Count
count.co
  • Getting Started
    • What is Count?
    • Count FAQ
    • Intro to your workspace
    • Example canvases
    • Getting started guides
      • Set up your workspace and projects
        • 1. Review workspace settings
        • 2. Create and organise your projects
      • Canvas orientation
      • Your first ad hoc analysis
        • 1. Examples and templates
        • 2. Build your first queries
        • 3. Create visuals
        • 4. Caching, local cells and scheduling
        • 5. Collaborating with a stakeholder
      • Your first report
        • 1. Examples and templates
        • 2. Filters and control cells
        • 3. Sharing your report
        • 4. Alerts
  • Connect your data
    • Database connection overview
      • Athena
      • Azure Synapse
      • BigQuery
      • Databricks
      • Microsoft SQL Server
      • MySQL
      • PostgreSQL
      • Redshift
      • Snowflake
    • Refresh database schema
    • Upload CSV files
    • dbt integration
      • ☁️dbt Cloud integration
      • 👩‍💻dbt Core integration
  • Import & Export
    • Import from other tools
      • Import Miro files
      • Import SQL files
      • Import Google Sheets
      • Import Jupyter notebooks
    • Export code and results
      • Export compiled SQL and Jinja-SQL
      • Export CSV files
      • Export images and PDF files
  • THE CANVAS
    • Navigating the canvas
      • Canvas tool bar
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      • Shared styles
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    • Count AI
    • Alerts and subscriptions
      • Slack integration
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  • Querying data
    • Cells overview
      • Dynamic query compilation engine
    • SQL cells
      • Referencing other cells
      • Jinja templating
      • SQL formatting
    • Python cells
    • Visual and low-code cells
      • Calculations in visuals and low-code cells
      • Joins in visuals and low-code cells
    • Control cells
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      • Date controls
      • Text, number, and boolean controls
      • Custom control cells
    • Local DuckDB cells
      • DuckDB on the server
    • Query caching and scheduling
    • Manage queries and results
    • Troubleshooting
  • Visualizing data
    • Visualization overview
    • Templated visuals
    • Custom visuals
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    • Formatting a visual
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        • Secondary Axis
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    • Every Visual Under the Sun
  • Presenting and Reporting
    • Reports and Slides
  • Count Metrics
    • Intro to Count Metrics
    • Build and edit a catalog
    • Views
      • Creating views
      • Customizing views
    • Datasets
      • Creating datasets
    • Save changes to the catalog
      • Catalog validation
      • Version control
    • Exposing catalogs to the workspace
    • Caching in Count Metrics
    • Using the catalog
      • Explore from cell
  • Sharing and Permissions
    • Real-time collaboration
    • Comments
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    • Shared links
    • Embedding canvases
  • History and Versions
    • Version control
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  • Settings and administration
    • Workspace settings
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        • Okta
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  • Quick guides
    • Interactive control guides
      • Date ranges
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On this page
  • Query a CSV file
  • Who can see uploaded CSV files?
  1. Connect your data

Upload CSV files

Upload any CSV file into your canvases.

PreviousRefresh database schemaNextdbt integration

Last updated 9 months ago

First, head to a canvas or create a new one.

In the data bar on the left-hand side of the page, you'll see the option to Upload a CSV file. Click this button to open a file browser and select a CSV file to upload. You can also upload multiple CSV files by dragging them onto the canvas from your file explorer.

Query a CSV file

When a CSV file is uploaded a CSV cell is created in the local DuckDB database, which contains all of the rows from the file.

To query this file just refer to it in a SQL statement by name, like any other cell.

Who can see uploaded CSV files?

Any other viewers of your canvas will see the CSV cells you've created.

If you wish to keep your CSV files private, you should use a private project, or make sure you are the only person permitted to view the canvas.