bucketplot.py
def x_edges_min_max(df_agg):
    """
    Set the x-axis edges to be the min and max values of `x_var`.
    Does not make sense to use this option when min and max are not numeric.
    This might result in zero width intervals, in which case a warning is given.
    """
    if not np.issubdtype(df_agg['x_min'].dtype, np.number):
        raise ValueError(
            "\n\tx_edges_min_max: This method can only be used when"
            "\n\tx_min and x_max are numeric data types."
        )
        
    if (df_agg['x_min'] == df_agg['x_max']).any():
        warning(
            "x_edges_min_max: At least one bucket has x_min == x_max, "
            "so using this method will result in zero width intervals."
        )
    
    res = df_agg.assign(
        # Get the coordinates for plot: interval edges
        x_left=lambda df: df['x_min'],
        x_right=lambda df: df['x_max'],
    )
    return(res)

    Set the x-axis edges to be the min and max values of `x_var`.
    Does not make sense to use this option when min and max are not numeric.
    This might result in zero width intervals, in which case a warning is given.
    
Set the x-axis edges to be the min and max values of `x_var`.
Does not make sense to use this option when min and max are not numeric.
This might result in zero width intervals, in which case a warning is given.
Set the x - axis edges to be the min and max values of ` x_var ` . Does not make sense to use this option when min and max are not numeric . This might result in zero width intervals in which case a warning is given .
def x_edges_min_max(df_agg):
    if not np.issubdtype(df_agg['x_min'].dtype, np.number):
        raise ValueError(
            "\n\tx_edges_min_max: This method can only be used when"
            "\n\tx_min and x_max are numeric data types."
        )
    if (df_agg['x_min'] == df_agg['x_max']).any():
        warning(
            "x_edges_min_max: At least one bucket has x_min == x_max, "
            "so using this method will result in zero width intervals."
        )
    res = df_agg.assign(
        x_left=lambda df: df['x_min'],
        x_right=lambda df: df['x_max'],
    )
    return(res)
def x_edges_min_max ( df_agg ) : if not np . issubdtype ( df_agg [ 'x_min' ] . dtype , np . number ) : raise ValueError ( "\n\tx_edges_min_max: This method can only be used when" "\n\tx_min and x_max are numeric data types." ) if ( df_agg [ 'x_min' ] == df_agg [ 'x_max' ] ) . any ( ) : warning ( "x_edges_min_max: At least one bucket has x_min == x_max, " "so using this method will result in zero width intervals." ) res = df_agg . assign ( x_left = lambda df : df [ 'x_min' ] , x_right = lambda df : df [ 'x_max' ] , ) return ( res )
Set the x-axis edges to be the min and max values of `x_var`.
Set the x - axis edges to be the min and max values of ` x_var ` .
"""
    Set the x-axis edges to be the min and max values of `x_var`.
    Does not make sense to use this option when min and max are not numeric.
    This might result in zero width intervals, in which case a warning is given.
    """ # Get the coordinates for plot: interval edges
df_agg