FYWLNmDQMHDmTWrFkEB9+9t1eTomPOyyFmwWSyzh0moM9TuNdpYetIhW7nbxeZPnc3JrOF54eH0eaeu3u1ShERufvYG+1o27ACbe4pz+EzCSzeeJIf1x9n4S8naNOwPBFtq1G9gretYxZbBZbsRx99lDp16rBp0ybs7e15+eWXad68OQcPHqRv377WyCh3EUteLjE/vEPm6QOU6T0G95A2to5UqPJMZmatPMzCDScILufFCyPCKFfG3daxREREbpvBYKBuVT/qVvXjYnw6yzaf4uedZ9kQdZ6Qan7c27YaTeqW1dz239zUYE39+vWpXr06FosFk8nE1q1badWqVVFnk7uMxZRLzMJpZJ7cg3+Px/Bo0MHWkQpVXGImU2dHcvhMAuEtqjAyIgRHB42HiIjI3aOsnxuP3Fufwd1qs+bXsyzbcoo3vt5JOX83+rQJplOTSprb/lOBX4UPPviAzz///PKL7e3JycmhevXqLFu2rMjDyd3DYjYRu/h9Mo7vwq/bSDwbdrF1pEIVeTiG9+buJs9kYtywxrRtWMHWkURERIrM5bnt6kS0DWbb/mgWbzrBp4sOMHv1Ebo1r0yv1sH4e5fuue0CS/aSJUv45ZdfmDx5Ms8//zw7duxgw4YNVogmdwuL2UTs0g9JP/Irvp3/hVdYuK0jFRqTyczs1Uf4Yf1xqgR5Mv6BJpTXeIiIiJQSRqMdbRqWp/U95ThyJpElm06yaMMJFm88SZt7/pzbruht65g2UWDJ9vX1JSAggODgYI4cOUJERATffPONNbLJXcBiMRO3Yibph7bg22EY3s162zpSoYlPzmTq7CgOnYqnW/PKPHJvfZw0HiIiIqWQwWCgTlVf6lT1vTy3veUUP+84x4bd56kX7EdE22o0rVcWYyma27Yr6AX29vacO3eO4OBgIiMjycvLIyUl5Y53PGXKFMaPH5//5169ejFu3Lj851euXMmcOXPueD9iOxaLmUsrPyNt/wZ82g7Cu+Xdc6Hs7iOxPDVtAyfPJzF2SCOeGHiPCraIiAh/zm1H1Ofrl7vycJ96xCVm8NZ/d/L45HUs23yKzOw8W0e0igJL9qhRo3j55Zdp3749P//8M+3bt6d58+Z3tNPt27ezaNEiAFJSUti8eTPLly8nOTmZI0eOkJuby8KFCxk0aNAd7Udsx2KxEP/Tl6TuXYt3q/54tx5o60iFwmQyM2vVYSb933Z8PJx47+l2tG9c0daxREREih03FwfubVedz//TmRdGhOHl7sjniw/w4Otr+O/yQ8QlZto6YpEqcFykbt26+eMhixcv5uzZs9jZFdjNbygpKYnp06fz2GOPceTIEYxGIyaTiby8PLKysnBwcGDevHkMGDAAe3tdnVoSWSwWEtb+l5So1Xg174NPu8EYDCX/10PxyZm8OyeKgyfj6dK0Eo/2rY+zo/4fFRER+SdGox2tQ8vTOrQ8R84ksPjPue1FG0/SOrQcEW2rUbOSj61jFrobNoSkpCQAHnnkEWbNmoXFYsFgMODv78+QIUNYs2bNbe3wlVde4ZlnniE6OhoANzc3+vfvT79+/WjTpg2BgYFs3bqVzz777La2L7ZlsVhI+GU2yTuX49mkB74dR9wVBXvP0VimzY0iK8fEM4Mb0jGskq0jiYiIlDi1q/gyvoovMQkZLN9yip9+PcumPX9Qt6ov97arRtN6QXfN3LbBYrFYrvfEww8/zNatW6953N7ens6dO/P+++/f8s4WLFjAiRMn+M9//sPChQvZuXMnkydPvuo106dPp3Xr1iQkJLBw4UIqVqzIiy++eNNnz7Ozszl48OAtZ5PC4Xx8Ey4nt5BdsSEZdbtDCS/YZrOFDQdT2HQwlTJe9gxs7UeAl4OtY4mIiNwVsnLN7DmZzo6jaSSlm/BxN9KsljsNg91wcrj9yQlrCgkJwcnJ6ZrHb1iyr/jPf/7D22+/XSghHnzwQeLi4jAajSQnJ5ORkcG9997Liy++CEBMTAxvv/0277//Pt26dWPp0qW8+eabdO3aldatW9/UPq6U7Bu94aIUFRVF48aNrbrP4iRxyw8kbpyHR2hH/Hs+jsFQMg6OG0lMyeLdOVHsP3GJjmEVebxfA91gv5CU9mNF5GbpWJHSwmQy8+vBiyzZdJLDZxJwc7ana/Mq9GpdlQAf1wI/3xbHSkGds8DG8Pbbb/PHH3+QnJzMX/t4vXr1bjnM119/nf/nK2eyrxRsgBkzZjBmzBgAcnNzsbOzw87Ojuzs7Fvel1hX0q9LSNw4D/f67fDv8ViJL9j7jsfx7pwoMrLy+PeghnRuqvEQERGRomI02tEqtBytQstx9GwCSzadYsmmkyzZdJJWDcpxb7uSN7ddYMl+9913mTVrFn5+fvmPGQwG1q1bV6hBjh07BkCNGjUAGDFiBBEREZQvX542bdoU6r6kcCXvWkHCum9xq9uKMr3GYLArubeyM5ktzP/5KPN+PkqFAHfeeKwllct62jqWiIhIqVGrsi/PD/clNjGD5VtO89OvZ9i89w/qVPElol01moeUjLntAsdFOnbsyLx58wgMDLRWpjuicRHrSon6iUurP8e1VjMC+z6LwVhyxykSU7N4b85u9h6Po0PjCjzePxQXjYcUidJ4rIjcDh0rIpCRlcvanedYuvkUMQkZBPq60rtNMF2aVmLnoYt8u+owcYmZlPFxYUR4HavdWveOx0WCgoJKTMEW60rZu+5ywa7emMC+z5Togn3gxCWmzo4kPTOXJ++7hy5NK90Vd0UREREp6VydHejTtho9Wwez42A0Szad5P+WHOSbFYcwmy//FhogLjGTGQv2ARSLNSwKbEUtWrTgnXfeoVOnTjg7O+c/fjsz2XL3SD2wgUsrPsEl+B4C+4/DYCyZd9wwmy0sWHeMuT8dIcjfnddGtaRKkMZDREREihujnYGWDcrRskE5jp1L5MWZW8k1m656TXauiW9XHS4ZJXvhwoUArF69Ov+xopjJlpIj7betxC37GOcqIQQOeB6Dfcks2Emp2UybG8XeY3G0a1iB0QMa4OpcMt+LiIhIaVKzkg85uabrPnepmKwkWWDJXr9+vTVySAmRfmQHsYvfx7lCLcoOHI+dg3Xn3gvLwZOXmDo7itSMHMYMCKVb88oaDxERESlB/H1crrs0u7+Piw3SXKvA+6ylp6fz2muv8cADD5CUlMQrr7xCenq6NbJJMZN+PJKYRe/hVK4GZQe9hJ2jc8GfVMxcGQ956ZOtODsamfbvtnRvUUUFW0REpIQZEV4HJ4er72jm5GBkRHgdGyW6WoEl+4033sDDw4P4+HicnJxIS0vjlVdesUY2KUYyTu4h5sepOAVWJuj+l7BzKh4/Jd6K5LRsXv3yV75deZhWoeWZ/kw7qpbzsnUsERERuQ3tG1fkiYGhlPnzzHUZHxeeGBhaLOax4SbGRQ4fPszbb7/Nxo0bcXFx4d1336VXr17WyCbFRObp/cT88A6O/hUpO/gV7JzdbB3plv12Op53ZkWSkp7D6P4NdPZaRETkLtC+cUXaN65YLG93WWDJtrO7+mS3yWS65jG5e2WeO8TFBZOx9ylL0JBXMLq42zrSLTGbLSzacIJvVx0m0MeVqU+2oVoFb1vHEhERkbtcgSW7SZMmTJ06laysLDZv3szs2bNp1qyZNbKJjWWdP8rF79/C3tOfckMnYXQtWbe2S0nPYfq83UQejqFVaDmeuu8e3T1ERERErKLAU9LPPfccrq6ueHh4MH36dGrXrs3zzz9vjWxiQ1kXThD93RvYu/sQNPRVjG4la3b5yJkE/v3eBvYei+OxvvV5YXiYCraIiIhYTYFnsh0cHGjatCljxowhKSmJyMhIqy9XLtaVffEUF+e9htHFnaChr2Lv4WPrSDfNYrGwaMNJvl35G/7eLkx9sg3VK3rbOpaIiIiUMgWW7OnTp7N7925mzZpFVlYWn3/+OceOHWP06NHWyCdWlhN7lui5r2Hn6HK5YHv62TrSTUvNyOH9eXvY+dtFWtQP4qlBDXF30dlrERERsb4Cx0XWrVvHV199BUDZsmWZPXs2K1euLPJgYn05l85zYc4kDPYOBA17FQfvAFtHumlHzl4eD9l9NIZH7g3hPw80UcEWERERmynwTHZubi4ODv8rKw4ODrr12V0oJ/4C0bMnYjDYETR0Eg4+ZW0d6aZYLBaWbDrFf5cfws/bhSlPtKFmpZIz3iIiIiJ3pwJLdqNGjRg7diwDBgzAYDCwePFiQkNDrZFNrCQ38SLRcyZisZgpN+w1HP3K2zrSTUnLyOH97/aw49BFmtUry9P3N8Td1dHWsUREREQKLtkvv/wyH374IW+//Tb29va0aNGCJ554whrZxApyk2OJnjMJS14OQUNfxbFM8VglqSDHziUyZVYk8UmZjIwIoU+bYP2GRURERIqNAkv2J598wvjx462RRawsLyWe6NmTMGdnEDRkEk6BVWwdqUAWi4VlW07x9bJD+Hg6M+WJ1tSq7GvrWCIiIiJXKfDCxw0bNlghhlhbXmoi0XMmYcpIoez9L+MUFGzrSAVKy8zl7W928cXigzSqFcgHz7ZXwRYREZFiqcAz2RUqVOChhx6iUaNGuLm55T/+4IMPFmkwKTqm9GSi504iLzWBoMEv41y+hq0jFejE70lMmbWLuMRMHupdj3vbVdN4iIiIiBRbBZZsb29vAP7444+iziJWYMpIvVywk2Ipe/8EnCvWtnWkf2SxWFix9TRfLj2Et4cTk8e0pnYVnb0WERGR4q3Akv32228DkJKSgqenZ5EHkqJjykwjeu6r5MZHEzjoP7hUrmfrSP8oPTOXjxbsZeu+C4TVCeSZwY3wdNPdQ0RERKT4K3Am+/Tp0/Ts2ZOePXsSExNDeHg4J0+etEY2KUTm7AwufvcGOZd+J3DA87hWLd63YTxxPolnpm9k+4Fo/tWzLi8/1EwFW0REREqMAkv266+/zosvvoifnx+BgYEMGzaMV155xRrZpJCYczKJ/u4Nsi+eIrDfc7hWb2TrSDdksVhYue004z7cTE6eibceb0X/jjWws9P8tYiIiJQcBZbspKQkWrVqlf/x0KFDSUtLK9JQUnjMudlc/P5tsv84TkDfZ3Cr2cTWkW4oIyuXqbOj+OTH/TSo4c8Hz7anXrCfrWOJiIiI3LICZ7IBsrOz8+/kEBcXh9lsLtJQUjjMudnELJhM1u+HCYh4CvfaLWwd6YZOX0hm8je7uJiQwYgedejfQWevRUREpOQqsGQPGTKEhx9+mPj4eKZNm8aKFSsYOXKkNbLJHbDk5RLzw1QyTx+gTO8ncK/XxtaRrstisfDTr2f5fPEBPFwdeevxVjp7LSIiIiVegSV7wIABVK5cmQ0bNpCXl8drr71G69atrZFNbpPFlEvMwnfJPLUH/x6P49Ggva0jXVdGVi4zf9jPxj3naVizDM8OaYy3h5OtY4mIiIjcsX8s2ceOHePMmTOEhoYybtw4a2WSO2Axm4hd/D4ZxyPx6/YIng072zrSdZ2+kMyUb3cRfSmdYeG1GdixpsZDRERE5K5xw5L9448/MmXKFCpXrsy5c+eYNm2azmAXcxazidglH5B+5Ff8ujyIV1h3W0e6hsViYc2Oc3y+aD9uLg688Vgr6lf3t3UsERERkUJ1w5I9a9Ysli1bRmBgIHv27GH69Okq2cWYxWImbvlM0n/bim/H4Xg17WXrSNfIzM5j5o/72BB1ntAa/owd2hgfD2dbxxIREREpdP84LhIYGAhAw4YNSUxMtEoguXUWi5lLKz8j7cAGfNrej3eLe20d6Rpno1OY/O0u/ohLY0i32tzXuSZGjYeIiIjIXeqGJfvKLfuuMBqNRR5Gbp3FYiH+py9J3bsW71YD8Gkz0NaRrrF25zk+WbgfV2d7Xh/VktAaZWwdSURERKRI3dR9suHa0i22Z7FYiF/7X1KiVuPVPAKfdvfbOtJVsrLz+GThftZH/k6D6v48N7QxPp4aDxEREZG73w1L9tGjR2nU6H/Lb2dlZdGoUSMsFgsGg4Hdu3dbJaBcn8ViIeGX2aTsXI5nk574dhxerH4QOncxhcnfRnI+NpX7u9Ti/q61NB4iIiIipcYNS/bPP/9szRxyixI3fUfy9sV4NuqGX5cHi1XBXh/5OzN/3IeLoz2vPdqCe2oG2DqSiIiIiFXdsGSXL1/emjnkFiRu+YGkLT/gEdoJv+4ji03BzsrJ4/NFB/h55zlCqvnx3NDG+Hm52DqWiIiIiNXd9Ey2FA9J2xeTuHEe7vXb4d/zMQwGO1tHAuD3mFSmfLuLczGp3Ne5JkO61sJoLB7ZRERERKxNJbsESd65nIT1s3Cr24oyvcYUm4K9Iep3Pv5hH44ORiaNbEGj2hoPERERkdJNJbuESIlaTfzPX+NaqxkBfZ7CYGf7Wypm55r4YvEBfvr1LPWC/Rg3TOMhIiIiIqCSXSKk7F3LpdVf4FojjMC+z2Aw2v4/2x9xaUz+ZhdnolMY0LEGw7rX1niIiIiIyJ9s39bkH6Ue2MClFZ/iEtyQwH7PYTA62DoSm/acZ8aCvdgbjUwc2ZywOoG2jiQiIiJSrKhkF2Npv20lbtnHuFQJIXDAOAz2ti3YObkm/m/JQVZtP0OdKr6MGxZGGR+Nh4iIiIj8nUp2MZV+5FdiF7+Pc8XaBA4cj52Dk03zXLiUxpRvIjl1IZn+HaozLLwO9hoPEREREbkulexiKP3YLmIWvYdTuRqUve9F7BxtuxT55r1/8NH8vdgbDbz8cDOa1i1r0zwiIiIixZ1KdjGTcXIPMQvfxSmwKkH3v4Sdk+3GMXJyTXy59CArt52hdmUfxg0PI8DH1WZ5REREREoKlexiJPP0fmIWTMHRvyJlB7+MnbObzbJEX0pnyqxdnDyfzL3tqvFAz7oaDxERERG5SSrZxUTm2UNcnP82Dn5BBA15BaOLu82ybN1/gQ+/34PBYGDCg01pFhJksywiIiIiJZFKdjGQdf4IF79/C3vvAIKGTMLo6mmTHLl5Jr5adojlW05Ts5I3zw9vQqCvxkNEREREbpVKto1l/XGc6O/exN7D53LBdvOySY6L8elMmRXJid+T6NM2mH/1rIeDvcZDRERERG6HSrYNZUef4uJ3r2N08SBo6KvYe/jYJMf2Axf44Ls9ALz4rya0qF/OJjlERERE7hYq2TaSHXOG6HmvYufoQtCwSdh7+lk9Q26emf+uOMTSTaeoXtGbF4aHUdbPdhdbioiIiNwtVLJtICfud6LnvorB3pGgYa/i4BVg9QwxCRm8M2sXx84l0btNMA/2qouDvdHqOURERETuRirZVpYTf4HoOZMwGOwIGvoqDj7WX9hlx8Fopn+3B4vFwvgHmtCqgcZDRERERAqTSrYV5SZeJHrORCwWM+WGvYajn3XLbZ7JzDcrfmPxxpNUq+DFC8ObEOSv8RARERGRwqaSbSW5ybFEz56IJS+HoKGv4limolX3H5uYwTuzIjl6NpGerarycJ96Gg8RERERKSIq2VaQlxJP9OxJmHMyCRoyCafAKlbd/87fLjJ97m5MZgvPDw+jzT3lrbp/ERERkdLGJiV7xowZrFq1CoB27drx/PPP8+yzz3Ls2DE6dOjA2LFjAfjiiy+oWbMm7dq1s0XMQpGXmkj0nImYMlIIGjIRp6Bg6+3bZGbWysMs3HCC4HJevDAijHJlbLeSpIiIiEhpYfXVRrZt28aWLVtYtGgRixcv5tChQ8yYMYPU1FSWL1/Ohg0bSE1NJSkpib1795bogm1KTyZ67iTyUhMJGjwB5/I1rLbvuMRMXpy5lYUbThDeogpTn2qjgi0iIiJiJVY/k12mTBnGjx+Po6MjANWqVcPDw4OcnBzy8vLIzc3FaDQyc+ZMRo0aZe14hcaUkXq5YCfFUnbwBJwr1LbaviMPx/De3N3kmUyMG9aYtg0rWG3fIiIiImKDkl2jxv/O5p45c4aVK1fy3Xff8ccff9CvXz8GDx5MQkIC8fHxNGjQwNrxCoUpM43oua+Sm3CRsvf9B5dK9ayzX5OZ2auP8MP641QJ8mT8A00or7PXIiIiIlZnsFgsFlvs+Pjx44waNYonn3ySvn37XvXc+PHjefzxx9myZQubNm0iNDSU0aNH39R2s7OzOXjwYFFEvjm5WXhEzsOYEktaowHklalmld2mZJj4YWs85+JyaFzdje6NvHGwN1hl3yIiIiKlVUhICE5OTtc8bpMLH6Oionjqqad48cUX6dmz51XPHTx4EHd3d/z8/Jg3bx7Lli1j5MiRnD59mqpVq970Pm70hotC6sFNJP4yh9yUSxiM9mAyETjwBdxqNrHK/ncfieX/lkSRk2ti7JBGtG9s3dsDityqqKgoGjdubOsYIsWejhWRm2OLY6WgE7tWL9nR0dGMGTOG6dOn06JFi2uenzlzJm+88QZXTrAbDAbs7OzIzs62dtSbknpwE5dWfIolLxsDgCkPjPaYczKLfN8mk5k5Px1hwbrjVC7rwQsjmlAx0KPI9ysiIiIi/8zqJfvLL78kOzubyZMn5z92//33M3jwYDZu3Ei9evXw9fUFoFWrVvTs2ZOQkBBq17behYO3IvGXOVjy/vYDgCmPxF/m4BHStsj2G5+cybtzojh4Mp4uTSvxaN/6ODvqtuciIiIixYHVW9mECROYMGHCdZ9r167dVbfs+89//mOtWLctLyX+lh4vDHuOxjJtbhRZOSaeGdyQjmGVimxfIiIiInLrdOrzDtl7+pGXcum6jxc2k9nCvDVHmL/2GBUCPHjr8TAqlfUs9P2IiIiIyJ1Ryb5DPh2G5s9kX2Gwd8Knw9BC3U9iShbvzoli/4lLdAyryOP9GuDspP98IiIiIsWRWtodujJ3feXuIg6e/vh0GFqo89j7jsfx7pwoMrLy+PeghnRuqvEQERERkeJMJbsQeIS0xSOkbaHfPsZktjD/56PM+/koFQLceeOxllTWeIiIiIhIsaeSXUwlpmYxbU4U+45fokPjCjzePxQXjYeIiIiIlAhqbcXQgROXmDo7kvTMXJ687x66NK2EwaDVG0VERERKCpXsYsRstrBg3THm/nSEIH93XhvVkipBGg8RERERKWlUsouJpNRsps2NYu+xONo1rMDoAQ1wdXawdSwRERERuQ0q2cXAwZOXmDo7itSMHMYMCKVb88oaDxEREREpwVSybchstvDjL8eZveowZf3cmPRIW6qW87J1LBERERG5QyrZNpKcls1783az+0gsbe4pzxMDQzUeIiIiInKXUMm2gd9Ox/POrEhS0nMY3b8B3VtU0XiIiIiIyF1EJduKzGYLizac4NtVhwn0cWXqk22oVsHb1rFEREREpJCpZFtJSnoO0+ftJvJwDK1Cy/HUffdoPERERETkLqWSbQWHTyfwzqxdJKXl8Fjf+vRoVVXjISIiIiJ3MZXsImSxWFi04STfrvwNf28Xpj7ZhuoVvW0dS0RERESKmEp2EUnNyOH9eXvY+dtFWtQP4qlBDXF30XiIiIiISGmgkl0INkT9zrerDhOXmEmZVfF0blqJtTvPkZiSxSP3htC7dbDGQ0RERERKEZXsO7Qh6ndmLNhHdq4JgLjETOb9dBQPNwemPNGGmpV8bJxQRERERKzNztYBSrpvVx3OL9h/5WRvVMEWERERKaVUsu/QpcTM6z4en5xl5SQiIiIiUlyoZN8hfx+XW3pcRERERO5+Ktl3aER4HZwcjFc95uRgZER4HRslEhERERFb04WPd6h944oA/7u7iI8LI8Lr5D8uIiIiIqWPSnYhaN+4Iu0bVyQqKorGjRvbOo6IiIiI2JjGRURERERECplKtoiIiIhIIVPJFhEREREpZCrZIiIiIiKFTCVbRERERKSQqWSLiIiIiBQylWwRERERkUKmki0iIiIiUshUskVERERECplKtoiIiIhIIVPJFhEREREpZCrZIiIiIiKFTCVbRERERKSQqWSLiIiIiBQylWwRERERkUKmki0iIiIiUshUskVERERECplKtoiIiIhIIVPJFhEREREpZCrZIiIiIiKFTCVbRERERKSQqWSLiIiIiBQylWwRERERkUKmki0iIiIiUshUskVERERECplKtoiIiIhIIVPJFhEREREpZCrZIiIiIiKFTCVbRERERKSQqWSLiIiIiBQylWwRERERkUJmk5K9bNkyevToQZcuXZgzZw4Azz77LL169WLatGn5r/viiy/YuHGjLSKKiIiIiNw2q5fsmJgYpk+fzty5c1myZAnff/89R44cITU1leXLl7NhwwZSU1NJSkpi7969tGvXztoRRURERETuiL21d7ht2zaaN2+Ot7c3AN26dWPt2rXk5OSQl5dHbm4uRqORmTNnMmrUKGvHExERERG5Y1Yv2bGxsZQpUyb/44CAAPbv30+tWrXo168fgwcPJiEhgfj4eBo0aHDL27dYLADk5OQUWuZbkZ2dbZP9ipQ0OlZEbo6OFZGbY+1j5UrXvNI9/87qJft6QQwGAy+++GL+x+PHj2fMmDHMmTOHTZs2ERoayujRo29q+7m5uQAcO3ascALfooMHD9pkvyIljY4VkZujY0Xk5tjqWMnNzcXZ2fmax61esgMDA4mMjMz/ODY2loCAgPyPDx48iLu7O35+fsybN49ly5YxcuRITp8+TdWqVQvcvpubGzVr1sTBwQGDwVAk70FERERESjeLxUJubi5ubm7Xfd7qJbtly5Z89NFHJCQk4OLiwpo1a3j99dfzn585cyZvvPFG/hlvg8GAnZ3dTf8KwM7ODg8PjyLJLiIiIiJyxfXOYF9hkzPZzzzzDCNGjCA3N5cBAwbkz15v3LiRevXq4evrC0CrVq3o2bMnISEh1K5d29pRRURERERui8Fyo2ltERERERG5LVrxUURERESkkKlki4iIiIgUMpVsEREREZFCppItIiIiIlLIVLJFRERERAqZSnYBZsyYQc+ePenZsyfvvPMOANu2baN379507dqV6dOnX/M5L7zwAgsXLgQgPj6eiIiI/H86duxIw4YNrfoeRKzhTo8VgPPnzzN06FAiIiIYPnw4f/zxh9Xyi1hLYRwr+/fvp3///vTu3ZtRo0YRFxdntfwi1nIrx8ratWuJiIigT58+jB49muTkZAAuXLjA0KFD6d69O48//jjp6enWewMWuaGtW7daBg0aZMnOzrbk5ORYRowYYVm2bJmlXbt2lnPnzllyc3MtDz30kGXDhg0Wi8ViuXjxomXUqFGWBg0aWH788cdrtmcymSzDhg2zLF261NpvRaRIFdax8txzz1nmzJljsVgslm+//dYyduxYm7wfkaJSGMeK2Wy2tGvXzrJ9+3aLxWKxrFixwjJq1CibvSeRonArx0pqaqqlVatWlosXL1osFovl/ffft7z++usWi8ViefTRRy3Lly+3WCwWy4wZMyzvvPOO1d6DzmT/gzJlyjB+/HgcHR1xcHCgWrVqnDlzhsqVK1OxYkXs7e3p3bs3q1evBmDZsmV06tSJ8PDw627vxx9/xMXFhd69e1vzbYgUucI6VsxmM2lpaQBkZmb+40paIiVRYRwriYmJZGVl0bx5cwA6dOjAli1byMnJscl7EikKt3Ks5ObmMmnSJAIDAwGoVasW0dHR5ObmsmvXLrp16wZAv3798o8ta1DJ/gc1atTgnnvuAeDMmTOsXLkSg8FAmTJl8l8TEBBATEwMACNHjmTgwIHX3ZbJZOKTTz5h7NixRZ5bxNoK61j597//zX//+1/atGnDV199xSOPPGKV/CLWUhjHio+PD66urmzZsgWAFStWkJubS2JionXehIgV3Mqx4uPjQ+fOnQHIysri888/p3PnziQmJuLu7o69/eUFzsuUKZN/bFmDSvZNOH78OA899BAvvPAClSpVuuZ5g8FQ4DY2b95M1apVqVWrVlFEFCkW7vRYeeGFF3jttdfYvHkzr776Kk888QQWLUord6E7OVYMBgMffvghn332Gffeey+pqal4e3vj4OBQlJFFbOJWjpXU1FQeeeQRateuTd++fa/7/eNmOlthUckuQFRUFP/6178YO3Ysffv2JTAwkEuXLuU/HxsbS0BAQIHbWbt2LT169CjKqCI2dafHSkJCAqdOnco/G9GtWzfi4uJ0dk7uOoXxfcXe3p5Zs2axePFi+vbti9lsxtvbu4iTi1jXrRwrsbGxDBkyhNq1a/Pmm28C4OvrS1paGiaTCYC4uLib6myFRSX7H0RHRzNmzBjeffddevbsCUBoaCinT5/m7NmzmEwmli9fTtu2bQvc1t69ewkLCyvqyCI2URjHio+PD05OTkRGRgKX/3J1c3PD19fXKu9BxBoK6/vKiy++yP79+wH46quv6N69O3Z2+pYud49bOVZMJhOPPfYY4eHhvPTSS/lnqx0cHAgLC2PlypUALF68+KY6W2Gxt9qeSqAvv/yS7OxsJk+enP/Y/fffz+TJk3nyySfJzs6mXbt2dO/evcBt/f7775QtW7Yo44rYTGEcKwaDgRkzZvD666+TlZWFm5sbH330kTXii1hNYX1fmTRpEhMnTiQzM5NatWrln7kTuVvcyrGydu1afvvtN0wmEz/99BMAISEhvPnmm0ycOJHx48fzySefEBQUxHvvvWe192CwaOBRRERERKRQ6XdLIiIiIiKFTCVbRERERKSQqWSLiIiIiBQylWwRERERkUKmki0iIiIiUshUskVEbGjv3r0MHz6c3r1706tXL0aOHMnx48dtHQuA5ORk2rVrx+zZs696/MiRI4SFhXH06FEbJRMRKf50Cz8RERvJycmhTZs2fPXVV9SrVw+AJUuWMH36dNatW4fRaLRxQti5cyejRo1i4cKFVK1alZycHPr168eIESO47777bB1PRKTY0plsEREbyczMJDU1lYyMjPzH+vTpw8svv4zJZGLHjh306tUr/7m/fvzRRx/x8MMP07t3b8aOHUu7du04cOBA/mufeeYZ5s6dC8Ann3xC3759iYiIYPTo0cTExHDhwgUaNmxIamoqABaLhW7dunHkyJGrMjZt2pQRI0bwwgsvYDabmT59OrVq1eK+++4jNTWV8ePH069fP3r37s1bb71FXl4eAD/88AMDBw7k3nvvpUOHDvlZFi5cyJAhQ+jbty/Dhw8nLi6Ohx56iL59+9K3b1/ef//9wv9Ci4jYgEq2iIiNeHl5MW7cOEaOHEmnTp0YN24cP/74Iy1btsTR0bHAz//jjz9YtGgR06ZNo3///ixatAi4POaxbds2evfuzeLFizl27BgLFixgyZIltGvXjgkTJlCuXDlatGjB0qVLAfj111/x9vamdu3a1+znySefBC4v5b1hwwZeffVVAN566y3q1avHwoULWbx4MYmJiXz99dekp6ezYMECPv/8cxYvXsz06dOZOnVq/vZOnDjBrFmzmDVrFvPnz6dChQosWrSIOXPmcPbs2fziLyJSkmlZdRERG3rwwQcZOHAgu3btYteuXXzxxRd88cUX/PDDDwV+7j333IO9/eW/xvv378+AAQMYP348y5cvp0OHDnh4ePDLL79w4MAB+vfvD4DZbCYzMxOAoUOHMnXqVIYOHcr333/P4MGDr7sfe3t7pk2bRufOnZk/fz7u7u4AbNiwgQMHDuRnzcrKAsDNzY1PP/2UjRs3cubMGY4cOXLV2fpatWrlb6NNmzY8+uijREdH07JlS8aOHYuHh8ftfClFRIoVlWwRERuJiopiz549jBw5kg4dOtChQweeffZZevfuzdatW/H19eWvl83k5uZe9fmurq75fy5fvjx169Zlw4YNLFy4kBdffBG4XKpHjhzJkCFDgMtz4MnJyQC0bNmSzMxMtm/fTmRkJFOmTLlh1ooVK1717yvb/uCDD6hWrRoAKSkpGAwGLl68yKBBg7jvvvto3Lgx3bt355dffrlu7gYNGrBu3Tq2b9/Or7/+ysCBA/n4449p1KjRrX0xRUSKGY2LiIjYiK+vL5988gmRkZH5j8XFxZGZmUnNmjXx9fXlwoULxMfHY7FYWLt27T9u77777uOLL74gKyuLxo0bA9C6dWt++OEH0tLSAPjggw94/vnnATAYDAwZMoSXXnqJXr164eTkdEv5W7duzX//+18sFgs5OTk8/vjjzJ49m4MHD+Lr68vo0aNp06ZNfsE2mUzXbOPdd99l5syZdO7cmZdeeonq1atz5syZW8ohIlIc6Uy2iIiNVK1alY8//pjp06dz8eJFnJyc8PDw4LXXXiM4OBiA+++/n/79+1OmTBnat2//j9vr2LEjr776Ko888kj+YwMHDiQmJob77rsPg8FAUFAQkydPzn++b9++TJkyhUGDBt1y/pdeeok333yT3r17k5ubS8uWLRk5ciR5eXn88MMPdO/eHRcXFxo0aICvry9nz569ZhsPPPAA48ePp1evXjg6OlKrVq2rLvYUESmpdAs/EZFSbMWKFSxatIj/+7//s3UUEZG7is5ki4iUUsOHD+fSpUt89NFHto4iInLX0ZlsEREREZFCpgsfRUREREQKmUq2iIiIiEghU8kWERERESlkKtkiIiIiIoVMJVtEREREpJCpZIuIiIiIFLL/B9IMqVM+kUCkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = unemployed_evol.plot(figsize=(12, 6), marker='.', markersize=12)\n",
    "plt.ylim(0, 0.1)\n",
    "ax.yaxis.set_major_formatter(lambda x, p: f'{x:.0%}')\n",
    "plt.legend(['Other Developers', 'Data Scientists'])\n",
    "plt.xticks([2017, 2018, 2019, 2020])\n",
    "plt.xlabel('Survey Years')\n",
    "plt.ylabel('Percentage of Repondents')\n",
    "#plt.title('Comparasion of the Evolution of Unemployment Rate Between\\nData Scientists and Other Developers')\n",
    "plt.savefig('figs/ds_enemplyment.png', bbox_inches='tight', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a3ab832d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "2017    0.67\n",
       "2018    2.37\n",
       "2019    1.78\n",
       "2020    1.91\n",
       "dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(unemployed_evol[True] - unemployed_evol[False]).mul(100).round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0ab71b7",
   "metadata": {},
   "source": [
    "In all year the unemployment rate of data scientists were higher than that of the other developers. The highest difference occured in 2018 were we had a difference of 2.37p.p."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e18ff0f7",
   "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>Percent Unemployed</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>data_science</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>False</th>\n",
       "      <td>5.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>6.71</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Percent Unemployed\n",
       "data_science                    \n",
       "False                       5.06\n",
       "True                        6.71"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df.groupby('data_science')['employment'].\n",
    " value_counts(normalize=True)[:, \"Not employed, but looking for work\"].\n",
    " mul(100).round(2).to_frame('Percent Unemployed'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f51f7ac",
   "metadata": {},
   "source": [
    "With this additional evidence it becames more difficult to believe that data science is really promising at te moment. Maybe the oportunity is already gone. Maybe our survey data is biases and doesn't represent the real population of data scientists. But from the information available in this analysis we couldn't confirm our initial hypothesis that data science is a promissing profession."
   ]
  }
 ],
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