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@sfsheath
Last active March 2, 2019 14:41
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"https://gist.github.com/89eafc47971f0651144a79fe4e330bcb"
]
},
{
"cell_type": "code",
"execution_count": 155,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<Graph identifier=Nb2ff7a3b4270479195e18bd568f2cb1b (<class 'rdflib.graph.Graph'>)>"
]
},
"execution_count": 155,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import io\n",
"import json\n",
"import pandas as pd\n",
"from pyld import jsonld\n",
"import rdflib\n",
"import urllib\n",
"\n",
"%matplotlib inline\n",
"\n",
"import matplotlib\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# rdf prefixes/namespaces\n",
"ns = {\"dcterms\" : \"http://purl.org/dc/terms/\",\n",
" \"owl\" : \"http://www.w3.org/2002/07/owl#\",\n",
" \"rdf\" : \"http://www.w3.org/1999/02/22-rdf-syntax-ns#\",\n",
" \"rdfs\" : \"http://www.w3.org/2000/01/rdf-schema#\",\n",
" \"ramphsont\": \"http://purl.org/roman-amphitheaters/ontology#\"}\n",
"\n",
"# uncomment and run these line to load from github.\n",
"#url = \"https://raw.githubusercontent.com/sfsheath/roman-amphitheaters/master/roman-amphitheaters.geojson\"\n",
"#data = io.BytesIO(urllib.request.urlopen(url).read()).read() \n",
"#jsonstr = json.loads(data.decode(\"utf-8\"))\n",
"\n",
"# load from file in same directory\n",
"f = open('roman-amphitheaters.geojson').read()\n",
"jsonstr = json.loads(f)\n",
"\n",
"# expects the jsonstr variable to have been set above\n",
"nquads = jsonld.normalize(jsonstr, {'algorithm': 'URDNA2015', 'format': 'application/nquads'})\n",
"\n",
"# hackish call to .replace() to turn nquads into ntriples. necessary?\n",
"nt = nquads.replace(\" <http://purl.org/roman-amphitheaters/> .\",\" .\")\n",
"\n",
"g = rdflib.Graph()\n",
"g.parse(data = nt, format = \"nt\")"
]
},
{
"cell_type": "code",
"execution_count": 156,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 156,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# construct result set suitable for conversion to pandas DataFrame or other 'row and column' oriented datatype\n",
"result = g.query(\n",
" \"\"\"SELECT ?ramph ?title ?label ?type ?capacity ?pleiades ?moderncountry ?latintoponym ?province ?region\n",
" WHERE {\n",
" # for predicates that really should exist, a problem if they don't\n",
" ?ramph rdfs:isDefinedBy [ dcterms:title ?title ;\n",
" rdfs:label ?label ;\n",
" ramphsont:type ?type ;\n",
" ramphsont:pleiades ?pleiades ;\n",
" ramphsont:moderncountry ?moderncountry ;\n",
" ramphsont:chronogroup ?chronogroup ] .\n",
" \n",
" # for predicates that might not exist due to incomplete data entry or not known/necessary\n",
" OPTIONAL { ?ramph rdfs:isDefinedBy/ramphsont:capacity/ramphsont:quantity ?capacity }\n",
" OPTIONAL { ?ramph rdfs:isDefinedBy/ramphsont:province ?province }\n",
" OPTIONAL { ?ramph rdfs:isDefinedBy/ramphsont:region ?region }\n",
" OPTIONAL { ?ramph rdfs:isDefinedBy/ramphsont:latintoponym ?latintoponym }\n",
"\n",
" }\"\"\", initNs = ns)\n",
"len(result) == 259 # keep this current with number of known amphitheaters and the like."
]
},
{
"cell_type": "code",
"execution_count": 157,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# convert to pandas.DataFrame. What is the best/fastest/most pythonic way to do this?\n",
"\n",
"tmpids, tmptitles, tmplabels, tmptypes, tmpcapacities, tmppleiades, tmpmoderncountries, tmplatintoponyms, tmpprovinces, tmpregions = ([] for i in range(10))\n",
"\n",
"for row in result:\n",
" tmpids.append(str(row.ramph).replace('http://purl.org/roman-amphitheaters/id/',''))\n",
" tmptitles.append(row.title)\n",
" tmplabels.append(row.label)\n",
" tmptypes.append(row.type)\n",
" tmpcapacities.append(row.capacity)\n",
" tmppleiades.append(str(row.pleiades))\n",
" tmpmoderncountries.append(row.moderncountry)\n",
" tmplatintoponyms.append(row.latintoponym)\n",
" tmpprovinces.append(row.province)\n",
" tmpregions.append(row.region)\n",
" \n",
"ramphs = pd.DataFrame({\"id\": tmpids,\n",
" \"title\": tmptitles,\n",
" \"label\": tmplabels,\n",
" \"type\": tmptypes,\n",
" \"capacity\": tmpcapacities,\n",
" \"moderncountry\": tmpmoderncountries,\n",
" \"latintoponym\": tmplatintoponyms,\n",
" \"pleiades\": tmppleiades,\n",
" \"province\": tmpprovinces,\n",
" \"region\": tmpregions\n",
" })"
]
},
{
"cell_type": "code",
"execution_count": 158,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x115f96438>"
]
},
"execution_count": 158,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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CAgIRERFBAYGIiIiggEBEREToQ0BgZp81sxvNbLaZPWZmF5vZJrl1ljGz081shpk9Y2Y/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\nI7MS0ZxrLmkL+WBglfT3FODHZnZOnnFEQpADab/4LLxr2iCTDtzEotM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"text/plain": [
"<matplotlib.figure.Figure at 0x11548b278>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ramphs['provinces_and_italy'] = None\n",
"ramphs['provinces_and_italy'][ramphs.region.notnull() ] = \"italia\"\n",
"ramphs['provinces_and_italy'][ramphs.region.isnull() ] = ramphs['province']\n",
"ramphs.groupby('provinces_and_italy')['provinces_and_italy'].count().sort_values().plot.bar()"
]
},
{
"cell_type": "code",
"execution_count": 159,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x117b01e80>"
]
},
"execution_count": 159,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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EBQQiIiKCAgIRERFBAYGIiIiggEBERERQQCAiIiL0ISAws8PM7Doze9zMHjSz881svdw6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\nTjRx4oUAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x114c33710>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ramphs.groupby('provinces_and_italy')['provinces_and_italy'].count().plot.bar()"
]
},
{
"cell_type": "code",
"execution_count": 160,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x117bde2b0>"
]
},
"execution_count": 160,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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ZjuW1r0vjeSspJadGp+vzDUraRiPiDODXgRN6i/YB52bmhb37DwYyM/cv8xTt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\nMl/2NAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x117be44a8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# barchart by augustan region of italy\n",
"ramphs.groupby('region')['region'].count().sort_values().plot.bar()"
]
},
{
"cell_type": "code",
"execution_count": 161,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# create a dataframe that is just the rows that are NaN in both province and region\n",
"bothnull = ramphs[ramphs['province'].isnull() & ramphs['region'].isnull()]"
]
},
{
"cell_type": "code",
"execution_count": 162,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>moderncountry</th>\n",
" <th>latintoponym</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"Empty DataFrame\n",
"Columns: [id, moderncountry, latintoponym]\n",
"Index: []"
]
},
"execution_count": 162,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# display id, modcountry\n",
"pd.options.display.max_rows = 999\n",
"bothnull[['id','moderncountry','latintoponym']].sort_values('moderncountry')"
]
},
{
"cell_type": "code",
"execution_count": 163,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Series([], dtype: int64)"
]
},
"execution_count": 163,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bothnull[['moderncountry']].groupby('moderncountry').size()"
]
}
],
"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": 1
}
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