Data Selector

Tool to select or filter data of different types and in multiple dimensions.

Purpose

The Data Selector tool allows you to select a sample of the dataset, specifying a number of rows. It is possible to delete or keep columns. Another parameter allows the selection of data in a column according to its value(s).

The output result is saved in a .csv file.

Usage

The usage is given as follows:

Usage: data-selector [OPTIONS] COMMAND [ARGS]...

  Data selection interactive tool.

Options:
  --help  Show this message and exit.

Commands:
  selector  Tool to select Data to Keep/Delete
  version   Print the application version information

To use the command to select data in file:

Usage: data-selector selector [OPTIONS]

  Tool to select Data to Keep/Delete

Options:
  -i, --input FILE                Data file to convert  [required]
  -out, --output TEXT             name for the output files  [required]
  -f, --force                     Overwrite existing files
  -s, --file_sep TEXT             File separator (csv).
  -row, --nb_rows INTEGER         Number of rows to import from input_file.
  -keep, --columns_to_keep FILE   Path to file with columns to keep.
  -delete, --columns_to_delete FILE
                                  Path to file with columns to delete.
  -values, --values_to_keep FILE  Path to file with columns and data to keep.
  --help                          Show this message and exit.

Specification for json parameter files

For the -keep and the -delete option, the template is given below :

[
"<column#1>",
"<column#2>",
"<column#3>",
"<column#4>",
"<column#5>"
]

column#x are the columns you want to select/delete. Note that you can add as many columns as needed.

For the values option, the template is given below :

{  
  "<column#1>":["<value#1>", "<value#2"],    
  "<column#2>":["<value#1>", "<value#2", "<value#3", "<value#4"],  
  "<column#3>":["<value#1>"]   
}

column#x are the columns you want to select/delete. value is a list of the values you want to keep on this column. Note that you can add as many columns as needed.

Build & run docker image (locally)

Build a local docker image using the following command line:

docker build -t data-selector .

Once built, you can run the container locally with the following command line:

docker run -ti --rm -v <your_path>:/DATA data-selector selector -i DATA/<path_to_data> -out DATA/<out_name> -s <file_sep> -keep DATA/<path_to_select_columns> -delete DATA/<path_to_delete_columns> -values DATA/<path_to_select_data_columns>

-v allows to mount a volume and to use your local data on the docker environment.

your_path: Local directory where the data (data to be selected, and parameter json files) are stored

path_to_data: The name of the file to select data from (in the Directory).

out_name: The name you want to give to the output file.

file_sep: File separator of the input file.

path_to_select_columns: Path towards json parametrization file.

path_to_delete_columns: Path towards json parametrization file.

path_to_select_data_columns: Path towards json parametrization file.

Build

Project is built by poetry.

poetry install

Lint

Python linting

⚠️ Be sure to write code compliant with linters or else you'll be rejected by the CI. Code linting is performed by flake8.

poetry run flake8 --count --show-source --statistics

Static type check is performed by mypy.

poetry run mypy .

Unit Test

⚠️ Be sure to write tests that succeed or else you'll be rejected by the CI. Unit tests are performed by the pytest testing framework.

poetry run pytest -v

You want to get involved? 😍

Please check out OKP4 health files :

• Contributing
• Code of conduct