For each row, checks whether all values in that row are equal. The result is a boolean column indicating equality for each row as true or false.

Note that if the types of input columns are not compatible, the result will be False for all rows. Compatibility here means that input columns must be

  • all numeric or boolean (the latter being interpreted as 0.0/1.0), OR
  • all string-like (categorical or text), OR
  • all list-like

By default, missing values (NaNs) in the same location are considered equal in this step. However, check the parameter keep_nans below to control how the presence of NaNs affects the result.

Also, when performing numeric comparison, the parameters rel_tol and abs_tol can be used to check for approximate equality. The desired tolerance (precision) can then be expressed either as a proportion of a reference value; and/or as an absolute maximum difference). More specifically, the equation used to check for numeric equality between values a and b is:

absolute(a - b) <= (rel_tol * absolute(b) + abs_tol).

Also see the parameter descriptions below, or the corresponding numpy documentation for further details.

abs_tol
number

Absolute tolerance. The absolute (positive) difference of two numbers must be smaller than or equal to this value for them to be considered equal.

rel_tol
number

Relative tolerance. The absolute (positive) difference of two numbers a and b must be smaller than or equal to rel_tol * absolute(b) for them to be considered equal.

keep_nans
[boolean, string]

Whether to maintain missing values (NaNs) in the result. The possible values are {true, false, "any", "all"}:

  • If false: use default NaN comparison. I.e. NaN == value => false but NaN == NaN => true. Note that this means the result will never contain any NaNs.

  • If true or any: the result will be NaN if any value in a row is NaN

  • If all: the result will be NaN if all values in a row are NaN.

Values must be one of the following:

  • any
  • all
  • True
  • False

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