NUMPY Tutorial

🔣 NumPy Array Data Types (dtype)

Every NumPy array has a data type (called dtype) that tells us what kind of elements are stored in the array — for example, integers, floats, or strings.

✅ Common NumPy Data Types

• int32, int64 – Integer numbers
• float32, float64 – Floating point numbers
• bool_ – Boolean values (True or False)
• complex64, complex128 – Complex numbers
• str_ or unicode_ – Strings

📌 Check the Data Type of an Array

Use array.dtype to check the data type of a NumPy array:

import numpy as np

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

Output:

int64

🎯 Specify a Data Type Using dtype

You can specify the data type when creating an array:

# Create float array
arr = np.array([1, 2, 3], dtype='float32')
print(arr)

Output:

[1. 2. 3.]

🔄 Convert Data Type of Existing Array

Use astype() to convert an array’s data type.

arr = np.array([1.7, 2.8, 3.9])
int_arr = arr.astype('int')
print(int_arr)

Output:

[1 2 3]

🧠 Why Is dtype Important?

• Improves performance by optimizing memory usage
• Ensures type safety when performing operations
• Lets you control precision in scientific computing