Python: How to create weighted quantiles in Pandas?

Question

I understand how to create simple quantiles in Pandas using pd.qcut . But after searching, I see nothing to create weighted quantiles. In particular, I want to create a variable that fills the values ​​of the variable of interest (from the smallest to the largest), so that each bit contains equal weight. So far this is what I have:

def wtdQuantile(dataframe, var, weight = None, n = 10):
    if weight == None:
        return pd.qcut(dataframe[var], n, labels = False)
    else:
        dataframe.sort_values(var, ascending = True, inplace = True)
        cum_sum = dataframe[weight].cumsum()
        cutoff = max(cum_sum)/n
        quantile = cum_sum/cutoff
        quantile[-1:] -= 1
        return quantile.map(int)

Is there an easier way or something from Pandas that I am missing?

Edit: As requested, I provide some sample data. In the following, I try to knock out the variable "Var", using the weight "Weight" as the weight. Using pd.qcut, we get an equal number of observations in each box. Instead, I want equal weight in each box or in this case as close to it as possible.

Weight  Var  pd.qcut(n=5)  Desired_Rslt
   10     1            0              0
   14     2            0              0
   18     3            1              0
   15     4            1              1
   30     5            2              1
   12     6            2              2
   20     7            3              2
   25     8            3              3
   29     9            4              3
   45    10            4              4