This article provides examples of how to use the command datashader_cli to quickly visualize large datasets using the CLI based on datashader.

Use case 1: Create a shaded scatter plot of points and save it to a png file and set the background color

Code:

datashader_cli points path/to/input.parquet --x pickup_x --y pickup_y path/to/output.png --background black|white|#rrggbb

Motivation: This use case is useful when you have a dataset that contains points and you want to generate a scatter plot and save it as a PNG file. Specifying the background color allows you to customize the visualization.

Explanation:

• datashader_cli: The command to run the datashader_cli tool.
• points: Specifies the type of data in the input dataset.
• path/to/input.parquet: The path to the input dataset in Parquet format.
• --x pickup_x: Specifies the column in the input dataset to be used as the x-axis values for the scatter plot.
• --y pickup_y: Specifies the column in the input dataset to be used as the y-axis values for the scatter plot.
• path/to/output.png: The path to save the output PNG file.
• --background black|white|#rrggbb: Sets the background color of the scatter plot. The options are “black”, “white”, or a specific color code in the format #rrggbb.

Example Output: A scatter plot of the points in the input dataset with the specified background color is saved as a PNG file.

Use case 2: Visualize geospatial data

Code:

datashader_cli points path/to/input_data.geo.parquet path/to/output_data.png --geo true

Motivation: This use case is relevant when working with geospatial data in formats such as Geoparquet, shapefile, geojson, geopackage, etc. It allows for quick visualization of the data, which can aid in analyzing patterns and relationships.

Explanation:

• datashader_cli: The command to run the datashader_cli tool.
• points: Specifies the type of data in the input dataset.
• path/to/input_data.geo.parquet: The path to the input geospatial dataset.
• path/to/output_data.png: The path to save the output PNG file.
• --geo true: Enables geospatial visualization. When set to “true”, the tool understands and utilizes the geospatial information in the input dataset.

Example Output: The geospatial data from the input dataset is visualized in the output PNG file, allowing for easy identification of patterns and relationships within the data.

Use case 3: Use matplotlib to render the image

Code:

datashader_cli points path/to/input_data.geo.parquet path/to/output_data.png --geo true --matplotlib true

Motivation: This use case is useful when you prefer to use matplotlib for rendering the image instead of the default datashader rendering. It allows for more customization and flexibility in the visualization process.

Explanation:

• datashader_cli: The command to run the datashader_cli tool.
• points: Specifies the type of data in the input dataset.
• path/to/input_data.geo.parquet: The path to the input geospatial dataset.
• path/to/output_data.png: The path to save the output PNG file.
• --geo true: Enables geospatial visualization. When set to “true”, the tool understands and utilizes the geospatial information in the input dataset.
• --matplotlib true: Specifies the use of matplotlib for rendering the image. When set to “true”, the tool utilizes matplotlib instead of the default datashader renderer.

Example Output: The geospatial data from the input dataset is visualized using matplotlib and saved as the output PNG file, allowing for customized and flexible visualization options.

Conclusion:

The datashader_cli command provides a convenient way to visualize large datasets using the CLI based on datashader. With options for customizing the scatter plots, handling geospatial data, and using matplotlib for rendering, this command offers flexibility and efficiency in exploring and analyzing datasets.