random_numbers.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the random module.\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "41"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random.randint(-90,90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9548589893607113"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random.random()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "76.57815271463235"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_number = random.randint(-90,89) + random.random()\n",
    "random_number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[-6.099863777489656,\n",
       " -53.39377927857434,\n",
       " 37.03385084172115,\n",
       " 40.89384953603855,\n",
       " 83.63573714279758,\n",
       " 34.66259599459366,\n",
       " -46.17475365621125,\n",
       " 1.679355805704231,\n",
       " -0.905397395706581,\n",
       " 43.64260013567921]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "latitudes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-35"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random.randrange(-90, 90, step=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-86"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random.randrange(-90, 90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random.randrange(-90, 90, step=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "31.955276760798924"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random.uniform(-90, 90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import NumPy dependency\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-66.22473937,  66.91866896,  36.72006687,  52.46180529,\n",
       "       -11.54535455,  69.22606814, -34.06110072,  68.28122297,\n",
       "       -53.48612087, -31.46810458,  28.19710021, -11.14854171,\n",
       "        81.63709231, -51.26372566,  29.79141479,  15.13146527,\n",
       "        -0.87146323, -53.16357494, -30.18858833,  63.2738537 ,\n",
       "        27.20816262,  53.3048807 , -75.45713434, -29.95902341,\n",
       "       -83.06656042, -62.72603923, -67.73488247,   6.34711741,\n",
       "        24.40315707,  31.17738905])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.uniform(-90.000, 90.000, size=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import timeit dependency\n",
    "import timeit "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13.7 µs ± 204 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": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.46 ms ± 64.9 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
     ]
    }
   ],
   "source": [
    "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": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = 1\n",
    "latitudes = []\n",
    "while x < 1500:\n",
    "    random_lat = random.randint(-90,90) + random.random()\n",
    "    latitudes.append(random_lat)\n",
    "    x += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "26.8 ns ± 0.657 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit latitudes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = 0\n",
    "latitudes = []\n",
    "for x in range(1,1500):\n",
    "    \n",
    "    random_lat = random.randint(-90,90) + random.random()\n",
    "    latitudes.append(random_lat);  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
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       " -81.90065740205749,\n",
       " 48.50185314753673,\n",
       " 72.82820878823384,\n",
       " -64.99197614789632,\n",
       " 78.4278339305448,\n",
       " 24.86018959092937,\n",
       " 59.92628344681577,\n",
       " 3.923069483010212,\n",
       " -42.468468817263,\n",
       " -84.08564045109539,\n",
       " 43.92365199501649,\n",
       " 0.691377601703171,\n",
       " 83.24877780041604,\n",
       " -84.17398839206264,\n",
       " -47.32912299273248,\n",
       " 34.6892171697864,\n",
       " 11.161950376579739,\n",
       " 82.81664737376155,\n",
       " 41.18933842745217,\n",
       " -36.638831605908976,\n",
       " ...]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "latitudes "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "36.1 ns ± 12.2 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit latitudes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the dependencies.\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<zip at 0x24984af9500>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a set of random latitude and longitude combinations.\n",
    "lats = np.random.uniform(low=-90.000, high=90.000, size=1500)\n",
    "lngs = np.random.uniform(low=-180.000, high=180.000, size=1500)\n",
    "lat_lngs = zip(lats, lngs)\n",
    "lat_lngs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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-103.07120598414292), (58.73168758014728, 105.13017100290392), (-36.51381483105428, 125.47230644362395), (21.675922729664606, 120.11825112853614), (-54.725360262020516, 8.415386750445322), (71.39504696167276, -37.91074154529787), (-8.293417889577356, 82.55516708434789), (-80.19467122565581, -134.89210799013762), (3.9337367826964567, 35.253714979687715), (-5.297579621926744, -161.91592495961945), (-76.27744488780053, 71.82356156233675), (77.226112438614, -162.49334944932397), (-41.433761560584045, -72.80539518745707), (-24.171000859832105, -51.04363782582766), (14.178652952859025, 130.65635623948214), (-51.577099116455784, 58.231849884897), (30.560609915771536, -162.60117061523943), (15.581785824246936, 46.39777104532078), (11.199831656352217, 125.99690791561454), (-36.990465871378035, -51.471768157554266), (58.33313300984517, 113.08746788140292), (31.598831503345153, -35.48047486022284), (78.22541716559527, 3.8675148764683627), (-31.046452662150493, 177.13289326952656)]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Add the latitudes and longitudes to a list.\n",
    "coordinates = list(lat_lngs)\n",
    "print(coordinates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import linear regression from the SciPy stats module.\n",
    "from scipy.stats import linregress"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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": 3,
   "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, 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}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": 5,
   "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,
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}