How to calculate 95% confidence intervals using Bootstrap method
I have a simple statistical solution : 
Confidence intervals are based on the standard error. 
The standard error in your case is the standard deviation of your 1000 bootstrap means. Assuming a normal distribution of the sampling distribution of your parameter(mean), which should be warranted by the properties of the Central Limit Theorem, just multiply the equivalent z-score of the desired confidence interval with the standard deviation. Therefore:
lower boundary = mean of your bootstrap means - 1.96 * std. dev. of your bootstrap means
upper boundary = mean of your bootstrap means + 1.96 * std. dev. of your bootstrap means
95% of cases in a normal distribution sit within 1.96 standard deviations from the mean
hope this helps
I'm trying to calculate the confidence interval for the mean value using the method of bootstrap in python. Let say I have a vector a with 100 entries and my aim is to calculate the mean value of these 100 values and its 95% confidence interval using bootstrap. So far I have manage to resample 1000 times from my vector using the np.random.choice function. Then for each bootstrap vector with 100 entries I calculated the mean. So now I have 1000 bootstrap mean values and a single sample mean value from my initial vector but I'm not sure how to proceed from here. How could I use these mean values to find the confidence interval for the mean value of my initial vector? I'm relatively new in python and it's the first time I came across with the method of bootstrap so any help would be much appreciated.