Assignment - day 2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b1839652",
   "metadata": {},
   "source": [
    "### Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a00442c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from numpy.random import randint,randn,uniform,sample\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d004048f",
   "metadata": {},
   "source": [
    "### Creating a data frame of random number in 10 rows and 4 columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e1470304",
   "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>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.113014</td>\n",
       "      <td>-0.712283</td>\n",
       "      <td>0.515367</td>\n",
       "      <td>-1.247482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.479212</td>\n",
       "      <td>0.350776</td>\n",
       "      <td>0.774229</td>\n",
       "      <td>0.301254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.612330</td>\n",
       "      <td>-1.393596</td>\n",
       "      <td>0.961367</td>\n",
       "      <td>-0.642502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.965085</td>\n",
       "      <td>-0.668197</td>\n",
       "      <td>1.229849</td>\n",
       "      <td>-0.587815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.333309</td>\n",
       "      <td>-0.620067</td>\n",
       "      <td>0.833977</td>\n",
       "      <td>0.404035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.430652</td>\n",
       "      <td>0.977074</td>\n",
       "      <td>2.390739</td>\n",
       "      <td>-0.449360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.340530</td>\n",
       "      <td>-1.451758</td>\n",
       "      <td>-1.427980</td>\n",
       "      <td>-0.356819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.136503</td>\n",
       "      <td>0.801171</td>\n",
       "      <td>0.393479</td>\n",
       "      <td>0.534628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.666852</td>\n",
       "      <td>-0.289559</td>\n",
       "      <td>-0.007002</td>\n",
       "      <td>-0.695900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-0.356923</td>\n",
       "      <td>-0.200207</td>\n",
       "      <td>1.190602</td>\n",
       "      <td>-0.865203</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          a         b         c         d\n",
       "0  1.113014 -0.712283  0.515367 -1.247482\n",
       "1 -0.479212  0.350776  0.774229  0.301254\n",
       "2  0.612330 -1.393596  0.961367 -0.642502\n",
       "3 -0.965085 -0.668197  1.229849 -0.587815\n",
       "4 -0.333309 -0.620067  0.833977  0.404035\n",
       "5  0.430652  0.977074  2.390739 -0.449360\n",
       "6 -0.340530 -1.451758 -1.427980 -0.356819\n",
       "7  1.136503  0.801171  0.393479  0.534628\n",
       "8 -0.666852 -0.289559 -0.007002 -0.695900\n",
       "9 -0.356923 -0.200207  1.190602 -0.865203"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(randn(10,4),columns=['a','b','c','d'])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1909fea5",
   "metadata": {},
   "source": [
    "### Creating a bar graph for all the columns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "917679b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Bar graph')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+/fbb1N/Pnj3bqediSQMAAAAMjcILAAAAQ6PwAgAAwNAovAAAADA0Ci8AAAAMjcILAAAAQ/OYwjt37ly1atVKFSpUUFBQkPr06aPffvsty+MOHz6sjh07qnTp0qpevbrCw8PTPNEDAAAAxuYxhXfbtm0aPHiwvv/+e61fv1558uRR9+7ddeXKlUyPSUpKUo8ePVSqVClt3rxZM2fO1HvvvacFCxa4MDkAAADcyWMePLF27do0rxcvXqyAgADt2rVLHTp0yPCYL774Qjdu3FBERIQKFCigGjVq6OjRo1q0aJFefPFFmUwmV0QHAACAG3nMFd5/unbtmlJSUmQ2mzPdZ8+ePWratKkKFCiQuq1Nmza6cOGCTp8+7YKUAAAAcDePucL7T6+++qpq166tkJCQTPeJi4tT2bJl02zz8/NLHatYsWKGx1ksFoflvB/uOm9OkFvnnlvnLTH33KZsFuNG/5kYfX625Na5u3LePj4+yp8/f5pt93Z0d9n5Jcm72Vepv79586bTzpOUlKS4uLh024ODg20e55GFd+LEidq1a5c2btwob29vm/v+c9nC319Ys7WcIasfmjNYLBa3nDcnyK1zz63zlph7bpx78lnb40b+meTWz1zKvXN39bwTExPl4+OTZluyy87+l7/Pf/PmzXRZHMnX11cVKlS47+M8bknDhAkTtGbNGq1fvz7TK7R/K1WqVLr/F3Dp0iVJ/3elFwAAAK5ntVr13nvvqX79+ipVqpRq1KihqVOnOuVcHlV4x48fry+//FLr169XlSpVstw/JCREO3fuTHNpPTo6WmXKlFFgYKAzowIAAMCGN954Q7Nnz9Yrr7yiXbt26ZNPPlG5cuWcci6PKbxjxozRqlWrFBkZKbPZrNjYWMXGxuratWup+0ydOlVdu3ZNfd2rVy8VKFBAoaGh+u2337R+/XrNmzdPoaGh3KEBAADATa5du6ZFixYpLCxMzz77rCpVqqSQkBANGTLEKefzmDW8kZGRkqRu3bql2T5+/HhNmDBBkhQTE6OTJ0+mjhUtWlTr1q3TmDFj1KpVK5nNZo0cOVIvvvii64IDAAAgjSNHjujWrVtq2bKlS87nMYU3ISEhy30iIiLSbatZs6a+++47JyQCAABAdrj6qbces6QBAAAAxlC1alXlz59fP//8s0vO5zFXeAEAAGAMRYoU0fDhwzV16lTly5dPjzzyiOLj43XgwAENHjzY4eej8AIAAMDlpkyZIrPZnHqnhlKlSqlv375OOReFFwAAwGAKtd7o7ghZ8vLy0iuvvKJXXnnF+edy+hkAAAAAN6LwAgAAwNAovAAAADA0Ci8AAAAMjcILAAAAQ6PwAgAAwNAovAAAADA0Ci8AAAAMjcILAAAAQ6PwAgAAIEfo06ePRowY4fD35dHCAAAABvP6JwNcer5pA5e69Hz3iyu8AAAAMDQKLwAAAFzu+vXrGjFihMqVK6fg4GDNmTPHaeei8AIAAMDlXn/9dW3ZskXLli3T119/rYMHD2rHjh1OORdreAEAAOBS165d0/Lly7VgwQK1adNGkrRw4ULVqFHDKefjCi8AAABc6uTJk7p9+7ZCQkJStxUuXFg1a9Z0yvkovAAAAHApq9Xq0vNReAEAAOBSlSpVUt68ebV3797UbcnJyfrtt9+ccj7W8AIAAMClChcurGeffVZhYWEqWbKkSpcurVmzZiklJcUp56PwAgAAwOWmTZum5ORkPfPMMypQoIBeeOEFXb9+3SnnovACAAAYTE5/8pkkFSpUSIsXL3bJuVjDCwAAAEOj8AIAAMDQKLwAAAAwNAovAAAADI3CCwAAAEOj8AIAAMDQKLwAAAAeLE+ePEpOTnb543pdyWq1Kjk5WXnyZO+OutyHFwAAwIMVKlRIt27dUlJSkrujKCkpSb6+vk55bx8fH+XPnz9bx1J4AQAAPFz+/PmzXQYdKS4uThUqVHB3jHRY0gAAAABDo/ACAADA0Ci8AAAAMDSPKrzbt29X3759Vb16dZnNZq1cudLm/qdPn5bZbE73a9OmTS5KDAAAAHfzqC+tJScnq0aNGnr66ac1fPhwu49bs2aNatWqlfq6WLFizogHAACAHMijCm/btm3Vtm1bSVJoaKjdxxUvXlz+/v7OigUAAIAczKOWNGTXs88+q8qVK6tdu3b6+uuv3R0HAAAALuRRV3jvV+HChTVt2jQ1adJEefLk0YYNGzRo0CBFRESoT58+7o4HAAAAFzAlJCR45HPoypUrp1mzZql///73ddzo0aO1c+dO7dixI9N9LBbLg8YDAPyPsmdfsjl+vsJ7LkoCwIiCg4Ntjhv6Cm9GGjRokOXdHbL6oTmDxWJxy3lzgtw699w6b4m558a5J5+1PW7kn0lu/cyl3Dv33DpvKefOPVes4f1fhw4d4gtsAAAAuYhHXeG9du2aTpw4IUlKSUnRuXPndPDgQRUrVkwVKlTQ1KlTtW/fPq1fv16StGrVKuXNm1d16tSRl5eXNm7cqMjISIWFhblxFgAAAHAljyq8+/fvV5cuXVJfz5gxQzNmzNDTTz+tiIgIxcTE6OTJk2mOefvtt3X27Fl5e3srKChICxYs4AtrAAAAuYhHFd7mzZsrISEh0/GIiIg0r/v166d+/fo5ORUAAABysly3hhcAAAC5C4UXAAAAhkbhBQAAgKFReAEAAGBoFF4AAAAYGoUXAAAAhkbhBQAAgKFReAEAAGBoFF4AAAAYGoUXAAAAhkbhBQAAgKFReAEAAGBoFF4AAAAYGoUXAAAAhkbhBQAAgKFReAEAAGBoFF4AAAAYGoUXAAAAhkbhBQAAgKFReAEAAGBoeR70Dfbs2aOEhAQ98sgjKlSokCMyATC4smdfUvLZzMcLtd7oujAAAMOz+wrvrFmz1KNHjzTb+vTpo/bt26tv374KCQnRmTNnHB4QAAAAeBB2F96vvvpKNWrUSH29YcMG/fDDDxo1apQiIyN1+/ZtzZo1yykhAQAAgOyye0nDuXPnFBwcnPo6KipKQUFBmjJliiTJYrFoxYoVjk8IAAAAPID7WsN779691N///PPP6tKlS+rrsmXL6uLFi45LBgAAAI+SU7+jYfeShsqVK+vbb7+VJG3atEkxMTF6/PHHU8f//PNPmc1mhwcEAAAAHoTdV3hfeuklDR48WIGBgbp+/bqqVKmiVq1apY7//PPPql27tlNCAgAAANlld+Ht0aOHihcvru+//15FihTR4MGDlSfPX4dfuXJFJUqUUJ8+fZwWFAAAAMgOuwrvnTt3tGfPHpUvX17Tp09PN16sWDG+sAYAAIAcya7C6+3tre7du2v69OkKCgpydiYAAJADmD/+0+Z4wqByLkoCPBi7vrTm5eWlgIAAXbt2zdl5AAAAAIeyew1vaGio5s+fr2eeeUZ+fn7OzOSR+H/BAAAAOZPdhffatWsqVKiQ6tevr06dOqlixYoqUKBAmn1MJpNefvllh4cEAAAAssvuwhsWFpb6+9WrV2e4D4UXAAAAOY3dhffXX391Zg4AAADAKewuvAEBAc7MAQAAADiF3YX3b2fPntX27dt18eJF9ejRQ+XLl9fdu3d15coVFStWLPVhFAAAAEBOcF/tdOLEifrggw907949mUwm1alTR+XLl9f169dVv359vfrqqxo5cqSzsgIAAAD3za778ErS/PnzFRERoZEjR+qrr76S1WpNHfP19VWnTp30zTffOCXk37Zv366+ffuqevXqMpvNWrlyZZbHHD58WB07dlTp0qVVvXp1hYeHp8kOAAAAY7O78C5dulRPPfWUpk6dqtq1a6cbr1mzpo4fP+7QcP+UnJysGjVqaObMmeluiZaRpKQk9ejRQ6VKldLmzZs1c+ZMvffee1qwYIFTcwIAACDnsHtJw7lz52zecqxIkSJKTEx0SKjMtG3bVm3btpX014MwsvLFF1/oxo0bioiIUIECBVSjRg0dPXpUixYt0osvviiTyeTUvAAAAHA/u6/wFi9eXDExMZmOHz58WGXKlHFIKEfZs2ePmjZtmuZqcJs2bXThwgWdPn3ajckAAADgKnZf4W3btq2WLl2qIUOGpLsy+uuvv2rFihV6/vnnHR7wQcTFxals2bJptv39WOS4uDhVrFgxw+MsFks2zlbQ5qg975m98xpDbp17bp132SzGjf5zMfr8MsJn7qnz4++27Mqt83bXv+vBwcE2x+0uvBMnTtRPP/2kZs2aqV27djKZTFq5cqWWLl2qb775RhUqVNDYsWMfOLCj/bOc//2FNVvLGbL6oWVo2582h7N6T4vFkr3zGkBunXtunbckJZ+1PW7kn0tu/dz5zD10fvzdli25dd5Szv133e7C6+/vry1btmjatGlav369rFarvvjiCxUpUkR9+vRRWFiYzGazE6Pev1KlSikuLi7NtkuXLkn6vyu9AIAHV3jAYzbHkwf4uCYIAGTgvu7DW7JkSb377rt69913denSJaWkpKhkyZLy8rJ7KbBLhYSEKCwsTDdv3pSPz19/2EZHR6tMmTIKDAx0czoAAAC4QrYfi5Y/f35JcmnZvXbtmk6cOCFJSklJ0blz53Tw4EEVK1ZMFSpU0NSpU7Vv3z6tX79ektSrVy+Fh4crNDRUY8aM0bFjxzRv3jyNGzeOOzTA7cqefcnmf/op1Hqj68IAAGBg99VWT5w4oRdeeEEPPfSQAgMDFRgYqIceekgvvPCCjh075qyMqfbv368WLVqoRYsWunHjhmbMmKEWLVpo+vTpkqSYmBidPHkydf+iRYtq3bp1unDhglq1aqWxY8dq5MiRevHFF52eFQAAADmD3Vd49+3bpx49eujGjRtq3bq1KleuLKvVquPHj2vdunX67rvvtG7dOjVs2NBpYZs3b66EhIRMxyMiItJtq1mzpr777junZQIAAEDOZnfhffXVV1W4cGFFR0crKCgozdixY8fUpUsXTZw4UT/88IPDQwIAAADZZfeShv/3//6fhgwZkq7sSlLlypU1ZMgQHTp0yKHhAAAAgAdld+EtVaqUzS+oeXt7y9/f3yGhAAAAAEexu/COHDlSkZGROnfuXLqxM2fOKDIyUiNHjnRoOAAAAOBBZbqG99133023rWjRomrYsKE6duyoSpUqSZKOHz+ujRs3KigoSDdu3HBeUgAAACAbMi28YWFhmR60bt26dNsOHz6ssLAwvfzyyw4JBgAAADhCpoX3119/dWUOAAAAwCkyLbwBAQGuzAEAAAA4heueCwwAAAC4gd0PnpCkDRs2aPny5Tp16pQSEhJktVrTjJtMJv3+++8ODQgARlL27EtKPpvxWKHWG10bBgByCbsLb3h4uMLDw1W0aFHVqlUr9S4NAAAAQE5md+H98MMP1bJlS3322WfKnz+/MzMBAAAADmN34b1z5466du1K2QUAAJKkwgMes73DpA9dkgPIit1fWmvdurX279/vzCwAAACAw9ldeGfPnq39+/dr5syZOnv2bLovrAEAAAA5kd1LGkqWLKmePXvqjTfe0KxZszLcx2Qy6fLlyw4Lh9yBb60DAABnsrvwhoWFaf78+QoMDFSDBg3k6+vrzFwAAACAQ9hdeJctW6aOHTtqxYoVzswDAAAAOJTda3hTUlLUpk0bZ2YBAAAAHM7uwtuhQwdt27bNmVkAAAAAh7O78I4ePVoWi0WjRo3SL7/8opiYGF28eDHdLwAAACAnsXsNb6NGjSRJhw4d0vLlyzPdLz4+/sFTAQAAAA5id+EdN26cTCaTM7MAAAAADmd34Z0wYYIzcwAAAABOYfcaXgAAAMAT2X2FNzw8PMt9TCaTxo0b90CBAAAAAEeyu/DOnDkz0zGTySSr1UrhBQAAQI5jd+G9cuVKum0pKSk6c+aMFi9erN27d+vLL790aDgAAADgQT3QGl4vLy9VrFhRM2bMUGBgoF599VVH5QIAAAAcwmFfWmvevLm+//57R70dAAAA4BAOK7wWi0VWq9VRbwcAAAA4hN1reLdv357h9sTERG3dulUffvihunfv7qhcgEcwf/xnpmMJg8q5MAkAAMiM3YW3c+fOGT5pzWq1ytvbWz179rTr1mUAAACAK9ldeKOiotJtM5lMMpvNCggIUJEiRRwaDAAAAHAEuwvvo48+6swcAAAAgFPwaGEAAAAYmt1XeCVp2bJlWrp0qU6dOpXhgyhMJpMuX77ssHAAAADAg7K78L7xxhuaN2+eatasqd69e8tsNjsxVuYiIyM1f/58xcbGqlq1apoxY4aaNWuW4b6nT59W3bp1023/8ssv9fjjjzs7KgAAAHIAuwvvihUr1LFjR61YscKZeWxau3atXn31Vc2ZM0dNmjRRZGSkevfurV27dqlChQqZHrdmzRrVqlUr9XWxYsVcERcAAGTi9U8G2ByfNnCpi5IgN7B7DW9ycrLbr4ouXLhQ/fr104ABA1S1alXNnj1b/v7+WrJkic3jihcvLn9//9Rf+fLlc1FiAAAAuJvdV3ibNGmiw4cPOzOLTbdv39aBAwf00ksvpdneunVr7d692+axzz77rG7evKmgoCCFhoaqW7duzoyaLWXPvqTks5mPF2q90XVhAAAADMTuwjt79mx1795ddevWVf/+/TN8CIUzXb58Wffu3ZOfn1+a7X5+foqLi8vwmMKFC2vatGlq0qSJ8uTJow0bNmjQoEGKiIhQnz59Mj2XxWLJRsKCNkezes+yWbx79jJ5Bltzz/nzzvxz5zPPHHPPmCfP++EHPN6T524Pz52f7b/b7JHduXvuz+wvnp4/u9z153twcLDNcbsL79NPP63bt2/r5Zdf1rhx41S2bFl5e3un2cdkMmnXrl3ZS2qnfxZtq9WaafkuUaJEmivCDz/8sOLj4/Xuu+/aLLxZ/dAytC3zR8za8562ru7ac7wnszX3HD9vG587n3nmmHvGjDzvrBh57haLxXPnl8XfbfbIdO7bs3mcB/Doz/wB5dQ/3+0uvCVLlpSfn58qV67szDyZKlGihLy9vdNdzb106VK6q762NGjQQCtXrnR0PAAAAORQdhfeb7/91pk5spQvXz7Vq1dP0dHR6t69e+r26Ohode3a1e73OXTokPz9/Z2QEAAAADnRfT14wt1GjhypYcOGqUGDBmrcuLGWLFmimJgYDRo0SJI0depU7du3T+vXr5ckrVq1Snnz5lWdOnXk5eWljRs3KjIyUmFhYW6cBQAAAFzJowrvk08+qfj4eM2ePVuxsbGqXr26Pv/8cwUEBEiSYmJidPLkyTTHvP322zp79qy8vb0VFBSkBQsW2Fy/CwAAAGPxqMIrSUOGDNGQIUMyHIuIiEjzul+/furXr58rYgEAACCH8rjCCwBwD/PHmX9j/64LcwDA/bL7SWsAAACAJ6LwAgAAwNBY0gAAOcTrnwywOT5t4FIXJQEAY7mvwrthwwYtX75cp06dUkJCgqxWa5pxk8mk33//3aEBAQCAZyp79iUbT97invhwHbsLb3h4uMLDw1W0aFHVqlVLlSpVcmYuAAAAwCHsLrwffvihWrZsqc8++0z58+d3ZiYAAADAYez+0tqdO3fUtWtXyi4AAAA8it1XeFu3bq39+/enPsYXsFfhAY/ZHE8e4OOaIAAAIFey+wrv7NmztX//fs2cOVNnz55N94U1AAAAICey+wpvyZIl1bNnT73xxhuaNWtWhvuYTCZdvnzZYeEAAACAB2V34Q0LC9P8+fMVGBioBg0ayNfX15m5AAAAAIewu/AuW7ZMHTt21IoVK5yZBwAAAHAouwtvSkqK2rRp48wsQDo8eQoAADwou7+01qFDB23bts2ZWQAAAACHs7vwjh49WhaLRaNGjdIvv/yimJgYXbx4Md0vAAAAICexe0lDo0aNJEmHDh3S8uXLM90vPj7+wVMBAAAADmJ34R03bpxMJpMzswAAAAAOZ3fhnTBhgjNzAAAAAE5h9xref7p69aquXr3qyCwAAACAw91X4T1z5oyGDRumSpUqKTAwUIGBgapUqZKGDx+uM2fOOCsjAAAAkG12L2mwWCxq166dEhMT9dhjj6lq1aqyWq2yWCz64osv9OOPP+r7779X5cqVnZkXAAAAuC92F96pU6fKarUqOjpaderUSTN26NAhdevWTVOnTrV5BwcAAADA1exe0rBt2zYNGzYsXdmVpNq1a2vo0KHaunWrQ8MBAAAAD8ruwnv79m35+vpmOl60aFHdvn3bIaEAAAAAR7G78NaoUUOrV6/WjRs30o3dunVLq1evVo0aNRwaDgAAAHhQdq/h/fe//61nnnlGrVq10uDBgxUcHCxJOnr0qJYsWSKLxaIVK1Y4LSgAAACQHXYX3o4dO+qDDz7QpEmT0jx1zWq1yt/fXx988IE6dOjgtKAAAABAdthdeCWpV69e6t69uw4cOJB6392AgADVq1dPefLc11sBAAAALnHfLTVPnjxq2LChGjZs6Iw8AAAAgENlWni3b9+erTd85JFHsh0GAAAAcLRMC2/nzp1T1+lKf63V/d/XmYmPj3dMMgAAAMABMi28UVFRaV7fuXNHkydP1vXr1zVw4EBVrlxZVqtVx44d09KlS1WoUCG98cYbTg8MAAAA3I9MC++jjz6a5vXkyZPl7e2t7du3y8fHJ83YkCFD1KFDB0VHR+uxxx5zSlAAAAAgO+x+8MTq1avVt2/fdGVXkgoWLKi+ffvqs88+c2g4AAAA4EHZXXivXr2qK1euZDoeHx+vq1evOiQUAAAA4Ch2F94mTZooIiJCu3fvTje2a9cuLV68WE2aNHFoOAAAAOBB2V14Z82aJR8fH3Xo0EGtWrXSCy+8oBdeeEGtWrVSx44dlT9/foWHhzszqyQpMjJSderUkb+/v1q2bKkdO3bY3P/w4cPq2LGjSpcurerVqys8PFxWq9XpOQEAAJAz2F14K1eurB07dmjEiBG6du2a1q9fr/Xr1+vatWsaMWKEduzYoeDgYGdm1dq1a/Xqq69q9OjR+s9//qOQkBD17t1bZ8+ezXD/pKQk9ejRQ6VKldLmzZs1c+ZMvffee1qwYIFTcwIAACDnuK8nrZUsWVJvvfWW3nrrLWflsWnhwoXq16+fBgwYIEmaPXu2fvrpJy1ZskRTpkxJt/8XX3yhGzduKCIiQgUKFFCNGjV09OhRLVq0SC+++KJd9xUGAACAZ7P7Cq+73b59WwcOHFDr1q3TbG/dunWG64olac+ePWratKkKFCiQuq1Nmza6cOGCTp8+7dS8AAAAyBnu6wqvxWLRihUrdOrUKV25ciXdWliTyaT169c7NODfLl++rHv37snPzy/Ndj8/P8XFxWV4TFxcnMqWLZtu/7/HKlas6JSsAAAAyDnsLrxr1qzRsGHD5O3treDgYJnN5nT7uOLLYP9chpDVI48z2j+j7f/LYrHcd669j9oeLzzgMZvjlkkf2hxf9skAm+PPPTLJdgAnarStoM3xvVnMzZbnKtgez85n5Ui2Pvfc/Jnf3dLf5jhzz5gn//O+/9Hs/3suGfszLzvAR8kZf9VEkjTzjL/N490596z+bnuQzz0n//P+oJ+5Jn1oM/+y7W/aPNyT/3l/kD/fH2TeWX2PzO7CO336dNWoUUNr1qxJd5XVFUqUKCFvb+90V3MvXbqUaZ5SpUpluL8km3Nw9pfvsnXe7Q9wrLNt+9PmcFbZLBaLe/O7kVE/c3sw99wly3/PDf6ZPwhP/ufFY/9858+4B5LduTtz3nav4f3zzz/13HPPuaXsSlK+fPlUr149RUdHp9keHR2txo0bZ3hMSEiIdu7cqZs3b6bZv0yZMgoMDHRqXgAAAOQMdhfeKlWq6PLly87MkqWRI0dq1apVWrZsmY4cOaLx48crJiZGgwYNkiRNnTpVXbt2Td2/V69eKlCggEJDQ/Xbb79p/fr1mjdvnkJDQ7lDAwAAQC5h95KGyZMn68UXX1TPnj1VuXJlZ2bK1JNPPqn4+HjNnj1bsbGxql69uj7//HMFBARIkmJiYnTy5MnU/YsWLap169ZpzJgxatWqlcxms0aOHKkXX3zRLfkBAADgenYX3u+++05+fn5q1qyZWrRoofLly8vb2zvNPiaTSW+//bbDQ/6vIUOGaMiQIRmORUREpNtWs2ZNfffdd07NBAAAgJzL7sK7ZMmS1N//9NNPGe7jisILAAAA3A+7C++VK1ecmQMAAABwCo950hoAAACQHRReAAAAGNp9PVo4Ojpa7733ng4cOKDExMQMn6wWHx/vsHAAAADAg7L7Cu93332nXr166fz58+rRo4dSUlLUq1cv9ezZUz4+PqpVq5bGjRvnzKwAAADAfbP7Cu+cOXNUu3Ztbdq0SYmJiVqyZIn69++vli1b6tSpU3r88ccVFBTkzKwAAADAfbP7Cu/hw4fVu3dv5cmTJ/X+u/fu3ZMkVaxYUc8//7zeeecd56QEAAAAssnuwps/f34VKFBAklSoUCGZTCZdvHgxdbxcuXJpnnIGAAAA5AR2F96HHnpIR44ckSTlzZtXVatWVVRUVOr4hg0bVLp0accnBAAAAB6A3YX38ccf17p163Tnzh1J0ogRI/Ttt9+qfv36ql+/vn744Qc9//zzTgsKAAAAZIfdX1obN26cRowYoTx5/jrkueeeU8GCBbVu3Tp5e3tr7Nixevrpp50WFAAAAMgOuwtv3rx5Vbx48TTbevXqpV69eqW+vnfvXuoX2gAAAICc4L4ePJGZW7duadmyZXrvvfd08OBBR7wlAAAAcphrS7fY3sFicUmO+5Vl4b1165Y2btyokydPqlixYmrfvr38/f0lSdevX9fixYsVERGhixcvqlKlSk4PDAAAANwPm4X3woUL6tSpk06dOpX6GOFChQrps88+k5eXl4YOHarz588rJCREc+bMUefOnV0SGgAAALCXzcI7bdo0nTlzRi+//LKaNWum06dPa9asWRo1apQuXryoGjVq6KOPPlKTJk1clRcAAAC4LzYL75YtW9S/f3+FhYWlbitVqpQGDhyotm3b6tNPP5WXl913NgMAAABczmZbjYuLU8OGDdNsa9SokSTpmWeeoewCAAAgx7PZWO/duycfH5802/5+7evr67xUAAAAgINkeZeGU6dOad++famvk5KSJEkWi0WFCxdOt3+DBg0cGA8AAAB4MFkW3hkzZmjGjBnpto8bNy7Na6vVKpPJpPj4eMelAwAAAB6QzcK7cOFCV+UAAADwCJ768IXczGbh7devn6tyAAAAAE7BbRYAAABgaBReAAAAGFqWX1oDAPyfhEHlbO+wxSUxAAD3gcILAIAN/J8cwPNReAEAcKNpA5e6OwKQKsv/g+ehWMMLAAAAQ6PwAgAAwNAovAAAADA0Ci8AAAAMjcILAAAAQ6PwAgAAwNAovAAAADA0Ci8AAAAMzWMePHHr1i1NmjRJa9as0c2bN9WiRQvNmTNH5cplfoPklStXauTIkem2x8TEyMfHx5lxAQD3gYcvAHAmj7nCO2HCBEVFRemjjz7Shg0bdPXqVfXp00f37t2zeVzBggV15MiRNL8ouwAAALmHR1zhTUxM1PLly7Vw4UK1atVKkrR48WLVrl1bW7ZsUZs2bTI91mQyyd/f31VRAQAAkMN4ROE9cOCA7ty5o9atW6duK1++vKpWrardu3fbLLw3btxQrVq1lJKSotq1a2vixImqW7euK2LnGkZ97jYAADAGj1jSEBcXJ29vb5UoUSLNdj8/P8XFxWV6XHBwsBYsWKBVq1YpMjJS+fPnV/v27XX8+HFnRwYAAEAO4dYrvG+++abefvttm/tERUVlOma1WmUymTIdDwkJUUhISOrrxo0bq3nz5lq8eLFmzZqV6XEWi8Vmpux42I59HuS8zsjsSp6ePyPG/swLPvA7eO7cbXP25+7JjDpvez5zW4z6c/mbZ87P9p9x9szJqH/G2SO7+R9k3sHBwTbH3Vp4R4wYoaeeesrmPuXLl9fevXt17949Xb58WSVLlkwdu3Tpkpo1a2b3+by9vVWvXj2dOHHC5n5Z/dCcxeZ5tz/AsTmcxWLx6PwPwmM/821/PvBbeOzcHcDT82dHbv73PCtG/rl47OeexZ9xWc0py3kb+M+4B5m7M+ft1sJbokSJdMsUMlKvXj3lzZtX0dHR6t27tyTpzz//1JEjR9S4cWO7z2e1WnX48GHVqlUr25kBAADgWTziS2tFixbVs88+q8mTJ8vPz0/FihXTa6+9ppo1a+qxxx5L3a9r165q0KCBpkyZIkmaOXOmGjVqpKCgICUlJWnx4sU6fPiw5s6d66aZAAAAwNU8ovBK0vTp0+Xt7a1BgwalPnji/fffl7e3d+o+J0+eTPMgisTERI0aNUpxcXHy9fVVnTp1tGHDBjVo0MAdUwAAAIAbeEzh9fHx0ezZszV79uxM9zl06FCa1zNmzNCMGTOcHQ0AAAA5mEfclgwAAADILgovAAAADM1jljQAyD2mDVzq7ggAAAPhCi8AAAAMjcILAAAAQ6PwAgAAwNBYwwsADnRt6RbbOzzAs+IBANnDFV4AAAAYGld4gRyKOxUAAOAYXOEFAACAoXGFF4DDsY4VgCdLGFTO3RHgYFzhBQAAgKFReAEAAGBoLGkAAABwIb6U7HoUXhdhTSMAAIB7sKQBAAAAhkbhBQAAgKFReAEAAGBoFF4AAAAYGoUXAAAAhkbhBQAAgKFReAEAAGBoFF4AAAAYGoUXAAAAhkbhBQAAgKFReAEAAGBoFF4AAAAYGoUXAAAAhkbhBQAAgKHlcXcAAAAA5A7TBi51y3m5wgsAAABDo/ACAADA0Ci8AAAAMDQKLwAAAAyNwgsAAABDo/ACAADA0Ci8AAAAMDQKLwAAAAzNYwrvJ598os6dOysgIEBms1mnT5+267ivv/5ajRs3VqlSpdS4cWNFRUU5OSkAAAByEo950tr169fVunVrdezYURMnTrTrmD179uj555/XhAkT1KVLF0VFRWngwIH6/vvv1bBhQycnBowrYVA5d0cAAMBuHlN4Q0NDJUn79++3+5iIiAg1b95cY8aMkSRVrVpVW7duVUREhD766COn5AQAAEDO4jFLGrJj7969at26dZptbdq00e7du92UCAAAAK7mMVd4syM2NlZ+fn5ptvn5+SkuLs7mcRaLxZmxnHJed2V2FE/Pn5GH7djHiPO2F3PPfYw6b3v+XbfFqD+Xvxl9fpnJrfOW3DP34OBgm+NuLbxvvvmm3n77bZv7REVFqXnz5tk+h8lkSvPaarWm2/ZPWf3QnMFisdg+73bbx7sjs6NkOXcDy63zzs2feW6de26dtz2M/HPJrZ97bp23lHPn7tbCO2LECD311FM29ylfvny239/f3z/d1dxLly6lu+oLAAAA43Jr4S1RooRKlCjhtPdv1KiRoqOj9fLLL6dui46OVuPGjZ12TgAAAOQsHvOltdjYWB08eFDHjh2TJB05ckQHDx7UlStXUvfp2rWrpk6dmvp6+PDh+s9//qO5c+fq6NGjmjt3rrZu3aoRI0a4PD8AAADcw2MK75IlS9SiRQsNHTpUkvTUU0+pRYsW2rBhQ+o+J0+eVExMTOrrxo0ba8mSJfr000/1yCOP6LPPPtOSJUu4By8AAEAu4jF3aZgwYYImTJhgc59Dhw6l29atWzd169bNWbEAAACQw3nMFV4AAAAgOyi8AAAAMDQKLwAAAAyNwgsAAABDo/ACAADA0Ci8AAAAMDQKLwAAAAyNwgsAAABDo/ACAADA0Ci8AAAAMDQKLwAAAAyNwgsAAABDo/ACAADA0Ci8AAAAMDQKLwAAAAyNwgsAAABDo/ACAADA0PK4OwDsM23gUndHAAAA8Ehc4QUAAIChUXgBAABgaBReAAAAGBqFFwAAAIZG4QUAAIChUXgBAABgaBReAAAAGBqFFwAAAIZG4QUAAIChUXgBAABgaBReAAAAGBqFFwAAAIZG4QUAAICh5XF3AMCori3dYnsHi8UlOQAAyO24wgsAAABDo/ACAADA0Ci8AAAAMDTW8AIA4ETnK7yn4OBgd8cAcjWu8AIAAMDQPOYK7yeffKIvv/xSBw8eVFJSkn799VcFBgbaPGblypUaOXJkuu0xMTHy8fFxVlQAQC7CHVmAnM9jCu/169fVunVrdezYURMnTrT7uIIFC2r//v1ptlF2AQAAcg+PKbyhoaGSlK68ZsVkMsnf398ZkQAAAOABDL+G98aNG6pVq5Zq1KihPn366Ndff3V3JAAAALiQoQtvcHCwFixYoFWrVikyMlL58+dX+/btdfz4cXdHAwAAgIuYEhISrO46+Ztvvqm3337b5j5RUVFq3rx56uv9+/erVatWdn1p7Z/u3bun5s2b69FHH9WsWbMy3c/CFwwAAAA8Rla3/nPrGt4RI0boqaeesrlP+fLlHXY+b29v1atXTydOnLC5nzvul2ixWHLtfRpz69xz67wl5p4b555b5y0x99w499w6bynnzt2thbdEiRIqUaKEy85ntVp1+PBh1apVy2XnBAAAgHt5zF0aYmNjFRsbq2PHjkmSjhw5osTERFWoUEHFihWTJHXt2lUNGjTQlClTJEkzZ85Uo0aNFBQUpKSkJC1evFiHDx/W3Llz3TYPAAAAuJbHFN4lS5YoPDw89fXfSyEWLlyo/v37S5JOnjypcuXKpe6TmJioUaNGKS4uTr6+vqpTp442bNigBg0auDY8AAAA3MZjCu+ECRM0YcIEm/scOnQozesZM2ZoxowZzowFAACAHM7QtyUDAAAAKLwAAAAwNAovAAAADI3CCwAAAEOj8AIAAMDQKLwAAAAwNFNCQoLV3SEAAAAAZ+EKLwAAAAyNwgsAAABDo/ACAADA0Ci8AAAAMDQKLwAAAAyNwgsALnLnzh13RwCAXCmPuwPkRn/++aeWLFmi3bt3Ky4uTiaTSX5+fmrSpIkGDhyo8uXLuzsiACcoW7astm3bpqpVq7o7CgA8sJiYGH300UfatWuXYmNj5e3trYCAAHXq1En9+/eXt7e3uyOm4j68LrZz50717t1b/v7+at26tfz8/GS1WnXp0iVFR0crNjZWX3zxhZo0aeLuqC537tw5zZgxQwsXLnR3FIdLSEjQ7t27ZTabFRISIpPJlDqWnJysBQsWaPz48W5M6Dy//fab9u7dq5CQEFWvXl1//PGHFi1apFu3bqlPnz5q3bq1uyM63Lhx4zLcHhkZqV69eslsNkuSZs2a5cJU7pGQkKBVq1bpxIkT8vf319NPP23Y/1O/Y8cO+fn5KTg4WNJfn3dkZKTOnTunChUqaOjQoXr++efdnNI5+vTpoyeffFLdunWTj4+Pu+O4TEpKiubOnatffvlF7dq106BBg7RixQq98847SklJUZcuXTRp0iTly5fP3VEdbv/+/erWrZsqVaqkAgUKaM+ePerVq5fu3Lmjn376SVWrVtWaNWtUpEgRd0eVROF1uccee0whISGZ/kU3fvx47dmzR9HR0S5O5n6HDh1Sy5YtFR8f7+4oDvX777+re/fuunTpklJSUlS3bl0tW7ZMAQEBkqS4uDhVq1bNcPOWpB9++EH9+/dX4cKFdf36da1YsULDhw9X7dq1lZKSou3bt2vNmjV67LHH3B3VoYoVK6ZatWqpaNGiabZv375dDz/8sAoWLCiTyaSoqCg3JXSeatWqaceOHSpevLhOnTqldu3aKSUlRdWqVZPFYtH169e1adMmValSxd1RHa5p06YKDw9XixYt9OGHH2rq1KkaNmyYqlSpIovFog8++ECvv/66hg4d6u6oDlesWDGZTCb5+vqqT58+eu6551SzZk13x3K6N998U5GRkerQoYO2bt2qp59+WpGRkRo5cqS8vLy0aNEiDRw4UJMmTXJ3VIdr3769HnvsMb366quSpNWrV+vDDz/Upk2blJCQoC5duqhZs2YKDw93c9K/UHhdrHTp0tq6dWvqFYB/Onr0qFq0aKGYmBgXJ3O+Tz/91Ob431d4jVb8+vbtqzx58mjx4sW6evWqXn31Ve3Zs0dRUVEKCgoydOFt27atWrRooUmTJmnNmjUaPXq0Bg8erNdff12SNHXqVB04cEDr1q1zc1LHmjNnjpYtW6YFCxaoefPmqdtLliypbdu2qVq1am5M51zFihXT0aNH5efnp8GDBys2NlarV69WoUKFdPPmTQ0YMEA+Pj5aunSpu6M6XOnSpbVnzx4FBASoefPmGjFihPr165c6/tVXX+mtt97S3r173ZjSOYoVK6aff/5ZP/zwg1asWKEzZ86ofv36GjBggJ588kkVKlTI3RGdom7dugoPD1f79u31xx9/qFmzZnr//ff11FNPSZKioqI0efJk7d+/381JHa9MmTLauXOnKlasKOmvq93+/v46fPiwSpUqpejoaIWGhur33393b9D/H2t4Xczf31+7du3KtPDu2rVL/v7+Lk7lGqGhoalXtjKSkpLi4kSu8csvvygqKkqFChVSoUKF9Mknn2jixInq3LmzoqKi5Ovr6+6ITvPHH3/o/ffflyT16NFDw4YNU5cuXVLHe/furZUrV7orntOMHj1aLVq00AsvvKAePXrotddey1Fr2Vzll19+0fz581PLjo+Pj8aOHasBAwa4OZlzFC5cWPHx8QoICFBMTIxq166dZrxu3bo6d+6cm9I5X5kyZTRmzBiNGTNGmzdv1tKlSzV69Gi99tpr6tmzpwYMGKB69eq5O6ZDxcbGpl7Jrlatmry9vdN87nXr1lVsbKy74jlVyZIldf78+dTCGxsbq7t376YuYahUqZKuXLnixoRpcZcGF3vppZf073//W6+88oq+/vpr7dy5U7t27dLXX3+tV155RWPHjtWoUaPcHdMpypQpo4iICJ07dy7DXxs3bnR3RKe4fft2upI/ffp0de/eXZ06ddKRI0fclMw1vLy8Uv/Xx8cndf2q9FdBSEpKclMy52rUqJG2bNmikydP6vHHH9exY8fcHcll/v7n/c6dO/Lz80sz5ufnp0uXLrkjltM98cQT+uCDDyRJzZs311dffZVmfO3atQoKCnJDMtdr3bq1li5dqt9++02jR4/W1q1bDble39/fP/UKpsVi0b1799L8mf7HH3+oZMmS7ornVJ06ddK///1vbdy4UdHR0Ro8eLAeeeQRFShQQNJfP48yZcq4OeX/4Qqviw0ZMkTFixfXokWLtHz5ct27d0+S5O3trXr16un9999Xjx493JzSOerWrauDBw+qa9euGY6bTCZZrcZbYVO5cmXt378/3X/GnjFjhlJSUtS/f383JXO+ChUq6Pjx46lXAH744Yc0X1j6888/VapUKTelc76iRYvq448/1tKlS9W+fXvD/leMf+rUqZO8vb2VmJgoi8WiGjVqpI6dO3dOJUqUcGM65wkLC1O7du3UoUMHNWjQQIsWLdKOHTtS1/D+8ssvhvwvGraULFlSo0aN0qhRo7R161Z3x3G43r17a/jw4Wrfvr22bt2qf//735o0aZLi4uLk5eWlefPmZfp3nqebNGmSYmNj9cwzz+jevXsKCQnRokWLUse9vLw0ZcoUNyZMizW8bnTnzh1dvnxZklSiRAnlzZvXzYmca8eOHUpOTtYTTzyR4XhycrL279+vRx991MXJnGvu3LnasWOHvvzyywzHx4wZo48++ihH/acfR4mMjFS5cuXUoUOHDMenTp2q2NjYNH9IGtWxY8e0Z88ede7c2dDLWGbOnJnmdaNGjdSmTZvU16+//rrOnz+vjz76yNXRXCIxMVHvvvuuNmzYoFOnTqWua2zSpIlCQ0P18MMPuzuiU9SpU0dbtmxR8eLF3R3FpVJSUvTOO+9oz549atq0qf71r39pzZo1mjJliq5fv6727dtr9uzZhl3DLEk3b97U3bt3VbhwYXdHsYnCCwAAAENjDS8AAAAMjcILAAAAQ6PwAoCHWblypcxms06fPu3uKADgEbhLAwA42cWLF7Vw4UJt3LhRZ86ckdVq1UMPPaS2bdtq+PDhKl26tLsjAoChUXgBwIn279+v3r176+rVq+rZs6eGDh0qLy8vHT58WEuXLlVUVJT27dvn7pgAYGgUXgBwkoSEBPXv318mk0lbtmxR9erV04y//vrrmjdvnnvCAUAuwhpeAHCSTz75ROfPn9ebb76ZruxKfz2Y4p83Zt+wYYPatGmjMmXKKDAwUP3799fRo0ezPFft2rU1YsSIdNtHjBiR5lGnp0+fltls1jvvvKOlS5fq4YcfVpkyZdSlSxedOnVKkrRgwQLVrl1bpUuXVs+ePRUTE5PmPTt16qRGjRrp+PHj6tmzp8qWLavg4GBNnTo13cM11q1bp1atWqlChQoKCAhQs2bNFB4enuV8AMCRuMILAE7y3XffycfHx+6nJ3755ZcaOnSoatWqpddee01JSUn64IMP1LZtW23ZsiX1iXWOsG7dOt24cUPPP/+8rl27pnfffVf9+/dXz549tX79eoWGhiomJkYLFizQv//9b61atSrN8UlJSerWrZvat2+vTp06adOmTXrnnXcUGBiogQMHSpK2bNmi559/Xi1atNDkyZPl7e0ti8WiHTt2OGweAGAPCi8AOMmRI0dUuXJl5cuXL8t979y5o9dee02VK1fWxo0bU5/M1KlTJ7Vq1UrTp0/XBx984LBs586d03//+1+ZzWZJfz0GdMaMGbp586Z27Nih/PnzS5KuXbumJUuW6NKlSypZsmTq8bGxsZo/f76ee+45SdLzzz+vRx99VEuXLk0tvN9//72KFCmitWvXytvb22HZAeB+saQBAJzk6tWrKlKkiF37HjhwQLGxsRo8eHCax5DWrVtXjz32mH744QdZrY57MGbXrl1Ty64kNWzYUJLUq1ev1LIrSQ0aNJDVak13CzQfHx/1798/zbZHHnkkdVmEJBUpUkTJycnavHmzw3IDQHZQeAHASYoUKaKrV6/ate+ZM2ckSVWqVEk3VrVqVSUkJCgpKclh2cqXL5/mta+vrySpXLlyGW5PSEhIs71s2bLprtqazWZduXIl9fXgwYMVFBSk3r17q3r16ho+fLi+/fZbhxZ3ALAHhRcAnKRq1ao6duyYbt++/UDvY09BNJlMGW6/d+9ehtszW2KQ2fZ/ZrBniYK/v7+2bdum1atXq3PnztqzZ4/69++vp556itILwKUovADgJB06dNDNmzf11VdfZblvQECAJGV4RwaLxSKz2Zx6tTUjZrNZiYmJ6bafPXvW/sBOkC9fPrVr106zZ8/Wvn379K9//Us//vijdu/e7dZcAHIXCi8AOMnAgQNVtmxZTZo0SUeOHEk3npSUpDfeeEOSVK9ePfn7+2vJkiW6ceNG6j6HDh1SdHS02rZtm+lVXEmqVKmS9u7dq1u3bqVuO3DggFuLZXx8fJrXJpNJderUkZR+iQQAOBN3aQAAJzGbzVq5cqV69+6tli1bqlevXqpfv37qk9bWrFmj4sWLa/LkycqbN6/eeustDR06VO3atVOfPn1Sb0vm6+uriRMn2jzXoEGD9NVXX6lHjx7q2bOnLly4oI8//ljVqlWzex2xo7300kuKj49XixYtVK5cOV24cEEffvihSpcurUceecQtmQDkThReAHCihx9+WDt37tSCBQu0ceNGrVmzRlarVZUqVdKgQYM0bNiw1H179eqlAgUKaM6cOZo2bZry5cunRx99VGFhYVneg7dly5YKDw/Xe++9p4kTJ6patWqKjIzU559/rm3btjl5lhl76qmntGzZMn388cdKSEhQqVKl9MQTT2j8+PF2370CABzBlJCQwDcHAAAAYFis4QUAAIChUXgBAABgaBReAAAAGBqFFwAAAIZG4QUAAIChUXgBAABgaBReAAAAGBqFFwAAAIZG4QUAAIChUXgBAABgaP8f93NJClUh5LwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('fivethirtyeight')\n",
    "df.plot(kind='bar',grid=True,figsize=(10,7))\n",
    "plt.ylabel('Random numbers')\n",
    "plt.xlabel('Columns')\n",
    "plt.title('Bar graph')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6b3afd97",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}