random_numbers.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ec61fbd8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b516a0a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "42"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random.randint(-90,90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "14c4661e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2674252205117871"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random.random()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ff1dc974",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[39.35763232661785,\n",
       " 49.54587608048356,\n",
       " -70.83938645366378,\n",
       " -41.63876246878889,\n",
       " 51.85309668790717,\n",
       " 70.20331771430584,\n",
       " 13.794888465527396,\n",
       " 60.212371223970244,\n",
       " -72.33269950751372,\n",
       " 1.358188097255906]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = 1\n",
    "latitudes = []\n",
    "while x < 11:\n",
    "    random_lat = random.randint(-90, 89) + random.random()\n",
    "    latitudes.append(random_lat)\n",
    "    x += 1\n",
    "latitudes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "76eb2410",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-72"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " random.randrange(-90, 90, step=1)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fd700ad6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13.996082167163323"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random.uniform(-90, 90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9812f362",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the NumPy module. Numerical math library that make arrays or matrices of numbers\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7fc76c62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "70.03570362047626"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.uniform(-90.000, 90.000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "194d01a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 61.87356955, -10.45475567,  59.77243144,  -9.72317796,\n",
       "         6.06854808, -11.38693125, -65.65766998,  43.96652904,\n",
       "        30.34530447,  87.45422974, -72.50648646,   0.37312543,\n",
       "        75.10366315, -81.01207786, -82.53247698,  24.45272551,\n",
       "        12.82682681, -39.00814121, -23.40291757, -58.63409914,\n",
       "        31.55528535, -47.06663258,   3.17342788,  52.07385334,\n",
       "        18.25314657,  31.57717438, -10.17671791,  32.56253363,\n",
       "        37.91655528,  41.74992414,   4.16075308,  16.00820427,\n",
       "        16.33502459, -27.26949728, -54.11550214,  14.6966687 ,\n",
       "       -66.82056675, -87.2219494 , -55.42916599,  61.5495744 ,\n",
       "       -11.37638755, -79.36646744,  48.06208557,  15.71608437,\n",
       "        20.09000258, -12.80382297,  26.11620744,  46.31976699,\n",
       "        88.67087335,  78.76457487])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.uniform(-90.000, 90.000, size=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "282f6aba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  9.06389428, -38.80663415,  21.61340704, ...,  14.79589566,\n",
       "       -86.9973864 , -34.73926361])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.uniform(-90.000, 90.000, size=1500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "79204630",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import timeit. To test how long a piece of code or function takes to run\n",
    "import timeit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "dbabb11f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12.1 µs ± 36.7 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit np.random.uniform(-90.000, 90.000, size=1500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c1baf75d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.2 ms ± 8.56 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
     ]
    }
   ],
   "source": [
    "# let´s probe which is faster\n",
    "def latitudes(size):\n",
    "    latitudes = []\n",
    "    x = 0\n",
    "    while x < (size):\n",
    "        random_lat = random.randint(-90, 90) + random.random()\n",
    "        latitudes.append(random_lat)\n",
    "        x += 1\n",
    "    return latitudes\n",
    "# Call the function with 1500.\n",
    "%timeit latitudes(1500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "925c19c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import linear regression from the SciPy stats module.\n",
    "from scipy.stats import linregress"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "df4e201c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create an equal number of latitudes and temperatures.\n",
    "lats = [42.5, 43.9, 8.1, 36.8, 79.9, 69.1, 25.7, 15.3, 12.7, 64.5]\n",
    "temps = [80.5, 75.3, 90.9, 90.0, 40.4, 62.3, 85.4, 79.6, 72.5, 72.0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6898a1e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y = -0.45x + 92.94\n",
      "The p-value is: 0.011\n"
     ]
    }
   ],
   "source": [
    "# Perform linear regression.\n",
    "# Slope of the regression line as slope, y-intercept as intercept, Correlation coefficient as r_value\n",
    "# p-value as p_value, Standard error as std_err\n",
    "\n",
    "(slope, intercept, r_value, p_value, std_err) = linregress(lats, temps)\n",
    "# Get the equation of the line.\n",
    "line_eq = \"y = \" + str(round(slope,2)) + \"x + \" + str(round(intercept,2))\n",
    "print(line_eq)\n",
    "print(f\"The p-value is: {p_value:.3f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "308b47e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate the regression line \"y values\" from the slope and intercept.\n",
    "regress_values = [(lat * slope + intercept) for lat in lats]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5a5cd3e0",
   "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": [
    "# Import Matplotlib.\n",
    "import matplotlib.pyplot as plt\n",
    "# Create a scatter plot of the x and y values.\n",
    "plt.scatter(lats,temps)\n",
    "# Plot the regression line with the x-values and the y coordinates based on the intercept and slope.\n",
    "plt.plot(lats,regress_values,\"r\")\n",
    "# Annotate the text for the line equation and add its coordinates.\n",
    "plt.annotate(line_eq, (10,40), fontsize=15, color=\"red\")\n",
    "plt.xlabel('Latitude')\n",
    "plt.ylabel('Temp')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d8c3f267",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bcfd503e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3130c178",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5b123fb8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "PythonData",
   "language": "python",
   "name": "pythondata"
  },
  "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": 5
}