{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One of the most popular economic reports comes out the first Friday of every month. It is call the Employment Situation Summary, but it is also referred to as the **Friday Jobs Report** by the financial media. I'm going to use the Python *pandas* data analysis library and *pandas-datareader* library to retrieve the latest employment numbers.\n",
    "\n",
    "The first step is to simply import those two libraries. I'm also going to import the *datetime* library to use some dates to determine the start and end points for the data I'm going to download."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import pandas_datareader as pdr\n",
    "import datetime"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I'm going to set the end point to today and the start point to just before the financial crisis in 2008."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "start = datetime.datetime(2007, 1, 1)\n",
    "end = datetime.datetime.today()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The *pandas-datareader* library makes it easy to get data into *pandas*. One of the easy to use sources of data included in *pandas-datareader* is the St. Louis Federal Reserve [FRED](https://fred.stlouisfed.org/) database website. Just search there for the data you need and then note the series code for the data you want. I want to get the All Employees: Total Nonfarm Payrolls series so the code is PAYEMS. This series provides a monthly estimate of total employment that is reported in thousands of persons. The code to get that data is below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>PAYEMS</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-10-01</th>\n",
       "      <td>149852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01</th>\n",
       "      <td>150048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-01</th>\n",
       "      <td>150275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01</th>\n",
       "      <td>150586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-01</th>\n",
       "      <td>150606</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            PAYEMS\n",
       "DATE              \n",
       "2018-10-01  149852\n",
       "2018-11-01  150048\n",
       "2018-12-01  150275\n",
       "2019-01-01  150586\n",
       "2019-02-01  150606"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_employed = pdr.DataReader('PAYEMS', 'fred', start, end)\n",
    "df_employed.tail(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see that in February there were 150,606,000 employed. That was up from 150,586,000 in January. However, what we really want to see is the change in the number of jobs since the previous month. I can do that with the following pandas code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>PAYEMS</th>\n",
       "      <th>change</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-10-01</th>\n",
       "      <td>149852</td>\n",
       "      <td>277.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01</th>\n",
       "      <td>150048</td>\n",
       "      <td>196.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-01</th>\n",
       "      <td>150275</td>\n",
       "      <td>227.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01</th>\n",
       "      <td>150586</td>\n",
       "      <td>311.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-01</th>\n",
       "      <td>150606</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            PAYEMS  change\n",
       "DATE                      \n",
       "2018-10-01  149852   277.0\n",
       "2018-11-01  150048   196.0\n",
       "2018-12-01  150275   227.0\n",
       "2019-01-01  150586   311.0\n",
       "2019-02-01  150606    20.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_employed['change'] = df_employed['PAYEMS'] - df_employed['PAYEMS'].shift(1)\n",
    "df_employed.tail(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+20,000 jobs created last month.\n"
     ]
    }
   ],
   "source": [
    "print('{:+,} jobs created last month.'.format(int(df_employed['change'][-1] * 1000)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you want to compare the number of jobs added this past month to what happened a year a go, you can calculate the percent change over the past 12-months using the *pandas* pct_change function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DATE\n",
       "2018-10-01    1.8\n",
       "2018-11-01    1.8\n",
       "2018-12-01    1.8\n",
       "2019-01-01    1.9\n",
       "2019-02-01    1.7\n",
       "Name: pct_change, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_employed['pct_change'] = (df_employed['PAYEMS'].pct_change(periods=12, fill_method='bfill') * 100).round(1)\n",
    "df_employed['pct_change'].tail(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I looks like jobs were up 1.7% this February versus the number of jobs in February of 2018.  The graph below plots the trend in job growth year-over-year since 2007. You can clearly see the impacts of the 2008 recession in the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xa5aae80>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.set()\n",
    "%matplotlib inline\n",
    "df_employed.plot(y='pct_change')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another popular component of the **Employment Situation Summary** is the Civilian Unemployment Rate. You can get that data using the UNRATE series on FRED."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>Unemployment Rate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-10-01</th>\n",
       "      <td>3.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01</th>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-01</th>\n",
       "      <td>3.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01</th>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-01</th>\n",
       "      <td>3.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Unemployment Rate\n",
       "DATE                         \n",
       "2018-10-01                3.8\n",
       "2018-11-01                3.7\n",
       "2018-12-01                3.9\n",
       "2019-01-01                4.0\n",
       "2019-02-01                3.8"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_unemployment = pdr.DataReader('UNRATE', 'fred', start, end)\n",
    "df_unemployment = df_unemployment.rename(columns={'UNRATE': 'Unemployment Rate'})\n",
    "df_unemployment.tail(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The unemployment rate last month was 3.8%.\n"
     ]
    }
   ],
   "source": [
    "print('The unemployment rate last month was {:}%.'.format(df_unemployment['Unemployment Rate'][-1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xbe20668>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXgAAAEPCAYAAABIut/fAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJzt3Xd0XNW1+PHvFPVeRtWWrGJdyb3JNqaZDjYQOoQSwLQHgQQSkvcjlYQH5CWQkJDkhd5rIIDBFWyKwdjI3djSsS3LTb33OjO/P0aSJdwkTbkzo/1Zy2tJd+69s49mZvvMvueeY7Db7QghhPA/Rr0DEEII4R6S4IUQwk9JghdCCD8lCV4IIfyUJHghhPBTkuCFEMJPSYIXQgg/JQleCCH8lCR4IYTwU5LghRDCT0mCF0IIP2XW4TmDgHygHLDq8PxCCOGLTEAyUAB0DuUAPRJ8PrBGh+cVQgh/cCrw5VB21CPBlwPU17disw1/Jsu4uHBqa1tcHpQe/KUt0g7v4y9tkXYcZjQaiIkJg94cOhR6JHgrgM1mH1GC7zvWX/hLW6Qd3sdf2iLtOMKQS9tykVUIIfyUJHghhPBTepRohBh17HY79fXVdHV1AO4vOVRVGbHZbG5/Hncbfe0wEBgYTEyMBYPB4PTzDinBa5oWCawFLlRK7dM07Wzgz0AI8JZS6ldORyKEH2tpacRgMJCYOAaDwf1fnM1mIz09vp8YR1s77HYbDQ01tLQ0EhER7fTznvCdpmnaHBxDcnJ6fw8Bnge+B+QB+ZqmXeB0JEL4sfb2FiIioj2S3IXvMhiMRETE0N7umpFDQ3m33Qb8ECjr/X02sFspVaKU6gFeBa50STRC+CmbzYrJJBVRcWImkxmbzTX3gJ4wwSulblVKDbwxKYXB4zDLgTEuiUYM255Djdz75Jfsr2jWOxRxAq6oqQr/58r3yUi6FEYGXyUyAMMuksXFhY/gqR0slogRH+ttnG3LG6v30NTaxUsrFH++93QCzPqUAPzlNXFXO6qqjJg9/Noc6/nKysq4667beP/9JYO2z507g3XrNnkitEHuvPM2br31DmbOnHXUxz39d7vrrtv55z+fPmL7JZcsJDg4mICAAACam5vJy5vAb37ze0JCQo55vmee+T/y8+cwbdqMIcdgNBpd8l4cSYI/hGM+hD5JHC7fDFltbcuIBv5bLBFUV/tHb9XZtvRYbazdVkZyXCj7ypt4/oPtXHZapgsjHBp/eU3c2Q6bzebRi4XHu6hntTq2H+1xPS5o2u12rNaj/330uMi6adOGYz7nn/70V5KTUwDo7u7mrrtu4aOPPuTSS6845vk2btzIjBn5w2qHzWY74r1oNBqG3TEeSYJfD2iapmUDJcC1OC66Cg8r2l9Pa0cPixbmsWlXNUu+3se6HRUEmI1cf65GXnpM/752u51XVii+LakDID83gSvPyNYpcuHNli79kPXr19LU1ERZWSn5+XO5//7/B8Arr7zIp59+jNVqY86cudx554+oqCjngQfuJz09nZKSveTk5DJp0hSWLfuI5uYmHnnkMcaNy+CKKy7izDPPpqBgPQAPPPAbcnJyBz33yy8/z8qVyzAajeTnz+VHP7qXZ575FzabjTvu+CEADz/8IHPnzmPdurUEB4ewa1cRLS3N3H77D1mxYil79uzi1FPnc88992G1WvnnP//K5s0bsVptLFhwIVdffR2bNm3glVdeIDg4mH37SsjKyua3v32Yf/zjCQBuu+1GnnnmpeP+nVpammlpaSEyMhKAd999i+XLl9LR0U5AQAAPPvgwO3Z8i1KFPPro73n44ccICgrisccepampkaCgYO6772dH/A1cadgJXinVoWnaTcC7QDCwFHjHxXGJIfimqIqQIBOTMmLRxsYQFhxAc1s3uw818OxHO3noltmEBju+Tq7ZVs5nW8qYnBlHXVMHn28p4/L5WRilLuxxX20v58ttQ55OZFhOmZLMyZOTT7zjCWzfvo1XX30bo9HEtddeTnHxFVRXV6FUIc888zIGg4GHHvoNK1cuY8qUaRQX7+YXv/gN2dk5fP/7lxEfb+Gpp17g+eefZvHi//CjH/0UgODgEF544XW+/PILHn74QV566c3+5/z666/48ssvePbZVzCbzfzqVz/nvffeYcGCi7j33ru4/fa76OzsZOPGAu6//wHWrVtLTU01Tz31AsuWfcSjj/6ON974D0FBQVxyyQJuvvk2PvlkOQDPP/8aXV1d/OQnd5ObOwGAb7/dxmuvvUN8vIU77riJ9eu/5t57f8Y777x1zOT+s5/9GJPJRF1dHQkJiVx++VWceeY5tLa28MUXn/P3vz9FUFAwzz77L959923uu+/nLFmymNtu+y+ysrK5885F3Hffz8nJyaWkZC+/+MX9vPHGf5x+vY5lyAleKTVuwM+rgKnuCEgMTY/VxuZd1UzLjifAbCLADNecNR6AkvImHn55I2+s2s0tCydQ19TBW6t3k5sWzY+vnMJX28t5YWkRFbVtpMSH6dwS4WlHG6ppt9sHXdybPHkKoaGO90ZKSipNTY1s2PANO3d+yy233ABAZ2cHiYlJTJkyjdjYuP6eqMWSwMyZ+QAkJSWzefPhCu7FF18GwCmnnMbDDz9IQ0ND/2MbNxZw9tnnERwcDMDChRezfPkSLrnkSpKSktmyZROVlRXMm3cKQUFBAMydOw+AxMQkMjKyiImJBSAyMpLm5iY2bPiG3bt3sXHjBgDa29soLt7DuHEZZGRkkZCQCEB6egbNzU0n/Nv1lWg++2wVTz75F84442wMBgNhYeE8+OD/8MknKzl48ADr169l/Hht0LFtbW0UFu7kkUd+37+tvb2dxsYGoqKcH/N+NDJuy0cV9pZn8nMTj3gsIzmSC+amseTr/dQ2dlDX1InNBjctyMNoMJCdGgVAcWnjsBK83W7nrdV7OGliEulJ/nFRVQ8nT3ZNL3ukIiMjaGkZPM66vr6OiIjI/t8DA4MGPW6327HZrFx11fe55prrAcdFRpPJRGNjQ/+Fxz4mk+mozz1wu91uw2g0Dvp98HOC1eoYLrhw4cV8/PFyKisrWbTo9v59Bj7v0Z7TarVx110/4vTTzwSgoaGBkJAQduzYTmBgYP9+BoMBu33o1wTnzz+Lb75Zx6OP/p7HHvsblZUV3HPPHVx++VXMnTuP2Ng4du9Wg46x2WwEBgbx4ouv92+rqqokMjJqyM87XHLXhY8qKHSUZyZmxB718YtPzuDkyUn0WO1Ehgdy64V5JEQ7rvQnxoYSFmymuKxxWM9Z29TByoKDrNtZ4XT8Qj+hoWGMHTuWzz5b1b9t8eL3mDVr9nGPmzEjnxUrltLW1kZPTw8PPPDTQecYilWrVgDw+eefkp6e0V+/7jv/J5+soLOzg56eHpYuXdw/suaMM85i48YC6upqmDhx0pCfb+bMWSxe/D49PT20tbVx1123sGPH9uMeYzKZ6OnpOeG5b7vtTrZt28ratV9SVLSTMWPGcvXV15GXN4Evvvi0fyy7yWTGarUSHh7OmDFjWbFiKQAFBev44Q9vP95TOE168D6ox2pj065qpmVbjjksMsBs5JaFE476mNFgIDMliuLSE38lHaisphWA6oaO4QUsvM6vf/0Qjz/+B1544Vl6errJyhrPT37y38c95pRTTmPPnl3cfvtN2GxW5syZxwUXXEhFxdCvJ2zfvpWPPlpMSEgwv/zlg4MeO/nkU9m9W3HLLT/Aau1h9uy5XHnlNQAEBQUzadJkMjOHNzDgkkuu4NChg9x887VYrVYWLLiIGTNmsWnThuO286abruW5517pLwUdTUxMLNdd9wP++c+/8vTTL/Hee+9w/fVXYrfbmTZtBnv3FgMwZ85J/PGPD/PLX/6O3/72f/jTnx7h9ddfxmwO4Pe/f8St90cYhvO1xEXGASUyTHLkbdlWXMsT/97Kj66YwrTs+BE99+KvSvhgTQlP3ntq/4XYE1m+/gBvf7qHsQnh/G7R4d6ev7wm7mxHRcV+kpLS3XLuo/HGOVyuuOIinnzyqf5hhkNhNhvp7rbS1tbKHXcs4q9//SdxcSN7z+tpuK/H0d4vA4ZJZgD7hnIeKdH4oIKiSkKCzEwcd/TyzFBkpUZhB/aWDb0XX1rjqNtWN7QPq14phDMKC3dwxRUXc/HFl/pkcteTlGh8jGP0TA3Tx8c7dddqZnIkBmBPaSOTMuOGdExfiaajy0pzezeRoYEnOEKIw95558MRHTdhwiSWLVvt4mhGB+nB+5id++po6+whPzfBqfOEBJlJtYRRPMQevM1up6ymjfgoxxC26oZ2p55fCOF+kuB9iNVm4/01JUSEBhxz9MxwjB8bze5DDXR2n3jmurqmDjq7rf01f0nwwydlLTEUrnyfSIL3IcvXH2BfRTPXn6thNjn/0s3MsdDVbWN7ce0J9+0rz0zJdpRzZCTN8JjNgbS2NkmSF8dlt9tpbW3CbHZN+VNq8F6uqbWL3Yca6ejq4YMvS5iVm+B0eaaPlhZNRGgABUVVzBpwTpvdTuG+ejq6rIQGmchNj6G0N8FnJEcSFRZIdb304IcjJsZCfX01LS0NJ97ZBYxG/1jqbjS2w2wOJCbG4pLnlQTvxTq6eviflzdQ0+joLUeFBXL9uTkuO7/JaGSmlsDab8vp7LYSFOC4E/C9L/ay5Ov9/ftddlomlXVtRIUHEhYcgCUmREo0w2QymYmP99zdqzJ01bvo1Q5J8F7snc+KqW3s4M5LJpEYE0J8VAihwa59yfI1C59tLmV7cS2zchMoKW9i6br9nDQxkfNmp/HhV/v44MsSIsMCSe2d1sASFYI6WO/SOIQQric1eC9VuL+e1ZtKOXvWWPJzE0hLjHB5cgfISYsmMjSAr7aXc6CymeeWFBIdHsR15+SQlhjBDedrhAabqW/uJCWuN8FHB1Pf1Em3l91II4QYTBK8l3r382ISokO47HT3LuBhMhqZlZvA1uJaHnyhgLKaVm48P7f/7tbI0EBuONcxK97YRMdiAwkxIdiBmkYp0wjhzaRE44VsNjsHq1o4c0Zqf13cnS47LYsJ42Kx2+3ERgaTkRw56PFZuQn8+sZZjE1wJHhL76Rl1Q0dJMfJdMNCeCtJ8F6ouqGd7h6bx+ZqDw02MyPn+FftByb9wwleevBCeDMp0XihviGJqfEjX5jcnaLCAgkMMFJR26Z3KEKI45AE74X6EnxyXKjOkRydwWAgNy2GLXuq5cYdIbyYJHgvVFbTSlxkECFB3ltBy89NoLapk73lw5tTXgjhOZLgvVBZTSspXlqe6TN9fDwmo4ENRVV6hyKEOAZJ8F7GZrNTXtvWf1ORtwoNDmBSRiwbiqqkTCOEl5IE72WqGtrpsXpuBI0zZvWWaXYdkLtahfBGTiV4TdP+n6ZpStO0bZqm/dJVQY1mpdW9I2gs3p/gp4+Px2wy8NnGQ0c8tq24lldWKqy9EyyV1rTyzIc7aWrr8nSYQoxaI07wmqadDVwL5APTgTmapl3mqsBGq7LeZfG8dQTNQKHBAcydkMSyr/exv+LwREr1zZ08vXgHn24qZfn6A/RYbTy9eAdf76jg1RVKv4CFGGWc6cFPB1YopZqUUlZgOXCJa8IavUprWomLDCY40HtH0Ax01ZnZRIYF8tySQnqsNux2Oy8vL6LHaiM3LZoPvizhhaVFHKxqYUpWHBtUNd8UVuodthCjgjNZZBPwF03THgXagIuRmr7TympafaI80yc8JIC7r5zGQ8+v589vbSE40MzW4lquOWs8cyck8qtn1/P1jgpOmpjIooV5PPLKRl5Zodg2YJERo8HAeXPSvP7CshC+xuDMCAhN034C3ATUAZ8Ac5VSF57gsHFAyYif1I91dlu5+hdLuHR+NjcunKB3OMPy8tKdfL65FIAJGbHce80MxzDKwko+XLOX+6+fSURoIAcrm3n89Y00t3X3H1vX2M6p01L5ybUz9QpfCF+SAewbyo4jTvCapkUAsUqp/b2//wwYo5T68QkOHQeU1Na2YLMN/7n9ZQEAOLItuw428IfXNnHP5ZOZPt41K7p4grOvyfNLCtm4q4on7jmVALN+XwL9+b3lq6QdhxmNBuLiwmEYCd6ZT1MG8IGmaWZN06KAW4C3nTjfqFdc2ghAVmqUzpF4Vn5eAu2dVnaU1OkdihB+ZcQJXim1DXgX2AZ8AzyhlPrKVYGNRntKG0mICSEy1DUL7vqKvPQYwoLNFBTJxVchXMmpoRpKqYeAh1wUy6hmt9spLmti4rhYvUPxOLPJyPQcCxtVFd09VgLM7p8DX4jRQEa9eImaxg6aWrvITo088c5+aHauo0zzrZRphHAZSfBeYrTW3/vk9pdpZPIyIVxFEryXKC5tIijA5FNj4F3JbDIyI8fClt01dPdY9Q5HCL8gCd5L7ClrJCM5ApNx9L4k+XkJdHRZ+XavlGmEcIXRm028SHePlUNVLaO2PNMnN03KNEK4kiR4L1BR147VZmeMxbsX+XA3s8nITM3C5j01dHVLmUYIZ0mC9wKlvTNIylwskJ+bSGeXjKYRwhUkwXuBsppWjAYDibHeP0Wwu+WmRxMeEsCnm0tlpSghnCQJ3guUVreSGBui6zws3sJkNHLhvHHsKKlj7bcVeocjhE+TjOIFHItsS3mmz9mzxjB+TBSvf7Kb+uZOvcMRwmdJgtdZd4+VqoZ2UuIkwfcxGgwsWpiH1WrjjVW79Q5HCJ8lCV5n5bVt2O2+sQarJyXGhDJ/eipbdlfT1tF94gOEEEeQBK+zshrHIttSojlSfl4CPVY7m3fX6B2KED5JErzOSmtaMRkNJMkImiNkJkcSFxkkNz4JMUKS4HVWVtNKQkwIZpO8FN9lMBiYlZvAjpI6KdMIMQKSVXRWWtMqNzgdR35uIlablGmEGAmnFvwQzunstlLd0M7cCYl6h+K1MpIjiIsM5oMvS9i6p4YxCeFcNG8cBoNB79CE8HrSg9dRRU0rdjskxUn9/VgMBgML56UTGGDiQFUL768pYc22cr3DEsInSA9eRxW1jhE0CdGS4I9n/rRU5k9LxWa389gbm3lz1W4mjoslLipY79CE8GqS4HVUUdcGgCVaEtVQGA0Gbl6Qx2+e+4Z/vr+dvPRjr19rMho4fVoKsZHytxWjlyR4HVXUthISZCI8JEDvUHyGJTqEG87L4ZWVuzhY1XLM/axWO9+W1PGLG2aM6kVUxOgmCV5HFbVtWKJC5ILhMM2blMy8ScnH3Wf9zkqeWryDFd8cZMHcdA9FJoR3ka6NjipqW7FEh+gdhl+anZfAzBwL76/ZS2nv3cJCjDZOJXhN067XNG1H77/HXBXUaGCz26msa5ME7yYGg4EbztMwm4x8XHBQ73CE0MWIE7ymaaHA34DTganAqZqmne2qwPxdY0sX3T02LDGS4N0lMiyQ8WOiKS5t1DsUIXThTA/e1Ht8GBDQ+6/dFUGNBlX1MoLGE7JSIymraaWto+e4+1ltNspqjn3RVghfNOKLrEqpZk3Tfg0UAW3A58DaoR4fFzfyBaYtlogRH+sttpbUA6BlxmOJ9/3Ftr31NZk5IYn315RQ29ZN+tiYY+735NtbWL3hIC/99jwiwwI9GKH7eOtrMlzSjpEbcYLXNG0KsAhIBxqBV4H7gT8N5fja2hZstuGvuWmxRFBd3Tzs47zN3oP1GA1g6LH6fHu8+TWJDQ3AAGzaWcHY2KOXw7bvrWXl+v0AfLOtlKnZ8R6M0D28+TUZDmnHYUajYdgdY2dKNOcBq5RSVUqpTuBFYL4T5xtVqhvbiY8JlVkk3SwkyEyqJeyYdfi2jh5eXFZEUmwoRqOB4jKp1wv/4Ux22QqcrWlamKZpBuAioMA1Yfm/6vp2mQPeQ7JSoygua8JmP/Ib45J1+2ho6eS2iyaQkRJJcWmTDhEK4R4jTvBKqZXAG8BGYBuOi6x/cFFcfq+6oZ0kWYfVI7JSomjv7KG8tm3QdrvdzvqdlUzJjCMjOZLc9Fj2ljeNqHQohDdy6k5WpdT/Av/rolhGjY6uHpraumUWSQ/JSo0EoLi0cdDc+3vLmqhr6uTSUzMByE2PYclXJRyqbiEt0T8u7InRTQrAOqhp6ACQHryHJMWGEh4SwEvLi7jtj5/y9OId2O12CoqqMJsMTB/vuKiaO84xeZmMmxf+Quai0UFVg+N2AenBe4bBYGDRwjyKSxupbepg3Y5KtLRoNqgqJmXEERrsmOwtMTaUyNAA9pQ2ccYMnYMWwgUkweuguj/Bh9HR2qlzNKPDtOx4pmXHY7PbaWju5NWVu7Da7Fx2Wmb/PgaDofeCrPTghX+QEo0OqhvaCQkyyzTBOuibU95sMmI2GZiWbRn0eFZqFFX17TS1dukUoRCuIwleB9UNHSREyzTBerFEh3DHxRP5/tk5hAYP/hI7sbcOv2WPLPItfJ8keB1UNbTLHDQ6mzY+njOmpx6xPS0xHEt0MAVFVTpEJYRrSYL3MJvNTm1ju0wT7KUMBgP5uYkU7qunua2Lto4eXv94F5V1bSc+WAgvIwnewxpaOumx2iXBe7H83ARsdjubd9fw5urdfLLxEE8t3oHVZtM7NCGGRRK8h/WNoJF54L1XWmI4CTEhLP6qhC+3lZMzJop9Fc0sX39A79CEGBZJ8B5WVd+b4KUH77UcZZoE6po6SYkP46fXTGeWZuGDL0t4beUuXv9kF4eOs+C3EN5CEryHVTe2YzQYiI0I0jsUcRwnT04mOS6UWy/MI8Bs5PrzNFLiw/h6RwWrN5byny/26h2iECckNzp5WHVDB7GRQTJNsJdLig3l4dvm9v8eGRrIgzfPBuCNT3bz6eZDtHf2EBIkHyHhvSTLeFh1QzsJUn/3afl5CfRY7WzZLWPlhXeTBO9hVfUyRNLXZaZEEhMRJGPlhdeTBO9B7Z09tLR3S4L3ccbei7DfltSecDFvIfQkCd6D+oZIJkiC93n5ub1lmj3VeocixDFJgveg6t554KUH7/syUyKJiwxm5TcH6bHKDVDCO0mC96D+m5xkHhqfZzAYuOas8RyoamHp1/v1DkeIo5IxXh5U3dBOWLC5f4EJ4dtmahbmTEjkw7X7yEqNIjby8L0NUWGB8joL3UmC96DqBhlB42+uOyeHwv31PP7WlkHbo8IDefyHJ2OUKaGFjiTBe1B1Q7ss5uxnwkMC+PUPZrG7tKF/W3FpE6s2HqK8tm3QIt9CeJokeA+x2ezUNHYwU0vQOxThYnFRwcRFJfX/np4YwaqNhygubZQEL3Q14gSvadqtwN0DNmUAryil7j7GIaNaXXMHVptdLrCOAomxoYQFmykubeS0qSl6hyNGsREneKXUs8CzAJqmTQTeBx50TVj+p2+IpIyB939Gg4HMlCiKy5r0DkWMcq4aJvl/wC+UUjI5xzEcHiIpCX40yEqNpKymlbaObr1DEaOY0wle07SzgRCl1L9dEI/fqm5ox2Q0EBMp0wSPBlmpUQDslV680JErLrLeAfx5uAfFxYWP+AktFt8bidLU3kNCTChJiVGDtvtiW45G2jHY7IhgDG9tobyhgzN0+tvIa+Jd9GiHUwle07RA4HTgpuEeW1vbgs1mH/ZzWiwRVFc3D/s4vR2qbCI2MmhQ7L7alu+SdhxdanwY23ZXc86MVJedc6jkNfEurmiH0WgYdsfY2RLNFGCXUqrVyfP4veqGDqm/jzJZqVHsLWscUUdGCFdwNsFnAodcEYg/a+vomyZYhkiOJnnpMbR3Wtl9qOHEOwvhBk6VaJRSbwNvuygWv9U/giZKevCjyZSsOALNRgqKqtDSYvQOR4xCMpukB/TPAy9L9Y0qwYFmJmfFsUFVS5lG6EISvAccqm7BYJAEPxrl5ybQ1NrFroNSphGeJwneA4pLGxljCSc4UKb+GW2mZsU7yjRK1m8VnicJ3s1sdjt7y5v6b3wRo0tQoIkp2fFsLKrqL9PYbHa+3FbO0nX7Wb7+AM1tXTpHKfyVdCndrKymlfZOK1kpkXqHInRy0sRENhRV8cnGQ5ybP5YV3xzg358V9z9us9tZMDddxwiFv5IevJsVlzYCkC09+FFrWnY8U7Li+M/nxWzeVc17a0qYkWPhXz89ncjQAKrq2/QOUfgpSfBuVlzaRHhIgFxgHcUMBgM3np+L2WTkyf9sJzjQxA3naQQGmLDEhPTPNCqEq0mCd7PiskayUiIxyNJto1pMRBDXnZuD0WDg+nNziAoLBByzi/YNoxXC1aQG70Yt7d2U17Zx0sSkE+8s/N5JE5OYmhVPaPDhj50lKoT1Oyvpsdowm6S/JVxL3lFutLfMUX+XETSiz8DkDo4evN0OtU1SphGuJwnejfaWNWEwQEayf0x3Klyv79qMlGmEO0iCd6PSmlYSokPkBidxTH0zjMqFVuEOkuDdqKymlZT4ML3DEF4sKjwQs8koPXjhFpLg3aS7x0ZlXTupFknw4tiMBgOW6GCq6yXBC9eTBO8mlXVt2Ox2UuIkwYvjk6GSwl0kwbtJWa1jkSsp0YgTsUSHUN3Yjt0uUwoL15IE7yal1a0YDJAcF6p3KMLLWaJDaO+00tTWzZPvbmP5+gN6hyT8hCR4NymraSUhJpQAs0nvUISX61vK8bWVis27a1hZcACb9OaFC0iCd5PSmlZSpTwjhqBvqOQGVU1MRBANLV39k9QJ4QxJ8G7Q3WOjqr6dlHgpz4gT60vw4SEB/Pe10zGbjBQUOhYIWbO1jPe+2KtneMKHyR04btA/gkZ68GIIggJMXDA3jQnpsSTEhDI5M5YNqop5k5N4eYXCarOTagljdl6i3qEKHyM9eDcorXGMoEmND9c5EuErrpyfzcSMWADy8xJoaOniibe3Eh4aQHpiBK+u3EVTq6z8JIZHErwLbdldwzMf7mDZ+v0YDJAUKyUaMXxTs+Ixm4w0tXVz4/m53HrRBDq6enj14116hyZ8jFMlGk3TLgJ+C4QBK5VSP3ZJVD7q/TV7qaxvJzIsgJMmJhFglv8/xfCFBJk5b/ZYbDY707LjATh/Thofrd1PXVMHsZHBOkcofMWIE7ymaZnAv4A5QCWwWtO0C5RSy1wVnC/p6OrhYHULC08ax2WnZeodjvBxl5+eNej3eZOS+WjtfjYxfAsqAAAYeUlEQVSoas7NH6tTVMLXONPFvBR4Syl1SCnVDVwNrHdNWL6npKwJu13WXhXukRQbytiEcDYUVekdivAhzpRosoEuTdMWA2nAR8Cvh3pwXNzIL0BaLN43v/rqrWUAzJmSQnho4JCP88a2jIS0w/3mzxzLK8sKwWzGMoQ1fr25LcMh7Rg5ZxK8GTgNmA+0AIuBG4EXh3JwbW0LNtvw79azWCKorm4e9nHutm1XNclxobS3dtLe2jmkY7y1LcMl7fCMCWMd3w5Xfl1ywjKNt7dlqKQdhxmNhmF3jJ0p0VQAnyilqpVS7cB7wGwnzuez7HY7e8uayEqR8oxwn8TYUNISwikoqtQ7FOEjnEnwHwHnaZoWrWmaCbgA2OiasHxLZX07Le3dZKVG6h2K8HOzchMoLm2iTtZwFUMw4gSvlFoP/BH4EtgJ7AdecFFcPqVv3hBZXFu4W35uAoBcbBVD4tQ4eKXU88DzLorFZxWXNhISZJKpCYTbJcaGkpYYTkFRFefOTgMcC3b3zWcjxEByJ44LqIMNZKZEYTQY9A5FjAL5uQkUlzVR29jBZ5tL+e9/fY06UK93WMILSYJ3Uml1C+W1bUwfH693KGKUmNVbplm+/gBvrd4DwPqdcuFVHEkSvJMKiqowADNzLHqHIkaJxBhHmWbVpkMYDJCbFs0GVY3VZgOgx2rTOULhLSTBO8Fut1NQVIWWFk1UeJDe4YhRpO9i69VnZnPWzLG0tHdTdKCB8tpWfvTXNawqkGX/hMwH75TSmlbKa9s4a+YYvUMRo8zZs8aSEh/GtOx4untsBAWa+GZnJWU1rXR0Wfmk4ABTxsXoHabQmSR4JxQUVmEwSHlGeF5QgInp4x3vu8AAE9Oy41mzrRyAjOQIduytpbGlU75ZjnJSohkhu93OBlWFNlbKM0J/szRHyWb6+HgWLcjDbnes8Xo076/Zy3NLdnoyPKETSfAjVFrtKM/01UKF0NPU7DiuOiObmy7IJdUSztjEiKPeDNVjtfHJhkOs31lJd49cjPV3kuBHqKDIUZ6ZoUmCF/ozm4ycPyeNiN6ZTE+dmsKugw00tAye+G7nvjraOnvosdo5UOn7k3iJ45MEPwJ9o2dy02KIChv61MBCeMrJU1OwA+t2DB4fX1BURWCA42O/p3eKDeG/JMGPQGl1KxV1bf03nAjhbdKSIslLj+HDtSXUNjomJuux2ti0q4Z8LYH4qOD+OZSE/5IEPwLfFMnoGeH9brwgF5sNXlxWiN1uZ0dJHe2dPczKTSArNYrisia9QxRuJgl+BPrKM5FSnhFeLCE6hCvPyGLHvnoef2sLb63eQ0iQmYkZsWSlRFLf3CnTDvs5SfDD1NTWRWVdG5Mz4/QORYgTmj89lfnTU2ls7cJkMrDwpHTMJmP/1NZSh/dvcqPTMJVVtwIwxiJTAwvvZzQY+MF52hHbxyaEE2g2UlzaxOy8RB0iE54gPfhhKq1xJHiZ+134MrPJyLikCOnB+zlJ8MNUVtNKSJCJmAi5e1X4tkmZcZSUN/FtSa3eoQg3kQQ/TGU1raTEh2GQxT2Ejztv9liS40J5cVkRbR09eocj3EAS/DCV1rSSKuUZ4QcCzCYWLcyjvrmTtz/drXc4wg0kwQ9DU2sXLe3dpMRJghf+ISslivPnpPHF1nK+3SulGn8jCX4Y+i+wygga4UcuOSWDlPgwXpBSjd+RBD8EpTWt2O12ynoTfGp8uM4RCeE6AWYTixbk0dDSyZurj16qqWlox2a3ezgy4SynxsFrmvYpkAB09266Qym13umovMjuQw08+uomrpifRW1jByFBZqLD5Q5W4V8yUyK5YE46S9ftZ5aWwJSswzfyFe2v509vbOaMGalcf+6RY+qF9xpxgtc0zQDkAOlKKb/9Xte3Wv37a/YSExFEqoygEX7qe6dksHVPDS8tL+KhW2YTGhxAR1cPzy8txGAwsHpTKTNzLOSNi9U7VDFEzpRo+v4rX6lp2lZN0+52RUB6sdvt1DZ2UNPQTluH4wuJzWZno6omNy2a4EAz1Q0dcoOT8FsBZiOLFubR2NLFax/vpqahnbdW76G2sYP7rppKQkwILywroqPryP6czW6npqGdmob2oz5+NN09Vlc3QXyHMyWaGGAVcA8QAHymaZpSSn3sksg87OMNh3hzlaP+GBhg5IHrZtLR1UNjaxffP3s8AP/6YAdjE6T+LvxXRnIkF8xNY8nX+/l6RwUA5+aPZWJGLIsW5PG/r23i358Wc8OA6Q9sdjt/eXsrO0rqAIiPCuYPd5yE0Xjsb7oHKpt59LVNfO/kDM6fk+beRo1iI07wSqmvga/7ftc07TlgATCkBB8XN/JEabFEjPjYY9lT1kRCbCjfPyeHV5YV8tIKhZYeQ2CAibPmjCM4yExaSjTje3vzruKOtuhB2uF9RtqW2y6dwlQtgdb2HkKCzMyZlITZZMRiieDig4188EUxZ81JZ2rvot8frtnLjpI6Lj8jG6vNzvufF1PV0sXkrPijnr+7x8ZDL2+gs8vKf74o5rSZY0lPjnR5O7yNHu1wpgZ/ChCklFrVu8nA4YutJ1Rb24LNNvyr8hZLBNXVrl1qzG63U1hSy4wcC1MzYrGfq/G3d7axr7yJWZqF5qZ2moGkqCCaGx0/u4I72qIHaYf3cbYt2UmHk1F9XWv/z+fnj2Hd9jL+8vomfn/LbJrbunhxyQ4mZ8axYPZYurptLP2qhE/W7SMp8ujTeby/Zi8lZU3cdEEu735ezJ9e3cAvb5iJ2XRkxdhfXhNXtMNoNAy7Y+xMDT4a+JOmacGapkUANwLvOXE+3VTUtdHa0dM/heq07HhOnpQEIKs2CTFAUICJWxZOoK6pg7v/8gUPPLUOk9HITRfkYjAYCAo0MSUrjg2qGpvNsbTlL55eR1VDO+AozSz5ej9zJyZy2tQUbjhXY39FM59uLtW5Zf7JmRLNR5qmzQE2AybgH71lG59TXOpY2aYvwQNce04OGSmRzJBVm4QYJHtMFPdcMYW9ZY6ZKKdlWwZNvjcrN4ENqppvCit5deUu2jp7eGFJIT+5eirPflRIeEgA156d07/vuKQIvv62gnNmjdWlPf7MqWKyUurXwK9dFItu9pQ2EhpkJjkutH9bSJCZM2eM0TEqIbzXtOx4pmUfvcY+NSueQLOR55YUYjIauHBeOh+t3c8jr27iUHUL91w+mfCQgP798/MS+PenxVQ1tJMQHeKpJowKcicrUFzWSGZqJEYZ3y6E0/rKNFabnctPz+LSUzOZnBnH/opmTpqYxPTxg78V52uOMuiGoio9wvVroz7Bt3X0UFbdSnZK1Il3FkIMyYXzxnHRvHGcNWsMBoOBmxfksvCkdK49Z/wR+8ZHh5CRHEmBJHiXG/UJvqS8CTuD6+9CCOekJUZw6WmZ/d+Ko8ODuPz0LMKCA466f35uAvsrmqmqb/NkmH7Pr9dktdnsvPt5MVOz48kZGw3Aqo2H2FZ8eFrU2qYODDjm4hBC6GNWroW3P93DP977lujwwxdsAwNNdHVZyUyJ5HunZOgYoW/y6x78yoKDLFt/gP97/1ta2rsp3FfHax/voqq+jZb2LlrauwgKMHLmjDGEBPn1/3VCeLX4qBDOmjkGs8nQ/9lsae+iqbWLspoWPvxqH51dMrXBcPltViuvbeU/X+wlKzWSfeXNvLS8iH3lzSTGhvK7m/MJDDDpHaIQYoDrzsk5YpvFEsGqdSU88e9tlJQ3kZseo0NkvsunevClNa18tvHgCfez2e08v6SQoAAjd186mQvnjWOjqqauqYNbFuRJchfCh2T2DoAo7h13L4bOpxK8OlDP469vYltxzXH323OokeKyJq6Yn0VUeBALT0pnalYcl52eSfYYuZgqhC8JDwkgMTa0/4ZEMXQ+leBPnZJCWlIELy4rorXj2NPeFBRVEWA2MjsvEQCzyciPr5zKwpPGeShSIYQrZadEsqe0EbusKjUsPpXgA8xG7r1mOk2t3bz5yeGlxbp7bGzeXY3VZsNmt7NBVTElM04unArhJ7JSo2hp7+6f00YMjU8leIDxY2NYcFIaX31bwdY9jlLNvz/bw5PvbmfJ2v3sOdRIY0uXTBImhB/pu0+luFTq8MPhcwke4KJ5GaRawnhxeRGbd1ezasMhwoLNfLh2H4u/KiHAbGRqdtyJTySE8Amp8WEEB5qkDj9MPpngA8xGblmYR3NrN0++u5346GAevHk2YSEB7NxXz5TMOJcuyiGE0JfRaCAjOZLC/fVs2VPT/6/4GHX50ppWGTePjyZ4gHFJkVw4Lx2jwcCiBXnERQVzY+8yYnMnJuocnRDC1XLTY6ioa+Nv72zr//fwKxtZs6180H4l5U389rlveP/LvTpF6j18upv7vVMyOGvmGCJCAwGYnmPhsbvmDZqbWgjhHy6Yk8aUzDhsA3rsb6/ew1urdzMpI5bYyGC6e6w8t6QQm93ON4VVXHlG9qieJdZne/AABoOhP7n3iY0MxjCKX1Ah/JXZZCQ9KYKM5Mj+fzcvzMNmgxeWFlJW08o7n+2lrKaVkyYmUd/cyd6y0V2z9+kEL4QY3RKiQ7hifhY79tXzq2fX8/GGg5wyJZnrz83BbDJSUDi6pyD26RKNEEKcOSOV5LhQWtq7CTAZmZwVh9lkZFJGLBtUFVefNXrLNJLghRA+zWAwMGFc7BHb8/MS2LKnhr2lTYOmKLHb7aOmjCslGiGEX5qWHY/ZZOTzLaWAI7E/tXgHf35rC1abTefoPEMSvBDCL4UEmTl71hi++raCHSV1fLW9gvU7K9mxr56V35x4Vlp/ICUaIYTfuuSUDLbuqeH5pYV0dFnJGRNFRGgg763Zy5TseFLjw/QO0a2cTvCapj0GxCulbnI+HCGEcJ3AABOLFubxyCsbCTAbWbQwj+BAM+rZBh5+eQOhwYdTYGxEMHdfNpnIsMDjnNG3OJXgNU07C7gRWOKacIQQwrWyUqK44+KJhAaZSYgJBeBHV0zhi61l0HvPlB0763dW8upKxV2XTtYxWtcacYLXNC0WeBh4BJjqsoiEEMLF+taG6JOdGkV26uDFf5JiQ3n38718U1h5xP6+ypmLrE8BvwTqXRSLEELo5vw5aWQkR/Dqyl00tnYNesxmt/Py8iIK9w8/3ZXXtvLYqxtp6+hxVahDNqIevKZptwIHlVKrNE27aSTniIsLH8lhgGMhXn/hL22Rdngff2mLJ9tx//Wz+PGfP+ftz4p54Mb8/vHyRfvq+GxLGVuLa/n7z84ccp3earXxyGub2HOwgcnZ8Zw3N92d4R9hpCWaq4FkTdO2ALFAuKZpf1FK3TfUE9TWtmCzDX/5LYslgurq5mEf5438pS3SDu/jL23xdDtCTAYuOTWDdz4rZskXxcyZ4CjVfLxuHyajgabWLp58cxO3XzxxSOf7aO0+9hxsIDjQxKcbDjAj68gbsobKaDQMu2M8ogSvlDqn7+feHvz84SR3IYTwVufNHsumXdW8ulKRmxZNRFggBUVVTM6MIz0pgg++LGFWbgIzcizHPU9pTWv/vhmpUby7eg/NbV1HTJDoTnKjkxBCDGAyOhYU6uy28fIKxd7SJuqbO8nPTWDhSemkJYTz8gpFS3v3cc+zetMhTEYD15+bw8lTUrDZ7WzeXeOhVjg4neCVUi/KGHghhD9Jjgvj0tMy2Ly7hpeWF2E2GZk23jH1waKFebS2d/Pax7uOebzNZmejqmZKVhyRoYFkpkaREBNCQWGlB1shPXghhDiq8/LTyEqJpLSmlUkZsYQEOSraaYkRXHTyONbvrGSjOjwdcWV9G4X76gDYdbCBptYu8nuHWxoMBvJzEyjc30BzW9eRT+YmkuCFEOIojEYDixbmEREawGlTUwY9tmBuOumJEbyyQtHc1kV7Zw+Pv7mFx9/aSkl5EwWqikCzkSmZcf3HzJuURFR4IO2dnhsuKXPRCCHEMSTHhfHEPaccMb2w2eSo0//uxQJe+3gXocEB1DZ2EBYSwPNLCmlu72ZKdjxBgaZB53rsrnkenapYErwQQhzHsRLymIRwLj4lg/e+cCzufW7+WCaMi+WJf28FYHZuwpDP5S6S4IUQYoQWzE1j254a2rusXHZaJoEBJk6bmsxGVc3krLgTn8DNJMELIcQImYxG/vu6GdhsdgIDHOWYH5yfyxXzswkKMJ3gaPeTBC+EEE4wm4wwIJcbDQbCQwL0C2gAGUUjhBB+ShK8EEL4KUnwQgjhpyTBCyGEn5IEL4QQfkoSvBBC+Ck9hkmawDHPw0g5c6y38Ze2SDu8j7+0RdpxxPFDHmBvsNuHv6qSk04B1nj6SYUQwk+cCnw5lB31SPBBQD5QDlg9/eRCCOGjTEAyUAB0DuUAPRK8EEIID5CLrEII4ackwQshhJ+SBC+EEH5KErwQQvgpSfBCCOGnJMELIYSfkgQvhBB+yitWdNI07bfAVb2/LlFK/VzTtLOBPwMhwFtKqV/17jsNeBaIBL4A/guIBVYOOGUUYFFKhXuoCf2cbYtSqkfTtNnAP3DcFHYAuFUpVeGt7RhwzMvAaqXUi9/Z/hBgVUo96PbAv8MV7dA07VTgCSAQKAFuVErVe6gJA+NyRVtuBP4AVA44zy89Ef+AmJxqh6ZpCXjB591Fr4dbP+u69+B7/yDnAtOBacBMTdO+DzwPfA/IA/I1Tbug95BXgbuVUjmAAbhNKVWllJqmlJoGzAD2Abd7tiWuaYumaQbgHeDnSqkpwMvA097cDk3TUjRN+xC44jvnidI07Tngp56Mf8Dzu6QdwAvADUqpycBO4GceakI/F7ZlFvCTvs+LDsnd6XZ4w+fdFe3wxGdd9wSPY8qCnyqlupRS3UAhkAPsVkqVKKV6cCTCKzVNSwdClFLreo99EbjyO+e7GWhTSr3umfAHcUVb4nu3f9q7/SPgfE3TgryxHb37Xwd8ALz9nfN8D9gNPO6ZsI/gqnbkKaV2apoWAKQCHu+947q25AM3apq2XdO0VzVNi/FQ/H1c1Y4+en3eXdEOt3/WdS/RKKV29P2sadp4HF95nsTxB+xTDowBUo6xve94E/BLHInF41zUlhqgVdO0c5VSK4FrgAAgDihzawN6DbMdKKX+1LvvKd85z8u92x90b8RH58J2dGuaNhn4BOgGfuHeyI/kqrb07vMYsBZ4BPg7juTjES5sh66fdxe1w+2fdW/owQOgadpE4GMcX3/3AgMnyTEANhzxHm17n/Nx/A+63b3RHp8zbVFK2YHLgV9omrYZiAZqgS4PhD7IENvh9VzRDqXUdqVUIvAQ8JY74hwKZ9uilLpUKfVV7/vsj8AFx9vfXVz03tL98+5MOzzxWfeKBK9p2snAKuD/KaVeAg7hmDWtTxKO/9GOtb3PJcCb7o32+FzUlm6l1Hyl1HQcX/NMQJ27Yx9oGO3was62Q9O0YE3TLhmw6VVgijtiPREXtCVK07T7BmwyAD3uiPV4XPje0vXz7qJ2uPWzrnuC1zRtLPA+cK1Squ/FWu94SMvu/Rp2LbBMKbUf6Oj9wwLcACwbcLqT0HGueRe25QVN0/J7f/4J8G+llMd6y8Nph6diGgkXtaMb+IemaTN7f7+KIc7F7UouaksL8HNN0+b0/n438J67Yj4aF7+3dPu8u7Adbv2s616DB+4HgoE/a5rWt+1fwE3Au72PLcVxtRkc9cJnNE2LBDYBfxtwrkwc/4vqxVVtuRN4StO0UGAbcIsngh9guO3wVk63Qyll1TTtauDp3g9tKXCrG2M+Fle15Srg/zRNCwF2AT9wY8xH48r3lp6fd1e1w62fdZkPXggh/JTuJRohhBDuIQleCCH8lCR4IYTwU5LghRDCT0mCF0IIP+UNwySFcClN08YBxUDfHY5GHGPAn1BKvT1gv4uAxcA1Sqm3erdFA5/17hKOY+4Z1fv7xzhm/ht47oFmK6U8fsexEMciwySF3+lN8N8OnD62d3K3VcB/K6Xe7d22DMet4dlKqblHOc984O9KqUnHO7cQ3kpKNGJU6L1z+Df0TvWraVomMB/H3YPjNU07IsEL4eukRCNGk63A5N6f/wvHIg1Vmqa9CdwHXD3E84RomrblO9u+Ukr90EVxCuESkuDFaGIH2nrn274ZWNS7/SXgK03TxiqlDg7hPO29i00I4dWkRCNGk3wcF0evAmKAv2uatg/HIgx24B7dIhPCDSTBi1FB07Qc4Nc4Vpe6E3hYKZWulBqnlBqHo2Rzm6ZpYTqGKYRLSYlG+KuBdXIb0AE8gGP2wWnAxd/Z/2XgVzhmA/zHMM490E1KqaNtF0IXMkxSCCH8lJRohBDCT0mCF0IIPyUJXggh/JQkeCGE8FOS4IUQwk9JghdCCD8lCV4IIfyUJHghhPBT/x8A0iCKXRyQRgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_unemployment.plot(y='Unemployment Rate')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the chart above, you can clearly see the impact of the 2008 recession in the unemployment rate. Also note that the unemployment rate is below the 2007 rate at this point."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>Unemployment Rate</th>\n",
       "      <th>change</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-10-01</th>\n",
       "      <td>3.8</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01</th>\n",
       "      <td>3.7</td>\n",
       "      <td>-0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-01</th>\n",
       "      <td>3.9</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-01</th>\n",
       "      <td>3.8</td>\n",
       "      <td>-0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Unemployment Rate  change\n",
       "DATE                                 \n",
       "2018-10-01                3.8     0.1\n",
       "2018-11-01                3.7    -0.1\n",
       "2018-12-01                3.9     0.2\n",
       "2019-01-01                4.0     0.1\n",
       "2019-02-01                3.8    -0.2"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_unemployment['change'] = df_unemployment['Unemployment Rate'] - df_unemployment['Unemployment Rate'].shift(1)\n",
    "df_unemployment.tail(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The unemployment rate changed by -0.2% last month as compared to the previous month.\n"
     ]
    }
   ],
   "source": [
    "print('The unemployment rate changed by {:.1f}% last month as compared to the previous month.'.format(df_unemployment['change'][-1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are several other components of the Friday Jobs Report that I haven't covered yet. I hope to revisit this post next month to include the labor force participation rate and average hourly earnings. I might even make these graphs interactive using Plotly. Feel free to suggest any other enhancements in the comments section below."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  },
  "nikola": {
   "category": "",
   "date": "2019-03-10 16:24:20 UTC-04:00",
   "description": "",
   "link": "",
   "slug": "friday-jobs-report-numbers-in-python",
   "tags": "employment,pandas,pandas-datareader",
   "title": "Friday Jobs Report Numbers in Python",
   "type": "text"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}