iwantcoding.com
🔥 Daily 👥 Rooms 🏆 Top Log in Sign up

Array Creation

By · Reviewed & last updated · Part of NumPy & Pandas Tutorial

NumPy ships a handful of array constructors. np.array wraps a Python sequence; zeros / ones / full create constant arrays; arange / linspace create ranges; random creates random arrays.

Every constructor you actually use

EXAMPLE
import numpy as np

# From a Python sequence
np.array([1, 2, 3])              # 1-D
np.array([[1, 2], [3, 4]])       # 2-D

# Constants
np.zeros((3, 4))                 # all 0.0
np.ones((2, 3), dtype=np.int32)
np.full((2, 2), 7)
np.empty((2, 2))                 # uninitialised — faster, but random bytes

# Ranges
np.arange(0, 10, 2)              # [0, 2, 4, 6, 8]
np.linspace(0, 1, 5)             # 5 evenly spaced from 0 to 1
np.logspace(0, 3, 4)             # [1, 10, 100, 1000]

# Identity
np.eye(3)                        # 3x3 identity

# Random — modern API via default_rng
rng = np.random.default_rng(seed=42)
rng.integers(0, 10, size=5)
rng.normal(loc=0, scale=1, size=(2, 3))
rng.choice(['a', 'b', 'c'], size=4, p=[0.7, 0.2, 0.1])

# Like another array
base = np.zeros((3, 3))
np.empty_like(base)
np.ones_like(base, dtype=int)

Why it matters

np.empty is faster than np.zeros when you’ll overwrite the array immediately. Don’t use it if you need a known initial value — you’ll get whatever garbage was in memory.

Tip: Tweak the snippet with Try it Yourself », then sit the quiz at the bottom of the page.

Example

Example
import numpy as np
np.zeros((2, 3))           # zeros
np.ones((2, 3))            # ones
np.eye(3)                  # identity
np.arange(0, 10, 2)        # 0,2,4,6,8
np.linspace(0, 1, 5)       # evenly spaced
Try it Yourself »

Discussion

Loading…