2b:null 2c:["$","title",null,{"children":"Learning Observable: Getting data into Observable | Niko Kommenda | Observable"}] 31:T57d,## File attachments You can attach files to notebooks by clicking on the paper clip in the toolbar on the right , which opens the files pane. Then, either click on the plus sign or drag and drop files straight onto the pane to upload files for use. Attached files can be easily accessed by using the _insert into notebook_ function to insert a new cell with a code snippet like the one below.32:T674,## Cloud files ProLearn more about Pro For Pro and Enterprise workspaces, Observable supports access to [cloud files](https://observablehq.com/@observablehq/cloud-files) hosted on Google or Microsoft. To connect, open up the files pane by clicking the icon, the click on "Cloud Files." 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","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":8,"value":"In order to analyze your data, Observable needs to be able to access it. There are many ways to do this: upload as an attachment, access via an API, query from a database, or retrieve from a cloud document such as Google Sheets.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":14,"value":"## Data on Observable\n\nFirst, let's look at what data even looks like. You're probably familiar with the tabular representation of datasets, such as this one of olympic athletes.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":11,"value":"viewof table = Inputs.table(olympians)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":18,"value":"\nBut the actual data isn't encoded as a table, it's a list of objects, one for each line. They all have the same properties, so that we can fit them into a table. The `name` property gets put into the _name_ column, the `nationality` property into the _nationality_ column, etc.\n\nTo see the list, expand the `olympians` array by clicking on the triangle next to the word `Array`, then click on one of the triangles next to the word `Object` to see an individual data object representing one athlete. It should look like this:\n\n${await FileAttachment(\"array-object.png\").image({width: 276})}\n\nWhy should you care about how the data is represented? Because that determines how it is processed, e.g., when using functions such as `map()` and `filter()` with accessors that extract individual items from each object they operate on.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":21,"value":"olympians","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":29,"value":"## Importing from other notebooks\n\nPerhaps the easiest way to access data is to import it from an existing notebook. This might be one of our [curated datasets](https://observablehq.com/@observablehq/curated-datasets?collection=@observablehq/datasets), or data prepared within your organization.\n\nImporting a dataset works just like importing any other cell: copy the import statement using the menu in the left toolbar of the cell you're looking to import, and paste it into your notebook as shown below.\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":283,"value":"

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":281,"value":"\nBelow, see the live code that imports the `lasers` data (a list of laser strikes on aircraft in the U.S. in 2020 from a FAA database) into this notebook.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":41,"value":"import {lasers} from \"@observablehq/faa-laser-reports-data\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":43,"value":"viewof laserTable = Inputs.table(lasers)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":27,"value":"$31","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":47,"value":"laser_report_2020 = FileAttachment(\"Laser_Report_2020.xlsx\").xlsx()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":50,"value":"The `Laser_Report_2020.xlsx` file, which contains data that the laser strikes table above is based on, is an [Excel XLSX file](https://observablehq.com/@observablehq/xlsx?collection=@observablehq/getting-data-in-and-out). Other common formats are CSV, JSON, as well as images in formats like JPG and PNG.\n\nLearn more about Observable's [File Attachments](https://observablehq.com/@observablehq/file-attachments). ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":31,"value":"$32","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":368,"value":"

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":366,"value":"Once you've authenticated, you'll see all your cloud files available to import directly into a notebook, just like you would with file attachments. To learn more, read our walk through on [cloud files](https://observablehq.com/@observablehq/cloud-files).","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":34,"value":"## Database access \nProLearn more about Pro\n\nConnecting to secure databases is available for [Enterprise and Pro Teams](https://observablehq.com/pricing) on Observable. But anyone can work with databases in Observable, for example by uploading one, or creating a new one right in Observable from existing files (CSV, parquet, arrow, XLSX, txt, and more!) using an in-browser database client like [DuckDB](https://observablehq.com/@observablehq/duckdb).","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":67,"value":"Once your database is connected, you can query, clean and analyze the data in Observable's SQL cell. Create a SQL cell by choosing 'Database query' from the Add Cell menu. Select the connected database you want to start working with, then write your SQL code (learn more in our [Exploring a database](https://observablehq.com/guide/murder-mystery) Quick Guide). Your query's output will show as an interactive table like the one shown below. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":68,"value":"SELECT * FROM customers \n JOIN invoices ON customers.CustomerId = invoices.CustomerId\n WHERE Country = \"Brazil\" ","pinned":true,"hidden":false,"mode":"sql","author":"$undefined","summary":"$undefined","output":"","database":"chinook.db"},{"id":36,"value":"## APIs\n\nObservable supports fetching API results directly in notebooks, making it simple to work with live data. Here is an example of retrieving the results from a live API on New York City subway ridership.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":384,"value":"d3.csv('https://data.ny.gov/api/views/vxuj-8kew/rows.csv?accessType=DOWNLOAD&sorting=true')","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":386,"value":"Observable supports API secrets for Enterprise, Pro Teams, and Pro Individual workspaces. Free Teams or Free Individuals can still fetch API results, using basic authentication.\n\n| Resources | |\n| :-- | --- | \n| [Fetch with Basic Auth example](https://observablehq.com/@mbostock/fetch-with-basic-auth) | An example of using Basic Auth |\n| [Observable Secrets](https://observablehq.com/@observablehq/secrets) | Documentation for securely storing API tokens and secrets |\n| [GitHub API starter template](https://observablehq.com/@observablehq/github-api-starter) | Quickly get started using the GitHub API |\n| [AWS S3 starter template](https://observablehq.com/@observablehq/aws-s3-api-starter) | Quickly get started using the AWS S3 API |","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":62,"value":"### Data Table cell\n\nObservable's Data Table cell provides an easy way to put your data in table form and perform some no-code data wrangling and exploration. From the Add Cell menu, search for or scroll to \"Data table.\" Then, choose the data source you want to explore (if it's a connected database, you'll also choose a table within that database). ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":63,"value":"An interactive view of the table will appear, with summary information atop each variable and options for filtering, selecting columns, sorting, and slicing rows below. Play with the interactive table below, which shows the \"customers\" table from the attached \"chinook.db\" database.\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":64,"value":"await FileAttachment(\"chinook.db\")","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":65,"value":"While the existing features in a Data Table cell cover some common data wrangling needs, you'll eventually need to do more customized querying and analysis (e.g. joins, grouping, renaming, cleaning, and more). \n\nYou can switch over to SQL code using the 'Convert to SQL mode' button, which will convert your current query to SQL and keep adding or editing custom SQL code.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":407,"value":"Next up, learn about how to visualize data with [Observable Plot](https://observablehq.com/@observablehq/learning-observable-observable-plot?collection=@observablehq/intro-to-observable). ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":273,"value":"newUsersCourseSmGetData1 = FileAttachment(\"new-users-course-sm-get-data@1.png\").image({width: 200})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W33","stdlib":"1","titling":"auto"}} 30:"$2f:body"