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Mali","cells":[{"id":4,"value":"# 🙋‍♀️ Gender bias in politics: the cases of Singapore and Mali","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":530,"value":"This project aims to be an **explorational tool** to explore **political gender bias**. The exploration has two parts: \n1. Size of female representation (regional and international) and\n2. In-depth analysis into Singapore data and what that tells us about gender bias\n\nWe will be narrowing our focus on **active political participation**, specifically, candidates running for election to Parliament/candidates who were elected.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":551,"value":"

Caveat on missing data

\nThe initial goal was to have in-depth comparison of both Singapore and Mali. However, both countries do not have official datasets on candidates and information on candidates who ran in their latest election. This lack of data itself represents the challenges that we confront when we want to study gender bias. It is an additional barrier to studying such an issue.\n\nFor this analysis and project, the team built our own dataset for Singapore's latest elections in 2020. Some information could not be found online, and this was listed as 'unknown'. There is no counterpart dataset for Mali as much of the data couldn't be found online.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":540,"value":"

The Data

\n1. [IPU data for international countries](https://data.ipu.org/women-ranking?month=3&year=2023) & IPU [historical data](https://data.ipu.org/historical-women)\n2. [Gender quotas dataset](https://www.idea.int/data-tools/data/gender-quotas/database)\n3. Singapore 🇸🇬 [2020 elections candidates dataset](https://docs.google.com/spreadsheets/d/1JDY4aQ11uXGZtzR7HabSSoT5WuFMyk-ri80tQAs4G3k/edit#gid=1577871075) we built from scratch","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":555,"value":"await FileAttachment(\"generalElectionCandidates20201.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":569,"value":"

Part #1: Analysing female representation across regions and internationally

\n\n**Overall female representation in Parliament has improved over the years. However, the change is not even across countries** \n\nFurther in this section, we'll also see if quotas have impacted the proportion of female representation.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":579,"value":"In the charts below, Singapore and Mali will be shown in red for each of their respective region. The lines aren't the neatest as different countries have different election cycles and, sometimes, gaps in election periods.","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1104,"value":"Use the dropdown option to highlight another country. Hover over to compare any country of interest.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1041,"value":"### Proportion of female Members of Parliament in **Asia**","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":620,"value":"viewof countryAsia = Inputs.select([... new Set(ipuHistoricalFiltered.filter(d => d.region === \"ASIA\").map(d => d.country))], {label: \"Country\", multiple: false})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":736,"value":"Asia = lineChart(ipuHistoricalFiltered.filter(d => d[\"region\"] == \"ASIA\"), {\n x: d => +d.year,\n y: d => d[\"percentage-women\"],\n z: d => d.country,\n title: d => d.country + \", \"+ d[\"percentage-women\"] + \"%\",\n fixedZ: \"Singapore\",\n dropDownZ: countryAsia,\n yLabel: \"Proportion (%) - Single or Lower Chamber\",\n yDomain: [0,100],\n width,\n height: 500,\n color: (z) => z === countryAsia ? \"cyan\" : z === \"Singapore\" ? \"red\" : \"#ddd\",\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1069,"value":"### Proportion of female Members of Parliament in **Sub-Saharan Africa**","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1077,"value":"viewof countryAfrica = Inputs.select([... new Set(ipuHistoricalFiltered.filter(d => d.region === \"SUB-SAHARAN\").map(d => d.country))], {label: \"Country\", multiple: false})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1054,"value":"Africa = lineChart(ipuHistoricalFiltered.filter(d => d[\"region\"] == \"SUB-SAHARAN\"), {\n x: d => +d.year,\n y: d => d[\"percentage-women\"],\n z: d => d.country,\n title: d => d.country + \", \"+ d[\"percentage-women\"] + \"%\",\n fixedZ: \"Mali\",\n dropDownZ: countryAfrica,\n yLabel: \"Proportion (%) - Single or Lower Chamber\",\n yDomain: [0,100],\n width,\n height: 500,\n color: (z) => z === countryAfrica ? \"cyan\" : z === \"Mali\" ? \"red\" : \"#ddd\",\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1415,"value":"Across regions, most countries have 20% or less female representation in Parliament. The pace of progress is varied; some like Madagascar and Singapore slowly increasing their proportion, while others like Cambodia have seen a decline.\n\nThe biggest strides are seen amongst African countries, with Rwanda leading the way and even obtaining a female majority. This was brought about by a number of special measures, including a 2003 Constitution that set a 30% quota for women in elected positions.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":720,"value":"

So do quotas work?

Part #2: Guided in-depth analysis into Singapore's 🇸🇬 political gender bias

","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":71,"value":"We'll be analysing the proportion of female candidates running for Singapore Parliament, the nations decision making body. For context: All genders in the database are either male (M) or female (F). Singapore has no candidate who's openly non-binary. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":74,"value":"**Female candidates are a minority, forming about a fifth of a total of 192 candidates.**
\nThe disparity is wider when we apply an intersectionality lens and filter for minority race representation i.e. non-Chinese","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":102,"value":"viewof race = Inputs.select([... new Set(gecandidates_race.map(d => d.race))], {label: \"Race\", multiple: false})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":122,"value":"overallProp.legend(\"color\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":147,"value":"overallProp = Plot.plot({\n x: {\n percent: true\n },\n marks: [\n Plot.barX(gecandidates_race.filter(d => d.race === race), Plot.stackX(Plot.groupZ({x: \"proportion\", title: (d) => `Actual count: ${d.length}`}, {fill: \"gender\"}))),\n Plot.ruleX([0, 1])\n ], color: { scheme: \"pastel2\"}\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":139,"value":"Moreover, the **spread of female candidates is uneven**. Some political parties remain male-only.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":128,"value":"Plot.plot({ \n y: {\n grid: true,\n percent: true\n },\n marks: [\n Plot.barY(polParty, {y: \"genderProp\", x: \"party-2020\", title: (d) =>\n `${d[\"party-2020\"]} \\n Percentage: ${Math.round(d.genderProp*100)}% \\n Actual count: ${d.count}`, fill: \"gender\",sort:{x:\"y\", reverse:true}}),\n Plot.ruleY([0])\n ], color: { scheme: \"pastel2\"}\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":379,"value":"Low female proportion also plays out among elected members of parliament who are also **political office holders**.\nIn fact, out of 27 female elected candidates, only 3 of them are cabinet members. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":340,"value":"Plot.plot({ marginLeft: 200,\n y: { domain: ['Prime Minister','Deputy Prime Minister','Senior Minister','Minister', 'Senior Minister of State', 'Minister of State','Senior Parliamentary Secretary'] },\n marks: [\n Plot.barX(position, {x: \"count\", y: \"highest_pos_if_pol_appointee\", title: \"count\", fill: \"gender\", }),\n Plot.ruleX([0]),\n ], color: { scheme: \"pastel2\"}, \n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":199,"value":"**Most candidates are married and many use this martial status to show themselves as a \"family person\" in their campaigns.** However, a higher proportion of female candidates are single, relative to male candidates (even if we were to assume that those with \"unknown\" status are also legally single). This suggests that as a female, being married is something that costs them more than males.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":227,"value":"addTooltips(Plot.plot({\n x: {\n percent: true\n }, label: \"Proportion for female candidates (%)\",\n marks: [\n Plot.barX(generalElectionCandidates20201.filter(d => d.gender === \"F\"), Plot.stackX(Plot.groupZ({x: \"proportion\", title: d => `Actual count: ${d.length}`}, {fill: \"relationship-status\"}))),\n Plot.text(generalElectionCandidates20201.filter(d => d.gender === \"F\"), Plot.stackX(Plot.groupZ({x: \"proportion\", text: \"first\"}, {z: \"relationship-status\", text: \"relationship-status\"}))),\n Plot.ruleX([0, 1])\n ], color: { scheme: \"pastel2\"}\n}))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":234,"value":"Plot.plot({\n x: {\n percent: true\n }, label: \"Proportion for male candidates (%)\",\n marks: [\n Plot.barX(generalElectionCandidates20201.filter(d => d.gender === \"M\"), Plot.stackX(Plot.groupZ({x: \"proportion\", title: d => `Actual count: ${d.length}`}, {fill: \"relationship-status\"}))),\n Plot.text(generalElectionCandidates20201.filter(d => d.gender === \"M\"), Plot.stackX(Plot.groupZ({x: \"proportion\", text: \"first\"}, {z: \"relationship-status\", text: \"relationship-status\"}))),\n Plot.ruleX([0, 1])\n ], color: { scheme: \"pastel2\"}\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":652,"value":"Compared to male candidates, more female candidates do not have children. This could also imply that there is gender bias as the weight/responsibility of having children for female is more than male.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":644,"value":"Plot.plot({\n x: {\n percent: true\n }, label: \"Proportion of male candidates with children (%)\",\n marks: [\n Plot.barX(generalElectionCandidates20201.filter(d => d.gender === \"M\"), Plot.stackX(Plot.groupZ({x: \"proportion\", title: d => `Actual count: ${d.length}`}, {fill: \"children\"}))),\n Plot.text(generalElectionCandidates20201.filter(d => d.gender === \"M\"), Plot.stackX(Plot.groupZ({x: \"proportion\", text: \"first\"}, {z: \"children\", text: \"children\"}))),\n Plot.ruleX([0, 1])\n ], color: { scheme: \"pastel2\"}\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":646,"value":"Plot.plot({\n x: {\n percent: true\n }, label: \"Proportion of female candidates with children (%)\",\n marks: [\n Plot.barX(generalElectionCandidates20201.filter(d => d.gender === \"F\"), Plot.stackX(Plot.groupZ({x: \"proportion\", title: d => `Actual count: ${d.length}`}, {fill: \"children\"}))),\n Plot.text(generalElectionCandidates20201.filter(d => d.gender === \"F\"), Plot.stackX(Plot.groupZ({x: \"proportion\", text: \"first\"}, {z: \"children\", text: \"children\"}))),\n Plot.ruleX([0, 1])\n ], color: { scheme: \"pastel2\"}\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":330,"value":"**A greater proportion of female candidates have backgrounds in charity/social service relative to male candidates.** But the top professional background is business for both male and female candidates. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":254,"value":"Plot.plot({\n marginLeft: 200,\n //facet: {data: background, y: \"gender\", marginRight: 80}, \n y: {domain: d3.sort(background, d => -d.count).map(d => d.background)},\n grid: true,\n marks: [\n Plot.barX(background, {x: \"count\", y: \"background\", title: \"count\", fill: \"gender\"}),\n Plot.ruleX([0]),\n ], color: { scheme: \"pastel2\"}, \n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":78,"value":"**Ministry portfolio of members of parliament who are also political office holders**: No female representation in Defence, but a relatively higher female representation (40% vs 33%) in Home Affairs (policing and internal security), National Development (national land use planning and development), and Sustainability and the Environment (environment, and water and food supplies)\n\nJosephine Teo is the Minister-in-charge of Smart Nation and Cybersecurity, the only area that's owned by a female MP. This is related to her duties as Minister of Communications and Information.\n\nNote: Economic Policies, National Security, and Social Policies do not refer to ministries, but to areas overseen by the three Senior Ministers as part of special appointments.\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":397,"value":"Plot.plot({ marginLeft: 200,\n marks: [\n Plot.barX(portfolio, {x: \"count\", y: \"portfolio\", fill: \"gender\", title:\"count\", sort:{y:\"x\", reverse:true}}),\n Plot.ruleX([0]),\n ], color: { scheme: \"pastel2\"}, \n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1313,"value":"

Part #3: Guided in-depth analysis into Mali's political gender bias

","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1481,"value":"numbersByRegions3 = await FileAttachment(\"Numbers by Regions@3.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"numbersByRegions3","database":"$undefined"},{"id":1489,"value":"legislativesInRegions1 = await FileAttachment(\"Legislatives in Regions@1.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"legislativesInRegions1","database":"$undefined"},{"id":1585,"value":"legislativesByParties21 = await FileAttachment(\"Legislatives by parties 2@1.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"legislativesByParties21","database":"$undefined"},{"id":1563,"value":"ageRanges1 = await FileAttachment(\"Age ranges @1.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"ageRanges1","database":"$undefined"},{"id":1645,"value":"**In this section, we analyze female participation in Mali's legislative election from 1997 to 2020**. For this period, we collected data on the number of elected PMs, their regions in Mali, their age range, and their parties.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1654,"value":"Among the 9 regions of Mali, between 1997 and 2020, **Segou is the region with the most female PMs, followed by Bamako, and Koulikoro**. The population of Timbuktu, Gao, and Kidal, in the northern region of Mali, are practically not represented by women. Kayes has emerged, in the West, after the application of the quota system (**Law 2015-052**).","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1620,"value":"Plot.plot({\ny: {\n grid: true,\n},\n marks: [\n Plot.barY(numbersByRegions3, {x: \"Regions\", y: \"Number of elected Women\", fill: \"Grey\", title: \"Number of elected Women\", sort:{x:\"y\", reverse:true}}),\n Plot.ruleY([0])],\n tooltip: {\n fill: \"Green\",\n stroke: \"yellow\",\n r: 8\n }\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1486,"value":"num_women_regions1 = FileAttachment(\"Num_Women_Regions@1.png\").image()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1657,"value":"What we found interested in the graph below is the surge in women participation in the 2020. The reason is the application of the 30% quota in Mali. In fact, between 1997 and 2018 the average female participation in the parliament was about 10%. **In the 2020 it surged to 28.5%, a record in Mali**.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1498,"value":"Plot.plot({\n y: {\n grid: true\n },\n marks: [\n Plot.line(legislativesInRegions1, {x: \"Years\", y: \"Number of elected Women\", z: \"Regions\", title: \"Regions\"})\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1661,"value":"When it comes to the parties elected women belong to, we noticed that **ADEMA-PASJ, RPM, and URD are the ones that most inclusive**.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1591,"value":"Plot.plot({\n marginLeft: 100,\n marks: [\n Plot.barX(legislativesByParties21, {x: \"Counts\", y: \"Parties\", title: \"Years\", fill: \"Years\", sort:{y:\"x\", reverse:true}})\n ],\n tooltip: {\n fill: \"red\",\n stroke: \"blue\",\n r: 8\n },\ncolor: {\n type: \"linear\", scheme: \"Blues\", legend: true\n}\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1639,"value":"Inputs.table(ageRanges1)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":323,"value":"## Appendix (Imports)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":95,"value":"import { aq, op } from \"@uwdata/arquero\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":468,"value":"import {Plot} from \"@mkfreeman/plot-tooltip\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":693,"value":"import {addTooltips} from \"@mkfreeman/plot-tooltip\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":609,"value":"ipuHistorical = FileAttachment(\"ipu-historical-data@1.csv\").csv()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":630,"value":"ipuHistoricalFiltered = aq.from(ipuHistorical).filter(aq.escape( d => [\"Lower\", \"Single\"].includes(d[\"chamber-type\"]) ) ).derive({year: aq.escape(d => new Date(d.year,0,1))}).objects()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":611,"value":"ipuHistoricalbyCountry = aq.from(ipuHistorical).filter(d => d[\"chamber-type\"] == \"Lower\").groupby(\"country\").objects()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":747,"value":"ipuQuota = FileAttachment(\"ipu-quota-lowerhouse@6.csv\").csv()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":756,"value":"arr = [\"Yes\", \"No\"]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":944,"value":"selectedRegion = ipuQuota.filter(d => d.region===Region);","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":564,"value":"generalElectionCandidates20201 = FileAttachment(\"generalElectionCandidates20201.csv\").csv()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":701,"value":"gecandidates_race = aq.from(generalElectionCandidates20201).derive({race: d => d.race === \"Indo-Aryan\" ? \"Other\" : d.race === \"Indian-Chinese\" ? \"Indian\" : d.race}).objects()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1220,"value":"polParty = aq.from(generalElectionCandidates20201).select(\"party-2020\",\"gender\").groupby(\"party-2020\", \"gender\").count().groupby(\"party-2020\").derive({genderProp: d => d.count/op.sum(d.count)}).orderby(\"party-2020\", \"gender\")","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":252,"value":"background = aq.from(generalElectionCandidates20201).select(\"gender\", \"background\").groupby(\"background\",\"gender\").count().groupby(\"background\").pivot(\"gender\",\"count\").impute({ \"F\": () => 0 }).fold(['F', 'M'], { as: ['gender', 'count'] }).objects()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":336,"value":"position = aq.from(generalElectionCandidates20201).filter(d => d[\"Political-appointee\"] == \"Y\").select(\"gender\", \"highest_pos_if_pol_appointee\").groupby(\"highest_pos_if_pol_appointee\",\"gender\").count().groupby(\"highest_pos_if_pol_appointee\").pivot(\"gender\",\"count\").impute({ \"F\": () => 0 }).fold(['F', 'M'], { as: ['gender', 'count'] }).objects()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":387,"value":"portfolio = aq.from(generalElectionCandidates20201).filter(d => d[\"Political-appointee\"] == \"Y\").select(\"gender\", \"portfolio\").derive({ portfolio: d => op.split(d.portfolio, '_')}).unroll(\"portfolio\").derive({ portfolio: d => op.trim(d.portfolio)}).groupby(\"portfolio\",\"gender\").count().groupby(\"portfolio\").pivot(\"gender\",\"count\").impute({ \"F\": () => 0, \"M\": () => 0}).fold(['F', 'M'], { as: ['gender', 'count'] }).objects()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":734,"value":"$47","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1328,"value":"style = html`\n\n\n\n`","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W48","stdlib":"1"}} 45:"$44:body"