NumPy Data Types

NumPy provides a wide range of data types to efficiently handle numerical and array-based computations. These types ensure better performance and memory optimization compared to Python’s native data types.

Commonly Used NumPy Data Types

Example: Specifying Data Types

import numpy as np
 
# Create arrays with specific data types
int_array = np.array([1, 2, 3], dtype='int32')
float_array = np.array([1.1, 2.2, 3.3], dtype='float64')
 
print("Integer Array:", int_array)
print("Data Type:", int_array.dtype)
 
print("Float Array:", float_array)
print("Data Type:", float_array.dtype)

Output:

Integer Array: [1 2 3]
Data Type: int32

Float Array: [1.1 2.2 3.3]
Data Type: float64

Type Casting with astype()

The astype() method allows you to explicitly convert an array’s data type to another. This is useful when performing operations that require consistent data types.

Example: Type Casting

# Create a float array
float_array = np.array([1.5, 2.8, 3.9], dtype='float64')
 
# Convert to integer
int_array = float_array.astype('int32')
 
print("Original Array:", float_array)
print("Converted Array:", int_array)

Output:

Original Array: [1.5 2.8 3.9]
Converted Array: [1 2 3]

Inspecting Data Types

Use the dtype attribute to inspect an array’s data type.

array = np.array([10, 20, 30], dtype='int32')
print("Data Type:", array.dtype)  # Output: int32

Why Use NumPy Data Types?

1. Efficiency: NumPy types are more memory-efficient than Python’s native types.
2. Performance: Operations on NumPy types are faster.
3. Control: Enables precise control over memory and computations.

Try It Yourself

Problem 1: Create Arrays with Different Data Types

Create three arrays with int32, float64, and bool_ data types. Print their values and data types.

import numpy as np
 
int_array = np.array([1, 2, 3], dtype='int32')
float_array = np.array([1.1, 2.2, 3.3], dtype='float64')
bool_array = np.array([True, False, True], dtype='bool_')
 
print("Integer Array:", int_array, "| Data Type:", int_array.dtype)
print("Float Array:", float_array, "| Data Type:", float_array.dtype)
print("Boolean Array:", bool_array, "| Data Type:", bool_array.dtype)

Problem 2: Type Casting

Create an array with floating-point numbers and convert it to integers using astype(). Print both the original and converted arrays.

import numpy as np
 
float_array = np.array([10.5, 20.8, 30.2], dtype='float64')
int_array = float_array.astype('int32')
 
print("Original Array:", float_array, "| Data Type:", float_array.dtype)
print("Converted Array:", int_array, "| Data Type:", int_array.dtype)