From cbeab4f3a74567aca4081f57409fb658b5e0a7b7 Mon Sep 17 00:00:00 2001 From: BuddySwan Date: Wed, 7 Sep 2022 14:47:04 +0000 Subject: [PATCH 1/2] --- rswan.ipynb | 198 ++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 198 insertions(+) create mode 100644 rswan.ipynb diff --git a/rswan.ipynb b/rswan.ipynb new file mode 100644 index 0000000..4b16c03 --- /dev/null +++ b/rswan.ipynb @@ -0,0 +1,198 @@ +{ + "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": [ + "" + ] + }, + "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": {}, + "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": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "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": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "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": null, + "metadata": { + "collapsed": true + }, + "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": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} From 84b2ea21d05c3d449e9f4079746faf4f6b0cad11 Mon Sep 17 00:00:00 2001 From: BuddySwan Date: Wed, 7 Sep 2022 15:17:23 +0000 Subject: [PATCH 2/2] Completed Practice 0 --- rswan.ipynb | 103 +++++++++++++++++++++++++++++++++++++++------------- 1 file changed, 78 insertions(+), 25 deletions(-) diff --git a/rswan.ipynb b/rswan.ipynb index 4b16c03..bb80933 100644 --- a/rswan.ipynb +++ b/rswan.ipynb @@ -9,7 +9,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "IPython version: %6.6s 6.1.0\n" + "IPython version: %6.6s 8.3.0\n" ] } ], @@ -46,13 +46,13 @@ "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", + "[[0.17708329 0.59248858 0.12700972]\n", + " [0.77754595 0.22366339 0.71782274]]\n", "Add 5 to every element\n", - "[[ 5.20354485 5.87353642 5.79226415]\n", - " [ 5.26457656 5.23486214 5.8240387 ]]\n", + "[[5.17708329 5.59248858 5.12700972]\n", + " [5.77754595 5.22366339 5.71782274]]\n", "Get the first row\n", - "[ 5.20354485 5.87353642 5.79226415]\n" + "[5.17708329 5.59248858 5.12700972]\n" ] } ], @@ -102,12 +102,14 @@ "outputs": [ { "data": { - "image/png": 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -130,12 +132,23 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] + "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, 1346269]\n" + ] + } + ], + "source": [ + "sequence = [0,1]\n", + "while sequence[len(sequence)-1] < 1000000:\n", + " sequence.append(sequence[len(sequence)-1]+sequence[len(sequence)-2])\n", + "print(sequence)" + ] }, { "cell_type": "markdown", @@ -148,12 +161,24 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average value: 0.52\n", + "Standard deviation: 0.4996\n" + ] + } + ], + "source": [ + "results = np.random.binomial(1, .5, 1000)\n", + "avgValue = np.mean(results)\n", + "standardDev = np.std(results)\n", + "print(f\"Average value: {round(avgValue,3)}\\nStandard deviation: {round(standardDev,5)}\")" + ] }, { "cell_type": "markdown", @@ -166,17 +191,45 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": { - "collapsed": true + "scrolled": true }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "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 = []\n", + "for i in range(len(x)):\n", + " y.append((0.5 * x[i]) + e[i])\n", + "plt.scatter(x, y)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -190,7 +243,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.8.10" } }, "nbformat": 4,