notNull

Filters the format-selection to only include cells where the value is not null.

This is shorthand for .where{ it != null }.

For more information: See format on the documentation website.

Examples using notNull:

df.format { colsOf<Int?>() }.notNull().perRowCol { row, col ->
    linearBg(col[row], col.min() to red, col.max() to green)
}

Creates a new FormattedFrame that uses the specified CellFormatter to format selected non-null cells of the dataframe.

This function is shorthand for .notNull().with { }.

You need to specify formatter: A lambda function expecting a CellAttributes or null given an instance of a cell: C of the dataframe.

You have access to the FormattingDsl in the context.

The formatting DSL allows you to create and combine CellAttributes to apply to one or multiple cells of a dataframe such that they have specific CSS attributes applied to them when rendered to HTML.

For instance, to specify black, bold text on a white background, you could write:

background(white) and textColor(black) and bold

It's also possible to define your own colors using rgb or interpolate colors using linear.

Use attr if you want to specify a custom CSS attribute.

For more information: See format on the documentation website.

Examples using notNull:

df.format().notNull { bold and textColor(black) }

Check out the full Grammar.

Filters out null values from the columns previously selected by gather, keeping only non-null entries.

A special case of Gather.where.

It's an intermediate step; returns a new Gather with filtered value columns.

For more information: See gather on the documentation website.

See Grammar.

Example

// Gather `resultA` and `resultB` columns into a single "value" column,
// with the "series" column containing a key.
// Assuming these columns contain nullable `Double` values, `notNull` filters out nulls,
// allowing subsequent transformations like `mapValues` to treat values as non-null `Double`.
df.gather { resultA and resultB }
  .notNull()
  .mapValues { (it + 0.5).toFloat() }
  .into("series", "value")

Not Null

Filters the update-selection to only include cells where the value is not null.

This is shorthand for .where{ it != null }.

For example:

df.update{ colsOf<Int?>() }.notNull().perRowCol{ row, col ->

    row[col] / col.mean(skipNA = true)

}

For more information: See update on the documentation website.

Not Null

Selects only the rows where the values in the selected columns are not null.

Shorthand for: update{ ... }.where{ it != null }

For example:

df.update{ colsOf<Number?>() }.notNull().perCol{ mean() }

Optional

Provide an expression to update the rows with. This combines with with notNull.

For example:

df.update{ city }.notNull{ it.toUpperCase() }

For more information: See update on the documentation website.

Parameters