equal

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.

Usage

The following examples show how the step can be used in a recipe.

Examples

Example 1
Example 2
Signature

To check exact equality of numeric columns num1 and num2

equal(ds.num1, ds.num2) -> (ds.num1_num2_eq)

To check approximate equality of numeric columns num1 and num2, with differences of less than 0.001 being considered “equal” use the abs_tol parameter, (note that for reasons of limited precision in how numbers are stored it would be safer to use e.g. 0.0011 or even 0.002 to approximate equality to three decimals):

equal(ds.num1, ds.num2, {"abs_tol": 0.001}) -> (ds.aprox_eq)

General syntax for using the step in a recipe. Shows the inputs and outputs the step is expected to receive and will produce respectively. For futher details see sections below.

equal(*columns: column, {
    "param": value,
    ...
}) -> (result: boolean)

Inputs & Outputs

The following are the inputs expected by the step and the outputs it produces. These are generally columns (ds.first_name), datasets (ds or ds[["first_name", "last_name"]]) or models (referenced by name e.g. "churn-clf").

Inputs

column

One or more columns to check for equality.

Outputs

column[boolean]

required

Output column indicating row-wise equality of the input columns.

Configuration

The following parameters can be used to configure the behaviour of the step by including them in a json object as the last “input” to the step, i.e. step(..., {"param": "value", ...}) -> (output).

Parameters

number

default:"0"

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

number

default:"0"

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.

[boolean, string]

default:"false"

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

Analyse

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Usage

Inputs & Outputs

Configuration

​Usage

​Inputs & Outputs

​Configuration

Usage

Inputs & Outputs

Configuration