NumPy ndarray.dtype Attribute

NumPy ndarray.dtype

The dtype attribute of a NumPy ndarray object returns the data type of the array’s elements. It provides information about the type of data stored in the array, such as integers, floats, or custom-defined types.

Syntax

ndarray.dtype

Return Value

Returns a numpy.dtype object, which describes the type of data stored in the array. It includes information about the data type, byte order, and size.

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Examples

1. Checking the Data Type of an Array

In this example, we create an integer array and check its data type using the dtype attribute.

import numpy as np

# Creating a NumPy array with integer values
arr = np.array([1, 2, 3, 4, 5])

# Checking the data type of the array elements
print(arr.dtype)  # Output: int64 (or int32 depending on the system)

Output:

int64

The output shows that the array elements are stored as 64-bit integers.

2. Checking the Data Type of a Floating-Point Array

Here, we create a floating-point array and check its data type.

import numpy as np

# Creating a NumPy array with floating-point values
arr = np.array([1.5, 2.7, 3.8, 4.2])

# Checking the data type of the array elements
print(arr.dtype)  # Output: float64

Output:

float64

Since the elements are decimal numbers, NumPy assigns them the float64 data type by default.

3. Creating an Array with a Specific Data Type

We can explicitly specify the data type of a NumPy array using the dtype parameter.

import numpy as np

# Creating an array with integers but specifying dtype as float
arr = np.array([1, 2, 3, 4], dtype=np.float32)

# Checking the data type
print(arr.dtype)  # Output: float32

Output:

float32

Even though the values are integers, they are stored as 32-bit floating-point numbers due to the specified dtype.

4. Checking Data Type of a Boolean Array

Boolean arrays store elements as bool type.

import numpy as np

# Creating a boolean NumPy array
arr = np.array([True, False, True])

# Checking the data type
print(arr.dtype)  # Output: bool

Output:

bool

Since the array contains boolean values, NumPy assigns them the bool data type.

5. Checking Data Type of a String Array

NumPy stores string data using the U (Unicode) type with a fixed length.

import numpy as np

# Creating a NumPy array with string elements
arr = np.array(["apple", "banana", "cherry"])

# Checking the data type
print(arr.dtype)  # Output: <U6 (or similar based on longest string length)

Output:

<U6

The <U6 output means that the array elements are Unicode strings with a maximum length of 6 characters.