wparham1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IPython version:      %6.6s 6.1.0\n"
     ]
    }
   ],
   "source": [
    "import IPython\n",
    "import json\n",
    "# Numpy is a library for working with Arrays\n",
    "import numpy as np\n",
    "# SciPy implements many different numerical algorithms\n",
    "import scipy as sp\n",
    "# Pandas is good with data tables\n",
    "import pandas as pd\n",
    "# Module for plotting\n",
    "import matplotlib\n",
    "#BeautifulSoup parses HTML documents (once you get them via requests)\n",
    "import bs4\n",
    "# Nltk helps with some natural language tasks, like stemming\n",
    "import nltk\n",
    "# Bson is a binary format of json to be stored in databases\n",
    "import bson\n",
    "# Mongo is one of common nosql databases \n",
    "# it stores/searches json documents natively\n",
    "import pymongo\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",
      "[[ 0.20354485  0.87353642  0.79226415]\n",
      " [ 0.26457656  0.23486214  0.8240387 ]]\n",
      "Add 5 to every element\n",
      "[[ 5.20354485  5.87353642  5.79226415]\n",
      " [ 5.26457656  5.23486214  5.8240387 ]]\n",
      "Get the first row\n",
      "[ 5.20354485  5.87353642  5.79226415]\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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aIPsbgIu69+lFwOXj3P4RX/RJnsbg/4CrquqGofHXAK8ELqju5BkHcWuHw+lA2YdcBfxB\ntzzt2Z/H4HzqV5Ls6vLdleQXmf7sVNUTVfWjqvox8Nc8dapgarIfYH/ZDdzQnSr4EvBjBveNmZrc\nsOj7dBXw+8C1Q9NXQvZNwJPLf8+495dJXKCYli8GFzyuBD603/gZDG6pPLPf+Av5yYs8DzO5izwH\nyn7S0PKbgeu75bP5yYs8X5q2132/Obt46mLs1GcH1gwtX8TgHPHU7DOL5H498Kfd8gsYnDbItORe\nan/p3qtf2G9s6rMzOFf/sm75dODObnks+/ph/x8+TV/AbzG4KHIPcHf3dRaDizePDo19fOg5lzC4\niv8g3VXzKcv+KeDebvwfGFygfXKH+2iX/avA7LRl32/OcNFPfXbgb7ts9zC4r9Nw8U98n1kk99HA\n33X7zF3Ay6cp91L7C/BJ4PULPGeqs3fjdzL4hbQD+PVu/lj2df8yVpIad8Sfo5ek1ln0ktQ4i16S\nGmfRS1LjLHpJapxFL0mNs+glqXEWvSQ17v8ApNqW0K2lWVMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8a2433ca20>"
      ]
     },
     "metadata": {},
     "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": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "1\n",
      "2\n",
      "3\n",
      "5\n",
      "8\n",
      "13\n",
      "21\n",
      "34\n"
     ]
    }
   ],
   "source": [
    "#set place holder variables for our summed number in fibonacci sequence\n",
    "first_num = 0\n",
    "second_num = 1\n",
    "\n",
    "#create a for loop to iterate through 1,000,000\n",
    "for counter in range(0, 1000000):\n",
    "    #account for zero and 1 cases\n",
    "    if(counter <= 1):\n",
    "        next_num = counter\n",
    "    #account for all other cases\n",
    "    else:\n",
    "        next_num = first_num + second_num\n",
    "        first_num = second_num\n",
    "        second_num = next_num\n",
    "    #print results\n",
    "    print(next_num)"
   ]
  },
  {
   "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": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean:  0.49\n",
      "Standard deviation:  0.5001501276118508\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import math\n",
    "\n",
    "#simulate the coinflip 1000 times\n",
    "flip_results = np.random.binomial(1, .5, 1000)\n",
    "total = sum(flip_results)\n",
    "\n",
    "#calculate the mean\n",
    "mean = total / 1000\n",
    "\n",
    "#create a variance list\n",
    "variance_list = [x - mean for x in flip_results]\n",
    "\n",
    "#square each value in the variance list\n",
    "squared_list = [x*x for x in variance_list]\n",
    "\n",
    "#find the sum of the squared list\n",
    "sqr_total = sum(squared_list)\n",
    "\n",
    "#calculate the variance\n",
    "variance = sqr_total/(1000-1)\n",
    "\n",
    "#calcualte the standard deviation\n",
    "std_deviation = math.sqrt(variance)\n",
    "\n",
    "#print results\n",
    "print(\"Mean: \", mean)\n",
    "print(\"Standard deviation: \", std_deviation)\n",
    "\n",
    "#code fossils existing for documentation and error checking\n",
    "'''\n",
    "print(total, \" \", sqr_total, \" \", mean, \" \", variance, \" \", std_deviation)\n",
    "print(squared_list)\n",
    "print(variance_list)\n",
    "print(flip_results)\n",
    "'''"
   ]
  },
  {
   "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": 55,
   "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 numpy as np\n",
    "%matplotlib inline \n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#create gaussians for x and e\n",
    "x = np.random.normal(0, 5, 100)\n",
    "e = np.random.normal(0, 1, 100)\n",
    "\n",
    "#create y that we want to plot\n",
    "y = 0.5*x+e\n",
    "\n",
    "#plot our scatterplot\n",
    "plt.scatter(x, y)\n",
    "plt.show()\n"
   ]
  },
  {
   "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": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}