NumPy Arrays and Data Types

NumPy Arrays and Data Types

NumPy arrays (ndarray) are homogeneous – every element shares the same data type (dtype).

Creating arrays of different dims

import numpy as np

# 1‑D array (vector)
vec = np.arange(5)          # [0 1 2 3 4]

# 2‑D array (matrix)
mat = np.arange(12).reshape(3,4)
print(mat)

# 3‑D array (tensor)
# shape: (2, 3, 4)
tensor = np.arange(24).reshape(2,3,4)
print(tensor.shape)

Specifying data types

float_arr = np.array([1, 2, 3], dtype=np.float64)
int_arr = np.array([1.2, 2.5, 3.7], dtype=np.int32)  # truncates

Inspecting dtype

print(float_arr.dtype)   # float64
print(int_arr.dtype)     # int32

Common dtypes

• np.int8, np.int16, np.int32, np.int64
• np.uint8, np.uint16, …
• np.float16, np.float32, np.float64
• np.bool_