complete the Practice0 hw

zdong7.ipynb

@@ -0,0 +1,264 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "IPython version:      %6.6s 7.17.0\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",
+      "[[0.39860853 0.00172869 0.97968484]\n",
+      " [0.65524169 0.85791178 0.72916159]]\n",
+      "Add 5 to every element\n",
+      "[[5.39860853 5.00172869 5.97968484]\n",
+      " [5.65524169 5.85791178 5.72916159]]\n",
+      "Get the first row\n",
+      "[5.39860853 5.00172869 5.97968484]\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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\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": 5,
+   "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": [
+    "fn = 0\n",
+    "f0 = 0\n",
+    "f1 = 1\n",
+    "fnList = []\n",
+    "while fn < 1000000:\n",
+    "    if fn == 0:\n",
+    "        fnList.append(f0)\n",
+    "\n",
+    "    fnList.append(f1)\n",
+    "    fn = f0 + f1\n",
+    "    f0 = f1\n",
+    "    f1 = fn\n",
+    "\n",
+    "# Print the Fibonacci numbers list\n",
+    "print(fnList)\n"
+   ]
+  },
+  {
+   "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": [
+      "The mean is: 0.527000\n",
+      "\n",
+      "The standard deviation is: 0.499270\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "n, p = 1, 0.5\n",
+    "mean = np.random.binomial(n, p, 1000)\n",
+    "res = np.mean(mean)\n",
+    "print(\"The mean is: %f\\n\" % res)\n",
+    "\n",
+    "stan = np.std(mean)\n",
+    "print(\"The standard deviation is: %f\\n\" % stan )"
+   ]
+  },
+  {
+   "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": 41,
+   "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",
+    "x = np.random.normal(0, 5, 50)\n",
+    "e = np.random.normal(0, 1, 50)\n",
+    "y = 0.5*x + e\n",
+    "gram = plt.scatter(x, y)"
+   ]
+  },
+  {
+   "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
+}