MODE

This function returns the mode, the most frequently occurring numeric value, from a 2D dataset.

For frequency counts f(v) over unique values v, the mode is any value maximizing frequency:

v_{\text{mode}} \in \arg\max_{v} f(v)

In this wrapper, non-numeric cells are ignored and an error is returned when no value repeats (that is, when no mode exists under the selected rule).

Excel Usage

=MODE(data)

data (list[list], required): 2D array of numeric values to find the mode of. Non-numeric values are ignored.

Returns (float): The most common value in the data, or str error message if invalid.

Example 1: Simple mode with repeated value

Inputs:

data
1	2
2	3
2	4

Excel formula:

=MODE({1,2;2,3;2,4})

Expected output:

2

Example 2: Multiple modes returns smallest

Inputs:

data
1	2
2	1
3	3

Excel formula:

=MODE({1,2;2,1;3,3})

Expected output:

1

Example 3: Non-numeric values ignored

Inputs:

data
1	a
2	2
2	b

Excel formula:

=MODE({1,"a";2,2;2,"b"})

Expected output:

2

Example 4: Mode of a larger array

Inputs:

data
10	20	30
20	40	50
60	20	80

Excel formula:

=MODE({10,20,30;20,40,50;60,20,80})

Expected output:

20

Python Code

from scipy.stats import mode as scipy_mode

def mode(data):
    """
    Return the modal (most common) numeric value in the input data, returning the smallest if there are multiple modes.

    See: https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.mode.html

    This example function is provided as-is without any representation of accuracy.

    Args:
        data (list[list]): 2D array of numeric values to find the mode of. Non-numeric values are ignored.

    Returns:
        float: The most common value in the data, or str error message if invalid.
    """
    # Helper function to normalize scalar input to 2D list
    def to2d(x):
        return [[x]] if not isinstance(x, list) else x

    try:
      # Normalize input for 2D list args
      data = to2d(data)

      # If input is not a list of lists, return error
      if not isinstance(data, list) or not all(isinstance(row, list) for row in data):
        return "Error: Invalid input: data must be a 2D list."
      # Flatten and filter numeric values
      flat = []
      for row in data:
        for val in row:
          try:
            v = float(val)
            flat.append(v)
          except (ValueError, TypeError):
            continue
      if len(flat) < 2:
        return "Error: Invalid input: data must contain at least two numeric values."
      # Use scipy.stats.mode
      res = scipy_mode(flat, keepdims=False, axis=None, nan_policy='omit')
      if res.count < 2:
        return "Error: No mode found. At least one value must appear more than once."
      # Return as Python float scalar, not numpy type
      mode_value = float(res.mode.item()) if hasattr(res.mode, 'item') else float(res.mode)
      return mode_value
    except Exception as e:
      return f"Error: {str(e)}"

Online Calculator

data *

2D array of numeric values to find the mode of. Non-numeric values are ignored.