diff --git a/tnguye69.ipynb b/tnguye69.ipynb new file mode 100644 index 0000000..72a6205 --- /dev/null +++ b/tnguye69.ipynb @@ -0,0 +1,319 @@ +{ + "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": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# IPython is quite modern: just press at the end of the unfinished statement to see the documentation\n", + "# on possible completions.\n", + "# In the code cell below, type x., to find built-in operations for x\n", + "x.any" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "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": 57, + "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", + "current = 0\n", + "print(first)\n", + "print(second)\n", + "\n", + "while(current < 1000000):\n", + " current = first + second\n", + " if(current > 1000000):\n", + " break\n", + " print(current)\n", + " first = second\n", + " second = current" + ] + }, + { + "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": 58, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean: 0.509 Standard Deviation: 0.4999189934379369\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "\n", + "tosses = np.random.binomial(1, 0.5, 1000)\n", + "\n", + "mean = np.mean(tosses)\n", + "std = np.std(tosses)\n", + "print('Mean: ', mean, \"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": 60, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "def plot():\n", + " x = np.random.normal(0,5)\n", + " e = np.random.normal(0,1)\n", + " return(x, 0.5*x+e)\n", + "\n", + "xList = []\n", + "yList = []\n", + "for i in range(1000):\n", + " x, y = plot()\n", + " xList.append(x)\n", + " yList.append(y)\n", + "\n", + "plt.scatter(xList, yList)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "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 +}