bgullet1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IPython version:      %6.6s 8.3.0\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",
      "[[0.13969487 0.90766739 0.07098579]\n",
      " [0.44794381 0.1527587  0.79622505]]\n",
      "Add 5 to every element\n",
      "[[5.13969487 5.90766739 5.07098579]\n",
      " [5.44794381 5.1527587  5.79622505]]\n",
      "Get the first row\n",
      "[5.13969487 5.90766739 5.07098579]\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": {
    "scrolled": true
   },
   "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": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[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": [
    "# Declare list to hold Fibonacci numbers\n",
    "fibs = []\n",
    "\n",
    "# Add first two Fib number\n",
    "fibs.append(0)\n",
    "fibs.append(1)\n",
    "\n",
    "ind = 2\n",
    "\n",
    "# Add \n",
    "while True:\n",
    "    \n",
    "    cur_sum = fibs[ind - 1] + fibs[ind - 2]\n",
    "    \n",
    "    if cur_sum <= 1000000:\n",
    "        fibs.append(cur_sum)\n",
    "    \n",
    "        ind = ind + 1\n",
    "    else:\n",
    "        break\n",
    "    \n",
    "print(fibs)"
   ]
  },
  {
   "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": 109,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Heads: 506\n",
      "Number of Tails: 494\n",
      "Mean: 0.506\n",
      "STD: 0.4999639987039066\n"
     ]
    }
   ],
   "source": [
    "# Generate binomial distribution (0 => tails, 1 => heads)\n",
    "dist = np.random.binomial(1, 0.5, 1000)\n",
    "\n",
    "# Calc mean and standard deviation\n",
    "mean = np.mean(dist)\n",
    "std = np.std(dist)\n",
    "\n",
    "# Find number of heads and tails\n",
    "num_heads = np.count_nonzero(dist)\n",
    "num_tails = 1000 - num_heads\n",
    "\n",
    "# Print out results\n",
    "print(\"Number of Heads: \" + str(num_heads))\n",
    "print(\"Number of Tails: \" + str(num_tails))\n",
    "print(\"Mean: \" + str(mean))\n",
    "print(\"STD: \" + str(std))"
   ]
  },
  {
   "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": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fb29d967730>"
      ]
     },
     "execution_count": 99,
     "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": [
    "# Generate Gaussian distributions with 1000 data points each\n",
    "x = np.random.normal(0,5,1000)\n",
    "e = np.random.normal(0,1,1000)\n",
    "\n",
    "# Do math equation\n",
    "y = 0.5*x+e\n",
    "\n",
    "# Scatter plot math equation\n",
    "plt.scatter(x,y)"
   ]
  },
  {
   "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
}