Numpy Beginner's Guide (Update)

Time for action – handling NaNs with the nanmean(), nanvar(), and nanstd() functions

We will apply jackknife resampling to the stock data. Each value will be omitted by setting it to Not a Number (NaN). The nanmean(), nanvar(), and nanstd() can then be used to compute the arithmetic mean, variance, and standard deviation.

1. First, initialize a 30-by-3 array for the estimates as follows:

   estimates = np.zeros((len(c), 3))

2. Loop through the values and generate a new dataset by setting one value to NaN at each iteration of the loop. For each new set of values, compute the estimates:

   for i in xrange(len(c)):
      a = c.copy()
      a[i] = np.nan
   
      estimates[i,] = [np.nanmean(a), np.nanvar(a), np.nanstd(a)]

3. Print the variance for each estimate (you can also print the mean ...