NumPy Array Creation

Last Updated: 02 Nov 2025

NumPy gives us multiple easy ways to create arrays — from lists, using built-in functions, or generating patterns.

Creating Arrays from Python Lists

Convert a normal Python list into a NumPy ndarray (n-dimensional) array using np.array().

import numpy as np

arr = np.array([10, 20, 30, 40])
print(arr)
print(type(arr))

arr=np.array([1,2,3], dtype=np.float64)
print(arr)
print(type(arr))

Creating 2D and 3D Arrays

import numpy as np

arr = np.array([[1, 2, 3], [4, 5, 6]])
print(arr)

arr = np.array([[[1, 2, 3], [4, 5, 6]], [[1, 2, 3], [4, 5, 6]]])
print(arr)

arr = np.array([[[1, 2, 3], [4, 5, 6]], [[1, 2, 3], [4, 5, 6]], [[1, 2, 3], [4, 5, 6]]])
print(arr)

🧠 Tip:

  • 1D → simple list
  • 2D → list of lists
  • 3D → list of list of lists

Numpy Arrays Attributes

  • ndim: Returns the number of dimensions of the array.
  • shape: Returns the dimensions of the array.
  • dtype: Returns the data type of the array.
  • size: Returns the total number of elements in the array.
  • itemsize: Returns the size of each element in the array in bytes.
  • nbytes: Total bytes consumed by array
import numpy as np

arr = np.array([[1, 2, 3], [4, 5, 6]])

print("Array:\n", arr)
print("Dimensions:", arr.ndim)
print("Shape:", arr.shape)
print("Size:", arr.size)
print("Data Type:", arr.dtype)
print("Item Size:", arr.itemsize)
print("Total Bytes:", arr.nbytes)
print("Memory Address:", arr.data)
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