completed nb

cmathew9.ipynb

@@ -0,0 +1,255 @@
+{
+ "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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+      "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": 8,
+   "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 fib(n=10):\n",
+    "    if n == 0:\n",
+    "        return 0\n",
+    "    if n == 1:\n",
+    "        return 1\n",
+    "    return fib(n-1) + fib(n-2)\n",
+    "nums = []\n",
+    "n = 0\n",
+    "fibn = 0\n",
+    "while fibn <= 1000000:\n",
+    "    nums.append(fibn)\n",
+    "    n += 1\n",
+    "    fibn = fib(n)\n",
+    "print(nums)"
+   ]
+  },
+  {
+   "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": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "mean:  0.497\n",
+      "standard deviation:  0.4999909999189985\n"
+     ]
+    }
+   ],
+   "source": [
+    "tosses = np.random.binomial(1, 0.5, 1000)\n",
+    "mean = sum(tosses)/len(tosses)\n",
+    "std = np.std(tosses)\n",
+    "print(\"mean: \", mean)\n",
+    "print(\"standard deviation: \", 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": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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N9n9fsv1xtcpQBHnLf9t+fUS8aPv1kl4qukF5Kltp5XFJ99r+fttHJN0m6V8LblMh2h++LW9T6wFxFX1J0m22j9i+Ua2H4Y8X3KbC2f4h26/Z+lrSm1Xdz0gnj0t6Z/vrd0oa6SpAIZsv236bpD+VNCHp07bPRcRsRJy3/ZikZyRdkfS+iLhaRBtL4A9t365WaeVZSb9WaGsKEhFXbN8naVnSmKQPR8T5gptVBjdJ+rhtqfX3+O8j4jPFNqkYtv9B0s9KOmj7BUm/I+khSY/Zfq9aS2b/cnEtzB9T9AEgcWUrrQAA+kSQA0DiCHIASBxBDgCJI8gBIHEEOQAkjiAHgMT9Py/XUq9zWCz9AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "x = np.random.normal(0, 5, 25)\n",
+    "e = np.random.normal(0, 1, 25)\n",
+    "scatter = plt.scatter(x, 0.5*x+e)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}