aesser.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 32,
   "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": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Make a 2 row x 3 column array of random numbers\n",
      "[[0.20470923 0.22425775 0.89678096]\n",
      " [0.62572716 0.61722194 0.09676417]]\n",
      "Add 5 to every element\n",
      "[[5.20470923 5.22425775 5.89678096]\n",
      " [5.62572716 5.61722194 5.09676417]]\n",
      "Get the first row\n",
      "[5.20470923 5.22425775 5.89678096]\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": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function ndarray.any>"
      ]
     },
     "execution_count": 34,
     "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": 35,
   "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": 36,
   "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",
      "55\n",
      "89\n",
      "144\n",
      "233\n",
      "377\n",
      "610\n",
      "987\n",
      "1597\n",
      "2584\n",
      "4181\n",
      "6765\n",
      "10946\n",
      "17711\n",
      "28657\n",
      "46368\n",
      "75025\n",
      "121393\n",
      "196418\n",
      "317811\n",
      "514229\n",
      "832040\n"
     ]
    }
   ],
   "source": [
    "first = 0\n",
    "second = 1\n",
    "temp = 0\n",
    "print(\"0\\n1\")\n",
    "while second + first < 1000000:\n",
    "    print(first + second)\n",
    "    temp = first\n",
    "    first = second\n",
    "    second += temp"
   ]
  },
  {
   "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": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.505\n",
      "0.49997499937496875\n"
     ]
    }
   ],
   "source": [
    "n, p = 1, .5  # number of trials, probability of each trial\n",
    "s = np.random.binomial(n, p, 1000)\n",
    "print(np.mean(s))\n",
    "print(np.std(s))"
   ]
  },
  {
   "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": 38,
   "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": [
    "x = np.random.normal(0, 5, 500)\n",
    "y = (0.5*x) + np.random.normal(0, 1, 500)\n",
    "\n",
    "plt.scatter(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "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
}