In Python, variables can be created of different data types, and changing data types can be very helpful in solving a problem such as reversing an array which can be done easily by first converting the array into a string and then reversing it.

In the article “Manipulating Data Types in NumPy”, we will discuss the Numpy Arrays and manipulate the data types using the astype() function.

Numpy Arrays

In this section of the article “Manipulating Data Types in NumPy”, we will discuss the Numpy arrays because we are going to implement the Numpy arrays in this article. Numpy arrays are similar to the Python lists but more flexible as we have methods provided by Numpy which can be used for faster execution time and more efficiency.

Python

Python

# Importing Numpy library

import numpy as np # creating Numpy array using 'array()' function

Explanation In the above example, we have reversed the Python array by converting it into a numpy array.

Python astype() function

In this section of the article “Manipulating Data Types in NumPy”, we will see how the data types can be changed in Numpy arrays. In Python, the “astype()” function can be used to change or manipulate data types in NumPy arrays.

The dtype can be passed as an argument in the astype() function, where all the generic and built-in data types are supported by the astype() function.

Syntax of astype() function

Here is the syntax of the astype() function in Python to change the data type of the variable:

In the above syntax, four parameters can be passed to the astype() function, where the first parameter is mandatory, and the rest are optional. Here is the explanation of the parameters that can be passed to the astype() function:

dtype: The dtype is the data type, the mandatory parameter that can be passed, such as int32, float64, and complex128.

copy: The copy parameter is an optional parameter with a boolean value. If the value is true, then the new copy will be returned. By default, the value of copy is true.

errors: The error parameter is an optional parameter that can be used to control the raising of exceptions on invalid data.

kwargs: The kwargs is known as a keyword argument that is passed on to the constructor.

Changing Data Types in Numpy

In this section of the article “Manipulating Data Types in NumPy”, we will see how the data types of NumPy arrays can be changed using the astype() function in Python with some examples:

Integer to Float

In this example, we will see how the integer data type can be converted to the float data type with the astype() function in Python:

Python

Python

# Importing Numpy library import numpy as np

# Creating numpy array using 'array()' function arr = np.array([53, 21, 38, 100]) print('Array of Integer Data Types:', arr)

# Changing the integer array to float arr = arr.astype('float64') print('Array of Float Data Types:', arr)

You can also try this code with Online Python Compiler

Explanation In the above example, we changed the data type of the Python array from integer to float using the astype() function by converting it into a numpy array.

Integer to Complex

In this example, we will see how the integer data type can be converted to the complex data type with the astype() function in Python:

Python

Python

# Importing Numpy library import numpy as np

# Creating numpy array using 'array()' function arr = np.array([53, 21, 38, 100]) print('Array of Integer Data Types:', arr)

# Changing the integer array to complex arr = arr.astype('complex128') print('Array of Integer Data Types:', arr)

You can also try this code with Online Python Compiler

Explanation In the above example, we changed the data type of the Python array from integer to complex using the astype() function by converting it into a numpy array.

Complex to Integer

In this example, we will see how the integer data type can be converted to the integer data type with the astype() function in Python:

Python

Python

# Importing Numpy library import numpy as np

# Creating numpy array using 'array()' function arr = np.array([ 53.+0.j, 21.+0.j, 38.+0.j, 100.+0.j]) print('Array of Complex Data Types:', arr)

# Changing the integer complex to integer arr = arr.astype('int32') print('Array of Integer Data Types:', arr)

You can also try this code with Online Python Compiler

Explanation In the above example, we changed the data type of the Python array from complex to integer using the astype() function by converting it into a numpy array.

Integer to String

In this example, we will see how the integer data type can be converted to the string data type with the astype() function in Python:

Python

Python

# Importing Numpy library

import numpy as np

# Creating numpy array using 'array()' function

arr = np.array([53, 21, 38, 100])

print('Array of Integer Data Types:', arr) # Changing the integer array to string array arr = arr.astype('str') print('Array of String Data Types:', arr)

You can also try this code with Online Python Compiler

Explanation In the above example, we changed the data type of the Python array from integer to string using the astype() function by converting it into a numpy array.

Frequently Asked Questions

Why to use Numpy arrays over Python lists?

Numpy arrays offer faster execution time and contiguous memory allocation, and they can also be used with various libraries such as Pandas, Scipy, and Scikit-learn.

How can the random numbers be generated using Numpy?

In Numpy, the numpy.random module can be used to generate the random numbers. Specifically rand() method is used where the parameter can be passed for the limits.

How the Numpy arrays of custom data types can be created?

In Numpy, there is a constructor called dtype(), which can be used to create an array of custom data types such as integer, float, and complex.

What is the default data type of Numpy arrays?

When creating the array, you can specify the data type of a NumPy array using the dtype parameter. For example: numpy.array([1, 2, 3], dtype=np.float64).

Conclusion

In Python, Numpy is a library that can be used to change data types of the variables using the astype() function. The dtype parameter is passed to the function, which defines the new data to be changed.

In the article “Manipulating Data Types in NumPy”, we discussed the Numpy Arrays, the astype() function in Python, and then we discussed changing data types in Numpy using the astype() function. Here are more articles that are recommended to read: