In This Article, You Will Learn About Python Numpy Data Types.
Python Numpy Data Types – Before moving ahead, let’s know a little bit about Python NumPy Slicing
These data types default to Python
String – Used to represent text data, the text will be given under quotation marks.
Integer – Used to represent integer numbers.
Float – Used to represent real numbers
Boolean – Used to indicate True or False
Complex number – Used to represent complex numbers.
NumPy Data Types
NumPy also supports additional data types. These data types can be referred to with just one character such as f for float or O for the object.
Below is a listing of all data types available in NumPy and the characters that represent them.
i – integer
b – boolean
u – unsigned integer
f – float
c – complex float
m – timedelta
M – datetime
O – object
S – string
U – unicode string
V – fixed for other types of memory
Verifying the Data Type of an Array
A property of the NumPy array object is dtype which returns the data type for the array.
Example – Returning the data type for an array object.
import numpy as np Array = np.array([1, 2, 3, 4]) print(Array.dtype)
Output - int32
To sum up, it returned the data type of an array.
Example – Returning the data type for an array object.
import numpy as np Array = np.array([1+2j, 2+1j, 3+7j, 4+2j]) print(Array.dtype)
Output - complex128
In conclusion, it returned the data type of an array.
Create Arrays with a Defined Type of Data
To create arrays, we use the array() function. This function can accept an optional argument: the argument dtype. This allows us to specify the expected data type for the array elements.
Example – Creating an array with data type float.
import numpy as np Array = np.array([1, 2, 3, 4], dtype='f') print(Array) print(Array.dtype)
Output - [1. 2. 3. 4.] float32
Overall, it returned an array with specified data type float.
Example – Creating an array with data type 8 bytes integer.
import numpy as np Array = np.array([1, 2, 3, 4,5,6], dtype='i8') print(Array) print(Array.dtype)
Output - [1 2 3 4 5 6] int64
All things considered, it returned an array with data type 8 bytes integer.
Note: Data types – i, u, f, S, and U can be defined with size as well.
What happens if a value cannot be converted?
NumPy will raise an error if a type contains elements that can’t be cast.
ValueError: In Python, ValueError is raised when the type of passed argument to a function is unexpected/incorrect.
An error will be raised if a non-integer string, such as ‘k’, is converted to an integer.
Example – An error will be raised if a non-integer string will be converted.
import numpy as np Array = np.array(['k', '2', '3'], dtype='i') print (Array)
Output - ValueError: invalid literal for int() with base 10: 'k'.
As shown above, it returned an error cause of conversion of non-integer string.
Convert Data Types on Existing Arrays
You can change the data type of an array by making a copy using the astype() method.
astype() creates a copy from the array and allows you to specify the data type as parameters.
You can specify the data type using a string, like ‘c’ for complex float, ‘b’ for Boolean valuer, etc. You can also use the data type directly, such as int for integer.
Example – Changing data type from integer to float by using ‘f’ as parameter value:
import numpy as np
Array = np.array([8, 29, 31])
Array_type_float = Array.astype('f')
print(Array_type_float)
print(Array_type_float.dtype)
Output - [ 8. 29. 31.] float32
In the final analysis, it returned the changed data type of an array.
Example – Changing data type from float to integer by using ‘i’ as the parameter value.
import numpy as np
Array = np.array([1.1, 2.1, 8.1])
New_Array = Array.astype('i')
print(Array)
print(New_Array.dtype)
As can be seen, it returned the changed data type of an array.
Example – Changing data type from complex to integer numbers.
import numpy as np
Array = np.array([1+1j, 2+6j, 8+1j])
New_Array = Array.astype('i')
print(New_Array)
print(New_Array.dtype)
Output - [1 2 8] int32
As a result, it returned the changed data type of array i,e., integer data type.
If you find anything incorrect in the above-discussed topic and have any further questions, please comment below.
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