{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<img src=\"http://www.nserc-crsng.gc.ca/_gui/wmms.gif\" alt=\"Canada logo\" align=\"right\">\n",
"\n",
"<br>\n",
"\n",
"<img src=\"http://www.triumf.ca/sites/default/files/styles/gallery_large/public/images/nserc_crsng.gif?itok=H7AhTN_F\" alt=\"NSERC logo\" align=\"right\" width = 90>\n",
"\n",
"\n",
"\n",
"# Exploring NSERC Awards Data\n",
"\n",
"\n",
"Canada's [Open Government Portal](http://open.canada.ca/en) includes [NSERC Awards Data](http://open.canada.ca/data/en/dataset/c1b0f627-8c29-427c-ab73-33968ad9176e) from 1995 through 2016.\n",
"\n",
"The awards data (in .csv format) were copied to an [Amazon Web Services S3 bucket](http://docs.aws.amazon.com/AmazonS3/latest/dev/UsingBucket.html). This open Jupyter notebook shows the large trends over the data and compares entire selections with each other.\n",
"\n",
"> **Acknowledgement:** I thank [Ian Allison](https://github.com/ianabc) and [James Colliander](http://colliand.com) of the [Pacific Institute for the Mathematical Sciences](http://www.pims.math.ca/) for building the [JupyterHub service](http://syzygy.ca) and for help with this notebook. -- I. Heisz"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1995\n",
"1996\n",
"1997\n",
"1998\n",
"1999\n",
"2000\n",
"2001\n",
"2002\n",
"2003\n",
"2004\n",
"2005\n",
"2006\n",
"2007\n",
"2008\n",
"2009\n",
"2010\n",
"2011\n",
"2012\n",
"2013\n",
"2014\n",
"2015\n",
"2016\n"
]
},
{
"data": {
"text/plain": [
"Index(['Name', 'Department', 'OrganizationID', 'Institution', 'ProvinceEN',\n",
" 'CountryEN', 'FiscalYear', 'AwardAmount', 'ProgramID', 'Committee'],\n",
" dtype='object')"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import sys\n",
"\n",
"## Bring in a selection of the NSERC awards data starting with 1995 and ending with 2016.\n",
"## Throw away as much as you can to keep the DataFrame small enough to manipulate using a laptop.\n",
"\n",
"df = pd.DataFrame()\n",
"\n",
"startYear = 1995\n",
"endYear = 2017 # The last year is not included, so if it was 2017 it means we include the 2016 collection but not 2017.\n",
"\n",
"for year in range(startYear, endYear):\n",
" file = 'https://s3.ca-central-1.amazonaws.com/open-data-ro/NSERC/NSERC_GRT_FYR' + str(year) + '_AWARD.csv.gz'\n",
" df = df.append(pd.read_csv(file, \n",
" compression='gzip', \n",
" usecols = [1, 2, 3, 4, 5, 7, 9, 11, 12, 17], \n",
" encoding='latin-1'\n",
" )\n",
" )\n",
" print(year)\n",
" \n",
"## Rename columns for better readability.\n",
"df.columns = ['Name', 'Department', 'OrganizationID',\n",
" 'Institution', 'ProvinceEN', 'CountryEN',\n",
" 'FiscalYear', 'AwardAmount', 'ProgramID',\n",
" 'Committee'] \n",
"\n",
"## Strip out any leading or trailing whitespace\n",
"df.columns.str.strip()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"DataFrame: 207.20 Mb\n"
]
}
],
"source": [
"## Quantify data stored in memory.\n",
"print(\"DataFrame: {:4.2f} Mb\".format(sys.getsizeof(df) / (1024. * 1024)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Check the data"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Name 457894\n",
"Department 457677\n",
"OrganizationID 457896\n",
"Institution 457896\n",
"ProvinceEN 454115\n",
"CountryEN 457896\n",
"FiscalYear 457896\n",
"AwardAmount 457896\n",
"ProgramID 457896\n",
"Committee 457896\n",
"dtype: int64\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Name</th>\n",
" <th>Department</th>\n",
" <th>OrganizationID</th>\n",
" <th>Institution</th>\n",
" <th>ProvinceEN</th>\n",
" <th>CountryEN</th>\n",
" <th>FiscalYear</th>\n",
" <th>AwardAmount</th>\n",
" <th>ProgramID</th>\n",
" <th>Committee</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>16700</th>\n",
" <td>NaN</td>\n",
" <td>Head Office</td>\n",
" <td>11846</td>\n",
" <td>Pacific Institute for the Mathematical Sciences</td>\n",
" <td>British Columbia</td>\n",
" <td>CANADA</td>\n",
" <td>1999</td>\n",
" <td>40373</td>\n",
" <td>MISPJ</td>\n",
" <td>103</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16701</th>\n",
" <td>NaN</td>\n",
" <td>Head Office</td>\n",
" <td>14687</td>\n",
" <td>National Engineering Week</td>\n",
" <td>Ontario</td>\n",
" <td>CANADA</td>\n",
" <td>1999</td>\n",
" <td>7500</td>\n",
" <td>MISPJ</td>\n",
" <td>103</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Name Department OrganizationID \\\n",
"16700 NaN Head Office 11846 \n",
"16701 NaN Head Office 14687 \n",
"\n",
" Institution ProvinceEN \\\n",
"16700 Pacific Institute for the Mathematical Sciences British Columbia \n",
"16701 National Engineering Week Ontario \n",
"\n",
" CountryEN FiscalYear AwardAmount ProgramID Committee \n",
"16700 CANADA 1999 40373 MISPJ 103 \n",
"16701 CANADA 1999 7500 MISPJ 103 "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# missing column info check\n",
"columnAmounts = df.count()\n",
"print(str(columnAmounts))\n",
"\n",
"#\n",
"missingData = pd.DataFrame()\n",
"missingData.append(df.loc[(df['Name'].isnull())])\n",
"#missingData.append(df.loc[(df['Department'].isnull())])\n",
"#missingData.append(df.loc[(df['ProvinceEN'].isnull()) & (df['CountryEN'] == 'CANADA')])\n",
"\n",
"# \n",
"# df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Information about the Distribution of Wealth Among Researcher's Funding"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# If you do more than one year at a time you risk crashing the kernel, \n",
"# the distribution is very similar year to year.\n",
"year = 2016 # Year you want to select for\n",
"selectedYearData = df.loc[(df['FiscalYear'] == year)] # Gets the data for the selected year\n",
"\n",
"# The sum of all awards that each individual has received (during the selected year)\n",
"individualAwardSum = selectedYearData.groupby(['Name'])['AwardAmount'].transform('sum')\n",
"\n",
"# Make a new column to show each researchers total awards received\n",
"selectedYearData = selectedYearData.assign(TotalAward = individualAwardSum)\n",
"\n",
"# Drop the duplicate names, now that we have the award amount info from them.\n",
"deduplicatedData = selectedYearData.drop_duplicates(subset = 'Name')\n",
"\n",
"# Sort by total award for the given year\n",
"byAwardAmount = deduplicatedData.sort_values(by=['TotalAward'], ascending=[True])"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The mean of AwardAmount is 38118.8182775\n",
"The median of AwardAmount is 21000.0\n",
"The standard deviation of AwardAmount is 141524.555172\n"
]
}
],
"source": [
"## Statistical information about a specific column in the data\n",
"\n",
"# Change this value to get information about the column you are interested in\n",
"columnYouWantInformationOn = 'AwardAmount'\n",
"\n",
"mean = df[columnYouWantInformationOn].mean()\n",
"print('The mean of ' + str(columnYouWantInformationOn) + ' is ' + str(mean))\n",
"\n",
"median = df[columnYouWantInformationOn].median()\n",
"print('The median of ' + str(columnYouWantInformationOn) + ' is ' + str(median))\n",
"\n",
"standardDeviation = df[columnYouWantInformationOn].std()\n",
"print('The standard deviation of ' + str(columnYouWantInformationOn) + ' is ' + str(standardDeviation))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Wealth Distribution Plot"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r\n",
"Usage: \r\n",
" pip install [options] <requirement specifier> [package-index-options] ...\r\n",
" pip install [options] -r <requirements file> [package-index-options] ...\r\n",
" pip install [options] [-e] <vcs project url> ...\r\n",
" pip install [options] [-e] <local project path> ...\r\n",
" pip install [options] <archive url/path> ...\r\n",
"\r\n",
"no such option: --user:\r\n"
]
}
],
"source": [
"## Imports For Plotting\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.ticker as mtick\n",
"!pip3 install plotly --user: #Plotly for interactive graphing\n",
"import plotly.plotly as py\n",
"import plotly.graph_objs as go"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
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a2tfMrKsZ+9I7HHvFE02um794SZPLW7L5gOreHUC+O4TtgQ+Ba0jDXT8YEfOr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\nA5wQzMws44RgZmYA/H88fnl8+j7biwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fb3c35ed320>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"## Note: this is a static plot with hardcoded variables which are not meant to be changed.\n",
"## It is also processing a lot of data so it may take a minute.\n",
"\n",
"axes = plt.gca() # creates the axes\n",
"\n",
"## y axis\n",
"yAxis = 'AwardAmount'\n",
"y = byAwardAmount[yAxis]\n",
"y = y/10**6 # scales y\n",
"plt.ylabel(yAxis+' In Millions', fontsize=18) # y axis label\n",
"\n",
"## x axis\n",
"x = range(byAwardAmount.shape[0])\n",
"plt.xlabel('IndividualResearchers', fontsize=16) # x axis label\n",
"axes.set_xticklabels([]) # make it so the x axis is not numbered\n",
"\n",
"plt.title(str(year) + ' NSERC Funding Distribution') # plot title\n",
"plot = plt.scatter(x, y, s=1, alpha=0.3) # make the plot\n",
"plt.show() # draw the plot"
]
}
],
"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": "3.6.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}