28:null 29:["$","title",null,{"children":"Лабораторная работа №1 | Olga Polovikova | Observable"}] 2f:[{"id":"87c96bf4213f6b57f90ba0305066169d5a84b464fc01a0c864fc5af24622dd024ef95bc296a9da445b6772b9aa5dab8b26b1e0a5c54afbe3e5f6fddccdf90677","href":"https://static.observableusercontent.com/files/87c96bf4213f6b57f90ba0305066169d5a84b464fc01a0c864fc5af24622dd024ef95bc296a9da445b6772b9aa5dab8b26b1e0a5c54afbe3e5f6fddccdf90677","name":"data.csv","type":"text/csv","lastModified":1726476566741.61,"size":1475504},{"id":"0db9e865df293f04ae0629a5a1dbc689fa38cc4b7b909afaab9e1945d26a026d2a5520543e95da1a34adcdca322569a880f72abdf2084ebeba1b0e8527b10f0c","href":"https://static.observableusercontent.com/files/0db9e865df293f04ae0629a5a1dbc689fa38cc4b7b909afaab9e1945d26a026d2a5520543e95da1a34adcdca322569a880f72abdf2084ebeba1b0e8527b10f0c","name":"demografia.xlsx","type":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","lastModified":1757474557749.44,"size":114706},{"id":"0db9e865df293f04ae0629a5a1dbc689fa38cc4b7b909afaab9e1945d26a026d2a5520543e95da1a34adcdca322569a880f72abdf2084ebeba1b0e8527b10f0c","href":"https://static.observableusercontent.com/files/0db9e865df293f04ae0629a5a1dbc689fa38cc4b7b909afaab9e1945d26a026d2a5520543e95da1a34adcdca322569a880f72abdf2084ebeba1b0e8527b10f0c","name":"demografia@1.xlsx","type":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","lastModified":1757474576961.634,"size":114706}] 2c:{"id":"441760d58ece8035","slug":null,"title":"Лабораторная работа №1","thumbnail":"49e3b8e7b5c25c76853ac31b4ed41e895e2facb91573182f4e68e995d0ccb899","access_level":"public","create_time":"$D2024-09-16T08:49:26.741Z","update_time":"$D2025-09-10T05:16:58.819Z","view_time":null,"first_public_time":"$D2024-09-16T09:19:40.625Z","fork":{"id":"4ed3240395dd7cbf","slug":null,"title":"Лабораторная работа №1","owner":{"id":"397eb612fd309cf3","login":"qellex","name":"qellex"}},"stars":0,"starred":false,"owner":{"type":"team","id":"6d2574fdcec31556","login":"39bbe44713bf1f81","name":"Olga Polovikova","avatar_url":"https://avatars-next.observableusercontent.com/59cbdcabd03e36702a8f8df561a992cf1232c54b9c6502e5afb83e05a340c322","tier":"starter_2024"},"version":433,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"39bbe44713bf1f81","login":"39bbe44713bf1f81","name":"Olga Polovikova","avatar_url":"https://avatars-next.observableusercontent.com/59cbdcabd03e36702a8f8df561a992cf1232c54b9c6502e5afb83e05a340c322","tier":null}],"members":[{"type":"individual","id":"39bbe44713bf1f81","login":"39bbe44713bf1f81","name":"Olga Polovikova","avatar_url":"https://avatars-next.observableusercontent.com/59cbdcabd03e36702a8f8df561a992cf1232c54b9c6502e5afb83e05a340c322","tier":null,"role":"editor"},{"type":"team","id":"6d2574fdcec31556","login":"39bbe44713bf1f81","name":"Olga Polovikova","avatar_url":"https://avatars-next.observableusercontent.com/59cbdcabd03e36702a8f8df561a992cf1232c54b9c6502e5afb83e05a340c322","tier":"starter_2024","role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"title":"Лабораторная работа №1","cells":[{"id":0,"value":"# Лабораторная работа №1","pinned":false,"hidden":false,"mode":"md"},{"id":18,"value":"Первоисточник: https://www.kaggle.com/datasets/rupindersinghrana/car-features-and-prices-dataset/data?select=data.csv","pinned":false,"hidden":false,"mode":"md"},{"id":41,"value":"Make: Марка
\nModel: Модель
\nYear: Год
\nEngine Fuel Type: Тип топлива двигателя
\nEngine HP: Мощность двигателя л.с.
\nEngine Cylinders: Цилиндры двигателя
\nTransmission Type: Тип трансмиссии
\nDriven_Wheels: Ведущие колеса
\nNumber of Doors: Количество дверей
\nMarket Category: Категория рынка
\nVehicle Size: Размер автомобиля
\nVehicle Style: Стиль автомобиля
\nhighway MPG: Расход по шоссе
\ncity mpg: Расход по городу
\nPopularity: Популярность
\nMSRP: Рекомендованная цена
","pinned":false,"hidden":false,"mode":"md"},{"id":368,"value":"workbook = FileAttachment(\"demografia@1.xlsx\").xlsx()","pinned":true,"hidden":false,"mode":"ojs"},{"id":369,"value":"workbook.sheetNames","pinned":true,"hidden":false,"mode":"ojs"},{"id":370,"value":"data2 = workbook.sheet(0, {headers: true})","pinned":true,"hidden":false,"mode":"ojs"},{"id":371,"value":"data2","pinned":true,"hidden":false,"mode":"ojs"},{"id":404,"value":"viewof tab1 = Array(data2.columns)","pinned":true,"hidden":false,"mode":"ojs"},{"id":426,"value":"data2.length","pinned":true,"hidden":false,"mode":"ojs"},{"id":402,"value":"viewof table1 = Inputs.table(altayregion_data, {\n columns: [\n \"Район или город\",\n \"всего 2021\",\n \"всего 2020\"\n ],\n header: {\n \"Район или город\": \"Rayon\",\n \"всего 2021\": \"2021\",\n \"всего 2020\": \"2020\"}, \n sort: \"всего 2021\", reverse:true, required: false})","pinned":false,"hidden":false,"mode":"md"},{"id":397,"value":"viewof table1 = Inputs.table(data2, { columns: [ \"CountryNameRu\", \"2015\", \"2014\", \"2013\" ], header: {\"CountryNameRu\": \"country\", \"2015\": \"2015\", \"2014\": \"2014\", \"2013\": \"2013\"}, sort: \"country\", reverse:true, required: false})","pinned":true,"hidden":false,"mode":"ojs"},{"id":421,"value":"datac= data2.filter( (x)=> x[\"CountryNameRu\"] != \"World\")","pinned":true,"hidden":false,"mode":"ojs"},{"id":419,"value":"Plot.plot({\n marks: [\n Plot.ruleY([0]),\n Plot.lineY(datac.filter( (x)=> x[\"2015\"] > 100000000), {x: \"CountryNameRu\", y: \"2015\"})\n ]\n})","pinned":true,"hidden":false,"mode":"ojs"},{"id":376,"value":"date_new = data2.filter(C=>C[\"Year\"]>2000)","pinned":false,"hidden":false,"mode":"ojs"},{"id":12,"value":"cars = FileAttachment(\"data.csv\").csv()","pinned":true,"hidden":false,"mode":"ojs"},{"id":37,"value":"select * from cars order by 10","pinned":true,"hidden":false,"mode":"sql","output":"","database":"var:cars"},{"id":47,"value":"cars.columns","pinned":true,"hidden":false,"mode":"ojs"},{"id":182,"value":"Смотрим из каким марок по годам, у нас есть выборка.\n","pinned":false,"hidden":false,"mode":"md"},{"id":49,"value":"Plot.plot({\n marks: [\n \n Plot.circle(cars.filter(C=>C[\"Year\"]>2000), {y: \"Make\", x: \"Year\", fill:\"#aabfab\", sort: {y: \"x\", reverse: true}}),\n Plot.ruleX([0], {fill:\"#aaaaaa\"})],\n \n color: { legend: true },\n marginLeft: 100,\n})","pinned":true,"hidden":false,"mode":"ojs"},{"id":184,"value":"Смотрим сколько каких машин, с каким приводом.\n","pinned":false,"hidden":false,"mode":"md"},{"id":68,"value":"Plot.plot({\n marks: [\n \n Plot.barY(cars, Plot.groupX({y: \"count\"}, {x:\"Driven_Wheels\", fill:\"#aabfab\", sort: {y: \"x\", reverse: true}}),\n Plot.ruleX([0], {fill:\"#aaaaaa\"}))],\n \n color: { legend: true },\n marginLeft: 100,\n})","pinned":true,"hidden":false,"mode":"ojs"},{"id":186,"value":"Смотрим, в каком кузове выпускались машины.\n","pinned":false,"hidden":false,"mode":"md"},{"id":74,"value":"Plot.plot({\n marks: [\n \n Plot.barX(cars, Plot.groupY({x: \"count\"}, {y:\"Vehicle Style\", fill:\"#aabfab\", sort: {y: \"x\", reverse: true}}),\n Plot.ruleX([0], {fill:\"#aaaaaa\"}))],\n \n color: { legend: true },\n marginLeft: 150,\n})","pinned":true,"hidden":false,"mode":"ojs"},{"id":159,"value":"DROP TABLE cars_year_average;","pinned":true,"hidden":false,"mode":"sql","output":"","database":"var:cars"},{"id":136,"value":"CREATE TABLE IF NOT EXISTS cars_year_average AS\nSELECT cars.Year, AVG(CAST(cars.MSRP AS DECIMAL))*70 AS average\nFROM cars \nWHERE cars.\"Market Category\" == 'Crossover'\nGROUP BY cars.Year;\n","pinned":true,"hidden":false,"mode":"sql","output":"data","database":"var:cars"},{"id":262,"value":"SELECT * FROM cars_year_average\n","pinned":true,"hidden":false,"mode":"sql","output":"","database":"var:cars"},{"id":349,"value":"SELECT * FROM cars_year_average order by 2 desc","pinned":true,"hidden":false,"mode":"sql","output":"data1","database":"var:cars"},{"id":352,"value":"data1","pinned":true,"hidden":false,"mode":"ojs"},{"id":190,"value":"Было бы прекрасно, если бы подсказали, как мне обратиться к таблице созданной в sql и как разделять столбец с ценой ниже на диапазоны цен.","pinned":false,"hidden":false,"mode":"md"},{"id":364,"value":"

НИЖЕ как создать таблицу по Таблице из БД

","pinned":false,"hidden":false,"mode":"html"},{"id":211,"value":"null","pinned":true,"hidden":false,"mode":"ojs"},{"id":205,"value":"viewof table_car = Inputs.table(data1)","pinned":true,"hidden":false,"mode":"ojs"},{"id":359,"value":"data1","pinned":true,"hidden":false,"mode":"ojs"},{"id":361,"value":"Plot.plot({\n marks: [\n Plot.ruleY([0]),\n Plot.lineY(data1, {y: \"Year\", x: \"average\"})\n ]\n})","pinned":true,"hidden":false,"mode":"ojs"},{"id":366,"value":"

Чтобы разделить по диапазону строки: добавить ещё один столбец, отвечающий за ваш порядок или создать функцию, которая по значению, будет возвращать номер диапазона. В графиках можно использовать facet - для рисования отдельных графиков каждого диапазона

","pinned":false,"hidden":false,"mode":"html"},{"id":204,"value":"null","pinned":true,"hidden":false,"mode":"ojs"},{"id":97,"value":"Plot.plot({\n marks: [\n \n Plot.circle(cars.filter(d=>d['Market Category']=='Crossover'), {x: \"Year\", y: \"MSRP\", fill: \"Make\"})\n ],\n color: { legend: true },\n marginLeft: 150,\n})","pinned":true,"hidden":false,"mode":"ojs"}],"files":"$W2f","stdlib":"1","titling":"auto"}} 2d:"$2c:body"