{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## NSF Funding History 1990 - 2016\n",
"Ths script requests zipped files containing annual funding history from the NSF website, unpacks, parses and loads the resulting xml files (in-memory) to 4 relational data sets into SQLite, and finally performs some analysis using Pandas, Seaborn and Plotly."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import os\n",
"import sys\n",
"import json\n",
"import re\n",
"import csv\n",
"import datetime as dt\n",
"import requests\n",
"from collections import OrderedDict\n",
"from io import BytesIO\n",
"from zipfile import ZipFile\n",
"from lxml import etree as ET\n",
"\n",
"import pandas as pd\n",
"import numpy as np\n",
"import sqlalchemy as sql\n",
"import matplotlib.pylab as plt\n",
"import seaborn as sns\n",
"import plotly.plotly as py\n",
"from plotly.graph_objs import Bar, Scatter, Marker, Layout\n",
"from plotly.tools import FigureFactory as FF\n",
"from wordcloud import WordCloud, STOPWORDS\n",
"from bs4 import BeautifulSoup\n",
"from nltk.corpus import stopwords\n",
"from IPython.display import HTML\n",
"\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Engineering"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Scraping the NSF\n",
"downloaded from: http://www.nsf.gov/awardsearch/download.jsp"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Constants\n",
"data_in = 'data_in/'\n",
"data_out = 'data_out/' \n",
"\n",
"# URL for http-requests\n",
"req_root = 'http://www.nsf.gov/awardsearch/download?DownloadFileName={YR}&All=true'\n",
"\n",
"# When to start looking at awards, and when to end. It is the YR in the root\n",
"year_start = 1990\n",
"year_end = 2017\n",
"\n",
"csv_out = [\n",
" 'NSF_AWARDS.csv',\n",
" 'NSF_ABSTRACTS.csv',\n",
" 'NSF_INSTITUTIONS.csv',\n",
" 'NSF_PI.csv'\n",
"]\n",
"\n",
"department_normalization = {\n",
" 'Polar Progrms': 'Polar Programs',\n",
" 'Arctic Sciences Division' : 'Polar Programs',\n",
" 'Research On Learning In Formal And Informal Settings (DRL)': 'Research On Learning',\n",
" 'Information Systems': 'Information & Intelligent Systems',\n",
" 'Civil, Mechanical, And Manufacturing Innovation': 'Civil, Mechanical, And Manufacturing Inn',\n",
" 'Behavioral And Cognitive Sci' : 'Behavioral And Cognitive Sciences',\n",
" 'Integrative Organismal System' : 'Integrative Organismal Sys',\n",
" 'Information Systems': 'Information & Intelligent Systems'\n",
"\n",
"}\n",
"\n",
"modifiers_to_remove = {\n",
" 'Division ': '', \n",
" 'Of ': '',\n",
" 'Div ': '',\n",
" 'Directorate ':'',\n",
" 'Office ': '',\n",
" 'Direct ': '',\n",
" 'Divn ': '',\n",
" 'For ': '',\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Each annual zipped file contains one xml file per award:"
]
},
{
"cell_type": "code",
"execution_count": 621,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\n",
" \n",
" Dynamics of Extreme Precipitation in the Northeast United States in Observations and Models\n",
" 11/01/2016\n",
" 10/31/2019\n",
" 453807\n",
" \n",
" Standard Grant\n",
" \n",
" \n",
" 06020100
\n",
" \n",
" Directorate For Geosciences\n",
" \n",
" \n",
" Div Atmospheric & Geospace Sciences\n",
" \n",
" \n",
" \n",
" Anjuli S. Bamzai\n",
" \n",
" Extreme precipitation and its related impacts, especially flooding, result in significant loss of life, property and infrastructure damage, transportation disruption, and storm water pollution, and have economic costs of more than $8 billion per year for the US. This project will undertake fundamental research into understanding the causes of extreme precipitation in the Northeast US. This is the most economically developed and densely populated region of the country. Improved model representation of these processes is a crucial step in the overall goal of improving forecasts and projections of these high-cost events, thereby mitigating their impacts. <br/><br/>Processes that cause extreme precipitation over daily to weekly periods in the Northeast US will be identified. The ability of current climate models to reproduce these processes will be examined. The two motivating questions are: What types of storms cause extreme precipitation in the Northeast? Do current models correctly reproduce these storms types and their relationship to extreme precipitation? Using observational data, storm types associated with extreme precipitation will be identified by applying advanced analytic techniques. Characteristic patterns in the jet stream and other storm features that occur in association with extreme precipitation will be identified. This analysis will then be undertaken on the climate model output to identify the storm types that are produced in the models and compare the modeled types to the observed types. The differences will be highlighted for use in model development and for providing context for model forecasts and projections. The physical processes by which the extreme precipitation are generated within each storm type will also be investigated. The relative strength of different factors that are known to influence precipitation, such as the amount of moisture in the lower atmosphere, will be examined within each storm type. After the key factors are identified, their influence will then be further tested in a regional, high resolution model by changing the strength of individual factors and examining how the modeled precipitation changes in response.\n",
" 10/25/2016\n",
" 10/25/2016\n",
" \n",
" 1623912\n",
" \n",
" Mathew\n",
" Barlow\n",
" Mathew_Barlow@uml.edu\n",
" 10/25/2016\n",
" \n",
" Principal Investigator\n",
" \n",
" \n",
" JianHua\n",
" Qian\n",
" JianHua_Qian@uml.edu\n",
" 10/25/2016\n",
" \n",
" Co-Principal Investigator\n",
" \n",
" \n",
" University of Massachusetts Lowell\n",
" Lowell\n",
" 018543643\n",
" 9789344170\n",
" 600 Suffolk Street\n",
" United States\n",
" Massachusetts\n",
" MA\n",
" \n",
" \n",
" 5740
\n",
" CLIMATE & LARGE-SCALE DYNAMICS\n",
" \n",
" \n",
"\n",
"\n"
]
}
],
"source": [
"with open('data_in/2016/1623912.xml', 'r') as xml_view:\n",
" print(xml_view.read())"
]
},
{
"cell_type": "code",
"execution_count": 626,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def division_cleanse(raw_division):\n",
" \"\"\"\n",
" Use normalization dicts to standardize legacy division naming.\n",
" \"\"\"\n",
"\n",
" rep = dict((re.escape(k), v) for k, v in modifiers_to_remove.items())\n",
" pattern = re.compile(\"|\".join(rep.keys()))\n",
"\n",
" division = pattern.sub(lambda m: rep[re.escape(m.group(0))],\n",
" raw_division.title())\n",
"\n",
" return department_normalization.get(division, division)\n",
"\n",
"def get_PIs(list_investigators):\n",
" \"\"\"\n",
" Returns a semi-colon joined list of PI names (first and last)\n",
" \"\"\"\n",
" return ';'.join(\n",
" ['{} {}'.format(PI.find('FirstName').text, \n",
" PI.find('LastName').text) \n",
" for PI in list_investigators])\n",
"\n",
"\n",
"# Builder functions\n",
"def awards_builder(tree):\n",
" global awards\n",
" list_investigators = tree.findall('.//Award/Investigator')\n",
" return awards.append(\n",
" OrderedDict([\n",
" ('AwardID',tree.find('Award/AwardID').text),\n",
" ('AwardTitle',tree.find('Award/AwardTitle').text),\n",
" ('AwardEffectiveDate',tree.find('Award/AwardEffectiveDate').text),\n",
" ('AwardExpirationDate',tree.find('Award/AwardExpirationDate').text),\n",
" ('AwardAmount',tree.find('Award/AwardAmount').text),\n",
" ('InstitutionName',tree.find('Award/Institution/Name').text),\n",
" ('Division', division_cleanse(tree.find('Award/Organization/Division')[0].text)),\n",
" ('Directorate', division_cleanse(tree.find('Award/Organization/Directorate')[0].text)),\n",
" ('EmailAddress',tree.find('Award/Investigator/EmailAddress').text),\n",
" ('InvestigatorNames', get_PIs(list_investigators)),\n",
" ('NumInvestigators', len(list_investigators))\n",
" ]))\n",
"\n",
"def abstracts_builder(tree):\n",
" global abstracts\n",
" return abstracts.append(\n",
" OrderedDict([\n",
" ('AwardID',tree.find('Award/AwardID').text),\n",
" ('Abstract',tree.find('Award/AbstractNarration').text)\n",
" ]))\n",
"\n",
"def institutions_builder(tree):\n",
" global institutions\n",
" return institutions.append(\n",
" OrderedDict([\n",
" ('InstitutionName',tree.find('Award/Institution/Name').text),\n",
" ('StreetAddress',tree.find('Award/Institution/StreetAddress').text),\n",
" ('CityName',tree.find('Award/Institution/CityName').text),\n",
" ('StateCode',tree.find('Award/Institution/StateCode').text),\n",
" ('ZipCode',tree.find('Award/Institution/ZipCode').text),\n",
" ('CountryName',tree.find('Award/Institution/CountryName').text)\n",
" ]))\n",
"\n",
"def PI_builder(tree):\n",
" global PI\n",
" return PI.append(\n",
" OrderedDict([\n",
" ('EmailAddress',tree.find('Award/Investigator/EmailAddress').text),\n",
" ('FirstName',tree.find('Award/Investigator/FirstName').text),\n",
" ('LastName',tree.find('Award/Investigator/LastName').text)\n",
" ]))\n",
"\n",
"def xml_parse(file):\n",
" '''\n",
" Calls all four data set builders after parsing a xml file into a tree.\n",
" This function is called for each unzipped-file.\n",
" '''\n",
" try:\n",
" tree = ET.parse(file)\n",
" except:\n",
" return(\"empty\")\n",
" try:\n",
" awards_builder(tree)\n",
" except:\n",
" return(tree.find('Award/AwardID').text,\"is missing an award key\")\n",
" \n",
" abstracts_builder(tree)\n",
" institutions_builder(tree)\n",
" PI_builder(tree)\n",
" return\n",
"\n",
"def curl(path):\n",
" '''\n",
" Requests zip file of a year's Award info from the NSf site.\n",
" The zip file is unzipped and stored as a stream of bytes in memory.\n",
" Each of the unzipped xml files is parsed by xml_parse for all 4 data set builders.\n",
" '''\n",
" file_url = req_root.format(YR = str(path))\n",
" print(\"*** {} ***\\ndownloading {}\".format(path,file_url))\n",
" url = requests.get(file_url)\n",
" zipfile = ZipFile(BytesIO(url.content))\n",
" [xml_parse(zipfile.open(file)) for file in zipfile.namelist()]\n",
" return\n",
"\n",
"def remove_duplicates(dict_list):\n",
" return [dict(tupleized) for tupleized in \n",
" set(tuple(item.items()) for item in dict_list)]\n",
"\n",
"def json_dump():\n",
" '''\n",
" Iterates through each relational dataset, and dumps each to json.\n",
" '''\n",
" dict_NSF = [awards,\n",
" abstracts,\n",
" institutions,\n",
" PI]\n",
"\n",
" out_json = [\"NSF_AWARDS.json\",\n",
" \"NSF_ABSTRACTS.json\",\n",
" \"NSF_INSTITUTIONS.json\",\n",
" \"NSF_PI.json\"]\n",
"\n",
" for list_dicts, json_file in zip(dict_NSF, out_json):\n",
" with open(os.path.join(data_out, json_file), 'w') as outfile:\n",
" json.dump(list_dicts, outfile, indent=4)\n",
"\n",
"def csv_dump():\n",
" '''\n",
" Iterates through each relational dataset, and dumps each to csv.\n",
" '''\n",
" for list_dicts, file in zip(dict_NSF, csv_out):\n",
" with open(os.path.join(data_out,file), 'w') as outfile:\n",
" dict_writer = csv.DictWriter(outfile, list_dicts[0].keys())\n",
" dict_writer.writeheader()\n",
" dict_writer.writerows(list_dicts)"
]
},
{
"cell_type": "code",
"execution_count": 629,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"*** 1990 ***\n",
"downloading http://www.nsf.gov/awardsearch/download?DownloadFileName=1990&All=true\n",
"*** 1991 ***\n",
"downloading http://www.nsf.gov/awardsearch/download?DownloadFileName=1991&All=true\n",
"*** 1992 ***\n",
"downloading http://www.nsf.gov/awardsearch/download?DownloadFileName=1992&All=true\n",
"*** 1993 ***\n",
"downloading http://www.nsf.gov/awardsearch/download?DownloadFileName=1993&All=true\n",
"*** 1994 ***\n",
"downloading http://www.nsf.gov/awardsearch/download?DownloadFileName=1994&All=true\n",
"*** 1995 ***\n",
"downloading http://www.nsf.gov/awardsearch/download?DownloadFileName=1995&All=true\n",
"*** 1996 ***\n",
"downloading http://www.nsf.gov/awardsearch/download?DownloadFileName=1996&All=true\n",
"*** 1997 ***\n",
"downloading http://www.nsf.gov/awardsearch/download?DownloadFileName=1997&All=true\n",
"*** 1998 ***\n",
"downloading http://www.nsf.gov/awardsearch/download?DownloadFileName=1998&All=true\n",
"*** 1999 ***\n",
"downloading http://www.nsf.gov/awardsearch/download?DownloadFileName=1999&All=true\n",
"*** 2000 ***\n",
"downloading http://www.nsf.gov/awardsearch/download?DownloadFileName=2000&All=true\n",
"*** 2001 ***\n",
"downloading http://www.nsf.gov/awardsearch/download?DownloadFileName=2001&All=true\n",
"*** 2002 ***\n",
"downloading http://www.nsf.gov/awardsearch/download?DownloadFileName=2002&All=true\n",
"*** 2003 ***\n",
"downloading http://www.nsf.gov/awardsearch/download?DownloadFileName=2003&All=true\n",
"*** 2004 ***\n",
"downloading http://www.nsf.gov/awardsearch/download?DownloadFileName=2004&All=true\n",
"*** 2005 ***\n",
"downloading http://www.nsf.gov/awardsearch/download?DownloadFileName=2005&All=true\n",
"*** 2006 ***\n",
"downloading http://www.nsf.gov/awardsearch/download?DownloadFileName=2006&All=true\n",
"*** 2007 ***\n",
"downloading http://www.nsf.gov/awardsearch/download?DownloadFileName=2007&All=true\n",
"*** 2008 ***\n",
"downloading http://www.nsf.gov/awardsearch/download?DownloadFileName=2008&All=true\n",
"*** 2009 ***\n",
"downloading http://www.nsf.gov/awardsearch/download?DownloadFileName=2009&All=true\n",
"*** 2010 ***\n",
"downloading http://www.nsf.gov/awardsearch/download?DownloadFileName=2010&All=true\n",
"*** 2011 ***\n",
"downloading http://www.nsf.gov/awardsearch/download?DownloadFileName=2011&All=true\n",
"*** 2012 ***\n",
"downloading http://www.nsf.gov/awardsearch/download?DownloadFileName=2012&All=true\n",
"*** 2013 ***\n",
"downloading http://www.nsf.gov/awardsearch/download?DownloadFileName=2013&All=true\n",
"*** 2014 ***\n",
"downloading http://www.nsf.gov/awardsearch/download?DownloadFileName=2014&All=true\n",
"*** 2015 ***\n",
"downloading http://www.nsf.gov/awardsearch/download?DownloadFileName=2015&All=true\n",
"*** 2016 ***\n",
"downloading http://www.nsf.gov/awardsearch/download?DownloadFileName=2016&All=true\n",
"*** 2017 ***\n",
"downloading http://www.nsf.gov/awardsearch/download?DownloadFileName=2017&All=true\n",
"298261 records scraped in 711.08449 sec\n",
"Duplicates removed in 16.04135 sec.\n",
"Dumped into CSV in 36.556531 sec.\n"
]
}
],
"source": [
"awards = []\n",
"abstracts = []\n",
"institutions = []\n",
"PI = []\n",
"dict_NSF = [awards, abstracts, institutions, PI]\n",
"\n",
"start = dt.datetime.now()\n",
"[curl(year) for year in range(year_start, year_end+1)] \n",
"print(\"{ROWS} records scraped in {TIME} sec\".format(\n",
" ROWS=len(awards),\n",
" TIME=(dt.datetime.now()-start).total_seconds()))\n",
"\n",
"# remove doops\n",
"start = dt.datetime.now()\n",
"[remove_duplicates(data_set) for data_set in [awards, PI, institutions]]\n",
"print(\"Duplicates removed in %s sec.\" % (dt.datetime.now()-start).total_seconds())\n",
"\n",
"# dump'em\n",
"start = dt.datetime.now()\n",
"csv_dump()\n",
"print(\"Dumped into CSV in %s sec.\" % (dt.datetime.now()-start).total_seconds())"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"
\n",
" \n",
" \n",
" | \n",
" AwardID | \n",
" AwardTitle | \n",
" AwardEffectiveDate | \n",
" AwardExpirationDate | \n",
" AwardAmount | \n",
" InstitutionName | \n",
" Division | \n",
" Directorate | \n",
" EmailAddress | \n",
" InvestigatorNames | \n",
" NumInvestigators | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" 0 | \n",
" Regulation of Sn-Glycerol-3-Phosphate Metaboli... | \n",
" 07/01/1986 | \n",
" 07/01/1986 | \n",
" 0 | \n",
" Virginia Polytechnic Institute and State Unive... | \n",
" Molecular And Cellular Bioscience | \n",
" Biological Sciences | \n",
" tilarson@vt.edu | \n",
" Timothy Larson | \n",
" 1 | \n",
"
\n",
" \n",
" 1 | \n",
" 9 | \n",
" Design of Cutting Tools for High Speed Milling | \n",
" 06/15/2000 | \n",
" 05/31/2004 | \n",
" 280000 | \n",
" University of Florida | \n",
" Civil, Mechanical, & Manufact Inn | \n",
" Engineering | \n",
" jtlusty@ufl.edu | \n",
" Jiri Tlusty;Tony Schmitz;John Ziegert | \n",
" 3 | \n",
"
\n",
" \n",
" 2 | \n",
" 26 | \n",
" A Novel Ultrasonic Cooling Concept for Microel... | \n",
" 06/15/2000 | \n",
" 05/31/2004 | \n",
" 292026 | \n",
" North Carolina State University | \n",
" Electrical, Commun & Cyber Sys | \n",
" Engineering | \n",
" ro@ncsu.edu | \n",
" Paul Ro;Andrey Kuznetsov;Angus Kingon | \n",
" 3 | \n",
"
\n",
" \n",
" 3 | \n",
" 27 | \n",
" Development of a Wireless Sensor to Detect Cra... | \n",
" 04/15/2000 | \n",
" 03/31/2004 | \n",
" 238000 | \n",
" University of Texas at Austin | \n",
" Civil, Mechanical, & Manufact Inn | \n",
" Engineering | \n",
" neikirk@mail.utexas.edu | \n",
" Dean Neikirk;Sharon Wood;Karl Frank | \n",
" 3 | \n",
"
\n",
" \n",
" 4 | \n",
" 31 | \n",
" Development of Link-to-Column Connections for ... | \n",
" 09/01/2000 | \n",
" 08/31/2003 | \n",
" 285000 | \n",
" University of Texas at Austin | \n",
" Civil, Mechanical, & Manufact Inn | \n",
" Engineering | \n",
" mde@mail.utexas.edu | \n",
" Michael Engelhardt | \n",
" 1 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" AwardID AwardTitle \\\n",
"0 0 Regulation of Sn-Glycerol-3-Phosphate Metaboli... \n",
"1 9 Design of Cutting Tools for High Speed Milling \n",
"2 26 A Novel Ultrasonic Cooling Concept for Microel... \n",
"3 27 Development of a Wireless Sensor to Detect Cra... \n",
"4 31 Development of Link-to-Column Connections for ... \n",
"\n",
" AwardEffectiveDate AwardExpirationDate AwardAmount \\\n",
"0 07/01/1986 07/01/1986 0 \n",
"1 06/15/2000 05/31/2004 280000 \n",
"2 06/15/2000 05/31/2004 292026 \n",
"3 04/15/2000 03/31/2004 238000 \n",
"4 09/01/2000 08/31/2003 285000 \n",
"\n",
" InstitutionName \\\n",
"0 Virginia Polytechnic Institute and State Unive... \n",
"1 University of Florida \n",
"2 North Carolina State University \n",
"3 University of Texas at Austin \n",
"4 University of Texas at Austin \n",
"\n",
" Division Directorate \\\n",
"0 Molecular And Cellular Bioscience Biological Sciences \n",
"1 Civil, Mechanical, & Manufact Inn Engineering \n",
"2 Electrical, Commun & Cyber Sys Engineering \n",
"3 Civil, Mechanical, & Manufact Inn Engineering \n",
"4 Civil, Mechanical, & Manufact Inn Engineering \n",
"\n",
" EmailAddress InvestigatorNames \\\n",
"0 tilarson@vt.edu Timothy Larson \n",
"1 jtlusty@ufl.edu Jiri Tlusty;Tony Schmitz;John Ziegert \n",
"2 ro@ncsu.edu Paul Ro;Andrey Kuznetsov;Angus Kingon \n",
"3 neikirk@mail.utexas.edu Dean Neikirk;Sharon Wood;Karl Frank \n",
"4 mde@mail.utexas.edu Michael Engelhardt \n",
"\n",
" NumInvestigators \n",
"0 1 \n",
"1 3 \n",
"2 3 \n",
"3 3 \n",
"4 1 "
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.read_csv(os.path.join(data_out,\"NSF_AWARDS.csv\"), nrows=5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Getting the data into a SQLite database\n",
"We could have gone straight from the stream of bytes to the SQLite db, but there's nothing wrong with keeping the processed back-up files in disk :)"
]
},
{
"cell_type": "code",
"execution_count": 631,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [],
"source": [
"def SQLite_dump(infile, Type='csv'):\n",
" # open SQlite connection\n",
" disk_engine = sql.create_engine('sqlite:///NSF.db')\n",
" start = dt.datetime.now()\n",
" tableName = infile.replace(\"NSF_\",\"\")\\\n",
" .replace(\".csv\",\"\")\\\n",
" .replace(\".json\",\"\")\\\n",
" .replace(data_out,\"\")\n",
" \n",
" print(\"*********************************************************************\")\n",
" if Type == 'csv':\n",
" '''\n",
" Set Type to csv after csv_dump.\n",
" Break each CSV into chunks to reduce in memory stoarge of dataset.\n",
" Each chunk is sent to a SQLite table (tableName).\n",
" '''\n",
" chunksize = 20000\n",
" j = 0\n",
" index_start = 1\n",
" \n",
" for df in pd.read_csv(infile, chunksize=chunksize, iterator=True, encoding='utf-8'):\n",
" if tableName == 'AWARDS':\n",
" for col in ['AwardEffectiveDate','AwardExpirationDate']:\n",
" df[col] = pd.to_datetime(df[col], format='%m/%d/%Y')\n",
" df.index += index_start\n",
" index_start = df.index[-1] + 1\n",
" j += 1\n",
"\n",
" print(\"{X} seconds: {Y} records dumped into SQLite table {Z}\".format(\n",
" X=(dt.datetime.now() - start).total_seconds(),\n",
" Y=j*chunksize,\n",
" Z=tableName\n",
" ))\n",
" \n",
" if j == 1:\n",
" df.to_sql(tableName, disk_engine, if_exists='replace', index=False)\n",
" \n",
" else:\n",
" df.to_sql(tableName, disk_engine, if_exists='append', index=False)\n",
" \n",
" elif Type == 'json':\n",
" '''\n",
" After json_dump, file is opened in a dataframe and dumped into a sqlite table\n",
" '''\n",
" df = pd.read_json(infile)\n",
" if tableName == 'AWARDS':\n",
" for col in ['AwardEffectiveDate','AwardExpirationDate']:\n",
" df[col] = pd.to_datetime(df[col], format='%m/%d/%Y')\n",
" \n",
" print(\"{X} seconds: {Y} records dumped into SQLite table {Z}\".format(\n",
" X=(dt.datetime.now() - start).seconds,\n",
" Y=len(df),\n",
" Z=tableName\n",
" ))\n",
" \n",
" df.to_sql(tableName, disk_engine, if_exists='replace', index=False)"
]
},
{
"cell_type": "code",
"execution_count": 632,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"*********************************************************************\n",
"0.543148 seconds: 20000 records dumped into SQLite table AWARDS\n",
"5.331882 seconds: 40000 records dumped into SQLite table AWARDS\n",
"6.401413 seconds: 60000 records dumped into SQLite table AWARDS\n",
"7.47707 seconds: 80000 records dumped into SQLite table AWARDS\n",
"8.588326 seconds: 100000 records dumped into SQLite table AWARDS\n",
"9.827497 seconds: 120000 records dumped into SQLite table AWARDS\n",
"11.051997 seconds: 140000 records dumped into SQLite table AWARDS\n",
"12.171535 seconds: 160000 records dumped into SQLite table AWARDS\n",
"13.445196 seconds: 180000 records dumped into SQLite table AWARDS\n",
"15.086242 seconds: 200000 records dumped into SQLite table AWARDS\n",
"20.884972 seconds: 220000 records dumped into SQLite table AWARDS\n",
"22.812093 seconds: 240000 records dumped into SQLite table AWARDS\n",
"25.089158 seconds: 260000 records dumped into SQLite table AWARDS\n",
"26.491599 seconds: 280000 records dumped into SQLite table AWARDS\n",
"28.456642 seconds: 300000 records dumped into SQLite table AWARDS\n",
"*********************************************************************\n",
"0.996505 seconds: 20000 records dumped into SQLite table ABSTRACTS\n",
"29.262021 seconds: 40000 records dumped into SQLite table ABSTRACTS\n",
"30.11163 seconds: 60000 records dumped into SQLite table ABSTRACTS\n",
"32.335524 seconds: 80000 records dumped into SQLite table ABSTRACTS\n",
"34.743973 seconds: 100000 records dumped into SQLite table ABSTRACTS\n",
"37.305316 seconds: 120000 records dumped into SQLite table ABSTRACTS\n",
"40.759159 seconds: 140000 records dumped into SQLite table ABSTRACTS\n",
"44.580109 seconds: 160000 records dumped into SQLite table ABSTRACTS\n",
"48.659568 seconds: 180000 records dumped into SQLite table ABSTRACTS\n",
"52.77365 seconds: 200000 records dumped into SQLite table ABSTRACTS\n",
"56.958778 seconds: 220000 records dumped into SQLite table ABSTRACTS\n",
"61.348238 seconds: 240000 records dumped into SQLite table ABSTRACTS\n",
"66.34427 seconds: 260000 records dumped into SQLite table ABSTRACTS\n",
"68.026501 seconds: 280000 records dumped into SQLite table ABSTRACTS\n",
"69.319802 seconds: 300000 records dumped into SQLite table ABSTRACTS\n",
"*********************************************************************\n",
"0.055064 seconds: 20000 records dumped into SQLite table INSTITUTIONS\n",
"1.834172 seconds: 40000 records dumped into SQLite table INSTITUTIONS\n",
"2.236637 seconds: 60000 records dumped into SQLite table INSTITUTIONS\n",
"2.655564 seconds: 80000 records dumped into SQLite table INSTITUTIONS\n",
"3.029963 seconds: 100000 records dumped into SQLite table INSTITUTIONS\n",
"3.428473 seconds: 120000 records dumped into SQLite table INSTITUTIONS\n",
"3.829848 seconds: 140000 records dumped into SQLite table INSTITUTIONS\n",
"4.290678 seconds: 160000 records dumped into SQLite table INSTITUTIONS\n",
"12.752918 seconds: 180000 records dumped into SQLite table INSTITUTIONS\n",
"13.461992 seconds: 200000 records dumped into SQLite table INSTITUTIONS\n",
"13.893989 seconds: 220000 records dumped into SQLite table INSTITUTIONS\n",
"14.296411 seconds: 240000 records dumped into SQLite table INSTITUTIONS\n",
"14.689038 seconds: 260000 records dumped into SQLite table INSTITUTIONS\n",
"15.061441 seconds: 280000 records dumped into SQLite table INSTITUTIONS\n",
"15.420823 seconds: 300000 records dumped into SQLite table INSTITUTIONS\n",
"*********************************************************************\n",
"0.037913 seconds: 20000 records dumped into SQLite table PI\n",
"1.016936 seconds: 40000 records dumped into SQLite table PI\n",
"1.320865 seconds: 60000 records dumped into SQLite table PI\n",
"1.630687 seconds: 80000 records dumped into SQLite table PI\n",
"1.963261 seconds: 100000 records dumped into SQLite table PI\n",
"2.252138 seconds: 120000 records dumped into SQLite table PI\n",
"2.53908 seconds: 140000 records dumped into SQLite table PI\n",
"2.851338 seconds: 160000 records dumped into SQLite table PI\n",
"3.151069 seconds: 180000 records dumped into SQLite table PI\n",
"3.445793 seconds: 200000 records dumped into SQLite table PI\n",
"3.791598 seconds: 220000 records dumped into SQLite table PI\n",
"4.069519 seconds: 240000 records dumped into SQLite table PI\n",
"4.338497 seconds: 260000 records dumped into SQLite table PI\n",
"4.651899 seconds: 280000 records dumped into SQLite table PI\n",
"4.997948 seconds: 300000 records dumped into SQLite table PI\n"
]
}
],
"source": [
"for infile in csv_out:\n",
" SQLite_dump(os.path.join(data_out,infile), Type='csv')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Analysis"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Filtering SQL tables into Pandas.\n",
"There is a read_sql() function in Pandas, but who wants to open connections, and write querries every time?\n",
"This function takes care of that in a way that is SQL-native, with the advantage of default values and auto formatting to spend more time analyzing data and less time writing querries!"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def do(query):\n",
" '''\n",
" Direct sql queries to a Pandas dataframe.\n",
" '''\n",
" disk_engine = sql.create_engine('sqlite:///NSF.db')\n",
" df = pd.read_sql_query(query, disk_engine)\n",
" \n",
" for col in df.columns:\n",
" if \"Date\" in col:\n",
" df[col] = pd.to_datetime(df[col])\n",
" return df"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"
\n",
" \n",
" \n",
" | \n",
" AwardID | \n",
" AwardTitle | \n",
" AwardEffectiveDate | \n",
" AwardExpirationDate | \n",
" AwardAmount | \n",
" InstitutionName | \n",
" Division | \n",
" Directorate | \n",
" EmailAddress | \n",
" InvestigatorNames | \n",
" NumInvestigators | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" 0 | \n",
" Regulation of Sn-Glycerol-3-Phosphate Metaboli... | \n",
" 1986-07-01 | \n",
" 1986-07-01 | \n",
" 0 | \n",
" Virginia Polytechnic Institute and State Unive... | \n",
" Molecular And Cellular Bioscience | \n",
" Biological Sciences | \n",
" tilarson@vt.edu | \n",
" Timothy Larson | \n",
" 1 | \n",
"
\n",
" \n",
" 1 | \n",
" 9 | \n",
" Design of Cutting Tools for High Speed Milling | \n",
" 2000-06-15 | \n",
" 2004-05-31 | \n",
" 280000 | \n",
" University of Florida | \n",
" Civil, Mechanical, & Manufact Inn | \n",
" Engineering | \n",
" jtlusty@ufl.edu | \n",
" Jiri Tlusty;Tony Schmitz;John Ziegert | \n",
" 3 | \n",
"
\n",
" \n",
" 2 | \n",
" 26 | \n",
" A Novel Ultrasonic Cooling Concept for Microel... | \n",
" 2000-06-15 | \n",
" 2004-05-31 | \n",
" 292026 | \n",
" North Carolina State University | \n",
" Electrical, Commun & Cyber Sys | \n",
" Engineering | \n",
" ro@ncsu.edu | \n",
" Paul Ro;Andrey Kuznetsov;Angus Kingon | \n",
" 3 | \n",
"
\n",
" \n",
" 3 | \n",
" 27 | \n",
" Development of a Wireless Sensor to Detect Cra... | \n",
" 2000-04-15 | \n",
" 2004-03-31 | \n",
" 238000 | \n",
" University of Texas at Austin | \n",
" Civil, Mechanical, & Manufact Inn | \n",
" Engineering | \n",
" neikirk@mail.utexas.edu | \n",
" Dean Neikirk;Sharon Wood;Karl Frank | \n",
" 3 | \n",
"
\n",
" \n",
" 4 | \n",
" 31 | \n",
" Development of Link-to-Column Connections for ... | \n",
" 2000-09-01 | \n",
" 2003-08-31 | \n",
" 285000 | \n",
" University of Texas at Austin | \n",
" Civil, Mechanical, & Manufact Inn | \n",
" Engineering | \n",
" mde@mail.utexas.edu | \n",
" Michael Engelhardt | \n",
" 1 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" AwardID AwardTitle \\\n",
"0 0 Regulation of Sn-Glycerol-3-Phosphate Metaboli... \n",
"1 9 Design of Cutting Tools for High Speed Milling \n",
"2 26 A Novel Ultrasonic Cooling Concept for Microel... \n",
"3 27 Development of a Wireless Sensor to Detect Cra... \n",
"4 31 Development of Link-to-Column Connections for ... \n",
"\n",
" AwardEffectiveDate AwardExpirationDate AwardAmount \\\n",
"0 1986-07-01 1986-07-01 0 \n",
"1 2000-06-15 2004-05-31 280000 \n",
"2 2000-06-15 2004-05-31 292026 \n",
"3 2000-04-15 2004-03-31 238000 \n",
"4 2000-09-01 2003-08-31 285000 \n",
"\n",
" InstitutionName \\\n",
"0 Virginia Polytechnic Institute and State Unive... \n",
"1 University of Florida \n",
"2 North Carolina State University \n",
"3 University of Texas at Austin \n",
"4 University of Texas at Austin \n",
"\n",
" Division Directorate \\\n",
"0 Molecular And Cellular Bioscience Biological Sciences \n",
"1 Civil, Mechanical, & Manufact Inn Engineering \n",
"2 Electrical, Commun & Cyber Sys Engineering \n",
"3 Civil, Mechanical, & Manufact Inn Engineering \n",
"4 Civil, Mechanical, & Manufact Inn Engineering \n",
"\n",
" EmailAddress InvestigatorNames \\\n",
"0 tilarson@vt.edu Timothy Larson \n",
"1 jtlusty@ufl.edu Jiri Tlusty;Tony Schmitz;John Ziegert \n",
"2 ro@ncsu.edu Paul Ro;Andrey Kuznetsov;Angus Kingon \n",
"3 neikirk@mail.utexas.edu Dean Neikirk;Sharon Wood;Karl Frank \n",
"4 mde@mail.utexas.edu Michael Engelhardt \n",
"\n",
" NumInvestigators \n",
"0 1 \n",
"1 3 \n",
"2 3 \n",
"3 3 \n",
"4 1 "
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = do(\"SELECT * FROM AWARDS\")\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"
\n",
" \n",
" \n",
" | \n",
" year | \n",
" Division | \n",
" AwardSum | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" 2016 | \n",
" Ocean Sciences | \n",
" 6287996066 | \n",
"
\n",
" \n",
" 1 | \n",
" 2016 | \n",
" Research On Learning | \n",
" 5199775389 | \n",
"
\n",
" \n",
" 2 | \n",
" 2016 | \n",
" Astronomical Sciences | \n",
" 4801812127 | \n",
"
\n",
" \n",
" 3 | \n",
" 2016 | \n",
" Materials Research | \n",
" 4727804645 | \n",
"
\n",
" \n",
" 4 | \n",
" 2016 | \n",
" Physics | \n",
" 4391902343 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" year Division AwardSum\n",
"0 2016 Ocean Sciences 6287996066\n",
"1 2016 Research On Learning 5199775389\n",
"2 2016 Astronomical Sciences 4801812127\n",
"3 2016 Materials Research 4727804645\n",
"4 2016 Physics 4391902343"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"q = \"\"\"\n",
"SELECT \n",
" strftime(\\'%Y\\',AwardEffectiveDate) as year,\n",
" Division,\n",
" SUM(AwardAmount) as 'AwardSum'\n",
"FROM AWARDS GROUP BY Division ORDER BY AwardSum DESC;\"\"\"\n",
"do(q).head()"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"
\n",
" \n",
" \n",
" | \n",
" year | \n",
" Directorate | \n",
" AwardSum | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" 2016 | \n",
" Mathematical & Physical Scien | \n",
" 20959270657 | \n",
"
\n",
" \n",
" 1 | \n",
" 2016 | \n",
" Geosciences | \n",
" 17517102569 | \n",
"
\n",
" \n",
" 2 | \n",
" 2017 | \n",
" Education And Human Resources | \n",
" 15754031311 | \n",
"
\n",
" \n",
" 3 | \n",
" 2016 | \n",
" Computer & Info Scie & Enginr | \n",
" 12113647384 | \n",
"
\n",
" \n",
" 4 | \n",
" 2016 | \n",
" Engineering | \n",
" 11317262347 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" year Directorate AwardSum\n",
"0 2016 Mathematical & Physical Scien 20959270657\n",
"1 2016 Geosciences 17517102569\n",
"2 2017 Education And Human Resources 15754031311\n",
"3 2016 Computer & Info Scie & Enginr 12113647384\n",
"4 2016 Engineering 11317262347"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"q = \"\"\"\n",
"SELECT \n",
" strftime(\\'%Y\\',AwardEffectiveDate) as year,\n",
" Directorate,\n",
" SUM(AwardAmount) as 'AwardSum'\n",
"FROM AWARDS GROUP BY 2 ORDER BY 3 DESC;\"\"\"\n",
"do(q).head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Analysis\n",
"Let's use Plotly and Seaborn for some Analysis"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plotly.offline.init_notebook_mode()\n",
"with open(\"/Users/leonyin/Documents/creds/plotly_credentials.json\",'r') as data_file:\n",
" '''\n",
" Get username credentials.\n",
" '''\n",
" creds = json.load(data_file)['credentials']\n",
" \n",
" plotly.tools.set_credentials_file(\n",
" username=creds['USER'],\n",
" api_key=creds['API_KEY']\n",
" )\n",
"with open(\"/Users/leonyin/Documents/creds/plotly_presets.json\",'r') as data_file:\n",
" '''\n",
" Load graphy presets.\n",
" '''\n",
" layout_dict = json.load(data_file)['layout']"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [],
"source": [
"def top_funds(df,groupby,n=5):\n",
" return df.groupby(groupby) \\\n",
" .apply(lambda x:x['AwardAmount'].sum()) \\\n",
" .sort_values(ascending=False) \\\n",
" .head(n)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"years = list(range(year_start,year_end))\n",
"top_n = 10"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"annual_funds = {}\n",
"annual_funds['Other'] = {}\n",
"\n",
"for year in years:\n",
" df = do(\"\"\"SELECT strftime(\\'%Y\\',AwardEffectiveDate) as year,\n",
" Division, SUM(AwardAmount) as 'AwardSum'\n",
" FROM AWARDS WHERE YEAR='{}'\n",
" GROUP BY Division ORDER BY AwardSum DESC\"\"\".format(year))\n",
"\n",
" annual_funds['Other'][year] = sum(df.AwardSum)\n",
" # get top 10 to plot\n",
" for i in range(top_n):\n",
" division = df.get_value(i,'Division')\n",
" _sum =df.get_value(i,'AwardSum')\n",
" annual_funds['Other'][year] -= _sum\n",
" if division in annual_funds:\n",
" annual_funds[division][year] = _sum\n",
" else:\n",
" annual_funds[division] = {year:_sum}\n",
"\n",
"tops = []\n",
"for division in top_funds(do(\"SELECT * FROM AWARDS\"),groupby='Division', n=top_n).keys():\n",
" tops.append({\n",
" 'x': years,\n",
" 'y': [annual_funds[division].get(year,None) for year in years],\n",
" 'name': division,\n",
" 'type': 'bar'\n",
" })\n",
"\n",
"py.iplot({\n",
" 'data': tops, \n",
" 'layout': {\n",
" 'barmode': 'stack',\n",
" 'xaxis': layout_dict['axis'],\n",
" 'yaxis': layout_dict['axis'],\n",
" 'plot_bgcolor': layout_dict['plot_bgcolor'],\n",
" 'title': '{} Highest Funded NSF Divisions'.format(top_n)\n",
" }})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The plot can be viewed here.\n",
"
Ocean sciences happen to be the highest funded division from the NSF.
\n",
"We can also look at how different divisions' funding varies interannual and interdivisionally."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df = do('SELECT * FROM ABSTRACTS '\n",
" 'LEFT JOIN AWARDS '\n",
" 'USING(AwardID)'\n",
" 'WHERE Abstract NOT NULL')\n",
"\n",
"df['year'] = df['AwardEffectiveDate'].dt.year\n",
"df['AwardDuration'] = (abs(((df['AwardExpirationDate'] - df['AwardEffectiveDate']).dt.days)) + 1) / 365\n",
"df['AwardAmountStandardized'] = df['AwardAmount'] / (df['NumInvestigators'] * df['AwardDuration'])\n",
"df.replace({'AwardAmountStandardized' : {np.nan : 0}}, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 272,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def division_history(divisions_to_compare, start=1990, end=2016, \n",
" agg_key='Division', num_bins=3):\n",
" \n",
" metric1 = 'AwardAmount'\n",
" metric2 = 'AwardAmountStandardized'\n",
" \n",
" df_plot = df[(df[agg_key].isin(divisions_to_compare)) &\n",
" (df['year'] >= start) & (df['year'] <= end)]\n",
" \n",
" fig, ax = plt.subplots(2,2, figsize=(18,12))\n",
"\n",
" # Top two plots\n",
" for label, df_div in df_plot.groupby([agg_key,'year'])[metric1].sum() \\\n",
" .reset_index().groupby(agg_key):\n",
" \n",
" df_div[[metric1, 'year']].plot(x='year', y=metric1, logy=False,\n",
" ax=ax[0][0], label=label,\n",
" title='Annual Funding per Division (USD)')\n",
" \n",
" for label, df_div in df_plot.groupby([agg_key,'year'])[metric2].mean() \\\n",
" .reset_index().groupby(agg_key):\n",
" \n",
" df_div[[metric2,'year']].plot(x='year', y=metric2, logy=False,\n",
" ax=ax[0][1], label=label,\n",
" title='Average Annual Funding Available '\n",
" 'per Investigator per Year of Research (USD)')\n",
" \n",
" # Bottom two plots\n",
" if len(divisions_to_compare) > 2:\n",
" # Density distro for more than two divisions\n",
" for label, df_div in df_plot[df_plot['year']==end].groupby(agg_key):\n",
" df_div['AwardDuration'].plot(kind='kde', logx=False, xlim=(0,10),\n",
" ax=ax[1][0], label=label, legend=True,\n",
" title='{} Distribution of '\n",
" 'Funding Length (Years)'.format(end))\n",
"\n",
" for label, df_div in df_plot[df_plot['year']==end].groupby(agg_key):\n",
" df_div['NumInvestigators'].plot(kind='kde', logx=False, xlim=(0,8), \n",
" ax=ax[1][1], label=label,\n",
" title='{} Distribution of Number of '\n",
" 'Research Collaborators'.format(end))\n",
" else:\n",
" # Violins for 2 or less\n",
" pd.options.mode.chained_assignment = None\n",
" \n",
" bins = np.linspace(start, end, num=num_bins+1) \n",
" labels = ['{:.0f}-{:.0f}'.format(\n",
" bins[i], bins[i+1]\n",
" ) for i in range(len(bins) - 1)]\n",
" \n",
" df_plot['YearRange'] = pd.cut(df['year'], bins, labels=labels)\n",
" \n",
" ax[1, 0].set_title('Distribution of Funding Length (Years)')\n",
" sns.violinplot(data=df_plot, y='YearRange', x='AwardDuration', hue=agg_key, \n",
" split=True, inner=None, ax=ax[1][0])\n",
" \n",
" ax[1, 1].set_title('Distribution of Research Collaborators Per Award')\n",
" g = sns.violinplot(data=df_plot, y='YearRange', x='NumInvestigators', hue=agg_key,\n",
" split=True, inner=None, ax=ax[1][1])\n",
" \n",
" g.set(ylabel='')\n",
" g.set(yticks=[])\n",
" ax[0, 1].legend_.remove()\n",
" ax[1, 0].legend_.remove()\n",
" ax[1, 1].legend_.remove()"
]
},
{
"cell_type": "code",
"execution_count": 1030,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"divisions_to_compare = ['Molecular And Cellular Bioscience', 'Social And Economic Sciences']"
]
},
{
"cell_type": "code",
"execution_count": 1170,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def get_history(full_name):\n",
" '''\n",
" Returns a PI-centric SQLite query to a Pandas dataframe.\n",
" '''\n",
" return do(\n",
" \"SELECT DISTINCT AwardID, AwardTitle, AwardEffectiveDate,\"\n",
" \"InstitutionName, Division, AwardAmount FROM AWARDS \"\n",
" \"INNER JOIN PI using(EmailAddress) \"\n",
" \"WHERE FirstName||' '||LastName = '{}' \"\n",
" \"ORDER BY AwardEffectiveDate ASC\".format(full_name)\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 1031,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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hhMhPEjApAN8v3s3ancfyNc3m11bi/26uk+t6HTvexNKlf9C1a3d27NjGAw88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O9wrSNdkk6n42upX8LPzZd/nfmYis4qh52roqOKX2c/SWFrMbND0vj3Rd8myidiTGMsjphPjE8U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\n8pP7oeXhA16sM2mlYJHsS4X/7Y6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Gw+P909/X67uBIiIiPZlcO6nHffLieYwvH5uDaERE\nRAafEiYiQ1x+Ip+zpp3Wp2OWTzqWFg2JFhGRLGqvtR73aalqIh6L97ifiIjIwUAJE5GDQEt1M/Mb\ne/ekmym17SxuWTDIEYmIyEhTkl/MhPJx3e7TVtOao2hEREQGnxImIgeJk6cczxE9rGdSX1rHWdNP\nw/O8HEUlIiIjyeS6A0/LiXkxptcfmsNoREREBpcSJiIHiZgXY8UhJ3JC+3H7/XxJy5FcPf9yCpOF\nOY5MRERGiim17Qf8bGrdZIrzi3IYjYiIyOBKRB2AiPSe53ksblnAuPIGXnr7Zd7duZWdHTuZPf5w\nmiobow5PRESGudHFNYwpreONzW++57PZ42dGEJGIiMjgUcJE5CDUUtVES1VT1GGIiMgI43keKw45\niS8/cNM+2ysKy2nVYuMiIjLMaEqOiIiIiPRaU+UEjpgwe59tx7QtIeapWykiIsOLRpiIiIiISJ8s\nn3Qsr7zzGsV5RZwweRn1pWOiDklERCTrlDARERERkT4pTBZw7cIrow5DRERkUGnspIiIiIiIiIhI\nBiVMREREREREREQyKGEiIiIiIiIiIpJBCRMRERERERERkQxKmIiIiIiIiIiIZFDCREREREREREQk\ngxImIiIiIiIiIiIZlDAREREREREREcng+b4fdQwiIiIiIiIiIkOKRpiIiIiIiIiIiGRQwkRERERE\nREREJIMSJiIiIiIiIiIiGZQwERERERERERHJoISJiIiIiIiIiEgGJUxERERERERERDIkog7gYGRm\npcB3gFIgCXzEOffHaKPKDTPzgC8D04AdwCXOuRejjSq3zCwBfB1oBPKAf3XO/XekQUXEzEYDfwaW\nOueeizqeKJjZx4CTCa4FX3bOfSPikHIq/D58i+D70AFcOpL+LZjZHOB/OeeWmFkL8E0gBTzpnLsy\n0uByJOMcTAduJPi3sBO4wDm3PtIAhyG1xWqL0430tnikt8Ogtlhtsdpi2PccpG07B7jKOTevv+Vq\nhEn/fBj4jXNuMbAK+FK04eTUqUB++I/ueuBzEccThfOAt5xzC4HjgS9GHE8kwsb5K8C2qGOJipkt\nAo4Ivw+LgXHRRhSJ44G4c24+8C/AZyKOJ2fM7O+Bm4D8cNPngBucc4uAmJmdEllwObKfc/B54Ern\n3FHArcDHooptmFNbrLYYUFusdngPtcVqi0d0W7yfc0CYOLpooGUrYdI/nwP+K3ydBLZHGEuuLQDu\nAHDOPQTMjDacSNwCfDx87QG7I4wlSp8F/hP4W9SBRGgZ8KSZ/Qz4BXBbxPFE4TkgEd7xLgN2RRxP\nLq0GVqS9P9w5d1/4+lfA0tyHlHOZ5+As59wT4esEI6t9zCW1xWqLu4z0tljtcEBt8V5qi0dmW7zP\nOTCzKoLE4TUDLVgJkx6Y2UVm9oSZPd71E5jonNtpZnXAtxkBWbs0pcCmtPcdZjai/h0557Y557aa\nWQnwI+Afo44p18xsJbDOOfdrgo7qSFUNHA68D7gC+F604UTiXaAJeJYgkXxjtOHkjnPuVoLhrl3S\nvwtbCDqtw1rmOXDOrQUws3nAlcC/RxTacKe2WG2x2uKA2uGA2uK91BaPwLY4/RyE7eFXgWuBrQzw\n+jiiGtf+cM593Tk31Tl3aNrPR8xsKvBr4GPOufujjjOHNgMlae9jzrlUVMFExczGAXcD33LO/TDq\neCKwCjjGzO4BpgM3h3OoR5oNwJ3OuY5wrvAOM6uOOqgcuxa4wzlnBOsp3GxmeRHHFJX0a2EJsDGq\nQKJkZmcRrK9xvHNuQ9TxDFNqi1FbjNpiUDvcRW3xXmqLGfFt8QyglWD03feBdjPr99RVJUz6wcwm\nEwwFPcc5d1fU8eTYHwjmSWJmc4Enut99+DGzWuBO4KPOuW9FHU8UnHOLnHNLwkWV/kqwmNS6qOOK\nwP3AcQBmVg+MIui8jSRvs/dO90aCoZ/x6MKJ1KNmtjB8vRy4r7udhyMzO4/gbtZi59wrUcczjKkt\nVlustjigdjigtngvtcUjuy32nHN/Dgc6HAWcDTztnPtwfwvUU3L65zMEC8p8IZwruNE5t6KHY4aL\nWwnuZvwhfL8qymAicj1QDnzczP4Z8IHlzrmd0YYVGT/qAKLinLvdzI40sz8RDPf7oHNupJ2PzwNf\nN7N7CdZ0ut45NxLmyu7PdcBNZpYEngF+HHE8ORUOgf0C8Apwq5n5wO+dc5+KNrJhSW2x2uJMI63t\nAdQOp1FbvJfa4pHdFmf9++/5/ki8poiIiIiIiIiIHJim5IiIiIiIiIiIZFDCREREREREREQkgxIm\nIiIiIiIiIiIZlDAREREREREREcmghImIiIiIiIiISAYlTEREREREREREMihhIiL9ZmaHmFnKzFYM\ncj0XmtnXw9e/M7PnzexRM3vCzB42s+VZrOuTZjY/fH2Tmc3IVtkiIiLSN2Y2IexrHJ2x/SUzGz/A\nsr9hZhcMLMJe1XOJmZ0Vvv6UmZ3Yz3L29FFEJDeUMBGRgVgF3AJclsM6feAi59wM59xU4HLg22Y2\nKUvlLwLiAM65S51zj2apXBEREemf3cBNZlaUts2PKph+mA/kAzjnPuGcu62f5ezpo4hIbiSiDkBE\nDk5mlgDOBRYAD5hZE7ACGO2c+5iZHQv8CKhwzqXM7GmChn4x8GGgACgELnHO3W9m9wBvA5OBs4Bp\nwD8Cm4BXgS1p1XtdL5xzj5jZD4FLgOvM7CVgkXPuVTNbBHzSObdkP+UvBM4DRgGpcNtsYCbwVTM7\nDfgP4BPOuXvN7Ibw9+0A7gI+CowHbgWeBA4D3gTOcM5tHPgZFhERkdDfgF8Dn2PvTRoPWGJmK51z\nSyAYMQLcA/we+BnwIjAV+DPwO2AlUA6scM659ArC/sO3gWUEfYMLCNr87zrnDg33ORG42Dm3wsz+\nATiT4Ab0nWHfpwT4PlAbFvtpYBtwchjrG8A5wD3OuZvN7EPAVcA7gANWO+c+bWZX0X0fZQWwC/h/\nQCXwLvChsE/0DaAKaCHoqywGlobl/Nw59+k+n32REUwjTESkv04EXnbOrSbolHwAuA3oGjJ7FEEn\nYYaZNQIbnXPrw/1OcM4dBvxv4O/TynzMOdcOrA8/WwAcAZT0EMuTwIFGmKTfgeoq/yWCzsuisBP0\nc+CDzrlvE3SqLnbOPdl1kJkdF/6+MwgSIxMJRrZAkNj5bDjaZRNBUkVERESyxwc+AizLmJrjc+CR\nJocCn3LOtQGzgAnOuXnADwj6Ivuz3jk3B/gv4Abn3BNAp5lNDj8/G/iOmS0DDidIYMwAxprZuQQ3\njl5yzs0CzgcWOOd+C/wC+Gfn3K+7KjKzqcAVBP2KhQR9C8KkS099lKeA7wCfd85NI7gR9RMzS4bF\nv+WcmwI8ASwP+1zzgFYzyzvA7y4i+6GEiYj010qCuygQjCRZRZCIKDWzcoJkx5cI7mwsB24P9z0N\nOM7MPhWWUZxW5kPhz3nAH5xzbznnUsb3/sMAAAPvSURBVASdgu74wPZexPwQgHNuC0Fi4/1m9hng\npIw4vIzjjga+75zbGcbzdfYmhtY65x4PXz9JcKdHREREssg59y5wKcHUnOKe9gfeSGuf1wC/DV+/\nAlQc4Jg7w5/p7fl3gbPNrIAgsfHfBCM2ZgOPAI8SJE8mA38ATjWzWwn6Qf/STXxLgducc1udczsJ\n+1S96aOEU5NanHM/D495CNgAWLhPV3/qdWCbmd0PXAv8k3NuVzcxiUgGJUxEpM/MrIYgCfIRM3sR\nuImg83EacAfBHZYUQadiEXAccFvYwP8JaCQYLnsj+yYnupIePvtenzp6COlQ4Om0Y7vKTGbstz2M\nfyzwIFAG/BL4Ju9NkqTLvFZ67J3SuCNte3rdIiIikkXhCI1fA/+XoM19iX3b6PR2PzMx0FNfAva2\n6ent+XeBMwhGmt4ZJhziBKM7ZoSjN+YA/+qce4FgxOt3gCOBh7upq5P9rEfSyz7K/v6Gi7G3b7Id\nwDnXCcwF/okgAfRHM2vtJiYRyaCEiYj0xwXAb5xz451zzc65RuAzBNNUbgduAO5zzj0GtANt4es2\nIOWc+wzBHOPl7H/xsvuBuWY2xsxiBHN398vMZgOnA18NN60HpoSvTznAYbOA551zXyDozKTH0cF7\n13e6m+BOT0G4dsuqcBsoQSIiIjLY0tva6wjWGRlDMKqi2czyzKySIEmxv2P6zTn3BvAacD17R7ze\nDZxvZkVhv+DnwPvM7Erg0865nwBXAjVmVsr++xa/BZabWXE4TeZ0gkRNj32UcBTKi2Z2KoCZzSVY\nN+XJ9ArMbDrBDap7nXMfJbi5ZIhIrylhIiL9cQHBdJt0XyJo5F8G6ggWVwP4C0FyBOAx4K9m5giG\nsW4BJoSf7ZmD7JxbB1xN0Jn4I8HaIOm+Gj5W+FGCu0xnOudeCz/7JHCjmT1EsIhal/Q5zncBcTN7\nCniA4A5VU/jZHcBXws6HH8ZzO0Ei6M8E84FfBr64n3JFREQk+9L7CFsIpubkESx2ejvwFPBD4N79\nHcOB2+re7APBYrDVzrnfhzHcBvyEYOrL48CjzrmbgZsBM7PHCRIVn3DObQZ+A9wQLijf1bd4imBx\n+QfDfTcTjAy5k971Uc4DrgnrupFgIduOjHP117CMp8zsz2FZv+rm9xSRDJ7vq68vIiIiIiKSK2Y2\nkWAR/M+H738G3BTepBGRIUKPFRYREREREcmtV4BZZvYEwbpvdypZIjL0aISJiIiIiIiIiEgGrWEi\nIiIiIiIiIpJBCRMRERERERERkQxKmIiIiIiIiIiIZFDCREREREREREQkgxImIiIiIiIiIiIZlDAR\nEREREREREcnw/wERgk6WemhVewAAAABJRU5ErkJggg==\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"division_history(divisions_to_compare, start=2000, num_bins=3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's look at a PI's history in more depth..."
]
},
{
"cell_type": "code",
"execution_count": 1171,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"
\n",
" \n",
" \n",
" | \n",
" AwardID | \n",
" AwardTitle | \n",
" AwardEffectiveDate | \n",
" InstitutionName | \n",
" Division | \n",
" AwardAmount | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" 1053407 | \n",
" CAREER: New Approaches for Ranking in Machine ... | \n",
" 2011-09-01 | \n",
" Massachusetts Institute of Technology | \n",
" Information & Intelligent Systems | \n",
" 480000 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" AwardID AwardTitle \\\n",
"0 1053407 CAREER: New Approaches for Ranking in Machine ... \n",
"\n",
" AwardEffectiveDate InstitutionName \\\n",
"0 2011-09-01 Massachusetts Institute of Technology \n",
"\n",
" Division AwardAmount \n",
"0 Information & Intelligent Systems 480000 "
]
},
"execution_count": 1171,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"get_history('Cynthia Rudin')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To compare funding history of multiple PIs, we can recycle the `get_history` for PI funding timeseries."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def trace(PI):\n",
" '''\n",
" Generates trace for a given PI\n",
" '''\n",
" def build_annotation(fund_history):\n",
" '''\n",
" Returns a list of break delimtied json-looking-strings for html rendering on the plotly plot.\n",
" More meta-data!\n",
" '''\n",
" A = [\"Award ID: {}\".format(str(award)) for award in fund_history['AwardID'].tolist()]\n",
" I = [\"Institution : {}\".format(uni) for uni in fund_history['InstitutionName'].tolist()]\n",
" M = [\"Award Amount: ${}\".format(str(award)) for award in fund_history['AwardAmount'].tolist()]\n",
" return ['
'.join([award, inst, mone]) for (award, inst, mone) in zip(A,I,M)]\n",
" \n",
" fund_history = get_history(PI)\n",
" \n",
" return Scatter(\n",
" x = pd.to_datetime(fund_history['AwardEffectiveDate'], format='%Y/%m/%d'),\n",
" y = fund_history['AwardAmount'].cumsum(),\n",
" text = build_annotation(fund_history),\n",
" name = PI,\n",
" connectgaps = True\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"py.iplot({\n",
" 'data': [trace('Scott Doney'), trace('Jorge Sarmiento'), trace('Hugh Ducklow')],\n",
" 'layout': \n",
" {'title': \"Marine Biogeochemists' NSF Funding History in USD 2000-2016\",\n",
" 'xaxis': layout_dict['axis'],\n",
" 'yaxis': layout_dict['axis'],\n",
" 'plot_bgcolor': layout_dict['plot_bgcolor']\n",
" }\n",
" })"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The plot can be viewed here."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"With the annotations, we can backtrack see see which awards were fullfilled at what institution. This provides an accurate track record into each scientist's funding history. Unfortunately this plot oversimplified an award as being given to one recipeient, when in reality these rewards are split into many slices and may last several years."
]
},
{
"cell_type": "code",
"execution_count": 1173,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def read_abstract(AwardID):\n",
" '''\n",
" Renders the abstract in HTML so we can learn more!\n",
" '''\n",
" abstract = do(\"SELECT * FROM ABSTRACTS \"\n",
" \"WHERE AwardID={}\".format(AwardID))\n",
" \n",
" return HTML(abstract['Abstract'].loc[0])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We see a 20M huge spike for Jorge Sarmiento on Sept 2014.
\n",
"We can quickly addess the abstract of using the awardID _1425989_."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"read_abstract(1425989)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can also split the data by Abstract topics.
\n",
"Currently this is only supported by substrings.
\n",
"An unsupervised learning algorithm might be interesting to use here."
]
},
{
"cell_type": "code",
"execution_count": 1024,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def topic_history(topic):\n",
" \n",
" abstract_df = do(\n",
" 'SELECT * FROM AWARDS '\n",
" 'INNER JOIN ( '\n",
" 'SELECT * FROM ABSTRACTS '\n",
" \"WHERE ABSTRACT LIKE '%{}%' \"\n",
" 'AND Abstract NOT NULL '\n",
" \"AND Abstract != 'None'\"\n",
" ') USING(AwardID)'.format(topic))\n",
" \n",
" return abstract_df"
]
},
{
"cell_type": "code",
"execution_count": 1175,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"
\n",
" \n",
" \n",
" | \n",
" AwardID | \n",
" AwardTitle | \n",
" AwardEffectiveDate | \n",
" AwardExpirationDate | \n",
" AwardAmount | \n",
" InstitutionName | \n",
" Division | \n",
" Directorate | \n",
" EmailAddress | \n",
" InvestigatorNames | \n",
" NumInvestigators | \n",
" Abstract | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" 9418652 | \n",
" Analysis of New Drill Core Samples from within... | \n",
" 1995-09-15 | \n",
" 1998-08-31 | \n",
" 119848 | \n",
" Lunar and Planetary Institute | \n",
" Earth Sciences | \n",
" Geosciences | \n",
" Buck.Sharpton@alaska.edu | \n",
" Virgil Sharpton;Benjamin Shuraytz | \n",
" 2 | \n",
" 9418652 Sharpton Recent analyses of drill core... | \n",
"
\n",
" \n",
" 1 | \n",
" 628302 | \n",
" Collaborative Research: Dynamics of carbon rel... | \n",
" 2006-09-15 | \n",
" 2009-08-31 | \n",
" 179989 | \n",
" Purdue University | \n",
" Earth Sciences | \n",
" Geosciences | \n",
" gabe.bowen@utah.edu | \n",
" Gabriel Bowen | \n",
" 1 | \n",
" Intellectual Merit: The ocean is the largest s... | \n",
"
\n",
" \n",
" 2 | \n",
" 628336 | \n",
" Collaborative Research: Dynamics of carbon rel... | \n",
" 2006-09-15 | \n",
" 2008-04-30 | \n",
" 307481 | \n",
" University of Wisconsin-Madison | \n",
" Earth Sciences | \n",
" Geosciences | \n",
" awinguth@uta.edu | \n",
" Arne Winguth | \n",
" 1 | \n",
" ABSTRACT<br/><br/>Intellectual Merit: The ocea... | \n",
"
\n",
" \n",
" 3 | \n",
" 628358 | \n",
" Collaborative Research: Dynamics of carbon rel... | \n",
" 2006-09-15 | \n",
" 2010-08-31 | \n",
" 240002 | \n",
" Yale University | \n",
" Earth Sciences | \n",
" Geosciences | \n",
" mark.pagani@yale.edu | \n",
" Mark Pagani | \n",
" 1 | \n",
" ABSTRACT<br/><br/>Intellectual Merit: The ocea... | \n",
"
\n",
" \n",
" 4 | \n",
" 628366 | \n",
" Collaborative Research: Dynamics of carbon rel... | \n",
" 2006-09-15 | \n",
" 2009-04-30 | \n",
" 179261 | \n",
" Williams College | \n",
" Earth Sciences | \n",
" Geosciences | \n",
" hstoll@geo.umass.edu | \n",
" Heather Stoll | \n",
" 1 | \n",
" ABSTRACT<br/><br/>Intellectual Merit: The ocea... | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" AwardID AwardTitle \\\n",
"0 9418652 Analysis of New Drill Core Samples from within... \n",
"1 628302 Collaborative Research: Dynamics of carbon rel... \n",
"2 628336 Collaborative Research: Dynamics of carbon rel... \n",
"3 628358 Collaborative Research: Dynamics of carbon rel... \n",
"4 628366 Collaborative Research: Dynamics of carbon rel... \n",
"\n",
" AwardEffectiveDate AwardExpirationDate AwardAmount \\\n",
"0 1995-09-15 1998-08-31 119848 \n",
"1 2006-09-15 2009-08-31 179989 \n",
"2 2006-09-15 2008-04-30 307481 \n",
"3 2006-09-15 2010-08-31 240002 \n",
"4 2006-09-15 2009-04-30 179261 \n",
"\n",
" InstitutionName Division Directorate \\\n",
"0 Lunar and Planetary Institute Earth Sciences Geosciences \n",
"1 Purdue University Earth Sciences Geosciences \n",
"2 University of Wisconsin-Madison Earth Sciences Geosciences \n",
"3 Yale University Earth Sciences Geosciences \n",
"4 Williams College Earth Sciences Geosciences \n",
"\n",
" EmailAddress InvestigatorNames \\\n",
"0 Buck.Sharpton@alaska.edu Virgil Sharpton;Benjamin Shuraytz \n",
"1 gabe.bowen@utah.edu Gabriel Bowen \n",
"2 awinguth@uta.edu Arne Winguth \n",
"3 mark.pagani@yale.edu Mark Pagani \n",
"4 hstoll@geo.umass.edu Heather Stoll \n",
"\n",
" NumInvestigators Abstract \n",
"0 2 9418652 Sharpton Recent analyses of drill core... \n",
"1 1 Intellectual Merit: The ocean is the largest s... \n",
"2 1 ABSTRACT
Intellectual Merit: The ocea... \n",
"3 1 ABSTRACT
Intellectual Merit: The ocea... \n",
"4 1 ABSTRACT
Intellectual Merit: The ocea... "
]
},
"execution_count": 1175,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"topic_history(\"ocean acidification\").head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Natural Language Processing\n",
"How can we leverage the abstracts to understand what each field within a Division is _all about_.
\n",
"We can count words within abstracts and visualize them with sizr proportional to frequency as a world cloud.\n",
"\n",
"First we need to ignore common words called stopwords."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def download_english_stopwords():\n",
" '''\n",
" Stopwords are words that don't give any unique insight\n",
" across different texts.\n",
" '''\n",
" stop_url = 'https://raw.githubusercontent.com/nltk/nltk_data/' \\\n",
" 'gh-pages/packages/corpora/stopwords.zip'\n",
" url = requests.get(stop_url)\n",
" zipfile = ZipFile(BytesIO(url.content))\n",
" with zipfile.open('stopwords/english', 'r') as f:\n",
" return f.read().decode('utf8').split('\\n')"
]
},
{
"cell_type": "code",
"execution_count": 173,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"stops = download_english_stopwords()"
]
},
{
"cell_type": "code",
"execution_count": 174,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['i', 'me', 'my', 'myself', 'we']"
]
},
"execution_count": 174,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"stops[:5]"
]
},
{
"cell_type": "code",
"execution_count": 210,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"stops += [\n",
" 'lab', 'abstract', 'project', 'career', 'research', \n",
" 'give', 'given', 'gives', 'giving', 'get', 'fund', \n",
" 'funding', 'funds', 'gain', 'gained', 'gaining', \n",
" 'gains', 'go', 'good', 'goods', 'great', 'greater', \n",
" 'greatest', 'greatly', 'high', 'higher', 'improve', \n",
" 'improves', 'improvement', 'improving', 'inrease', \n",
" 'increased', 'increasing', 'increasingly', 'know', \n",
" 'large', 'larger', 'largely', 'learn', 'learned', \n",
" 'learner', 'less', 'like', 'investigator', 'also', \n",
" 'university', 'student', 'students', 'undergraduate', \n",
" 'graduate', 'br', 'new', 'pi', 'one', 'two', 'three',\n",
" 'four', 'five', 'year', 'well', 'studies', 'things', \n",
" 'using', 'many', 'use', 'used','using',' users', \n",
" 'school', 'work', 'nsf', 'science', 'professor', \n",
" 'researcher', 'researchers'\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": 1015,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def display_cloud(words, title, exclude=list(), save=False): \n",
" wc = WordCloud(max_words=300, stopwords=stops + exclude, margin=10, background_color=\"white\",\n",
" random_state=1, scale=.75, width=800, height=400).generate(words)\n",
" \n",
" plt.title(title)\n",
" plt.imshow(wc.to_array())\n",
" plt.axis(\"off\")\n",
" plt.figure()\n",
" \n",
" if save:\n",
" wc.to_file(\"clouds/{}_cloud.png\".format(title.replace(' ', '_').lower()))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can compare most common terms across divisions."
]
},
{
"cell_type": "code",
"execution_count": 1032,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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pbmaWsZBH47cTFCFmGPMJiyjLk0uZYSxgnJa7RGBfU+dHXy6mtdOjrcOjvVPS1umxak2a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U0koRyOsWp5PrODSi3EArOSJi/JTKOEUs1Pu0zjKo95Rjkb3TZKNT/TjRKOtmrZ6I7s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\n/bO73/ezBNt2uDz8O9+85LqKCTO6Onv74crcawwaFTEoAlwR8SkSboh8D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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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AE2Mqv1+Y5Bd7l/Gjl+OENIHtSq7eL0bUp/B/T7Uyp8ZHfdIhkXO5fH4ZNSHvnW1XcuWC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xg6zJmh89zjhdx2fV5Wwt+TGDVC47e/zOefjxSy73QD126SPhECKsoUTj/RwwlZ3uf/kImzT/fx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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"for division in divisions_to_compare:\n",
" \n",
" words = df[(df['year'] == 2016) & (df['Division'] == division)] \\\n",
" .groupby('Division')['Abstract'] \\\n",
" .apply(lambda x: x.sum()).iloc[0]\n",
" \n",
" display_cloud(words, division, exclude=['data'], save=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can create a wordcloud for a specific topic (substring)"
]
},
{
"cell_type": "code",
"execution_count": 1033,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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RhRtFuFCFC792VI/ArrCxFtMOYcsklkxgt3sURcwNNCQeRxFOFOHGqZbj7RXs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5NJ75o1FqtWWRx8cPuJfE3S4verg5xU4yVkOfyQsLh/1NYVx1jSIeik+NZsYHr8Bz411vcqyKEX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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"topic = 'policy'\n",
"\n",
"words = topic_history(topic) \\\n",
" .groupby('Division')['Abstract'] \\\n",
" .apply(lambda x: x.sum()).iloc[0]\n",
"\n",
"display_cloud(words, topic, save=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Network Graphs\n",
"How are researchers connected?
\n",
"Let's make a network of collaborators based on who has been award grants together.\n",
"\n",
"Work in progress...\n",
"But a beautiful mistake!."
]
},
{
"cell_type": "code",
"execution_count": 1176,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# df['Division'].value_counts().iloc[:40]\n",
"def count_PIS(PI_STRING):\n",
" \"\"\"\n",
" split PI string\n",
" \"\"\"\n",
" PI_list = [x.rstrip().lstrip().title() for x in PI_STRING.split(';')]\n",
" \n",
" for PI in PI_list:\n",
" if PI_count.get(PI):\n",
" PI_count[PI]['count'] += 1\n",
" else:\n",
" PI_count[PI] = {'count': 1}\n",
"\n",
"def parse_PIS(PI_STRING):\n",
" \"\"\"\n",
" split PI string\n",
" \"\"\"\n",
" PI_list = [x.rstrip().lstrip().title() for x in PI_STRING.split(';')]\n",
" \n",
" if len(PI_list) > 1:\n",
" for PI in PI_list:\n",
" if PI_dict.get(PI):\n",
" # check if PI exists\n",
" continue\n",
" else:\n",
" # if PI not in dict, add it\n",
" PI_dict[PI] = dict()\n",
" \n",
" #check other PIs\n",
" remaining_PI = [x for x in PI_list if x != PI]\n",
" for sub_PI in remaining_PI:\n",
" # for PI already collaborated add one\n",
" if PI_dict[PI].get(sub_PI):\n",
" PI_dict[PI][sub_PI] += 1\n",
" else:\n",
" PI_dict[PI][sub_PI] = 1\n",
" else:\n",
" return\n",
"\n",
"PI_dict = dict()\n",
"PI_count = dict()\n",
"\n",
"# df[\n",
"# (df['Division'].isin(['Ocean Sciences'])) \n",
"# & (df['year'] >= 2000)\n",
"# ]['InvestigatorNames'].apply(parse_PIS)\n",
"\n",
"df[df['year'] >= 2000]['InvestigatorNames'].apply(parse_PIS)\n",
"\n",
"# df[(df['Division'].isin(['Ocean Sciences'])) & (df['year'] >= 2000)]['InvestigatorNames'].apply(count_PIS)\n",
"\n",
"df[df['year'] >= 2000]['InvestigatorNames'].apply(count_PIS)\n",
"\n",
"\n",
"# PI_exclude = [PI for (PI, count) in PI_count.items() if count <= 2]\n",
"\n",
"df_connection = pd.DataFrame([\n",
" {'source' : source.replace(' ', '_'), \n",
" 'target': target.replace(' ', '_'), \n",
" 'value': value} \n",
" for (source, targets) in PI_dict.items() \n",
" for (target, value) in targets.items()])\n",
"# if source not in PI_exclude \n",
"# or target not in PI_exclude])\n",
"\n",
"force_directed_graph = {}\n",
"force_directed_graph['links'] = df_connection.to_dict(orient='records')\n",
"force_directed_graph['nodes'] = [{'id': source.replace(' ', '_'), 'group': 1}\\\n",
" for source in df_connection['source'].append(df_connection['target']).unique()]\n",
"\n",
"with open('data_out/nsf.json', 'w') as fp:\n",
" json.dump(force_directed_graph, fp)"
]
},
{
"cell_type": "code",
"execution_count": 1184,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"PI_count"
]
},
{
"cell_type": "code",
"execution_count": 1180,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def get_edges(PI):\n",
" '''\n",
" look through dataframes for PI and all PIs who have shared an award with that PI.\n",
" '''\n",
" df_PI = df[df['InvestigatorNames'].str.contains(PI)]\n",
" PI_count[PI]['Children'] = set(';'.join(df_PI['InvestigatorNames'].tolist()).split(';'))"
]
},
{
"cell_type": "code",
"execution_count": 1183,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"[get_edges(PI) for PI in PI_count.keys()]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"1. check other PI's children for new subchildren\n",
"2. append other PI's child to subchildren\n",
"3. seach other children"
]
},
{
"cell_type": "code",
"execution_count": 1166,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def depth(first_PI, PI):\n",
" if not PI_count[PI].get('subChildren'):\n",
" PI_count[PI]['subChildren'] = set()\n",
" \n",
" diff_set = PI_count[PI]['Children'] - PI_count[first_PI]['subChildren']\n",
" if diff_set:\n",
" print(\"difference found between {} and {}\".format(first_PI, PI))\n",
" for new_PI in diff_set:\n",
" # add new PI to OG PI's subchildren\n",
" PI_count[first_PI]['subChildren'].update(set(new_PI))\n",
" # add OG PI to new PI's subchildren\n",
" PI_count[new_PI]['subChildren'].update(set(first_PI))\n",
" # recursive call\n",
" depth(first_PI, new_PI)\n",
" else: # base case\n",
" return"
]
},
{
"cell_type": "code",
"execution_count": 1090,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ex = set(';'.join(df[df['InvestigatorNames'].str.lower().str.contains('Sarah Gille'.lower())]['InvestigatorNames'].tolist()).split(';'))"
]
},
{
"cell_type": "code",
"execution_count": 1161,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'Alexa Griesel',\n",
" 'B. Gregory Mitchell',\n",
" 'David Sandwell',\n",
" 'Farooq Azam',\n",
" 'Janet Sprintall',\n",
" 'Jennifer MacKinnon',\n",
" 'John Orcutt',\n",
" 'Julie McClean',\n",
" 'Katherine Barbeau',\n",
" 'Matthew Mazloff',\n",
" 'Osmund Holm-Hansen',\n",
" 'Paul Henkart',\n",
" 'Sarah Gille',\n",
" 'Teresa Chereskin'}"
]
},
"execution_count": 1161,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"PI_count['Sarah Gille'].get('Children')"
]
},
{
"cell_type": "code",
"execution_count": 1158,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"for PI in PI_count.keys():\n",
" PI_count[PI]['subChildren'] = PI_count[PI]['Children']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"check each set of Children, append their Children to dict, and start generating graph\n",
"\n",
"AKA links, all permutations of investigators "
]
},
{
"cell_type": "code",
"execution_count": 1159,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'Alison Buchan',\n",
" 'Brynn Voy',\n",
" 'DeEtta Mills',\n",
" 'Diana Downs',\n",
" 'Eberhard Voit',\n",
" 'Elizabeth Fozo',\n",
" 'Erik Zinser',\n",
" 'Gladys Alexandre',\n",
" 'Jay Whelan',\n",
" 'Jennifer DeBruyn',\n",
" 'Laurie Richardson',\n",
" 'Michael Best',\n",
" 'Patrick Gillevet',\n",
" 'Shawn Campagna',\n",
" 'Steven Wilhelm'}"
]
},
"execution_count": 1159,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"PI_count['Shawn Campagna']['subChildren']"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 0
}