What are the main ways to create NumPy arrays?

Knowing the idiomatic array-creation functions is a baseline NumPy skill. Each function is designed for a specific situation and picking the right one keeps code readable and avoids unnecessary copies.

import numpy as np

# From Python sequences
a = np.array([1, 2, 3, 4])              # 1-D, dtype inferred (int64)
b = np.array([[1, 2], [3, 4]], dtype=np.float32)  # 2-D, explicit dtype

# Pre-filled arrays
np.zeros((3, 4))          # 3×4 array of 0.0
np.ones((2, 2))           # 2×2 array of 1.0
np.full((3, 3), 7)        # 3×3 array filled with 7
np.eye(4)                 # 4×4 identity matrix

# Ranges
np.arange(0, 10, 2)       # [0 2 4 6 8]  — like range() but returns ndarray
np.linspace(0, 1, 5)      # [0.  0.25 0.5 0.75 1.]  — N evenly spaced points

# Random arrays (use default_rng for reproducibility)
rng = np.random.default_rng(seed=42)
rng.random((3, 3))        # uniform [0, 1)
rng.standard_normal(1000) # standard normal distribution
rng.integers(0, 100, size=10)  # random ints in [0, 100)

# From existing data without copying
np.asarray([1.0, 2.0, 3.0])   # no copy if already array-like and matching dtype
np.frombuffer(b'\x01\x02\x03', dtype=np.uint8)  # from raw bytes

np.linspace is preferred over np.arange for floating-point ranges because arange with a float step can produce unexpected element counts due to floating-point rounding. linspace guarantees exactly N points.

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