bucketplot.py
def x_point_wgt_av(df_agg, x_var):
    """
    Set the x_point to be the weighted average of x_var within the bucket,
    weighted by stat_wgt.
    """
    if not (x_var + '_wgt_av') in df_agg.columns:
        raise ValueError(
            "\n\tx_point_wgt_av: This method can only be used when"
            "\n\tthe weighted average has already been calculated."
        )
    
    res = df_agg.assign(
        x_point=lambda df: df[x_var + '_wgt_av']
    )
    return(res)

    Set the x_point to be the weighted average of x_var within the bucket,
    weighted by stat_wgt.
    
Set the x_point to be the weighted average of x_var within the bucket,
weighted by stat_wgt.
Set the x_point to be the weighted average of x_var within the bucket weighted by stat_wgt .
def x_point_wgt_av(df_agg, x_var):
    if not (x_var + '_wgt_av') in df_agg.columns:
        raise ValueError(
            "\n\tx_point_wgt_av: This method can only be used when"
            "\n\tthe weighted average has already been calculated."
        )
    res = df_agg.assign(
        x_point=lambda df: df[x_var + '_wgt_av']
    )
    return(res)
def x_point_wgt_av ( df_agg , x_var ) : if not ( x_var + '_wgt_av' ) in df_agg . columns : raise ValueError ( "\n\tx_point_wgt_av: This method can only be used when" "\n\tthe weighted average has already been calculated." ) res = df_agg . assign ( x_point = lambda df : df [ x_var + '_wgt_av' ] ) return ( res )
Set the x_point to be the weighted average of x_var within the bucket,
weighted by stat_wgt.
Set the x_point to be the weighted average of x_var within the bucket weighted by stat_wgt .
"""
    Set the x_point to be the weighted average of x_var within the bucket,
    weighted by stat_wgt.
    """
df_agg
x_var