Built-in Data Types in Python
Python provides several built-in data types by default to handle different kinds of data. Here are some of the most commonly used ones:
String (str): Used to represent text. Strings are enclosed in quotation marks.
Example: "Hello, World!", 'Python'
Integer (int): Represents whole numbers, both positive and negative.
Example: -3, 0, 42
Float (float): Used for representing real (decimal) numbers.
Example: 3.14, 0.99, -100.5Boolean (bool): Represents one of two values — True or False.
Example: True, False
Complex (complex): Used to represent complex numbers, which include a real and an imaginary part.
Example: 1 + 2j, 3.5 + 4.5j
Data Types in NumPy
NumPy extends Python’s built-in data types with additional types specifically designed for numerical computing. These data types are often represented by single-character codes for efficiency.
Below is a list of commonly used NumPy data types along with their character codes:
i – Integer
Used for signed integer numbers.
Example: int8, int16, int32, int64
u – Unsigned Integer
Represents non-negative integers.
Example: uint8, uint16
f – Float
Used for floating-point (decimal) numbers.
Example: float16, float32, float64
c – Complex Float
Represents complex numbers with float components.
Example: complex64, complex128
b – Boolean
Used to represent True or False.
m – Timedelta
Represents differences in time (durations).
M – Datetime
Used for date and time representation.
O – Object
Generic Python objects (useful for mixed types).
S – String
Represents byte strings (ASCII-encoded).
U – Unicode String
Used for Unicode text data.
V – Void
A fixed-size raw chunk of memory, often used for custom or structured data.
Checking the Data Type of a NumPy Array
In NumPy, every array has a special property called dtype that reveals the data type of its elements.
Program:
Get the data type of an array:
import numpy as np
arr = np.array([1, 2, 3, 4])
print(arr.dtype)
This will display the data type of the array arr.a
Output :
int64
Program:
import numpy as np
arr = np.array(['apple', 'banana', 'cherry'])
print(arr.dtype)
Output:
<U6
Creating Arrays with a Specific Data Type
In NumPy, we can create arrays using the array() function. This function includes an optional parameter called dtype that allows us to specify the desired data type for the array elements.
Program:
Create an array with elements of string data type:
import numpy as np
arr = np.array([1, 2, 3, 4], dtype='S')
print(arr)
print(arr.dtype)
Output:
[b'1' b'2' b'3' b'4']
|S1
Program:
Create an array with elements of 4-byte integer data type:
import numpy as np
arr = np.array([1, 2, 3, 4], dtype='i4')
print(arr)
print(arr.dtype)
Output:
[1234]
int34
What Happens if a Value Cannot Be Converted?
If you specify a data type (dtype) that cannot accommodate certain elements in the array, NumPy will raise a ValueError.
What is a ValueError?
In Python, a ValueError occurs when a function receives an argument of the correct type, but with an inappropriate or invalid value.
Example:
Trying to convert a non-numeric string, such as 'a', to an integer will result in a ValueError:
import numpy as np
arr = np.array(['a', '2', '3'], dtype='i')
# This will raise a ValueError
You can specify the desired data type either as:
A string, such as 'i' for integer or 'f' for float.
A Python data type, like int or float.
