tneuefei.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",
      "[[0.29097719 0.44945147 0.8145607 ]\n",
      " [0.11202385 0.08807172 0.4049387 ]]\n",
      "Add 5 to every element\n",
      "[[5.29097719 5.44945147 5.8145607 ]\n",
      " [5.11202385 5.08807172 5.4049387 ]]\n",
      "Get the first row\n",
      "[5.29097719 5.44945147 5.8145607 ]\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": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lUOcdSX6RyRkDVfVoVf2oqn4M/A2P/ck/MWNgcCZ4ffdn/xeAHzO4cdUkjYEkK4A/AK4Z6j5JY9gCHF7+B8btuTSKif4jfTG4APFJ4INz2jcC9wJTc9qfy09efHmQ0V98OdIYNgwtvwm4rlvexE9efPnCuP4e5vTZy2MXVCdmDMDqoeWLGMzvTtpz6XXAn3XLz2EwBZBJGkO3bSPwuTltEzMGBnPvL+6WzwZ2d8tj8XoY2Q/sCD/E32ZwkeIu4M7u6xwGF1UeHmr72NA+lzC4on4/3ZXrMR3Dp4C7u/Z/ZHCR9fAT5yPdGL4MTI/rGOb0GQ73iRkD8HddjXcxuN/RcNhPynPpRODvu+fTHcBLJm0M3bZPAK+bZ5+JGEPXvpvBP0a7gF/v+o/F68HbD0hSg8Zuzl2S1J/hLkkNMtwlqUGGuyQ1yHCXpAYZ7pLUIMNdkhr0/7PHexcvvoyjAAAAAElFTkSuQmCC\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": 9,
   "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]\n"
     ]
    }
   ],
   "source": [
    "fibarray = []\n",
    "fibarray.append(0)\n",
    "fibarray.append(1)\n",
    "\n",
    "for i in range(2,1000000) :\n",
    "    fibarray.insert(i, (fibarray[i-1] + fibarray[i-2]))\n",
    "    \n",
    "#commenting out the print statement in case anyone wants to test for grading\n",
    "#print (fibarray)"
   ]
  },
  {
   "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": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean is 499.587\n",
      "Standard Deviation is 15.101206276321125\n"
     ]
    }
   ],
   "source": [
    "from math import sqrt\n",
    "\n",
    "heads = np.random.binomial(1000, .5, size=1000)\n",
    "\n",
    "#find the mean\n",
    "sum = 0\n",
    "for val in heads:\n",
    "    sum += val\n",
    "    \n",
    "mean = sum / 1000\n",
    "\n",
    "\n",
    "#calculate the standard deviation\n",
    "sum_of_squares = 0\n",
    "for val in heads:\n",
    "    squared_val = (val - mean) * (val - mean)\n",
    "    sum_of_squares += squared_val\n",
    "\n",
    "variance = sum_of_squares / 1000\n",
    "std_dev = sqrt(variance)\n",
    "\n",
    "print(\"The mean is\", mean)\n",
    "print(\"Standard Deviation is\", std_dev)"
   ]
  },
  {
   "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": 28,
   "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",
    "\n",
    "x = np.random.normal(2.5, 1, size = 100) \n",
    "e = np.random.normal(0.5,1, size= 100)\n",
    "\n",
    "y = (0.5 * x + e)\n",
    "\n",
    "plot = plt.scatter(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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
}