Numerical Binning

Description

The Numerical Binning activity groups numerical data into “bins” or “intervals” based on a chosen rule. This helps to make large sets of continuous numbers easier to analyze by simplifying them into smaller, predefined ranges.

Input

Data Only

Output

Transformed Data

Configuration Fields

• Column

Choose the column with numerical data that you want to bin.

• Binning mode This is how the system decides how to split the numbers into bins. You can choose from the following

  • Sturges Good for smaller datasets; it calculates the number of bins based on a formula.
  • Freedman diaconis Works well when there are outliers in the data, calculates bins using a different formula.
  • Scott This method minimizes binning errors and is based on bin width.
  • Square Root A simple method where the number of bins is the square root of the data size.
  • Fixed Size Intervals You define how wide the bins should be, and it uses that size for each bin.

• Custom You define the exact bins yourself.

• Output column The new column that will store the binned values.

• Include original If you want to keep the original numbers alongside the new binned values.

  • Enabled Keep the original values.
  • Disabled Only show the binned values.

• Number Of Bins

How many bins you want to divide the data into.

• Minimum Value (rendered only when mode is fixed intervals)

The smallest number that should be included in the bins.

• Maximum Value (rendered only when mode is fixed intervals) The largest number that should be included in the bins.

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Sample Input

Age
23
35
47
59
72
89

Sample Configuration

Sample Output

Age	Age_Binned
23	20-40
35	20-40
47	40-60
59	40-60
72	60-80
89	80-100