You can add a column to your importer directly through the UI of your Fuse account, or you can do so in code using the documentation below. The Float column type can accept whole integers and decimals. It supports functionalities such as:
  • Validate that input is greater than or less than a certain number
  • Round values to the nearest integer

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addColumn Function

The addColumn function accepts an object as an argument.
  • internal_key (String) - required: The internal key used to access a column’s value.
  • label (String) - required: The user-facing column label that is shown in the interface.
  • column_type (String) - required: Should be “float”.
  • required (String) - required: Whether or not the column is required.
  • position (Optional): The position or order of the column.
  • unique (Optional): Whether values should be unique.
  • validations (Optional): An array of built-in data validations. See Built-in Float Validations.
  • transformations (Optional): An array of built-in data transformations. See Built-in Float Transformations.

​
Example Code

  const importer = new FuseImporter();

  importer.getSessionToken = ... // see https://fuse-docs.flatirons.com/getting-started/sessions


  const validations = [
    {
      validation_type: "less_than",
      message: "Must be less than 10",
      options: {
        less_than: 10,
      },
    },
  ];

  const transformations = [
    {
      validation_type: "number_of_decimals",
    },
  ];

  importer.addColumn({
    internal_key: "age",
    label: "Age",
    column_type: "float",
    required: true,
    position: 1,
    validations: validations,
    transformations: transformations,
  });

  importer.show();

​
Built-in Float Validations

The addColumn object accepts an array of validations. Each validation has three properties:
  • validation_type: the name of the validation
  • message: the message showed to the end user if the validation fails
  • options: options for the specific validation type
The validations property accepts an array of objects that contains these possible values: validation_type, message and options. As the example below: 
validations: [
  {
    validation_type: "less_than",
    message: "Must be less than 10",
    options: {
      less_than: 10,
    },
  },
];

​
less_than

Specifies that the value must be less than a certain number. 
validations: [
  {
    validation_type: "less_than",
    message: "Must be less than 10",
    options: {
      less_than: 10,
    },
  },
];

​
greater_than

Specifies that the value must be greater than a certain number. 
validations: [
  {
    validation_type: "greater_than",
    message: "Must be greater than 10",
    options: {
      greater_than: 10,
    },
  },
];

​
even

Specifies that the value must be even. 
validations: [
  {
    validation_type: "even",
    message: "Must be an even number",
  },
];

​
odd

Specifies that the value must be odd. 
validations: [
  {
    validation_type: "odd",
    message: "Must be an odd number",
  },
];

​
regex

Specifies that the value must match a regex 
validations: [
  {
    validation_type: "regex",
    message: "Must not have a leading zero",
    options: {
      pattern: /^([+-]?[1-9]\d*|0)$/,
    },
  },
];

​
Built-in Float Transformations

The options property of addColumn accepts an array of transformations. Each transformation has two possible properties:
  • transformation_type: the name of the transformation
  • options: options for the specific transformation type
The transformations property accepts an array of objects that contains these possible values: transformation_type and options. As the example below: 
transformations: [
  {
    transformation_type: "number_of_decimals",
    options: {
      number_of_decimals: 2,
    },
  },
];

​
number_of_decimals

This transformation allows you to specify the desired number of decimal places for the float values in the column. For example, if the original value is 3.14159 and you specify 2 decimal places, it becomes 3.14. You should pass an options object to the transformation object with a number_of_decimals integer value in your desired format. 
transformations: [
  {
    transformation_type: "number_of_decimals",
    options: {
      number_of_decimals: 2,
    },
  },
];