Querying a NumPy array of NumPy arrays saved as an

I generate a npz file as follows:

import numpy as np
import os

# Generate npz file
dataset_text_filepath = 'test_np_load.npz'
texts = []
for text_number in range(30000): 
    texts.append(np.random.random_integers(0, 20000, 
                 size = np.random.random_integers(0, 100)))
texts = np.array(texts)
np.savez(dataset_text_filepath, texts=texts)

This gives me this ~7MiB npz file (basically only 1 variable texts, which is a NumPy array of Numpy arrays):

which I load with numpy.load():

# Load data
dataset = np.load(dataset_text_filepath)

If I query it as follows, it takes several minutes:

# Querying data: the slow way
for i in range(20):
    print('Run {0}'.format(i))
    random_indices = np.random.randint(0, len(dataset['texts']), size=10)
    dataset['texts'][random_indices]

while if I query as follows, it takes less than 5 seconds:

# Querying data: the fast way
data_texts = dataset['texts']
for i in range(20):
    print('Run {0}'.format(i))
    random_indices = np.random.randint(0, len(data_texts), size=10)
    data_texts[random_indices]

How comes the second method is so much faster than the first one?

dataset['texts'] reads the file each time it is used. load of a npz just returns a file loader, not the actual data. It's a 'lazy loader', loading the particular array only when accessed. The load docs could be clearer, but they say:

- If the file is a ``.npz`` file, the returned value supports the context
  manager protocol in a similar fashion to the open function::

    with load('foo.npz') as data:
        a = data['a']

  The underlying file descriptor is closed when exiting the 'with' block.

and from the savez:

 When opening the saved ``.npz`` file with `load` a `NpzFile` object is
returned. This is a dictionary-like object which can be queried for
its list of arrays (with the ``.files`` attribute), and for the arrays
themselves.

More details in help(np.lib.npyio.NpzFile)