{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "\"Canada\n", "\n", "
\n", "\n", "\"NSERC\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": [ "
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NameDepartmentOrganizationIDInstitutionProvinceENCountryENFiscalYearAwardAmountProgramIDCommittee
16700NaNHead Office11846Pacific Institute for the Mathematical SciencesBritish ColumbiaCANADA199940373MISPJ103
16701NaNHead Office14687National Engineering WeekOntarioCANADA19997500MISPJ103
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" ], "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] [package-index-options] ...\r\n", " pip install [options] -r [package-index-options] ...\r\n", " pip install [options] [-e] ...\r\n", " pip install [options] [-e] ...\r\n", " pip install [options] ...\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": { "image/png": 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"text/plain": [ "" ] }, "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 }