Open In App

Check data type in NumPy

Improve
Improve
Like Article
Like
Save
Share
Report

Numpy, is originally called numerical python, but in short, we pronounce it as Numpy. NumPy is a general-purpose array-processing package in Python. It provides high-performance multidimensional data structures like array objects and tools for working with these arrays. Numpy provides faster and more efficient calculations of matrices and arrays.

NumPy provides familiarity with almost all mathematical functions. In numpy, these functions are called universal function ufunc.

Check Data Type in NumPy

Here, are various ways to check NumPy dtype attributes. here we are discussing some generally used methods for checking NumPy dtype attributes those are following.

  • Using dtype
  • Using array function array()

Check NumPy dtype Attributes Using dtype

Example 1: Check Integer Datatype

In this example, the code snippet utilizes the NumPy library to create an array, ‘arr,’ containing integers. The array is then printed along with its datatype, showcasing the flexibility and simplicity of NumPy for numerical operations in Python.

Python3




# importing numpy library
import numpy as np
 
# creating and initializing an array
arr = np.array([1, 2, 3, 23, 56, 100])
 
# printing the array and checking datatype
print('Array:', arr)
 
print('Datatype:', arr.dtype)


Output:

Array: [  1   2   3  23  56 100]
Datatype: int32

Example 2 : Check String Datatype

In this example, the NumPy library is used to create an array (`arr_1`) containing strings like ‘apple’, ‘ball’, ‘cat’, and ‘dog’. The script then prints the array and its data type, revealing that the elements are of type Unicode string.

Python3




import numpy as np
 
# creating and initializing array of string
arr_1 = np.array(['apple', 'ball', 'cat', 'dog'])
 
# printing array and its datatype
print('Array:', arr_1)
 
print('Datatype:', arr_1.dtype)


Output:

Array: ['a' 'b' 'c' 'd']
Datatype: <U1

Using array function array()

Creating the array with a defined datatype. Creating numpy array by using an array function array(). This function takes argument dtype that allows us to define the expected data type of the array elements:

Example 1: In this example code utilizes the NumPy library to create an array ‘arr’ containing elements [1, 2, 3, 8, 7, 5], with a specified datatype ‘S’ (string). The script then prints both the array and its datatype, showcasing the flexibility of NumPy arrays to handle various data types.

Python3




import numpy as np
 
# Creating and initializing array with datatype
arr = np.array([1, 2, 3, 8, 7, 5], dtype='S')
 
# printing array and its datatype
print("Array:", arr)
print("Datatype:", arr.dtype)


Output:

S is used for defining string datatype. We use i, u, f, S and U for defining various other data types along with their size.

Array: [b'1' b'2' b'3' b'8' b'7' b'5']
Datatype: |S1

Example 2: In this example a NumPy array named ‘arr’ is created and initialized with values [1, 2, 3, 4], specifying a 32-bit integer datatype (‘i4’). The array and its datatype are then printed using the NumPy library.

Python3




import numpy as np
 
# creating and initialising array along
# with datatype and its size 4 i.e. 32bytes
arr = np.array([1, 2, 3, 4], dtype='i4')
 
# printing array and datatype
print('Array:', arr)
print('Datatype:', arr.dtype)


Output:

The size of integer elements is 4 i.e. 32bytes

Array: [1 2 3 4 8 9 5]
Datatype: int32

Example 3: In this example code utilizes NumPy to create and initialize an array named ‘arr’ with elements [1, 2, 3, 4], specifying a data type of 64-bit integers (‘i8’). The print statements then display the array and its data type, showcasing the resulting array and the specified data type, which is 64-bit integers.

Python3




import numpy as np
 
# creating and initialising array along
# with datatype and its size 8 i.e. 64bytes
arr = np.array([1, 2, 3, 4], dtype='i8')
 
# printing array and datatype
print('Array:', arr)
print('Datatype:', arr.dtype)


Output:

And in this example the size of elements is 64bytes.

Array: [1 2 3 4 8 9 7]
Datatype: int64

Example 4 : In this example, a NumPy array named arr is created and initialized with the values 1, 2, 3, 4, 8, 9, 7. The array is explicitly assigned a data type of 32-bit floating-point (‘f4’). The resulting array and its data type are then printed using the print function

Python3




import numpy as np
 
# creating and initialising array along
# with datatype and its size 4 i.e. 32bytes
arr = np.array([1, 2, 3, 4, 8, 9, 7], dtype='f4')
 
# printing array and datatype
print('Array:', arr)
print('Datatype:', arr.dtype)


Output:

In this example, the data type is float and the size is 32bytes.

Array: [1. 2. 3. 4. 8. 9. 7.]
Datatype: float32

Example 5: In this example code utilizes the NumPy library to create an array named ‘arr’ containing integers [1, 2, 3, 4, 8, 9, 7] with a specified datatype ‘S2’, indicating a string of length 2. The code then prints the array and its datatype, showcasing the result as ‘Array: [b’1′ b’2′ b’3′ b’4′ b’8′ b’9′ b’7′]’ and ‘Datatype: |S2’, respectively.

Python3




import numpy as np
 
# creating and initialising array along
# with datatype and its size 2
arr = np.array([1, 2, 3, 4, 8, 9, 7], dtype='S2')
 
# printing array and datatype
print('Array:', arr)
print('Datatype:', arr.dtype)


Output:

In this example, the datatype is a string and the size is 2.

Array: [b'1' b'2' b'3' b'4' b'8' b'9' b'7']
Datatype: |S2



Last Updated : 10 Jan, 2024
Like Article
Save Article
Previous
Next
Share your thoughts in the comments
Similar Reads