How to Use np.array in NumPy: Syntax and Examples

Use np.array() to create a NumPy array from a list or other sequence. Pass your data inside the parentheses, like np.array([1, 2, 3]), to get a fast, efficient array for numerical operations.

Syntax

The basic syntax of np.array() is simple:

• np.array(object, dtype=None, copy=True, order='K', subok=False, ndmin=0)

Here, object is the data you want to convert into an array, usually a list or tuple. dtype sets the data type (like int or float). Other parameters control copying and array shape but are optional.

python

np.array(object, dtype=None, copy=True, order='K', subok=False, ndmin=0)

Example

This example shows how to create a NumPy array from a Python list and check its type and contents.

python

import numpy as np

# Create a NumPy array from a list
arr = np.array([10, 20, 30, 40])

# Print the array
print(arr)

# Print the type to confirm it's a NumPy array
print(type(arr))

Output

[10 20 30 40]
<class 'numpy.ndarray'>

Common Pitfalls

One common mistake is passing a list of lists with uneven lengths, which creates an array of dtype=object instead of a numeric array. Another is forgetting to import NumPy as np.

Also, using np.array() on a list of mixed data types can lead to unexpected data type conversions.

python

import numpy as np

# Wrong: uneven nested lists
arr_wrong = np.array([[1, 2], [3, 4, 5]])
print(arr_wrong)
print(arr_wrong.dtype)

# Right: uniform nested lists
arr_right = np.array([[1, 2], [3, 4]])
print(arr_right)
print(arr_right.dtype)

Output

[list([1, 2]) list([3, 4, 5])]
object
[[1 2]
 [3 4]]
int64

Quick Reference

Parameter	Description
object	Input data (list, tuple, etc.) to convert to array
dtype	Optional: desired data type (e.g., int, float)
copy	Optional: whether to copy data (default True)
order	Optional: memory layout ('C', 'F', or 'K')
subok	Optional: if True, subclasses are preserved
ndmin	Optional: minimum number of dimensions for output array

Key Takeaways

Use np.array() to convert lists or tuples into fast NumPy arrays.

Ensure input data is uniform in shape and type for best results.

Remember to import NumPy as np before using np.array().

Specify dtype if you need a specific data type for the array.

Avoid uneven nested lists to prevent object arrays.