TransformMissing

TransformMissing[tab,spec]

replaces instances of Missing[…] in tabular data tab according to the specification spec.

TransformMissing[tab,{col1spec1,…}]

uses the specification speci to replace missing elements in coli.

Details and Options

  • TransformMissing is also known as missing handling and missing imputation.
  • TransformMissing is used to replace missing elements in a dataset with values that can be specified or inferred from the known part of the data.
  • Missing value handling is typically used to maintain dataset integrity (often required for further data processing) and to preserve the original dataset sample size.
  • Possible forms of tab include:
  • {assoc1,…}a list of associations
    {x11,…}a matrix
    Dataset[…]a dataset object
    Tabular[…]a tabular object
  • Transformation specification spec based on the all the non-missing column values includes:
  • " Mean"the mean value
    "Median"the median value
    "Mode"the most frequent value
  • These methods assume missing data is random (MCAR) and the imputed value reflects a consistent, representative dataset, working best when data lacks skew or outliers.
  • Transformation based on a constant value includes:
  • {"Constant", val}a constant value val
  • Transformation based on sampling includes:
  • "RandomElement"randomly selected value
    {"RandomVariate",dist}value sampled from dist
  • If the distribution dist has free parameters, these will be estimated from the non-missing data using EstimatedDistribution.
  • Transformation based on the element position includes:
  • "NearestElement"use the closest non-missing element
    "NextElement"use the next non-missing element
    "PreviousElement"use the previous non-missing element
  • These methods assume that missing values are similar to nearby data points and are useful for filling small gaps in sequential or time-based data.
  • Transformation based on interpolation includes:
  • "Interpolation"use interpolation
    {"Interpolation",InterpolationOrderdegree}specify the interpolation order degree
  • Interpolation assumes that missing data lies on a smooth, continuous path between known points, making it most effective for datasets with gradual changes and minimal sharp fluctuations.
  • Additional methods can be implemented using TransformColumns and other transformation functions.
  • When specifying the desired columns, coli can have the following formats:
  • nthe nth column
    "name"the column "name"
    TypeSpecifier[…]columns matching the specified type
    Allall the columns
    Automaticautomatically find columns based on spec (default)

Examples

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Basic Examples  (2)

Replace missing elements by using the mean value of the columns:

Use a literal value:

Fit a distribution on the column data and sample from it:

Scope  (15)

Column Specification  (4)

Transform the specified column:

Transform a list of columns:

Transform all the columns:

Transform the columns that are compatible with the "Mean" transformation:

This is equivalent to:

Transformations  (11)

Constant  (4)

Impute missing values with the mean of a column:

Impute missing values with the median of a column:

Impute missing values with the most common value of a column:

Impute missing values with a constant value:

Random  (2)

Impute each missing value with a random choice from its column:

Sample from a given distribution:

Sample after estimating the distribution on the (non-missing) column values:

Position Based  (3)

Replace missing values using the previous non-missing value:

Replace missing values using the next non-missing value:

Replace missing values using the closest non-missing value:

Interpolation Based  (2)

Impute missing values sampling from an InterpolatingFunction:

Specify an interpolation order:

Possible Issues  (1)

Not all transformations are compatible with every data type:

Wolfram Research (2025), TransformMissing, Wolfram Language function, https://reference.wolfram.com/language/ref/TransformMissing.html.

Text

Wolfram Research (2025), TransformMissing, Wolfram Language function, https://reference.wolfram.com/language/ref/TransformMissing.html.

CMS

Wolfram Language. 2025. "TransformMissing." Wolfram Language & System Documentation Center. Wolfram Research. https://reference.wolfram.com/language/ref/TransformMissing.html.

APA

Wolfram Language. (2025). TransformMissing. Wolfram Language & System Documentation Center. Retrieved from https://reference.wolfram.com/language/ref/TransformMissing.html

BibTeX

@misc{reference.wolfram_2024_transformmissing, author="Wolfram Research", title="{TransformMissing}", year="2025", howpublished="\url{https://reference.wolfram.com/language/ref/TransformMissing.html}", note=[Accessed: 15-January-2025 ]}

BibLaTeX

@online{reference.wolfram_2024_transformmissing, organization={Wolfram Research}, title={TransformMissing}, year={2025}, url={https://reference.wolfram.com/language/ref/TransformMissing.html}, note=[Accessed: 15-January-2025 ]}