14-Intro_to_NumPy.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction to NumPy\n",
    "* NumPy is written in C and the primary data type is a powerful array n-dimensional \n",
    "* Arrays are much faster than lists \n",
    "* Arrays allow vectorized operations\n",
    "* Require all data to be of the same type --> trade off for speed\n",
    "* NumPy arrays are of a fixed size \n",
    "* NumPy is huge and very powerful --> we will just hit the highlights\n",
    "* Part of a Data \"ecosystem\" which includes data science libaries, machine learning and image processing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Documentation\n",
    "https://numpy.org/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 4, 9, 16, 25, 36, 49, 64, 81, 100]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Write code to transform the data into the data squared\n",
    "squares = list(range(11))\n",
    "squares = [x ** 2 for x in squares]\n",
    "squares"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0,   1,   4,   9,  16,  25,  36,  49,  64,  81, 100])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "squares = np.array(list(range(11)))\n",
    "squares * squares"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# let's take a look at time consideration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "array = np.arange(5e6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5000000"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array.ndim\n",
    "array.shape\n",
    "array.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "array_list = array.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "806 ms ± 44.3 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit -n10 [value * 5 for value in array_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19.1 ms ± 949 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit -n10  x = array * 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zero = np.zeros(27).reshape(3,3,3)\n",
    "zero.reshape(-1).ndim\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0., 0., 0.],\n",
       "       [0., 1., 0., 0., 0.],\n",
       "       [0., 0., 1., 0., 0.],\n",
       "       [0., 0., 0., 1., 0.],\n",
       "       [0., 0., 0., 0., 1.]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.eye(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "normals = np.random.standard_normal(100000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.004421126999014449"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normals.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.004381859550871397"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.median(normals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.96073171, -1.64487863,  0.00438186,  1.65013612,  1.96319131])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = [2.5, 5, 50, 95, 97.5]\n",
    "np.percentile(normals, p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.77504595,  0.72365519, -0.1081774 , -1.26384606,  0.55632299,\n",
       "       -1.01631192, -0.38009222,  1.42032114, -0.72107824, -0.69030702])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_normals = np.random.standard_normal(10)\n",
    "new_normals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True, False, False,  True, False, False,  True, False,\n",
       "       False])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.where(new_normals > 0, True, False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "85.25"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weights = [.25,.15, .20, .40]\n",
    "\n",
    "scores = [90, 85, 100, 75]\n",
    "np.dot(weights, scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# what about simulating ending values of stock price?\n",
    "r = .00375\n",
    "price = 100\n",
    "vol = .30\n",
    "T = 1 / 252\n",
    "days = 21\n",
    "iterations = 10000\n",
    "S = np.zeros((days + 1, iterations))\n",
    "S[0] = price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "for iteration in range(iterations):\n",
    "    for day in range(1, days + 1):\n",
    "        S[day][iteration] = (S[day - 1][iteration] * \n",
    "        np.exp((r -5 * vol ** 2)* T + np.sqrt(T) * vol * np.random.standard_normal()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "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(S[:, [5,10,15,20,25]]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10000"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(S[21])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.503883941679929"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "S[21].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "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(S[21], bins = 50, edgecolor= 'w');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "# instantaneous return \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}