diff --git a/Practice0.ipynb b/Practice0.ipynb deleted file mode 100644 index 4b16c03..0000000 --- a/Practice0.ipynb +++ /dev/null @@ -1,198 +0,0 @@ -{ - "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 -} diff --git a/mcox59.ipynb b/mcox59.ipynb new file mode 100644 index 0000000..f5a6450 --- /dev/null +++ b/mcox59.ipynb @@ -0,0 +1,253 @@ +{ + "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": 1, + "metadata": {}, + "outputs": [ + { + "ename": "SyntaxError", + "evalue": "invalid syntax (, line 4)", + "output_type": "error", + "traceback": [ + "\u001b[0;36m File \u001b[0;32m\"\"\u001b[0;36m, line \u001b[0;32m4\u001b[0m\n\u001b[0;31m x.\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n" + ] + } + ], + "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." + ] + }, + { + "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": [ + "import numpy as np\n", + "nums = np.arange(1,1000001)\n", + "ROOT_FIVE = np.sqrt(5)\n", + "alpha = (1 + ROOT_FIVE) / 2\n", + "beta = (1 - ROOT_FIVE) / 2 \n", + "fib = np.rint(((alpha ** a) - (beta ** a) / (ROOT_FIVE)))" + ] + }, + { + "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": 4, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "499.412\n", + "16.145347812915027\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "heads = np.random.binomial(1000, .5, size=1000)\n", + "mean = np.mean(heads)\n", + "stdev = np.std(heads)" + ] + }, + { + "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": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "x = np.random.normal(0,5, size=1000)\n", + "e = np.random.normal(0,1, size=1000)\n", + "y = np.add(np.multiply(0.5, x), e)\n", + "plt.plot(x,y, 'o', color='black')" + ] + }, + { + "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 +}