acook46.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IPython version:      %6.6s 7.16.1\n"
     ]
    }
   ],
   "source": [
    "# Adam Cook\n",
    "# acook46\n",
    "\n",
    "import IPython\n",
    "import json\n",
    "\n",
    "# Numpy is a library for working with Arrays\n",
    "import numpy as np\n",
    "\n",
    "# SciPy implements many different numerical algorithms\n",
    "import scipy as sp\n",
    "\n",
    "# Pandas is good with data tables\n",
    "import pandas as pd\n",
    "\n",
    "# Module for plotting\n",
    "import matplotlib\n",
    "\n",
    "#BeautifulSoup parses HTML documents (once you get them via requests)\n",
    "import bs4\n",
    "\n",
    "# Nltk helps with some natural language tasks, like stemming\n",
    "import nltk\n",
    "\n",
    "# Bson is a binary format of json to be stored in databases\n",
    "# import bson\n",
    "\n",
    "# Mongo is one of common nosql databases \n",
    "# it stores/searches json documents natively\n",
    "# import pymongo\n",
    "\n",
    "print (\"IPython version:      %6.6s\", IPython.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Make a 2 row x 3 column array of random numbers\n",
      "[[0.43719086 0.24051404 0.52106047]\n",
      " [0.06300796 0.14284395 0.00840616]]\n",
      "Add 5 to every element\n",
      "[[5.43719086 5.24051404 5.52106047]\n",
      " [5.06300796 5.14284395 5.00840616]]\n",
      "Get the first row\n",
      "[5.43719086 5.24051404 5.52106047]\n"
     ]
    }
   ],
   "source": [
    "#Here is what numpy can do\\n\",\n",
    "print (\"Make a 2 row x 3 column array of random numbers\")\n",
    "x = np.random.random((2, 3))\n",
    "print (x)\n",
    "\n",
    "#array operation (as in R)\n",
    "print (\"Add 5 to every element\")\n",
    "x = x + 5\n",
    "print (x)\n",
    "\n",
    "# get a slice (first row) (as in R)\n",
    "print (\"Get the first row\")\n",
    "print (x[0, :])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function ndarray.any>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# IPython is quite modern: just press <TAB> at the end of the unfinished statement to see the documentation\n",
    "#       on possible completions.\n",
    "#       In the code cell below, type x.<TAB>, to find built-in operations for x\n",
    "x.any"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": [
    "%matplotlib inline \n",
    "import matplotlib.pyplot as plt\n",
    "heads = np.random.binomial(500, .5, size=500)\n",
    "histogram = plt.hist(heads, bins=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Task 1\n",
    "## write a program to produce Fibonacci numbers up to 1000000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The fibonacci sequence under 1,000,000 is:\n",
      "[0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, 2584, 4181, 6765, 10946, 17711, 28657, 46368, 75025, 121393, 196418, 317811, 514229, 832040]\n"
     ]
    }
   ],
   "source": [
    "# Establish the first two numbers of the Fibonacci sequence\n",
    "fib = [0, 1]\n",
    "\n",
    "# Append array with sum of previous two indices\n",
    "def fibonacci(n):\n",
    "    while (fib[-1] < n):\n",
    "        fib.append(fib[-1] + fib[-2])\n",
    "    fib.remove(fib[-1])\n",
    "    return fib\n",
    "\n",
    "print(\"The fibonacci sequence under 1,000,000 is:\")\n",
    "print(fibonacci(1000000))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Task 2\n",
    "## write a program to simulate 1000 tosses of a fair coin (use np.random.binomial)\n",
    "## Calculate the mean and standard deviation of that sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean: 0.485\n",
      "Standard Deviation: 0.49977494935220584\n"
     ]
    }
   ],
   "source": [
    "# Flip 1000 coins\n",
    "flip = np.random.binomial(n=1, p=0.5, size=1000)\n",
    "\n",
    "# Calculate and print the mean\n",
    "mean = np.mean(flip)\n",
    "print(\"Mean:\", mean)\n",
    "\n",
    "# Calculate and print the standard deviation\n",
    "stdev = np.std(flip)\n",
    "print(\"Standard Deviation:\", stdev)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Task 3\n",
    "## Produce a scatterplot of y = 0.5*x+e where x has gaussian (0, 5) and e has gaussian (0, 1) distributions \n",
    "### use numpy.random.normal to generate gaussian distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1af90932f10>"
      ]
     },
     "execution_count": 7,
     "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": [
    "# Apply gaussians to x and e\n",
    "x = np.random.normal(0, 5, 100)\n",
    "e = np.random.normal(0, 1, 100)\n",
    "\n",
    "# Calculate y and plot in a scatterplot\n",
    "y = 0.5*x + e\n",
    "plt.scatter(x, y)"
   ]
  },
  {
   "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}