import numpy as np


# In data analysis, NaN stands for "Not a Number". It is used to represent missing or undefined values in a dataset.
print(np.nan)
print(np.array([1, 2, np.nan]))

# np.linspace(start, stop, num) Generates an array of num evenly spaced values between start and stop.
a = np.linspace(0, 1, 5)
print(a)

# When using reshape, total number of elements must match the original array. 
# If the number of elements doesn't match, it cause an error.
c1 = np.array([1, 2, 3, 4, 5, 6])
c2 = c1.reshape((2, 3))

# Generate normally distribution of random numbers.
rng = np.random.default_rng(123) # Set a random seed. Only for debugging.
print(rng.standard_normal(size=(4, 2)))