Charts.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: Seaborn in /anaconda2/lib/python2.7/site-packages (0.8.1)\n",
      "Requirement already satisfied: numpy in /anaconda2/lib/python2.7/site-packages (from Seaborn) (1.14.3)\n",
      "Requirement already satisfied: scipy in /anaconda2/lib/python2.7/site-packages (from Seaborn) (1.1.0)\n",
      "Requirement already satisfied: matplotlib in /anaconda2/lib/python2.7/site-packages (from Seaborn) (2.2.2)\n",
      "Requirement already satisfied: pandas in /anaconda2/lib/python2.7/site-packages (from Seaborn) (0.23.0)\n",
      "Requirement already satisfied: cycler>=0.10 in /anaconda2/lib/python2.7/site-packages (from matplotlib->Seaborn) (0.10.0)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /anaconda2/lib/python2.7/site-packages (from matplotlib->Seaborn) (2.2.0)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in /anaconda2/lib/python2.7/site-packages (from matplotlib->Seaborn) (2.7.3)\n",
      "Requirement already satisfied: pytz in /anaconda2/lib/python2.7/site-packages (from matplotlib->Seaborn) (2018.4)\n",
      "Requirement already satisfied: six>=1.10 in /anaconda2/lib/python2.7/site-packages (from matplotlib->Seaborn) (1.11.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /anaconda2/lib/python2.7/site-packages (from matplotlib->Seaborn) (1.0.1)\n",
      "Requirement already satisfied: backports.functools_lru_cache in /anaconda2/lib/python2.7/site-packages (from matplotlib->Seaborn) (1.5)\n",
      "Requirement already satisfied: subprocess32 in /anaconda2/lib/python2.7/site-packages (from matplotlib->Seaborn) (3.5.0)\n",
      "Requirement already satisfied: setuptools in /anaconda2/lib/python2.7/site-packages (from kiwisolver>=1.0.1->matplotlib->Seaborn) (39.1.0)\n"
     ]
    }
   ],
   "source": [
    "! pip install Seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting pip\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/5f/25/e52d3f31441505a5f3af41213346e5b6c221c9e086a166f3703d2ddaf940/pip-18.0-py2.py3-none-any.whl (1.3MB)\n",
      "\u001b[K    100% |████████████████████████████████| 1.3MB 5.4MB/s ta 0:00:01\n",
      "\u001b[31mdistributed 1.21.8 requires msgpack, which is not installed.\u001b[0m\n",
      "\u001b[31mgrin 1.2.1 requires argparse>=1.1, which is not installed.\u001b[0m\n",
      "\u001b[?25hInstalling collected packages: pip\n",
      "  Found existing installation: pip 10.0.1\n",
      "    Uninstalling pip-10.0.1:\n",
      "      Successfully uninstalled pip-10.0.1\n",
      "Successfully installed pip-18.0\n"
     ]
    }
   ],
   "source": [
    "!pip install --upgrade pip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: Seaborn in /anaconda2/lib/python2.7/site-packages (0.8.1)\n",
      "Requirement already satisfied: numpy in /anaconda2/lib/python2.7/site-packages (from Seaborn) (1.14.3)\n",
      "Requirement already satisfied: scipy in /anaconda2/lib/python2.7/site-packages (from Seaborn) (1.1.0)\n",
      "Requirement already satisfied: matplotlib in /anaconda2/lib/python2.7/site-packages (from Seaborn) (2.2.2)\n",
      "Requirement already satisfied: pandas in /anaconda2/lib/python2.7/site-packages (from Seaborn) (0.23.0)\n",
      "Requirement already satisfied: cycler>=0.10 in /anaconda2/lib/python2.7/site-packages (from matplotlib->Seaborn) (0.10.0)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /anaconda2/lib/python2.7/site-packages (from matplotlib->Seaborn) (2.2.0)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in /anaconda2/lib/python2.7/site-packages (from matplotlib->Seaborn) (2.7.3)\n",
      "Requirement already satisfied: pytz in /anaconda2/lib/python2.7/site-packages (from matplotlib->Seaborn) (2018.4)\n",
      "Requirement already satisfied: six>=1.10 in /anaconda2/lib/python2.7/site-packages (from matplotlib->Seaborn) (1.11.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /anaconda2/lib/python2.7/site-packages (from matplotlib->Seaborn) (1.0.1)\n",
      "Requirement already satisfied: backports.functools_lru_cache in /anaconda2/lib/python2.7/site-packages (from matplotlib->Seaborn) (1.5)\n",
      "Requirement already satisfied: subprocess32 in /anaconda2/lib/python2.7/site-packages (from matplotlib->Seaborn) (3.5.0)\n",
      "Requirement already satisfied: setuptools in /anaconda2/lib/python2.7/site-packages (from kiwisolver>=1.0.1->matplotlib->Seaborn) (39.1.0)\n"
     ]
    }
   ],
   "source": [
    "! pip install Seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from numpy.random import randn\n",
    "import pandas as pd\n",
    "from pandas import Series, DataFrame\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from  matplotlib import rcParams\n",
    "import seaborn as sb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "rcParams['figure.figsize'] = 5,4\n",
    "sb.set_style('whitegrid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a1c79c250>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = range(1,10)\n",
    "y = [1,2,2,3,4,2,4,2,4]\n",
    "\n",
    "plt.plot(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     21.0\n",
       "1     21.0\n",
       "2     22.8\n",
       "3     21.4\n",
       "4     18.7\n",
       "5     18.1\n",
       "6     14.3\n",
       "7     24.4\n",
       "8     22.8\n",
       "9     19.2\n",
       "10    17.8\n",
       "11    16.4\n",
       "12    17.3\n",
       "13    15.2\n",
       "14    10.4\n",
       "15    10.4\n",
       "16    14.7\n",
       "17    32.4\n",
       "18    30.4\n",
       "19    33.9\n",
       "20    21.5\n",
       "21    15.5\n",
       "22    15.2\n",
       "23    13.3\n",
       "24    19.2\n",
       "25    27.3\n",
       "26    26.0\n",
       "27    30.4\n",
       "28    15.8\n",
       "29    19.7\n",
       "30    15.0\n",
       "31    21.4\n",
       "Name: msg, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "address = \"/Users/virendsingh/Downloads/Ex_Files_Python_Data_Science_EssT/Exercise Files/Ch01/01_05/mtcars.csv\"\n",
    "cars = pd.read_csv(address)\n",
    "cars.columns = ['car_names','msg','cyl','disp','hp','drat','wt','qsec','vs','am','gear','carb']\n",
    "mpg = cars['msg']\n",
    "mpg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1c8a0b90>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAATQAAAD3CAYAAACTiqgxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJztnXl8VPW99z9ntiQzk8wkmYQl+waGQFhlURa1YLze4tKqVL34XOG2YvUq9qosBbQNV/RqbZVH61Jv+1S0lFpLad3FarQEkIQ1BAgkZCeTZZJZktnP88fkTCbJJDOTnG1Ofu/Xi9eLzJxz5puTmc98f7/vRtE0TYNAIBAkgExoAwgEAoEtiKARCATJQASNQCBIBiJoBAJBMhBBIxAIkoEIGoFAkAyKUAd4PB5s27YNdXV1kMvl2LVrFywWCzZs2IDs7GwAwN13342bb76Za1sJBAJhVKhQeWiff/45Dh48iF27duHIkSP43e9+hxtuuAEWiwXr1q3jy04CgUAISUhBAwC32w2FQoG//OUvqKyshEwmQ11dHTweD7KysrB161ZotVo+7CUQCIQRCUvQAGDTpk347LPP8PLLL6OtrQ3Tp0/HzJkz8etf/xpmsxmbNm0adHxFRQUnBhMIBML8+fODP0FHgNFopK+77jr6ypUr/sdqamro++67b9ixx44di+TSNE3T9NmzZyM+h0/Ebh9NExvZgtjIDlzYOJq2hIxy7t+/H6+//joAIC4uDhRF4eGHH8apU6cAAOXl5SgqKmJPegkEAmGMhIxy3njjjdiyZQvuvfdeuN1ubN26FVOmTEFpaSmUSiUMBgNKS0v5sJVAIBBGJaSgqdVqvPTSS8Me37t3LycGEQgEwlghibUEAkEyEEEjEAiSgQgagUCQDETQCASCZCCCRphwPLr3OJ75sFpoMwgcEDLKSSBICYvdhQ9OtaI4XSe0KQQOIB4aYUJRfqkTbi8NU69LaFMIHEAEjTChKKtpBwB02ZwCW0LgAiJohAkDTdP46oJP0Hr6XHB7vAJbRGAbImiECcPlzl40dvUhP9XX6qq7jyw7pQYRNMKEoazfO7ttzlQAgIksOyUHETTChKHsQjsyk9SYm5kIgOyjSREiaIQJgdPtRXltJ5ZPMyBRrQIAmHqJoEkNImiECcGx+i70Oj1YXpCCJI1P0DqJhyY5SGItYUJQdqEDChmFa/INUMgoAGQPTYoQQSNMCL660I75WYnQxvje8hqVHF02EuWUGmTJSYg6vq5ph9Mdfg6Z0WJHdasZy6el+B9L1KjIHpoEIYJGiCoud9iw9q2jeOUfF8M+5+sLHQCAFQGClqRRkSinBCGCRogquvq9qt/+sw4We3hLxrKadiRrVJgxJcH/WKKaeGhShAgaIaqw2t0AALPdjd+X14c83uul8XVNB5YVGCDrDwYAQDLx0CQJETRCVGF1+AQtM0mNt76pQ6/TPerxVS1mdNmcg/bPgP49NCJokoMIGiGqYDy0x0umo8vmxLtHGkY9/qsLRgDAsoLBgpakUcHm9MDu8nBjKEEQiKARogpLv4e2YloKrslLxutltaOKUtmFDhRNTUBKfMygx5lqgW7SF01SEEEjRBWMh6aNUeDhG/LRbnFg37HGoMda7C5UNpiGLTcBIEmjBEDqOaUGETRCVGGxu6BWySGXUViSm4wFWYl47ctLQfPSDvV3p11eMFzQSD2nNCGCRogqrA63P9ufoig8fEM+WnrseL+yadixZRfaoVHJMT8rcdhzTD0n8dCkBRE0QlRhcbihjR2o2FsxLQXF6Tq8+uWlQR1oaZpGWU07luQlQ6UY/jZP1BAPTYoQQSNEFVa7G/ExA4JGURQevj4fDV29+NupFv/jTHfaYPtnAKCP8+2hdVqJoEkJImiEqMI6xEMDgJWFk3DV5Hj83y8uwuOlAQBfnfelawTbPwMAhVwGXZySeGgSgwgaIaqw2gf20BhkMt9e2qV2Gz4+cwUAUFbTgcwkNbINmhGvReo5pQcRNEJU4QsKKIc9/i8zpyAvRYPdX9TA7vKg/FLnoGL0YCSqiYcmNYigEaIKi92F+NjhbfzkMgoPXZ+Pc1cseO7jc+hzeUbcP2PweWgksVZKEEEjRA00TQ9K2xjKLbOnIjNJjd/+8zIUMgpL8pJHvV4SqeeUHETQCFFDn8sDL41hQQEGhVyGH1+XBwCDutOORKJGha5eJ2iaZt1WsWOxu/Cbr2vh9Urrdw/Zgtvj8WDbtm2oq6uDXC7Hrl27QNM0Nm/eDIqiUFBQgKeeegoyGdFGArcElj2NxPfmpeP9481Yc3VGyOslqVVwur3odXqgCSF+UuPPFU3Y+UE1rskzYMbUhNAnRAkh/4r/+Mc/AAB79+7FkSNH/IK2ceNGLFq0CDt27MDBgwexatUqzo0lTGyYwvRge2gMKoUM+x5YEtb1EgOqBSaaoB1v7AYAdPdJa8kd0q1auXIlSktLAQAtLS0wGAyoqqrCwoULAQDLly/HoUOHuLWSQMCAhzaaoEVC0gSu56xsMAEAzH3SCoqEtU5UKBTYtGkTSktLUVJSApqmQVG+7p8ajQYWi4VTIwkEYKC5Y7C0jbGQOEHrOdstDjR29QEAeiQmaGF/1T333HN4/PHHcdddd8HhcPgft9lsSEgIvgavrq6OyBi73R7xOXwidvsAadtYXW8DALS3NKDa3jZuO0xm34e5quYyJnk7WbGRT8ZqY3mDzf//C5ebUa21jXL0+OD7PoYUtP3796OtrQ0PPPAA4uLiQFEUZs6ciSNHjmDRokUoKyvD4sWLg55bWFgYkTHV1dURn8MnYrcPkLaNVb1NANpQXDgNGUnqcdsxtdcF/KURsfoUFBbmsGIjn4zVxr9ePgel3AgvDcTEJ6Kw8CoOrPPBxX2sqKgY8bmQgnbjjTdiy5YtuPfee+F2u7F161bk5eVh+/btePHFF5Gbm4uSkhJWDSYQgsFMeQqVjhEu8bEKyGXUhMtFO95gwoypOjR29U68JadarcZLL7007PE9e/ZwYhCBMBJMUICtiKRMRiFRrUTnBBI0t8eLU009WHN1Bsx9LskJGkkeI0QNVocbMQpZ0P5mYyVRPbGqBc5dsaDP5cG8rEQkxCmJoBEIQmFxuFlL2WBgqgUmCsf70zXmZuihi1NOzLQNAkEMBGsdNF6SJ1g9Z2VDN1LiY5CeGAcd8dAIBOEI1txxvCRqVBMqsfZ4gwnzMvWgKAq6OAURNAJBKLjw0JLUKph6XZIr0g5Gp9WBy529mJvpGxqji1PCbHdLqjifCBoharCM0NxxPCRqVPB4aVj6I6hS5kR//ea8AEHzeGl/BYYUIIJGiBqsjuDNHceDf+DwBFh2VjaYoJBRmJWmA+ATNEBa5U9E0AhRAxdLTmbg8ESo56ys70bhlATEqeQAiKARCILh71bLuofW33FD4oLm8dI42dSNeZl6/2MJRNAIBGFwuL1weWj289AYD03iS87zVyzodXr8AQFgwEOTUi4aETRCVMBsXMezHeWcIB7a8UZfQu28IIJGPDQCgWf87bdZ9tDUKjlUCpnkPbTK+m4ka1TISIrzP0YEjUAQCLabOzJQFOXLRZO6h9ZgwtzMRH9jVsDXtUQuo4igEQh8YwljQMpYSZT4BHWTzYnaDhvmZekHPU5RFBJipVUtQASNEBUwvdDYDgoAvlw0KQsak1A7NyNx2HO+ek6SWEsg8MrAkpMLQYuBqVc6XspQjjeYIKOA2Rm6Yc/p1CrioREIfOMXNC48NLW0PbTKhm5cNTkBatXwe6eLU6JHQgERImiEqIDrPbSePhfcHi/r1+aKM809ONnaF/I4j5fGicbuYftnDFJrIUQEjRAVWB1uKOUUYljsVsvA5KJ1R9EH+7mPz2Hb560409wz6nEXjVZYHe5B+WeBSK2FEBE0QlTA1HEGph2wBVMtEE2pG41dvXB7gYferYTZPrIgMQOF544oaNJqIUQEjRAVcFHHyZAUZQOHvV4aLT12FE+ORZOpD1v+fHpEQTreYEKSRoXs5OBj/6TWQogIGiEqsNjZ74XG4PfQomRzvMPmgNPtxdIsDZ4omY4PTrdiz5GGoMdWNnRjboZ+RM9WatUCRNAIUYHV4WK9jpNhwEOLjg91s8kXDEjVKPCjZbm4bnoKSv92dth+Wk+fCxeNVszNDB4QAIigEQiCYOVg4hODXu37UEeLh9bcPSBoMhmFF++agySNCg+/W+lPQAaGd6gNhtRaCBFBI0QFVjt3e2ixSjk0KnnU7KG1MIKm9YlRkkaF3ffMRaOpD1veH9hPq6z3JdQWZ4T20KTSQogIGiEqsDrY71YbSGIUjbNrNvUhPlYBjWrg43t1dhL+68Zp+PupVrzTv592vLEb0ybFj3rfyJKTQBAAC4ceGuDzcqKlhVBzdx/S9HHDHt+wPA8rpqXg53/37acdbzBhXtbIy02ACBqBwDtOtxcOt5ezoADQL2hR4qE1mYILmm8/bTYS1Ur8+2+PwmJ3Y+4oy01Aei2EiKARRI+Nw8J0hiR19AhaS3cf0hKHCxoAJGtj8PIP5vp/l1AemtRaCBFBI4gefx1nLDd5aED07KFZ7C6Y7e6gHhrDotxk/PRfZ2B+ViJykjUhrymlFkLcfeURCCxhcfi8B049NI0KNqcHdpcHsUo5Z68zXpiUDZ+HNrIAr1+ag/VLc8K6ppQK1ImHRhA9zDwBrvLQgIFqgW6R90VjkmqnjuKhRUoCETQCgT+4bO7I4J+gLvJlJ5ODls6ioOnilCQPjUDgCy6bOzJESz1nU3cfVHIZDNoY1q4ppSXnqO8Ql8uFrVu3orm5GU6nEw8++CAmT56MDRs2IDs7GwBw99134+abb+bDVsIEhQkKcJ22AYjfQ2s29WGqPhYyGXttlPRqn6DRNM1JeyY+GfUdcuDAAej1ejz//PMwmUy4/fbb8dBDD+H+++/HunXr+LKRMMHhxUPTRIeH1tzdx+r+GTC4hVA8h5FkPhj1HXLTTTehpKTE/7NcLseZM2dQV1eHgwcPIisrC1u3boVWq+XcUMLExWp3Q0YBcRxGH/Vx0bOHtrwghdVrBlYLRLugjbqHptFooNVqYbVa8cgjj2Djxo0oLi7Gk08+iXfeeQcZGRl45ZVX+LKVMEFh6ji5XA4p5DLo4pSizkVzur0wWhwjJtWOFSmVP4X04VtbW/HQQw/hnnvuwerVq2E2m5GQkAAAWLVqFUpLS0c8t7q6OiJj7HZ7xOfwidjtA6RpY1NbB2Llkb+fIkWrBC63dqC6ulqU97HF7AJNA1RvN6s2dht9kdPT5y5B1sOuWPJ9H0cVtI6ODqxbtw47duzAkiVLAADr16/H9u3bUVxcjPLychQVFY14fmFhYUTGVFdXR3wOn4jdPkCaNsqP2ZAUT3H+e03+0gSPQo7CwkJR3kfTxQ4Ajbh6Ri4K8w2s2ejV9QCftkKfOgWFhVPGb2gAXNzHioqKEZ8bVdBee+01mM1mvPrqq3j11VcBAJs3b8YzzzwDpVIJg8EwqodGILAB162DGBLVKn+elxgZXCXAHhNmyblt2zZs27Zt2ON79+7lzCAp4/J4oZBRUR8a5xur3e2PQnJJkkYZciyckDR394GigMm6WFavKyVBI4m1PEHTNK57/ks8+/E5oU2JOix8eWj9PdHEOtKt2dSHFG0MYhTsRnul1EKICBpPWBxuNHf34c2yWlS1iNcLECNWO3fzBAJJUqvgdHvR6/Rw/lpjoaVn5LZB40FKLYSIoPGE0ewAAHhpYPv+M/B6xekFiBHe9tBEXi3QPEJjRzaQSgshImg8YbTYAQDfm5eGyoZu/KmiUWCLogO3x+cxcTWTM5AkEddzer00WrrtnHhogHTqOYmg8US7xeehPbgiDwuzk/DsR+dEncQpFmwO3/KPy7InBjF7aB1WB5weL2cemlRaCBFB4wlG0FITYvHz24pgtrvxP5+QAEEomOaOXBamMySJuJ7Tn7LB4ZJTCi2EiKDxhNHigEohQ0KsAldNTsC6a7Pxh6ONqGwwCW2aqOGjMJ2BWXKKcYI6VzloDGTJSYiIdosDqfEx/hy0R1dOw+SEWGzffwZuj1dg68QL062Wj6BAfKwvfUGMWwFMp1pugwIu0aashAsRNJ4wWuxIjR9oyqeNUWD7d2egqsWMPYfrBbRM3Fh49NBkMgqJaqUo53M2d/uGC3PVDYNpIWQTacpKuBBB4wmj2YGU+MFdRm+eNRnLCgz4xacXYDTbBbJM3Fh5aO4YSKJanNOfWkYYLswWUqkWIILGE+1WB1LjB5esUBSFn986Ew63F898KK7ODmKB2UPjq0+XWAcON5n6kM7R/hkQIGgiHxITCiJoPOBwe9Dd6xrmoQFAjkGDDStysf9ECw5d6hDAOnHj30PjYckJ+ARNrFFOTj00tU/QuvvE97tHAhE0HvCnbAQRNAD48fX5yEiKw46/VsHpJgGCQCwONygKUPM0KzNRoxJdlNNsd8Fid7PeejsQxkOL9tQNyQja3qMNKL/UKbQZQRnIQQsuaLFKOX5+y0xcNFrxv/+s49M00WO1u6FVKVgdCjIaSWqfh+YVUbSvheOUDYDsoYmK00092Pz+afzo98f8+TpiwtgvaCnakdu+XH9VKpbkJuNPx0hJVCBWh4u35Sbg89B80T7xeMpcp2wARNBEA03TePbjaiSqlfDSNB7fd1J0hd/GEB4aw3cKU3Gp3SbqJoN8w1dhOgMzcNjsEJGg8eChSaWFUNQL2tc1HfjnxU488p0C7Fg9A+W1naJbtrVbHKAoIDlEk8Jl/dN8vqkhwQEGi93Nr4fWXy1gtosnH6vZ1D9cWMPecOGhSKWFUFQLmtdL49mPziEjKQ73LMrEXQsysLJwEv7nk/M4f8UitHl+2i12JGtUUMhHv93TJmmRGh+Dry8SQWPg30PzCVqPQ0SC1s3+cOFgSKGFUFQL2oGTLTjbasbjN05HjEIOiqLw7PdnIT5GgY1/PAGHWxxvynaLAynxodsmUxSFpfkG/PNih+iWzULBV3NHhgEPTVxLTi6XmwxSqOeMWkFzuD144dPzKJqagNXFU/2PG7QxePb7xahuNeNXn9cIaOEAxv46znBYWmBAl82Js61mjq2KDix2YTw0s5g8NA4bOwYihRZCUSto7xxuQJOpD5v/5aphrviqGZOwZkEGXvvqEr693CWQhQMEK3saiaX5BgDAN2TZCYBZcvI3zVutkkOlkIlmyelwe2C0ODjNQWOQQguhqBQ0s92F3V/UYGm+wb+RPpTtq2cgPTEOP9l3wl8+IwReL40Oa/geWmpCLKZPiieBAfjundXBb1CAoigkqVWiWXJe6fHV+PLhoZElp0C88VUtTL0ubP6Xq0Y8RhujwC/vmoNmUx9K/3aWR+sGY+p1wu2lwxY0AFhWYMDRy12wu8ThJQiFzclvYTpDkkYlmiWnPweNxz20aG4hFHWCZjTb8ZtvanHL7KmYmaYb9dgF2UnYsCIPfzzWiE+rrvBk4WD8SbVhBAUYlhYY4HR7cbRO+OWykPDZ3DGQJI1KNGkbTf05aOl6NeevJYUWQlEnaL/8vAYeL43Hb5we1vEbV07DjCkJ2PL+aX8JEp+EKnsKxqKcZKjksgm/j8Znc8dAEjUq9IgksbbZxM1w4WBIoVogqgTtotGKfccace+iLGQmh/eNpVLI8KsfzIHF4cbTf6vi2MLhGEMUpgcjTiXH/KxEfD3B99Es/tZB/Ara5IQYtNvc8Iggdaaluw+p8TFQKbj/qPLdQuivJ5qx8+/sbgdFlaC98Ml5xCnl+M8b8iM6b9qkeNx/TTY+PnOFdy+NGV8XbpSTYWmBAdWtZkG8SrHgb+7Is6AVpMbD6aHRZOrl9XWDwXXboED49tD+crwZh1huKBE1glZRb8LHVVfwwPJcJGsjLwH5/vx0eLw0/n6qhQPrRqbd4oA2RgG1KrIP5bICX/rGRO6R5t9D4zFtAwDyUrUAfCsCofEl1XK/fwb48tAA/gSttt2G3BQNq9fk96tvBBxuD/5wpAEXG0wwNF8IeswnVW0waGOwflnOmF5j2qR4zJiSgP3Hm3H/tWO7xliIJKk2kKKpOujVSpRd6MCtc9I4sEz88N3ckSG/X9BqjFZ8p3ASr68diNdLo7Xbjptmcr9/BvDbE83h9qDJ1Ivb5rL73haFoPX0uvDLz2v6vxmCj3VTyWV4/s7iiD2dQL43Lw07P6jGpXYr8lK0Y75OJLSbHTCMQdDkMgrX5hvwzcV20DTtnxY1kfAPSOE5KKCLUyIpTi64h8YMF07na8nJY9fa+s5eeGkgT4oeWmpCLE7sWIXq6moUFhaOeNx4P9SrZ0/FMx9WY//xZvxXmFHS8dJudaBoasKYzl2Wb8AHp1px0WhFwaR4li0TP0JFOQEgU69CjcCC1sRD26BA4nlsIVTb7ru3uQZ2HQvR7KFRFBXy33iZlBCLa/MN+MvxZt6SB41me8QBAYal/ftoEzXaaXW4oFbJIeepW20gGTolLhmtgiaZMkm1fJQ9Afy2ELrUbgMA5LDsoYlG0PjitjlpaDL1oaKe+4nlNocbNqdn2LSncElPVCPHoJmw+Wh8tw4KJFOngtXhxhUBxwv6W2/zJGgAfy2EatttmJQQw/rfd8IJWsnMyYhVyvCX482cv1ao4SjhsDTfgMO1nRNyeArfzR0DydT79pOE3Edr7u5DAofDhYPBVz1nXYeV9eUmEELQXC4XnnjiCdxzzz244447cPDgQdTX1+Puu+/GPffcg6eeegpeb3R90LQxCpQUTcbfT7VyLhIDZU/jELQCA3qdHlQ2cO9Rig2L3c17HSdDps7XRqimTUBBM/GXssHAVwuh2g72UzaAEIJ24MAB6PV6vPvuu3jzzTdRWlqKXbt2YePGjXj33XdB0zQOHjzIulFcc9vcNPT0ufCP80ZOX4dJqo2k7GkoS/KSIZdRE7L7Bt+dNgLRxcqgVytxsV1YDy1Nz0/KBgMfLYS6bE5097qQy0GmwaiCdtNNN+HRRx/1/yyXy1FVVYWFCxcCAJYvX45Dhw6xbhTXLMs3IFmjwn6Ol50DS86xvykTYpWYk6GfkG25rTw3dwyEoigUpGpxUUgPjccqAQY+lpwDEU6ePTSNRgOtVgur1YpHHnkEGzduHJQTpdFoYLGIp3d/uCjkMqyePRUHq42c/vGMFgcUMgr6uPHtgSzNN+B0UzdvNXZige/mjkPJT9UK5qExw4X5Stlg4KOFUG1/hJOLJWfIr7/W1lY89NBDuOeee7B69Wo8//zz/udsNhsSEkbOsaquro7IGLvdHvE5Y2VOohO/83jxv59W4qZp4eWJRWrfxUYj9LEynD9/bqxmAgAyVHZ4aWBf2QkszRrdTefzHo6VcG3s6XXA1WsW5Pex2+2Ipx3osjlRfvwM9LH8TG5nqOvqr+G1daG6OniiKxd/a4elGx4vjcrTZ6FWjj9mGMzGo+c7oZABliv1qDaym5IzqqB1dHRg3bp12LFjB5YsWQIAmDFjBo4cOYJFixahrKwMixcvHvH80ZJkgxEqsZZNrqJpvHy0B+VXvHjs1vBeM1L7HOUWpCXJx/075U/z4ukvjKjtjcUPQ1yLz3s4VsKxkaZp9LpqkTklFYWF/CRBB1JdXY1rZ6XjjW87QemmoDA3mdfXbz7bBqAZVxflozAzcUQb2f5bF1gagIouTM7MDbncfb+yCXMy9KPuhQWzsefbY8gxeDCzaMaYbKyoqBjxuVEl+LXXXoPZbMarr76KtWvXYu3atdi4cSN2796NNWvWwOVyoaSkZExGCQ1FUbh9ThqO1nVx1lVhPEm1gSjlMizOTcY3F9tZsCo66HN54KX577QRSIGAReotPfxWCTCE20Ko0+rAT/adxGtfXYr4NWrbrZwsN4EQHtq2bduwbdu2YY/v2bOHE2P45tY5afjFZxfw1xMteOj6yFoShUOH1YG5I3y7RsqyAgM+r25DfacNWcncvBnEhFCF6YFM0cVCoxKmprPZ1AeVgtvhwsEIt4UQ0035VFNPRNd3e7xo6OrFjUWTx2ZgCCZcYm0gmclqLMhK5KQUyu3xotPmHFdSbSDLJlgZlFCF6YFQFOULDAggaE39EU6uhwsPJdwWQodrfX3MLrRZ0OsMv7Kg0dQHl4fmJMIJTHBBA4Db56XhotGKqhZ252B2WJ2g6fEl1QaSY9AgTR83YfLRhGruOJS8VC1qjPxH8ptNvmnpfBNuC6HDtV2IU8rhpYGzEXx2/CkbHHW7mfCC9q+zpkApp1gvhWKj7CkQiqJw3fQUfHHeiJq26EuViRShmjsOpSA1Hm1mB8x2flNmmky9vAxGGQrTQmg0D63L5sT5NgvWXJ0BADgZwbKTSdlgu20Qw4QXNL1aheunp+LAyRa4PeyVQg1UCbD3Lbtx5TRoYxR4bN8Jydd2WgRsHRRIvgCBgS6bEx1WJwom8dOzLxCtSgEZNbqgHa3zLTdXz56CyQmxONXUHfb1aztsSNKooFerxm1rMCa8oAHA7XPT0G5xsNrfnI06zqGkxMdg1/dm4UyzGS8frGHtumLEKtCAlKEIEelkXosRUz6RyaiQ9ZzMcnNWmh7F6bqIAgO17VbkcLR/BhBBAwBcf1Uq4mMVrJZCMUvOlDHMPxiNkqLJuGtBOl798iIq6qU7t9Pav8QT2kPLSFJDpZDxKmjMnp1QTT1DlT8dru3EguxEqBQyzM7Qo67DFnbFTW2HjbOAAEAEDQAQq5Tju8VTceBkC1787AIrE8uNFjsS1UpOxo/tWF2EtMQ4PPbHk7A5uO9dJQSMh6YRWNDkMgq5Bg2/gtZmhUYlx1QeZnEGQxenRPcIAtVlc+LcFQsW9ycaF6f7hn2fDsNLs9hdaLc4OAsIAETQ/Gy6aTr+tXgKXj5Yg5t+VYayC+NLYjWaHawuNwPRxijw4l1z0Gjqxc4P2J1rKBYsDjdiFDJe5lGGIp/nSGeN0YL8VK1gcyRG89CY/bPFuUkAgOI0PQDgZBj7aFzWcDII/24RCXq1Ci/9YC72rF8EiqJw3/8exX/+4TiMY+xY2m51jKvLRiiuzk7ChhV5+MPRRnx+to2z1xEKi90t+P4ZQ0FqPJpMfehzjt9zD4ffudtMAAAXUklEQVSaNmFnSIzWQihw/wzwRUWzk9VhBQZqO3xeLlcRToAI2jCWFhjw0aPLsHFlAT45cwXf+cVX+H355YinaBvNYxtfFwmPrZyGwikJ2Pz+KXRYpTWQWMjWQUPJT9WCpoFLPHTe6Ol1wWhx+IMRQjCah3a4thPzsxIHec7F6fqwAgO17TbIZRQyk4ig8UqsUo6NK6fhk8eWY3aGHjv+WoXbX/0nLnaGJxo0TaPdwt2Sk0GlkOFXa+bAbHdjy/unBR3owTZCNnccCpM+wYegXWxnAgLCC9rQ95PJv3+WNOjx4nQdWnvs/lSlkahttyEjMY7TbQQiaKOQY9Dg7fUL8fLdc9HaY8djHzajsSt0Ibu5zw2nx8u5oAHA9MnxeLJkOj4724Y/HWvi/PX4QkweWnayBnIZxUs7buY1ClKFXXJ6vDRsQ5bYR/rrNxcP6TwyO8O3/DzVOLqXdqndymlAACCCFhKKonDL7KnY+6PFcHuBr8IIFjDfVHwIGgCsuzYHS3KT8bO/VaHVIo0mkBaHm9fhIKOhUsiQlazmJdJ5oc2KOKWc9061gYxUoH64thOxShmK0/WDHi+amgAZhVH30bxeGpc7uU3ZAIighU2uQQODWo7y2tDJt0YWWm9HgkxG4YW7ZkNGUfjFN+0R7/eJEavDJdiAlGDkp/AT6WQinHwXpQcyUguhw7WdWJCVNGzJqFYpMG1S/KglUC09fbC7vKzP4RwKEbQwoSgKs6fE4fClzpB7Vf46znEMR4mUNH0cfn5bEaqMdrxRVsvb63KFVcARdsEomKRFfWcv5yVnF41WQQMCQHAPbaT9MwZfxUD3iJ8Nf8oGB6PrAiGCFgGzJ8eh0+bEhRB7KXwvORlum5OGZVkavPjZeVS1RNanSkzQNC3okOFg5Kdq4fbSqO+0cfYaFrsLrT125AsYEACCtxA6ejn4/hlDcboepl4XmvqnvQ+lroPbonQGImgRUDzZt4Q8dGn0Fj7tFgdilTLel0wUReHhxQYkqlV47I8nWKl4EAKH2wuXhxaXh9a/Sc/lPhpzbSEDAkDwFkIj7Z8xzE4fPcG2tt0KbYyC8y95ImgRMEmrRGaSOmQRu9HiS6oVItM7IVaO/7mjGBfarHjhk/MRnUvTNI7UdgouhP7CdBF5aEx2ew2HgjYQ4RR4yRmkhdDh2q5h+WeBTJ8cD5VcNmI+GjNYmOvPBBG0CLkmLxlHajtH3XjnsuwpHK6bnoq1i7Pwm2/qcCjMeZ5eL43//qAaa944jF0fCjs1Sgztt4eiVimQnhjHqYdWY7QgRiFDRhL/fdACGdpCqLvXiXNXzFicM/KgGJVChsKpCTjZOJKHxn2EEyCCFjFL8pJhtrtH7dLpK3sSTtAAYMvNVyHXoMHjfzoZshOC2+PFE++dwm++qcOkhBj88VgjumzBR6fxgViaOw6lIFXLrYdmtCIvRQu5gBFOYHgLoSN1XaBpYHHe6JOvZqfrcKa5Z9iXfZ/Tg+buPs5z0AAiaBGzpH9TtLx2ZM/HaLYLLmhqlQIvrpmDNosDPztQNeJxdpcHD75TiT9XNuGxldOwZ/0i2F1e/L9Dl/kzdghiae44lPxULWrbrZylxfhqOIVdbjIElj8N7J/pRj2nOF0Pm9Pjb7PNwAQEuCxKZyCCFiGpCbHIS9GMuI9md3lgtrsFXXIyzMnQ4z9vyMf7x5vxwanWYc9b7C78+2+P4rOzbfjZLUV4dGUBCibFY2VhKn5ffjmi4RdsIpbmjkMpSI2Hw+3lZOyhzeFGc3ef4PtnDIMFzbd/FqMYfdgyI3hD89GYonSuUzYAImhj4po8A76t64IrSMvudp6TakPx0PX5mJ2uw0/3n0ZbQOeQTqsD97x5BMcum/DSD+bg/1yT7X9uw4o8mHpd2PdtowAW+5JqAfF5aHkcdq8d6FIrbISTgRE0Zv9s0Sj7Zwx5KVqoVfJhFQNMDhqXnWoZiKCNgSV5ybA5PUEjOv7W2zwm1Y6GUi7Di2vmwO7y4In3ToGmaTR39+HO18pxoc2CN+6bj1vnpA06Z0F2EhZkJeLNr+tYnbMQLmIMCgADLbG52EdjrjlNJEvOhP4WQkeZ/bMwJsfLZRRmpumGe2jtVkzVxSJONbqHxwZE0MYA88c9HKQMqp1JqmW59fZ4yEvR4qc3F6LsQjue/egc7vj1IbRbHdjzH4tww1WTgp7zwIo8NHf34YPTw5eqXGMW6R6aLk6J1PgYTjy0GqMFKrkMmQJHOBmYrrWHa7sQo5Bhdsbo+2cMs9N1qG4xD6qo8KVs8CPURNDGQJJGhcIpCUETbIUoewqHf1ucheXTUvB6WS1cHhp//NESXJ0dvIwFAL5zVSryU7V47ata3tsSWR1uKOUUYkTQrXYoBZO4iXRebLMiN0UDhVwcv7O+f8lZ3t//LNT+GUNxuh5Ojxfnr/jqXmma9qVs8BAQAIigjZkluck4dtkEh3twEqrR4oCMApI14hI0iqLwwp3F+LfFmXhvwxLMmJow6vEyGYUfLc9FdasZZTwPN2ZaBwnVgno08lO0uGS0si7yNUarIFOeRoJpIVTdag5rucnAVAycavbto5nsHlgdbl5y0AAiaGPmmrxkONxeHG8YvAFqNDuQrI0RPJcoGKnxsdh52yxkh/nmum1OGiYlxOC1Ly9xbNlgrCJqHTSU/EnxsDrcuDLG1uzB6HN60GjqFbzkKRCm/AkIb/+MISMpDolqpb83WlOPL8BDlpwiZ2FuEmQUhqVviCGpli1UChnWL81BeW3niBngXGARUXPHoeT3fzDZbPZ4qd0KmhZPQAAYELRI9s8A30pgVrreX9PZZGYEjXhooiYhVolZaTocHiJoRovwSbVscvfCTMTHKvB6GX9emtXhEl2Ek4FJfGUzMDAwh1N8gjYvM/z9M4bZ6TrUGK2+CoEeF2KVMkzV8dOwkgjaOFicl4zjjaZB04CEruNkm/hYJf5tcRY+OnMFlzu4a50TiNXhFlVheiDJGhX0aiWrgYGaNisUMgpZyfx4MeHAtBCKZLnJUJyuh8dLo6qlB01mF7KTNbw1rCSCNg6uyTPA5aFxrH+CucdLo9PmFE1SLVvcf202lDIZ3vian8aRYmvuGAhFUShI9QUG2KLGaEWOQQOlSCKcgM9bvHN+Or4/Py30wUOYHVAx0NTjRB5P+2cAEbRxsSArEQoZ5d9H67I54fHSkvLQAF8w4fvz0/BeRZM/LYVLxNbccSjM4GEvSzWdNW0WUS03ASBGIcfzd85GemLkeXGpCbGYnBCLivouXLG6eds/A4igjQtNjAJzMvQo7xc0plOtlPbQGH64LBcujxe/O1TH+WtZROyhAUDhlASYel1YtOsgtrx/Cl+caxtzDzm7y4OGLnFFONmgOF2HL84Z4aX5KXliCEvQTp48ibVr1wIAqqqqsGzZMqxduxZr167Fhx9+yKmBYmdJXjJON/fAYneJNqmWDXJTtLipaDLeLq/3F49zgdPthcPtFe0eGgD84OpM/GrNHCzMTsKBEy1Y97tjmFf6GTa8XYE/VzTBFEHrpdp2G7y0uAICbDA7Qw+7y1ctwFfKBgCEfNe8+eabOHDgAOLifFGKs2fP4v7778e6des4Ny4aWJKXjN1fXMS3l7vQYfW9kVO00tpDY3hgRR4+OnMFe4824D+W5XLyGjaHOMueAlEpZLhtbhpum5sGh9uD8kud+OxsGz6vbsPHVVcgl1FYkpuMX66ZE3L7wR/hlKCHxiCqJWdmZiZ2797t//nMmTP48ssvce+992Lr1q2wWrmfVShm5mX62hIfutgpaQ8N8LUjumpyfFizSceKv7mjSBNrhxKjkOO66an479tnoXzzd/DXh67FgyvycKSuEy8dvBDy/ItGK+QyCtkGcdRwskVxmq9iIDFWjgQe/5YhBa2kpAQKxcC3ZXFxMZ588km88847yMjIwCuvvMKpgWInVinH/MxElNf6BC0+VoFYJfddBYRibqYep5p6OKvvFGtzx3CQySjMztDj8ZLpuGtBBv74bWPI3mkX2izISlZHnOsldnRqJXINGmTo+f1iivhds2rVKiQkJPj/X1paOuKx1dWR9aa32+0Rn8MnI9mXn+DFnlozFF4ndCpK0N+B63tokPehp8+Ff3x7GlPix/ZmHc3GqjbfGLQuYwuqq01jtnO8jPc+lmQA+74FSt8/ho3XpIx4XFVjF7L0yjG9ltg/L48s0gFuF682Rixo69evx/bt21FcXIzy8nIUFRWNeGxhYWFE166uro74HD4Zyb5b47rw9olynG6zY2FOkqC/A9f30JPQg93l36A31oDCwqljusZoNragDUArigpyUZgRfGQaH4z3PhYCuLdFhrcP12PLrfOD1s863B60Wutw+4IsFBZO591GrikENzZWVFSM+FzEaRtPP/00nnnmGaxduxaVlZX48Y9/PC7jpEBxuh5xSjm8tHg61XLFtEm+cWWnRxhXNl5aun0emkECqS8/vj4PSjmFlw/WBH3+ckcvPF5aVF02op2wPLT09HTs27cPAFBUVIS9e/dyalS0oVLIcHVOEsoutEsuqXYoKoUMV02Jx+lmbgStsqEbqfExmKqL/i+G1PhY3LckG7/5uhY/vj5/mHBJNcIpJCSxliWYaVBSTKodyqw0HU4397CWKR9IRb0J8zITRdkLbSw8sDwXsUo5fvX58IjnhTYrZBS/aQ1ShwgaSywrMAAA0hL56SogJMXpOljsbtR3sTv9yGixo6GrF/OzElm9rpAka2Nw/7XZ+PupVpy7MniW60WjBVnJGklHxfmGCBpLzEzT4U8blqCkaLLQpnDOrP4co6HTfcZLZb3vevMkJGiAr2wsPkaBX3422EuraRNXl1opQASNRa7OThJVxwSuKJikRYyC/cBAZYMJKrkMM9NGbw8ebejVKqxfloNPqtpwpn/v0eXxoq7DJpo5nFJB+p8+Auso5TLMmJqAUywHBirqTZiVrpNckikArFuaA12cEi/2e2n1nTa4vbTkajiFhggaYUwUp+lQ1dwDD0uBAYfbg9NNPZLaPwskIVaJHy3PxRfnjKhsMOFCfwtvEuFkFyJohDExM00Hm9ODug52annPNJvh9HgxL1OaggYA/35NNpI1KvzyswuoabOCosBr88OJABE0wpgo7h9XxlY+2vEGX5nTvCzhqgO4RhOjwIYVefi6pgPvH29CRqKal2niEwkiaIQxkZeiQZxSjlMsBQYq6k3ITFJLvtLi3xZnISU+BvWdvSQgwAFE0AhjQiGXoWhqAiuRTpqmcazeJNn9s0DiVHI8dF0eACCfBARYhwgaYczMStehqsUMt8c7rus0mfrQbnFILv9sJO5elInvzUvDd2eNrbifMDJE0Ahjpjhdhz6XB5faxzferrJ//2y+hAMCgcQo5HjxrjmYlR7+AF9CeBBBI4wZtioGKupN0KjkmD6ZpDAQxgcRNMKYyTVooFHJxx3prKg3YU6mHnKehtESpAsRNMKYkckoFPV33hgrNocb1a3mCbPcJHALETTCuChO0+FsixmuMQYGTjZ2w0tLryCdIAxE0AjjYla6Dg63FzVtY6sYqKj3BQTmEg+NwAJE0AjjYqBiYGyBgcoGE6ZN0kIXFx1j6wjihggaYVxkJakRH6sYU8WA10ujsqF7QiTUEviBCBphXMhklL8ld6TUdljR0+eSdEE6gV+IoBHGzax0Hc61WuB0RxYYYPbPiIdGYAsiaIRxU5ymh9PjxYU2S0TnVdSbkKhWIifIzEoCYSwQQSOMm1lpvhKeSPfRKvoL0qUy4YkgPETQCOMmIykOujhlRJFOk82JS+02kn9GYBUiaIRxQ1EUitN1EXloxxsnVkE6gR+IoBFYYVaaDuevWGB3ecI6vqLeBIWM8uexEQhsQASNwArF6Tq4vTTOXQkvMFBRb8KMqQmkBTWBVYigEVhhFlMxEEYrIY+XxsnGHpJ/RmAdImgEVpiqi0WyRhXWPlqdyYk+l4fknxFYhwgagRUoisKs9PAqBs4a7QBIQi2BfYigEVhjVpoONUYr+pyjBwaq2+2YoovFVH0cT5YRJgpE0AisMStNB4+XxtlW86jHnTVOnIEoBH4hgkZgjeIwAgNXeuww2twk/4zACQqhDSBIh0kJMUiJj8Hn1UakjDAw+EyLb4+N7J8RuCAsQTt58iReeOEFvP3226ivr8fmzZtBURQKCgrw1FNPQSYjjh7BFxhYmJOED0614puLHSMep1XJUDglgUfLCBOFkIL25ptv4sCBA4iL823g7tq1Cxs3bsSiRYuwY8cOHDx4EKtWreLcUEJ08Is7Z+PR7xSMekxnSz1UCvIlSGCfkO+qzMxM7N692/9zVVUVFi5cCABYvnw5Dh06xJ11hKgjVinHtEnxo/7Tx5LqAAI3hBS0kpISKBQDjhxN0/52LxqNBhZLZD2wCAQCgSsiDgoE7pfZbDYkJIy8F1JdXR3Rte12e8Tn8InY7QOIjWxBbGQHvm2MWNBmzJiBI0eOYNGiRSgrK8PixYtHPLawsDCia1dXV0d8Dp+I3T6A2MgWxEZ24MLGioqKEZ+LeGd206ZN2L17N9asWQOXy4WSkpJxGUcgEAhsEZaHlp6ejn379gEAcnJysGfPHk6NIhAIhLFAYucEAkEyEEEjEAiSgaJpmubiwqNt3BEIBMJ4mD9/ftDHORM0AoFA4Buy5CQQCJKBCBqBQJAMgrcP8nq9ePrpp3H+/HmoVCrs3LkTWVlZQps1jNtuuw3x8fEAfGksu3btEtiiAaKhG0qgjVVVVdiwYQOys7MBAHfffTduvvlmwWxzuVzYunUrmpub4XQ68eCDDyI/P19U9zGYjZMnTxbVffR4PNi2bRvq6uogl8uxa9cu0DTN732kBeaTTz6hN23aRNM0TR8/fpzesGGDwBYNx26307feeqvQZgTljTfeoL/73e/Sd955J03TNP3AAw/Qhw8fpmmaprdv305/+umnQppH0/RwG/ft20e/9dZbAls1wHvvvUfv3LmTpmma7urqolesWCG6+xjMRrHdx88++4zevHkzTdM0ffjwYXrDhg2830fBv7orKiqwbNkyAMCcOXNw5swZgS0azrlz59DX14d169bhvvvuw4kTJ4Q2yU80dEMZauOZM2fw5Zdf4t5778XWrVthtVoFtA646aab8Oijj/p/lsvloruPwWwU231cuXIlSktLAQAtLS0wGAy830fBBc1qtUKr1fp/lsvlcLvdAlo0nNjYWKxfvx5vvfUWfvazn+Hxxx8XjY3R0A1lqI3FxcV48skn8c477yAjIwOvvPKKgNb57pNWq4XVasUjjzyCjRs3iu4+BrNRbPcRABQKBTZt2oTS0lKUlJTwfh8FFzStVgubzeb/2ev1Dnrzi4GcnBzccsstoCgKOTk50Ov1aG9vF9qsoETSDUUoVq1ahZkzZ/r/f/bsWYEtAlpbW3Hffffh1ltvxerVq0V5H4faKMb7CADPPfccPvnkE2zfvh0Oh8P/OB/3UXBBmzdvHsrKygAAJ06cwLRp0wS2aDjvvfcenn32WQBAW1sbrFYrUlJSBLYqOEw3FAAoKyvDggULBLZoOOvXr8epU6cAAOXl5SgqKhLUno6ODqxbtw5PPPEE7rjjDgDiu4/BbBTbfdy/fz9ef/11AEBcXBwoisLMmTN5vY+CJ9YyUc4LFy6Apmk888wzyMvLE9KkYTidTmzZsgUtLS2gKAqPP/445s2bJ7RZfpqamvCTn/wE+/btQ11dHbZv3w6Xy4Xc3Fzs3LkTcrnwHWIDbayqqkJpaSmUSiUMBgNKS0sHbTvwzc6dO/HRRx8hNzfX/9hPf/pT7Ny5UzT3MZiNGzduxPPPPy+a+9jb24stW7ago6MDbrcbP/zhD5GXl8fr+1FwQSMQCAS2EHzJSSAQCGxBBI1AIEgGImgEAkEyEEEjEAiSgQgagUCQDETQCASCZCCCRiAQJAMRNAKBIBn+P27BD0OgHy0uAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mpg.plot()"
   ]
  },
  {
   "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>cyl</th>\n",
       "      <th>wt</th>\n",
       "      <th>msg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>2.620</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>2.875</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>2.320</td>\n",
       "      <td>22.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>3.215</td>\n",
       "      <td>21.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>3.440</td>\n",
       "      <td>18.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>3.460</td>\n",
       "      <td>18.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8</td>\n",
       "      <td>3.570</td>\n",
       "      <td>14.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4</td>\n",
       "      <td>3.190</td>\n",
       "      <td>24.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4</td>\n",
       "      <td>3.150</td>\n",
       "      <td>22.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>6</td>\n",
       "      <td>3.440</td>\n",
       "      <td>19.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>6</td>\n",
       "      <td>3.440</td>\n",
       "      <td>17.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>8</td>\n",
       "      <td>4.070</td>\n",
       "      <td>16.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>8</td>\n",
       "      <td>3.730</td>\n",
       "      <td>17.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>8</td>\n",
       "      <td>3.780</td>\n",
       "      <td>15.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>8</td>\n",
       "      <td>5.250</td>\n",
       "      <td>10.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>8</td>\n",
       "      <td>5.424</td>\n",
       "      <td>10.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>8</td>\n",
       "      <td>5.345</td>\n",
       "      <td>14.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>4</td>\n",
       "      <td>2.200</td>\n",
       "      <td>32.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>4</td>\n",
       "      <td>1.615</td>\n",
       "      <td>30.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>4</td>\n",
       "      <td>1.835</td>\n",
       "      <td>33.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>4</td>\n",
       "      <td>2.465</td>\n",
       "      <td>21.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>8</td>\n",
       "      <td>3.520</td>\n",
       "      <td>15.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>8</td>\n",
       "      <td>3.435</td>\n",
       "      <td>15.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>8</td>\n",
       "      <td>3.840</td>\n",
       "      <td>13.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>8</td>\n",
       "      <td>3.845</td>\n",
       "      <td>19.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>4</td>\n",
       "      <td>1.935</td>\n",
       "      <td>27.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>4</td>\n",
       "      <td>2.140</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>4</td>\n",
       "      <td>1.513</td>\n",
       "      <td>30.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>8</td>\n",
       "      <td>3.170</td>\n",
       "      <td>15.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>6</td>\n",
       "      <td>2.770</td>\n",
       "      <td>19.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>8</td>\n",
       "      <td>3.570</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>4</td>\n",
       "      <td>2.780</td>\n",
       "      <td>21.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    cyl     wt   msg\n",
       "0     6  2.620  21.0\n",
       "1     6  2.875  21.0\n",
       "2     4  2.320  22.8\n",
       "3     6  3.215  21.4\n",
       "4     8  3.440  18.7\n",
       "5     6  3.460  18.1\n",
       "6     8  3.570  14.3\n",
       "7     4  3.190  24.4\n",
       "8     4  3.150  22.8\n",
       "9     6  3.440  19.2\n",
       "10    6  3.440  17.8\n",
       "11    8  4.070  16.4\n",
       "12    8  3.730  17.3\n",
       "13    8  3.780  15.2\n",
       "14    8  5.250  10.4\n",
       "15    8  5.424  10.4\n",
       "16    8  5.345  14.7\n",
       "17    4  2.200  32.4\n",
       "18    4  1.615  30.4\n",
       "19    4  1.835  33.9\n",
       "20    4  2.465  21.5\n",
       "21    8  3.520  15.5\n",
       "22    8  3.435  15.2\n",
       "23    8  3.840  13.3\n",
       "24    8  3.845  19.2\n",
       "25    4  1.935  27.3\n",
       "26    4  2.140  26.0\n",
       "27    4  1.513  30.4\n",
       "28    8  3.170  15.8\n",
       "29    6  2.770  19.7\n",
       "30    8  3.570  15.0\n",
       "31    4  2.780  21.4"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = cars[['cyl', 'wt', 'msg']]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1c950f90>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 9 artists>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1cada790>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mpg.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1cc8cbd0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mpg.plot(kind='barh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = [1,2,3,4,0,5]\n",
    "plt.pie(x)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 360x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.savefig('pie_chart.jpeg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "u'/Users/virendsingh/Documents/workSpace/ML-learning'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%pwd"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}