question about `sklearn.ensemble.BaggingClassifier`
Found the answer hiding in lines 93-100 in the bagging.py file.
Here is what I understand - the bootstrapping process works in three steps:  


Calculate number of samples to train each estimator on (the max_samples variable in the bagging.py code). In your case its $1.0 * x.shape[0] = 150$.  
Select with repetition the needed max samples(as calculated in the previous step).
The selection is done by using randint function, and it generates an array of the x series indices. A given index can appear in this array more than once.
In order to account for indices(samples) that were selected more than one, a weights vector is passed into the base estimator fit function. So, for example, a sample that was selected twice will have $weight * 2$ and it will have the desired impact on the fitting algorithm.  


As to my understanding, one can use the estimators_samples_ only to find out which samples were included and not how many times each of them did.