Numpy data Types - Coding

NumPy is a powerful Python library that can manage different types of data. Here we will explore the Datatypes in NumPy and How we can check and create datatypes of the NumPy array.

DataTypes in NumPy

A data type in NumPy is used to specify the type of data stored in a variable. Here is the list of characters to represent data types available in NumPy.

Character	Meaning
b	Boolean
f	Float
m	Time Delta
O	Object
U	Unicode String
i	Integer
u	Unsigned Integer
c	Complex Float
M	DateTime
S	String
V	A fixed chunk of memory for other types (void)

The list of various types of data types provided by NumPy are given below:

Data Type	Description
bool_	Boolean
int_	Default integer type (int64 or int32)
intc	Identical to the integer in C (int32 or int64)
intp	Integer value used for indexing
int8	8-bit integer value (-128 to 127)
int16	16-bit integer value (-32768 to 32767)
int32	32-bit integer value (-2147483648 to 2147483647)
int64	64-bit integer value (-9223372036854775808 to 9223372036854775807)
uint8	Unsigned 8-bit integer value (0 to 255)
uint16	Unsigned 16-bit integer value (0 to 65535)
uint32	Unsigned 32-bit integer value (0 to 4294967295)
uint64	Unsigned 64-bit integer value (0 to 18446744073709551615)
float_	Float values
float16	Half precision float values
float32	Single-precision float values
float64	Double-precision float values
complex_	Complex values
complex64	Represent two 32-bit float complex values (real and imaginary)
complex128	Represent two 64-bit float complex values (real and imaginary)

Checking the Data Type of NumPy Array

We can check the datatype of Numpy array by using dtype. Then it returns the data type all the elements in the array. In the given example below we import NumPy library and craete an array using “array()” method with integer value. Then we store the data type of the array in a variable named “data_type” using the ‘dtype’ attribute, and after then we can finally, we print the data type.

Python3

# Import NumPy Module
import numpy as np
 
# Create a NumPy array
arr = np.array([1, 2, 3, 4, 5])
 
# Check the data type of the array
data_type = arr.dtype
print("Data type:", data_type)

Output:

Data type: int64

Create Arrays With a Defined Data Type

We can create an array with a defined data type by specifying “dtype” attribute in numpy.array() method while initializing an array. In the below code we have created various types of defined arrays such as ‘float64’, ‘int32’, ‘complex128’, and ‘bool’.

Python3

# Import NumPy Array
import numpy as np
 
# Creating a float64 array
arr1 = np.array([1, 2, 3, 4], dtype=np.float64)  
 
# Creating a 3x3 int32 array of zeros
arr2 = np.zeros((3, 3), dtype=np.int32)  
 
# Creating a 2x2 complex128 array of ones
arr3 = np.ones((2, 2), dtype=np.complex128)  
 
# Creating a 1D bool array
arr4 = np.empty((4,), dtype=np.bool_)  
 
# Print the arrays and their data types
print("arr1:\n", arr1)
print("Data type of arr1:", arr1.dtype)
 
print("\narr2:\n", arr2)
print("Data type of arr2:", arr2.dtype)
 
print("\narr3:\n", arr3)
print("Data type of arr3:", arr3.dtype)
 
print("\narr4:\n", arr4)
print("Data type of arr4:", arr4.dtype)

Output:
Screenshot-2023-08-18-123440

Convert Data Type of NumPy Arrays

We can convert data type of an arrays from one type to another using astype() function. In the below code we have initialize an array with float type values. After that we have convert that float64 type array to int32 type using astype() function. Finally, print the array and their types of original array and new array.

Python3

import numpy as np
 
# Create an array with float data type elements
arr_original = np.array([1.2, 2.5, 3.7])
 
# Converting to int32
arr_new = arr_original.astype(np.int32)  
 
# Print the original its type
print("Original array:", arr_original)
print("Data type of original array:", arr_original.dtype)
 
# Print new array and its type
print("\nNew array:", arr_new)
print("Data type of new array:", arr_new.dtype)

Output:

Screenshot-2023-08-18-125409

Reffered: https://www.geeksforgeeks.org

Numpy

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