class11.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "math.ceil(3.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "math.floor(3.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "120"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "math.factorial(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0986122886681098"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "math.log(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on built-in function log in module math:\n",
      "\n",
      "log(...)\n",
      "    log(x[, base])\n",
      "    \n",
      "    Return the logarithm of x to the given base.\n",
      "    If the base not specified, returns the natural logarithm (base e) of x.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(math.log)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "math.pow(2,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.4142135623730951"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "math.sqrt(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random.randrange(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random.randrange(10, 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random.randrange(10,20,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random.randint(2,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random.choice([1,2,3,4,5,6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'h'"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random.choice('hello python')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[4, 2]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random.choices([1,2,3,4,5,6,7], k=2, weights = [2,1,1,33,4,2,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "mylist = [2,3,4,5,6,67,23]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "random.shuffle(mylist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2, 5, 67, 3, 6, 4, 23]\n"
     ]
    }
   ],
   "source": [
    "print(mylist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "cards = 'Ace two three'.split()\n",
    "random.shuffle(cards)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['two', 'three', 'Ace']\n"
     ]
    }
   ],
   "source": [
    "print(cards)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.array([1,2,33,4,5,5,6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1  2 33  4  5  5  6]\n"
     ]
    }
   ],
   "source": [
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7,)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "b = np.array([[1] , [2], [3], [4], [5]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, 1)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1]\n",
      " [2]\n",
      " [3]\n",
      " [4]\n",
      " [5]]\n"
     ]
    }
   ],
   "source": [
    "print(b)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "c = np.array([[1,2,3] , [4,5,6]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2 3]\n",
      " [4 5 6]]\n"
     ]
    }
   ],
   "source": [
    "print(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 3)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = np.zeros((3,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 0.]]\n"
     ]
    }
   ],
   "source": [
    "print(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = np.ones((3,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1. 1. 1. 1.]\n",
      " [1. 1. 1. 1.]\n",
      " [1. 1. 1. 1.]]\n"
     ]
    }
   ],
   "source": [
    "print(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = np.full()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_matrix = np.random.random((2,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.82757314 0.24843271 0.05440831 0.26221324]\n",
      " [0.56189551 0.61306393 0.13574634 0.26517901]]\n"
     ]
    }
   ],
   "source": [
    "print(random_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.array([[1,2] , [3,4] , [5,6]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = np.array([[0,1], [3,4], [3,9]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  3],\n",
       "       [ 6,  8],\n",
       "       [ 8, 15]])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x + y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  3],\n",
       "       [ 6,  8],\n",
       "       [ 8, 15]])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.add(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  1],\n",
       "       [ 0,  0],\n",
       "       [ 2, -3]])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x - y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  1],\n",
       "       [ 0,  0],\n",
       "       [ 2, -3]])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.subtract(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 9, 12])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(x, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3,  7, 11])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(x, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "z = np.arange(14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13]\n"
     ]
    }
   ],
   "source": [
    "print(z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.shuffle(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[33  2  5  5  6  4  1]\n"
     ]
    }
   ],
   "source": [
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "pick_one = np.random.choice(z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n"
     ]
    }
   ],
   "source": [
    "print(pick_one)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.array([1,2,3,4,5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x)\n",
    "plt.plot(x**2)\n",
    "plt.plot(x**3)\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Speed')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x,x**2, marker='^', color='red', label='speed')\n",
    "plt.title('speed analysis')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "numbers = np.random.randint(1,10,5)\n",
    "indices = np.arange(5)\n",
    "\n",
    "plt.bar(indices, numbers, 0.5, color='green')\n",
    "plt.bar(indices, numbers+1, 0.25, color='orange')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x10d95f5f8>,\n",
       "  <matplotlib.patches.Wedge at 0x10d95fac8>,\n",
       "  <matplotlib.patches.Wedge at 0x10d95ff98>,\n",
       "  <matplotlib.patches.Wedge at 0x10fa764a8>,\n",
       "  <matplotlib.patches.Wedge at 0x10fa76978>],\n",
       " [Text(0.9088626448409262, 0.6196520740002058, 'apples'),\n",
       "  Text(-0.21796079517432285, 1.078189729021278, 'oranges'),\n",
       "  Text(-1.0659850315925596, 0.27143307171494335, 'berry'),\n",
       "  Text(-1.7351013298097433, -1.1829722631120563, 'banana'),\n",
       "  Text(0.4772722257032133, -0.991065700431763, 'lichi')])"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "label = ['apples', 'oranges', 'berry', 'banana', 'lichi']\n",
    "values = [24,23,22,12,45]\n",
    "plt.pie(values, labels=label, radius=1, explode = (0,0,0,1,0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "mydictionary = {\"country\": [\"Brazil\", \"Russia\", \"India\", \"China\", \"South Africa\"],\n",
    "       \"capital\": [\"Brasilia\", \"Moscow\", \"New Dehli\", \"Beijing\", \"Pretoria\"],\n",
    "       \"area\": [8.516, 17.10, 3.286, 9.597, 1.221],\n",
    "       \"population\": [200.4, 143.5, 1252, 1357, 52.98] }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "mydata = pd.DataFrame(mydictionary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        country    capital    area  population\n",
      "0        Brazil   Brasilia   8.516      200.40\n",
      "1        Russia     Moscow  17.100      143.50\n",
      "2         India  New Dehli   3.286     1252.00\n",
      "3         China    Beijing   9.597     1357.00\n",
      "4  South Africa   Pretoria   1.221       52.98\n"
     ]
    }
   ],
   "source": [
    "print(mydata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "mydata.to_csv('country_data.csv', encoding='utf-8', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         country    capital    area  population\n",
      "BR        Brazil   Brasilia   8.516      200.40\n",
      "RU        Russia     Moscow  17.100      143.50\n",
      "IN         India  New Dehli   3.286     1252.00\n",
      "CH         China    Beijing   9.597     1357.00\n",
      "SA  South Africa   Pretoria   1.221       52.98\n"
     ]
    }
   ],
   "source": [
    "mydata.index = ['BR', 'RU', 'IN', 'CH', 'SA']\n",
    "print(mydata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_df = pd.read_csv('sample.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    month_number  facecream  facewash  toothpaste  bathingsoap  shampoo  \\\n",
      "0              1       2500      1500        5200         9200     1200   \n",
      "1              2       2630      1200        5100         6100     2100   \n",
      "2              3       2140      1340        4550         9550     3550   \n",
      "3              4       3400      1130        5870         8870     1870   \n",
      "4              5       3600      1740        4560         7760     1560   \n",
      "5              6       2760      1555        4890         7490     1890   \n",
      "6              7       2980      1120        4780         8980     1780   \n",
      "7              8       3700      1400        5860         9960     2860   \n",
      "8              9       3540      1780        6100         8100     2100   \n",
      "9             10       1990      1890        8300        10300     2300   \n",
      "10            11       2340      2100        7300        13300     2400   \n",
      "11            12       2900      1760        7400        14400     1800   \n",
      "\n",
      "    moisturizer  total_units  total_profit  \n",
      "0          1500        21100        211000  \n",
      "1          1200        18330        183300  \n",
      "2          1340        22470        224700  \n",
      "3          1130        22270        222700  \n",
      "4          1740        20960        209600  \n",
      "5          1555        20140        201400  \n",
      "6          1120        29550        295500  \n",
      "7          1400        36140        361400  \n",
      "8          1780        23400        234000  \n",
      "9          1890        26670        266700  \n",
      "10         2100        41280        412800  \n",
      "11         1760        30020        300200  \n"
     ]
    }
   ],
   "source": [
    "print(sample_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0     2500\n",
      "1     2630\n",
      "2     2140\n",
      "3     3400\n",
      "4     3600\n",
      "5     2760\n",
      "6     2980\n",
      "7     3700\n",
      "8     3540\n",
      "9     1990\n",
      "10    2340\n",
      "11    2900\n",
      "Name: facecream, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(sample_data['facecream'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "profitList = sample_df['total_profit'].tolist()\n",
    "monthList = sample_df['month_number'].tolist()\n",
    "plt.plot(monthList, profitList, label= 'Month wise profits')\n",
    "plt.xlabel('MOnth')\n",
    "plt.ylabel('profit')\n",
    "plt.yticks([100000, 200000, 300000, 400000, 500000])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = {\n",
    "    \n",
    "    \"language\": \"python\",\n",
    "    \"code\": \"print('hello')\",\n",
    "    \"input\": [1,2,3,4]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'{\"language\": \"python\", \"code\": \"print(\\'hello\\')\", \"input\": [1, 2, 3, 4]}'"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "json.dumps(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "json_string = '[{\"name\": \"jack\", \"phone\":58475893275}]'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = json.loads(json_string)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'name': 'jack', 'phone': 58475893275}]\n"
     ]
    }
   ],
   "source": [
    "print(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"sample.json\", \"r\") as file:\n",
    "    contents = json.load(file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'name': 'python', 'phone': 9876543210}]\n"
     ]
    }
   ],
   "source": [
    "print(contents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "from selenium import webdriver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "browser = webdriver.Chrome('/Users/himankbhalla/Downloads/chromedriver_chrome77')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "browser.get(\"https://codechef.com\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "username = browser.find_element_by_id('edit-name')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "password = browser.find_element_by_id('edit-pass')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "username.send_keys(\"himankb\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "from getpass import getpass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter password········\n"
     ]
    }
   ],
   "source": [
    "password.send_keys(getpass(\"Enter password\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "login_button = browser.find_element_by_id(\"edit-submit\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "login_button.click()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "browser.get(\"https://codechef.com/submit/TEST\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "browser.find_element_by_id(\"edit_area_toggle_checkbox_edit-program\").click()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "textArea = browser.find_element_by_id(\"edit-program\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"code.py\", 'r') as f:\n",
    "    code = f.read()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t = int(input())\n",
      "while t != 42:\n",
      "    print(t)\n",
      "    t = int(input())\n",
      "else:\n",
      "    pass\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(code)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "textArea.send_keys(code)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "browser.find_element_by_id(\"edit-submit-1\").click()"
   ]
  },
  {
   "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": "3.6.5rc1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}