import numpy as np

# Data size
N = 5
M = 5

# How many entries to replace
REPLACE_COUNT = 4

# What to replace with
REPLACE_WITH = 10

# Just for clarity of display
np.set_printoptions(precision=2, suppress=True)

# Geherate some data.
data = np.random.rand(N, M)

print('Original: ')
print(data)

# Replacement is done by replacing randon items in flattened view.
# This boils down to selecting numbers from 0 to N * M.
# Notice replace=False, so we don't select the same number two times.
# Instead of using N * M one could use np.prod(data.shape)
data.flat[np.random.choice(N * M, REPLACE_COUNT, replace=False)] = REPLACE_WITH
print('After replacement: ')
print(data)