BINNED_STATISTIC

=BINNED_STATISTIC(x, values, [statistic], [bins], [lowerlimit], [upperlimit])

=BINNED_STATISTIC({1;1;2;5;7}, {1.0;1.0;2.0;1.5;3.0}, "mean", 2)

=BINNED_STATISTIC({1;1;2;5;7}, {1.0;1.0;2.0;1.5;3.0}, "sum", 2)

=BINNED_STATISTIC({1;2;3;4;5;6;7}, {10;20;30;40;50;60;70}, "median", 3, 1, 7)

=BINNED_STATISTIC({1;2;3;4;5}, {10;20;30;40;50}, "count", 2)

from scipy.stats import binned_statistic as scipy_binned_statistic
 
def binned_statistic(x, values, statistic='mean', bins=10, lowerlimit=None, upperlimit=None):
    """
    Computes a binned statistic (mean, sum, median, etc.) for the input data.
 
    Args:
        x: 2D list or scalar of float values. Data to bin.
        values: 2D list or scalar of float values. Data to compute statistics on.
        statistic: Statistic to compute ('mean', 'sum', 'median', 'count', 'std', 'min', 'max'). Default is 'mean'.
        bins: Number of bins (integer, default 10).
        lowerlimit: Lower bound for the bin range (optional).
        upperlimit: Upper bound for the bin range (optional).
 
    Returns:
        2D list of statistic values for each bin, or an error message (str) if input is invalid.
 
    This example function is provided as-is without any representation of accuracy.
    """
    # Convert scalars to 2D lists if needed
    if not isinstance(x, list):
        x = [[x]]
    if not isinstance(values, list):
        values = [[values]]
    # Flatten 2D lists to 1D
    try:
        flat_x = [float(item) for row in x for item in (row if isinstance(row, list) else [row])]
        flat_values = [float(item) for row in values for item in (row if isinstance(row, list) else [row])]
    except Exception:
        return "Invalid input: x and values must be 2D lists or scalars of numbers."
    if len(flat_x) == 0 or len(flat_values) == 0:
        return "Invalid input: x and values must not be empty."
    if len(flat_x) != len(flat_values):
        return "Invalid input: x and values must have the same length."
    if statistic not in ('mean', 'sum', 'median', 'count', 'std', 'min', 'max'):
        return "Invalid input: statistic must be one of 'mean', 'sum', 'median', 'count', 'std', 'min', 'max'."
    try:
        nbins = int(bins)
    except Exception:
        return "Invalid input: bins must be an integer."
    if nbins < 1:
        return "Invalid input: bins must be >= 1."
    # Prepare range
    range_arg = None
    if lowerlimit is not None and upperlimit is not None:
        try:
            range_arg = (float(lowerlimit), float(upperlimit))
        except Exception:
            return "Invalid input: lowerlimit and upperlimit must be numbers."
    try:
        if range_arg:
            res = scipy_binned_statistic(flat_x, flat_values, statistic=statistic, bins=nbins, range=range_arg)
        else:
            res = scipy_binned_statistic(flat_x, flat_values, statistic=statistic, bins=nbins)
        stat = res.statistic
    except Exception as e:
        return f"scipy.stats.binned_statistic error: {e}"
    # Return as 2D list (column vector)
    return [[float(x)] if x is not None else [None] for x in stat]