{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "666ccd0c-8076-4dc6-a6c0-204c1fb3d239",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "67adc236-04c9-4e21-9c8d-19d08dc0c378",
   "metadata": {},
   "outputs": [],
   "source": [
    "a1 = np.array([2,3.27,4,4.13,5.44,6,8,10,12,14,16,18,20,22,24,26,28,30], dtype=int)\n",
    "a2 = np.array([[1,3,5,7,9],\n",
    "               [2,4,6,8,10],\n",
    "               [0,1,2,3,4]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6d0671f7-fbf3-4bdd-aaa4-6f5050506c4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2,  3,  4,  4,  5,  6,  8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28,\n",
       "       30])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7721494d-2e08-4f81-9a77-9fe45068b0f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a1.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7845ebde-ad26-4fb6-a070-a84ed41e9602",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a2.itemsize #in bytes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d6492342-2763-4413-a342-87d41f2ff62c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 5)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a2.shape #rows n columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "df9cdc73-0d24-4376-adae-d578aa30ab6d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  1   9  25  49  81]\n",
      " [  4  16  36  64 100]\n",
      " [  0   1   4   9  16]]\n"
     ]
    }
   ],
   "source": [
    "a3 = a2 ** 2\n",
    "print(a3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6f77c338-8883-406d-aba9-91eda00cdbe1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 3, 4])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a1[0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4b1d2fa9-156d-428f-80e0-811933c06dba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4,  5,  6,  8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a1[3:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "74d1bf1a-c856-4b76-89fe-31957ab38505",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.int64(30)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a1[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0facf598-826f-4430-9675-5919e85c07db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2,  3,  4,  4,  5,  6,  8, 10, 12, 14, 16, 18, 20, 22, 24, 26])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a1[:-2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a43e74d5-da97-4989-b1b2-43c559fc971e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2,  4,  8, 14, 20, 26])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a1[0::3] #skips numbers according to the last number in brackets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e31bc6d9-6835-457d-9f80-eb72a209611b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 2  3  4  4  5  6  8 10 12 14 16 18 20 22 24 26 28 30]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([10,  6,  4,  3])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(a1)\n",
    "a1[7::-2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "bb542f32-a3d7-4cbe-ba3e-7d389c2c7039",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 3],\n",
       "       [2, 4]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a2[:2, :2] #print specified rows and columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "89ae5245-f506-464f-a5d6-e4eedd90065b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a2[:1, :1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "667f9641-e3f6-42b5-964d-655216c05153",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['1' '1' '1' '1']\n",
      " ['1' '1' '1' '1']\n",
      " ['1' '1' '1' '1']]\n"
     ]
    }
   ],
   "source": [
    "a4 = np.ones([3,4], dtype=str) #print zeros or ones...change data type - str, float, \n",
    "print(a4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a2353214-e87b-4e04-baaf-7299f870bca9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#create an array and sequence\n",
    "\n",
    "x = 4\n",
    "b = np.arange(4,64, x-2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c192f6a8-ad4d-4f23-8470-42bd02d5a628",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4,  6,  8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36,\n",
       "       38, 40, 42, 44, 46, 48, 50, 52, 54, 56, 58, 60, 62])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6bfc6c92-addc-4d46-bd4d-8be1029e88f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3.        , 3.33333333, 3.66666667, 4.        , 4.33333333,\n",
       "       4.66666667, 5.        , 5.33333333, 5.66666667, 6.        ])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import numpy library which contains the linspace function\n",
    "import numpy as np\n",
    "\n",
    "# Create values within spaced numbers\n",
    "c = np.linspace(3, 6, 10)\n",
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "620b3488-5da0-4730-9b9d-415713651df5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3.        , 3.33333333],\n",
       "       [3.66666667, 4.        ],\n",
       "       [4.33333333, 4.66666667],\n",
       "       [5.        , 5.33333333],\n",
       "       [5.66666667, 6.        ]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#reshape arrange into new dim\n",
    "d = c.reshape(5,2)\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5a9d12bc-5abc-4615-a0ae-29e2dffee3c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3.        , 3.33333333, 3.66666667, 4.        , 4.33333333],\n",
       "       [4.66666667, 5.        , 5.33333333, 5.66666667, 6.        ]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = np.reshape(d,(2,5))\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6c7f2bc9-4314-4c59-90f9-6b6b3fbcdfa5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2.        ,  3.        ,  4.        ,  4.        ,  5.        ,\n",
       "        6.        ,  8.        , 10.        , 12.        , 14.        ,\n",
       "       16.        , 18.        , 20.        , 22.        , 24.        ,\n",
       "       26.        , 28.        , 30.        ,  3.        ,  3.33333333,\n",
       "        3.66666667,  4.        ,  4.33333333,  4.66666667,  5.        ,\n",
       "        5.33333333,  5.66666667,  6.        ])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#concatenate - join 2 arrays\n",
    "b =np.concatenate([a1,c])\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "168329ae-cbb4-4e98-b2dc-eede58f086a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1  2  3  4  5  6  7  8  9 10]\n",
      "[ 1  3  5  7  9 11 13 15 17 19]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.arange(1,11,1)\n",
    "b = np.arange(1,20,2)\n",
    "\n",
    "print(a)\n",
    "print(b)\n",
    "c = np.array_equal(a,b)\n",
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c16e4581-696a-435f-b7dd-d236bca75ebc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False, False, False, False, False, False, False, False,\n",
       "       False])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = np.logical_xor(a,b)\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c010c8d6-546c-4f10-847e-8930078b0601",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([102,  48,  19,  94,  30,  25,  10,  92,  37,  73,  74,  64,  61,\n",
       "       102,  77,  11,  21,  76,  20,  58,  92,  68,  54,  33,  92,  11,\n",
       "        38,  23,   6,  85,  30, 100,  16,  37, 102,  70,  74,  10,  62,\n",
       "        54,  37,  67,  44,  36,   8,  70,   8,  98,  45,  26,   4,  51,\n",
       "        16,  72,  17,  42,  88,  50,  10,  62,  74,  11,  96,  70,  32,\n",
       "        46,  76,  57,  44,  85,  11,  41,  65,  44,  98,  10,  50,  81,\n",
       "        45,  81,  50,  48,  18,  50,  41,   5,  91,  94,  29, 101,  58,\n",
       "        17,  27,  32,  94,   1,  84,  42,  82,  71,   1,   1,  28,  81,\n",
       "       101,  59,  28,  55,  91,  83,  30,  17,  86,  12,  48,  86, 100,\n",
       "        73,  50,   5,  83,  48,  98,  27,   6,  73,  15,  85,  92,  27,\n",
       "        82,  91,  11,  71,  94,  59,   8,  59,  91,  83,   7,  56,  59,\n",
       "        62,  59,  91,  17,  73,   8,  88,  65,  21,   7,  38,  14,  42,\n",
       "        15,  11,  41,  38,  28,  31,  12,  93, 101,  36,  96,   7,   1,\n",
       "        74,  97,  71,  81,  90,  63,  18,  85,  75,  74,  98,  45,  45,\n",
       "        79,  73,  19,  40,  17,   8,  25,  24,  15,   7,  88,   3,  74,\n",
       "        43,  72,   9,  24,  30])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# big_data = np.random.random_integers(1,101,1000)\n",
    "big_data2 = np.random.randint(1,101 + 2,200)\n",
    "big_data2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0b96d8f4-d6fb-4f0a-8816-2f1dca85f43b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "display_name": "Python [conda env:base] *",
   "language": "python",
   "name": "conda-base-py"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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