pytorch-stuff/Visualization.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.arange(0,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = x**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Y label')"
      ]
     },
     "execution_count": 12,
     "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": [
    "plt.plot(x,y,'r--')\n",
    "plt.xlim(0,4)\n",
    "plt.ylim(0,10)\n",
    "plt.title('TITLE')\n",
    "plt.xlabel('X label')\n",
    "plt.ylabel('Y label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "mat = np.arange(0,100).reshape(10,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9],\n",
       "       [10, 11, 12, 13, 14, 15, 16, 17, 18, 19],\n",
       "       [20, 21, 22, 23, 24, 25, 26, 27, 28, 29],\n",
       "       [30, 31, 32, 33, 34, 35, 36, 37, 38, 39],\n",
       "       [40, 41, 42, 43, 44, 45, 46, 47, 48, 49],\n",
       "       [50, 51, 52, 53, 54, 55, 56, 57, 58, 59],\n",
       "       [60, 61, 62, 63, 64, 65, 66, 67, 68, 69],\n",
       "       [70, 71, 72, 73, 74, 75, 76, 77, 78, 79],\n",
       "       [80, 81, 82, 83, 84, 85, 86, 87, 88, 89],\n",
       "       [90, 91, 92, 93, 94, 95, 96, 97, 98, 99]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x10ec20668>"
      ]
     },
     "execution_count": 15,
     "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": [
    "plt.imshow(mat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "mat = np.random.randint(0,1000,(10,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x1103a5a20>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(mat)\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('./Tensorflow-Bootcamp-master/00-Crash-Course-Basics/salaries.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>Name</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>John</td>\n",
       "      <td>50000</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sally</td>\n",
       "      <td>120000</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Alyssa</td>\n",
       "      <td>80000</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name  Salary  Age\n",
       "0    John   50000   34\n",
       "1   Sally  120000   45\n",
       "2  Alyssa   80000   27"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10fe6b860>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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l85xJcXHOcfDgQapWzfvVfOpukjLlmy37eXj6Sk5mZvOnmzpzY/emwU6p3Dt+OotZK3YyOT6Z7/cfp36NKvy8bzNG9mlGg5rFc+lxZmYmqampnDql+2pUrVqV8PBwKlX68S/pA3U3qUhImZCd45jw+RYmLNxC6wY1eGNUT1o1qBHstMRPTo7jm6QDxMUn8+Wm/VQOqcB1XRoTGxNF5/Divw2s/FigInHeSzzM7G0z22dma/1i9cxsgZlt8f6t68XNzCaYWZKZrTazHn7LjPbabzGz0X7xnma2xltmgnnHgoG2IXK2/cdOc8fbS3nl8y3c1L0pH98fowJRAlWoYAxoE8bk2N58/ssBjOgdwX/W7eH6177l1jcX8elqDSxYEp33SMLMLgHSgXedc5282F+AQ865F83sSaCuc+4JMxsMjAcGA32AV5xzfcysHpAARAMOWA70dM4dNrNlwIPAUuAzYIJzbl6gbZzvBelIonxZuvUg46etIO1kJn8Y0onbosPLdZ9zaXP0VOYPAwvuOHSSJrWrcnu/SEb0jqDORaX3Hh6lUYG6m8wsEpjrVyQ2AZc653abWWPgS+dcWzN7y5ue5t/uzMM5d48Xfwv40nt84Zxr58VHnGkXaBvny1VFonzIyXH88+vv+dv8TTQPrc4bo3rQvnGtYKcl+ZSd4/h8w17i4lNYvPUgVStV4Kbu4cTGRNKmoQYWLA6FfQlsQ+fcmcHN9wBnxjZoCuzwa5fqxc4VT80lfq5t/ISZjQXGAjRr1uxCX4uUMoePZ/DojJV8sWk/13VpzAs3d6Zm1TI2nHU5E1LBuLpjI67u2IgNu4/yzqIUZiWmMm3ZdmJahRLbP4rL25XygQVLqQJfAuucc2ZWpGe/z7cN59xEYCL4jiSKMhcJrsTth3lgSiIH0jP4w5CO/Lxvc3UvlTHtG9fixVu68Pigdkxbtp33Fm/j7ncTaB56EaP7RXJbdLi+FBSj/I5NsNfrAsL7d58X3wlE+LUL92LniofnEj/XNqQccs4x6dtkhv5zMRUqGDPv68ft/SJVIMqwetUrc/9lrfjmict4dUR3QqtX5rm56+n3wkKenbOO5APHg51iuZDfIjEHOHOF0mhgtl/8Du8qp75AmtdlNB+42szqelcpXQ3M9+YdNbO+3lVNd5y1rty2IeVM2slM7n1/OX+Yu57L2jXg0/E/o0t4nWCnJcWkUkgFru/ahFnjYph9fwxXtm/AlKXbuPzvX3Ln5O/4Zsv+C/4VseRdXq5umobvxHN9YC/wO+BjYAbQDNgGDHXOHfL+0L8GDAJOALHOuQRvPXcCv/ZW+7xzLs6LRwOTgWrAPGC8170Umts2zveCdOK6bFm7M41xUxLZdeQkT17TjrsujtLRg7Dv6CneX7qdqUu3cSA9g9YNajAmJpKbu4drYMF80o/ppFRxzjFl6Xaem7ue0OqVeW1kd3o2rxfstKSEOZWZzdzVu4mLT2bdrqPUrlaJ4b0juKNfJE1LwMCCpYmKhJQa6aez+PWsNcxZtYsBbcJ4eVg36lXXNfMSmHOO71IOExefzPx1ewAY2LERsTFR9IoM7sCCpYVGgZVSYeOeo4ybkkjKgeM8NrAt9w1oqcse5bzMjN5R9egdVY+dR07y7uIUpi/bwby1e+jYpBaxMVFc37X4BxYsC3QkISXGhwk7+O3stdSsWokJw7vTr2VosFOSUuxERhb/XrGTyfEpbNmXTv0alRnZpzk/71t8AwuWJupukhLrZEY2z8xey4fLU+nXIpRXRnTTf2IpNM45vk06QFx8Cgs37qNSiHFdlyaM6R9J1whdJXeGupukRPp+fzr3T0lk095jPHh5Kx66sg0h6l6SQmRm/Kx1GD9rHUbygeO8syiFDxN28O8VO+nRrA6xMVEM6tSISiG6pW1udCQhQTNn1S6e+mg1VSqF8PKwbgxoExbslKScOHYqkw8TUnlncQrbDp6gUa2q3N6vOSN6Nyu3F0mou0lKjFOZ2fzx0/W8v2Q70c3r8urI7jSurcsVpfhl5zi+2LiPuEXJxCcdpErFCtzYrSmxF0fSrlH5GjBSRUJKhO0HTzBu6nLW7jzKPZe04FcD2+owX0qETXuOMXlRMrMSd3I6K4d+LUKJjYnkivYNy0UXqIqEBN1/1u7hsZmrMODvQ7txVYeAA/uKBM3h4xlM/24H7y1OYVfaKZrVu4g7+jVnaK8IapXhgQVVJCRoMrNz+PO8jfzr22S6htfmtZE9iKh3UbDTEjmnrOwc5q/bS1x8MgnbDlO9cgi39gxndP9IWoSVvTsfqkhIUOw8cpIHpiayYvsRxvSP5KnB7fSDJil11qSmERefzCerd5GZ7bi0bRixMVFc0rp+mfk1t4qEFLsvNu7jkRkrycp2/PmWLlzbpXGwUxIpkH3HTjF16XbeX7KdA+mnaRlWnTExUdzSoykXVS7dvyhQkZBik5Wdw0sLNvPGl9/TvnEt3hjVg6j61YOdlkihOZ2VzaerdxMXn8KanWnUqlqRYb18AwuW1q5UFQkpFvuOnmL8tBUsTT7EiN4R/O76jlStpO4lKZuccyzfdpi4+BT+s24Pzjmu6tCQ2Jgo+kTVK1VdUfrFtRS5RUkHeHD6Co6fzualoV25uUf4+RcSKcXMjOjIekRH1mPXkZO8u3gb07/bzvx1e2nfuBaxMZHc0LVJqf6ipCMJKbDsHMdrC5P4x+ebaRlWgzdG9aBNw5rBTkskKE5mZPPxyp3ExSezeW86odUrM7JPM37etzkNa5XcMcnU3SRF4kD6aR75YCXfbDnATd2b8scbO1G9ig5QRZxzLPr+IHHxyXy+cR8hZgzu3JjYmEi6N6sb7PR+Qt1NUui+SznEA1MTOXwikxdv7sywXhGlqg9WpCiZGTGt6hPTqj4pB47z7uJtzEjYwZxVu+gWUYfYmEgGd25c4kcc0JGEXLCcHMf/+2Yrf5m/iYi61Xh9VA86Nqkd7LRESrz001nMTNjB5EUppBw8QcNaVbi9r29gwdAaVYKam7qbpFAcOZHBrz5cxf827GNw50b8+ZYu1CzDQxWIFIWcHMeXm/cRF5/CN1sOULliBYZ0bUJsTBQdmgRnYEF1N0mBrdxxhPunJLLv2Cmevb4Do/tHqntJJB8qVDAub9eQy9s1ZMveY8QtSmFWYiofLk+lT1Q9YmOiuKpDyRhYUEcScl7OOSYvSuFPn22gQc2qvD6qB910Ry+RQnXkRAYffLeDdxdvY+eRk4TXrcbofpEM7RVB7WpFf7Su7ibJl6OnMnnyo9V8tmYPV7ZvwN9u60qdi8rnTVlEikNWdg4L1u8lLj6FZSmHqFYphFt6NmVM/yhaNSi6gQVVJOSCrduVxv1TEtlx+CSPD2zL2EtaqHtJpBit3ZlGXHwKn6zaRUZ2Dpe0CSM2JpIBrcOoUMhdUYGKRIGuvTKzR8xsnZmtNbNpZlbVzKLMbKmZJZnZB2ZW2WtbxXue5M2P9FvPU158k5kN9IsP8mJJZvZkQXKVvHPOMW3Zdm56YxEnM7OZPrYv9wxoqQIhUsw6Na3N34d2Jf7Jy3nkyjZs2H2U2LjvuPLlr3h3cQrHT2cVeQ75PpIws6bAt0AH59xJM5sBfAYMBmY556ab2T+BVc65N81sHNDFOXevmQ0HbnLODTOzDsA0oDfQBPgf0MbbzGbgKiAV+A4Y4Zxbf668dCRRMMdPZ/Gbj9fy7xU7+Vnr+vxjWLegX5onIj4ZWTl8tmY3cfHJrEpNo2bVigyL9g0s2Cy0YAMLFtXVTRWBamaWCVwE7AYuB0Z6898BngXeBIZ40wAzgdfM99V0CDDdOXcaSDazJHwFAyDJObfVewHTvbbnLBKSf1v2HuO+KYls3Z/Oo1e14f7LWpWIqytExKdyxQrc2L0pQ7o1IXH7EeLik4lblMKk+GSubN+QX17dptDvzZ3vIuGc22lmfwO2AyeB/wLLgSPOuTPHQKlAU2+6KbDDWzbLzNKAUC++xG/V/svsOCveJ7dczGwsMBagWbNm+X1J5dqsxFSe/vdaqlepyPt39aF/q/rBTklEAjAzejavS8/mddmddpL3l2xj6tLtZGYV/jnmfBcJM6uL75t9FHAE+BAYVEh5XRDn3ERgIvi6m4KRQ2l1KjObZ+esY/p3O+gTVY9XR3SnQQkehExEfqxx7Wo8NrAdD17Rukju+liQ7qYrgWTn3H4AM5sFxAB1zKyidzQRDuz02u8EIoBUM6sI1AYO+sXP8F8mUFwKwdb96YybksjGPce4/7KWPHJlGyqW8HFkRCR3RXVb4IL8RdgO9DWzi7xzC1fgO1/wBXCr12Y0MNubnuM9x5u/0PnOms8BhntXP0UBrYFl+E5Ut/aulqoMDPfaSiGYu3oXN7wWz56jp4iL7cVjA9upQIjITxTknMRSM5sJJAJZwAp8XT6fAtPN7I9ebJK3yCTgPe/E9CF8f/Rxzq3zroxa763nfudcNoCZPQDMB0KAt51z6/Kbr/iczsrmT59u4J3F2+jRrA6vjexBkzrVgp2WiJRQ+jFdObLj0Anun5rI6tQ0fvGzKB4f1K7ED1MsIsVDA/yVcwvW7+WXM1bigLdu78nAjo2CnZKIlAIqEmVcZnYOf52/iYlfb6VT01q8MbJngX90IyLlh4pEGbY77SQPTF3B8m2Hub1vc56+tn2pviG7iBQ/FYky6qvN+3l4+goysnKYMKI7N3RtEuyURKQUUpEoY7JzHP/432Ze+yKJtg1r8vqoHrQMK7rhhUWkbFORKEP2HTvFQ9NWsnjrQYZGh/P7GzpRrbK6l0Qk/1QkyohF3x/gwWkrST+dyV9v7cJt0RHnX0hE5DxUJEq5nBzHG18m8dKCzUTWr86Uu/vQtlHNYKclImWEikQpduh4Bg9/sJKvN+9nSLcm/OmmzlSvordURAqP/qKUUsu3HeKBqSs4mJ7B8zd1YmTvZrpznIgUOhWJUsY5x7++SebP/9lIkzrVmDWuP52a1g52WiJSRqlIlCJpJzL51cxVLFi/l0EdG/GX27pQq2qlYKclImWYikQpsTr1COOmJLIn7RTPXNeB2JhIdS+JSJFTkSjhnHO8t2Qbf5y7gbCaVZhxbz96NKsb7LREpJxQkSjBjp3K5MlZa/h09W4uaxvGS0O7Ubd65WCnJSLliIpECbV+11Hun5rI9kMneGJQO+65pAUVKqh7SUSKl4pECeOcY0bCDp6ZvY7a1Sox9e4+9GkRGuy0RKScUpEoQU5kZPGbj9cyK3EnF7eqzz+Gd6N+jSrBTktEyjEViRIiad8xxk1JZMu+dB6+sjXjL29NiLqXRCTIVCRKgI9X7OTX/15DtUohvHtnb37WOizYKYmIACoSQXUqM5vff7Keacu20zuyHq+O7E7DWlWDnZaIyA9UJIIk5cBxxk1JZP3uo9x3aUt+eVUbKoZUCHZaIiI/oiIRBPPW7OaxmasJqWC8PSaay9s1DHZKIiK5UpEoRhlZObwwbwNx8Sl0i6jDayO7E173omCnJSISUIH6N8ysjpnNNLONZrbBzPqZWT0zW2BmW7x/63ptzcwmmFmSma02sx5+6xnttd9iZqP94j3NbI23zAQrxYMVpR4+wW1vLSYuPoU7Y6KYcU8/FQgRKfEK2gn+CvAf51w7oCuwAXgS+Nw51xr43HsOcA3Q2nuMBd4EMLN6wO+APkBv4HdnCovX5hd+yw0qYL5B8fmGvVw74Vu27kvnzVE9eOb6DlSuqPMPIlLy5fsvlZnVBi4BJgE45zKcc0eAIcA7XrN3gBu96SHAu85nCVDHzBoDA4EFzrlDzrnDwAJgkDevlnNuiXPOAe/6ratUyMr2dS/d9U4C4XWrMffBi7mmc+NgpyUikmcFOScRBewH4sysK7AceAho6Jzb7bXZA5w5K9sU2OG3fKoXO1c8NZf4T5jZWHxHJzRr1iz/r6gQ7Uk7xYPTVrAs5RCj+jTjt9d1oGqlkGCnJSJyQQrS51ER6AG86ZzrDhzn/7qWAPCOAFwBtpEnzrmJzrlo51x0WFjwf4j2zZb9XDvhG9buSuOV4d14/qbOKhAiUioVpEikAqnOuaXe85n4isZer6sI79993vydQITf8uFe7Fzx8FziJVZ2juOlBZu54+1lhLMXVFwAAA4iSURBVNaozJwHLmZIt1wPfkRESoV8Fwnn3B5gh5m19UJXAOuBOcCZK5RGA7O96TnAHd5VTn2BNK9baj5wtZnV9U5YXw3M9+YdNbO+3lVNd/itq8TZf+w0d7y9lAmfb+Hm7uF8fH8MrRrUCHZaIiIFUtDfSYwHpphZZWArEIuv8Mwws7uAbcBQr+1nwGAgCTjhtcU5d8jM/gB857V7zjl3yJseB0wGqgHzvEeJs3TrQcZPW0HayUz+cmsXhkZHnH8hEZFSwHynDcqO6Ohol5CQUCzbyslx/PPr7/nb/E1Ehlbn9VE9aN+4VrFsW0SkMJnZcudc9Nlx/eI6nw4fz+DRGSv5YtN+ruvSmBdu7kzNqpWCnZaISKFSkciHxO2HeWBKIgfSM/jDkI78vG9zSvGPwUVEAlKRuADOOSZ9m8yL8zbSuE5VPrqvP53Dawc7LRGRIqMikUdpJzN5fOYq5q/by9UdGvLX27pSu5q6l0SkbFORyIO1O9MYNyWRXUdO8ptr23PXxVHqXhKRckFF4hycc0xZup3nPllPaI3KfHBPX3o2rxfstEREio2KRADpp7P49aw1zFm1iwFtwnh5WDfqVa8c7LRERIqVikQuNu45yrgpiaQcOM5jA9ty34CWVKig7iURKX9UJM7yYcIOfjt7LTWrVmLK3X3p1zI02CmJiASNioTnZEY2z8xey4fLU+nXIpRXRnSjQc2qwU5LRCSoVCQ8T81azexVu3jw8lY8dGUbQtS9JCKiInHGI1e14aYe4QxoE/z7UYiIlBQqEp7modVpHlo92GmIiJQoBbnpkIiIlHEqEiIiEpCKhIiIBKQiISIiAalIiIhIQCoSIiISkIqEiIgEpCIhIiIBqUiIiEhAKhIiIhKQioSIiARU4CJhZiFmtsLM5nrPo8xsqZklmdkHZlbZi1fxnid58yP91vGUF99kZgP94oO8WJKZPVnQXEVE5MIUxpHEQ8AGv+d/Bl52zrUCDgN3efG7gMNe/GWvHWbWARgOdAQGAW94hScEeB24BugAjPDaiohIMSlQkTCzcOBa4F/ecwMuB2Z6Td4BbvSmh3jP8eZf4bUfAkx3zp12ziUDSUBv75HknNvqnMsApnttRUSkmBT0SOIfwONAjvc8FDjinMvynqcCTb3ppsAOAG9+mtf+h/hZywSK/4SZjTWzBDNL2L9/fwFfkoiInJHvImFm1wH7nHPLCzGffHHOTXTORTvnosPCdNMgEZHCUpCbDsUAN5jZYKAqUAt4BahjZhW9o4VwYKfXficQAaSaWUWgNnDQL36G/zKB4iIiUgzyfSThnHvKORfunIvEd+J5oXNuFPAFcKvXbDQw25ue4z3Hm7/QOee8+HDv6qcooDWwDPgOaO1dLVXZ28ac/OYrIiIXrihuX/oEMN3M/gisACZ58UnAe2aWBBzC90cf59w6M5sBrAeygPudc9kAZvYAMB8IAd52zq0rgnxFRCQA832ZLzuio6NdQkJCsNMQESlVzGy5cy767Lh+cS0iIgGpSIiISEAqEiIiEpCKhIiIBKQiISIiAalIiIhIQCoSIiISkIqEiIgEpCIhIiIBqUiIiEhAKhIiIhKQioSIiASkIiEiIgGpSIiISEAqEiIiEpCKhIiIBKQiISIiAalIiIhIQCoSIiISkIqEiIgEpCIhIiIBqUiIiEhAKhIiIhKQioSIiASU7yJhZhFm9oWZrTezdWb2kBevZ2YLzGyL929dL25mNsHMksxstZn18FvXaK/9FjMb7RfvaWZrvGUmmJkV5MWKiMiFKciRRBbwS+dcB6AvcL+ZdQCeBD53zrUGPveeA1wDtPYeY4E3wVdUgN8BfYDewO/OFBavzS/8lhtUgHxFROQC5btIOOd2O+cSveljwAagKTAEeMdr9g5wozc9BHjX+SwB6phZY2AgsMA5d8g5dxhYAAzy5tVyzi1xzjngXb91iYhIMSiUcxJmFgl0B5YCDZ1zu71Ze4CG3nRTYIffYqle7Fzx1FziuW1/rJklmFnC/v37C/RaRETk/xS4SJhZDeAj4GHn3FH/ed4RgCvoNs7HOTfRORftnIsOCwsr6s2JiJQbBSoSZlYJX4GY4pyb5YX3el1FeP/u8+I7gQi/xcO92Lni4bnERUSkmBTk6iYDJgEbnHMv+c2aA5y5Qmk0MNsvfod3lVNfIM3rlpoPXG1mdb0T1lcD8715R82sr7etO/zWJSIixaBiAZaNAW4H1pjZSi/2a+BFYIaZ3QVsA4Z68z4DBgNJwAkgFsA5d8jM/gB857V7zjl3yJseB0wGqgHzvIeIiBQT8502KDuio6NdQkJCsNMQESlVzGy5cy767Lh+cS0iIgGpSIiISEAqEiIiEpCKhIiIBKQiISIiAalIiIhIQCoSIiISkIqEiIgEpCIhIiIBqUiIiEhAKhIiIhKQioSIiASkIiEiIgGpSIiISEAqEiIiEpCKhIiIBKQiISIiAalIiIhIQCoSIiISkIqEiIgEpCIhIiIBqUiIiEhAKhIiIhKQioSIiARU4ouEmQ0ys01mlmRmTwY7HxGR8qREFwkzCwFeB64BOgAjzKxDcLMSESk/KgY7gfPoDSQ557YCmNl0YAiwvtC39OWLsObDQl9tnjgXnO0SrO1SPl9zobNCXFUhrqs85AUlM7fr/wHN+xfOujwlvUg0BXb4PU8F+pzdyMzGAmMBmjVrlr8t1WoKjbvmb9lCUcgf4DxvNkjb9W08SJsN5msuJIVaZAtxXeUiLyixuVWuUXjr8pT0IpEnzrmJwESA6Ojo/O3xHrf7HiIi8oMSfU4C2AlE+D0P92IiIlIMSnqR+A5obWZRZlYZGA7MCXJOIiLlRonubnLOZZnZA8B8IAR42zm3LshpiYiUGyW6SAA45z4DPgt2HiIi5VFJ724SEZEgUpEQEZGAVCRERCQgFQkREQnIXNCGRygaZrYf2JbPxesDBwoxncKivC6M8rowyuvClNS8oGC5NXfOhZ0dLHNFoiDMLME5Fx3sPM6mvC6M8rowyuvClNS8oGhyU3eTiIgEpCIhIiIBqUj82MRgJxCA8rowyuvCKK8LU1LzgiLITeckREQkIB1JiIhIQCoSIiISULkpEmY2yMw2mVmSmT2Zy/wqZvaBN3+pmUX6zXvKi28ys4HFnNejZrbezFab2edm1txvXraZrfQehTqEeh7yGmNm+/22f7ffvNFmtsV7jC7mvF72y2mzmR3xm1ck+8vM3jazfWa2NsB8M7MJXs6rzayH37yi3Ffny2uUl88aM1tkZl395qV48ZVmllDMeV1qZml+79UzfvPO+f4XcV6P+eW01vs81fPmFeX+ijCzL7y/A+vM7KFc2hTdZ8w5V+Yf+IYZ/x5oAVQGVgEdzmozDvinNz0c+MCb7uC1rwJEeesJKca8LgMu8qbvO5OX9zw9iPtrDPBaLsvWA7Z6/9b1pusWV15ntR+Pb3j5ot5flwA9gLUB5g8G5uG7X2tfYGlR76s85tX/zPaAa87k5T1PAeoHaX9dCswt6Ptf2Hmd1fZ6YGEx7a/GQA9vuiawOZf/j0X2GSsvRxK9gSTn3FbnXAYwHRhyVpshwDve9EzgCjMzLz7dOXfaOZcMJHnrK5a8nHNfOOdOeE+X4Ls7X1HLy/4KZCCwwDl3yDl3GFgADApSXiOAaYW07YCcc18Dh87RZAjwrvNZAtQxs8YU7b46b17OuUXedqH4Plt52V+BFORzWdh5FctnC8A5t9s5l+hNHwM2AE3PalZkn7HyUiSaAjv8nqfy0538QxvnXBaQBoTmcdmizMvfXfi+LZxR1cwSzGyJmd1YSDldSF63eIe2M83szG1mS8T+8rrlooCFfuGi2l/nEyjvotxXF+rsz5YD/mtmy81sbBDy6Wdmq8xsnpl19GIlYn+Z2UX4/tB+5Bculv1lvm7w7sDSs2YV2WesxN90SHzM7OdANDDAL9zcObfTzFoAC81sjXPu+2JK6RNgmnPutJndg+8o7PJi2nZeDAdmOuey/WLB3F8llpldhq9IXOwXvtjbVw2ABWa20fumXRwS8b1X6WY2GPgYaF1M286L64F455z/UUeR7y8zq4GvMD3snDtamOs+l/JyJLETiPB7Hu7Fcm1jZhWB2sDBPC5blHlhZlcCTwM3OOdOn4k753Z6/24FvsT3DaNY8nLOHfTL5V9Az7wuW5R5+RnOWd0BRbi/zidQ3kW5r/LEzLrge/+GOOcOnon77at9wL8pvC7W83LOHXXOpXvTnwGVzKw+JWB/ec712SqS/WVmlfAViCnOuVm5NCm6z1hRnGgpaQ98R0xb8XU/nDnh1fGsNvfz4xPXM7zpjvz4xPVWCu/EdV7y6o7vZF3rs+J1gSredH1gC4V0Ei+PeTX2m74JWOL+70RZspdfXW+6XnHl5bVrh+9EohXH/vLWGUngE7HX8uOTisuKel/lMa9m+M6x9T8rXh2o6Te9CBhUjHk1OvPe4ftju93bd3l6/4sqL29+bXznLaoX1/7yXvu7wD/O0abIPmOFtnNL+gPf2f/N+P7gPu3FnsP37RygKvCh959mGdDCb9mnveU2AdcUc17/A/YCK73HHC/eH1jj/UdZA9xVzHm9AKzztv8F0M5v2Tu9/ZgExBZnXt7zZ4EXz1quyPYXvm+Vu4FMfH2+dwH3Avd68w143ct5DRBdTPvqfHn9Czjs99lK8OItvP20ynuPny7mvB7w+2wtwa+I5fb+F1deXpsx+C5k8V+uqPfXxfjOeaz2e68GF9dnTMNyiIhIQOXlnISIiOSDioSIiASkIiEiIgGpSIiISEAqEiIiEpCKhIiIBKQiISIiAf1/jGrmBZ8CVBMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}