cearhear.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IPython version:      %6.6s 8.3.0\n",
      "[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": [
    "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__)\n"
   ]
  },
  {
   "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",
      "[[0.21759606 0.33573653 0.36131715]\n",
      " [0.55510547 0.63439092 0.15811647]]\n",
      "Add 5 to every element\n",
      "[[5.21759606 5.33573653 5.36131715]\n",
      " [5.55510547 5.63439092 5.15811647]]\n",
      "Get the first row\n",
      "[5.21759606 5.33573653 5.36131715]\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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j+xn9ANoH/PRaHN++/YAkdei4v+YuST2y3CWpQ5a7JHXIcpekDlnuktQhy12SOmS5S1KH/g/0Ig+PMFB4LAAAAABJRU5ErkJggg==\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": 31,
   "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": [
    "def fibonacci(n):\n",
    "    a = 0\n",
    "    b = 1\n",
    "    for _ in range(n):\n",
    "        yield a\n",
    "        a, b = b, a + b\n",
    "\n",
    "print(list(fibonacci(31)))"
   ]
  },
  {
   "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": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.505\n",
      "0.49997499937496875\n"
     ]
    }
   ],
   "source": [
    "np.random.seed()\n",
    "n = 1\n",
    "p = 0.5\n",
    "x = np.random.binomial(n, p, 1000)\n",
    "print(np.mean(x))\n",
    "print(np.std(x))\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": 37,
   "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, 1000)\n",
    "e = np.random.normal(0, 1, 1000)\n",
    "y = 0.5*x + e\n",
    "\n",
    "plt.scatter(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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
}