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"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Programming Assignment I: Exploratory Analysis over 2012 FEC Presidential Election Dataset"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Team Details\n",
"--------------\n",
"\n",
"When submitting, fill your team details in this cell. Note that this is a markdown cell.\n",
"\n",
"**Student 1 Name and ID:** Saravanan Thirumuruganathan, 1000XXXXXX\n",
"\n",
"**Student 2 Name and ID:** Satishkumar Masilamani, 1000XXXXXX\n",
"\n",
"**Student 3 Name and ID:** "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Assignment Details\n",
"-------------------\n",
"\n",
"In this assignment, you will be learn more about three key steps in a data analytics process. \n",
"\n",
"### 1. Data Collection\n",
"\n",
"In the first set of tasks, you will put to use the various techniques we learnt in web scraping and use it to scrape three popular websites. \n",
"\n",
"### 2. Exploratory Analysis\n",
"In the second and third set of tasks, you will conduct a guided exploration over 2012 FEC Presidential Election Dataset. You will learn and use some of the most common exploration/aggregation/descriptive operations. This should also help you learn most of the key functionalities in Pandas.\n",
"\n",
"### 3. Visualization\n",
"In the third set of tasks (that is done in conjunction with #2), you will also learn how to use visualization libraries to identify patterns in data that will help in your further data analysis. You will also explore most popular chart types and how to use different libraries and styles to make your visualizations more attractive.\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###Python Packages\n",
"\n",
"Here are the set of packages that you will use extensively in this assignment. Do NOT import any new packages without confirming with the instructor. The objective is to make you identify and use the various functions in the packages imported below. Besides, to my knowledge, the libraries below do offer a concise way to achieve the homework tasks anyway."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# special IPython command to prepare the notebook for matplotlib\n",
"%matplotlib inline \n",
"\n",
"#Array processing\n",
"import numpy as np\n",
"#Data analysis, wrangling and common exploratory operations\n",
"import pandas as pd\n",
"from pandas import Series, DataFrame\n",
"\n",
"#A sane way to get web data\n",
"import requests\n",
"\n",
"#Packages for web scraping. No need to use both. Feel free to use one of them.\n",
"from pattern import web\n",
"from BeautifulSoup import BeautifulSoup\n",
"\n",
"#For visualization. Matplotlib for basic viz and seaborn for more stylish figures + statistical figures not in MPL.\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"\n",
"#For some of the date operations\n",
"import datetime\n",
"\n",
"\n",
"pd.set_option('display.notebook_repr_html',True)\n",
"from IPython.display import display"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 21
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Part 1: Web Scraping\n",
"----------------\n",
"\n",
"In the first series of tasks, we will seek to scrape information from diverse websites. To make your job easier, you can assume that you do not need to do any complex webdriver stuff. For each task, you will implement a function that accepts an url and produces output in the format that the question asks.\n",
"\n",
"NOTE: Make sure that you follow the input and output requirements. The assignment will be evaluated and graded automatically by a script. Any error will result in you getting a score of 0 for that task.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"(5 points) Website 1: Wikipedia\n",
"-----------------------\n",
"\n",
"In the first task, we will seek to parse contents from a table in a Wikipedia page. You will implement a function that accepts two parameters. The first is the url and the second is the name of the table. For example, if the url was http://en.wikipedia.org/wiki/List_of_Test_cricket_records and the table name was \"Team wins, losses and draws\", then you have to parse the first table. Your code must produce as an output a Pandas data frame where the columns have same name as the table ('Team',\t'First Test match',\t'Matches',\t'Won',\t'Lost',\t'Tied',\t'Drawn' and '% Won') in this example. \n",
"\n",
"Do NOT worry about the data format of the Pandas data frame. Simply treat each of them as a string.\n",
"\n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#Input:\n",
"# url: URL of a wikipedia page\n",
"# table_id: Index of the table to scrape\n",
"#Output:\n",
"# df is a Pandas data frame that contains a tabular representation of the table\n",
"# The columns are named the same as the table columns\n",
"# Each row of df corresponds to a row in the table\n",
"def scraping_wikipedia_table(url, table_id):\n",
" website_html = requests.get(url).text\n",
" dom = web.Element(website_html)\n",
" tbls = [t for t in dom.by_tag('table')]\n",
" tbl = tbls[table_id-1] #correct the off by one error\n",
" headerRow = tbl.by_tag(\"tr\")[0] #simple trick - first row always gives the header\n",
" #the title could be inside a th tag or td tag\n",
" # use the in operator to determine which is the case and use the right filtering\n",
" if len(headerRow.by_tag(\"th\")) > 0:\n",
" heading = [web.plaintext(th.content) for th in headerRow.by_tag('th')]\n",
" else:\n",
" heading = [web.plaintext(td.content) for td in headerRow.by_tag('td')]\n",
"\n",
" #DataFrame has many constructors. On a whim I am choosing the list of dicts\n",
" arrayOfDict = [] \n",
" for row in tbl.by_tag(\"tr\")[1:-1] : #ignore the last row about updated date \n",
" thContents = [web.plaintext(td.html) for td in row.by_tag(\"th\")]\n",
" tdContents = [web.plaintext(td.html) for td in row.by_tag(\"td\")]\n",
" contents = thContents + tdContents #big time hack - assumes th, if present is always the first column\n",
" #Convert contents to a dictionary with appropriate headings\n",
" rowAsDict = {heading[i] : contents[i] for i in range(len(contents))}\n",
" arrayOfDict.append(rowAsDict)\n",
" df = DataFrame(arrayOfDict,columns=heading)\n",
" return df\n",
"\n",
"print display(scraping_wikipedia_table(\"http://en.wikipedia.org/wiki/List_of_Test_cricket_records\", 1))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"
\n",
"
\n",
" \n",
" \n",
" | \n",
" Team | \n",
" First Test match | \n",
" Matches | \n",
" Won | \n",
" Lost | \n",
" Tied | \n",
" Drawn | \n",
" % Won | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" Australia | \n",
" 01877-03-15-000015 March 1877 | \n",
" 773 | \n",
" 362 | \n",
" 205 | \n",
" 2 | \n",
" 204 | \n",
" 46.83 | \n",
"
\n",
" \n",
" 1 | \n",
" Bangladesh | \n",
" 02000-11-10-000010 November 2000 | \n",
" 88 | \n",
" 7 | \n",
" 70 | \n",
" 0 | \n",
" 11 | \n",
" 7.95 | \n",
"
\n",
" \n",
" 2 | \n",
" England | \n",
" 01877-03-15-000015 March 1877 | \n",
" 952 | \n",
" 339 | \n",
" 275 | \n",
" 0 | \n",
" 338 | \n",
" 35.60 | \n",
"
\n",
" \n",
" 3 | \n",
" India | \n",
" 01932-06-25-000025 June 1932 | \n",
" 487 | \n",
" 122 | \n",
" 156 | \n",
" 1 | \n",
" 208 | \n",
" 25.05 | \n",
"
\n",
" \n",
" 4 | \n",
" New Zealand | \n",
" 01930-01-10-000010 January 1930 | \n",
" 399 | \n",
" 80 | \n",
" 160 | \n",
" 0 | \n",
" 159 | \n",
" 20.05 | \n",
"
\n",
" \n",
" 5 | \n",
" Pakistan | \n",
" 01952-10-16-000016 October 1952 | \n",
" 387 | \n",
" 121 | \n",
" 110 | \n",
" 0 | \n",
" 156 | \n",
" 31.26 | \n",
"
\n",
" \n",
" 6 | \n",
" South Africa | \n",
" 01889-03-12-000012 March 1889 | \n",
" 390 | \n",
" 144 | \n",
" 129 | \n",
" 0 | \n",
" 117 | \n",
" 36.92 | \n",
"
\n",
" \n",
" 7 | \n",
" Sri Lanka | \n",
" 01982-02-17-000017 February 1982 | \n",
" 235 | \n",
" 71 | \n",
" 84 | \n",
" 0 | \n",
" 80 | \n",
" 30.21 | \n",
"
\n",
" \n",
" 8 | \n",
" West Indies | \n",
" 01928-06-23-000023 June 1928 | \n",
" 503 | \n",
" 163 | \n",
" 170 | \n",
" 1 | \n",
" 169 | \n",
" 32.40 | \n",
"
\n",
" \n",
" 9 | \n",
" Zimbabwe | \n",
" 01992-10-18-000018 October 1992 | \n",
" 97 | \n",
" 11 | \n",
" 60 | \n",
" 0 | \n",
" 26 | \n",
" 11.34 | \n",
"
\n",
" \n",
" 10 | \n",
" ICC World XI | \n",
" 02005-10-14-000014 October 2005 | \n",
" 1 | \n",
" 0 | \n",
" 1 | \n",
" 0 | \n",
" 0 | \n",
" 0.00 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"metadata": {},
"output_type": "display_data",
"text": [
" Team First Test match Matches Won Lost Tied \\\n",
"0 Australia 01877-03-15-000015 March 1877 773 362 205 2 \n",
"1 Bangladesh 02000-11-10-000010 November 2000 88 7 70 0 \n",
"2 England 01877-03-15-000015 March 1877 952 339 275 0 \n",
"3 India 01932-06-25-000025 June 1932 487 122 156 1 \n",
"4 New Zealand 01930-01-10-000010 January 1930 399 80 160 0 \n",
"5 Pakistan 01952-10-16-000016 October 1952 387 121 110 0 \n",
"6 South Africa 01889-03-12-000012 March 1889 390 144 129 0 \n",
"7 Sri Lanka 01982-02-17-000017 February 1982 235 71 84 0 \n",
"8 West Indies 01928-06-23-000023 June 1928 503 163 170 1 \n",
"9 Zimbabwe 01992-10-18-000018 October 1992 97 11 60 0 \n",
"10 ICC World XI 02005-10-14-000014 October 2005 1 0 1 0 \n",
"\n",
" Drawn % Won \n",
"0 204 46.83 \n",
"1 11 7.95 \n",
"2 338 35.60 \n",
"3 208 25.05 \n",
"4 159 20.05 \n",
"5 156 31.26 \n",
"6 117 36.92 \n",
"7 80 30.21 \n",
"8 169 32.40 \n",
"9 26 11.34 \n",
"10 0 0.00 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"None\n"
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"prompt_number": 22
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"### (5 points) Website 2: Walmart\n",
"\n",
"In the second task, we will seek to scrape the results of Walmart. Specifically, we will focus on Movies department. Your job is to implement a function that accepts a valid search result url (such as http://www.walmart.com/search/?query=marvel&cat_id=4096_530598 ), scrapes the 10-15 product listings in that url only and outputs a Pandas data frame with the following fields (with the EXACT names and data types as below): \n",
"\n",
"+ Product Title: String. Title of the product (such as Samsung Red WB1100F Smart Camera with 16.2 Megapixels and 35x Optical Zoom)\n",
"+ Sale Price: Float. Price of the product (such as 249.99). This is the price that is highlighted in blue typically. Ignore the dollar sign and return the value only\n",
"+ Number of Ratings: Integer. The number of ratings that users have provided for the product. This is the number associated with the star.\n",
"+ Free Shipping: Boolean. Is True if the product has free shipping above $50 and false otherwise. \n",
"+ Free Store Pickup: Boolean. Is True if the product has free store pickup and false otherwise. \n",
"+ Starring: String. Contains the name of starring actors. Convert it to a simple string such as \"Chris Pratt Zoe Saldana Vin Diesel\" is okay. No need to parse it further.\n",
"+ Running: Integer. The running time of the movie. Only the integer value is required (ie. NNNN minutes then return only NNNN).\n",
"+ Format: String. Values such as Widescreen. Beware, some entries might not have this value.\n",
"+ Rating: String. MPAA rating.\n",
"\n",
"Make sure that you observe the following:\n",
"\n",
"* If a data type is specified, then your data should be in that format. For eg, if a field is int, then ensure that it has an integer value.\n",
"* Your code should not crash if some product did not have a valid value such as price. Instead you must fill it with NA (see Pandas tutorial for what NA is). "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#Input:\n",
"# url: URL of a Walmart results page for a search query in Movies department\n",
"#Output:\n",
"# df is a Pandas data frame that contains a tabular representation of the results\n",
"# The df must have 9 columns that must have the same name and data type as described above\n",
"# Each row of df corresponds to a movie in the results table\n",
"\n",
"def getValuesSafely(domObj, domSearchString):\n",
" results = domObj(domSearchString)\n",
" if len(results) > 0:\n",
" return web.plaintext(results[0].content)\n",
" else:\n",
" return np.nan\n",
" \n",
"def scraping_walmart_movies(url):\n",
" website_html = requests.get(url).text\n",
" dom = web.Element(website_html)\n",
" heading = ['Product Title', 'Sale Price', 'Number of Ratings', 'Free Shipping', 'Free Store Pickup', \n",
" 'Starring', 'Running', 'Format', 'Rating']\n",
"\n",
" arrayOfDict = []\n",
"\n",
" movies = dom(\"div.js-tile.tile-landscape\")\n",
" for movie in movies:\n",
" productTitle = getValuesSafely(movie, 'h4 > a')\n",
" salesPrice = getValuesSafely(movie, 'div.item-price-container > span.price.price-display')\n",
" numRatings = getValuesSafely(movie,'span.stars-reviews')\n",
" freeShipping = \"shipping on orders over $50\" in movie #Horrible hack\n",
" freeStorePickup = \"store pickup\" in movie #Horrible Hack\n",
" dl = movie('dl.media-details.dl-horizontal')\n",
" dt = dl[0].by_tag(\"dt\")\n",
" dd = dl[0].by_tag(\"dd\")\n",
"\n",
" tempOrder = [\"Director:\", \"Starring:\", \"Running:\", \"Format:\", \"Release:\", \"Rating:\"]\n",
" tempDict = {key : np.nan for key in tempOrder} \n",
" index = 0\n",
" for key in tempOrder:\n",
" if key in movie:\n",
" tempDict[key] = web.plaintext(dd[index].content)\n",
" index = index + 1\n",
" \n",
" movieAsDict = {'Product Title': productTitle, 'Sale Price': salesPrice, 'Number of Ratings': numRatings, \n",
" 'Free Shipping':freeShipping, 'Free Store Pickup':freeStorePickup, \n",
" 'Starring': tempDict[\"Starring:\"], 'Running': tempDict[\"Running:\"], \n",
" 'Format': tempDict[\"Format:\"], 'Rating': tempDict[\"Rating:\"]}\n",
" arrayOfDict.append(movieAsDict)\n",
" \n",
"\n",
" df = DataFrame(arrayOfDict,columns=heading)\n",
" return df \n",
"\n",
"print display(scraping_walmart_movies('http://www.walmart.com/search/?query=marvel&cat_id=4096_530598'))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Product Title | \n",
" Sale Price | \n",
" Number of Ratings | \n",
" Free Shipping | \n",
" Free Store Pickup | \n",
" Starring | \n",
" Running | \n",
" Format | \n",
" Rating | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" Marvel Heroes: Collection | \n",
" $33.98 | \n",
" (1)\\nratings | \n",
" True | \n",
" True | \n",
" * Rebecca Romijn\\n* Hugh Jackman\\n* Chris Evan... | \n",
" 856 minutes | \n",
" NaN | \n",
" PG-13 | \n",
"
\n",
" \n",
" 1 | \n",
" Marvel's The Avengers (Widescreen) | \n",
" $19.96 | \n",
" (68)\\nratings | \n",
" True | \n",
" True | \n",
" * Robert Downey Jr.\\n* Chris Evans\\n* Mark Ruf... | \n",
" 145 minutes | \n",
" Widescreen | \n",
" PG-13 | \n",
"
\n",
" \n",
" 2 | \n",
" Marvel: Guardians Of The Galaxy (Widescreen) | \n",
" $19.96 | \n",
" (44)\\nratings | \n",
" True | \n",
" True | \n",
" * Chris Pratt\\n* Zoe Saldana\\n* Vin Diesel\\n* ... | \n",
" 121 minutes | \n",
" Widescreen | \n",
" PG-13 | \n",
"
\n",
" \n",
" 3 | \n",
" Marvel Knights: Wolverine Versus Sabretooth - ... | \n",
" $13.47 | \n",
" NaN | \n",
" False | \n",
" False | \n",
" * Jeph Loeb\\n* Simone Bianchi | \n",
" 44 minutes | \n",
" Widescreen | \n",
" Not Rated | \n",
"
\n",
" \n",
" 4 | \n",
" Marvel's The Avengers (DVD + Blu-ray) (Widescr... | \n",
" $29.96 | \n",
" (5)\\nratings | \n",
" True | \n",
" True | \n",
" * Robert Downey Jr.\\n* Chris Evans\\n* Mark Ruf... | \n",
" 145 minutes | \n",
" Widescreen | \n",
" PG-13 | \n",
"
\n",
" \n",
" 5 | \n",
" Marvel Complete Giftset (Widescreen) | \n",
" $26.96 | \n",
" NaN | \n",
" True | \n",
" True | \n",
" * Justin Gross\\n* Grey Delisle\\n* Michael Mass... | \n",
" 640 minutes | \n",
" Widescreen | \n",
" PG-13 | \n",
"
\n",
" \n",
" 6 | \n",
" Elektra / Fantastic Four / Daredevil (Director... | \n",
" $13.99 | \n",
" NaN | \n",
" True | \n",
" True | \n",
" * Jessica Alba\\n* Chris Evans\\n* Jennifer Garn... | \n",
" NaN | \n",
" NaN | \n",
" Not Rated | \n",
"
\n",
" \n",
" 7 | \n",
" The Punisher (Extended Cut) (Widescreen) | \n",
" $7.00 | \n",
" (4)\\nratings | \n",
" True | \n",
" True | \n",
" * John Travolta\\n* Thomas Jane\\n* Ben Foster\\n... | \n",
" 140 minutes | \n",
" Widescreen | \n",
" R | \n",
"
\n",
" \n",
" 8 | \n",
" Spider-Man 2 (Widescreen) | \n",
" $12.96 | \n",
" (3)\\nratings | \n",
" True | \n",
" True | \n",
" * Tobey Maguire\\n* Kirsten Dunst\\n* James Fran... | \n",
" 128 minutes | \n",
" Widescreen | \n",
" PG-13 | \n",
"
\n",
" \n",
" 9 | \n",
" Superheroes Collection: The Incredible Hulk Re... | \n",
" $13.47 | \n",
" NaN | \n",
" True | \n",
" True | \n",
" * Bill Bixby\\n* Jack Colvin\\n* Lou Ferrigno\\n*... | \n",
" 247 minutes | \n",
" Widescreen | \n",
" Not Rated | \n",
"
\n",
" \n",
" 10 | \n",
" Marvel: Iron Man & Hulk - Heroes United (Wides... | \n",
" $19.96 | \n",
" (1)\\nratings | \n",
" True | \n",
" True | \n",
" * Adrian Pasdar\\n* Fred Tatasciore\\n* David Kaye | \n",
" 72 minutes | \n",
" Widescreen | \n",
" PG | \n",
"
\n",
" \n",
" 11 | \n",
" Captain America: The Winter Soldier (Widescreen) | \n",
" $19.96 | \n",
" (19)\\nratings | \n",
" True | \n",
" True | \n",
" * Chris Evans\\n* Scarlett Johansson\\n* Anthony... | \n",
" 136 minutes | \n",
" Widescreen | \n",
" PG-13 | \n",
"
\n",
" \n",
" 12 | \n",
" Iron Man 3 (DVD + Digital Copy) (Widescreen) | \n",
" $19.96 | \n",
" (26)\\nratings | \n",
" True | \n",
" True | \n",
" * Robert Downey Jr.\\n* Gwyneth Paltrow\\n* Don ... | \n",
" 135 minutes | \n",
" Widescreen | \n",
" PG-13 | \n",
"
\n",
" \n",
" 13 | \n",
" Thor: The Dark World (Widescreen) | \n",
" $19.96 | \n",
" (22)\\nratings | \n",
" True | \n",
" True | \n",
" * Chris Hemsworth\\n* Natalie Portman\\n* Tom Hi... | \n",
" 112 minutes | \n",
" Widescreen | \n",
" PG-13 | \n",
"
\n",
" \n",
" 14 | \n",
" Spider-Man (2-Disc) (Special Edition) (Widescr... | \n",
" $12.96 | \n",
" (11)\\nratings | \n",
" True | \n",
" True | \n",
" * Tobey Maguire\\n* Willem Dafoe\\n* Kirsten Dun... | \n",
" 121 minutes | \n",
" Widescreen | \n",
" PG-13 | \n",
"
\n",
" \n",
" 15 | \n",
" Spider-Man / Spider-Man 2 / Spider-Man 3 (Wide... | \n",
" $25.46 | \n",
" NaN | \n",
" False | \n",
" True | \n",
" * Tobey Maguire\\n* Kirsten Dunst\\n* James Fran... | \n",
" 523 minutes | \n",
" Widescreen | \n",
" PG-13 | \n",
"
\n",
" \n",
" 16 | \n",
" DC Showcase: Superman / Shazam!: The Return Of... | \n",
" $9.07 | \n",
" (10)\\nratings | \n",
" True | \n",
" True | \n",
" NaN | \n",
" 63 minutes | \n",
" NaN | \n",
" PG-13 | \n",
"
\n",
" \n",
" 17 | \n",
" The Next Avengers: Heroes Of Tomorrow (Widescr... | \n",
" $5.00 | \n",
" (9)\\nratings | \n",
" True | \n",
" True | \n",
" * Noah Crawford\\n* Nicole Oliver\\n* Shawn MacD... | \n",
" 78 minutes | \n",
" Widescreen | \n",
" PG | \n",
"
\n",
" \n",
" 18 | \n",
" Ultimate Avengers Movie Collection (Widescreen) | \n",
" $11.17 | \n",
" (5)\\nratings | \n",
" True | \n",
" True | \n",
" NaN | \n",
" 222 minutes | \n",
" Widescreen | \n",
" PG-13 | \n",
"
\n",
" \n",
" 19 | \n",
" Ultimate Avengers: The Movie (Widescreen) | \n",
" $5.39 | \n",
" (7)\\nratings | \n",
" True | \n",
" True | \n",
" * Justin Gross\\n* Grey DeLisle\\n* Michael Mass... | \n",
" 71 minutes | \n",
" Widescreen | \n",
" PG-13 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"metadata": {},
"output_type": "display_data",
"text": [
" Product Title Sale Price \\\n",
"0 Marvel Heroes: Collection $33.98 \n",
"1 Marvel's The Avengers (Widescreen) $19.96 \n",
"2 Marvel: Guardians Of The Galaxy (Widescreen) $19.96 \n",
"3 Marvel Knights: Wolverine Versus Sabretooth - ... $13.47 \n",
"4 Marvel's The Avengers (DVD + Blu-ray) (Widescr... $29.96 \n",
"5 Marvel Complete Giftset (Widescreen) $26.96 \n",
"6 Elektra / Fantastic Four / Daredevil (Director... $13.99 \n",
"7 The Punisher (Extended Cut) (Widescreen) $7.00 \n",
"8 Spider-Man 2 (Widescreen) $12.96 \n",
"9 Superheroes Collection: The Incredible Hulk Re... $13.47 \n",
"10 Marvel: Iron Man & Hulk - Heroes United (Wides... $19.96 \n",
"11 Captain America: The Winter Soldier (Widescreen) $19.96 \n",
"12 Iron Man 3 (DVD + Digital Copy) (Widescreen) $19.96 \n",
"13 Thor: The Dark World (Widescreen) $19.96 \n",
"14 Spider-Man (2-Disc) (Special Edition) (Widescr... $12.96 \n",
"15 Spider-Man / Spider-Man 2 / Spider-Man 3 (Wide... $25.46 \n",
"16 DC Showcase: Superman / Shazam!: The Return Of... $9.07 \n",
"17 The Next Avengers: Heroes Of Tomorrow (Widescr... $5.00 \n",
"18 Ultimate Avengers Movie Collection (Widescreen) $11.17 \n",
"19 Ultimate Avengers: The Movie (Widescreen) $5.39 \n",
"\n",
" Number of Ratings Free Shipping Free Store Pickup \\\n",
"0 (1)\\nratings True True \n",
"1 (68)\\nratings True True \n",
"2 (44)\\nratings True True \n",
"3 NaN False False \n",
"4 (5)\\nratings True True \n",
"5 NaN True True \n",
"6 NaN True True \n",
"7 (4)\\nratings True True \n",
"8 (3)\\nratings True True \n",
"9 NaN True True \n",
"10 (1)\\nratings True True \n",
"11 (19)\\nratings True True \n",
"12 (26)\\nratings True True \n",
"13 (22)\\nratings True True \n",
"14 (11)\\nratings True True \n",
"15 NaN False True \n",
"16 (10)\\nratings True True \n",
"17 (9)\\nratings True True \n",
"18 (5)\\nratings True True \n",
"19 (7)\\nratings True True \n",
"\n",
" Starring Running \\\n",
"0 * Rebecca Romijn\\n* Hugh Jackman\\n* Chris Evan... 856 minutes \n",
"1 * Robert Downey Jr.\\n* Chris Evans\\n* Mark Ruf... 145 minutes \n",
"2 * Chris Pratt\\n* Zoe Saldana\\n* Vin Diesel\\n* ... 121 minutes \n",
"3 * Jeph Loeb\\n* Simone Bianchi 44 minutes \n",
"4 * Robert Downey Jr.\\n* Chris Evans\\n* Mark Ruf... 145 minutes \n",
"5 * Justin Gross\\n* Grey Delisle\\n* Michael Mass... 640 minutes \n",
"6 * Jessica Alba\\n* Chris Evans\\n* Jennifer Garn... NaN \n",
"7 * John Travolta\\n* Thomas Jane\\n* Ben Foster\\n... 140 minutes \n",
"8 * Tobey Maguire\\n* Kirsten Dunst\\n* James Fran... 128 minutes \n",
"9 * Bill Bixby\\n* Jack Colvin\\n* Lou Ferrigno\\n*... 247 minutes \n",
"10 * Adrian Pasdar\\n* Fred Tatasciore\\n* David Kaye 72 minutes \n",
"11 * Chris Evans\\n* Scarlett Johansson\\n* Anthony... 136 minutes \n",
"12 * Robert Downey Jr.\\n* Gwyneth Paltrow\\n* Don ... 135 minutes \n",
"13 * Chris Hemsworth\\n* Natalie Portman\\n* Tom Hi... 112 minutes \n",
"14 * Tobey Maguire\\n* Willem Dafoe\\n* Kirsten Dun... 121 minutes \n",
"15 * Tobey Maguire\\n* Kirsten Dunst\\n* James Fran... 523 minutes \n",
"16 NaN 63 minutes \n",
"17 * Noah Crawford\\n* Nicole Oliver\\n* Shawn MacD... 78 minutes \n",
"18 NaN 222 minutes \n",
"19 * Justin Gross\\n* Grey DeLisle\\n* Michael Mass... 71 minutes \n",
"\n",
" Format Rating \n",
"0 NaN PG-13 \n",
"1 Widescreen PG-13 \n",
"2 Widescreen PG-13 \n",
"3 Widescreen Not Rated \n",
"4 Widescreen PG-13 \n",
"5 Widescreen PG-13 \n",
"6 NaN Not Rated \n",
"7 Widescreen R \n",
"8 Widescreen PG-13 \n",
"9 Widescreen Not Rated \n",
"10 Widescreen PG \n",
"11 Widescreen PG-13 \n",
"12 Widescreen PG-13 \n",
"13 Widescreen PG-13 \n",
"14 Widescreen PG-13 \n",
"15 Widescreen PG-13 \n",
"16 NaN PG-13 \n",
"17 Widescreen PG \n",
"18 Widescreen PG-13 \n",
"19 Widescreen PG-13 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"None\n"
]
}
],
"prompt_number": 23
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### (2.5 * 4 = 10 points) Website 3: Facebook\n",
"\n",
"In the third task, we will scrape some specific sub-pages of Facebook profiles. In order to avoid using sophisticated tools like Selenium, for this task you can assume that the input to the function is the DOM of the relevant page. You can then use it to parse relevant contents."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#Input: dom - DOM of the books page corresponding to an FB account's profile. Eg, DOM of https://www.facebook.com/zuck/books\n",
"#Output: An array (Python list) of books listed in the profile page. \n",
"# Note that this function requires a list as an output not a Pandas data frame\n",
"def scraping_facebook_books(dom):\n",
" return [li(\"a._gx7\")[0].title for li in dom(\"ul.uiList > li\")]\n",
" \n",
"#Input: dom - DOM of the groups page corresponding to an FB account's profile. Eg, DOM of https://www.facebook.com/zuck/groups\n",
"#Output: A Pandas data frame with one row per group. \n",
"# It must have three columns - 'Group Name', 'Number of Members', 'Group Description'\n",
"# Note that all information of a group is in the same page (eg. https://www.facebook.com/zuck/groups)\n",
"# You can collect all data from same page even if they are incomplete (such as group description)\n",
"# Ensure that the column names as given above\n",
"def scraping_facebook_groups(dom):\n",
" arrayOfDict = []\n",
" for li in dom(\"ul.uiList > li\"):\n",
" groupAsDict = {}\n",
" groupAsDict[\"Group Name\"] = web.plaintext(li(\"div.mbs.fwb\")[0].content)\n",
" groupAsDict[\"Number of Members\"] = web.plaintext(li(\"div.mbs.fcg\")[0].content)\n",
" groupAsDict[\"Group Description\"] = web.plaintext(li(\"div.mtm > span\")[0].content)\n",
" arrayOfDict.append(groupAsDict)\n",
" df = DataFrame(arrayOfDict,columns=[\"Group Name\", \"Number of Members\", \"Group Description\"])\n",
" return df\n",
"\n",
"#Input: dom - DOM of the music page corresponding to an FB account's profile. Eg, DOM of https://www.facebook.com/zuck/music\n",
"#Output: A Pandas data frame with one row per group. \n",
"# It must have four columns \n",
"# 'Name', 'Type' (eg. Musician/Band or Bands&Musicians) and 'Verified' (boolean: True/False), 'Profile Url'\n",
"# Note that all information of a group is in the same page (eg. https://www.facebook.com/zuck/music)\n",
"# Ensure that the column names as given above\n",
"def scraping_facebook_music(dom):\n",
" arrayOfDict = []\n",
" for li in dom(\"ul.uiList > li\"):\n",
" musicAsDict = {}\n",
" musicAsDict[\"Name\"] = li(\"a._gx7\")[0].title\n",
" musicAsDict[\"Type\"] = web.plaintext(li(\"div.fsm.fwn.fcg\")[0].content)\n",
" musicAsDict[\"Verified\"] = \"Verified Page\" in li\n",
" musicAsDict[\"Profile Url\"] = li(\"a._gx7\")[0].href\n",
" arrayOfDict.append(musicAsDict)\n",
" df = DataFrame(arrayOfDict,columns=[\"Name\", \"Type\", \"Verified\", \"Profile Url\"])\n",
" return df\n",
"\n",
"#Input: dom - DOM of the music page corresponding to an FB account's profile. Eg, DOM of https://www.facebook.com/zuck/movies\n",
"#Output: A Pandas data frame with one row per group. \n",
"# It must have following columns - \n",
"# 'Name', 'Type' (eg. Movie), 'Verified', 'Profile Url' - as before\n",
"# Ensure that the column names as given above\n",
"def scraping_facebook_movies(dom):\n",
" arrayOfDict = []\n",
" for li in dom(\"ul.uiList > li\"):\n",
" movieAsDict = {}\n",
" movieAsDict[\"Name\"] = li(\"a._gx7\")[0].title\n",
" movieAsDict[\"Type\"] = web.plaintext(li(\"div.fsm.fwn.fcg\")[0].content)\n",
" movieAsDict[\"Verified\"] = \"Verified Page\" in li\n",
" movieAsDict[\"Profile Url\"] = li(\"a._gx7\")[0].href\n",
" arrayOfDict.append(movieAsDict)\n",
" df = DataFrame(arrayOfDict,columns=[\"Name\", \"Type\", \"Verified\", \"Profile Url\"])\n",
" return df\n",
"\n",
"books_html = open(\"zuckBooks.html\").read()\n",
"#Uncomment one of these lines based on whether you used Pattern or BeautifulSoup\n",
"dom = web.Element(books_html)\n",
"#dom = BeautifulSoup(books_html)\n",
"print \"Books in Zuck's FB:\\n\", scraping_facebook_books(dom)\n",
"\n",
"group_html = open(\"zuckGroups.html\").read()\n",
"dom = web.Element(group_html)\n",
"#dom = BeautifulSoup(group_html)\n",
"print \"\\n\\nGroups in Zuck's FB:\\n\"\n",
"display(scraping_facebook_groups(dom))\n",
"\n",
"\n",
"music_html = open(\"zuckMusic.html\").read()\n",
"dom = web.Element(music_html)\n",
"#dom = BeautifulSoup(music_html)\n",
"print \"\\n\\nMusic in Zuck's FB:\\n\"\n",
"display(scraping_facebook_music(dom))\n",
"\n",
"\n",
"movies_html = open(\"zuckMovies.html\").read()\n",
"dom = web.Element(movies_html)\n",
"#dom = BeautifulSoup(movies_html)\n",
"print \"\\n\\nMovies in Zuck's FB:\\n\"\n",
"display(scraping_facebook_movies(dom))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Books in Zuck's FB:\n",
"[u'Gang Leader for a Day: A Rogue Sociologist Takes to the Streets', u'The Better Angels of Our Nature', u'American Lion: Andrew Jackson in the White House', u\"The House of Rothschild: Volume 1: Money's Prophets: 1798-1848\", u'Open: An Autobiography', u'The Information: A History, a Theory, a Flood', u\"Surely You're Joking, Mr. Feynman!\", u'The Idea Factory: Bell Labs and the Great Age of American Innovation', u'Decoded', u'Einstein: His Life and Universe', u'Physics of the Impossible', u\"Ender's Shadow\", u'The Giving Tree', u\"Ender's Game\"]\n",
"\n",
"\n",
"Groups in Zuck's FB:\n",
"\n"
]
},
{
"html": [
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Group Name | \n",
" Number of Members | \n",
" Group Description | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" Pulis | \n",
" 30 members | \n",
" This is a group for our favorite Hungarian bre... | \n",
"
\n",
" \n",
"
\n",
"
"
],
"metadata": {},
"output_type": "display_data",
"text": [
" Group Name Number of Members \\\n",
"0 Pulis 30 members \n",
"\n",
" Group Description \n",
"0 This is a group for our favorite Hungarian bre... "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"\n",
"Music in Zuck's FB:\n",
"\n"
]
},
{
"html": [
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Name | \n",
" Type | \n",
" Verified | \n",
" Profile Url | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" Imagine Dragons | \n",
" Musician/Band | \n",
" True | \n",
" https://www.facebook.com/ImagineDragons?ref=pr... | \n",
"
\n",
" \n",
" 1 | \n",
" Beyonc\u00e9 | \n",
" Musician/Band | \n",
" True | \n",
" https://www.facebook.com/beyonce?ref=profile | \n",
"
\n",
" \n",
" 2 | \n",
" Rihanna | \n",
" Artist | \n",
" True | \n",
" https://www.facebook.com/rihanna?ref=profile | \n",
"
\n",
" \n",
" 3 | \n",
" The All-American Rejects | \n",
" Musician/Band | \n",
" True | \n",
" https://www.facebook.com/AllAmericanRejects?re... | \n",
"
\n",
" \n",
" 4 | \n",
" LCD Soundsystem | \n",
" Musician/Band | \n",
" True | \n",
" https://www.facebook.com/lcdsoundsystem?ref=pr... | \n",
"
\n",
" \n",
" 5 | \n",
" Daft Punk | \n",
" Musician/Band | \n",
" True | \n",
" https://www.facebook.com/daftpunk?ref=profile | \n",
"
\n",
" \n",
" 6 | \n",
" Linkin Park | \n",
" Musician/Band | \n",
" True | \n",
" https://www.facebook.com/linkinPark?ref=profile | \n",
"
\n",
" \n",
" 7 | \n",
" Ingrid Michaelson | \n",
" Musician/Band | \n",
" True | \n",
" https://www.facebook.com/ingridmichaelson?ref=... | \n",
"
\n",
" \n",
" 8 | \n",
" Radiohead | \n",
" Musician/Band | \n",
" True | \n",
" https://www.facebook.com/radiohead?ref=profile | \n",
"
\n",
" \n",
" 9 | \n",
" Shakira | \n",
" Musician/Band | \n",
" True | \n",
" https://www.facebook.com/shakira?ref=profile | \n",
"
\n",
" \n",
" 10 | \n",
" Nirvana | \n",
" Musician/Band | \n",
" True | \n",
" https://www.facebook.com/Nirvana?ref=profile | \n",
"
\n",
" \n",
" 11 | \n",
" JAY Z | \n",
" Musician/Band | \n",
" True | \n",
" https://www.facebook.com/JayZ?ref=profile | \n",
"
\n",
" \n",
" 12 | \n",
" The Killers | \n",
" Musician/Band | \n",
" True | \n",
" https://www.facebook.com/Thekillers?ref=profile | \n",
"
\n",
" \n",
" 13 | \n",
" Green Day | \n",
" Musician/Band | \n",
" True | \n",
" https://www.facebook.com/GreenDay?ref=profile | \n",
"
\n",
" \n",
" 14 | \n",
" Taylor Swift | \n",
" Musician/Band | \n",
" True | \n",
" https://www.facebook.com/TaylorSwift?ref=profile | \n",
"
\n",
" \n",
" 15 | \n",
" Lady Gaga | \n",
" Musician/Band | \n",
" True | \n",
" https://www.facebook.com/ladygaga?ref=profile | \n",
"
\n",
" \n",
" 16 | \n",
" Katy Perry | \n",
" Musician/Band | \n",
" True | \n",
" https://www.facebook.com/katyperry?ref=profile | \n",
"
\n",
" \n",
" 17 | \n",
" U2 | \n",
" Musician/Band | \n",
" True | \n",
" https://www.facebook.com/u2?ref=profile | \n",
"
\n",
" \n",
" 18 | \n",
" John Mayer | \n",
" Musician/Band | \n",
" True | \n",
" https://www.facebook.com/johnmayer?ref=profile | \n",
"
\n",
" \n",
"
\n",
"
"
],
"metadata": {},
"output_type": "display_data",
"text": [
" Name Type Verified \\\n",
"0 Imagine Dragons Musician/Band True \n",
"1 Beyonc\u00e9 Musician/Band True \n",
"2 Rihanna Artist True \n",
"3 The All-American Rejects Musician/Band True \n",
"4 LCD Soundsystem Musician/Band True \n",
"5 Daft Punk Musician/Band True \n",
"6 Linkin Park Musician/Band True \n",
"7 Ingrid Michaelson Musician/Band True \n",
"8 Radiohead Musician/Band True \n",
"9 Shakira Musician/Band True \n",
"10 Nirvana Musician/Band True \n",
"11 JAY Z Musician/Band True \n",
"12 The Killers Musician/Band True \n",
"13 Green Day Musician/Band True \n",
"14 Taylor Swift Musician/Band True \n",
"15 Lady Gaga Musician/Band True \n",
"16 Katy Perry Musician/Band True \n",
"17 U2 Musician/Band True \n",
"18 John Mayer Musician/Band True \n",
"\n",
" Profile Url \n",
"0 https://www.facebook.com/ImagineDragons?ref=pr... \n",
"1 https://www.facebook.com/beyonce?ref=profile \n",
"2 https://www.facebook.com/rihanna?ref=profile \n",
"3 https://www.facebook.com/AllAmericanRejects?re... \n",
"4 https://www.facebook.com/lcdsoundsystem?ref=pr... \n",
"5 https://www.facebook.com/daftpunk?ref=profile \n",
"6 https://www.facebook.com/linkinPark?ref=profile \n",
"7 https://www.facebook.com/ingridmichaelson?ref=... \n",
"8 https://www.facebook.com/radiohead?ref=profile \n",
"9 https://www.facebook.com/shakira?ref=profile \n",
"10 https://www.facebook.com/Nirvana?ref=profile \n",
"11 https://www.facebook.com/JayZ?ref=profile \n",
"12 https://www.facebook.com/Thekillers?ref=profile \n",
"13 https://www.facebook.com/GreenDay?ref=profile \n",
"14 https://www.facebook.com/TaylorSwift?ref=profile \n",
"15 https://www.facebook.com/ladygaga?ref=profile \n",
"16 https://www.facebook.com/katyperry?ref=profile \n",
"17 https://www.facebook.com/u2?ref=profile \n",
"18 https://www.facebook.com/johnmayer?ref=profile "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"\n",
"Movies in Zuck's FB:\n",
"\n"
]
},
{
"html": [
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Name | \n",
" Type | \n",
" Verified | \n",
" Profile Url | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" Hero | \n",
" Movie | \n",
" True | \n",
" https://www.facebook.com/HeroMovie?ref=profile | \n",
"
\n",
" \n",
" 1 | \n",
" Django Unchained | \n",
" Movie | \n",
" True | \n",
" https://www.facebook.com/unchainedmovie?ref=pr... | \n",
"
\n",
" \n",
" 2 | \n",
" MoneyBall | \n",
" Movie | \n",
" True | \n",
" https://www.facebook.com/MoneyballMovie?ref=pr... | \n",
"
\n",
" \n",
" 3 | \n",
" Gladiator | \n",
" Movie | \n",
" True | \n",
" https://www.facebook.com/gladiatormovie?ref=pr... | \n",
"
\n",
" \n",
" 4 | \n",
" The Matrix | \n",
" Movie | \n",
" True | \n",
" https://www.facebook.com/TheMatrixMovie?ref=pr... | \n",
"
\n",
" \n",
" 5 | \n",
" Star Wars | \n",
" Movie | \n",
" True | \n",
" https://www.facebook.com/StarWars?ref=profile | \n",
"
\n",
" \n",
" 6 | \n",
" Batman: The Dark Knight | \n",
" Movie | \n",
" True | \n",
" https://www.facebook.com/darkknight?ref=profile | \n",
"
\n",
" \n",
" 7 | \n",
" Waiting for \"Superman\" | \n",
" Movie | \n",
" True | \n",
" https://www.facebook.com/waitingforsuperman?re... | \n",
"
\n",
" \n",
" 8 | \n",
" Iron Man | \n",
" Movie | \n",
" True | \n",
" https://www.facebook.com/ironman?ref=profile | \n",
"
\n",
" \n",
" 9 | \n",
" Fearless | \n",
" Movie | \n",
" False | \n",
" https://www.facebook.com/pages/Fearless/112430... | \n",
"
\n",
" \n",
" 10 | \n",
" The Godfather | \n",
" Movie | \n",
" True | \n",
" https://www.facebook.com/thegodfather?ref=profile | \n",
"
\n",
" \n",
" 11 | \n",
" Disney Pixar | \n",
" Company | \n",
" True | \n",
" https://www.facebook.com/DisneyPixar?ref=profile | \n",
"
\n",
" \n",
"
\n",
"
"
],
"metadata": {},
"output_type": "display_data",
"text": [
" Name Type Verified \\\n",
"0 Hero Movie True \n",
"1 Django Unchained Movie True \n",
"2 MoneyBall Movie True \n",
"3 Gladiator Movie True \n",
"4 The Matrix Movie True \n",
"5 Star Wars Movie True \n",
"6 Batman: The Dark Knight Movie True \n",
"7 Waiting for \"Superman\" Movie True \n",
"8 Iron Man Movie True \n",
"9 Fearless Movie False \n",
"10 The Godfather Movie True \n",
"11 Disney Pixar Company True \n",
"\n",
" Profile Url \n",
"0 https://www.facebook.com/HeroMovie?ref=profile \n",
"1 https://www.facebook.com/unchainedmovie?ref=pr... \n",
"2 https://www.facebook.com/MoneyballMovie?ref=pr... \n",
"3 https://www.facebook.com/gladiatormovie?ref=pr... \n",
"4 https://www.facebook.com/TheMatrixMovie?ref=pr... \n",
"5 https://www.facebook.com/StarWars?ref=profile \n",
"6 https://www.facebook.com/darkknight?ref=profile \n",
"7 https://www.facebook.com/waitingforsuperman?re... \n",
"8 https://www.facebook.com/ironman?ref=profile \n",
"9 https://www.facebook.com/pages/Fearless/112430... \n",
"10 https://www.facebook.com/thegodfather?ref=profile \n",
"11 https://www.facebook.com/DisneyPixar?ref=profile "
]
}
],
"prompt_number": 24
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Part 2 and 3: Exploratory Analysis and Visualization\n",
"======================\n",
"\n",
"Data Set Description\n",
"---------------------\n",
"\n",
"In this assignment, you will use the FEC dataset. The US Federal Election Commission publishes data on contributions to political campaigns. This includes contributor names, occupation and employer, address, and contribution amount. Specifically, we will be using the FEC data from 2012 election between Barack Obama and Mitt Romney. This is widely considered as a landmark election as both sides spent an unprecedented amount of 1 Billion dollars each (i.e. more than 6000 Crores in INR each). If you are interested, you can download the entire list of contributor details at the [FEC site](http://www.fec.gov/disclosurep/PDownload.do). It is relatively large (150 MB compressed, 1 GB uncompressed). For our experiments, we will use a smaller subset of the data collected (and cleaned) by Wes McKinney (the creator of Pandas). For the download link, see the [Assignments page](http://saravanan-thirumuruganathan.github.io/cse5334Spring2015/assignments.html). It is small by most standards (around 1.4 million records, 20 MB compressed, 160 MB uncompressed) but large enough to give a taste of why data mining is compute intensive. Hopefully, this will also give you an appreciation as to the awesomeness of Pandas/Numpy - you can do really cool stuff with 2-3 lines of code that runs in seconds. \n",
"\n",
"**Dataset Description:** You can find the details (such as meaning of the column names) of the dataset in the [FEC website](ftp://ftp.fec.gov/FEC/Presidential_Map/2012/DATA_DICTIONARIES/CONTRIBUTOR_FORMAT.txt).\n",
"\n",
"While a knowledge of US Elections or FEC Campaign finance rules is not necessary for solving this assignment, you can check out the following links if you are curious. \n",
"\n",
"[How to Become the US President: A Step-by-Step Guide](http://2012election.procon.org/view.resource.php?resourceID=004333)\n",
"\n",
"[US Election guide: how does the election work?](http://www.telegraph.co.uk/news/worldnews/us-election/9480396/US-Election-guide-how-does-the-election-work.html)\n",
"\n",
"If you are fascinated with Campaign finance reform (as I am), here are some good links:\n",
"\n",
"[Super-PACs and Dark Money: ProPublica\u2019s Guide to the New World of Campaign Finance](http://www.propublica.org/blog/item/super-pacs-propublicas-guide-to-the-new-world-of-campaign-finance)\n",
"\n",
"[40 charts that explain money in politics](http://www.vox.com/2014/7/30/5949581/money-in-politics-charts-explain)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Visualization\n",
"-------------\n",
"\n",
"Visualization is a key component of exploration. You will perform a number of 1-D and 2-D charts to get some intuition about the data. You can choose to use either Matplotlib or Seaborn for plotting. The default figures generated from Matplotlib might look a bit ugly. So you might want to try Seaborn to get better figures. The defaults in Seaborn are much saner and sometimes makes your life lot easier. Seaborn has number of styles - so feel free to experiment with them and choose the one you like. We have earmarked 10 points for the aesthetics of your visualizations. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Pandas\n",
"----------\n",
"Almost all the tasks below could be purely solved using Pandas. Most analytic tasks should not require more than 2-3 lines of code (visualization on the other hand is a different matter - you might need 5-10 lines for each chart unless you use Seaborn). Here is a NON COMPREHENSIVE list of functions that you might want to know to solve this assignment : agg, apply, argmax, argmin, count, crosstab, cumsum, cut, describe, groupby, head, idxmax, idxmin, info, isin, map, max, min, pivot_table, size, sum, transform, unique, value_counts .\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Exploration Tasks\n",
"==================\n",
"\n",
"You can find a set of exploratory analytics tasks below. Ensure that you clearly follow the instructions. The assignment will be graded automatically - so failure to follow might cause some issues. Also do NOT rename the functions or variables that the instructor has set. Of course, feel free to create new variables/functions that will hep your code. \n",
"\n",
"Reading and Filtering Dataset\n",
"------------------------------\n",
"The FEC data contains information about all presidential candidates. As a sitting president, Barack Obama was the only candidate from the Democratic party. In the Republican party, there was a process called Primary (see links above) where number of candidates competed to be the nominee. Mitt Romney won the Republican primary and competed with Barack Obama in the elections, which Obama won. \n",
"\n",
"The Python code below reads the FEC dataset into a Pandas data frame with the name fec_all. If your machine has less than 2 GB of RAM, then change the function argument low_memory to True. Once the frame is loaded, we remove all negative contributions (where the campaign refunded amount to a contributor for some reason). Finally, we create a new data frame called fec that contains the contributions to Barack Obama and Mitt Romney alone. \n",
"\n",
"For this code to work, the file 'fec_2012_contribution_subset.csv' must be in the same folder as the notebook. \n",
"\n",
"To reduce my typing, I might refer to Obama as BO and Romney as MR in the text below."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#read the csv file into a Pandas data frame\n",
"fec_all = pd.read_csv('fec_2012_contribution_subset.csv', low_memory=False)\n",
"\n",
"#We will be doing party wise analysis later. So, we want to associate each candidate with their party\n",
"parties = {'Bachmann, Michelle': 'Republican',\n",
" 'Cain, Herman': 'Republican',\n",
" 'Gingrich, Newt': 'Republican',\n",
" 'Huntsman, Jon': 'Republican',\n",
" 'Johnson, Gary Earl': 'Republican',\n",
" 'McCotter, Thaddeus G': 'Republican',\n",
" 'Obama, Barack': 'Democrat',\n",
" 'Paul, Ron': 'Republican',\n",
" 'Pawlenty, Timothy': 'Republican',\n",
" 'Perry, Rick': 'Republican',\n",
" \"Roemer, Charles E. 'Buddy' III\": 'Republican',\n",
" 'Romney, Mitt': 'Republican',\n",
" 'Santorum, Rick': 'Republican'}\n",
"\n",
"#create a new column called party that sets the value to the party of the candidate\n",
"# The way this line works is as follows:\n",
"# 1. fec_all.cand_nm gives a vector (or Series in Pandas terminology)\n",
"# 2. For each row, the code looks up the candidate name to the dictionary parties\n",
"# 3. If the name of the candidate (cand_nm) is in parties, it returns the value (i.e. Republican or Democrat)\n",
"# 4. This whole thing is done for each row and you get another vector as output\n",
"# 5. Finally, you create a new column in fec_all called 'party' and assign the vector you got to it.\n",
"# 6. All in a single line :)\n",
"fec_all['party'] = fec_all.cand_nm.map(parties)\n",
"\n",
"#ignore the refunds\n",
"# Get the subset of dataset where contribution amount is positive\n",
"fec_all = fec_all[fec_all.contb_receipt_amt > 0]\n",
"\n",
"#fec_all contains details about all presidential candidates. \n",
"#fec contains the details about contributions to Barack Obama and Mitt Romney only\n",
"#for the rest of the tasks, unless explicitly specified, work on the fec data frame.\n",
"fec = fec_all[fec_all.cand_nm.isin(['Obama, Barack', 'Romney, Mitt'])]\n",
"\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 25
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Task 1: Descriptive Statistics\n",
"--------------------------------\n",
"\n",
"Let us start with some easy stuff. As of now, you do not know anything about the dataset. So the first task will be to get some basic sense. \n",
"\n",
" "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#Task 1a: print the details of the data frame. \n",
"# Basically, this should contain information such as number of rows,columns, name of columns, #non-null values for each column etc\n",
"# Hint: there is a Pandas function to do this\n",
"#replace None with your code\n",
"print \"Task 1a: Details of FEC data frame are: \\n\", fec.info() \n",
"\n",
"#Task 1b: finding the number of rows and columns in the data frame.\n",
"#Hint: find the property of the data frame that gives the number of rows and columns\n",
"#replace None with your code\n",
"t1b_num_rows = fec.shape[0]\n",
"t1b_num_cols = fec.shape[1]\n",
"print \"\\n\\nTask 1b: #Rows=%s, #Columns=%s\" % (t1b_num_rows, t1b_num_cols) \n",
"\n",
"#Task 1c: The only numeric data is 'contb_receipt_amt' which is the amount of contribution. \n",
"# Print the descriptive details (min, max, quantiles etc) for 'contb_receipt_amt'\n",
"#Hint: as above there is a direct pandas command for it.\n",
"#replace None with your code\n",
"print \"\\n\\nTask 1c: Descriptive details of contb_receipt_amt is \\n\", fec.contb_receipt_amt.describe()\n",
"\n",
"#Task 1d: Let us now print the number of unique values for few columns\n",
"#Hint: Look for a Pandas function to do this.\n",
"t1d_num_uniq_cand_id = fec.cand_id.nunique()\n",
"t1d_num_uniq_cand_nm = fec.cand_nm.nunique()\n",
"t1d_num_uniq_contbr_city = fec.contbr_city.nunique()\n",
"t1d_num_uniq_contbr_st = fec.contbr_st.nunique()\n",
"print \"\\n\\nTask 1d: #Uniq cand_id = \", t1d_num_uniq_cand_id\n",
"print \"Task 1d: #Uniq cand_num = \", t1d_num_uniq_cand_nm\n",
"print \"Task 1d: #Uniq contbr_city = \", t1d_num_uniq_contbr_city\n",
"print \"Task 1d: #Uniq contbr_st = \", t1d_num_uniq_contbr_st"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Task 1a: Details of FEC data frame are: \n",
"\n",
"Int64Index: 694282 entries, 411 to 701385\n",
"Data columns (total 17 columns):\n",
"cmte_id 694282 non-null object\n",
"cand_id 694282 non-null object\n",
"cand_nm 694282 non-null object\n",
"contbr_nm 694282 non-null object\n",
"contbr_city 694275 non-null object\n",
"contbr_st 694278 non-null object\n",
"contbr_zip 694234 non-null object\n",
"contbr_employer 693607 non-null object\n",
"contbr_occupation 693524 non-null object\n",
"contb_receipt_amt 694282 non-null float64\n",
"contb_receipt_dt 694282 non-null object\n",
"receipt_desc 2345 non-null object\n",
"memo_cd 87387 non-null object\n",
"memo_text 90672 non-null object\n",
"form_tp 694282 non-null object\n",
"file_num 694282 non-null int64\n",
"party 694282 non-null object\n",
"dtypes: float64(1), int64(1), object(15)\n",
"memory usage: 95.3+ MB\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"None\n",
"\n",
"\n",
"Task 1b: #Rows=694282, #Columns=17\n",
"\n",
"\n",
"Task 1c: Descriptive details of contb_receipt_amt is \n",
"count 694282.000000\n",
"mean 322.942745\n",
"std 4482.130250\n",
"min 0.010000\n",
"25% 35.000000\n",
"50% 100.000000\n",
"75% 250.000000\n",
"max 2014490.510000\n",
"Name: contb_receipt_amt, dtype: float64"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"\n",
"\n",
"Task 1d: #Uniq cand_id = "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 2\n",
"Task 1d: #Uniq cand_num = 2\n",
"Task 1d: #Uniq contbr_city = 11856\n",
"Task 1d: #Uniq contbr_st = 67\n"
]
}
],
"prompt_number": 26
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Task 2: Basic Filtering\n",
"-----------------------\n",
"In this task, we will perform some very high level filtering. Pandas has a convenient and powerful syntax for filtering (for eg, see above for how I filtered negative contributions and non-Obama, Romney candidates). "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#Task 2a: Let us find out how much contributions did Obama and Romney made in this dataset\n",
"# Remember, that this is not the complete amount as it excludes other sources like PACs, Super PACs and \n",
"# spending by party committes.\n",
"#Hint: use cand_nm field\n",
"t2a_tot_amt_obama = fec.contb_receipt_amt[fec.cand_nm == 'Obama, Barack'].sum()\n",
"t2a_tot_amt_romney = fec.contb_receipt_amt[fec.cand_nm == 'Romney, Mitt'].sum()\n",
"print \"Task 2a: Total Contribution for Obama is %s and for Romney is %s\" % (t2a_tot_amt_obama, t2a_tot_amt_romney)\n",
"\n",
"#Task 2b: How much contribution did folks from California, New York and Texas make totally (i.e. to both Obama and Romney).\n",
"#use contbr_st field\n",
"t2b_tot_amt_CA = fec.contb_receipt_amt[fec.contbr_st == 'CA'].sum()\n",
"t2b_tot_amt_NY = fec.contb_receipt_amt[fec.contbr_st == 'NY'].sum()\n",
"t2b_tot_amt_TX = fec.contb_receipt_amt[fec.contbr_st == 'TX'].sum()\n",
"print \"\\n\\nTask 2b: Total contributions from California is %s, New York is %s and Texas is %s\" % (t2b_tot_amt_CA, t2b_tot_amt_NY, t2b_tot_amt_TX)\n",
"\n",
"#Task 2c: Let us now use multiple filtering criteria\n",
"# How much money did folks from Texas made to BO and MR?\n",
"# How much money did folks from UT Arlington made to BO and MR?\n",
"t2c_tot_contr_tx_bo = fec.contb_receipt_amt[(fec.cand_nm == 'Obama, Barack') & (fec.contbr_st == 'TX')].sum()\n",
"t2c_tot_contr_tx_mr = fec.contb_receipt_amt[(fec.cand_nm == 'Romney, Mitt') & (fec.contbr_st == 'TX')].sum()\n",
"t2c_tot_contr_uta_bo = fec.contb_receipt_amt[(fec.cand_nm == 'Obama, Barack') & (fec.contbr_employer == 'UT ARLINGTON') \n",
" & (fec.contbr_st == 'TX')].sum()\n",
"t2c_tot_contr_uta_mr = fec.contb_receipt_amt[(fec.cand_nm == 'Romney, Mitt') & (fec.contbr_employer == 'UT ARLINGTON') \n",
" & (fec.contbr_st == 'TX')].sum()\n",
"\n",
"print \"\\n\\nTask 2c: From TX, BO got %s and MR got %s dollars\" % (t2c_tot_contr_tx_bo, t2c_tot_contr_tx_mr)\n",
"print \"Task 2c: From UTA, BO got %s and MR got %s dollars\" % (t2c_tot_contr_uta_bo, t2c_tot_contr_uta_mr)\n",
"\n",
"#Task 2d: How much did Engineers from Google gave to BO and MR.\n",
"# This task is a bit tricky as there are many variations: eg, SOFTWARE ENGINEER vs ENGINEER and GOOGLE INC. vs GOOGLE\n",
"t2d_tot_engr_goog_bo = fec.contb_receipt_amt[(fec.cand_nm == 'Obama, Barack') & (fec.contbr_employer.str.contains(\"GOOGLE\") == True) \n",
" & (fec.contbr_occupation.str.contains(\"ENGINEER\") == True)].sum()\n",
"t2d_tot_engr_goog_mr = fec.contb_receipt_amt[(fec.cand_nm == 'Romney, Mitt') & (fec.contbr_employer.str.contains(\"GOOGLE\") == True) \n",
" & (fec.contbr_occupation.str.contains(\"ENGINEER\") == True)].sum()\n",
"\n",
"print \"\\n\\nTask 2d: From Google Engineers, BO got %s and MR got %s dollars\" % (t2d_tot_engr_goog_bo, t2d_tot_engr_goog_mr)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Task 2a: Total Contribution for Obama is 135877427.24 and for Romney is 88335907.53\n",
"\n",
"\n",
"Task 2b: Total contributions from California is 35062620.84, New York is 24836131.14 and Texas is 12792822.13"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"\n",
"\n",
"Task 2c: From TX, BO got 6570832.45 and MR got 6221989.68 dollars"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Task 2c: From UTA, BO got 750.0 and MR got 0 dollars\n",
"\n",
"\n",
"Task 2d: From Google Engineers, BO got 87212.4 and MR got 2850.0 dollars"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
}
],
"prompt_number": 27
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Task 3: Basic Aggregation \n",
"--------------------------\n",
"In this task, we will perform some very high level aggregation. Pandas has some convenient functions for aggregation (do NOT write a for loop - Pandas has some very efficient, vectorized code)."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#Task 3a: For each state, print the total contribution they made to both candidates. \n",
"t3a_state_contr_both = fec.contb_receipt_amt.groupby(fec.contbr_st).sum()\n",
"print \"Task 3a: Total contribution made to both candidates by each state are:\\n\", t3a_state_contr_both\n",
"\n",
"#Task 3b: Now let us limit ourselves to TX. For each city in TX, print the total contribution made to both candidates\n",
"t3b_tx_city_contr_both = fec.contb_receipt_amt[fec.contbr_st == 'TX'].groupby(fec.contbr_city).sum()\n",
"print \"\\n\\nTask 3b: Total contribution made to both candidates by each city in TX are:\\n\", t3b_tx_city_contr_both\n",
"\n",
"#Task 3c: Now let us zoom into Arlington, TX. For each zipcode in Arlington, print the total contribution made to both candidates\n",
"t3c_arlington_contr_both = fec.contb_receipt_amt[(fec.contbr_st == 'TX') & (fec.contbr_city == 'ARLINGTON')].groupby(fec.contbr_zip).sum()\n",
"print \"\\n\\nTask 3c: Total contribution made to both candidates by each zipcode in Arlington are:\\n\", t3c_arlington_contr_both"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Task 3a: Total contribution made to both candidates by each state are:\n",
"contbr_st\n",
"AA 56540.00\n",
"AB 2048.00\n",
"AE 48653.75\n",
"AK 368044.39\n",
"AL 1070426.99\n",
"AP 38785.50\n",
"AR 464803.28\n",
"AS 2955.00\n",
"AZ 3394913.21\n",
"CA 35062620.84\n",
"CO 3639143.61\n",
"CT 5567766.71\n",
"DC 5398676.30\n",
"DE 419381.14\n",
"FF 99030.00\n",
"...\n",
"SC 1033475.80\n",
"SD 253088.76\n",
"TN 2636233.06\n",
"TX 12792822.13\n",
"UK 2500.00\n",
"UT 4237151.85\n",
"VA 7725743.04\n",
"VI 84212.00\n",
"VT 1041740.03\n",
"WA 5592454.72\n",
"WI 1400471.78\n",
"WV 295879.59\n",
"WY 446642.58\n",
"XX 400250.00\n",
"ZZ 5963.00\n",
"Name: contb_receipt_amt, Length: 67, dtype: float64\n",
"\n",
"\n",
"Task 3b: Total contribution made to both candidates by each city in TX are:\n",
"contbr_city\n",
"ABERNATHY 210.00\n",
"ABILENE 45748.00\n",
"ADDISON 12244.25\n",
"ADKINS 600.00\n",
"ALAMO 152.00\n",
"ALAMO HEIGHTS 2500.00\n",
"ALBA 69.00\n",
"ALBANY 6500.00\n",
"ALEDO 5880.00\n",
"ALICE 2760.00\n",
"ALLEN 21066.00\n",
"ALLISON 474.00\n",
"ALPINE 5166.00\n",
"ALTO 120.00\n",
"ALTON 125.00\n",
"...\n",
"WIMBERLEY 19060\n",
"WINDCREST 525\n",
"WINDTHORST 400\n",
"WINFIELD 1000\n",
"WINNIE 230\n",
"WINNSBORO 375\n",
"WOODLANDS 5112\n",
"WOODVILLE 175\n",
"WOODWAY 2965\n",
"WORTHAM 250\n",
"WYLIE 8405\n",
"YANTIS 340\n",
"YOAKUM 100\n",
"YORKTOWN 100\n",
"ZAPATA 300\n",
"Name: contb_receipt_amt, Length: 729, dtype: float64\n",
"\n",
"\n",
"Task 3c: Total contribution made to both candidates by each zipcode in Arlington are:\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"contbr_zip\n",
"75237 85\n",
"76001 1130\n",
"760015368 5175\n",
"760015696 150\n",
"760017554 300\n",
"76002 904\n",
"760023037 550\n",
"760024202 215\n",
"760025010 2474\n",
"760025461 35\n",
"760041185 1000\n",
"76006 10050\n",
"760062026 400\n",
"760062725 1000\n",
"760062778 365\n",
"...\n",
"760172730 125\n",
"760172754 1000\n",
"760172768 80\n",
"760173004 135\n",
"760173541 636\n",
"760174554 200\n",
"760176032 210\n",
"760176250 145\n",
"760177925 25\n",
"760177973 650\n",
"76018 105\n",
"760181913 170\n",
"760182501 100\n",
"760184920 100\n",
"760185108 35\n",
"Name: contb_receipt_amt, Length: 121, dtype: float64\n"
]
}
],
"prompt_number": 28
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Task 4: Aggregation+Filtering+Ranking\n",
"-----------------------------------------\n",
"In this task, you will try to combine aggregation with filtering and then rank the results based on the results. Pandas is often quite clever and might sort the data for you already. \n",
"\n",
"**Hint:** Pandas has ready made functions for all the following."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#Task 4a: Print the number of contributors to Obama in each state.\n",
"t4a_num_contr_obama_per_state = fec.contbr_nm[fec.cand_nm == 'Obama, Barack'].groupby(fec.contbr_st).count()\n",
"print \"Number of contributions to Obama in each state is \\n\", t4a_num_contr_obama_per_state\n",
"\n",
"#Task 4b: Print the top-10 states (based on number of contributors) that contributed to Obama.\n",
"# print both state name and number of contributors\n",
"t4b_top10_obama_contr_states = fec[fec.cand_nm == 'Obama, Barack'].contbr_st.value_counts()[:10]\n",
"print \"\\n\\nTop-10 states with most contributors to Obama are :\\n\", t4b_top10_obama_contr_states\n",
"\n",
"#Task 4c: Print the top-20 occupations that contributed overall (to both BO and MR)\n",
"t4c_top20_contr_occupation = fec.contbr_occupation.value_counts()[:20]\n",
"print \"\\n\\nTop-20 Occupations with most contributors are :\\n\", t4c_top20_contr_occupation\n",
"\n",
"#Task 4d: Print the top-10 Employers that contributed overall (to both BO and MR)\n",
"t4d_top10_contr_employer_all = fec.contbr_employer.value_counts()[:10]\n",
"print \"\\n\\nTop-10 Employers with most contributors are :\\n\", t4d_top10_contr_employer_all"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Number of contributions to Obama in each state is \n",
"contbr_st\n",
"AA 74\n",
"AB 4\n",
"AE 395\n",
"AK 2036\n",
"AL 3854\n",
"AP 158\n",
"AR 1747\n",
"AS 31\n",
"AZ 10509\n",
"CA 100182\n",
"CO 12289\n",
"CT 9977\n",
"DC 11491\n",
"DE 1782\n",
"FL 29797\n",
"...\n",
"QU 1\n",
"RI 2038\n",
"SC 4228\n",
"SD 713\n",
"TN 6534\n",
"TX 32292\n",
"UT 2790\n",
"VA 21451\n",
"VI 415\n",
"VT 3563\n",
"WA 20783\n",
"WI 8050\n",
"WV 1330\n",
"WY 1055\n",
"ZZ 15\n",
"Name: contbr_nm, Length: 64, dtype: int64\n",
"\n",
"\n",
"Top-10 states with most contributors to Obama are :\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"CA 100182\n",
"NY 50383\n",
"IL 33240\n",
"TX 32292\n",
"FL 29797\n",
"MA 24864\n",
"MD 22552\n",
"VA 21451\n",
"WA 20783\n",
"PA 19280\n",
"dtype: int64\n",
"\n",
"\n",
"Top-20 Occupations with most contributors are :\n",
"RETIRED 177473\n",
"ATTORNEY 30133\n",
"INFORMATION REQUESTED 24747\n",
"HOMEMAKER 19626\n",
"PHYSICIAN 17206\n",
"INFORMATION REQUESTED PER BEST EFFORTS 12545\n",
"PROFESSOR 11804\n",
"TEACHER 11512\n",
"CONSULTANT 10061\n",
"NOT EMPLOYED 9696\n",
"LAWYER 7438\n",
"ENGINEER 6107\n",
"PRESIDENT 4864\n",
"MANAGER 4745\n",
"WRITER 4439\n",
"SELF-EMPLOYED 3763\n",
"SALES 3697\n",
"EXECUTIVE 3609\n",
"OWNER 3408\n",
"EDUCATOR 3360\n",
"dtype: int64\n",
"\n",
"\n",
"Top-10 Employers with most contributors are :\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"RETIRED 173287\n",
"SELF-EMPLOYED 79638\n",
"NOT EMPLOYED 45727\n",
"INFORMATION REQUESTED 25524\n",
"INFORMATION REQUESTED PER BEST EFFORTS 13104\n",
"HOMEMAKER 12081\n",
"OBAMA FOR AMERICA 1741\n",
"STUDENT 1417\n",
"SELF 1228\n",
"DISABLED 1096\n",
"dtype: int64\n"
]
}
],
"prompt_number": 29
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Task 5: Basic Visualization\n",
"-----------------------------\n",
"\n",
"Let us take a break from analytics to do some Visualization. Notice that the visualization tasks are NOT ordered based on their complexity. So some of them might be more challenging than others."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#Task 5a: Draw a \"horizontal\" bar chart with one bar each for Obama and Romney with the value corresponding to total amount they raised.\n",
"# Remember to make the bar chart into a horizontal bar chart\n",
"#########################begin code for Task 5a\n",
"plt.figure()\n",
"figObj5a = fec.contb_receipt_amt.groupby(fec.cand_nm).sum().plot(kind='barh', title='Task 5a',color=['blue','red'])\n",
"figObj5a.set_xlabel('Total Amount Raised')\n",
"figObj5a.set_ylabel('Candidate Names')\n",
"#########################end code for Task 5a\n",
"\n",
"\n",
"#Task 5b: Draw the \"horizontal\" bar chart of total NUMBER of contributions made per Candidate.\n",
"# ie Candidate name vs number of contributions for that candidate\n",
"#########################begin code for Task 5b\n",
"plt.figure()\n",
"figObj5b = fec.contbr_nm.groupby(fec.cand_nm).agg('count').plot(kind='barh', title='Task 5b',color=['blue','red'])\n",
"figObj5b.set_xlabel('#Contributors')\n",
"figObj5b.set_ylabel('Candidate Names')\n",
"#########################end code for Task 5b\n",
"\n",
"#Task 5c: Draw the \"horizontal\" bar chart of average contributions made per Candidate.\n",
"# ie Candidate Name vs avg contribution\n",
"#########################begin code for Task 5c\n",
"plt.figure()\n",
"figObj5c = fec.contb_receipt_amt.groupby(fec.cand_nm).mean().plot(kind='barh',title='Task 5c',color=['blue','red'])\n",
"figObj5c.set_xlabel('Average Value of Contributions')\n",
"figObj5c.set_ylabel('Candidate Names') \n",
"#########################end code for Task 5c\n",
"\n",
"\n",
"#Task 5d: Draw a \"horizontal\" bar chart that lists the top-10 states based on the TOTAL contribution they made to both candidates\n",
"# For each state, draw two lines - one in blue for Obama and one in red for Romney\n",
"# Display the proportion of the total contribution that came from the state.\n",
"# For eg, if Obama made 1 billion and TX gave 100 million of it, the proportion is 10% \n",
"# Remember to make the bar chart into a horizontal bar chart\n",
"#########################begin code for Task 5d\n",
"plt.figure()\n",
"t5dTotalContrib = fec.groupby(['cand_nm', 'contbr_st']).contb_receipt_amt.sum().unstack(0).fillna(0)\n",
"t5dTotalContrib['Total'] = t5dTotalContrib['Obama, Barack'] + t5dTotalContrib['Romney, Mitt']\n",
"t5dPercentContrib = t5dTotalContrib / t5dTotalContrib.sum(axis=0)\n",
"t5dTotalContrib['Obama Pct'], t5dTotalContrib['Romney Pct'] = t5dPercentContrib['Obama, Barack'], t5dPercentContrib['Romney, Mitt']\n",
"t5dTop10States = t5dTotalContrib.sort('Total', ascending=False)[:10]\n",
"figObj5d = t5dTop10States[['Obama Pct', 'Romney Pct']].plot(kind='barh', title='Task 5d', color=['blue','red'])\n",
"figObj5d.set_xlabel('Percent Contributions')\n",
"figObj5d.set_ylabel('States') \n",
"#########################end code for Task 5d\n",
"\n",
"\n",
"#Task 5e: Now repeat the same process based on Occupation (again top-10)\n",
"#########################begin code for Task 5e\n",
"plt.figure()\n",
"t5eTotalContrib = fec.groupby(['cand_nm', 'contbr_occupation']).contb_receipt_amt.sum().unstack(0).fillna(0)\n",
"t5eTotalContrib['Total'] = t5eTotalContrib['Obama, Barack'] + t5eTotalContrib['Romney, Mitt']\n",
"t5ePercentContrib = t5eTotalContrib / t5eTotalContrib.sum(axis=0)\n",
"t5eTotalContrib['Obama Pct'], t5eTotalContrib['Romney Pct'] = t5ePercentContrib['Obama, Barack'], t5ePercentContrib['Romney, Mitt']\n",
"t5eTop10Occuation = t5eTotalContrib.sort('Total', ascending=False)[:10]\n",
"figObj5e = t5eTop10Occuation[['Obama Pct', 'Romney Pct']].plot(kind='barh', title='Task 5e', color=['blue','red'])\n",
"figObj5e.set_xlabel('Percent Contributions')\n",
"figObj5e.set_ylabel('Occupations') \n",
"#########################end code for Task 5e\n",
"\n",
"\n",
"#Task 5f: Now repeat the same process based on Employers (again top-10)\n",
"#########################begin code for Task 5f\n",
"plt.figure()\n",
"t5fTotalContrib = fec.groupby(['cand_nm', 'contbr_employer']).contb_receipt_amt.sum().unstack(0).fillna(0)\n",
"t5fTotalContrib['Total'] = t5fTotalContrib['Obama, Barack'] + t5fTotalContrib['Romney, Mitt']\n",
"t5fPercentContrib = t5fTotalContrib / t5fTotalContrib.sum(axis=0)\n",
"t5fTotalContrib['Obama Pct'], t5fTotalContrib['Romney Pct'] = t5fPercentContrib['Obama, Barack'], t5fPercentContrib['Romney, Mitt']\n",
"t5fTop10Employers = t5fTotalContrib.sort('Total', ascending=False)[:10]\n",
"figObj5f = t5fTop10Employers[['Obama Pct', 'Romney Pct']].plot(kind='barh', title='Task 5f', color=['blue','red'])\n",
"figObj5f.set_xlabel('Percent Contributions')\n",
"figObj5f.set_ylabel('Employers')\n",
"#########################end code for Task 5f\n",
"\n",
"\n",
"#Task 5g: Draw the histogram of total NUMBER of contributions made per each state.\n",
"# X-axis : state, Y-axis : number of contribution from that state\n",
"#########################begin code for Task 5g\n",
"plt.figure(figsize=(16, 8)) \n",
"figObj5g = fec.contbr_nm.groupby(fec.contbr_st).count().plot(kind = 'bar',title='Task 5g')\n",
"figObj5g.set_xlabel('State Name')\n",
"figObj5g.set_ylabel('Number of Contributors')\n",
"#########################end code for Task 5g\n",
"\n",
"\n",
"#Task 5h: Draw a histogram of actual contributions made for Obama. Set bin size as 50\n",
"#X-axis: contribution amount bin, Y-axis: frequency\n",
"#########################begin code for Task 5h\n",
"\n",
"#####Note: Hist by default, tries to create bins that are neatly arranged from min to max. \n",
"# However, the contribution amounts are very skewed. \n",
"# If you run the command fec.contb_receipt_amt.describe() you can see that 75% of them are below 250 but max value is 2014490.51\n",
"# Hence default hist will not work here as most people gave small amount\n",
"# For eg, only 10 contributions to Obama was above $5000 (you can verify by the line below)\n",
"# len(fec.contb_receipt_amt[(fec.cand_nm == 'Obama, Barack') & (fec.contb_receipt_amt > 5000)])\n",
"#There are many ways to fix it - including cut and qcut of pandas.\n",
"# I am going for a simple solution - which is to ignore the top-10 contributions (above 5000)\n",
"# and plt only the rest\n",
"plt.figure()\n",
"figObj5h = fec.contb_receipt_amt[(fec.cand_nm == 'Obama, Barack') & (fec.contb_receipt_amt <= 5000)].hist(bins = 50)\n",
"figObj5h.set_xlabel('Contribution Bins')\n",
"figObj5h.set_ylabel('Frequency')\n",
"figObj5h.set_title('Task 5h')\n",
"#########################end code for Task 5h\n",
"\n",
"\n",
"#Task 5i: Draw a histogram of actual contributions made for Romney. Set bin size as 50\n",
"#X-axis: contribution amount bin, Y-axis: frequency\n",
"#########################begin code for Task 5i\n",
"plt.figure()\n",
"figObj5i = fec.contb_receipt_amt[(fec.cand_nm == 'Romney, Mitt') & (fec.contb_receipt_amt <= 5000)].hist(bins = 50)\n",
"figObj5i.set_xlabel('Contribution Bins')\n",
"figObj5i.set_ylabel('Frequency')\n",
"figObj5i.set_title('Task 5i')\n",
"\n",
"#########################end code for Task 5i\n",
"\n",
"\n",
"#Harder\n",
"#Task 5j: Draw a line chart showing how the campaign contribution of Obama and Romney varied every quarter\n",
"#Use blue for Obama and red for Romney\n",
"#This is a bit tricky because, you have to convert contribution date to quarter.\n",
"#You can either do it on your own or see if Pandas has some function\n",
"#########################begin code for Task 5j\n",
"\n",
"#Ignore the warning \n",
"plt.figure()\n",
"t5jContbDateTime = pd.to_datetime(fec.contb_receipt_dt)\n",
"fec.loc[:,'contb_receipt_dt_quarter'] = map(lambda dateTime: dateTime.quarter, t5jContbDateTime)\n",
"fec.loc[:,'contb_receipt_dt_year'] = map(lambda dateTime: dateTime.year, t5jContbDateTime)\n",
"figObj5j = fec['contb_receipt_amt'][fec.cand_nm=='Obama, Barack'].groupby(\n",
" [fec.contb_receipt_dt_year,fec.contb_receipt_dt_quarter]).sum().plot(color='Blue')\n",
"fec['contb_receipt_amt'][fec.cand_nm=='Romney, Mitt'].groupby(\n",
" [fec.contb_receipt_dt_year,fec.contb_receipt_dt_quarter]).sum().plot(color='Red')\n",
"figObj5j.set_title('Task 5j')\n",
"figObj5j.set_xlabel('Quarters/Years')\n",
"figObj5j.set_ylabel('Per Quarter Contributions')\n",
"figObj5j.legend (['Obama', 'Romney'])\n",
"#########################end code for Task 5j\n",
"\n",
"\n",
"#Harder\n",
"#Task 5k: Draw a line chart showing the CUMULATIVE campaign contribution of Obama and Romney varied every quarter\n",
"# In other words, if Obama made X, Y, Z in first, second and third quarters\n",
"# then plot X for first quarter, X+Y for second quarter and X+Y+Z for third quarter.\n",
"#Use blue for Obama and red for Romney\n",
"#This is a bit tricky because, you have to convert contribution date to quarter.\n",
"#You can either do it on your own or see if Pandas has some function\n",
"#########################begin code for Task 5k\n",
"plt.figure()\n",
"figObj5k = fec['contb_receipt_amt'][fec.cand_nm=='Obama, Barack'].groupby(\n",
" [fec.contb_receipt_dt_year,fec.contb_receipt_dt_quarter]).sum().cumsum().plot(color='Blue')\n",
"fec['contb_receipt_amt'][fec.cand_nm=='Romney, Mitt'].groupby(\n",
" [fec.contb_receipt_dt_year,fec.contb_receipt_dt_quarter]).sum().cumsum().plot(color='Red')\n",
"figObj5k.set_title('Task 5j')\n",
"figObj5k.set_xlabel('Quarters/Years')\n",
"figObj5k.set_ylabel('Per Quarter Contributions')\n",
"figObj5k.legend (['Obama', 'Romney'])\n",
"#########################end code for Task 5k\n",
"\n",
"#Tasks 5l and 5m\n",
"#Repeat 5j and 5k but do it for NUMBER of contributors\n",
"#In other words, 5l plots the number of contributors for Obama and Romney, quarter over quarter\n",
"#5m plots cumulative number of contributors quarter over quarter.\n",
"\n",
"#########################begin code for Task 5l\n",
"plt.figure()\n",
"figObj5l = fec['contb_receipt_amt'][fec.cand_nm=='Obama, Barack'].groupby(\n",
" [fec.contb_receipt_dt_year,fec.contb_receipt_dt_quarter]).count().plot(color='Blue')\n",
"fec['contb_receipt_amt'][fec.cand_nm=='Romney, Mitt'].groupby(\n",
" [fec.contb_receipt_dt_year,fec.contb_receipt_dt_quarter]).count().plot(color='Red')\n",
"figObj5l.set_title('Tak 5l')\n",
"figObj5l.set_xlabel('Quarters/Years')\n",
"figObj5l.set_ylabel('#Contributions')\n",
"figObj5l.legend (('Obama', 'Romney'))\n",
"#########################end code for Task 5l\n",
"\n",
"\n",
"#########################begin code for Task 5m\n",
"plt.figure()\n",
"figObj5m = fec['contb_receipt_amt'][fec.cand_nm=='Obama, Barack'].groupby(\n",
" [fec.contb_receipt_dt_year,fec.contb_receipt_dt_quarter]).count().cumsum().plot(color='Blue')\n",
"fec['contb_receipt_amt'][fec.cand_nm=='Romney, Mitt'].groupby(\n",
" [fec.contb_receipt_dt_year,fec.contb_receipt_dt_quarter]).count().cumsum().plot(color='Red')\n",
"figObj5m.set_title('Tak 5m')\n",
"figObj5m.set_xlabel('Quarters/Years')\n",
"figObj5m.set_ylabel('#Contributions')\n",
"figObj5m.legend (('Obama', 'Romney'))\n",
"#########################end code for Task 5m\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stderr",
"text": [
"/usr/local/lib/python2.7/dist-packages/pandas/core/indexing.py:245: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" self.obj[key] = np.nan\n",
"/usr/local/lib/python2.7/dist-packages/pandas/core/indexing.py:415: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" self.obj[item] = s\n"
]
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 30,
"text": [
""
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": [
""
]
},
{
"metadata": {},
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"text": [
""
]
},
{
"metadata": {},
"output_type": "display_data",
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"text": [
""
]
},
{
"metadata": {},
"output_type": "display_data",
"text": [
""
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": [
""
]
},
{
"metadata": {},
"output_type": "display_data",
"text": [
""
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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Dc+IOv9/dJ5pZCevXtcsH3gUucfcVZjYKGAwsDI/pADzo7leH6+HdDfSNS/Lz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\ndfdrzOylhLaKAr8GJia5zqH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"text": [
""
]
},
{
"metadata": {},
"output_type": "display_data",
"text": [
""
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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q0GGIiDQp+SRmmVtjpMOvqchrEREREakndSZm7j7KzMqB7YG/A53cfXqDRyYi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\nBkw0s/0JpghLgFlAZtq0Xdg2Hf77m7u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ASUVORK5CYII=\n",
"text": [
""
]
},
{
"metadata": {},
"output_type": "display_data",
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jux8xvv2kqrpbkucnOSXJY5O8vKq2J3luko+Ml31TkheP7/Y1Sb6/ux+e5MFV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\n/mmmt9GP38w+VDrHxvis1oY0u0zU1W9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"text": [
""
]
},
{
"metadata": {},
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"text": [
""
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": [
""
]
},
{
"metadata": {},
"output_type": "display_data",
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5c81s22binnucasLBQuq++1zUq+diwwYz331nL7aTd/paQmkN3IS+jnzzHA9F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\nSZs8jfhHHwq7OHMTjtczNypO/8otzuh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"text": [
""
]
},
{
"metadata": {},
"output_type": "display_data",
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12PXgI/h790XrzqS/uBKKiEwxxrTE3kPeDXwmIt8nMzCl0sWKFU569oRffnFx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\nQrke6Cwim4odW4m9FkU7mMv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"text": [
""
]
},
{
"metadata": {},
"output_type": "display_data",
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1ZEckku3xx/18952D6dPN8aju3dN3PKq8Lr4uAEqpkVrrOxMXkhAiGsuW2Rk4\n0IvPB9On53HMMbJ1hgCPB15/3RyPeuABL6edto8mTdJzPCqalSSWK6V67V+otZ4ah3iEEFFYt85G\nr14+gkF4++08mjdPzw8gER8NGhiMGpXPzTeb41GzZu2jatVkR3XoorkPqkuxn/OAJ4Fu8QxKCFG2\nHTvg+usz2L7dzrPP+snJkXudxIEuvDBE//4Bfv3Vwb33ejnIokEpKZr7oG4u/lgpVRP4v3gFJIQo\nW34+3HSTj7Vr7dxxh5+bbpKtM0TZHn3Uz7JlDj74wEWHDmF69Uqvfy+Hs1PuXqBBjOMQQhxEJAJ3\n3ull6VInl10W5JFH5F4nUT6XyxyPqlHD4OGHPaxceViboydNNNttfFnsoQ1oCHwat4iEEKV6+mk3\nH37o4owzwowenY89vT5rRJIce6zB2LF59OiRwS23+Pj8871pM9szmkkSj1t/GtbPdq316viFJITY\n39SpLkaN8tCwYYSpU/PwepMdkUgnOTlh7rzTz8iRHoYM8TJpUn5a3Mx90O9gWut5wDbgVOAUSm7h\nLoSIs7lzHTzwgIeaNSNMn76PWrXScLRbJN0DDwRo3z7EJ5+4eOON9LibO5qVJAYC72OOOyngP0qp\nm+MblhACYOVKO7fe6sPphKlT82jYUJKTODxOJ0ycmE/t2hGGDfOwfHnq9xFHE+EA4Ayt9T1a6yFA\nG+C++IYlhPjrLxs9e/rYu9fGuHH5tGkj9zqJI5OdbTB+fD6hEPTt6+Pvv5MdUfmiSVB7MbdcL5AL\n7ItPOEIIgNxc6NHDx6ZNdoYNy+eSS6RnXcRG585h7r03wPr1dgYP9qX0/VHlrcV3j/XrJmCeUmo6\n5jp81wK/JCA2ISqlYBD69PHx008OevcOcPvt6XXvikh9d98dYOlSB7NmORk3zsXAgan5b6y8FlQm\nUBX4HpgF1ASygHlIghIiLgwDBgyAr75y0q1biKeekq0zROw5HDBuXD516kQYMcLD0qWpub1feYvF\nDk9gHEJUapEIzJrlZOxYF998A6eeGmbixDyc0dwIIsRhOPpog9dey+eKK3z06+dl7tx91K6dWv19\nsqOuEEmUnw//+peLceNc/Pab+V/q4ovhmWfy0nJxT5Fe2rcP89BDAUaM8DBwoJd3381LqRvAD7qj\nLtBSa70iQfEIUSns3Alvvunm9dddbN1qx+UyuP76IAMGBDjrrCps3Zpa32RFxXXHHQG+/trB5587\nGTnSzZCPu17KAAAgAElEQVQhqbOEVjQdCP8AmsQ7ECEqgz//tDFxopu333axb5+NzEyDO+7w07dv\nkLp1JSmJxLPbYcyYPLp2rcJzz7k544wwHTumxgr50SSoNUqpx4ClQB7menyG1np+XCMTogJZtcrO\n2LFuPvrISThso27dCPfd56dXryCZmcmOTlR2NWvCa6/lcdllGdx2m5cvvthHnTrJ/8IUTYKqRdF+\nUMXt/1gIUYxhwPz5DsaMcfPVV+Z/tSZNwtx+e4Arrgjhdic5QCGKOeOMCI895uexx7wMGODlvffy\ncCR5pkE0CeoOrfWq4gVKqfZxikeItBcKwb//7WTsWDerVpn/wzt2DDFwYIBzzgnLtHGRsm67LciS\nJQ5mzHDxwgtuhg5N7nhUebP4OgIO4HWl1K3FnnIB44FGcY5NiLSyZw9Mn+5i4kQ369fbsdsNLrss\nyMCBAVq0kGWKROqz2WDkyHxWr3bwyitu2rQJc845yRuPKq8F1Q3oBNSlaMsNMFcznxjPoIRIJ1u2\n2HjjDRdTprjZudOGz2fQp0+A/v0DNGiQ/H58IQ7FUUfBpEl5XHxxBgMHmvdH1auXnH/H5U0zHwag\nlOqltZ6auJCESA+//WZj/Hg3//d/Lvx+G7VqRbjvvgB9+gRlSwyR1lq0iPDEE36GDvVy221ePvgg\nD1cSduiIZgxqvlLqRcyljgp6zw2tdZ/4hSVE6vrmG3NG3syZTgzDRv36EW6/3c911wXJyEh2dELE\nRu/eQb7+2sFHH7l45hk3jz2W+PGoaBLU/wHzrZ8C8vVQVCollyIy/9u0bBnmjjsCXHhhKOmznYSI\nNZsNXnopnx9/dDBmjIe2bcOcd15ix6OiSVBOrfW9cY9EiBRU2lJE3bqZM/Lat5cZeaJiy8w0x6Mu\nvDCDQYN8zJ27l6ysxF0/mlWXFiqlLlVKyV0botLYuRNGjnTTunUV7r7by//+Z6d79yDz5+9l2rQ8\nOnSQ5CQqh2bNIjzzjJ+dO2307etL6LWjaUFdA9wBoJQqKJPFYkWFJEsRCXGgHj2CLF7s4L33EjtT\n4qAJSmtdNxGBCJFMZS1FdOONQapVS3Z0QiSXzQbPP5/P33/biK5dExsHvZJSqgowDOhqHf8F8IjW\nem+cYxMirgqWIho71s28eeZ/hcaNzaWIrrxSliISorgqVWD69DzMvWwTI5pUOAbYC/TGHLPqC0wA\nbozmAkqptsCzWusuSqmTgDeBCLAKGKi1NpRSfYF+mDcBj9Baf6qU8gHvYO7imwvcpLXeppRqB7xq\nHTtba/2EdZ1hwIVW+V1a62XRxCcqn1AIPv7YXIpo5Uqzp/rMM82JD127ytiSEKkimgTVWmt9arHH\nA5VSP0VzcqXU/cANwB6r6GXgIa31fKXUeOAypdTXwCCgNeDDnJQxBxgArNBaP6GUug54BLgLMzle\nobVep5T6VCnVAjNxdtJat1VKHQe8D7SJJkZReezZA+++Cy++WKVwKaJLLzWXImrZUpYiEiLVRDOL\nz6aUqlHwwPo9GOX5fwOupOgG31bFtumYAeQAZwCLtNZBrfVu6zWnAmcCM61jZwI5SqlMwK21XmeV\nz7LOcSYwG0BrvR5wKqVqRRmjqOC2bLHxzDNuWrWqyp13wrZtNvr0CbBkyV4mTcqX5CREioqmBfUy\n8I1S6mPMRHMp8Ew0J9daf6CUalCsqHjnSS5QHagG7CqjfHc5ZQXlDYF8YHsp5yheJiqZ//7Xxrhx\nJZciGj7cxnXX7ZWliIRIA9HM4puilPoW6IyZYK7QWq88zOsV/6paDdiJmXCKj7plllJeWlnxcwTK\nOEe5srLSY6c4ifPQLFkCL7wAH31kToRo2BDuuQduvtluLUVUNdkhHlSq1OXBSJyxlS5xJkq5CUop\n1RjIsxLSSmss6EjWuvheKdVZa/0VcAEwF/gGeEop5QG8mNvLrwIWYU56WGYdO19rnauUCiilGgLr\ngHOB4VZMz1trBh4H2LXWOw4WzNatuUfwVhIjKytT4oxCJAKzZ5ubA5a1FNHevZCRkfr1mey6jJbE\nGVvpFGeilLcfVFfMWXTXAf+ziusCryilemqtvzyE6xT0p9yDub+UG1gD/MuaxTcKWIA5JvaQ1tpv\nTaJ4Sym1APADPaxz9AemYe5VNatgtp513BLrHLcfQmwijRUsRTR+vItff5WliISoSGyGUXpfvFJq\nCdC3lN10WwJjtdYdEhBfPBnp8m1F4jzQzp3w1ltuXn/dxZYtdlwug6uuCnH77QEaNy570kM61Gc6\nxAgSZ6ylUZwJ+9pXXhefd//kBKC1/l4pJZsKiKQoWIronXdc7N1rLkU0cGCAfv0CshSREBVMeQnK\noZRya61LbAJijRV54huWECXtvxRRdnaEe++VpYiEqMjKS1AfA+OUUoO01nkASikvMBqYk4jgROUm\nSxEJUbmVl6AeB94CNiul1mBOMW8MfAYMTkBsopKSpYiEEFB+groZeBLoBjxvlb2gtb4+3kGJyslc\nisjFhAluWYpICFFugnJhrn9XBbgT894kl1LqNOBHrbWMSIuY2LLFxhtvuJgyxc3OnTZ8PoPevQP0\n7x/ghBPkn5kQlVWZCUprPQ5zDOp74CKgOXAJZvfeqZhr6Alx2PZfiqhmzQj33RegT5+gLEUkhCj3\nRt3lwK9ADcyVwVcC27XWtyQoNlFBLVtmzsibMcOJYdioXz/CgAF+uncPWksRCSFE+S2oVkqpRpgb\nFJ6PuQpEI6XUR8BirfXzZb1WiP0VLEU0dqybpUuLliIaODDARReZSxEJIURx5a7Fp7X+RSm1Wmt9\nP4BSahbm3k3tExGcSH9+v7kU0bhxRUsR5eSYM/I6dJAZeUKIskWzmvl5pfy+Pm4RiQqhtKWIuncP\nHnQpIiGEKBDNflBCRE2WIhJCxIokKBETP/4ITz7p5aOPnIRC5lJE99zjp1cvWYpICHF4JEGJw/bH\nHzbmzHHy2WdOFiwAcMlSREKImJEEJaIWDMKyZQ7mzHHy+ecOtC6aete5M9x22z5ZikgIETOSoES5\ntm2z8cUXZlL68ksnu3eb2cfrNejWLUROTohu3UK0bFmVrVuPZLNlIYQoSRKUKMEwzK0t5sxxMmeO\nk+XL7RiGmZSOPTbCVVcF6dYtxJlnhvH5khysEKJCkwQl2LMHFiwwu+3mzHGyaZMdAIfDoG3bMDk5\nYbp1C9G4cUS674QQCSMJqpJat87G55+braTFix0EAmbmqVkzwtVXm62kLl1CHHVUkgMVQlRakqAq\niWAQli51WF13Dn77rWiCQ9Om4cLxpNatI7LskBAiJUiCqsC2bCma4DBvnpPcXLOVlJFhcN55ocKk\nVK+e3EArhEg9kqAqkEgEVq60W9PAnXz/fdEEh+OPj3DttWbXXYcOYbzeJAcrhBAHIQkqze3ZA/Pm\nmRMcPv/cyZYtRRMc2rc3u+66dQtz8skywUEIkV4kQaWhtWtthdPAlyxxEAyamad27aJW0tlnh6he\nPcmBCiHEEZAElQYCAViyxFE4627tWnvhc82bF7SSQrRoIRMchBAVhySoFLV5s425cx3Mnw+zZ1dl\nz56iCQ4XXBCkW7cwOTkhsrN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"text": [
""
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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t+24RuRfYHvCr6sq66isuLqnPbqRVUVGB1ZlEjbnOigo4//w83nsvyNFHVzJq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\n2ro1TqvWxFu1piHvMklpQKn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"text": [
""
]
}
],
"prompt_number": 30
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Task 6: Discretization\n",
"-----------------------\n",
"\n",
"Recall the IMDB dataset that we discussed in the class where we were able to draw lot more interesting plots. The key challenge here is that most of the attributes have too many values (ie. the domain cardinality is very large). There are few ways to work around this issue. We already did something above (ie focussing only top-k). The other option is **discretization** where we create buckets and put contributions based on the buckets. Discretization in Pandas is acheived by cut function."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#The following set of tasks are a bit tricky: \n",
"# you need to use multiple commands to achieve. Specifically look at cut, groupby and unstack\n",
"\n",
"#Task 6a: Discretize the contributions of Obama and Romney based on the bins given below.\n",
"# For example, if Obama got contributions such as 2, 6, 16, 18, 120, then he has \n",
"# 0 contribution in (0,1], 2 contributions in (1,10], 2 contributions in (10, 100] and 1 contribution in (100, 1000]\n",
"bins = np.array([0, 1, 10, 100, 1000, 10000, 100000, 1000000, 10000000])\n",
"labels = pd.cut(fec.contb_receipt_amt, bins) # set the variable labels to the output of pd.cut\n",
"grouped = fec.groupby(['cand_nm', labels]) #Group the data based on labels and candidate names\n",
"#Replace None below in the print statement with grouped.size().unstack(0) . \n",
"# If your code for labels and grouped is correct, this should print number of people in each bin per candidate\n",
"print \"Task 6a:\\n\", grouped.size().unstack(0)\n",
"\n",
"#Task 6b: In Task 6a, we calculated the COUNT (i.e. the number of contributors in each bin)\n",
"# This by itself is not sufficient.\n",
"# In this task, let us compute the TOTAL AMOUNT in each bucket.\n",
"# Specifically, compute for each candidate the total amount of contributions that each got in each bucket.\n",
"# Continuing the example above, Obama's total contribution in each bucket is\n",
"# 0 in (0,1], 8 in (1,10], 34 in (10, 100] and 120 in (100, 1000]\n",
"#This could be done in 1 line from the variable grouped above\n",
"t6b_bucket_sums = grouped.contb_receipt_amt.sum().unstack(0)\n",
"print \"\\n\\nTask 6b:\\n\", t6b_bucket_sums\n",
"\n",
"\n",
"#Task 6c: Even this does not fully specify the disparity in the funding scenario.\n",
"# To see this let us now compute the PROPORTION of total contribution in each bucket\n",
"# This is called normalization and is a common operation. \n",
"# Typically normalization is done over the same candidate.\n",
"# But for the point I am trying to make, let us normalize within bucket.\n",
"# For example, if obama made X in a bucket and Y in another bucket, \n",
"# then normalization will give X/(X+Y) for Obama and Y/(X+Y) for Romney\n",
"# This is also quite easy in Pandas and can be done in 1 line\n",
"# The key idea is to realize that sum function takes an argument called axis\n",
"# that does different things for axis=0 and axis=1 (figure out which does what)\n",
"t6c_normed_bucket_sums = t6b_bucket_sums.div(t6b_bucket_sums.sum(axis=1), axis=0)\n",
"print \"\\n\\nTask 6c:\\n\", t6c_normed_bucket_sums\n",
"\n",
"#Once you have done this , uncomment the following line to a horizontal stacked bar chart\n",
"t6c_normed_bucket_sums[:-2].plot(kind='barh', stacked=True, color=['blue','red'])\n",
"\n",
"\n",
"#Task 6d: Let us go back and try to do the other analysis\n",
"# Let us now try to see what PROPORTION of each candidate's amount came from each bucket.\n",
"# This is the classical case of normalization.\n",
"# Continuing the example above, Obama has made 0+8+34+120=162\n",
"# We can normalize it by diving each bucket by the total\n",
"# For example, 0/162, 8/162, 34/162 and 120/162.\n",
"# If you finished t6c, then this just a matter of playing with axis values.\n",
"t6d_normed_bucket_sums = t6b_bucket_sums.div(t6b_bucket_sums.sum(axis=0),axis=1)\n",
"print \"\\n\\nTask 6d:\\n\", t6d_normed_bucket_sums\n",
"\n",
"\n",
"#Once you have done this , uncomment the following line to a horizontal stacked bar chart\n",
"t6d_normed_bucket_sums.plot(kind='barh', stacked=True,color=['blue','red'])\n",
"\n",
"\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Task 6a:\n",
"cand_nm Obama, Barack Romney, Mitt\n",
"contb_receipt_amt \n",
"(0, 1] 493 77\n",
"(1, 10] 40070 3681\n",
"(10, 100] 372280 31853\n",
"(100, 1000] 153991 43357\n",
"(1000, 10000] 22284 26186\n",
"(10000, 100000] 2 1\n",
"(100000, 1000000] 3 NaN\n",
"(1000000, 10000000] 4 NaN\n",
"\n",
"\n",
"Task 6b:\n",
"cand_nm Obama, Barack Romney, Mitt\n",
"contb_receipt_amt \n",
"(0, 1] 318.24 77.00\n",
"(1, 10] 337267.62 29819.66\n",
"(10, 100] 20288981.41 1987783.76\n",
"(100, 1000] 54798531.46 22363381.69\n",
"(1000, 10000] 51753705.67 63942145.42\n",
"(10000, 100000] 59100.00 12700.00\n",
"(100000, 1000000] 1490683.08 NaN\n",
"(1000000, 10000000] 7148839.76 NaN\n",
"\n",
"\n",
"Task 6c:\n",
"cand_nm Obama, Barack Romney, Mitt\n",
"contb_receipt_amt \n",
"(0, 1] 0.805182 0.194818\n",
"(1, 10] 0.918767 0.081233\n",
"(10, 100] 0.910769 0.089231\n",
"(100, 1000] 0.710176 0.289824\n",
"(1000, 10000] 0.447326 0.552674\n",
"(10000, 100000] 0.823120 0.176880\n",
"(100000, 1000000] 1.000000 NaN\n",
"(1000000, 10000000] 1.000000 NaN\n",
"\n",
"\n",
"Task 6d:\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"cand_nm Obama, Barack Romney, Mitt\n",
"contb_receipt_amt \n",
"(0, 1] 0.000002 0.000001\n",
"(1, 10] 0.002482 0.000338\n",
"(10, 100] 0.149318 0.022503\n",
"(100, 1000] 0.403294 0.253163\n",
"(1000, 10000] 0.380885 0.723852\n",
"(10000, 100000] 0.000435 0.000144\n",
"(100000, 1000000] 0.010971 NaN\n",
"(1000000, 10000000] 0.052612 NaN\n"
]
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 31,
"text": [
""
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": [
""
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": [
""
]
}
],
"prompt_number": 31
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Task 7: Big Money in Politics\n",
"------------------------------\n",
"\n",
"Using the tool of discretization, we were able to analyze how much people contributed in each bucket. While it showed some sense of imbalance, it did not show it clearly. Recently, the concept of income inequality is becoming a rallying cry among liberals. Let us see if there is any inequality in the contributions (spoiler alert: yes!). We can see if we find anything interesting, not by analyzing, total amount but by the amount contributed by top-X%.\n",
"\n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#Task 7a: Write two functions: one for Obama and one for Romney that does the following:\n",
"# Given a value of N (N can be 1, 2, 5, 10 etc), sort the contributions made to the candidate in decreasing order\n",
"# Then find how much contribution the top-N% made\n",
"# Then compute the fraction to the overall campaign collection\n",
"# For example, if Obama collected 1 billion dollars and the top-1% gave 100 million then their contribution is 10%\n",
"\n",
"def t7a_helper_contributions_by_top_N_pct(candidate_name, N):\n",
" pivotTblUniqContr = pd.tools.pivot.pivot_table(fec[fec.cand_nm == candidate_name], \n",
" values='contb_receipt_amt', \n",
" index=['contbr_nm', 'contbr_occupation', 'contbr_employer', 'contbr_zip', 'contbr_city','contbr_st'], \n",
" aggfunc=np.sum).order(ascending=False)\n",
" totalContrib = pivotTblUniqContr.sum()\n",
" numContrib = pivotTblUniqContr.count()\n",
" percent = int(N/100.0 * numContrib)\n",
" topNContrib = pivotTblUniqContr.iloc[:percent].sum()\n",
" \n",
" return topNContrib/totalContrib\n",
"\n",
"\n",
"def t7a_contributions_by_top_N_pct_obama(N):\n",
" return t7a_helper_contributions_by_top_N_pct('Obama, Barack', N)\n",
" \n",
"def t7a_contributions_by_top_N_pct_romney(N):\n",
" return t7a_helper_contributions_by_top_N_pct('Romney, Mitt', N)\n",
"\n",
"\n",
"for N in [1, 2, 5, 10, 20]:\n",
" print \"N=%s, Obama proportion=%s and Romney proportion = %s\" % (N, \n",
" t7a_contributions_by_top_N_pct_obama(N), t7a_contributions_by_top_N_pct_romney(N))\n",
"\n",
" \n",
"\n",
"#Task 7b: Now let us see who these people in 1% are\n",
"# Compute the top-1% based on total contribution (ie. money they gave to Obama + Romney)\n",
"# Now let us see some information about them.\n",
"# For each of the folks in 1%, print the following:\n",
"# name, state, occupation, employer, total amount they gave to Obama, total amount they gave to Romney\n",
"contributors_and_contrib = fec.pivot_table(values= 'contb_receipt_amt', \n",
" index= ['contbr_nm', 'contbr_occupation', 'contbr_employer', 'contbr_city', 'contbr_st'],\n",
" columns='cand_nm', aggfunc=np.sum, fill_value=0)\n",
"num1Percenters = int(0.01 * len(contributors_and_contrib))\n",
"contributors_and_contrib[\"Total\"] = contributors_and_contrib[\"Obama, Barack\"] + contributors_and_contrib[\"Romney, Mitt\"]\n",
"contributors_ordered_by_contrib = contributors_and_contrib.sort([\"Total\"], ascending = False)\n",
"t7b_1pcters = contributors_ordered_by_contrib.iloc[:num1Percenters]\n",
"\n",
"display(contributors_ordered_by_contrib)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"N=1, Obama proportion=0.150775678905 and Romney proportion = 0.0463774533432\n",
"N=2, Obama proportion=0.233644483744 and Romney proportion = 0.078658416654"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"N=5, Obama proportion=0.424594528848 and Romney proportion = 0.138822888029"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"N=10, Obama proportion=0.5666857722 and Romney proportion = 0.2390652498"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"N=20, Obama proportion=0.709150146108 and Romney proportion = 0.439549973343"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
},
{
"html": [
"\n",
"
\n",
" \n",
" \n",
" | \n",
" | \n",
" | \n",
" | \n",
" cand_nm | \n",
" Obama, Barack | \n",
" Romney, Mitt | \n",
" Total | \n",
"
\n",
" \n",
" contbr_nm | \n",
" contbr_occupation | \n",
" contbr_employer | \n",
" contbr_city | \n",
" contbr_st | \n",
" | \n",
" | \n",
" | \n",
"
\n",
" \n",
" \n",
" \n",
" OBAMA VICTORY FUND 2012 - UNITEMIZED | \n",
" ZOOLOGY EDUCATION | \n",
" ] | \n",
" CHICAGO | \n",
" IL | \n",
" 8187796.84 | \n",
" 0 | \n",
" 8187796.84 | \n",
"
\n",
" \n",
" WASHINGTON | \n",
" DC | \n",
" 451726.00 | \n",
" 0 | \n",
" 451726.00 | \n",
"
\n",
" \n",
" MURPHY, CYNTHIA C. | \n",
" PUBLIC RELATIONS | \n",
" MURPHY GROUP | \n",
" LITTLE ROCK | \n",
" AR | \n",
" 35800.00 | \n",
" 0 | \n",
" 35800.00 | \n",
"
\n",
" \n",
" DAVIS, STEPHEN JAMES | \n",
" ATTORNEY | \n",
" BANNEKER PARTNERS | \n",
" SAN FRANCISCO | \n",
" CA | \n",
" 30800.00 | \n",
" 0 | \n",
" 30800.00 | \n",
"
\n",
" \n",
" BOULIND, JEANNETTE | \n",
" NOT EMPLOYED | \n",
" NOT EMPLOYED | \n",
" PHILADELPHIA | \n",
" PA | \n",
" 20000.00 | \n",
" 0 | \n",
" 20000.00 | \n",
"
\n",
" \n",
" HIEMSTRA, SHERRON | \n",
" RETIRED | \n",
" RETIRED | \n",
" WASHINGTON | \n",
" DC | \n",
" 20000.00 | \n",
" 0 | \n",
" 20000.00 | \n",
"
\n",
" \n",
" PEROT, FRANCIS KINCAID | \n",
" CONSULTANT | \n",
" SELF-EMPLOYED | \n",
" WARREN | \n",
" VT | \n",
" 20000.00 | \n",
" 0 | \n",
" 20000.00 | \n",
"
\n",
" \n",
" KLEIN, PATRICIA | \n",
" RETIRED | \n",
" RETIRED | \n",
" ZIONSVILLE | \n",
" PA | \n",
" 17750.00 | \n",
" 0 | \n",
" 17750.00 | \n",
"
\n",
" \n",
" MEDORE, MARK | \n",
" INVESTOR | \n",
" SELF-EMPLOYED | \n",
" OAKLAND PARK | \n",
" FL | \n",
" 15195.00 | \n",
" 0 | \n",
" 15195.00 | \n",
"
\n",
" \n",
" ABRAMS, EDWIN | \n",
" RETIRED | \n",
" RETIRED | \n",
" VERO BEACH | \n",
" FL | \n",
" 15000.00 | \n",
" 0 | \n",
" 15000.00 | \n",
"
\n",
" \n",
" ANDERSON, SALLY | \n",
" ADMINISTRATOR | \n",
" ROSWELL ARTIST IN RESIDENCE FOUNDATION | \n",
" ROSWELL | \n",
" NM | \n",
" 15000.00 | \n",
" 0 | \n",
" 15000.00 | \n",
"
\n",
" \n",
" TRAUB, JANET E. | \n",
" NOT EMPLOYED | \n",
" NOT EMPLOYED | \n",
" SAN FRANCISCO | \n",
" CA | \n",
" 15000.00 | \n",
" 0 | \n",
" 15000.00 | \n",
"
\n",
" \n",
" ROLFE, RONALD | \n",
" LAWYER | \n",
" SELF-EMPLOYED | \n",
" NEW YORK | \n",
" NY | \n",
" 15000.00 | \n",
" 0 | \n",
" 15000.00 | \n",
"
\n",
" \n",
" BISHOP, ROBERT C. | \n",
" RETIRED | \n",
" RETIRED | \n",
" REDWOOD CITY | \n",
" CA | \n",
" 15000.00 | \n",
" 0 | \n",
" 15000.00 | \n",
"
\n",
" \n",
" STEINBRING, YVONNE | \n",
" RETIRED | \n",
" RETIRED | \n",
" GREEN VALLEY | \n",
" AZ | \n",
" 15000.00 | \n",
" 0 | \n",
" 15000.00 | \n",
"
\n",
" \n",
" COHN, NANCY | \n",
" VOLUNTEER | \n",
" VOLUNTEER | \n",
" TALLAHASSEE | \n",
" FL | \n",
" 15000.00 | \n",
" 0 | \n",
" 15000.00 | \n",
"
\n",
" \n",
" ANSTROM, DECKER | \n",
" RETIRED | \n",
" RETIRED | \n",
" WASHINGTON | \n",
" DC | \n",
" 15000.00 | \n",
" 0 | \n",
" 15000.00 | \n",
"
\n",
" \n",
" DENNE, CONSTANCE AYERS | \n",
" RETIRED | \n",
" RETIRED | \n",
" GREENPORT | \n",
" NY | \n",
" 15000.00 | \n",
" 0 | \n",
" 15000.00 | \n",
"
\n",
" \n",
" LANDIS, HOWARD KEL | \n",
" FINANCIAL SERVICES | \n",
" PLEXUS CAPITAL | \n",
" RALEIGH | \n",
" NC | \n",
" 15000.00 | \n",
" 0 | \n",
" 15000.00 | \n",
"
\n",
" \n",
" WEST, RUTH ANN | \n",
" INVESTOR | \n",
" SELF-EMPLOYED | \n",
" DURANGO | \n",
" CO | \n",
" 15000.00 | \n",
" 0 | \n",
" 15000.00 | \n",
"
\n",
" \n",
" FRANKLIN, VIRGINIA | \n",
" RETIRED | \n",
" RETIRED | \n",
" TULSA | \n",
" OK | \n",
" 14000.00 | \n",
" 0 | \n",
" 14000.00 | \n",
"
\n",
" \n",
" CARY, LAURA | \n",
" RETIRED | \n",
" RETIRED | \n",
" DENVER | \n",
" CO | \n",
" 13000.00 | \n",
" 0 | \n",
" 13000.00 | \n",
"
\n",
" \n",
" WEISSLER, STUART C. | \n",
" RETIRED | \n",
" RETIRED | \n",
" COCONUT CREEK | \n",
" FL | \n",
" 13000.00 | \n",
" 0 | \n",
" 13000.00 | \n",
"
\n",
" \n",
" NORPAC | \n",
" ZOOLOGY EDUCATION | \n",
" ] | \n",
" ENGLEWOOD CLIFFS | \n",
" NJ | \n",
" 0.00 | \n",
" 12700 | \n",
" 12700.00 | \n",
"
\n",
" \n",
" BRANDT, RICK MR. | \n",
" EXECUTIVE | \n",
" BRANDT CONSOLUDATED INC | \n",
" TAMPA | \n",
" FL | \n",
" 0.00 | \n",
" 12500 | \n",
" 12500.00 | \n",
"
\n",
" \n",
" BROCKMAN, DOROTHY K. MRS. | \n",
" INVESTMENTS | \n",
" SELF-EMPLOYED | \n",
" HOUSTON | \n",
" TX | \n",
" 0.00 | \n",
" 12500 | \n",
" 12500.00 | \n",
"
\n",
" \n",
" OKUNO, DALE MR. | \n",
" ANGEL INVESTOR | \n",
" OKUNO ASSOCIATES INC. | \n",
" PASADENA | \n",
" CA | \n",
" 0.00 | \n",
" 12500 | \n",
" 12500.00 | \n",
"
\n",
" \n",
" FRYE, MEGAN MS. | \n",
" COMMODITY BROKER | \n",
" WATER STREET ADVISORY | \n",
" PEORIA | \n",
" IL | \n",
" 0.00 | \n",
" 12500 | \n",
" 12500.00 | \n",
"
\n",
" \n",
" GREEN, JEFFREY MR. | \n",
" OWNER | \n",
" GREEN CHEVROLET | \n",
" PEORIA | \n",
" IL | \n",
" 0.00 | \n",
" 12500 | \n",
" 12500.00 | \n",
"
\n",
" \n",
" BRINCAT, JEFF MR. | \n",
" PRESIDENT | \n",
" CONSUMER FINANCIAL SERVICES | \n",
" LAKE FOREST | \n",
" IL | \n",
" 0.00 | \n",
" 12500 | \n",
" 12500.00 | \n",
"
\n",
" \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
"
\n",
" \n",
" GREENBAUM MD, SCOTT DR. | \n",
" PHYSICIAN | \n",
" SELF-EMPLOYED | \n",
" KINGS POINT | \n",
" NY | \n",
" 0.00 | \n",
" 3 | \n",
" 3.00 | \n",
"
\n",
" \n",
" BERKOWITZ, PAUL | \n",
" NOT EMPLOYED | \n",
" NOT EMPLOYED | \n",
" FOREST GROVE | \n",
" OR | \n",
" 3.00 | \n",
" 0 | \n",
" 3.00 | \n",
"
\n",
" \n",
" SNYDER, KAREN A. MRS. | \n",
" HOMEMAKER | \n",
" HOMEMAKER | \n",
" MCLEAN | \n",
" VA | \n",
" 0.00 | \n",
" 3 | \n",
" 3.00 | \n",
"
\n",
" \n",
" GRAVES, CRYSTAL | \n",
" ADMINISTRATION/INSTRUCTOR | \n",
" INTER-CITY SERVICES | \n",
" BERKELEY | \n",
" CA | \n",
" 3.00 | \n",
" 0 | \n",
" 3.00 | \n",
"
\n",
" \n",
" ENGLANDER, EVELYN | \n",
" RETIRED | \n",
" RETIRED | \n",
" ESCONDIDO | \n",
" CA | \n",
" 3.00 | \n",
" 0 | \n",
" 3.00 | \n",
"
\n",
" \n",
" ARD, COURTNEY | \n",
" SPRINKLER FITTER FIRE PROTECTION SPECI | \n",
" LOCAL 281 SPRINKLER FITTERS UN EMPL.3Y | \n",
" CHICAGO | \n",
" IL | \n",
" 3.00 | \n",
" 0 | \n",
" 3.00 | \n",
"
\n",
" \n",
" MANTONE, MARC L. MR. | \n",
" SALES | \n",
" MERRILL LYNCH | \n",
" TOMS RIVER | \n",
" NJ | \n",
" 0.00 | \n",
" 3 | \n",
" 3.00 | \n",
"
\n",
" \n",
" LARKIN, KATHLEEN | \n",
" UNDERGRADUATE STUDENT | \n",
" PENN STATE UNIVERSITY | \n",
" WEST ISLIP | \n",
" NY | \n",
" 3.00 | \n",
" 0 | \n",
" 3.00 | \n",
"
\n",
" \n",
" PRESSLEY, NORM MR. | \n",
" FARMER | \n",
" SELF-EMPLOYED | \n",
" SAN DIEGO | \n",
" CA | \n",
" 0.00 | \n",
" 3 | \n",
" 3.00 | \n",
"
\n",
" \n",
" KUNESH, JASON | \n",
" LEAD UI/UX DEVELOPER | \n",
" OBAMA FOR AMERICA | \n",
" CHICAGO | \n",
" IL | \n",
" 3.00 | \n",
" 0 | \n",
" 3.00 | \n",
"
\n",
" \n",
" RICHO, JOHN H | \n",
" ZOOLOGY EDUCATION | \n",
" RETIRED | \n",
" LOS ANGELES | \n",
" CA | \n",
" 3.00 | \n",
" 0 | \n",
" 3.00 | \n",
"
\n",
" \n",
" SCHNEIDER, THOMAS | \n",
" DIRECTOR | \n",
" COPS TRUST | \n",
" ROCHESTER HILLS | \n",
" MI | \n",
" 3.00 | \n",
" 0 | \n",
" 3.00 | \n",
"
\n",
" \n",
" SCOTT, MATTHEW | \n",
" STUDENT | \n",
" GWU | \n",
" WEST ORANGE | \n",
" NJ | \n",
" 3.00 | \n",
" 0 | \n",
" 3.00 | \n",
"
\n",
" \n",
" GREYF, JANE MS. | \n",
" ATTORNEY | \n",
" GOODWIN PROCTER | \n",
" NEW YORK | \n",
" NY | \n",
" 0.00 | \n",
" 3 | \n",
" 3.00 | \n",
"
\n",
" \n",
" ODLE, TERESA | \n",
" EDITOR | \n",
" SELF-EMPLOYED | \n",
" ALBUQUERQUE | \n",
" NM | \n",
" 3.00 | \n",
" 0 | \n",
" 3.00 | \n",
"
\n",
" \n",
" MCCARTHY, JAMES | \n",
" PROGRAMMER | \n",
" SELF | \n",
" BUTTE | \n",
" MT | \n",
" 3.00 | \n",
" 0 | \n",
" 3.00 | \n",
"
\n",
" \n",
" GALLAGHER, PATRICK | \n",
" 3RD GENERATION FAMILY BUSINESS OWNER | \n",
" GALLAGHER ASPHALT CORPORATION | \n",
" CHICAGO | \n",
" IL | \n",
" 0.00 | \n",
" 3 | \n",
" 3.00 | \n",
"
\n",
" \n",
" KERBER, JOHN BERNARD | \n",
" NOT EMPLOYED | \n",
" NOT EMPLOYED | \n",
" EDEN PRAIRIE | \n",
" MN | \n",
" 3.00 | \n",
" 0 | \n",
" 3.00 | \n",
"
\n",
" \n",
" GREEN, PETER | \n",
" FILM AND TELEVISION PRODUCER | \n",
" NOT EMPLOYED | \n",
" SHERMAN OAKS | \n",
" CA | \n",
" 3.00 | \n",
" 0 | \n",
" 3.00 | \n",
"
\n",
" \n",
" PLEAS, OCTAVIA | \n",
" RETIRED MSW | \n",
" RETIRED | \n",
" OVERLAND PARK | \n",
" KS | \n",
" 3.00 | \n",
" 0 | \n",
" 3.00 | \n",
"
\n",
" \n",
" WHEELER, LINDA L | \n",
" PHYSICIAN | \n",
" CASS REGIONAL MED. CENTER | \n",
" HARRISONVILLE | \n",
" MO | \n",
" 2.50 | \n",
" 0 | \n",
" 2.50 | \n",
"
\n",
" \n",
" BROWN, SHERESE MONIQUE MONIQUE | \n",
" DATA MANAGER | \n",
" SEIU | \n",
" HYATTSVILLE | \n",
" MD | \n",
" 2.00 | \n",
" 0 | \n",
" 2.00 | \n",
"
\n",
" \n",
" GOULT, RODERICK | \n",
" CONSULTANT | \n",
" THE VICTORIA GROUP, INC. | \n",
" SALEM | \n",
" NH | \n",
" 2.00 | \n",
" 0 | \n",
" 2.00 | \n",
"
\n",
" \n",
" JOYCE, LOUIS | \n",
" FIELD ORGANIZER | \n",
" ORGANIZING FOR AMERICA - VIRGINIA | \n",
" LEESBURG | \n",
" VA | \n",
" 2.00 | \n",
" 0 | \n",
" 2.00 | \n",
"
\n",
" \n",
" WINTERSTEINER, PETER P. | \n",
" SCIENTIST | \n",
" ARCON CORP | \n",
" WABAN | \n",
" MA | \n",
" 2.00 | \n",
" 0 | \n",
" 2.00 | \n",
"
\n",
" \n",
" FOSS, MARNA | \n",
" NOT EMPLOYED | \n",
" NOT EMPLOYED | \n",
" APEX | \n",
" NC | \n",
" 2.00 | \n",
" 0 | \n",
" 2.00 | \n",
"
\n",
" \n",
" TRAN, KHIEM | \n",
" INFORMATION REQUESTED PER BEST EFFORTS | \n",
" INFORMATION REQUESTED PER BEST EFFORTS | \n",
" QUINCY | \n",
" MA | \n",
" 0.00 | \n",
" 2 | \n",
" 2.00 | \n",
"
\n",
" \n",
" MICHAL, JUNE B | \n",
" LEGAL SECRETARY | \n",
" RETIRED | \n",
" PUYALLUP | \n",
" WA | \n",
" 1.00 | \n",
" 0 | \n",
" 1.00 | \n",
"
\n",
" \n",
" SHUBIN, ALAN | \n",
" CONSULTANT | \n",
" TOWERS WATSON | \n",
" CHARLOTTE | \n",
" NC | \n",
" 1.00 | \n",
" 0 | \n",
" 1.00 | \n",
"
\n",
" \n",
" WILSON, LAURA | \n",
" WRITER | \n",
" OBAMA FOR AMERICA | \n",
" CHICAGO | \n",
" IL | \n",
" 0.08 | \n",
" 0 | \n",
" 0.08 | \n",
"
\n",
" \n",
"
\n",
"
289862 rows \u00d7 3 columns
\n",
"
"
],
"metadata": {},
"output_type": "display_data",
"text": [
"cand_nm Obama, Barack \\\n",
"contbr_nm contbr_occupation contbr_employer contbr_city contbr_st \n",
"OBAMA VICTORY FUND 2012 - UNITEMIZED ZOOLOGY EDUCATION ] CHICAGO IL 8187796.84 \n",
" WASHINGTON DC 451726.00 \n",
"MURPHY, CYNTHIA C. PUBLIC RELATIONS MURPHY GROUP LITTLE ROCK AR 35800.00 \n",
"DAVIS, STEPHEN JAMES ATTORNEY BANNEKER PARTNERS SAN FRANCISCO CA 30800.00 \n",
"BOULIND, JEANNETTE NOT EMPLOYED NOT EMPLOYED PHILADELPHIA PA 20000.00 \n",
"HIEMSTRA, SHERRON RETIRED RETIRED WASHINGTON DC 20000.00 \n",
"PEROT, FRANCIS KINCAID CONSULTANT SELF-EMPLOYED WARREN VT 20000.00 \n",
"KLEIN, PATRICIA RETIRED RETIRED ZIONSVILLE PA 17750.00 \n",
"MEDORE, MARK INVESTOR SELF-EMPLOYED OAKLAND PARK FL 15195.00 \n",
"ABRAMS, EDWIN RETIRED RETIRED VERO BEACH FL 15000.00 \n",
"ANDERSON, SALLY ADMINISTRATOR ROSWELL ARTIST IN RESIDENCE FOUNDATION ROSWELL NM 15000.00 \n",
"TRAUB, JANET E. NOT EMPLOYED NOT EMPLOYED SAN FRANCISCO CA 15000.00 \n",
"ROLFE, RONALD LAWYER SELF-EMPLOYED NEW YORK NY 15000.00 \n",
"BISHOP, ROBERT C. RETIRED RETIRED REDWOOD CITY CA 15000.00 \n",
"STEINBRING, YVONNE RETIRED RETIRED GREEN VALLEY AZ 15000.00 \n",
"COHN, NANCY VOLUNTEER VOLUNTEER TALLAHASSEE FL 15000.00 \n",
"ANSTROM, DECKER RETIRED RETIRED WASHINGTON DC 15000.00 \n",
"DENNE, CONSTANCE AYERS RETIRED RETIRED GREENPORT NY 15000.00 \n",
"LANDIS, HOWARD KEL FINANCIAL SERVICES PLEXUS CAPITAL RALEIGH NC 15000.00 \n",
"WEST, RUTH ANN INVESTOR SELF-EMPLOYED DURANGO CO 15000.00 \n",
"FRANKLIN, VIRGINIA RETIRED RETIRED TULSA OK 14000.00 \n",
"CARY, LAURA RETIRED RETIRED DENVER CO 13000.00 \n",
"WEISSLER, STUART C. RETIRED RETIRED COCONUT CREEK FL 13000.00 \n",
"NORPAC ZOOLOGY EDUCATION ] ENGLEWOOD CLIFFS NJ 0.00 \n",
"BRANDT, RICK MR. EXECUTIVE BRANDT CONSOLUDATED INC TAMPA FL 0.00 \n",
"BROCKMAN, DOROTHY K. MRS. INVESTMENTS SELF-EMPLOYED HOUSTON TX 0.00 \n",
"OKUNO, DALE MR. ANGEL INVESTOR OKUNO ASSOCIATES INC. PASADENA CA 0.00 \n",
"FRYE, MEGAN MS. COMMODITY BROKER WATER STREET ADVISORY PEORIA IL 0.00 \n",
"GREEN, JEFFREY MR. OWNER GREEN CHEVROLET PEORIA IL 0.00 \n",
"BRINCAT, JEFF MR. PRESIDENT CONSUMER FINANCIAL SERVICES LAKE FOREST IL 0.00 \n",
"... ... \n",
"GREENBAUM MD, SCOTT DR. PHYSICIAN SELF-EMPLOYED KINGS POINT NY 0.00 \n",
"BERKOWITZ, PAUL NOT EMPLOYED NOT EMPLOYED FOREST GROVE OR 3.00 \n",
"SNYDER, KAREN A. MRS. HOMEMAKER HOMEMAKER MCLEAN VA 0.00 \n",
"GRAVES, CRYSTAL ADMINISTRATION/INSTRUCTOR INTER-CITY SERVICES BERKELEY CA 3.00 \n",
"ENGLANDER, EVELYN RETIRED RETIRED ESCONDIDO CA 3.00 \n",
"ARD, COURTNEY SPRINKLER FITTER FIRE PROTECTION SPECI LOCAL 281 SPRINKLER FITTERS UN EMPL.3Y CHICAGO IL 3.00 \n",
"MANTONE, MARC L. MR. SALES MERRILL LYNCH TOMS RIVER NJ 0.00 \n",
"LARKIN, KATHLEEN UNDERGRADUATE STUDENT PENN STATE UNIVERSITY WEST ISLIP NY 3.00 \n",
"PRESSLEY, NORM MR. FARMER SELF-EMPLOYED SAN DIEGO CA 0.00 \n",
"KUNESH, JASON LEAD UI/UX DEVELOPER OBAMA FOR AMERICA CHICAGO IL 3.00 \n",
"RICHO, JOHN H ZOOLOGY EDUCATION RETIRED LOS ANGELES CA 3.00 \n",
"SCHNEIDER, THOMAS DIRECTOR COPS TRUST ROCHESTER HILLS MI 3.00 \n",
"SCOTT, MATTHEW STUDENT GWU WEST ORANGE NJ 3.00 \n",
"GREYF, JANE MS. ATTORNEY GOODWIN PROCTER NEW YORK NY 0.00 \n",
"ODLE, TERESA EDITOR SELF-EMPLOYED ALBUQUERQUE NM 3.00 \n",
"MCCARTHY, JAMES PROGRAMMER SELF BUTTE MT 3.00 \n",
"GALLAGHER, PATRICK 3RD GENERATION FAMILY BUSINESS OWNER GALLAGHER ASPHALT CORPORATION CHICAGO IL 0.00 \n",
"KERBER, JOHN BERNARD NOT EMPLOYED NOT EMPLOYED EDEN PRAIRIE MN 3.00 \n",
"GREEN, PETER FILM AND TELEVISION PRODUCER NOT EMPLOYED SHERMAN OAKS CA 3.00 \n",
"PLEAS, OCTAVIA RETIRED MSW RETIRED OVERLAND PARK KS 3.00 \n",
"WHEELER, LINDA L PHYSICIAN CASS REGIONAL MED. CENTER HARRISONVILLE MO 2.50 \n",
"BROWN, SHERESE MONIQUE MONIQUE DATA MANAGER SEIU HYATTSVILLE MD 2.00 \n",
"GOULT, RODERICK CONSULTANT THE VICTORIA GROUP, INC. SALEM NH 2.00 \n",
"JOYCE, LOUIS FIELD ORGANIZER ORGANIZING FOR AMERICA - VIRGINIA LEESBURG VA 2.00 \n",
"WINTERSTEINER, PETER P. SCIENTIST ARCON CORP WABAN MA 2.00 \n",
"FOSS, MARNA NOT EMPLOYED NOT EMPLOYED APEX NC 2.00 \n",
"TRAN, KHIEM INFORMATION REQUESTED PER BEST EFFORTS INFORMATION REQUESTED PER BEST EFFORTS QUINCY MA 0.00 \n",
"MICHAL, JUNE B LEGAL SECRETARY RETIRED PUYALLUP WA 1.00 \n",
"SHUBIN, ALAN CONSULTANT TOWERS WATSON CHARLOTTE NC 1.00 \n",
"WILSON, LAURA WRITER OBAMA FOR AMERICA CHICAGO IL 0.08 \n",
"\n",
"cand_nm Romney, Mitt \\\n",
"contbr_nm contbr_occupation contbr_employer contbr_city contbr_st \n",
"OBAMA VICTORY FUND 2012 - UNITEMIZED ZOOLOGY EDUCATION ] CHICAGO IL 0 \n",
" WASHINGTON DC 0 \n",
"MURPHY, CYNTHIA C. PUBLIC RELATIONS MURPHY GROUP LITTLE ROCK AR 0 \n",
"DAVIS, STEPHEN JAMES ATTORNEY BANNEKER PARTNERS SAN FRANCISCO CA 0 \n",
"BOULIND, JEANNETTE NOT EMPLOYED NOT EMPLOYED PHILADELPHIA PA 0 \n",
"HIEMSTRA, SHERRON RETIRED RETIRED WASHINGTON DC 0 \n",
"PEROT, FRANCIS KINCAID CONSULTANT SELF-EMPLOYED WARREN VT 0 \n",
"KLEIN, PATRICIA RETIRED RETIRED ZIONSVILLE PA 0 \n",
"MEDORE, MARK INVESTOR SELF-EMPLOYED OAKLAND PARK FL 0 \n",
"ABRAMS, EDWIN RETIRED RETIRED VERO BEACH FL 0 \n",
"ANDERSON, SALLY ADMINISTRATOR ROSWELL ARTIST IN RESIDENCE FOUNDATION ROSWELL NM 0 \n",
"TRAUB, JANET E. NOT EMPLOYED NOT EMPLOYED SAN FRANCISCO CA 0 \n",
"ROLFE, RONALD LAWYER SELF-EMPLOYED NEW YORK NY 0 \n",
"BISHOP, ROBERT C. RETIRED RETIRED REDWOOD CITY CA 0 \n",
"STEINBRING, YVONNE RETIRED RETIRED GREEN VALLEY AZ 0 \n",
"COHN, NANCY VOLUNTEER VOLUNTEER TALLAHASSEE FL 0 \n",
"ANSTROM, DECKER RETIRED RETIRED WASHINGTON DC 0 \n",
"DENNE, CONSTANCE AYERS RETIRED RETIRED GREENPORT NY 0 \n",
"LANDIS, HOWARD KEL FINANCIAL SERVICES PLEXUS CAPITAL RALEIGH NC 0 \n",
"WEST, RUTH ANN INVESTOR SELF-EMPLOYED DURANGO CO 0 \n",
"FRANKLIN, VIRGINIA RETIRED RETIRED TULSA OK 0 \n",
"CARY, LAURA RETIRED RETIRED DENVER CO 0 \n",
"WEISSLER, STUART C. RETIRED RETIRED COCONUT CREEK FL 0 \n",
"NORPAC ZOOLOGY EDUCATION ] ENGLEWOOD CLIFFS NJ 12700 \n",
"BRANDT, RICK MR. EXECUTIVE BRANDT CONSOLUDATED INC TAMPA FL 12500 \n",
"BROCKMAN, DOROTHY K. MRS. INVESTMENTS SELF-EMPLOYED HOUSTON TX 12500 \n",
"OKUNO, DALE MR. ANGEL INVESTOR OKUNO ASSOCIATES INC. PASADENA CA 12500 \n",
"FRYE, MEGAN MS. COMMODITY BROKER WATER STREET ADVISORY PEORIA IL 12500 \n",
"GREEN, JEFFREY MR. OWNER GREEN CHEVROLET PEORIA IL 12500 \n",
"BRINCAT, JEFF MR. PRESIDENT CONSUMER FINANCIAL SERVICES LAKE FOREST IL 12500 \n",
"... ... \n",
"GREENBAUM MD, SCOTT DR. PHYSICIAN SELF-EMPLOYED KINGS POINT NY 3 \n",
"BERKOWITZ, PAUL NOT EMPLOYED NOT EMPLOYED FOREST GROVE OR 0 \n",
"SNYDER, KAREN A. MRS. HOMEMAKER HOMEMAKER MCLEAN VA 3 \n",
"GRAVES, CRYSTAL ADMINISTRATION/INSTRUCTOR INTER-CITY SERVICES BERKELEY CA 0 \n",
"ENGLANDER, EVELYN RETIRED RETIRED ESCONDIDO CA 0 \n",
"ARD, COURTNEY SPRINKLER FITTER FIRE PROTECTION SPECI LOCAL 281 SPRINKLER FITTERS UN EMPL.3Y CHICAGO IL 0 \n",
"MANTONE, MARC L. MR. SALES MERRILL LYNCH TOMS RIVER NJ 3 \n",
"LARKIN, KATHLEEN UNDERGRADUATE STUDENT PENN STATE UNIVERSITY WEST ISLIP NY 0 \n",
"PRESSLEY, NORM MR. FARMER SELF-EMPLOYED SAN DIEGO CA 3 \n",
"KUNESH, JASON LEAD UI/UX DEVELOPER OBAMA FOR AMERICA CHICAGO IL 0 \n",
"RICHO, JOHN H ZOOLOGY EDUCATION RETIRED LOS ANGELES CA 0 \n",
"SCHNEIDER, THOMAS DIRECTOR COPS TRUST ROCHESTER HILLS MI 0 \n",
"SCOTT, MATTHEW STUDENT GWU WEST ORANGE NJ 0 \n",
"GREYF, JANE MS. ATTORNEY GOODWIN PROCTER NEW YORK NY 3 \n",
"ODLE, TERESA EDITOR SELF-EMPLOYED ALBUQUERQUE NM 0 \n",
"MCCARTHY, JAMES PROGRAMMER SELF BUTTE MT 0 \n",
"GALLAGHER, PATRICK 3RD GENERATION FAMILY BUSINESS OWNER GALLAGHER ASPHALT CORPORATION CHICAGO IL 3 \n",
"KERBER, JOHN BERNARD NOT EMPLOYED NOT EMPLOYED EDEN PRAIRIE MN 0 \n",
"GREEN, PETER FILM AND TELEVISION PRODUCER NOT EMPLOYED SHERMAN OAKS CA 0 \n",
"PLEAS, OCTAVIA RETIRED MSW RETIRED OVERLAND PARK KS 0 \n",
"WHEELER, LINDA L PHYSICIAN CASS REGIONAL MED. CENTER HARRISONVILLE MO 0 \n",
"BROWN, SHERESE MONIQUE MONIQUE DATA MANAGER SEIU HYATTSVILLE MD 0 \n",
"GOULT, RODERICK CONSULTANT THE VICTORIA GROUP, INC. SALEM NH 0 \n",
"JOYCE, LOUIS FIELD ORGANIZER ORGANIZING FOR AMERICA - VIRGINIA LEESBURG VA 0 \n",
"WINTERSTEINER, PETER P. SCIENTIST ARCON CORP WABAN MA 0 \n",
"FOSS, MARNA NOT EMPLOYED NOT EMPLOYED APEX NC 0 \n",
"TRAN, KHIEM INFORMATION REQUESTED PER BEST EFFORTS INFORMATION REQUESTED PER BEST EFFORTS QUINCY MA 2 \n",
"MICHAL, JUNE B LEGAL SECRETARY RETIRED PUYALLUP WA 0 \n",
"SHUBIN, ALAN CONSULTANT TOWERS WATSON CHARLOTTE NC 0 \n",
"WILSON, LAURA WRITER OBAMA FOR AMERICA CHICAGO IL 0 \n",
"\n",
"cand_nm Total \n",
"contbr_nm contbr_occupation contbr_employer contbr_city contbr_st \n",
"OBAMA VICTORY FUND 2012 - UNITEMIZED ZOOLOGY EDUCATION ] CHICAGO IL 8187796.84 \n",
" WASHINGTON DC 451726.00 \n",
"MURPHY, CYNTHIA C. PUBLIC RELATIONS MURPHY GROUP LITTLE ROCK AR 35800.00 \n",
"DAVIS, STEPHEN JAMES ATTORNEY BANNEKER PARTNERS SAN FRANCISCO CA 30800.00 \n",
"BOULIND, JEANNETTE NOT EMPLOYED NOT EMPLOYED PHILADELPHIA PA 20000.00 \n",
"HIEMSTRA, SHERRON RETIRED RETIRED WASHINGTON DC 20000.00 \n",
"PEROT, FRANCIS KINCAID CONSULTANT SELF-EMPLOYED WARREN VT 20000.00 \n",
"KLEIN, PATRICIA RETIRED RETIRED ZIONSVILLE PA 17750.00 \n",
"MEDORE, MARK INVESTOR SELF-EMPLOYED OAKLAND PARK FL 15195.00 \n",
"ABRAMS, EDWIN RETIRED RETIRED VERO BEACH FL 15000.00 \n",
"ANDERSON, SALLY ADMINISTRATOR ROSWELL ARTIST IN RESIDENCE FOUNDATION ROSWELL NM 15000.00 \n",
"TRAUB, JANET E. NOT EMPLOYED NOT EMPLOYED SAN FRANCISCO CA 15000.00 \n",
"ROLFE, RONALD LAWYER SELF-EMPLOYED NEW YORK NY 15000.00 \n",
"BISHOP, ROBERT C. RETIRED RETIRED REDWOOD CITY CA 15000.00 \n",
"STEINBRING, YVONNE RETIRED RETIRED GREEN VALLEY AZ 15000.00 \n",
"COHN, NANCY VOLUNTEER VOLUNTEER TALLAHASSEE FL 15000.00 \n",
"ANSTROM, DECKER RETIRED RETIRED WASHINGTON DC 15000.00 \n",
"DENNE, CONSTANCE AYERS RETIRED RETIRED GREENPORT NY 15000.00 \n",
"LANDIS, HOWARD KEL FINANCIAL SERVICES PLEXUS CAPITAL RALEIGH NC 15000.00 \n",
"WEST, RUTH ANN INVESTOR SELF-EMPLOYED DURANGO CO 15000.00 \n",
"FRANKLIN, VIRGINIA RETIRED RETIRED TULSA OK 14000.00 \n",
"CARY, LAURA RETIRED RETIRED DENVER CO 13000.00 \n",
"WEISSLER, STUART C. RETIRED RETIRED COCONUT CREEK FL 13000.00 \n",
"NORPAC ZOOLOGY EDUCATION ] ENGLEWOOD CLIFFS NJ 12700.00 \n",
"BRANDT, RICK MR. EXECUTIVE BRANDT CONSOLUDATED INC TAMPA FL 12500.00 \n",
"BROCKMAN, DOROTHY K. MRS. INVESTMENTS SELF-EMPLOYED HOUSTON TX 12500.00 \n",
"OKUNO, DALE MR. ANGEL INVESTOR OKUNO ASSOCIATES INC. PASADENA CA 12500.00 \n",
"FRYE, MEGAN MS. COMMODITY BROKER WATER STREET ADVISORY PEORIA IL 12500.00 \n",
"GREEN, JEFFREY MR. OWNER GREEN CHEVROLET PEORIA IL 12500.00 \n",
"BRINCAT, JEFF MR. PRESIDENT CONSUMER FINANCIAL SERVICES LAKE FOREST IL 12500.00 \n",
"... ... \n",
"GREENBAUM MD, SCOTT DR. PHYSICIAN SELF-EMPLOYED KINGS POINT NY 3.00 \n",
"BERKOWITZ, PAUL NOT EMPLOYED NOT EMPLOYED FOREST GROVE OR 3.00 \n",
"SNYDER, KAREN A. MRS. HOMEMAKER HOMEMAKER MCLEAN VA 3.00 \n",
"GRAVES, CRYSTAL ADMINISTRATION/INSTRUCTOR INTER-CITY SERVICES BERKELEY CA 3.00 \n",
"ENGLANDER, EVELYN RETIRED RETIRED ESCONDIDO CA 3.00 \n",
"ARD, COURTNEY SPRINKLER FITTER FIRE PROTECTION SPECI LOCAL 281 SPRINKLER FITTERS UN EMPL.3Y CHICAGO IL 3.00 \n",
"MANTONE, MARC L. MR. SALES MERRILL LYNCH TOMS RIVER NJ 3.00 \n",
"LARKIN, KATHLEEN UNDERGRADUATE STUDENT PENN STATE UNIVERSITY WEST ISLIP NY 3.00 \n",
"PRESSLEY, NORM MR. FARMER SELF-EMPLOYED SAN DIEGO CA 3.00 \n",
"KUNESH, JASON LEAD UI/UX DEVELOPER OBAMA FOR AMERICA CHICAGO IL 3.00 \n",
"RICHO, JOHN H ZOOLOGY EDUCATION RETIRED LOS ANGELES CA 3.00 \n",
"SCHNEIDER, THOMAS DIRECTOR COPS TRUST ROCHESTER HILLS MI 3.00 \n",
"SCOTT, MATTHEW STUDENT GWU WEST ORANGE NJ 3.00 \n",
"GREYF, JANE MS. ATTORNEY GOODWIN PROCTER NEW YORK NY 3.00 \n",
"ODLE, TERESA EDITOR SELF-EMPLOYED ALBUQUERQUE NM 3.00 \n",
"MCCARTHY, JAMES PROGRAMMER SELF BUTTE MT 3.00 \n",
"GALLAGHER, PATRICK 3RD GENERATION FAMILY BUSINESS OWNER GALLAGHER ASPHALT CORPORATION CHICAGO IL 3.00 \n",
"KERBER, JOHN BERNARD NOT EMPLOYED NOT EMPLOYED EDEN PRAIRIE MN 3.00 \n",
"GREEN, PETER FILM AND TELEVISION PRODUCER NOT EMPLOYED SHERMAN OAKS CA 3.00 \n",
"PLEAS, OCTAVIA RETIRED MSW RETIRED OVERLAND PARK KS 3.00 \n",
"WHEELER, LINDA L PHYSICIAN CASS REGIONAL MED. CENTER HARRISONVILLE MO 2.50 \n",
"BROWN, SHERESE MONIQUE MONIQUE DATA MANAGER SEIU HYATTSVILLE MD 2.00 \n",
"GOULT, RODERICK CONSULTANT THE VICTORIA GROUP, INC. SALEM NH 2.00 \n",
"JOYCE, LOUIS FIELD ORGANIZER ORGANIZING FOR AMERICA - VIRGINIA LEESBURG VA 2.00 \n",
"WINTERSTEINER, PETER P. SCIENTIST ARCON CORP WABAN MA 2.00 \n",
"FOSS, MARNA NOT EMPLOYED NOT EMPLOYED APEX NC 2.00 \n",
"TRAN, KHIEM INFORMATION REQUESTED PER BEST EFFORTS INFORMATION REQUESTED PER BEST EFFORTS QUINCY MA 2.00 \n",
"MICHAL, JUNE B LEGAL SECRETARY RETIRED PUYALLUP WA 1.00 \n",
"SHUBIN, ALAN CONSULTANT TOWERS WATSON CHARLOTTE NC 1.00 \n",
"WILSON, LAURA WRITER OBAMA FOR AMERICA CHICAGO IL 0.08 \n",
"\n",
"[289862 rows x 3 columns]"
]
}
],
"prompt_number": 32
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Task 8: Political Polarization in USA\n",
"------------------------------------------\n",
"\n",
"It has been argued that political polarization has dramatically increased in USA in the last 10-15 years. While there are many ways to analyze the dataset for polarization, let us use some simple measures."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#Task 8a: One way to measure polarity is to see how different some distribution are. \n",
"# For both Obama and Romney, print the top-10 states based on their total AMOUNT of contribution\n",
"# Do you see lot of common states? \n",
"t8a_top_10_states_obama = fec.contb_receipt_amt[fec.cand_nm == 'Obama, Barack'].groupby(fec.contbr_st).sum().order(ascending=False)[:10]\n",
"t8a_top_10_states_romney = fec.contb_receipt_amt[fec.cand_nm == 'Romney, Mitt'].groupby(fec.contbr_st).sum().order(ascending=False)[:10]\n",
"\n",
"print \"\\n\\nT8a: Top 10 states for Obama:\\n\",t8a_top_10_states_obama\n",
"print \"\\n\\nT8a: Top 10 states for Romney:\\n\",t8a_top_10_states_romney\n",
"\n",
"#Task 8b: For both Obama and Romney, print the top-10 occupation based on their total AMOUNT of contribution\n",
"# Do you see lot of common occupation? \n",
"t8b_top_10_occu_obama = fec['contb_receipt_amt'][fec.cand_nm == 'Obama, Barack'].groupby(fec.contbr_occupation).sum().order(ascending=False)[:10]\n",
"t8b_top_10_occu_romney = fec['contb_receipt_amt'][fec.cand_nm == 'Romney, Mitt'].groupby(fec.contbr_occupation).sum().order(ascending=False)[:10]\n",
"\n",
"print \"\\n\\nT8b: Top 10 Occupation for Obama:\\n\", t8b_top_10_occu_obama\n",
"print \"\\n\\nT8b: Top 10 Occupation for Romney:\\n\", t8b_top_10_occu_romney\n",
"\n",
"\n",
"\n",
"#Task 8c: For both Obama and Romney, print the top-10 employers based on their total AMOUNT of contribution\n",
"# Do you see lot of common employers? \n",
"t8c_top_10_emp_obama = fec['contb_receipt_amt'][fec.cand_nm == 'Obama, Barack'].groupby(fec.contbr_employer).sum().order(ascending=False)[:10]\n",
"t8c_top_10_emp_romney = fec['contb_receipt_amt'][fec.cand_nm == 'Romney, Mitt'].groupby(fec.contbr_employer).sum().order(ascending=False)[:10]\n",
"\n",
"print \"\\n\\nT8b: Top 10 Employers for Obama:\\n\", t8c_top_10_emp_obama\n",
"print \"\\n\\nT8b: Top 10 Employers for Romney:\\n\", t8c_top_10_emp_romney\n",
"\n",
"\n",
"#Harder\n",
"#Task 8d: Here is another way to compute polarization\n",
"# Find the top-1000 contributors based on their TOTAL contribution (to both Obama and Romney)\n",
"# For each of the top-1000 folks count the number of people who donated to both, to Obama only and to Romney only\n",
"top1000Contributors = fec.pivot_table(values='contb_receipt_amt',\n",
" index=['contbr_nm','contbr_employer','contbr_st','cand_nm'],\n",
" aggfunc = np.sum).order(ascending = False).head(1000).unstack().fillna(0)\n",
"t8d_top_1000_both = len(top1000Contributors[ (top1000Contributors[\"Obama, Barack\"] > 0) & (top1000Contributors[\"Romney, Mitt\"] > 0)])\n",
"t8d_top_1000_BO_only = len(top1000Contributors[ (top1000Contributors[\"Obama, Barack\"] > 0) & (top1000Contributors[\"Romney, Mitt\"] == 0)])\n",
"t8d_top_1000_MR_only = len(top1000Contributors[ (top1000Contributors[\"Obama, Barack\"] == 0) & (top1000Contributors[\"Romney, Mitt\"] > 0)])\n",
"\n",
"print \"\\n\\nT8c:Both, Obama only, Romney only = \", t8d_top_1000_both, t8d_top_1000_BO_only, t8d_top_1000_MR_only\n",
"\n",
"#Harder:\n",
"#Task 8e: Here is yet another way\n",
"# For each state, compute what fraction of amount went to Obama and what fraction went to Romney\n",
"# If there is no polarization, then both will get more or less equal amount. \n",
"# If there is polarization, then the amount will skewed.\n",
"# Let us use a very crude measure to compute polarization\n",
"# If X is what Obama got from a state and Y is what Romney got, compute the value of max(X,Y)/ (min(X,Y) + 1)\n",
"# For each state compute this value and sort the results in a descending order. \n",
"# Do you see any pattern?\n",
"t8e = fec.pivot_table(values = 'contb_receipt_amt', index = ['contbr_st'], columns = ['cand_nm'], aggfunc = np.sum, fill_value=0)\n",
"t8e[\"polarity\"] = t8e.apply(\n",
" lambda row: max(row[\"Obama, Barack\"], row[\"Romney, Mitt\"]) / (min(row[\"Obama, Barack\"], row[\"Romney, Mitt\"])+1), \n",
" axis=1)\n",
"t8e_state_contr_ranked_by_polarity = t8e.sort([\"polarity\"], ascending=False)\n",
"print \"\\n\\nt8e:States ordered by polarity \\n\", t8e_state_contr_ranked_by_polarity\n",
"\n",
"\n",
"#Harder:\n",
"#Task 8f: Repeat the above analysis for occupation\n",
"# However, instead of taking all occupations, let us only take the top-50 occupations based on TOTAL contributions made\n",
"# For each occupation compute this value and sort the results in a descending order and displ\n",
"# Do you see any pattern?\n",
"t8f = fec.pivot_table(values = 'contb_receipt_amt', index = ['contbr_occupation'], columns = ['cand_nm'], aggfunc = np.sum, fill_value=0)\n",
"t8f['Total'] = t8f['Obama, Barack'] + t8f['Romney, Mitt']\n",
"t8f = t8f.sort(['Total'], ascending=False)[:50]\n",
"t8f[\"polarity\"] = t8f.apply(\n",
" lambda row: max(row[\"Obama, Barack\"], row[\"Romney, Mitt\"]) / (min(row[\"Obama, Barack\"], row[\"Romney, Mitt\"])+1), \n",
" axis=1)\n",
"t8f_occu_contr_ranked_by_polarity = t8f.sort([\"polarity\"], ascending=False)\n",
"print \"\\n\\nt8f: Occupation ordered by polarity:\\n\", t8f_occu_contr_ranked_by_polarity\n",
"\n",
"\n",
"#Harder:\n",
"#Task 8g: A known variable of polarization is based on where a person lives.\n",
"# At the risk of too much generalization, liberals dominate cities while conservations dominate rural areas\n",
"# Let us see if this holds in Texas.\n",
"# Texas is a known solid red (i.e. conservative) state.\n",
"# For each city in Texas, compute the polarity and order them by polarity.\n",
"# Do you see any pattern?\n",
"t8g = fec[fec.contbr_st == 'TX'].pivot_table(values = 'contb_receipt_amt', \n",
" index = ['contbr_city'], columns=['cand_nm'], aggfunc=np.sum, fill_value=0)\n",
"t8g['Total'] = t8g['Obama, Barack'] + t8g['Romney, Mitt']\t\n",
"t8g[\"polarity\"] = t8g.apply(\n",
" lambda row: max(row[\"Obama, Barack\"], row[\"Romney, Mitt\"]) / (min(row[\"Obama, Barack\"], row[\"Romney, Mitt\"])+1), \n",
" axis=1)\n",
"t8g_tx_city_contr_ranked_by_polarity = t8g.sort(['polarity'], ascending=False)\n",
"print \"\\n\\nt8f: Texas cities ordered by polarity:\\n\", t8g_tx_city_contr_ranked_by_polarity\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"\n",
"T8a: Top 10 states for Obama:\n",
"contbr_st\n",
"CA 23824984.24\n",
"IL 16443895.84\n",
"NY 14651918.51\n",
"FL 7318178.58\n",
"MA 6649015.25\n",
"TX 6570832.45\n",
"MD 4832663.93\n",
"DC 4373538.80\n",
"VA 4259977.19\n",
"WA 4250933.16\n",
"Name: contb_receipt_amt, dtype: float64\n",
"\n",
"\n",
"T8a: Top 10 states for Romney:\n",
"contbr_st\n",
"CA 11237636.60\n",
"NY 10184212.63\n",
"FL 8338458.81\n",
"TX 6221989.68\n",
"MA 4710542.30\n",
"UT 3717300.48\n",
"IL 3628571.53\n",
"CT 3499475.45\n",
"VA 3465765.85\n",
"NJ 3333320.20\n",
"Name: contb_receipt_amt, dtype: float64\n",
"\n",
"\n",
"T8b: Top 10 Occupation for Obama:\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"contbr_occupation\n",
"RETIRED 25305116.38\n",
"ATTORNEY 11141982.97\n",
"INFORMATION REQUESTED 4866973.96\n",
"HOMEMAKER 4248875.80\n",
"PHYSICIAN 3735124.94\n",
"LAWYER 3160478.87\n",
"CONSULTANT 2459912.71\n",
"PROFESSOR 2165071.08\n",
"CEO 2073284.79\n",
"PRESIDENT 1878509.95\n",
"Name: contb_receipt_amt, dtype: float64\n",
"\n",
"\n",
"T8b: Top 10 Occupation for Romney:\n",
"contbr_occupation\n",
"RETIRED 11508473.59\n",
"INFORMATION REQUESTED PER BEST EFFORTS 11396894.84\n",
"HOMEMAKER 8147446.22\n",
"ATTORNEY 5364718.82\n",
"PRESIDENT 2491244.89\n",
"EXECUTIVE 2300947.03\n",
"C.E.O. 1968386.11\n",
"INVESTOR 1537595.12\n",
"CONSULTANT 1424894.01\n",
"PHYSICIAN 1368023.96\n",
"Name: contb_receipt_amt, dtype: float64\n",
"\n",
"\n",
"T8b: Top 10 Employers for Obama:\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"contbr_employer\n",
"RETIRED 22694358.85\n",
"SELF-EMPLOYED 17080985.96\n",
"NOT EMPLOYED 8586308.70\n",
"INFORMATION REQUESTED 5053480.37\n",
"HOMEMAKER 2605408.54\n",
"SELF 1076531.20\n",
"SELF EMPLOYED 469290.00\n",
"STUDENT 318831.45\n",
"VOLUNTEER 257104.00\n",
"MICROSOFT 215585.36\n",
"Name: contb_receipt_amt, dtype: float64\n",
"\n",
"\n",
"T8b: Top 10 Employers for Romney:\n",
"contbr_employer\n",
"INFORMATION REQUESTED PER BEST EFFORTS 12059527.24\n",
"RETIRED 11506225.71\n",
"HOMEMAKER 8147196.22\n",
"SELF-EMPLOYED 7409860.98\n",
"STUDENT 496490.94\n",
"CREDIT SUISSE 281150.00\n",
"MORGAN STANLEY 267266.00\n",
"GOLDMAN SACH & CO. 238250.00\n",
"BARCLAYS CAPITAL 162750.00\n",
"H.I.G. CAPITAL 139500.00\n",
"Name: contb_receipt_amt, dtype: float64\n",
"\n",
"\n",
"T8c:Both, Obama only, Romney only = "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 0 747 253\n",
"\n",
"\n",
"t8e:States ordered by polarity \n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"cand_nm Obama, Barack Romney, Mitt polarity\n",
"contbr_st \n",
"XX 0.00 400250 400250.000000\n",
"FF 0.00 99030 99030.000000\n",
"ZZ 5963.00 0 5963.000000\n",
"AS 2955.00 0 2955.000000\n",
"UK 0.00 2500 2500.000000\n",
"AB 2048.00 0 2048.000000\n",
"ON 1955.00 0 1955.000000\n",
"FM 600.00 0 600.000000\n",
"QU 500.00 0 500.000000\n",
"AA 56405.00 135 414.742647\n",
"VI 80712.00 3500 23.053985\n",
"AP 37130.50 1655 22.421800\n",
"VT 986510.59 55229 17.861861\n",
"ME 1167760.12 117152 9.967821\n",
"AE 42973.75 5680 7.564469\n",
"PR 216019.00 29125 7.416707\n",
"UT 519851.37 3717300 7.150684\n",
"HI 795212.64 111763 7.115105\n",
"MN 1744387.14 293656 5.940220\n",
"NM 906162.36 168011 5.393438\n",
"IL 16443895.84 3628572 4.531780\n",
"DC 4373538.80 1025137 4.266293\n",
"WI 1130155.46 270316 4.180852\n",
"DE 336669.14 82712 4.070329\n",
"ID 197538.06 787158 3.984822\n",
"AR 359247.28 105556 3.403349\n",
"AK 281840.15 86204 3.269418\n",
"WA 4250933.16 1341522 3.168737\n",
"MP 3855.00 1250 3.081535\n",
"GU 11581.50 3850 3.007401\n",
"... ... ... ...\n",
"MT 300225.24 161629 1.857485\n",
"NC 2357067.63 1273604 1.850705\n",
"LA 548013.54 991237 1.808779\n",
"CT 2068291.26 3499475 1.691963\n",
"MS 195197.17 330183 1.691527\n",
"IN 883691.81 542086 1.630166\n",
"SC 630732.94 402743 1.566089\n",
"ND 58999.90 39392 1.497725\n",
"NH 616994.85 424839 1.452299\n",
"NY 14651918.51 10184213 1.438689\n",
"CO 2132429.49 1506714 1.415284\n",
"OK 594342.52 839484 1.412456\n",
"MA 6649015.25 4710542 1.411518\n",
"NE 251311.97 178600 1.407114\n",
"GA 2786399.49 1995726 1.396183\n",
"KS 448038.57 326634 1.371680\n",
"TN 1119315.02 1516918 1.355219\n",
"WV 169154.47 126725 1.334805\n",
"WY 194046.74 252596 1.301721\n",
"AZ 1506476.98 1888436 1.253544\n",
"VA 4259977.19 3465766 1.229159\n",
"FL 7318178.58 8338459 1.139417\n",
"NV 710693.67 630049 1.127996\n",
"KY 714954.32 666903 1.072050\n",
"TX 6570832.45 6221990 1.056066\n",
"MI 2570307.25 2448110 1.049915\n",
"OH 1822728.83 1901561 1.043249\n",
"NJ 3203257.93 3333320 1.040603\n",
"MO 1320780.71 1371333 1.038274\n",
"AL 543123.48 527304 1.029999\n",
"\n",
"[67 rows x 3 columns]\n",
"\n",
"\n",
"t8f: Occupation ordered by polarity:\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"cand_nm Obama, Barack Romney, Mitt \\\n",
"contbr_occupation \n",
"INFORMATION REQUESTED PER BEST EFFORTS 0.00 11396894.84 \n",
"INFORMATION REQUESTED 4866973.96 0.00 \n",
"NOT EMPLOYED 1709188.20 0.00 \n",
"PRESIDENT & C.E.O. 0.00 574161.28 \n",
"C.P.A. 50.00 412107.11 \n",
"C.E.O. 1690.00 1968386.11 \n",
"LAWYER 3160478.87 7705.20 \n",
"SOFTWARE ENGINEER 396985.65 19589.54 \n",
"PSYCHOLOGIST 427299.92 21341.00 \n",
"PROFESSOR 2165071.08 161362.12 \n",
"WRITER 1084188.88 96613.12 \n",
"EDUCATOR 436600.89 40735.00 \n",
"ARTIST 763125.00 84920.12 \n",
"TEACHER 1250969.15 141338.39 \n",
"PRIVATE EQUITY 60374.00 418240.00 \n",
"EXECUTIVE DIRECTOR 348180.94 54498.00 \n",
"ARCHITECT 483859.89 82980.00 \n",
"CEO 2073284.79 355910.92 \n",
"BANKER 224084.40 1009186.24 \n",
"INVESTMENT BANKER 164011.70 661035.00 \n",
"INVESTMENTS 160480.00 634922.00 \n",
"FINANCE 296031.40 1143592.25 \n",
"ENGINEER 951525.55 326049.24 \n",
"INVESTMENT MANAGER 136840.00 382826.00 \n",
"INVESTMENT MANAGEMENT 138224.00 382332.12 \n",
"PHYSICIAN 3735124.94 1368023.96 \n",
"DIRECTOR 471741.73 186255.12 \n",
"REAL ESTATE DEVELOPER 139454.00 317940.00 \n",
"RETIRED 25305116.38 11508473.59 \n",
"ATTORNEY 11141982.97 5364718.82 \n",
"FINANCIAL ADVISOR 169075.50 344951.00 \n",
"REAL ESTATE 528902.09 1054801.00 \n",
"HOMEMAKER 4248875.80 8147446.22 \n",
"BUSINESS EXECUTIVE 155780.00 278301.48 \n",
"INVESTOR 884133.00 1537595.12 \n",
"CONSULTANT 2459912.71 1424894.01 \n",
"EXECUTIVE 1355161.05 2300947.03 \n",
"SELF-EMPLOYED 672393.40 1092538.44 \n",
"CHAIRMAN 496547.00 805131.75 \n",
"VICE PRESIDENT 325647.15 525540.89 \n",
"MANAGING DIRECTOR 329688.25 519665.12 \n",
"PRINCIPAL 250005.44 162740.00 \n",
"BUSINESS OWNER 449979.30 617389.86 \n",
"PRESIDENT 1878509.95 2491244.89 \n",
"MANAGER 762883.22 577924.94 \n",
"SALES 392886.91 501946.86 \n",
"STUDENT 628099.75 496590.94 \n",
"PARTNER 395759.50 488100.11 \n",
"OWNER 1001567.36 875186.24 \n",
"ACCOUNTANT 249660.24 262555.62 \n",
"\n",
"cand_nm Total polarity \n",
"contbr_occupation \n",
"INFORMATION REQUESTED PER BEST EFFORTS 11396894.84 11396894.840000 \n",
"INFORMATION REQUESTED 4866973.96 4866973.960000 \n",
"NOT EMPLOYED 1709188.20 1709188.200000 \n",
"PRESIDENT & C.E.O. 574161.28 574161.280000 \n",
"C.P.A. 412157.11 8080.531569 \n",
"C.E.O. 1970076.11 1164.036730 \n",
"LAWYER 3168184.07 410.121574 \n",
"SOFTWARE ENGINEER 416575.19 20.264150 \n",
"PSYCHOLOGIST 448640.92 20.021550 \n",
"PROFESSOR 2326433.20 13.417385 \n",
"WRITER 1180802.00 11.221847 \n",
"EDUCATOR 477335.89 10.717814 \n",
"ARTIST 848045.12 8.986280 \n",
"TEACHER 1392307.54 8.850818 \n",
"PRIVATE EQUITY 478614.00 6.927371 \n",
"EXECUTIVE DIRECTOR 402678.94 6.388758 \n",
"ARCHITECT 566839.89 5.830972 \n",
"CEO 2429195.71 5.825275 \n",
"BANKER 1233270.64 4.503579 \n",
"INVESTMENT BANKER 825046.70 4.030389 \n",
"INVESTMENTS 795402.00 3.956369 \n",
"FINANCE 1439623.65 3.863064 \n",
"ENGINEER 1277574.79 2.918340 \n",
"INVESTMENT MANAGER 519666.00 2.797597 \n",
"INVESTMENT MANAGEMENT 520556.12 2.766013 \n",
"PHYSICIAN 5103148.90 2.730305 \n",
"DIRECTOR 657996.85 2.532758 \n",
"REAL ESTATE DEVELOPER 457394.00 2.279875 \n",
"RETIRED 36813589.97 2.198825 \n",
"ATTORNEY 16506701.79 2.076899 \n",
"FINANCIAL ADVISOR 514026.50 2.040207 \n",
"REAL ESTATE 1583703.09 1.994318 \n",
"HOMEMAKER 12396322.02 1.917553 \n",
"BUSINESS EXECUTIVE 434081.48 1.786492 \n",
"INVESTOR 2421728.12 1.739097 \n",
"CONSULTANT 3884806.72 1.726382 \n",
"EXECUTIVE 3656108.08 1.697913 \n",
"SELF-EMPLOYED 1764931.84 1.624848 \n",
"CHAIRMAN 1301678.75 1.621458 \n",
"VICE PRESIDENT 851188.04 1.613830 \n",
"MANAGING DIRECTOR 849353.37 1.576227 \n",
"PRINCIPAL 412745.44 1.536217 \n",
"BUSINESS OWNER 1067369.16 1.372038 \n",
"PRESIDENT 4369754.84 1.326181 \n",
"MANAGER 1340808.16 1.320036 \n",
"SALES 894833.77 1.277583 \n",
"STUDENT 1124690.69 1.264821 \n",
"PARTNER 883859.61 1.233322 \n",
"OWNER 1876753.60 1.144404 \n",
"ACCOUNTANT 512215.86 1.051648 \n",
"\n",
"\n",
"t8f: Texas cities ordered by polarity:\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"cand_nm Obama, Barack Romney, Mitt Total polarity\n",
"contbr_city \n",
"STAFFORD 29982.25 0.00 29982.25 29982.250000\n",
"WHEELER 11622.00 0.00 11622.00 11622.000000\n",
"DAINGERFIELD 10125.00 0.00 10125.00 10125.000000\n",
"LUMBERTON 10000.00 0.00 10000.00 10000.000000\n",
"FARMERS BRANCH 9225.00 0.00 9225.00 9225.000000\n",
"DUNCANVILLE 9089.20 0.00 9089.20 9089.200000\n",
"FLORESVILLE 8898.50 0.00 8898.50 8898.500000\n",
"TROUP 8686.00 0.00 8686.00 8686.000000\n",
"BUDA 7056.00 0.00 7056.00 7056.000000\n",
"DEER PARK 6693.00 0.00 6693.00 6693.000000\n",
"ALBANY 0.00 6500.00 6500.00 6500.000000\n",
"ROSHARON 5830.00 0.00 5830.00 5830.000000\n",
"HOCKLEY 5696.00 0.00 5696.00 5696.000000\n",
"FREDERICKSBRG 5607.00 0.00 5607.00 5607.000000\n",
"NAPLES 5300.00 0.00 5300.00 5300.000000\n",
"UVALDE 5080.00 0.00 5080.00 5080.000000\n",
"GILMER 0.00 5055.00 5055.00 5055.000000\n",
"STERLING CITY 0.00 5000.00 5000.00 5000.000000\n",
"MOORE 5000.00 0.00 5000.00 5000.000000\n",
"DANGERFIELD 5000.00 0.00 5000.00 5000.000000\n",
"GAIL 0.00 5000.00 5000.00 5000.000000\n",
"FULSHEAR 0.00 5000.00 5000.00 5000.000000\n",
"GRAPELAND 0.00 5000.00 5000.00 5000.000000\n",
"JONESBORO 0.00 5000.00 5000.00 5000.000000\n",
"HENRIETTA 3980.00 0.00 3980.00 3980.000000\n",
"CARTHAGE 3910.00 0.00 3910.00 3910.000000\n",
"FRESNO 3905.00 0.00 3905.00 3905.000000\n",
"ELGIN 3780.00 0.00 3780.00 3780.000000\n",
"LLANO 3550.00 0.00 3550.00 3550.000000\n",
"HIGHLAND VILLAGE 3231.00 0.00 3231.00 3231.000000\n",
"... ... ... ... ...\n",
"BAYTOWN 10847.00 8600.00 19447.00 1.261132\n",
"HUNTSVILLE 2750.00 2200.00 4950.00 1.249432\n",
"SUNRISE BEACH 200.00 250.00 450.00 1.243781\n",
"SHENANDOAH 160.00 200.00 360.00 1.242236\n",
"COMFORT 205.00 250.00 455.00 1.213592\n",
"CORSICANA 2200.00 2650.00 4850.00 1.203998\n",
"SPRING BRANCH 2960.00 3555.00 6515.00 1.200608\n",
"KATY 28411.30 33605.12 62016.42 1.182767\n",
"BASTROP 5923.75 5050.00 10973.75 1.172788\n",
"BELTON 2713.00 2340.12 5053.12 1.158847\n",
"FAIRVIEW 3499.00 3030.00 6529.00 1.154404\n",
"MANSFIELD 7785.00 8850.00 16635.00 1.136656\n",
"PLANO 92154.00 81357.12 173511.12 1.132696\n",
"SAN ANTONIO 373400.76 422340.48 795741.24 1.131062\n",
"GRAPEVINE 13723.00 12188.00 25911.00 1.125851\n",
"DALLAS 1068911.89 1186395.96 2255307.85 1.109909\n",
"WESTLAKE 7500.00 8255.00 15755.00 1.100520\n",
"PROSPER 2631.00 2895.36 5526.36 1.100061\n",
"DENTON 20477.00 21916.36 42393.36 1.070239\n",
"GRANBURY 3114.00 2925.00 6039.00 1.064252\n",
"HARLINGEN 7374.00 7750.00 15124.00 1.050847\n",
"BOERNE 15968.09 16657.00 32625.09 1.043078\n",
"HEMPSTEAD 1395.00 1350.00 2745.00 1.032568\n",
"KINGWOOD 18729.75 18176.00 36905.75 1.030409\n",
"SUGAR LAND 57750.56 58951.65 116702.21 1.020780\n",
"MELISSA 750.00 735.00 1485.00 1.019022\n",
"MCALLEN 10397.00 10436.00 20833.00 1.003655\n",
"COLLEGE STA 1000.00 1000.00 2000.00 0.999001\n",
"CHINA SPRING 1000.00 1000.00 2000.00 0.999001\n",
"HORIZON CITY 250.00 250.00 500.00 0.996016\n",
"\n",
"[729 rows x 4 columns]\n"
]
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