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November 21, 2013 18:07
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Assignment4-Hopefully WOrkin
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| { | |
| "cells": [ | |
| { | |
| "cell_type": "heading", | |
| "level": 2, | |
| "metadata": {}, | |
| "source": [ | |
| "<h1>Assignment 4</h1>\n", | |
| "<h4>by Kevin Fox</h4>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "heading", | |
| "level": 3, | |
| "metadata": {}, | |
| "source": [ | |
| "PART 1 - Analytics with SQL" | |
| ] | |
| }, | |
| { | |
| "cell_type": "heading", | |
| "level": 4, | |
| "metadata": {}, | |
| "source": [ | |
| "1) - How many shops are there??" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "SELECT COUNT(shops.shop_id) as count <br/>\n", | |
| "FROM shops;<br/>\n", | |
| "<br/>\n", | |
| "OUTPUT:<br/>\n", | |
| "<br/><b>46426</b>\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "heading", | |
| "level": 4, | |
| "metadata": {}, | |
| "source": [ | |
| "2) - What is the average price over all listings? What is the average \u201cprice\u201d across all transactions?" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "SELECT AVG(listings.price) as avgPriceListing<br/>\n", | |
| "FROM listings;<br/>\n", | |
| "<br/>\n", | |
| "OUTPUT:<br/>\n", | |
| "Average Price over all listing = \n", | |
| "<b>3521.7482 Cents</b><br/><br/>\n", | |
| "SELECT AVG(price*100) as avgPriceTrans<br/>\n", | |
| "FROM transactions;<br/>\n", | |
| "<br/>\n", | |
| "OUTPUT:<br/>Average Price over all transactions = \n", | |
| "<b>2075.8080 Cents</b>\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "heading", | |
| "level": 4, | |
| "metadata": {}, | |
| "source": [ | |
| "3) - What is the average individual price of each listing purchased (note that the price field in the transactions table is the total price for the transaction; you need to control for quantity). How does this compare to the average listing price?" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "SELECT AVG((transactions.price*100)/transactions.`quantity`) as avgPriceTransAdjForQuantity<br/>\n", | |
| "FROM transactions <br/>\n", | |
| "SELECT AVG(listings.price) as avgPriceListing<br/>\n", | |
| "FROM listings<br/><br/>\n", | |
| "OUTPUT: <br/>\n", | |
| "Average Indv. Price of each listing purchased = <b>2045.05588738 Cents</b><br/>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "The average listing price is \\$35.21 while the average individual price of actual listings purchased is $20.45, which may indicate that customers are more likley to buy cheaper items more frequently than the more expensive items on Etsy. " | |
| ] | |
| }, | |
| { | |
| "cell_type": "heading", | |
| "level": 4, | |
| "metadata": {}, | |
| "source": [ | |
| "4) - Remove listings with a price or quantity of 0 and recompute the average price. How does this compare to the average price of each listing purchased?" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "SELECT AVG(listings.price) as avgPriceListing<br/>\n", | |
| "FROM listings<br/>\n", | |
| "WHERE listings.price <> '0' and listings.quantity <> '0'<br/>\n", | |
| "<br/>\n", | |
| "SELECT AVG(price*100) as avgPriceTrans<br/>\n", | |
| "FROM transactions<br/><br/>\n", | |
| "OUTPUT:<br/>\n", | |
| "Average price of listings w/o Zeros = <b>3753.6990 Cents</b>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "The average listing price is \\$35.21 while the average listing price accounting for listings that are priced at Zero or have a qunatity of Zero is \\$37.53, which is expected since removing lower bound elements (ie. Zeros) will increase the average price of listings." | |
| ] | |
| }, | |
| { | |
| "cell_type": "heading", | |
| "level": 4, | |
| "metadata": {}, | |
| "source": [ | |
| "5) - What are the 5 most expensive listings" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "SELECT listing_id,user_id,price <br/>\n", | |
| "FROM listings<br/>\n", | |
| "ORDER BY price DESC<br/>\n", | |
| "LIMIT 5<br/>\n", | |
| "<br/>\n", | |
| "SELECT listing_id,user_id, price/quantity as priceAdjustedForQuantity<br/>\n", | |
| "FROM listings<br/>\n", | |
| "ORDER BY priceAdjustedForQuantity DESC<br/>\n", | |
| "LIMIT 5<br/><br/>\n", | |
| "OUTPUT:<br/>\n", | |
| "<table>\n", | |
| " <tr>\n", | |
| " <td>\n", | |
| " <b> Listing ID</b>\n", | |
| " </td>\n", | |
| " <td>\n", | |
| " <b>User ID</b>\n", | |
| " </td>\n", | |
| " <td>\n", | |
| " <b>Listing Price</b>\n", | |
| " </td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>\n", | |
| " 929296\n", | |
| " </td>\n", | |
| " <td>\n", | |
| " 760430\n", | |
| " </td>\n", | |
| " <td>\n", | |
| " 25000000\n", | |
| " </td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>\n", | |
| " 825605\n", | |
| " </td>\n", | |
| " <td>\n", | |
| " 168439\n", | |
| " </td>\n", | |
| " <td>\n", | |
| " 3500000\n", | |
| " </td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>\n", | |
| " 149300\n", | |
| " </td>\n", | |
| " <td>\n", | |
| " 440712\n", | |
| " </td>\n", | |
| " <td>\n", | |
| " 2000000\n", | |
| " </td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>\n", | |
| " 92558\n", | |
| " </td>\n", | |
| " <td>\n", | |
| " 594058\n", | |
| " </td>\n", | |
| " <td>\n", | |
| " 1100000\n", | |
| " </td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>\n", | |
| " 65276\n", | |
| " </td>\n", | |
| " <td>\n", | |
| " 445767\n", | |
| " </td>\n", | |
| " <td>\n", | |
| " 1000000\n", | |
| " </td>\n", | |
| " </tr>\n", | |
| "</table>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "heading", | |
| "level": 4, | |
| "metadata": {}, | |
| "source": [ | |
| "6) - How many listings has each user purchased? (Explain your interpretation(s))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "SELECT buyer_user_id, COUNT(quantity)<br/>\n", | |
| "from transactions<br/>\n", | |
| "group by buyer_user_id<br/>\n", | |
| "</br>\n", | |
| "OUTPUT:<br/>\n", | |
| "<table>\n", | |
| " <tr>\n", | |
| " <td>\n", | |
| " <b>Buyer_user_id</b>\n", | |
| " </td>\n", | |
| " <td>\n", | |
| " <b>Quantity</b>\n", | |
| " </td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>\n", | |
| " 11\n", | |
| " </td>\n", | |
| " <td>\n", | |
| " 1\n", | |
| " </td>\n", | |
| "\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>\n", | |
| " 21\n", | |
| " </td>\n", | |
| " <td>\n", | |
| " 1\n", | |
| " </td>\n", | |
| "\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>\n", | |
| " 24\n", | |
| " </td>\n", | |
| " <td>\n", | |
| " 1\n", | |
| " </td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>\n", | |
| " 37\n", | |
| " </td>\n", | |
| " <td>\n", | |
| " 1\n", | |
| " </td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>\n", | |
| " 51\n", | |
| " </td>\n", | |
| " <td>\n", | |
| " 1\n", | |
| " </td>\n", | |
| " </tr>\n", | |
| "</table>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "I grouped by the buyer_user_ids in the transactions table to get just the buyers that bought items and then counted the qunaity of items they bought in order to answer the question. " | |
| ] | |
| }, | |
| { | |
| "cell_type": "heading", | |
| "level": 3, | |
| "metadata": {}, | |
| "source": [ | |
| "EXTRA CREDIT " | |
| ] | |
| }, | |
| { | |
| "cell_type": "heading", | |
| "level": 4, | |
| "metadata": {}, | |
| "source": [ | |
| "7 ) - Compute the distribution of how many users purchase different numbers of listings (# listings purchased vs. # users with that many purchases). You can ignore users with 0 purchases. (Could you plot this? Hint: click export) " | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "\n", | |
| "<br/>\n", | |
| "\tSELECT u.user_id , COUNT(t.transaction_id) <br/>\n", | |
| "\tFROM users u<br/>\n", | |
| "\tJOIN transactions t<br/>\n", | |
| "\tON u.user_id = t.buyer_user_id<br/>\n", | |
| "\tGROUP BY u.user_id<br/>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "%matplotlib inline\n", | |
| "\n", | |
| "import matplotlib.pyplot as plt\n", | |
| "import collections\n", | |
| "queryResults = open(\"query_result.csv\",\"r\")\n", | |
| "\n", | |
| "#Skip header\n", | |
| "queryResults.next()\n", | |
| "\n", | |
| "userDist = collections.Counter()\n", | |
| "userDistOrdered = collections.OrderedDict()\n", | |
| "\n", | |
| "for line in queryResults:\n", | |
| " parts = line.split(',')\n", | |
| " userDist[parts[1].replace('\\n','')]+= 1\n", | |
| " if parts[1].replace('\\n','') in userDistOrdered:\n", | |
| " userDistOrdered[parts[1].replace('\\n','')]+= 1\n", | |
| " else:\n", | |
| " userDistOrdered[parts[1].replace('\\n','')]= 1\n", | |
| "\n", | |
| "final = collections.OrderedDict(sorted(userDistOrdered.items(), key=lambda t: t[0]))\n", | |
| "\n", | |
| "\n", | |
| "plt.plot(final.keys(),final.values())\n", | |
| "plt.title(\"Distributions of User Listing Purchase\")\n", | |
| "plt.xlabel(\"Number of Purchases\")\n", | |
| "plt.ylabel(\"Number of Users\")\n", | |
| "plt.show()" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "metadata": {}, | |
| "output_type": "display_data", | |
| "png": 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bBcC330JEBPz0k00OJ4QQzdINN3B/9NFHNaqbNBoNXbp0YfDgwbi7u9suUhuy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\nsEiShRBCCIskWQghhLBIkoUQQgiL/h/1fA2+2n+yvAAAAABJRU5ErkJggg==\n", | |
| "text": [ | |
| "<matplotlib.figure.Figure at 0x1060d0c10>" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 91 | |
| }, | |
| { | |
| "cell_type": "heading", | |
| "level": 4, | |
| "metadata": {}, | |
| "source": [ | |
| "8) - Compute the number of users with each gender." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "SELECT u.gender as gender, COUNT(u.user_id) as Num_Users<br/>\n", | |
| "FROM users u<br/>\n", | |
| "GROUP BY u.`gender`;<br/>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "OUTPUT:<br/>\n", | |
| "<b>\n", | |
| "private\t52228<br/>\n", | |
| "male\t6863<br/>\n", | |
| "female\t36052<br/>\n", | |
| "</b>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "heading", | |
| "level": 4, | |
| "metadata": {}, | |
| "source": [ | |
| "9) - Among the users with purchases, compute the number of users with each gender." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "SELECT u.gender as gender, COUNT(u.user_id) as Num_Users<br/>\n", | |
| "FROM users u<br/>\n", | |
| "JOIN transactions t<br/>\n", | |
| "ON u.user_id = t.buyer_user_id<br/>\n", | |
| "GROUP BY u.`gender`;<br/>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "\n", | |
| "OUTPUT : <br/>\n", | |
| "<b>\n", | |
| "female\t3676<br/>\n", | |
| "male\t681<br/>\n", | |
| "private\t5222<br/>\n", | |
| "</b>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "heading", | |
| "level": 4, | |
| "metadata": {}, | |
| "source": [ | |
| "10) - If you needed to combine information from two or more tables and then use Python to perform further analytics on the result, are you now prepared to do so? Explain briefly." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Yes. I am now well aware how to perform a join amongst two tables and export the results in a csv format which I could easily parse and analze using Python. " | |
| ] | |
| }, | |
| { | |
| "cell_type": "heading", | |
| "level": 2, | |
| "metadata": {}, | |
| "source": [ | |
| "PART 2 - Dealing with Data: Parsing Text and Extracting Information" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "First Step is download the roster.html in order to do the analysis." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "!curl http://people.stern.nyu.edu/ja1517/data/pds_2012_roster.html > roster.html\n" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| " % Total % Received % Xferd Average Speed Time Time Time Current\r\n", | |
| " Dload Upload Total Spent Left Speed\r\n", | |
| "\r", | |
| " 0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "\r", | |
| "100 25218 100 25218 0 0 59416 0 --:--:-- --:--:-- --:--:-- 535k\r\n" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 1 | |
| }, | |
| { | |
| "cell_type": "heading", | |
| "level": 4, | |
| "metadata": {}, | |
| "source": [ | |
| "1 ) Within the document, there are several student IDs (the column actually is titled E-mail). Extract these IDs from the html and print them to a file, one per line." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "import re as re\n", | |
| "\n", | |
| "file1 = open('roster.html')\n", | |
| "outputFile1 = open('output1.txt','a')\n", | |
| "regex = re.compile('>[a-z]{2,3}[0-9]{3,4}<')\n", | |
| "studentIDs = {}\n", | |
| "\n", | |
| "for line in file1:\n", | |
| " four_letter_words = regex.findall(line)\n", | |
| " for word in four_letter_words:\n", | |
| " studentIDs[word] = 1\n", | |
| "print \"Number of Student IDs : \",\n", | |
| "print len(studentIDs)\n", | |
| "print \"\"\n", | |
| "for key in studentIDs.keys():\n", | |
| " print key.replace('>',' ').replace('<',' ')\n", | |
| " outputFile1.write(str(key.replace('>','').replace('<','')))\n", | |
| " outputFile1.write(\"\\n\")\n", | |
| "outputFile1.close()\n", | |
| "file1.close()\n" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "Number of Student IDs : 44\n", | |
| "\n", | |
| " jhr320 \n", | |
| " kg1155 \n", | |
| " vd511 \n", | |
| " bct248 \n", | |
| " jz996 \n", | |
| " svs290 \n", | |
| " aj1149 \n", | |
| " arb492 \n", | |
| " ssb405 \n", | |
| " jh3347 \n", | |
| " zf309 \n", | |
| " aka300 \n", | |
| " hnk224 \n", | |
| " ksj222 \n", | |
| " djs507 \n", | |
| " kjs418 \n", | |
| " amc723 \n", | |
| " ll1691 \n", | |
| " ar3109 \n", | |
| " das607 \n", | |
| " jc4794 \n", | |
| " vj384 \n", | |
| " ds3569 \n", | |
| " jh924 \n", | |
| " as6317 \n", | |
| " asa384 \n", | |
| " xyh202 \n", | |
| " sr1927 \n", | |
| " ss6335 \n", | |
| " ajc591 \n", | |
| " dhk332 \n", | |
| " mrp295 \n", | |
| " cp1105 \n", | |
| " cmc616 \n", | |
| " awl270 \n", | |
| " zbh203 \n", | |
| " rjz226 \n", | |
| " srh371 \n", | |
| " tfp211 \n", | |
| " crg272 \n", | |
| " sp2710 \n", | |
| " rs2715 \n", | |
| " aml601 \n", | |
| " dcc327 \n" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 2 | |
| }, | |
| { | |
| "cell_type": "heading", | |
| "level": 4, | |
| "metadata": {}, | |
| "source": [ | |
| "2 ) - Constrain your search to print only those students with four letters in their last names or less. How many students were removed? " | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "import re as re\n", | |
| "\n", | |
| "file2 = open('roster.html')\n", | |
| "outputFile2 = open('output2.txt','a')\n", | |
| "regex2 = re.compile('[A-Z]{1,1}[a-zA-Z]{0,3}[,]{1,1}[ ]*[a-zA-Z]*[ ]*[a-zA-Z.]*')\n", | |
| "studentNames = {}\n", | |
| "\n", | |
| "for line in file2:\n", | |
| " four_letter_words = regex2.findall(line)\n", | |
| " for word in four_letter_words:\n", | |
| " studentNames[word] = 1\n", | |
| " \n", | |
| "print \"Number of Student IDs : \",\n", | |
| "print len(studentNames)\n", | |
| "print \"\"\n", | |
| "for key in studentNames.keys():\n", | |
| " print key\n", | |
| " outputFile2.write(str(key))\n", | |
| " outputFile2.write(\"\\n\")\n", | |
| "print \"\"\n", | |
| "print \"Number of students removed = \",\n", | |
| "print len(studentIDs)-len(studentNames)\n", | |
| "file2.close()\n", | |
| "outputFile2.close()" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "Number of Student IDs : 14\n", | |
| "\n", | |
| "Saat, Daniel A.\n", | |
| "Hao, Jerry\n", | |
| "Lin, Liwen\n", | |
| "Hill, Zelos B.\n", | |
| "Shi, Shenglun\n", | |
| "Kim, Hyung\n", | |
| "Feng, Zhengyang\n", | |
| "John, Kevin C.\n", | |
| "Rhyu, Joseph H.\n", | |
| "Liss, Alexander W.\n", | |
| "Choi, Jaesik\n", | |
| "Ho, Xias Yi\n", | |
| "Zhu, Jianping\n", | |
| "Kim, Dong H.\n", | |
| "\n", | |
| "Number of students removed = 30\n" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 3 | |
| }, | |
| { | |
| "cell_type": "heading", | |
| "level": 4, | |
| "metadata": {}, | |
| "source": [ | |
| "3 - For every student in the class, in addition to extracting their student ID, extract their name. Present the results by printing out, one student per line:<br/>\n", | |
| " first (and middle) name [tab] last name [tab] student id" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "import re as re\n", | |
| "\n", | |
| "file3 = open('roster.html')\n", | |
| "outputFile3 = open('output3.txt','a')\n", | |
| "regex3 = re.compile('[A-Z]{1,1}[a-zA-Z]+[,]{1,1}[ ]*[a-zA-Z]*[ ]*[a-zA-Z.]*')\n", | |
| "regex4 = re.compile('>[a-z]{2,3}[0-9]{3,4}<')\n", | |
| "studentFullNames = []\n", | |
| "\n", | |
| "##skip first 120 lines to avoid instructor names\n", | |
| "for x in range(0,120):\n", | |
| " file3.readline()\n", | |
| "\n", | |
| "for line in file3:\n", | |
| " result1 = regex3.findall(line)\n", | |
| " result2 = regex4.findall(line)\n", | |
| " for word in result1:\n", | |
| " studentFullNames.append(word)\n", | |
| " for word in result2:\n", | |
| " studentFullNames.append(word.replace('>','').replace('<',''))\n", | |
| " \n", | |
| "\n", | |
| "print \"First and Middle \\t Last Name \\t NetID\"\n", | |
| "\n", | |
| "count=0\n", | |
| "for key in studentFullNames:\n", | |
| " ##Check if odd, which means its a name not ID\n", | |
| " if(count%2==0):\n", | |
| " names = key.strip().split()\n", | |
| " lastName = names[0].replace(',','').strip()\n", | |
| " firstName = names[1].strip()\n", | |
| " if(len(names)==3):\n", | |
| " middleName = names[2].strip()\n", | |
| " fullName = firstName+' '+middleName+'\\t'+lastName\n", | |
| " else:\n", | |
| " fullName = firstName+'\\t'+lastName\n", | |
| " outputFile3.write(fullName)\n", | |
| " print fullName,\n", | |
| " else:\n", | |
| " print '\\t',\n", | |
| " outputFile3.write('\\t')\n", | |
| " studentID = key\n", | |
| " print key\n", | |
| " outputFile3.write(studentID)\n", | |
| " outputFile3.write('\\n')\n", | |
| " \n", | |
| " \n", | |
| " \n", | |
| " count+=1\n", | |
| "print \"\"\n", | |
| "file3.close()\n", | |
| "outputFile3.close()\n" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "First and Middle \t Last Name \t NetID\n", | |
| "Abhishek Kumar\tAiyangar \taka300\n", | |
| "Antik Subodh\tAvasthi \tasa384\n", | |
| "Sonal Suresh\tBathe \tssb405\n", | |
| "Aditya\tBhandari \tarb492\n", | |
| "Corinne M.\tCasagrande \tcmc616\n", | |
| "Ann M.\tCedrone \tamc723\n", | |
| "Alejandro J.\tChahin \tajc591\n", | |
| "Jaesik\tChoi \tjc4794\n", | |
| "David C.\tCohodes \tdcc327\n", | |
| "Vitalii\tDoban \tvd511\n", | |
| "Zhengyang\tFeng \tzf309\n", | |
| "Charlotte R.\tGeorge \tcrg272\n", | |
| "Kelly\tGoldston \tkg1155\n", | |
| "Jerry\tHao \tjh3347\n", | |
| "Sneha R\tHarchwani \tsrh371\n", | |
| "Zelos B.\tHill \tzbh203\n", | |
| "Xias Yi\tHo \txyh202\n", | |
| "Joseph A.\tHooper \tjh924\n", | |
| "Vamsee\tJasti \tvj384\n", | |
| "Alex\tJoseph \taj1149\n", | |
| "Dong H.\tKim \tdhk332\n", | |
| "Hyung\tKim \thnk224\n", | |
| "Abby M.\tLerner \taml601\n", | |
| "Liwen\tLin \tll1691\n", | |
| "Alexander W.\tLiss \tawl270\n", | |
| "Smriti\tPandey \tsp2710\n", | |
| "Tyler F.\tPennell \ttfp211\n", | |
| "Michael R.\tPeres \tmrp295\n", | |
| "Craig S.\tPerler \tcp1105\n", | |
| "Suresh\tRangarajan \tsr1927\n", | |
| "Ashwath\tRavichandran \tar3109\n", | |
| "Joseph H.\tRhyu \tjhr320\n", | |
| "Daniel A.\tSaat \tdas607\n", | |
| "Deepa\tSaini \tds3569\n", | |
| "Douglas J.\tSanto \tdjs507\n", | |
| "Reshama\tShaikh \trs2715\n", | |
| "Shenglun\tShi \tss6335\n", | |
| "Amrish\tSingh \tas6317\n", | |
| "Kevin C.\tJohn \tksj222\n", | |
| "Kurt J.\tStanton \tkjs418\n", | |
| "Brandon C.\tTikalsky \tbct248\n", | |
| "Sebastian\tSanchez \tsvs290\n", | |
| "Ryan J.\tZaczynski \trjz226\n", | |
| "Jianping\tZhu \tjz996\n", | |
| "\n" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 6 | |
| }, | |
| { | |
| "cell_type": "heading", | |
| "level": 3, | |
| "metadata": {}, | |
| "source": [ | |
| "EXTRA CREDIT" | |
| ] | |
| }, | |
| { | |
| "cell_type": "heading", | |
| "level": 4, | |
| "metadata": {}, | |
| "source": [ | |
| "4 ) - The E-mail column seems misnamed. Create a new html document that replaces all student ids in this field with student email addresses." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "import re as re\n", | |
| "\n", | |
| "HTML = open('roster.html')\n", | |
| "outputHTML = open('rosterUpdated.html','a')\n", | |
| "regex = re.compile('>[a-z]{2,3}[0-9]{3,4}<')\n", | |
| "studentIDS = []\n", | |
| "\n", | |
| "\n", | |
| "for line in HTML:\n", | |
| " result = regex.findall(line)\n", | |
| " if(len(result)>0):\n", | |
| " for word in result:\n", | |
| " word = word.strip().replace('<','').replace('>','')\n", | |
| " email = word+'@nyu.edu'\n", | |
| " line=line.replace(word,email)\n", | |
| " ##print line\n", | |
| " outputHTML.write(line)\n", | |
| " \n", | |
| "outputHTML.close()\n", | |
| "HTML.close()\n" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [], | |
| "prompt_number": 8 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [] | |
| } | |
| ], | |
| "metadata": {} | |
| } | |
| ] | |
| } |
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