MasteringPythonZeroToHero_part2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Structures "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "L = [1,3,4.9,\"name\",3]\n",
    "T = (1,3,4.9,\"name\",3)\n",
    "S = {1,3,4.9,\"name\",3}\n",
    "D = {23:\"twothree\",'B':43,'C':'CCD'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The type of L is  <class 'list'>\n",
      "The type of T is  <class 'tuple'>\n",
      "The type of S is  <class 'set'>\n",
      "The type of D is  <class 'dict'>\n"
     ]
    }
   ],
   "source": [
    "print(\"The type of L is \", type(L))\n",
    "print(\"The type of T is \", type(T))\n",
    "print(\"The type of S is \", type(S))\n",
    "print(\"The type of D is \", type(D))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n",
      "3\n",
      "True\n",
      "twothree\n"
     ]
    }
   ],
   "source": [
    "print(L[1])\n",
    "print(T[1])\n",
    "print(3 in S)\n",
    "print(D[23])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "43\n"
     ]
    }
   ],
   "source": [
    "print(D['B'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1, 3, 4.9, 'name'}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "S"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 3, 4.9, 'name', 3]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[3, 4.9]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L[1:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[3, 'name', 4.9, 3, 1]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L[::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 3, 4.9)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 3, 4.9, 'name', 3]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "L = L + [\"how\",\"are\",6,\"you\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 3, 4.9, 'name', 3, 'how', 'are', 6, 'you']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "L.append(6.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 3, 4.9, 'name', 3, 'how', 'are', 6, 'you', 6.8]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "T2 = ('a','b',45)\n",
    "T3 = T+T2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 3, 4.9, 'name', 3, 'a', 'b', 45)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1, 3, 4.9, 'name'}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "S"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "S.add(56)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1, 3, 4.9, 56, 'name'}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "S"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "S.update({23,\"game\",1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1, 23, 3, 4.9, 56, 'game', 'name'}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "S"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{23: 'twothree', 'B': 43, 'C': 'CCD'}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "D['newKey'] = \"newValue\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{23: 'twothree', 'B': 43, 'C': 'CCD', 'newKey': 'newValue'}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "D2 = {\"y\":\"YY\",\"z\":10}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 3, 4.9, 'name', 3, 'how', 'are', 6, 'you', 6.8]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "del L[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 3, 4.9, 3, 'how', 'are', 6, 'you', 6.8]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1, 23, 3, 4.9, 56, 'game', 'name'}"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "S"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "S.remove('game')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1, 23, 3, 4.9, 56, 'name'}"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "S"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{23: 'twothree', 'B': 43, 'C': 'CCD', 'newKey': 'newValue'}"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "del D['C']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{23: 'twothree', 'B': 43, 'newKey': 'newValue'}"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "unsupported operand type(s) for +: 'dict' and 'dict'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-35-02f7553b163b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mD\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mD\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mD2\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'dict' and 'dict'"
     ]
    }
   ],
   "source": [
    "D = D + D2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "D.update(D2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{23: 'twothree', 'B': 43, 'newKey': 'newValue', 'y': 'YY', 'z': 10}"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 3, 4.9, 3, 'how', 'are', 6, 'you', 6.8]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "L2 = L"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 3, 4.9, 3, 'how', 'are', 6, 'you', 6.8]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "L2[2] = \"four point nine\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 3, 'four point nine', 3, 'how', 'are', 6, 'you', 6.8]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 3, 'four point nine', 3, 'how', 'are', 6, 'you', 6.8]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "L2 = L.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 3, 'four point nine', 3, 'how', 'are', 6, 'you', 6.8]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "L2[2] = 4.9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 3, 4.9, 3, 'how', 'are', 6, 'you', 6.8]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 3, 'four point nine', 3, 'how', 'are', 6, 'you', 6.8]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 3, 'four point nine', 3, 'how', 'are', 6, 'you', 6.8]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "L3 = L[1:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[3, 'four point nine', 3, 'how']"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "L3[0] = \"three\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 3, 'four point nine', 3, 'how', 'are', 6, 'you', 6.8]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on built-in function append:\n",
      "\n",
      "append(object, /) method of builtins.list instance\n",
      "    Append object to the end of the list.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(L.append)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "L.clear?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "L.pop?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "L.reverse()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[6.8, 'you', 6, 'are', 'how', 3, 'four point nine', 3, 1]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 3, 'four point nine', 3, 'how', 'are', 6, 'you', 6.8]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L[::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_items([(23, 'twothree'), ('B', 43), ('newKey', 'newValue'), ('y', 'YY'), ('z', 10)])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D.items()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[6.8, 'you', 6, 'are', 'how', 3, 'four point nine', 3, 1]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 3, 4.9, 'name', 3)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1, 23, 3, 4.9, 56, 'name'}"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "S"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{23: 'twothree', 'B': 43, 'newKey': 'newValue', 'y': 'YY', 'z': 10}"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "D2 = {'A':L,'B':T,'C':S,'D':D}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'are'"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D2['A'][3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "K = D2['D']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{23: 'twothree', 'B': 43, 'newKey': 'newValue', 'y': 'YY', 'z': 10}"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "23 twothree\n",
      "B 43\n",
      "newKey newValue\n",
      "y YY\n",
      "z 10\n"
     ]
    }
   ],
   "source": [
    "for x in K:\n",
    "    print(x,K[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "L3 = [L,T,D,23,\"game\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(L3[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "L3 = [x**2 for x in range(10)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "S3 = {x**2 for x in range(2,20,3)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{4, 25, 64, 121, 196, 289}"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "S3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{23: 'twothree', 'B': 43, 'newKey': 'newValue', 'y': 'YY', 'z': 10}"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "23 in D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "43 in D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"Let say you are a teacher and you have different student\n",
    "records containing id of a student and the marks list in each subject\n",
    "where different students have taken different number of subjects. All\n",
    "these records are in hard copy. You want to enter all the data in computer\n",
    "and want to compute the average marks of each student and display\"\"\"\n",
    "\n",
    "def getDataFromUser():\n",
    "    D = {}\n",
    "    while True:\n",
    "        studentId = input(\"Enter student ID: \")\n",
    "        marksList = input(\"Enter the marks by comma separted values: \")\n",
    "        moreStudents = input('Enter \"no\" to quit insertion: ')\n",
    "        if studentId in D:\n",
    "            print(studentId, \"is already insterted\")\n",
    "        else:\n",
    "            D[studentId] = marksList.split(\",\")\n",
    "        if moreStudents.lower() == \"no\":\n",
    "            return D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter student ID: 12\n",
      "Enter the marks by comma separted values: 56,45,13\n",
      "Enter \"no\" to quit insertion: asdf\n",
      "Enter student ID: 45\n",
      "Enter the marks by comma separted values: 44,55,66,77,4\n",
      "Enter \"no\" to quit insertion: asdfa\n",
      "Enter student ID: 12\n",
      "Enter the marks by comma separted values: 45,45\n",
      "Enter \"no\" to quit insertion: asdfa\n",
      "12 is already insterted\n",
      "Enter student ID: 23\n",
      "Enter the marks by comma separted values: 45,45\n",
      "Enter \"no\" to quit insertion: no\n"
     ]
    }
   ],
   "source": [
    "studentData = getDataFromUser()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'12': ['56', '45', '13'],\n",
       " '45': ['44', '55', '66', '77', '4'],\n",
       " '23': ['45', '45']}"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "studentData"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getAvgMarks(D):\n",
    "    avgMarks = {}\n",
    "    for x in D:\n",
    "        L = D[x]\n",
    "        s = 0\n",
    "        for marks in L:\n",
    "            s += int(marks)\n",
    "        avgMarks[x] = s/len(L)\n",
    "    return avgMarks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "avgM = getAvgMarks(studentData)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'12': 38.0, '45': 49.2, '23': 45.0}"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avgM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Student : 12 got avg Marks as:  38.0\n",
      "Student : 45 got avg Marks as:  49.2\n",
      "Student : 23 got avg Marks as:  45.0\n"
     ]
    }
   ],
   "source": [
    "for x in avgM:\n",
    "    print(\"Student :\",x,\"got avg Marks as: \",avgM[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.array([1,2,3,5,7],dtype='i')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "b = np.array((2,3,5),dtype='f')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3 5 7]\n"
     ]
    }
   ],
   "source": [
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int32')"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float32')"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "a = np.array([[1,2,3],[4,5,6]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[0,2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "B = np.array([[1,2,3,-1],[2,4,5,9]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B[1,2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "C = np.array([[[1,2,3],[4,5,6],[0,0,-1]],[[-1,-2,-3],[-4,-5,-6],[0,0,1]]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "C.shape[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-3"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "C[1,0,2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(C)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "A = np.array([2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "B = np.array(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "C.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "72"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "C.nbytes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[20 23 26 29 32 35 38 41 44 47 50 53 56 59 62 65 68 71 74 77 80 83 86 89\n",
      " 92 95 98]\n"
     ]
    }
   ],
   "source": [
    "A = np.arange(20,100,3) # for i in range(20,100,3)\n",
    "print(A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23\n",
      " 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47\n",
      " 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71\n",
      " 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95\n",
      " 96 97 98 99]\n"
     ]
    }
   ],
   "source": [
    "print(A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "range(0, 10)\n"
     ]
    }
   ],
   "source": [
    "print(range(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n"
     ]
    }
   ],
   "source": [
    "print(list(range(10)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3 9 5 7 1 0 6 2 4 8]\n"
     ]
    }
   ],
   "source": [
    "A = np.random.permutation(np.arange(10))\n",
    "print(A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.randint?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "v = np.random.randint(20,300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "int"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "A = np.random.rand(1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 5.,  8.,  8., 10.,  6.,  7., 10., 11., 10., 14.,  9., 10., 13.,\n",
       "         5., 12.,  8.,  5.,  6.,  9., 19., 13., 15., 10., 17., 11.,  7.,\n",
       "         8., 13.,  8.,  6., 15.,  7.,  7., 10., 10.,  5., 13., 11.,  7.,\n",
       "        12., 10.,  6., 12., 12., 19., 11., 12.,  8.,  9., 13.,  8., 10.,\n",
       "         9.,  8.,  8., 11., 12., 12.,  4., 15., 15.,  9., 12.,  7., 15.,\n",
       "        10.,  8.,  8.,  5., 10., 13.,  6.,  6., 11.,  8., 10.,  7., 14.,\n",
       "        10.,  9., 11., 15., 12.,  8., 12., 10., 10.,  7.,  9., 12., 16.,\n",
       "        10.,  6., 11.,  9.,  4.,  8., 11., 15., 12.]),\n",
       " array([2.13332953e-04, 1.02065081e-02, 2.01996832e-02, 3.01928583e-02,\n",
       "        4.01860335e-02, 5.01792086e-02, 6.01723837e-02, 7.01655589e-02,\n",
       "        8.01587340e-02, 9.01519091e-02, 1.00145084e-01, 1.10138259e-01,\n",
       "        1.20131434e-01, 1.30124610e-01, 1.40117785e-01, 1.50110960e-01,\n",
       "        1.60104135e-01, 1.70097310e-01, 1.80090485e-01, 1.90083660e-01,\n",
       "        2.00076836e-01, 2.10070011e-01, 2.20063186e-01, 2.30056361e-01,\n",
       "        2.40049536e-01, 2.50042711e-01, 2.60035886e-01, 2.70029061e-01,\n",
       "        2.80022237e-01, 2.90015412e-01, 3.00008587e-01, 3.10001762e-01,\n",
       "        3.19994937e-01, 3.29988112e-01, 3.39981287e-01, 3.49974462e-01,\n",
       "        3.59967638e-01, 3.69960813e-01, 3.79953988e-01, 3.89947163e-01,\n",
       "        3.99940338e-01, 4.09933513e-01, 4.19926688e-01, 4.29919863e-01,\n",
       "        4.39913039e-01, 4.49906214e-01, 4.59899389e-01, 4.69892564e-01,\n",
       "        4.79885739e-01, 4.89878914e-01, 4.99872089e-01, 5.09865264e-01,\n",
       "        5.19858440e-01, 5.29851615e-01, 5.39844790e-01, 5.49837965e-01,\n",
       "        5.59831140e-01, 5.69824315e-01, 5.79817490e-01, 5.89810666e-01,\n",
       "        5.99803841e-01, 6.09797016e-01, 6.19790191e-01, 6.29783366e-01,\n",
       "        6.39776541e-01, 6.49769716e-01, 6.59762891e-01, 6.69756067e-01,\n",
       "        6.79749242e-01, 6.89742417e-01, 6.99735592e-01, 7.09728767e-01,\n",
       "        7.19721942e-01, 7.29715117e-01, 7.39708292e-01, 7.49701468e-01,\n",
       "        7.59694643e-01, 7.69687818e-01, 7.79680993e-01, 7.89674168e-01,\n",
       "        7.99667343e-01, 8.09660518e-01, 8.19653693e-01, 8.29646869e-01,\n",
       "        8.39640044e-01, 8.49633219e-01, 8.59626394e-01, 8.69619569e-01,\n",
       "        8.79612744e-01, 8.89605919e-01, 8.99599094e-01, 9.09592270e-01,\n",
       "        9.19585445e-01, 9.29578620e-01, 9.39571795e-01, 9.49564970e-01,\n",
       "        9.59558145e-01, 9.69551320e-01, 9.79544496e-01, 9.89537671e-01,\n",
       "        9.99530846e-01]),\n",
       " <a list of 100 Patch objects>)"
      ]
     },
     "execution_count": 53,
     "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.hist(A,bins=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([1.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00,\n",
       "        1.000e+00, 1.000e+00, 0.000e+00, 1.000e+00, 2.000e+00, 0.000e+00,\n",
       "        1.000e+00, 1.000e+00, 2.000e+00, 3.000e+00, 2.000e+00, 2.000e+00,\n",
       "        2.000e+00, 3.000e+00, 4.000e+00, 6.000e+00, 7.000e+00, 8.000e+00,\n",
       "        6.000e+00, 7.000e+00, 1.100e+01, 1.100e+01, 1.000e+01, 1.100e+01,\n",
       "        1.700e+01, 9.000e+00, 1.300e+01, 3.100e+01, 3.200e+01, 2.900e+01,\n",
       "        3.600e+01, 3.400e+01, 3.800e+01, 4.500e+01, 6.000e+01, 4.200e+01,\n",
       "        5.700e+01, 5.600e+01, 9.200e+01, 9.100e+01, 1.140e+02, 1.240e+02,\n",
       "        1.330e+02, 1.520e+02, 1.600e+02, 1.470e+02, 1.760e+02, 1.830e+02,\n",
       "        2.040e+02, 2.150e+02, 2.430e+02, 2.800e+02, 3.490e+02, 3.350e+02,\n",
       "        3.430e+02, 4.030e+02, 3.880e+02, 4.560e+02, 4.850e+02, 4.930e+02,\n",
       "        5.240e+02, 5.760e+02, 6.510e+02, 6.400e+02, 6.800e+02, 7.730e+02,\n",
       "        8.100e+02, 8.000e+02, 8.820e+02, 9.300e+02, 1.032e+03, 1.055e+03,\n",
       "        1.045e+03, 1.107e+03, 1.175e+03, 1.136e+03, 1.241e+03, 1.312e+03,\n",
       "        1.326e+03, 1.368e+03, 1.395e+03, 1.402e+03, 1.472e+03, 1.426e+03,\n",
       "        1.549e+03, 1.608e+03, 1.599e+03, 1.615e+03, 1.657e+03, 1.642e+03,\n",
       "        1.677e+03, 1.660e+03, 1.743e+03, 1.768e+03, 1.734e+03, 1.700e+03,\n",
       "        1.720e+03, 1.826e+03, 1.783e+03, 1.691e+03, 1.658e+03, 1.597e+03,\n",
       "        1.664e+03, 1.705e+03, 1.622e+03, 1.619e+03, 1.542e+03, 1.469e+03,\n",
       "        1.483e+03, 1.402e+03, 1.376e+03, 1.378e+03, 1.287e+03, 1.325e+03,\n",
       "        1.300e+03, 1.150e+03, 1.161e+03, 1.059e+03, 1.059e+03, 9.860e+02,\n",
       "        9.960e+02, 9.060e+02, 8.700e+02, 8.320e+02, 8.060e+02, 7.340e+02,\n",
       "        6.750e+02, 6.220e+02, 5.950e+02, 6.030e+02, 5.330e+02, 5.340e+02,\n",
       "        4.970e+02, 4.400e+02, 4.200e+02, 3.770e+02, 3.560e+02, 3.100e+02,\n",
       "        3.200e+02, 2.700e+02, 2.540e+02, 2.210e+02, 2.180e+02, 1.820e+02,\n",
       "        1.700e+02, 1.670e+02, 1.440e+02, 1.530e+02, 1.220e+02, 1.190e+02,\n",
       "        8.500e+01, 7.700e+01, 7.100e+01, 7.500e+01, 6.800e+01, 4.500e+01,\n",
       "        5.400e+01, 3.800e+01, 3.700e+01, 3.800e+01, 3.100e+01, 1.900e+01,\n",
       "        2.600e+01, 2.100e+01, 1.200e+01, 1.900e+01, 1.400e+01, 1.200e+01,\n",
       "        9.000e+00, 1.000e+01, 6.000e+00, 9.000e+00, 9.000e+00, 6.000e+00,\n",
       "        4.000e+00, 3.000e+00, 3.000e+00, 2.000e+00, 3.000e+00, 0.000e+00,\n",
       "        0.000e+00, 1.000e+00, 3.000e+00, 0.000e+00, 0.000e+00, 1.000e+00,\n",
       "        0.000e+00, 0.000e+00, 2.000e+00, 0.000e+00, 0.000e+00, 0.000e+00,\n",
       "        0.000e+00, 1.000e+00]),\n",
       " array([-4.43054629, -4.38685293, -4.34315956, -4.2994662 , -4.25577283,\n",
       "        -4.21207946, -4.1683861 , -4.12469273, -4.08099936, -4.037306  ,\n",
       "        -3.99361263, -3.94991927, -3.9062259 , -3.86253253, -3.81883917,\n",
       "        -3.7751458 , -3.73145243, -3.68775907, -3.6440657 , -3.60037234,\n",
       "        -3.55667897, -3.5129856 , -3.46929224, -3.42559887, -3.3819055 ,\n",
       "        -3.33821214, -3.29451877, -3.25082541, -3.20713204, -3.16343867,\n",
       "        -3.11974531, -3.07605194, -3.03235857, -2.98866521, -2.94497184,\n",
       "        -2.90127848, -2.85758511, -2.81389174, -2.77019838, -2.72650501,\n",
       "        -2.68281165, -2.63911828, -2.59542491, -2.55173155, -2.50803818,\n",
       "        -2.46434481, -2.42065145, -2.37695808, -2.33326472, -2.28957135,\n",
       "        -2.24587798, -2.20218462, -2.15849125, -2.11479788, -2.07110452,\n",
       "        -2.02741115, -1.98371779, -1.94002442, -1.89633105, -1.85263769,\n",
       "        -1.80894432, -1.76525095, -1.72155759, -1.67786422, -1.63417086,\n",
       "        -1.59047749, -1.54678412, -1.50309076, -1.45939739, -1.41570402,\n",
       "        -1.37201066, -1.32831729, -1.28462393, -1.24093056, -1.19723719,\n",
       "        -1.15354383, -1.10985046, -1.0661571 , -1.02246373, -0.97877036,\n",
       "        -0.935077  , -0.89138363, -0.84769026, -0.8039969 , -0.76030353,\n",
       "        -0.71661017, -0.6729168 , -0.62922343, -0.58553007, -0.5418367 ,\n",
       "        -0.49814333, -0.45444997, -0.4107566 , -0.36706324, -0.32336987,\n",
       "        -0.2796765 , -0.23598314, -0.19228977, -0.1485964 , -0.10490304,\n",
       "        -0.06120967, -0.01751631,  0.02617706,  0.06987043,  0.11356379,\n",
       "         0.15725716,  0.20095053,  0.24464389,  0.28833726,  0.33203062,\n",
       "         0.37572399,  0.41941736,  0.46311072,  0.50680409,  0.55049746,\n",
       "         0.59419082,  0.63788419,  0.68157755,  0.72527092,  0.76896429,\n",
       "         0.81265765,  0.85635102,  0.90004438,  0.94373775,  0.98743112,\n",
       "         1.03112448,  1.07481785,  1.11851122,  1.16220458,  1.20589795,\n",
       "         1.24959131,  1.29328468,  1.33697805,  1.38067141,  1.42436478,\n",
       "         1.46805815,  1.51175151,  1.55544488,  1.59913824,  1.64283161,\n",
       "         1.68652498,  1.73021834,  1.77391171,  1.81760508,  1.86129844,\n",
       "         1.90499181,  1.94868517,  1.99237854,  2.03607191,  2.07976527,\n",
       "         2.12345864,  2.16715201,  2.21084537,  2.25453874,  2.2982321 ,\n",
       "         2.34192547,  2.38561884,  2.4293122 ,  2.47300557,  2.51669893,\n",
       "         2.5603923 ,  2.60408567,  2.64777903,  2.6914724 ,  2.73516577,\n",
       "         2.77885913,  2.8225525 ,  2.86624586,  2.90993923,  2.9536326 ,\n",
       "         2.99732596,  3.04101933,  3.0847127 ,  3.12840606,  3.17209943,\n",
       "         3.21579279,  3.25948616,  3.30317953,  3.34687289,  3.39056626,\n",
       "         3.43425963,  3.47795299,  3.52164636,  3.56533972,  3.60903309,\n",
       "         3.65272646,  3.69641982,  3.74011319,  3.78380656,  3.82749992,\n",
       "         3.87119329,  3.91488665,  3.95858002,  4.00227339,  4.04596675,\n",
       "         4.08966012,  4.13335349,  4.17704685,  4.22074022,  4.26443358,\n",
       "         4.30812695]),\n",
       " <a list of 200 Patch objects>)"
      ]
     },
     "execution_count": 54,
     "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": [
    "B = np.random.randn(100000)\n",
    "plt.hist(B,bins=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "C = np.random.rand(2,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.4435152 , 0.69440385, 0.31639453],\n",
       "       [0.18575164, 0.41289566, 0.46056886]])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "C"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "C.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "C = np.random.rand(2,3,4,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "C.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "D = np.arange(100).reshape(4,25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4, 25)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "D = np.arange(100).reshape(4,5,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4, 5, 5)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.zeros?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.ones?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "A = np.arange(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3 4 5 6 7 8 9]\n"
     ]
    }
   ],
   "source": [
    "b = A[3:10]\n",
    "print(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "b[0] = -1200"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1200,     4,     5,     6,     7,     8,     9])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([    0,     1,     2, -1200,     4,     5,     6,     7,     8,\n",
       "           9,    10,    11,    12,    13,    14,    15,    16,    17,\n",
       "          18,    19,    20,    21,    22,    23,    24,    25,    26,\n",
       "          27,    28,    29,    30,    31,    32,    33,    34,    35,\n",
       "          36,    37,    38,    39,    40,    41,    42,    43,    44,\n",
       "          45,    46,    47,    48,    49,    50,    51,    52,    53,\n",
       "          54,    55,    56,    57,    58,    59,    60,    61,    62,\n",
       "          63,    64,    65,    66,    67,    68,    69,    70,    71,\n",
       "          72,    73,    74,    75,    76,    77,    78,    79,    80,\n",
       "          81,    82,    83,    84,    85,    86,    87,    88,    89,\n",
       "          90,    91,    92,    93,    94,    95,    96,    97,    98,\n",
       "          99])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "b = A[3:10].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80,\n",
       "       85, 90, 95])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A[::5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([99, 94, 89, 84, 79, 74, 69, 64, 59, 54, 49, 44, 39, 34, 29, 24, 19,\n",
       "       14,  9,  4])"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A[::-5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   99,    98,    97,    96,    95,    94,    93,    92,    91,\n",
       "          90,    89,    88,    87,    86,    85,    84,    83,    82,\n",
       "          81,    80,    79,    78,    77,    76,    75,    74,    73,\n",
       "          72,    71,    70,    69,    68,    67,    66,    65,    64,\n",
       "          63,    62,    61,    60,    59,    58,    57,    56,    55,\n",
       "          54,    53,    52,    51,    50,    49,    48,    47,    46,\n",
       "          45,    44,    43,    42,    41,    40,    39,    38,    37,\n",
       "          36,    35,    34,    33,    32,    31,    30,    29,    28,\n",
       "          27,    26,    25,    24,    23,    22,    21,    20,    19,\n",
       "          18,    17,    16,    15,    14,    13,    12,    11,    10,\n",
       "           9,     8,     7,     6,     5,     4, -1200,     2,     1,\n",
       "           0])"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A[::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([    0,     1,     2, -1200,     4,     5,     6,     7,     8,\n",
       "           9,    10,    11,    12,    13,    14,    15,    16,    17,\n",
       "          18,    19,    20,    21,    22,    23,    24,    25,    26,\n",
       "          27,    28,    29,    30,    31,    32,    33,    34,    35,\n",
       "          36,    37,    38,    39,    40,    41,    42,    43,    44,\n",
       "          45,    46,    47,    48,    49,    50,    51,    52,    53,\n",
       "          54,    55,    56,    57,    58,    59,    60,    61,    62,\n",
       "          63,    64,    65,    66,    67,    68,    69,    70,    71,\n",
       "          72,    73,    74,    75,    76,    77,    78,    79,    80,\n",
       "          81,    82,    83,    84,    85,    86,    87,    88,    89,\n",
       "          90,    91,    92,    93,    94,    95,    96,    97,    98,\n",
       "          99])"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "idx = np.argwhere(A==-1200)[0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "A[idx] = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "       17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
       "       34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,\n",
       "       51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67,\n",
       "       68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84,\n",
       "       85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99])"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "A = np.round(10*np.random.rand(5,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 3.,  6., 10.,  4.],\n",
       "       [ 6.,  4.,  8.,  8.],\n",
       "       [ 5.,  2.,  7.,  8.],\n",
       "       [ 5., 10.,  8.,  0.],\n",
       "       [ 4.,  1.,  5.,  7.]])"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.0"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A[1,2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6., 4., 8., 8.])"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A[1,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 6.,  4.,  2., 10.,  1.])"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[8., 8.],\n",
       "       [7., 8.]])"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A[1:3,2:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 3.,  6., 10.,  4.],\n",
       "       [ 6.,  4.,  8.,  8.],\n",
       "       [ 5.,  2.,  7.,  8.],\n",
       "       [ 5., 10.,  8.,  0.],\n",
       "       [ 4.,  1.,  5.,  7.]])"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 3.,  6.,  5.,  5.,  4.],\n",
       "       [ 6.,  4.,  2., 10.,  1.],\n",
       "       [10.,  8.,  7.,  8.,  5.],\n",
       "       [ 4.,  8.,  8.,  0.,  7.]])"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy.linalg as la"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-22.3370061 ,   0.92592966,  18.50388119],\n",
       "       [  3.82344926,   1.64838003,  -4.78140642],\n",
       "       [ 14.16692615,  -1.53539266,  -9.7793494 ]])"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "la.inv(np.random.rand(3,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 3.,  6., 10.,  4.],\n",
       "       [ 6.,  4.,  8.,  8.],\n",
       "       [ 5.,  2.,  7.,  8.],\n",
       "       [ 5., 10.,  8.,  0.],\n",
       "       [ 4.,  1.,  5.,  7.]])"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "A.sort(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 3.,  1.,  5.,  0.],\n",
       "       [ 4.,  2.,  7.,  4.],\n",
       "       [ 5.,  4.,  8.,  7.],\n",
       "       [ 5.,  6.,  8.,  8.],\n",
       "       [ 6., 10., 10.,  8.]])"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "A.sort(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  1.,  3.,  5.],\n",
       "       [ 2.,  4.,  4.,  7.],\n",
       "       [ 4.,  5.,  7.,  8.],\n",
       "       [ 5.,  6.,  8.,  8.],\n",
       "       [ 6.,  8., 10., 10.]])"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "A = np.arange(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "B = A[[3,5,6]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 5, 6])"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "B[0] = -4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-4,  5,  6])"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "       17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
       "       34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,\n",
       "       51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67,\n",
       "       68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84,\n",
       "       85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99])"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "B = A[A<40]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "       17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
       "       34, 35, 36, 37, 38, 39])"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "B = A[(A<40) & (A>30)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([31, 32, 33, 34, 35, 36, 37, 38, 39])"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "# &, and \n",
    "# |, or\n",
    "# ~, not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "A = np.round(10*np.random.rand(2,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2., 7., 5.],\n",
       "       [0., 0., 7.]])"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 5., 10.,  8.],\n",
       "       [ 3.,  3., 10.]])"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A+3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2., 7., 5.],\n",
       "       [1., 1., 8.]])"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A+(np.arange(2).reshape(2,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2., 7., 5.],\n",
       "       [0., 0., 7.]])"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "B = np.round(10*np.random.rand(2,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2., 7., 5.],\n",
       "       [0., 0., 7.]])"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4., 2.],\n",
       "       [8., 1.]])"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "C = np.hstack((A,B))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2., 7., 5., 4., 2.],\n",
       "       [0., 0., 7., 8., 1.]])"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "C"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "A = np.random.permutation(np.arange(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 4, 3, 6, 9, 8, 5, 2, 7])"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "A.sort()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sort(A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "A.sort()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "A = A[::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9, 8, 7, 6, 5, 4, 3, 2, 1, 0])"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "A = np.array([\"abc\",'howare you','u765','13er'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "A.sort()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['13er', 'abc', 'howare you', 'u765'], dtype='<U10')"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "307 ms ± 22.2 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
      "2.77 ms ± 260 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "B = np.random.rand(1000000)\n",
    "%timeit sum(B)\n",
    "%timeit np.sum(B)  # B.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mySum(G):\n",
    "    s = 0\n",
    "    for x in G:\n",
    "        s+=x\n",
    "    return s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.24.2\n"
     ]
    }
   ],
   "source": [
    "print(pd.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "A = pd.Series([2,3,4,5],index=['a','b','c','d'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(A.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['a', 'b', 'c', 'd'], dtype='object')"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A['a']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    2\n",
       "b    3\n",
       "c    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A['a':'c']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "grads_dict = {'A':4,'B':3.5,'C':3,'D':2.5}\n",
    "grads = pd.Series(grads_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4. , 3.5, 3. , 2.5])"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grads.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "marks_dict = {'A':85,'B':75,'C':65,'D':55}\n",
    "marks = pd.Series(marks_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    85\n",
       "B    75\n",
       "C    65\n",
       "D    55\n",
       "dtype: int64"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "marks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "85"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "marks['A']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    85\n",
       "B    75\n",
       "dtype: int64"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "marks[0:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    85\n",
       "B    75\n",
       "C    65\n",
       "D    55\n",
       "dtype: int64"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "marks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    4.0\n",
       "B    3.5\n",
       "C    3.0\n",
       "D    2.5\n",
       "dtype: float64"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grads"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "D = pd.DataFrame({'Marks':marks,'Grades':grads})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Marks</th>\n",
       "      <th>Grades</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>85</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>75</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>65</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>55</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Marks  Grades\n",
       "A     85     4.0\n",
       "B     75     3.5\n",
       "C     65     3.0\n",
       "D     55     2.5"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
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       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Marks</th>\n",
       "      <td>85.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Grades</th>\n",
       "      <td>4.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           A     B     C     D\n",
       "Marks   85.0  75.0  65.0  55.0\n",
       "Grades   4.0   3.5   3.0   2.5"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Marks</th>\n",
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       "  <tbody>\n",
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       "      <th>B</th>\n",
       "      <td>75</td>\n",
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       "      <th>D</th>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Marks  Grades\n",
       "A     85     4.0\n",
       "B     75     3.5\n",
       "C     65     3.0\n",
       "D     55     2.5"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[85. ,  4. ],\n",
       "       [75. ,  3.5],\n",
       "       [65. ,  3. ],\n",
       "       [55. ,  2.5]])"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "65.0"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D.values[2,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Marks', 'Grades'], dtype='object')"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Marks</th>\n",
       "      <th>Grades</th>\n",
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       "  </thead>\n",
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       "      <td>4.0</td>\n",
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       "      <th>B</th>\n",
       "      <td>75</td>\n",
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       "      <th>C</th>\n",
       "      <td>65</td>\n",
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       "    <tr>\n",
       "      <th>D</th>\n",
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       "      <td>2.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Marks  Grades\n",
       "A     85     4.0\n",
       "B     75     3.5\n",
       "C     65     3.0\n",
       "D     55     2.5"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "D['ScaledMarks'] = 100*(D['Marks']/90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Marks</th>\n",
       "      <th>Grades</th>\n",
       "      <th>ScaledMarks</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>85</td>\n",
       "      <td>4.0</td>\n",
       "      <td>94.444444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>75</td>\n",
       "      <td>3.5</td>\n",
       "      <td>83.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>65</td>\n",
       "      <td>3.0</td>\n",
       "      <td>72.222222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>55</td>\n",
       "      <td>2.5</td>\n",
       "      <td>61.111111</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Marks  Grades  ScaledMarks\n",
       "A     85     4.0    94.444444\n",
       "B     75     3.5    83.333333\n",
       "C     65     3.0    72.222222\n",
       "D     55     2.5    61.111111"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [],
   "source": [
    "del D['ScaledMarks']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>4.0</td>\n",
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       "      <td>3.5</td>\n",
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       "      <th>C</th>\n",
       "      <td>65</td>\n",
       "      <td>3.0</td>\n",
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       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>55</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Marks  Grades\n",
       "A     85     4.0\n",
       "B     75     3.5\n",
       "C     65     3.0\n",
       "D     55     2.5"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "G = D[D['Marks']>70]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Marks</th>\n",
       "      <th>Grades</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>85</td>\n",
       "      <td>4.0</td>\n",
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       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>75</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Marks  Grades\n",
       "A     85     4.0\n",
       "B     75     3.5"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "G"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [],
   "source": [
    "A = pd.DataFrame([{'a':1,'b':4},{'b':-3,'c':9}])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-3</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     a  b    c\n",
       "0  1.0  4  NaN\n",
       "1  NaN -3  9.0"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "        vertical-align: top;\n",
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       "\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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-3</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     a  b    c\n",
       "0  1.0  4  0.0\n",
       "1  0.0 -3  9.0"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [],
   "source": [
    "A.dropna?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "A = pd.Series(['a','b','c'],index=[1,3,5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'a'"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    b\n",
       "5    c\n",
       "dtype: object"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A[1:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    a\n",
       "3    b\n",
       "dtype: object"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A.loc[1:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    b\n",
       "5    c\n",
       "dtype: object"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A.iloc[1:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "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>Marks</th>\n",
       "      <th>Grades</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>85</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>75</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>65</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>55</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Marks  Grades\n",
       "A     85     4.0\n",
       "B     75     3.5\n",
       "C     65     3.0\n",
       "D     55     2.5"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Marks     65.0\n",
       "Grades     3.0\n",
       "Name: C, dtype: float64"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D.iloc[2,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "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>Marks</th>\n",
       "      <th>Grades</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>55</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>65</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>75</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>85</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Marks  Grades\n",
       "D     55     2.5\n",
       "C     65     3.0\n",
       "B     75     3.5\n",
       "A     85     4.0"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D.iloc[::-1,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.impute import SimpleImputer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('E:/covid/covid_19_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>SNo</th>\n",
       "      <th>ObservationDate</th>\n",
       "      <th>Province/State</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Last Update</th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1/22/2020</td>\n",
       "      <td>Anhui</td>\n",
       "      <td>Mainland China</td>\n",
       "      <td>1/22/2020 17:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1/22/2020</td>\n",
       "      <td>Beijing</td>\n",
       "      <td>Mainland China</td>\n",
       "      <td>1/22/2020 17:00</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1/22/2020</td>\n",
       "      <td>Chongqing</td>\n",
       "      <td>Mainland China</td>\n",
       "      <td>1/22/2020 17:00</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1/22/2020</td>\n",
       "      <td>Fujian</td>\n",
       "      <td>Mainland China</td>\n",
       "      <td>1/22/2020 17:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1/22/2020</td>\n",
       "      <td>Gansu</td>\n",
       "      <td>Mainland China</td>\n",
       "      <td>1/22/2020 17:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>1/22/2020</td>\n",
       "      <td>Guangdong</td>\n",
       "      <td>Mainland China</td>\n",
       "      <td>1/22/2020 17:00</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>1/22/2020</td>\n",
       "      <td>Guangxi</td>\n",
       "      <td>Mainland China</td>\n",
       "      <td>1/22/2020 17:00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>1/22/2020</td>\n",
       "      <td>Guizhou</td>\n",
       "      <td>Mainland China</td>\n",
       "      <td>1/22/2020 17:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>1/22/2020</td>\n",
       "      <td>Hainan</td>\n",
       "      <td>Mainland China</td>\n",
       "      <td>1/22/2020 17:00</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>1/22/2020</td>\n",
       "      <td>Hebei</td>\n",
       "      <td>Mainland China</td>\n",
       "      <td>1/22/2020 17:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SNo ObservationDate Province/State  Country/Region      Last Update  \\\n",
       "0    1       1/22/2020          Anhui  Mainland China  1/22/2020 17:00   \n",
       "1    2       1/22/2020        Beijing  Mainland China  1/22/2020 17:00   \n",
       "2    3       1/22/2020      Chongqing  Mainland China  1/22/2020 17:00   \n",
       "3    4       1/22/2020         Fujian  Mainland China  1/22/2020 17:00   \n",
       "4    5       1/22/2020          Gansu  Mainland China  1/22/2020 17:00   \n",
       "5    6       1/22/2020      Guangdong  Mainland China  1/22/2020 17:00   \n",
       "6    7       1/22/2020        Guangxi  Mainland China  1/22/2020 17:00   \n",
       "7    8       1/22/2020        Guizhou  Mainland China  1/22/2020 17:00   \n",
       "8    9       1/22/2020         Hainan  Mainland China  1/22/2020 17:00   \n",
       "9   10       1/22/2020          Hebei  Mainland China  1/22/2020 17:00   \n",
       "\n",
       "   Confirmed  Deaths  Recovered  \n",
       "0          1       0          0  \n",
       "1         14       0          0  \n",
       "2          6       0          0  \n",
       "3          1       0          0  \n",
       "4          0       0          0  \n",
       "5         26       0          0  \n",
       "6          2       0          0  \n",
       "7          1       0          0  \n",
       "8          4       0          0  \n",
       "9          1       0          0  "
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['SNo','Last Update'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Confirmed</th>\n",
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       "      <th>Recovered</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/22/2020</td>\n",
       "      <td>Anhui</td>\n",
       "      <td>Mainland China</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/22/2020</td>\n",
       "      <td>Beijing</td>\n",
       "      <td>Mainland China</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/22/2020</td>\n",
       "      <td>Chongqing</td>\n",
       "      <td>Mainland China</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/22/2020</td>\n",
       "      <td>Fujian</td>\n",
       "      <td>Mainland China</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/22/2020</td>\n",
       "      <td>Gansu</td>\n",
       "      <td>Mainland China</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  ObservationDate Province/State  Country/Region  Confirmed  Deaths  Recovered\n",
       "0       1/22/2020          Anhui  Mainland China          1       0          0\n",
       "1       1/22/2020        Beijing  Mainland China         14       0          0\n",
       "2       1/22/2020      Chongqing  Mainland China          6       0          0\n",
       "3       1/22/2020         Fujian  Mainland China          1       0          0\n",
       "4       1/22/2020          Gansu  Mainland China          0       0          0"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.rename(columns={'ObservationDate':'Date','Province/State':'Province','Country/Region':'Country'},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Country</th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Deaths</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/22/2020</td>\n",
       "      <td>Anhui</td>\n",
       "      <td>Mainland China</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/22/2020</td>\n",
       "      <td>Beijing</td>\n",
       "      <td>Mainland China</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/22/2020</td>\n",
       "      <td>Chongqing</td>\n",
       "      <td>Mainland China</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/22/2020</td>\n",
       "      <td>Fujian</td>\n",
       "      <td>Mainland China</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/22/2020</td>\n",
       "      <td>Gansu</td>\n",
       "      <td>Mainland China</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date   Province         Country  Confirmed  Deaths  Recovered\n",
       "0  1/22/2020      Anhui  Mainland China          1       0          0\n",
       "1  1/22/2020    Beijing  Mainland China         14       0          0\n",
       "2  1/22/2020  Chongqing  Mainland China          6       0          0\n",
       "3  1/22/2020     Fujian  Mainland China          1       0          0\n",
       "4  1/22/2020      Gansu  Mainland China          0       0          0"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Date'] = pd.to_datetime(df['Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Province</th>\n",
       "      <th>Country</th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>Anhui</td>\n",
       "      <td>Mainland China</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>Beijing</td>\n",
       "      <td>Mainland China</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>Chongqing</td>\n",
       "      <td>Mainland China</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>Fujian</td>\n",
       "      <td>Mainland China</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>Gansu</td>\n",
       "      <td>Mainland China</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date   Province         Country  Confirmed  Deaths  Recovered\n",
       "0 2020-01-22      Anhui  Mainland China          1       0          0\n",
       "1 2020-01-22    Beijing  Mainland China         14       0          0\n",
       "2 2020-01-22  Chongqing  Mainland China          6       0          0\n",
       "3 2020-01-22     Fujian  Mainland China          1       0          0\n",
       "4 2020-01-22      Gansu  Mainland China          0       0          0"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6162.000000</td>\n",
       "      <td>6162.000000</td>\n",
       "      <td>6162.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>588.957481</td>\n",
       "      <td>18.960240</td>\n",
       "      <td>220.271503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4908.724815</td>\n",
       "      <td>198.962911</td>\n",
       "      <td>2466.089672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>67798.000000</td>\n",
       "      <td>3099.000000</td>\n",
       "      <td>55142.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Confirmed       Deaths     Recovered\n",
       "count   6162.000000  6162.000000   6162.000000\n",
       "mean     588.957481    18.960240    220.271503\n",
       "std     4908.724815   198.962911   2466.089672\n",
       "min        0.000000     0.000000      0.000000\n",
       "25%        2.000000     0.000000      0.000000\n",
       "50%       11.000000     0.000000      0.000000\n",
       "75%      100.000000     1.000000     12.000000\n",
       "max    67798.000000  3099.000000  55142.000000"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6162 entries, 0 to 6161\n",
      "Data columns (total 6 columns):\n",
      "Date         6162 non-null datetime64[ns]\n",
      "Province     3700 non-null object\n",
      "Country      6162 non-null object\n",
      "Confirmed    6162 non-null int64\n",
      "Deaths       6162 non-null int64\n",
      "Recovered    6162 non-null int64\n",
      "dtypes: datetime64[ns](1), int64(3), object(2)\n",
      "memory usage: 288.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.fillna('NA')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6162 entries, 0 to 6161\n",
      "Data columns (total 6 columns):\n",
      "Date         6162 non-null datetime64[ns]\n",
      "Province     6162 non-null object\n",
      "Country      6162 non-null object\n",
      "Confirmed    6162 non-null int64\n",
      "Deaths       6162 non-null int64\n",
      "Recovered    6162 non-null int64\n",
      "dtypes: datetime64[ns](1), int64(3), object(2)\n",
      "memory usage: 288.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>Anhui</td>\n",
       "      <td>Mainland China</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>Beijing</td>\n",
       "      <td>Mainland China</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>Chongqing</td>\n",
       "      <td>Mainland China</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>Fujian</td>\n",
       "      <td>Mainland China</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>Gansu</td>\n",
       "      <td>Mainland China</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>Guangdong</td>\n",
       "      <td>Mainland China</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>Guangxi</td>\n",
       "      <td>Mainland China</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>Guizhou</td>\n",
       "      <td>Mainland China</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>Hainan</td>\n",
       "      <td>Mainland China</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>Hebei</td>\n",
       "      <td>Mainland China</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date   Province         Country  Confirmed  Deaths  Recovered\n",
       "0 2020-01-22      Anhui  Mainland China          1       0          0\n",
       "1 2020-01-22    Beijing  Mainland China         14       0          0\n",
       "2 2020-01-22  Chongqing  Mainland China          6       0          0\n",
       "3 2020-01-22     Fujian  Mainland China          1       0          0\n",
       "4 2020-01-22      Gansu  Mainland China          0       0          0\n",
       "5 2020-01-22  Guangdong  Mainland China         26       0          0\n",
       "6 2020-01-22    Guangxi  Mainland China          2       0          0\n",
       "7 2020-01-22    Guizhou  Mainland China          1       0          0\n",
       "8 2020-01-22     Hainan  Mainland China          4       0          0\n",
       "9 2020-01-22      Hebei  Mainland China          1       0          0"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df.groupby('Country')[['Country','Confirmed','Deaths','Recovered']].sum().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df.groupby(['Country','Date'])[['Country','Date','Confirmed','Deaths','Recovered']].sum().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3 = df2[df2['Confirmed']>100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>Australia</td>\n",
       "      <td>2020-03-10</td>\n",
       "      <td>107</td>\n",
       "      <td>3</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>Australia</td>\n",
       "      <td>2020-03-11</td>\n",
       "      <td>128</td>\n",
       "      <td>3</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>Australia</td>\n",
       "      <td>2020-03-12</td>\n",
       "      <td>128</td>\n",
       "      <td>3</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>Australia</td>\n",
       "      <td>2020-03-13</td>\n",
       "      <td>200</td>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>Australia</td>\n",
       "      <td>2020-03-14</td>\n",
       "      <td>250</td>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>Australia</td>\n",
       "      <td>2020-03-15</td>\n",
       "      <td>297</td>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>Australia</td>\n",
       "      <td>2020-03-16</td>\n",
       "      <td>377</td>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>Austria</td>\n",
       "      <td>2020-03-08</td>\n",
       "      <td>104</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>Austria</td>\n",
       "      <td>2020-03-09</td>\n",
       "      <td>131</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>Austria</td>\n",
       "      <td>2020-03-10</td>\n",
       "      <td>182</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>Austria</td>\n",
       "      <td>2020-03-11</td>\n",
       "      <td>246</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>Austria</td>\n",
       "      <td>2020-03-12</td>\n",
       "      <td>302</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>Austria</td>\n",
       "      <td>2020-03-13</td>\n",
       "      <td>504</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>Austria</td>\n",
       "      <td>2020-03-14</td>\n",
       "      <td>655</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>Austria</td>\n",
       "      <td>2020-03-15</td>\n",
       "      <td>860</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>Austria</td>\n",
       "      <td>2020-03-16</td>\n",
       "      <td>1018</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>Bahrain</td>\n",
       "      <td>2020-03-10</td>\n",
       "      <td>110</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212</th>\n",
       "      <td>Bahrain</td>\n",
       "      <td>2020-03-11</td>\n",
       "      <td>195</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>Bahrain</td>\n",
       "      <td>2020-03-12</td>\n",
       "      <td>195</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>Bahrain</td>\n",
       "      <td>2020-03-13</td>\n",
       "      <td>189</td>\n",
       "      <td>0</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>Bahrain</td>\n",
       "      <td>2020-03-14</td>\n",
       "      <td>210</td>\n",
       "      <td>0</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>Bahrain</td>\n",
       "      <td>2020-03-15</td>\n",
       "      <td>214</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>Bahrain</td>\n",
       "      <td>2020-03-16</td>\n",
       "      <td>214</td>\n",
       "      <td>1</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>2020-03-06</td>\n",
       "      <td>109</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>2020-03-07</td>\n",
       "      <td>169</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>2020-03-08</td>\n",
       "      <td>200</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>2020-03-09</td>\n",
       "      <td>239</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>2020-03-10</td>\n",
       "      <td>267</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>2020-03-11</td>\n",
       "      <td>314</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>282</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>2020-03-12</td>\n",
       "      <td>314</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2521</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>2020-03-16</td>\n",
       "      <td>2200</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2631</th>\n",
       "      <td>Thailand</td>\n",
       "      <td>2020-03-15</td>\n",
       "      <td>114</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2632</th>\n",
       "      <td>Thailand</td>\n",
       "      <td>2020-03-16</td>\n",
       "      <td>147</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2701</th>\n",
       "      <td>UK</td>\n",
       "      <td>2020-03-05</td>\n",
       "      <td>115</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2702</th>\n",
       "      <td>UK</td>\n",
       "      <td>2020-03-06</td>\n",
       "      <td>163</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2703</th>\n",
       "      <td>UK</td>\n",
       "      <td>2020-03-07</td>\n",
       "      <td>206</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2704</th>\n",
       "      <td>UK</td>\n",
       "      <td>2020-03-08</td>\n",
       "      <td>273</td>\n",
       "      <td>3</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2705</th>\n",
       "      <td>UK</td>\n",
       "      <td>2020-03-09</td>\n",
       "      <td>321</td>\n",
       "      <td>4</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2706</th>\n",
       "      <td>UK</td>\n",
       "      <td>2020-03-10</td>\n",
       "      <td>382</td>\n",
       "      <td>6</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2707</th>\n",
       "      <td>UK</td>\n",
       "      <td>2020-03-11</td>\n",
       "      <td>459</td>\n",
       "      <td>8</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2708</th>\n",
       "      <td>UK</td>\n",
       "      <td>2020-03-12</td>\n",
       "      <td>459</td>\n",
       "      <td>8</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2709</th>\n",
       "      <td>UK</td>\n",
       "      <td>2020-03-13</td>\n",
       "      <td>801</td>\n",
       "      <td>8</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2710</th>\n",
       "      <td>UK</td>\n",
       "      <td>2020-03-14</td>\n",
       "      <td>1143</td>\n",
       "      <td>21</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2711</th>\n",
       "      <td>UK</td>\n",
       "      <td>2020-03-15</td>\n",
       "      <td>1144</td>\n",
       "      <td>21</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2712</th>\n",
       "      <td>UK</td>\n",
       "      <td>2020-03-16</td>\n",
       "      <td>1551</td>\n",
       "      <td>56</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2753</th>\n",
       "      <td>US</td>\n",
       "      <td>2020-03-02</td>\n",
       "      <td>101</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2754</th>\n",
       "      <td>US</td>\n",
       "      <td>2020-03-03</td>\n",
       "      <td>122</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2755</th>\n",
       "      <td>US</td>\n",
       "      <td>2020-03-04</td>\n",
       "      <td>153</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2756</th>\n",
       "      <td>US</td>\n",
       "      <td>2020-03-05</td>\n",
       "      <td>221</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2757</th>\n",
       "      <td>US</td>\n",
       "      <td>2020-03-06</td>\n",
       "      <td>278</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2758</th>\n",
       "      <td>US</td>\n",
       "      <td>2020-03-07</td>\n",
       "      <td>417</td>\n",
       "      <td>17</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2759</th>\n",
       "      <td>US</td>\n",
       "      <td>2020-03-08</td>\n",
       "      <td>537</td>\n",
       "      <td>21</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2760</th>\n",
       "      <td>US</td>\n",
       "      <td>2020-03-09</td>\n",
       "      <td>605</td>\n",
       "      <td>22</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2761</th>\n",
       "      <td>US</td>\n",
       "      <td>2020-03-10</td>\n",
       "      <td>959</td>\n",
       "      <td>28</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2762</th>\n",
       "      <td>US</td>\n",
       "      <td>2020-03-11</td>\n",
       "      <td>1281</td>\n",
       "      <td>36</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2763</th>\n",
       "      <td>US</td>\n",
       "      <td>2020-03-12</td>\n",
       "      <td>1663</td>\n",
       "      <td>40</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2764</th>\n",
       "      <td>US</td>\n",
       "      <td>2020-03-13</td>\n",
       "      <td>2179</td>\n",
       "      <td>47</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2765</th>\n",
       "      <td>US</td>\n",
       "      <td>2020-03-14</td>\n",
       "      <td>2726</td>\n",
       "      <td>54</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2766</th>\n",
       "      <td>US</td>\n",
       "      <td>2020-03-15</td>\n",
       "      <td>3499</td>\n",
       "      <td>63</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2767</th>\n",
       "      <td>US</td>\n",
       "      <td>2020-03-16</td>\n",
       "      <td>4632</td>\n",
       "      <td>85</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>470 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Country       Date  Confirmed  Deaths  Recovered\n",
       "152     Australia 2020-03-10        107       3         21\n",
       "153     Australia 2020-03-11        128       3         21\n",
       "154     Australia 2020-03-12        128       3         21\n",
       "155     Australia 2020-03-13        200       3         23\n",
       "156     Australia 2020-03-14        250       3         23\n",
       "157     Australia 2020-03-15        297       3         23\n",
       "158     Australia 2020-03-16        377       3         23\n",
       "171       Austria 2020-03-08        104       0          0\n",
       "172       Austria 2020-03-09        131       0          2\n",
       "173       Austria 2020-03-10        182       0          4\n",
       "174       Austria 2020-03-11        246       0          4\n",
       "175       Austria 2020-03-12        302       1          4\n",
       "176       Austria 2020-03-13        504       1          6\n",
       "177       Austria 2020-03-14        655       1          6\n",
       "178       Austria 2020-03-15        860       1          6\n",
       "179       Austria 2020-03-16       1018       3          6\n",
       "211       Bahrain 2020-03-10        110       0         22\n",
       "212       Bahrain 2020-03-11        195       0         35\n",
       "213       Bahrain 2020-03-12        195       0         35\n",
       "214       Bahrain 2020-03-13        189       0         44\n",
       "215       Bahrain 2020-03-14        210       0         44\n",
       "216       Bahrain 2020-03-15        214       0         60\n",
       "217       Bahrain 2020-03-16        214       1         77\n",
       "276       Belgium 2020-03-06        109       0          1\n",
       "277       Belgium 2020-03-07        169       0          1\n",
       "278       Belgium 2020-03-08        200       0          1\n",
       "279       Belgium 2020-03-09        239       0          1\n",
       "280       Belgium 2020-03-10        267       0          1\n",
       "281       Belgium 2020-03-11        314       3          1\n",
       "282       Belgium 2020-03-12        314       3          1\n",
       "...           ...        ...        ...     ...        ...\n",
       "2521  Switzerland 2020-03-16       2200      14          4\n",
       "2631     Thailand 2020-03-15        114       1         35\n",
       "2632     Thailand 2020-03-16        147       1         35\n",
       "2701           UK 2020-03-05        115       1          8\n",
       "2702           UK 2020-03-06        163       2          8\n",
       "2703           UK 2020-03-07        206       2         18\n",
       "2704           UK 2020-03-08        273       3         18\n",
       "2705           UK 2020-03-09        321       4         18\n",
       "2706           UK 2020-03-10        382       6         18\n",
       "2707           UK 2020-03-11        459       8         19\n",
       "2708           UK 2020-03-12        459       8         19\n",
       "2709           UK 2020-03-13        801       8         19\n",
       "2710           UK 2020-03-14       1143      21         19\n",
       "2711           UK 2020-03-15       1144      21         19\n",
       "2712           UK 2020-03-16       1551      56         21\n",
       "2753           US 2020-03-02        101       6          7\n",
       "2754           US 2020-03-03        122       7          8\n",
       "2755           US 2020-03-04        153      11          8\n",
       "2756           US 2020-03-05        221      12          8\n",
       "2757           US 2020-03-06        278      14          8\n",
       "2758           US 2020-03-07        417      17          8\n",
       "2759           US 2020-03-08        537      21          8\n",
       "2760           US 2020-03-09        605      22          8\n",
       "2761           US 2020-03-10        959      28          8\n",
       "2762           US 2020-03-11       1281      36          8\n",
       "2763           US 2020-03-12       1663      40         12\n",
       "2764           US 2020-03-13       2179      47         12\n",
       "2765           US 2020-03-14       2726      54         12\n",
       "2766           US 2020-03-15       3499      63         12\n",
       "2767           US 2020-03-16       4632      85         17\n",
       "\n",
       "[470 rows x 5 columns]"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x27c8f5e2dd8>]"
      ]
     },
     "execution_count": 220,
     "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": [
    "x = np.linspace(0,10,1000)\n",
    "y = np.sin(x)\n",
    "plt.plot(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x27c8f2a9908>"
      ]
     },
     "execution_count": 224,
     "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.scatter(x[::10],y[::10],color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x27c8f217a90>]"
      ]
     },
     "execution_count": 225,
     "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,color='b')\n",
    "plt.plot(x,np.cos(x),color='g')"
   ]
  },
  {
   "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": 2
}