py4ds-Session9-drafts.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1983b440",
   "metadata": {},
   "source": [
    "## Session 9 (numpy part e)\n",
    "\n",
    "### Advanced (fancy) Indexing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "9cd1fb89",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f6598c39",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 6, 3, 9, 8, 9, 3, 6, 1, 3])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.random.randint(1, 10, size=10)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b389c70a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9\n",
      "[9 8 9 3 6 1 3]\n"
     ]
    }
   ],
   "source": [
    "# simple indexing discussed earlier\n",
    "print(a[3])\n",
    "print(a[3:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fde468f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6, 9, 3])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ix = np.array([1, 3, 6]) # it could be also a python list (i.e. ix = [1, 3, 6])\n",
    "a[ix]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "54aae437",
   "metadata": {},
   "outputs": [],
   "source": [
    "# result of indexing will have the shape of index array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9104357d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 3],\n",
       "       [1, 7]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ix = np.array([[1, 3], [1, 7]])\n",
    "ix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2da5b626",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6, 9],\n",
       "       [6, 6]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[ix]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36f57463",
   "metadata": {},
   "source": [
    "### indexing a 2d array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d6918d9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3],\n",
       "       [ 4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.arange(12).reshape((3, 4))\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2f921066",
   "metadata": {},
   "outputs": [],
   "source": [
    "# simple indexing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "60c32278",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[1, 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2118ee3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[1][2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4f0fabff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5, 11])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rows = [1, 2]\n",
    "cols = [1, 3]\n",
    "a[rows, cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3de094aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5, 11])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[[1, 2], [1, 3]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "fe3a5cad",
   "metadata": {},
   "outputs": [],
   "source": [
    "# in case of different index shapes, the broadcasting rules will be applied :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8b62f06e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 7])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[1, [1, 3]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f679bba",
   "metadata": {},
   "source": [
    "### advanced indexing can be used in combination with slicing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f58f6df9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3],\n",
       "       [ 4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "612885d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "55a5e58b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 4,  7],\n",
       "       [ 8, 11]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[1:, [0, 3]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef6497b1",
   "metadata": {},
   "source": [
    "### advanced indexing example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "9dc6fce1",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.random.randint(0, 100, size=100)\n",
    "y = np.random.uniform(300, 400, size=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "726fa7e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([14, 69, 92, 24, 55, 99, 76, 35, 67, 10, 71, 68,  5, 92, 86, 77, 20,\n",
       "       75, 42, 54, 10,  3, 67, 98, 70, 40, 97, 43, 66, 38, 89, 49, 15, 69,\n",
       "       72, 25, 29, 55, 54, 90, 86, 47, 20, 45, 27, 56, 66, 64, 26,  0, 12,\n",
       "       60, 73, 40, 17, 78, 96, 77, 17, 90, 42, 16, 23, 78, 56, 13,  8, 59,\n",
       "        5,  3, 88, 90, 25, 50, 49,  6, 61, 87, 82, 27, 29,  5, 66, 24, 62,\n",
       "        2, 88, 40, 33, 58, 16, 34, 86, 77,  2,  2, 70, 48,  1, 87])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b4a4bb19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([357.90588006, 328.58128447, 317.60203007, 322.56991853,\n",
       "       334.66186712, 370.33075519, 323.44649002, 320.71475098,\n",
       "       337.46662255, 318.80282745, 397.32040916, 324.70305103,\n",
       "       328.69743707, 313.1124383 , 388.35055685, 353.6217657 ,\n",
       "       342.93509246, 343.43975706, 316.054278  , 339.2595008 ,\n",
       "       372.0980077 , 304.62300166, 301.70476295, 367.12677111,\n",
       "       312.08349885, 348.18403687, 370.56262994, 398.02148597,\n",
       "       347.76524593, 327.35921578, 393.97513358, 366.29421326,\n",
       "       330.15322341, 342.88094501, 373.23259772, 308.3000393 ,\n",
       "       388.05820069, 384.21111755, 302.12987082, 395.93099285,\n",
       "       388.21394956, 356.42805436, 310.00284549, 318.78606678,\n",
       "       338.61640313, 374.15179907, 316.75430139, 368.12223617,\n",
       "       338.8736942 , 302.00096032, 332.12588216, 329.06707908,\n",
       "       353.38433931, 329.85101269, 339.60176545, 372.74106937,\n",
       "       318.47914118, 396.62552372, 322.40818234, 355.88423321,\n",
       "       350.2727035 , 373.42800358, 358.77756491, 337.27922592,\n",
       "       327.49398809, 346.88472828, 314.95493608, 301.28186712,\n",
       "       341.1391577 , 335.75410269, 388.74731301, 317.89766437,\n",
       "       349.23926491, 316.5324502 , 358.18175507, 389.12396392,\n",
       "       342.72314121, 326.40696041, 369.49522646, 336.62628953,\n",
       "       311.4483397 , 322.06600788, 383.5465496 , 305.94517993,\n",
       "       347.50956573, 361.59520556, 375.65403567, 320.35646982,\n",
       "       315.52682622, 322.42573657, 315.29134005, 378.44088449,\n",
       "       302.74771006, 385.83088636, 396.0268879 , 375.09889287,\n",
       "       313.87612109, 344.74944019, 393.69737528, 358.07071983])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "3c61b364",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fddafa92a60>"
      ]
     },
     "execution_count": 29,
     "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, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "14d64e18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([87, 95, 62, 13, 14, 23, 97, 11,  2, 27, 85,  8,  4, 42, 38, 63, 15,\n",
       "       61, 26, 68])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ix = np.random.choice(100, 20, replace=False) # setting replace parameter to False will prevent duplicate results\n",
    "ix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "4fd5df4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fddaa90b940>"
      ]
     },
     "execution_count": 33,
     "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[ix], y[ix])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "35b48f1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# for merging the plots, we could plot the values in the same cell :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "e513ad2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fddae66a9d0>"
      ]
     },
     "execution_count": 35,
     "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, y)\n",
    "plt.scatter(x[ix], y[ix], color='red')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c62d73ca",
   "metadata": {},
   "source": [
    "### advanced indexing can be used to modify array elements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "2357b409",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  2,  4,  6,  8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.arange(15)*2\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "d0f3f7c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 6,  8, 10, 12])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ix = [3, 4, 5, 6]\n",
    "a[ix]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "11256396",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  2,  4,  0,  0,  0,  0, 14, 16, 18, 20, 22, 24, 26, 28])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[ix] = 0\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "50f128d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  2,  4,  1,  1,  1,  1, 14, 16, 18, 20, 22, 24, 26, 28])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[ix] = [1, 1, 1, 1]\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "4ab2967f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  2,  4,  1,  2,  3,  4, 14, 16, 18, 20, 22, 24, 26, 28])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[ix] = [1, 2, 3, 4]\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "a35138a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# what happens if duplicate values exist in the index array ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "bbc29323",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.arange(15)*2\n",
    "ix = [3, 4, 4, 6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "80455e95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  2,  4,  6,  8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "8633faa7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 6,  8,  8, 12])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[ix]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "9fde149b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  2,  4,  7,  9, 10, 13, 14, 16, 18, 20, 22, 24, 26, 28])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[ix] += 1\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "30b0371a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}