2f:T7f4,plot = Plot.plot({ height: 600, width: 700, style: {fontFamily:"Martian Mono", backgroundColor:"transparent"}, y: {label:null, ticks:false, domain:[-8,8]}, //adjust square rect color palette marks: [ //annotation for Aretha Franklin /*Plot.text(annotations, {x: 2019, y:-7.5, fontSize: 11.5, fill: "rgb(108, 108, 108)", text: (d)=> "2019\nStevie Nicks \nfirst woman to be\ninducted twice"}), Plot.text(annotations, {x: 1991.5, y:-5.2, fontSize: 11.5, fill: "rgb(108, 108, 108)", textAnchor:"start", text: (d)=> "1990. Carole King \nfirst woman to win\nAhmet Ertegun Award"}), Plot.text(annotations, {x: 1987, y:-7.2, fontSize: 11.5, fill: 'rgb(108, 108, 108)', text: (d)=> "1987\nAretha Franklin \nfirst woman inducted"}),*/ //square tiles Plot.rect(stacked_data2, {x1: (d) => d.Released_Year -0.45, x2: (d) => d.Released_Year + 0.45, y1: (d) => d.class_index - 0.45, y2: (d) => d.class_index + 0.45, title: (d) => d.Released_Year + '\n' + d.movie_title + '\nInclusivity Score: ' + d.inclusivity_score, fill: (d) => getColorScheme(d.inclusivity_score)(d.inclusivity_score), tip:true}), //mark for category (if added) Plot.axisX({label: null, fontSize:15, tickSize:0, tickFormat: d3.format("d")}), //image for Aretha Franklin - circle crop added with CSS end of notebook /*Plot.dot(annotations, {x: "class_year", y:"y2", r:30, fill:"white"}), Plot.image(annotations, {x: "class_year", y:"y2", src:"small", width:50})*/ ] })30:T4a3,horizontal_swarm = Plot.plot({ height: 400, width: 800, marginBottom: 50, className: "horizontal-swarm", x: { tickSize: 0, label: 'Movie Inclusivity Score', type: "linear" }, y: {ticks:false}, marks: [ Plot.dot(stacked_data.filter(d => d.Released_Year >= date_filter[0] && d.Released_Year <= date_filter[1]), Plot.dodgeY({ r: 9, anchor: "middle", title : "movie_title", padding: 2, fill: (d) => d[selected_index] >= 5 ? inclusive_color : non_inclusive_color, x: selected_index, })), Plot.dot(stacked_data.filter(d => d.Released_Year >= date_filter[0] && d.Released_Year <= date_filter[1]), Plot.dodgeY( Plot.pointer({ r: 9, anchor: "middle", title : d=> d.movie_title + '\n' + d.Released_Year + '\nScore: ' + d[selected_index], padding: 2, fill: '#167C80', tip: "xy", x: selected_index}) )), Plot.ruleX(stacked_data.filter(d => d.Released_Year >= date_filter[0] && d.Released_Year <= date_filter[1]), Plot.groupZ({ x: "median" }, { x: selected_index, stroke: "#393939", strokeWidth: 2.5, strokeDasharray: [5, 5], })), ], });31:T702,stacked_data2 = { const dataZ = []; const grouped_years = d3.group(merged_df1, d => d.Released_Year); debugger; grouped_years.forEach((yearData, index) => { const yearDataSingle = []; const grouped_movies = d3.group(yearData, d => d.movie_title); grouped_movies.forEach((movie, id) => { yearDataSingle.push(movie[0]) }); const positive_movies = yearDataSingle.filter((d) => d.inclusivity_score >= threshold); const negative_movies = yearDataSingle.filter((d) => d.inclusivity_score < threshold); var sorted_negative_data = []; var sorted_positive_data = []; if(positive_movies.length == 1) { sorted_positive_data = [positive_movies[0]]; } else if (positive_movies.length > 1) { sorted_positive_data = d3.sort(positive_movies, (a, b) => d3.ascending(a.inclusivity_score, b.inclusivity_score)); } if(negative_movies.length == 1) { sorted_negative_data = [negative_movies[0]]; } else if (negative_movies.length > 1) { sorted_negative_data = d3.sort(negative_movies, (a, b) => d3.descending(a.inclusivity_score, b.inclusivity_score)); } sorted_positive_data.forEach((movie_data, index) => { dataZ.push({ movie_title: movie_data.movie_title, inclusivity_score: movie_data.inclusivity_score, class_index: index + 1, Released_Year: movie_data.Released_Year }); }); sorted_negative_data.forEach((movie_data, index) => { dataZ.push({ movie_title: movie_data.movie_title, inclusivity_score: movie_data.inclusivity_score, class_index: -index, Released_Year: movie_data.Released_Year }); }); }); return dataZ; }32:T4fc,select t1.Released_Year,t1.Gender,(t1.conteig/t2.conteig_total) as percentatge_total from (SELECT DISTINCT Released_Year, CASE WHEN "Gender" = 'mostly_male' THEN 'male' WHEN "Gender" = 'mostly_female' THEN 'female' WHEN "Gender" = 'unknown' THEN 'male' WHEN "Gender" = 'andy' THEN 'male' else "Gender" END AS Gender, COUNT(*) AS conteig FROM merged_df1 GROUP BY Released_Year, CASE WHEN "Gender" = 'mostly_male' THEN 'male' WHEN "Gender" = 'mostly_female' THEN 'female' WHEN "Gender" = 'unknown' THEN 'male' WHEN "Gender" = 'andy' THEN 'male' else "Gender" END)t1 left join ( select Released_Year,sum(conteig) as conteig_total from (SELECT DISTINCT Released_Year, CASE WHEN "Gender" = 'mostly_male' THEN 'male' WHEN "Gender" = 'mostly_female' THEN 'female' WHEN "Gender" = 'unknown' THEN 'male' WHEN "Gender" = 'andy' THEN 'male' else "Gender" END AS Gender, COUNT(*) AS conteig FROM merged_df1 GROUP BY Released_Year, CASE WHEN "Gender" = 'mostly_male' THEN 'male' WHEN "Gender" = 'mostly_female' THEN 'female' WHEN "Gender" = 'unknown' THEN 'male' WHEN "Gender" = 'andy' THEN 'male' else "Gender" END) group by Released_Year )t2 on t1.Released_Year=t2.Released_Year33:[{"id":"b7afd34aa9062f9decec2ce5efccf5e7503cfd72676199cbeb0859a9836dbd063f5f2b34e7295c171c03659f2785a1d62a608f039ccc50bce306abc4f2e36f1f","href":"https://static.observableusercontent.com/files/b7afd34aa9062f9decec2ce5efccf5e7503cfd72676199cbeb0859a9836dbd063f5f2b34e7295c171c03659f2785a1d62a608f039ccc50bce306abc4f2e36f1f","name":"merged_df (1).csv","type":"text/csv","lastModified":1706970405242.702,"size":140768},{"id":"665c74b5b3c65e5b7008031ddc038444e20ea617232c29b7e36823d73cba5f7a9d9fe406671d0e698994718a30c16c941c2ccbfbfa9812c697c426316501a472","href":"https://static.observableusercontent.com/files/665c74b5b3c65e5b7008031ddc038444e20ea617232c29b7e36823d73cba5f7a9d9fe406671d0e698994718a30c16c941c2ccbfbfa9812c697c426316501a472","name":"crew_members_count.csv","type":"text/csv","lastModified":1706978353934.607,"size":964},{"id":"5589ca90df15c58daaa8572dcb0ddd616cb7ca078012e7877619b3b356ac056ff668d68956d476c5e10c70c7fd81f7462ab7ad1ee95f062641245a834ed25175","href":"https://static.observableusercontent.com/files/5589ca90df15c58daaa8572dcb0ddd616cb7ca078012e7877619b3b356ac056ff668d68956d476c5e10c70c7fd81f7462ab7ad1ee95f062641245a834ed25175","name":"output_file.csv","type":"text/csv","lastModified":1706980287608.415,"size":46471},{"id":"4dd593a26200a9458b60067ff3881cbd4b37c158109503c7fb2e7735d5804bf89f1e0cf3af722800ba0633400f6b434b4ec080cce941f4b626a64bd75c9f7820","href":"https://static.observableusercontent.com/files/4dd593a26200a9458b60067ff3881cbd4b37c158109503c7fb2e7735d5804bf89f1e0cf3af722800ba0633400f6b434b4ec080cce941f4b626a64bd75c9f7820","name":"merged_df (2).csv","type":"text/csv","lastModified":1706980939127.428,"size":142979},{"id":"975023ef53c9c2b610ed0e23ddc6d675cb5add51c7f8f6d7f2fef597362fa71cabb258b96a06e300fba1c76fe3b38f19926eed790abd6d43d84811ae5ff4a94f","href":"https://static.observableusercontent.com/files/975023ef53c9c2b610ed0e23ddc6d675cb5add51c7f8f6d7f2fef597362fa71cabb258b96a06e300fba1c76fe3b38f19926eed790abd6d43d84811ae5ff4a94f","name":"merged_df (3).csv","type":"text/csv","lastModified":1706982897550.887,"size":146910},{"id":"987ebca91e791da357f3c5c7d27afb9462d889339037f41998b533e595ffab52e6b6635f94e32f83618e389acafa22a4c112901241a34853c00567e4a6945c89","href":"https://static.observableusercontent.com/files/987ebca91e791da357f3c5c7d27afb9462d889339037f41998b533e595ffab52e6b6635f94e32f83618e389acafa22a4c112901241a34853c00567e4a6945c89","name":"Best Picture nominees 2004 - 2024 - Placheholder@1.csv","type":"text/csv","lastModified":1706960496228.158,"size":5980},{"id":"19f4458e54f58b0f336b92112885ae952d507a3d29b31995db574069dc48eaebf312f143b0ac1b0ae3729a2222158f54491f4966dde75bb376f0602b5bc34df7","href":"https://static.observableusercontent.com/files/19f4458e54f58b0f336b92112885ae952d507a3d29b31995db574069dc48eaebf312f143b0ac1b0ae3729a2222158f54491f4966dde75bb376f0602b5bc34df7","name":"title_basics_2004_2023.csv","type":"text/csv","lastModified":1706970146489.796,"size":14084}] 2d:{"title":"A Mirror to Society: How Inclusivity Has Evolved in Oscar-Nominated Films","cells":[{"id":0,"value":"# A Mirror to Society: How Inclusivity Has Evolved in Oscar-Nominated Films","pinned":false,"hidden":false,"mode":"md"},{"id":19,"value":"https://www.mediaversityreviews.com/how-we-grade","pinned":false,"hidden":false,"mode":"md"},{"id":23,"value":"[Exemple Inclusivity Scores](https://docs.google.com/spreadsheets/d/1-vqRNK_F0_D3V-e1eeUcZVhDBqg9ogixENbVPNoiSz8/edit#gid=1991719371)","pinned":false,"hidden":false,"mode":"md"},{"id":21,"value":"[Exemple de analisis](https://docs.google.com/document/d/1ImBxCkjCCT-P6UzykRmzt3m52u-YMP5eNge5UUdCqug/edit)","pinned":false,"hidden":false,"mode":"md"},{"id":12,"value":"## Color Palettes","pinned":false,"hidden":false,"mode":"md"},{"id":6,"value":"https://access.mymind.com/colors","pinned":false,"hidden":false,"mode":"md"},{"id":10,"value":"https://observablehq.com/@saneef/mindfully-curated-colors","pinned":false,"hidden":false,"mode":"md"},{"id":14,"value":"## Datasets","pinned":false,"hidden":false,"mode":"md"},{"id":16,"value":"[Best Picture nominees 2004 - 2024](https://docs.google.com/spreadsheets/d/14F56DwKLEX-Fqvrx85hj0tjMZXMbwRBbj5xB_j5yIV8/edit?usp=sharing)","pinned":false,"hidden":false,"mode":"md"},{"id":28,"value":"---","pinned":false,"hidden":false,"mode":"md"},{"id":389,"value":"Given how the Oscars have a huge impact on the film industry and the pop-culture, examining the selection of films considered \"award-worthy\" each year serves as a key indicator for assessing progress toward inclusivity.","pinned":false,"hidden":false,"mode":"md"},{"id":356,"value":"## How has Inclusivity evolved over time in Academy Award-Nominated Films?","pinned":false,"hidden":false,"mode":"md"},{"id":377,"value":"Even though the Academy of Motion Picture Arts and Sciences has made some **recent efforts to increase diversity**, the reality is that Hollywood's biggest awards ceremony still has **a long way to go in becoming truly inclusive**. A quick glance at the nomination history over the past two decades reveals a consistent pattern of the Oscars being **male-dominated** and **lacking inclusivity**.","pinned":false,"hidden":false,"mode":"md"},{"id":306,"value":"viewof threshold = Inputs.range([0, 10], {step: 1, label: 'Inclusivity index'})","pinned":false,"hidden":false,"mode":"ojs"},{"id":94,"value":"$2f","pinned":false,"hidden":false,"mode":"ojs"},{"id":347,"value":"## Tracking Inclusivity in Academy Award Nominations","pinned":false,"hidden":false,"mode":"md"},{"id":59,"value":"viewof selected_index = Inputs.select(new Map([[\"Inclusivity Score\", \"inclusivity_score\"], [\"Gender Score\", \"gender_score\"], [\"Race Score\", \"race_score\"]]), {value: \"Inclusivity Score\", label: \"Select an inclusivity index:\"})","pinned":false,"hidden":false,"mode":"ojs"},{"id":308,"value":"viewof date_filter = interval([2004, 2023], {\n step: 1,\n value: [2004, 2023],\n label: 'Date filter:',\n })","pinned":false,"hidden":false,"mode":"ojs"},{"id":35,"value":"$30","pinned":false,"hidden":false,"mode":"ojs"},{"id":340,"value":"## The Changing Face of Hollywood: Measuring Progress in Racial and Gender Diversity in Oscar Nominations","pinned":false,"hidden":false,"mode":"md"},{"id":253,"value":"miniPlots = html`\n\n

${[line_1()]}

\n ${[area_2()]}\n

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":265,"value":"line_1 = () => {\n return Plot.plot({\n width: width,\n color: { legend: true, scheme: \"Viridis\" },\n marks: [\n Plot.lineY(\n stacked_data_unpivot,\n Plot.binX(\n { y: \"mean\" },\n { x: \"Released_Year\", y: \"score\", stroke: \"score_type\", tip: true, curve: \"catmull-rom\"}\n ),\n \n )\n ]\n})}","pinned":false,"hidden":false,"mode":"ojs"},{"id":395,"value":"Plot.plot({\n x: {axis: null},\n y: {tickFormat: \"s\", grid: true},\n color: {scheme: \"spectral\", legend: true},\n marks: [\n Plot.barY(crew_members_count, {\n x: \"gender_prediction\",\n y: \"count_percentage\",\n fill: \"gender_prediction\",\n fx: \"startYear\",\n sort: {x: null, color: null, fx: {value: \"-y\", reduce: \"sum\"}}\n }),\n Plot.ruleY([0])\n ]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":479,"value":"width = 1024","pinned":true,"hidden":false,"mode":"ojs"},{"id":481,"value":"height = 600","pinned":true,"hidden":false,"mode":"ojs"},{"id":491,"value":"scale = d3.scaleBand()\n .domain(sequence(10))\n .range([0, waffleSize])\n .padding(0.1)","pinned":true,"hidden":false,"mode":"ojs"},{"id":431,"value":"chart = {\n const svg = d3.create(\"svg\")\n .style(\"cursor\", \"default\")\n .attr(\"viewBox\", [0, 0, width, height]);\n \n const g = svg.selectAll(\".waffle\") \n .data(waffles)\n .join(\"g\")\n .attr(\"class\", \"waffle\");\n \n const cellSize = scale.bandwidth();\n const half = cellSize / 2;\n const cells = g.append(\"g\")\n .selectAll('circle')\n .data(d => d)\n .join('circle')\n .attr(\"fill\", d => d.index === -1 ? \"#ddd\" : color(d.index));\n \n cells.attr(\"cx\", d => scale(d.x) + half)\n .attr(\"cy\", 0)\n .attr(\"r\", half);\n \n // cells.append(\"title\").text(d => {\n // const cd = crew2[d.index];\n // return `${cd.territory}\\n${toCurrency(cd.profit)} (${cd.ratio.toFixed(1)}%)`;\n // }); \n \n // cells.transition()\n // .duration(d => d.y * 100)\n // .ease(d3.easeBounce)\n // .attr(\"cy\", d => scale(d.y) + (half));\n // svg.transition().delay(550)\n // .on(\"end\", () => drawLegend(svg, cells));\n \n return svg.node();\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":527,"value":"waffleData = crew2.filter((d) => d.startYear == \"2005\")","pinned":true,"hidden":false,"mode":"ojs"},{"id":319,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":484,"value":"waffles = {\n const array = [];\n const max = waffleData.length; \n let index = 0, curr = 1, \n accu = Math.round(crew2[0].ratio), waffle = [];\n \n for (let y = 9; y >= 0; y--)\n for (let x = 0; x < 10; x ++) {\n if (curr > accu && index < max) {\n let r = Math.round(crew2[++index].ratio);\n while(r === 0 && index < max) r = Math.round(crew2[++index].ratio);\n accu += r;\n }\n waffle.push({x, y, index});\n curr++;\n } \n array.push(waffle);\n return array;\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":399,"value":"crew_members_count = FileAttachment(\"crew_members_count.csv\").csv()","pinned":true,"hidden":false,"mode":"ojs"},{"id":442,"value":"crew2 = crew_members_count.map(d => {\n return { ...d, ratio: d.count_percentage * 100 };\n return d;\n});\n \n","pinned":true,"hidden":false,"mode":"ojs"},{"id":249,"value":"getColorScheme(6, 2009)","pinned":true,"hidden":false,"mode":"ojs"},{"id":244,"value":"function getColorScheme(inc_score) {\n const color_scheme = inc_score >= threshold ? d3.interpolatePurples : d3.interpolateOranges;\n return d3.scaleSequential(color_scheme).domain(inc_score >= threshold ? [0, 10] : [10,0]);\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":192,"value":"","pinned":false,"hidden":false,"mode":"sql","output":"","database":"null"},{"id":500,"value":"sequence = (length) => Array.apply(null, {length: length}).map((d, i) => i);","pinned":true,"hidden":false,"mode":"ojs"},{"id":498,"value":"color = d3.scaleOrdinal(d3.schemeTableau10)\n .domain(sequence(crew2.length))","pinned":true,"hidden":false,"mode":"ojs"},{"id":438,"value":"drawLegend = (svg, cells) => {\n const legend = svg.selectAll(\".legend\")\n .data(crew_members_count.map(d => d.gender_prediction))\n .join(\"g\") \n .attr(\"opacity\", 1)\n .attr(\"transform\", (d, i) => `translate(${waffleSize + 20},${i * 30})`)\n .on(\"mouseover\", highlight)\n .on(\"mouseout\", restore);\n\n legend.append(\"rect\")\n .attr(\"rx\", 3).attr(\"ry\", 3)\n .attr(\"width\", 30).attr(\"height\", 20)\n .attr(\"fill\", (d, i) => color(i)); \n\n legend.append(\"text\")\n .attr(\"dx\", 40)\n .attr(\"alignment-baseline\", \"hanging\")\n .text((d, i) => `${d} (${chartData[i].ratio.toFixed(1)}%)`);\n \n function highlight(e, d, restore) {\n const i = legend.nodes().indexOf(e.currentTarget);\n cells.transition().duration(500)\n .attr(\"fill\", d => d.index === i ? color(d.index) : \"#ccc\"); \n }\n \n function restore() {\n cells.transition().duration(500).attr(\"fill\", d => color(d.index))\n }\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":477,"value":"waffleSize = true ? width < height ? width : height : 150;","pinned":true,"hidden":false,"mode":"ojs"},{"id":450,"value":"Plot.plot({\n color: {\n scheme: \"Inferno\",\n legend: true\n },\n marks: [\n Plot.auto(grouped_merged_df1, {x: \"Released_Year\", y: {value: \"gender\", reduce: \"count\"}, color: \"gender\"})\n ]\n})","pinned":true,"hidden":false,"mode":"ojs"},{"id":465,"value":"Plot.plot({\n color: {\n type: \"categorical\",\n range: [\"#6e58a7ff\", \"#f67926ff\"]\n },\n marks: [\n Plot.areaY(\n grouped_merged_df1,\n Plot.binX(\n { y: \"count\" },\n { x: \"Released_Year\", y: \"gender\", fill: \"gender\", tip: true }\n )\n ),\n Plot.ruleY([0])\n ]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":259,"value":"area_2 = () => {\n return Plot.plot({\n color: {\n range: [\"green\", \"purple\"],\n legend: true\n },\n marks: [\n Plot.areaY(\n grouped_merged_df1,\n Plot.binX(\n { y: \"count\" },\n { x: \"Released_Year\", y: \"gender\", tip: true }\n )\n ),\n Plot.ruleY([0])\n ]\n})}","pinned":true,"hidden":false,"mode":"ojs"},{"id":255,"value":"area_1 = () => {\n return Plot.plot({\n color: { legend: true, scheme: \"Inferno\" },\n marks: [\n Plot.areaY(\n grouped_merged_df1,\n Plot.binX(\n { y: \"count\" },\n { x: \"Released_Year\", y: \"race\", fill: \"race\", tip: true }\n )\n ),\n Plot.ruleY([0])\n ]\n});\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":49,"value":"---\n### Addendum: data, libraries, other ...\n---","pinned":false,"hidden":false,"mode":"md"},{"id":65,"value":"import {form} from \"@mbostock/form-input\"","pinned":true,"hidden":false,"mode":"ojs"},{"id":313,"value":"import {interval} from '@mootari/range-slider'","pinned":true,"hidden":false,"mode":"ojs"},{"id":30,"value":"data = FileAttachment(\"Best Picture nominees 2004 - 2024 - Placheholder@1.csv\").csv()","pinned":false,"hidden":false,"mode":"ojs"},{"id":101,"value":"data2 = FileAttachment(\"title_basics_2004_2023.csv\").csv()","pinned":true,"hidden":false,"mode":"ojs"},{"id":61,"value":"index_labels = [\"inclusivity_score\",\"race_score\",\"gender_score\"]","pinned":false,"hidden":false,"mode":"ojs"},{"id":104,"value":"merged_df1 = await FileAttachment(\"merged_df (2).csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs"},{"id":222,"value":"select distinct movie_title, released_year,\ncase when gender = 'mostly_male' then 'male' when gender = 'mostly_female' then 'female'\nwhen gender = 'unknown' then 'male' when gender = 'andy' then 'male'\n else gender end as gender\n ,case when race = 'White' then 'White' else 'Non White' end as race from merged_df1\n","pinned":true,"hidden":false,"mode":"sql","output":"grouped_merged_df1","database":"var:merged_df1"},{"id":367,"value":"stacked_data2.filter((d) => d.Released_Year == 2015)","pinned":true,"hidden":false,"mode":"ojs"},{"id":107,"value":"SELECT\n *,\n ROW_NUMBER() OVER (PARTITION BY Released_year ORDER BY inclusivity_score ASC) AS class_index\nFROM (\n SELECT DISTINCT movie_title, inclusivity_score, gender_score, race_score, Released_year\n FROM merged_df1\n where Released_year >= 2004\n AND INCLUSIVITY_SCORE >= 5\n)\nUNION ALL\nSELECT\n *,\n ((ROW_NUMBER() OVER (PARTITION BY Released_year ORDER BY inclusivity_score DESC))-1)*-1 AS class_index\nFROM (\n SELECT DISTINCT movie_title, inclusivity_score, gender_score, race_score, Released_year\n FROM merged_df1\n where Released_year >= 2004\n AND INCLUSIVITY_SCORE < 5\n)\n","pinned":true,"hidden":false,"mode":"sql","output":"stacked_data","database":"var:merged_df1"},{"id":154,"value":"SELECT DISTINCT movie_title, Released_year, inclusivity_score as score, 'total_score' as score_type\n FROM merged_df1\n where Released_year >= 2004\nunion all\nSELECT DISTINCT movie_title, Released_year, gender_score as score, 'gender_score' as score_type\n FROM merged_df1\n where Released_year >= 2004\nunion all\nSELECT DISTINCT movie_title, Released_year, race_score as score, 'race_score' as score_type\n FROM merged_df1\n where Released_year >= 2004\n","pinned":true,"hidden":false,"mode":"sql","output":"stacked_data_unpivot","database":"var:merged_df1"},{"id":473,"value":"directors = await FileAttachment(\"output_file.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs"},{"id":476,"value":"$32","pinned":true,"hidden":false,"mode":"sql","output":"directors_2","database":"var:merged_df1"},{"id":131,"value":"https://observablehq.com/@eesur/waffle-chart","pinned":false,"hidden":false,"mode":"md"},{"id":208,"value":"https://observablehq.com/d/d2aaf9ee9ed258cb","pinned":true,"hidden":false,"mode":"ojs"},{"id":143,"value":"inclusive_color = \"#9357A9\"","pinned":true,"hidden":false,"mode":"ojs"},{"id":147,"value":"non_inclusive_color = \"#F87C4C\"","pinned":true,"hidden":false,"mode":"ojs"},{"id":424,"value":"import gender_guesser.detector as gender","pinned":true,"hidden":false,"mode":"ojs"}],"files":"$W33","stdlib":"1","titling":"auto"}