{ "cells": [ { "cell_type": "markdown", "id": "41ce46ff", "metadata": { "editable": true, "slideshow": { "slide_type": "slide" }, "tags": [] }, "source": [ "# Welcome to GGR274\n", "\n", "- What is Data Science?\n", "- What is the role of Statistics in Data Science?" ] }, { "cell_type": "markdown", "id": "320af18d", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Where is Data Science Used in Society?\n", "\n", "### Questions\n", "\n", "- Is there a Bike Share bike available?\n", "\n", "- Will I experience another TTC delay today?\n", "\n", "- Will this person commit a crime in the future?\n", "\n", "- Does COVID-19 affect males more than females?\n", "\n", "- What movies might this person enjoy?\n", "\n", "- How many people attended Indian Day Schools in Canada?" ] }, { "cell_type": "markdown", "id": "c5abf094", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Bike Share Toronto\n", "\n", "**Is there a bike available at St. George St. /Hoskin Ave.?**\n", "\n", " \n", "\n" ] }, { "cell_type": "markdown", "id": "19a6a68c", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Visualization using maps \n", "\n", " **Don't worry about understanding the code today - it's complicated, but we will be learning as the course goes on** " ] }, { "cell_type": "code", "execution_count": 12, "id": "e77fafc5", "metadata": { "editable": true, "slideshow": { "slide_type": "slide" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "
Make this Notebook Trusted to load map: File -> Trust Notebook
" ], "text/plain": [ "" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import folium\n", "\n", "m = folium.Map(location=[43.664288100559816, -79.39800603825044], zoom_start =18)\n", "m" ] }, { "cell_type": "markdown", "id": "403f6401", "metadata": { "jp-MarkdownHeadingCollapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "## Bike Share data is ugly\n", "\n", "But, it can be made beautiful with a little bit of programming ..." ] }, { "cell_type": "markdown", "id": "9578440c", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "## Step 1\n", "\n", "- Read the data from into Python." ] }, { "cell_type": "markdown", "id": "dab7bb35", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "```\n", "{\"last_updated\":1637611674,\"ttl\":30,\"data\":{\"stations\":[{\"station_id\":\"7000\",\"name\":\"Fort York Blvd / Capreol Ct\",\"physical_configuration\":\"REGULAR\",\"lat\":43.639832,\"lon\":-79.395954,\"altitude\":0.0,\"address\":\"Fort York Blvd / Capreol Ct\",\"capacity\":35,\"rental_methods\":[\"KEY\",\"TRANSITCARD\",\"CREDITCARD\",\"PHONE\"],\"groups\":[],\"obcn\":\"647-643-9607\",\"nearby_distance\":500.0},{\"station_id\":\"7001\",\"name\":\"Wellesley Station Green P\",\"physical_configuration\":\"REGULAR\",\"lat\":43.66496415990742,\"lon\":-79.38355031526893,\"altitude\":0.0,\"address\":\"Yonge / Wellesley\",\"post_code\":\"M4Y 1G7\",\"capacity\":17,\"rental_methods\":[\"KEY\",\"TRANSITCARD\",\"CREDITCARD\",\"PHONE\"],\"groups\":[],\"obcn\":\"416-617-9576\",\"nearby_distance\":500.0},\n", "\n", "```" ] }, { "cell_type": "markdown", "id": "2a2bb792", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "🙀 Don't worry about understanding all the details for now ... " ] }, { "cell_type": "code", "execution_count": 2, "id": "f861c4d0", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "import pandas as pd\n", "\n", "station_url = 'https://tor.publicbikesystem.net/ube/gbfs/v1/en/station_information'\n", "stationinfo = pd.read_json(station_url)\n", "\n", "stationlist = stationinfo['data'].iloc[0]" ] }, { "cell_type": "markdown", "id": "451b4b3a", "metadata": {}, "source": [ "The first station data looks a bit better:" ] }, { "cell_type": "code", "execution_count": 3, "id": "6698ad6b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'station_id': '7000',\n", " 'name': 'Fort York Blvd / Capreol Ct',\n", " 'physical_configuration': 'REGULAR',\n", " 'lat': 43.639832,\n", " 'lon': -79.395954,\n", " 'altitude': 0.0,\n", " 'address': 'Fort York Blvd / Capreol Ct',\n", " 'capacity': 35,\n", " 'is_charging_station': False,\n", " 'rental_methods': ['KEY', 'TRANSITCARD', 'CREDITCARD', 'PHONE'],\n", " 'groups': [],\n", " 'obcn': '647-643-9607',\n", " 'nearby_distance': 500.0,\n", " '_ride_code_support': True,\n", " 'rental_uris': {}}" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stationlist[0]" ] }, { "cell_type": "markdown", "id": "bd4c91c5", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Step 2\n", "\n", "- A bit of programming is needed to **transform the data** into a format that can be displayed on the map above." ] }, { "cell_type": "code", "execution_count": 4, "id": "d885beb3", "metadata": { "scrolled": true, "slideshow": { "slide_type": "-" } }, "outputs": [ { "data": { "text/plain": [ "[{'type': 'Feature',\n", " 'geometry': {'type': 'Point', 'coordinates': [-79.395954, 43.639832]},\n", " 'properties': {'station_id': '7000',\n", " 'name': 'Fort York Blvd / Capreol Ct',\n", " 'capacity': 35}},\n", " {'type': 'Feature',\n", " 'geometry': {'type': 'Point',\n", " 'coordinates': [-79.38355031526893, 43.66496415990742]},\n", " 'properties': {'station_id': '7001',\n", " 'name': 'Wellesley Station Green P',\n", " 'capacity': 23}}]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# convert JSON format to GeoJSON format \n", "def featuredict(lon,lat, id1, name, capacity):\n", " dict = {\n", " \"type\": \"Feature\",\n", " \"geometry\": {\n", " \"type\": \"Point\",\n", " \"coordinates\": [lon,lat]\n", " },\n", " \"properties\": {\n", " \"station_id\": id1,\n", " \"name\": name,\n", " \"capacity\": capacity\n", " }\n", " }\n", " return(dict)\n", "\n", "\n", "# featuredict1 = create_station_list\n", "def create_station_list(l):\n", " m = []\n", " for x in range(0,len(l)):\n", " m.append(\n", " featuredict(\n", " l[x]['lon'],\n", " l[x]['lat'],\n", " l[x]['station_id'],\n", " l[x]['name'],\n", " l[x]['capacity']\n", " )\n", " )\n", " x += 1\n", " return(m)\n", "\n", "stations = create_station_list(stationlist)\n", "stations[0:2]" ] }, { "cell_type": "markdown", "id": "05cc9d7e", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Step 2 (Cont'd)\n", "\n", "- More data transformation ..." ] }, { "cell_type": "code", "execution_count": 5, "id": "ebc8212c", "metadata": { "slideshow": { "slide_type": "-" } }, "outputs": [ { "data": { "text/plain": [ "{'type': 'Feature',\n", " 'geometry': {'type': 'Point', 'coordinates': [-79.395954, 43.639832]},\n", " 'properties': {'station_id': '7000',\n", " 'name': 'Fort York Blvd / Capreol Ct',\n", " 'capacity': 35}}" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# convert stations list to GeoJSON format\n", "\n", "def add_GeoJSON_formatting(features):\n", " dict = {\n", " \"type\": \"FeatureCollection\",\n", " \"features\": features\n", " }\n", " return(dict)\n", "\n", "\n", "stations_geoj = add_GeoJSON_formatting(stations)\n", "stations_geoj['features'][0]" ] }, { "cell_type": "markdown", "id": "0b0899ce", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## A Data Science Application\n", "\n", "**Question:** How many bikes are available\n", "\n", "**Data:** TO Bike Share data\n", "\n", "**Communicate the results:** Visualize the data on a map where the bikes are located\n", "\n", "**Next Steps:** Predict how many bikes are available at hourly intervals" ] }, { "cell_type": "code", "execution_count": 6, "id": "3f943b82", "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/html": [ "
Make this Notebook Trusted to load map: File -> Trust Notebook
" ], "text/plain": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "folium.GeoJson(\n", " stations_geoj, \n", " name = \"Bikes\", \n", " tooltip = folium.features.GeoJsonTooltip(fields=['station_id','name','capacity'], localize=True)\n", ").add_to(m)\n", "m" ] }, { "cell_type": "markdown", "id": "71c5eb78", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## TTC Subway Delays" ] }, { "cell_type": "markdown", "id": "cd30d28b-2f7f-4321-99d3-f25f5fdec99d", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "id": "5c1219f2-51b7-4938-a193-530371d7ecf5", "metadata": { "editable": true, "slideshow": { "slide_type": "subslide" }, "tags": [] }, "source": [ "## Steps to Read the Public Data\n", "\n", "1. Extract data file locations." ] }, { "cell_type": "code", "execution_count": 7, "id": "13b38d3b-b4cf-4ea6-ab65-f0a1f0ae3c41", "metadata": { "editable": true, "scrolled": true, "slideshow": { "slide_type": "" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "https://ckan0.cf.opendata.inter.prod-toronto.ca/dataset/996cfe8d-fb35-40ce-b569-698d51fc683b/resource/3900e649-f31e-4b79-9f20-4731bbfd94f7/download/ttc-subway-delay-codes.xlsx\n", "https://ckan0.cf.opendata.inter.prod-toronto.ca/dataset/996cfe8d-fb35-40ce-b569-698d51fc683b/resource/ca43ac3d-3940-4315-889b-a9375e7b8aa4/download/ttc-subway-delay-readme.xlsx\n", "https://ckan0.cf.opendata.inter.prod-toronto.ca/dataset/996cfe8d-fb35-40ce-b569-698d51fc683b/resource/8ca4a6ed-5e7e-4b9d-b950-bf45e4b2fe20/download/ttc-subway-delay-jan-2014-april-2017.xlsx\n", "https://ckan0.cf.opendata.inter.prod-toronto.ca/dataset/996cfe8d-fb35-40ce-b569-698d51fc683b/resource/e2ee9f63-3130-4d6a-a259-ce79c9c2f1bc/download/ttc-subway-delay-may-december-2017.xlsx\n", "https://ckan0.cf.opendata.inter.prod-toronto.ca/dataset/996cfe8d-fb35-40ce-b569-698d51fc683b/resource/32bd0973-e83d-4df1-8219-c8c55cf34c6d/download/ttc-subway-delay-data-2018.xlsx\n", "https://ckan0.cf.opendata.inter.prod-toronto.ca/dataset/996cfe8d-fb35-40ce-b569-698d51fc683b/resource/1df6aace-fa16-40e9-a0d3-416a912bfaf1/download/ttc-subway-delay-data-2019.xlsx\n", "https://ckan0.cf.opendata.inter.prod-toronto.ca/dataset/996cfe8d-fb35-40ce-b569-698d51fc683b/resource/1ba66ead-cddf-453d-859d-b349d3286f02/download/ttc-subway-delay-2020.xlsx\n", "https://ckan0.cf.opendata.inter.prod-toronto.ca/dataset/996cfe8d-fb35-40ce-b569-698d51fc683b/resource/c6e4f5eb-6ed7-4db1-944f-87406faa5a09/download/ttc-subway-delay-2021.xlsx\n", "https://ckan0.cf.opendata.inter.prod-toronto.ca/dataset/996cfe8d-fb35-40ce-b569-698d51fc683b/resource/441143ca-8194-44ce-a954-19f8141817c7/download/ttc-subway-delay-2022.xlsx\n", "https://ckan0.cf.opendata.inter.prod-toronto.ca/dataset/996cfe8d-fb35-40ce-b569-698d51fc683b/resource/2fbec48b-33d9-4897-a572-96c9f002d66a/download/ttc-subway-delay-2023.xlsx\n" ] } ], "source": [ "import requests\n", "# Toronto Open Data is stored in a CKAN instance. It's APIs are documented here:\n", "# https://docs.ckan.org/en/latest/api/\n", "\n", "# To hit our API, you'll be making requests to:\n", "base_url = \"https://ckan0.cf.opendata.inter.prod-toronto.ca\"\n", "\n", "# Datasets are called \"packages\". Each package can contain many \"resources\"\n", "# To retrieve the metadata for this package and its resources, use the package name in this page's URL:\n", "url = base_url + \"/api/3/action/package_show\"\n", "params = { \"id\": \"ttc-subway-delay-data\"}\n", "package = requests.get(url, params = params).json()\n", "xlsx_list = []\n", "\n", "# To get resource data:\n", "for idx, resource in enumerate(package[\"result\"][\"resources\"]):\n", "\n", " # To get metadata for non datastore_active resources:\n", " if not resource[\"datastore_active\"]:\n", " url = base_url + \"/api/3/action/resource_show?id=\" + resource[\"id\"]\n", " resource_metadata = requests.get(url).json()['result']\n", " # From here, you can use the \"url\" attribute to download this file\n", " print(resource_metadata['url'])\n", " xlsx_list.append(resource_metadata['url'])\n" ] }, { "cell_type": "markdown", "id": "e580eb9c-c90d-47f0-9282-fcf3907d8fe3", "metadata": { "editable": true, "slideshow": { "slide_type": "subslide" }, "tags": [] }, "source": [ "2. Read the files of interest, say files for 2022 and 2023." ] }, { "cell_type": "code", "execution_count": 8, "id": "1696ce1d-5074-45a7-9e32-1a484493fb1e", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "https://ckan0.cf.opendata.inter.prod-toronto.ca/dataset/996cfe8d-fb35-40ce-b569-698d51fc683b/resource/441143ca-8194-44ce-a954-19f8141817c7/download/ttc-subway-delay-2022.xlsx\n", "https://ckan0.cf.opendata.inter.prod-toronto.ca/dataset/996cfe8d-fb35-40ce-b569-698d51fc683b/resource/2fbec48b-33d9-4897-a572-96c9f002d66a/download/ttc-subway-delay-2023.xlsx\n" ] }, { "data": { "text/html": [ "
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DateTimeDayStationCodeMin DelayMin GapBoundLineVehicle
02022-01-0115:59SaturdayLAWRENCE EAST STATIONSRDP00NSRT3023
12022-01-0102:23SaturdaySPADINA BD STATIONMUIS00NaNBD0
22022-01-0122:00SaturdayKENNEDY SRT STATION TOMRO00NaNSRT0
32022-01-0102:28SaturdayVAUGHAN MC STATIONMUIS00NaNYU0
42022-01-0102:34SaturdayEGLINTON STATIONMUATC00SYU5981
.................................
208772023-11-3001:57ThursdaySHEPPARD STATIONMUATC510NYU6066
208782023-11-3001:58ThursdayFINCH STATIONSUDP00NaNYU0
208792023-11-3006:29ThursdayBESSARION STATIONTUML70ESHP6186
208802023-11-3007:39ThursdayLESLIE STATIONPUOPO916WSHP6181
208812023-11-3012:31ThursdayDON MILLS STATIONSUO00NaNSHP0
\n", "

40777 rows × 10 columns

\n", "
" ], "text/plain": [ " Date Time Day Station Code Min Delay \\\n", "0 2022-01-01 15:59 Saturday LAWRENCE EAST STATION SRDP 0 \n", "1 2022-01-01 02:23 Saturday SPADINA BD STATION MUIS 0 \n", "2 2022-01-01 22:00 Saturday KENNEDY SRT STATION TO MRO 0 \n", "3 2022-01-01 02:28 Saturday VAUGHAN MC STATION MUIS 0 \n", "4 2022-01-01 02:34 Saturday EGLINTON STATION MUATC 0 \n", "... ... ... ... ... ... ... \n", "20877 2023-11-30 01:57 Thursday SHEPPARD STATION MUATC 5 \n", "20878 2023-11-30 01:58 Thursday FINCH STATION SUDP 0 \n", "20879 2023-11-30 06:29 Thursday BESSARION STATION TUML 7 \n", "20880 2023-11-30 07:39 Thursday LESLIE STATION PUOPO 9 \n", "20881 2023-11-30 12:31 Thursday DON MILLS STATION SUO 0 \n", "\n", " Min Gap Bound Line Vehicle \n", "0 0 N SRT 3023 \n", "1 0 NaN BD 0 \n", "2 0 NaN SRT 0 \n", "3 0 NaN YU 0 \n", "4 0 S YU 5981 \n", "... ... ... ... ... \n", "20877 10 N YU 6066 \n", "20878 0 NaN YU 0 \n", "20879 0 E SHP 6186 \n", "20880 16 W SHP 6181 \n", "20881 0 NaN SHP 0 \n", "\n", "[40777 rows x 10 columns]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import re\n", "import pandas as pd\n", "p = re.compile(\".+(2022|2023)\")\n", "df_list = []\n", "\n", "for x in xlsx_list:\n", " if p.match(x):\n", " print(x)\n", " df_list.append(pd.read_excel(x))\n", "\n", "ttc_delays = pd.concat(df_list)\n", "ttc_delays" ] }, { "cell_type": "markdown", "id": "f6b2fddc", "metadata": { "editable": true, "slideshow": { "slide_type": "slide" }, "tags": [] }, "source": [ "## What can we learn from the data?\n", "\n", "Plotting (with a bit of manipulation) usually helps us see patterns that we might not see otherwise." ] }, { "cell_type": "code", "execution_count": 9, "id": "fa2b0fc2-cf86-4341-80cb-86518c27d235", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "[Text(0.5, 0, 'Total Length of Delays (min)'), Text(0, 0.5, 'Top 15 Stations')]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import seaborn as sns\n", "ttc_delays_stn = ttc_delays.groupby(\n", " \"Station\" # station names need cleaning...\n", ").agg(\n", " {\"Min Delay\": \"sum\"}\n", ").reset_index().sort_values(\n", " \"Min Delay\", ascending = False\n", ").head(15)\n", "# print(ttc_delays_stn)\n", "g = sns.barplot(\n", " data = ttc_delays_stn,\n", " x = \"Min Delay\",\n", " y = \"Station\"\n", ")\n", "g.set(xlabel = \"Total Length of Delays (min)\", ylabel = \"Top 15 Stations\")" ] }, { "cell_type": "code", "execution_count": 10, "id": "59140615", "metadata": { "editable": true, "slideshow": { "slide_type": "subslide" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "[[,\n", " ],\n", " [Text(1, 0, 'Jan'), Text(12, 0, 'Dec')],\n", " Text(0.5, 0, '')]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ttc_delays[\"Year\"] = ttc_delays[\"Date\"].apply(lambda x: x.year)\n", "ttc_delays[\"Month\"] = ttc_delays[\"Date\"].apply(lambda x: x.month)\n", "ttc_delays_ymd = ttc_delays.groupby(\n", " [\"Year\", \"Month\"]\n", ").size().reset_index(name = \"Number of Delays\")\n", "\n", "g = sns.lineplot(\n", " data = ttc_delays_ymd,\n", " x = \"Month\",\n", " y = \"Number of Delays\",\n", " hue = \"Year\"\n", ")\n", "g.set(xticks = [1, 12], xticklabels = [\"Jan\", \"Dec\"], xlabel = \"\")" ] }, { "cell_type": "markdown", "id": "eeeb4198", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## How many people attended Federal Day Schools?\n" ] }, { "cell_type": "code", "execution_count": 11, "id": "598e6d95", "metadata": { "editable": true, "slideshow": { "slide_type": "slide" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import IFrame\n", "IFrame(\"https://indiandayschools.org/\", 1200,1000)" ] }, { "cell_type": "markdown", "id": "3e199e87", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "![](FederalDaySchools/cover_expertreport.png)" ] }, { "cell_type": "markdown", "id": "6cbd4c44", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "![](FederalDaySchools/title_expertreport.png)" ] }, { "cell_type": "markdown", "id": "f97f06da", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "![](FederalDaySchools/estimates_expertreport.png)" ] }, { "cell_type": "markdown", "id": "622ee42b", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "![](FederalDaySchools/datapg1_expertreport.png)" ] }, { "cell_type": "markdown", "id": "f7d8f745", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "![](FederalDaySchools/datapg2_expertreport.png)" ] }, { "cell_type": "markdown", "id": "748cbf17", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "![](FederalDaySchools/datapg3_expertreport.png)" ] }, { "cell_type": "markdown", "id": "ad472b24", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "\n", "![](FederalDaySchools/c-8149-00418.png)" ] }, { "cell_type": "markdown", "id": "44f749e3", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "![](FederalDaySchools/c-8171-00008.png)" ] }, { "cell_type": "markdown", "id": "35c37979", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Questions \n", "\n", "- What is the provenance of the data used in the expert report?" ] }, { "cell_type": "markdown", "id": "448aebb6", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- How does this data relate to the original documents?" ] }, { "cell_type": "markdown", "id": "f92f28dd", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- What were record retention policies of Day Schools?" ] }, { "cell_type": "markdown", "id": "8c45482b", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- If we don't know the data's provenance then it's not possible to assess the reliability of the total number of people that attended Day School from this data." ] } ], "metadata": { "celltoolbar": "Slideshow", "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.10.1" }, "latex_envs": { "LaTeX_envs_menu_present": true, "autoclose": true, "autocomplete": true, "bibliofile": "biblio.bib", "cite_by": "apalike", "current_citInitial": 1, "eqLabelWithNumbers": true, "eqNumInitial": 1, "hotkeys": { "equation": "Ctrl-E", "itemize": "Ctrl-I" }, "labels_anchors": false, "latex_user_defs": false, "report_style_numbering": false, "user_envs_cfg": false } }, "nbformat": 4, "nbformat_minor": 5 }