BINNED_STATISTIC_2D

=BINNED_STATISTIC_2D(x, y, values, [statistic], [bins], [xlowerlimit], [xupperlimit], [ylowerlimit], [yupperlimit])

=BINNED_STATISTIC_2D({0.1;0.1;0.1;0.6}, {2.1;2.6;2.1;2.1}, {1;1;1;1}, "count", 2, 0, 1, 2, 3)

=BINNED_STATISTIC_2D({0.1;0.1;0.1;0.6}, {2.1;2.6;2.1;2.1}, {10;20;30;40}, "mean", 2, 0, 1, 2, 3)

=BINNED_STATISTIC_2D({0.1;0.1;0.1;0.6}, {2.1;2.6;2.1;2.1}, {1;2;3;4}, "sum", 2, 0, 1, 2, 3)

=BINNED_STATISTIC_2D({0.1;0.1;0.1;0.6}, {2.1;2.6;2.1;2.1}, {10;20;30;40}, "median", 2, 0, 1, 2, 3)

from scipy.stats import binned_statistic_2d as scipy_binned_statistic_2d
 
def binned_statistic_2d(x, y, values, statistic='mean', bins=10, xlowerlimit=None, xupperlimit=None, ylowerlimit=None, yupperlimit=None):
    """
    Computes a bidimensional binned statistic (mean, sum, median, etc.) for the input data.
 
    Args:
        x: 2D list or scalar of float values. Data to bin along the first dimension.
        y: 2D list or scalar of float values. Data to bin along the second dimension.
        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).
        xlowerlimit: Lower bound for the x bin range (optional).
        xupperlimit: Upper bound for the x bin range (optional).
        ylowerlimit: Lower bound for the y bin range (optional).
        yupperlimit: Upper bound for the y 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(y, list):
        y = [[y]]
    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_y = [float(item) for row in y 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, y, and values must be 2D lists or scalars of numbers."
    if len(flat_x) == 0 or len(flat_y) == 0 or len(flat_values) == 0:
        return "Invalid input: x, y, and values must not be empty."
    if len(flat_x) != len(flat_y) or len(flat_x) != len(flat_values):
        return "Invalid input: x, y, 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 (xlowerlimit is not None and xupperlimit is not None and
        ylowerlimit is not None and yupperlimit is not None):
        try:
            range_arg = [(float(xlowerlimit), float(xupperlimit)), (float(ylowerlimit), float(yupperlimit))]
        except Exception:
            return "Invalid input: xlowerlimit, xupperlimit, ylowerlimit, yupperlimit must be numbers."
    try:
        if range_arg:
            res = scipy_binned_statistic_2d(flat_x, flat_y, flat_values, statistic=statistic, bins=nbins, range=range_arg)
        else:
            res = scipy_binned_statistic_2d(flat_x, flat_y, flat_values, statistic=statistic, bins=nbins)
        stat = res.statistic
    except Exception as e:
        return f"scipy.stats.binned_statistic_2d error: {e}"
    # Return as 2D list (row-major)
    return [[float(x) if x is not None else None for x in row] for row in stat.tolist()]