python - How to print values of a 3 dimensional array that are less than a specific value? -

so have dataset named data_low looks this:

(array([ 0,  0,  0, ..., 30, 30, 30]), array([  2,   2,   5, ..., 199, 199, 199]), array([113, 114,  64, ...,  93,  94,  96])) 

and shape: (84243,3).

i can unique value precipitation dataset this:

in [63]: print(data_low[0, 2, 113]) out [63]: 1.74 

what trying print values in dataset have value of less 3.86667. i'm pretty new python, , don't know loop use in order this. appreciated. thanks.

edit: here program have. context, used ncecat combine 31 datasets, why have 3 1d arrays: first array day, , 2nd , 3rd represent longitude , latitude.

data_path = r"c:\users\matth\downloads\trmm_3b42rt\3b42rt_daily.201001.7.nc4" f = dataset(data_path)  latbounds = [ -38 , -20 ] lonbounds = [ 115 , 145 ] # degrees east ?  lats = f.variables['lat'][:]  lons = f.variables['lon'][:]  # latitude lower , upper index latli = np.argmin( np.abs( lats - latbounds[0] ) ) latui = np.argmin( np.abs( lats - latbounds[1] ) )   # longitude lower , upper index lonli = np.argmin( np.abs( lons - lonbounds[0] ) ) lonui = np.argmin( np.abs( lons - lonbounds[1] ) )  precip_subset = f.variables['precipitation'][ : , lonli:lonui , latli:latui ]  print(precip_subset.shape) print(precip_subset.size) print(np.mean(precip_subset))  data_low = np.nonzero((precip_subset > 0) & (precip_subset < 3.86667)) print(data_low)  x = list(zip(*data_low))[:] xx = np.array(x) print(xx.shape) print(xx.size)  in range(0,84243,1):     print(data_low[i, i, i]) 

out:

in [136]: %run "c:\users\matth\precip_anomalies.py" (31, 120, 72) 267840 1.51398 (array([ 0,  0,  0, ..., 30, 30, 30]), array([  7,   7,   7, ..., 119, 119,  119]), array([ 9, 10, 11, ..., 23, 53, 54])) (13982, 3) 41946 [ 0  0  0 ..., 30 30 30]  typeerrortraceback (most recent call last) c:\users\matth\precip_anomalies.py in <module>()      53       54 in range(0,84243,1): ---> 55     print(data_low[i, i, i])  typeerror: tuple indices must integers, not tuple 

given data_low numpy matrix (based on question not, 3-tuple 3 arrays), can use masking:

data_low[data_low < 3.86667] 

this return 1d numpy array contains values less 3.86667.

if want these vanilla python list, can use:

list(data_low[data_low < 3.86667]) 

but if want further processing (in numpy) better use numpy array anyway.