Nonparametric estimation of population parameters for finite populations has been used with great success for data that fit the independent and identically distributed framework. However, most of these approaches do not extend to data from multistage samples. In this work, we present a method for developing a nonparametric distribution function for a finite population that has been stratified. Proportional allocation of sampling weights has been utilized alongside kernel weights. Asymptotic properties of the estimator are derived and are compared with those of existing model based estimators using the simulated sets of data. The results show that applying the bias reduction technique to a stratified population greatly improves precision of the estimator