lwrinkle.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": 2,
   "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": [
    "a = c = 0\n",
    "b = 1\n",
    "results = [0, 1]\n",
    "while a+b < 1000000:\n",
    "    c = a+b\n",
    "    results.append(c)\n",
    "    a = b\n",
    "    b = c\n",
    "print(results)"
   ]
  },
  {
   "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": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.489] 0.49987898535545583\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import statistics\n",
    "mean = sd = 0\n",
    "t = []\n",
    "for x in range(0,1000):\n",
    "    t.append( np.random.binomial(1, 0.5, 1))\n",
    "\n",
    "mean = sum(t)/1000\n",
    "sd = np.std(t)\n",
    "print(mean, sd)"
   ]
  },
  {
   "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": 27,
   "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": [
    "# 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\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "data = np.random.normal(0, 5, 100)\n",
    "data2 = np.random.normal(0, 1, 100)\n",
    "\n",
    "formula = 0.5 * data + data2\n",
    "plt.scatter(data, formula)\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
}