Vikram Chiluka

Python code.compile_command() Function

Python code Module:

The code module contains functions for implementing read-eval-print loops in Python. There are two classes and two convenience functions available that can be used to build applications that give an interactive interpreter prompt.

The code module includes a variety of functions for emulating the behaviour of the standard interpreter’s interactive mode.

The compile command works in the same way as the built-in compile function, but it does some additional checks(tests) to ensure that you provide it with a complete Python statement.

Python code.compile_command() Function:

We can use the code.compile_command() method to compile a single or numerous lines of code in order to check for syntax errors.

Syntax:

 code.compile_command(code)

Return Value:

An object or compilation error if occurred is returned by the compile_command() function.

code.compile_command() Function in Python

 

Method #1: Using compile_command() Function (Static Input)

Approach:

  • Import compile_command function from code module using the import keyword.
  • Give the code as static input and store it in a variable.
  • Pass the above-given code as an argument to the compile_command() function
    to compile the given code.
  • The Exit of the Program.

Below is the implementation:

# Import compile_command function from code module using the import keyword
from code import compile_command

# Give the code as static input and store it in a variable.
code = 'x = 3 y = 4; print(x*y)'

# Pass the above given code as an argument to the compile_command() function
# to compile the given code
compile_command(code)

Output:

 File "<input>", line 1
x = 3 y = 4; print(x*y)
^
SyntaxError: invalid syntax

Method #2: Using compile_command() Function (User Input)

Approach:

  • Import compile_command function from code module using the import keyword.
  • Give the code as user input using the input() function and store it in a variable.
  • Pass the above-given code as an argument to the compile_command() function
    to compile the given code.
  • The Exit of the Program.

Below is the implementation:

# Import compile_command function from code module using the import keyword
from code import compile_command

# Give the code as user input using the input() function and store it in a variable.
code = input("Enter some random code:\n")
print()

# Pass the above given code as an argument to the compile_command() function
# to complie the given code
compile_command(code)

Output:

Enter some random code:
+x=5

File "<input>", line 1
SyntaxError: can't assign to operator

 

 

 

 

Python linecache.getline() Function

Python linecache Module:

The linecache module allows you to read any line from a Python source file while attempting to optimise internally using a cache, which is useful in the case where numerous lines are read from a single file. The traceback module uses this to retrieve source lines for inclusion in the formatted traceback.

Python linecache.getline() Function:

We can obtain a specific line by providing a line number using the linecache.getline() method. One use case for this method is to locate/find the syntax error in the specified line number within a large file by using the linecache.getline() function.

Syntax:

linecache.getline(file_name, line_number)

Return Value:

The content(data) of the given text file is returned by the getline() fnction.

Let us take the text file samplefile.txt as an example here.

samplefile.txt:

Hello this is Python-programs
Good morning all
Welcome to the Python coding platform
Hurry up!!!!

linecache.getline() Function in Python

Method #1: Using getline() Function (Static Input)

Approach:

  • Import linecache module using the import keyword
  • Pass some random text file name, line number as arguments to the getline() function of the linecache module to get the content of the given line number.
  • Store it in a variable.
  • Print the content of the given line.
  • The Exit of the Program.

Below is the implementation:

# Import linecache module using the import keyword
import linecache 

# Pass some random text file name, line number as arguments to the 
# getline() function of the linecache module to get the content of the 
# given line number.
# Store it in a variable.
rslt = linecache.getline('samplefile.txt', 2)

# Print the content of the given line.
print("The content of the given line-2 is:")
print(rslt)

Output:

The content of the given line-2 is:
Good morning all

Method #2: Using getline() Function (User Input)

Approach:

  • Import linecache module using the import keyword.
  • Give the line number as user input using the int(input()) function and store it in a variable.
  • Pass some random text file name, above given line number as arguments to the getline() function of the linecache module to get the content of the given line number.
  • Store it in a variable.
  • Print the content of the given line.
  • The Exit of the Program.

Below is the implementation:

# Import linecache module using the import keyword
import linecache 

# Give the line number as user input using the int(input()) function 
# and store it in a variable.
gvn_line = int(input("Enter some random number = "))

# Pass some random text file name, above given line number as arguments to the 
# getline() function of the linecache module to get the content of the 
# given line number.
# Store it in a variable.
rslt = linecache.getline('samplefile.txt', gvn_line)

# Print the content of the given line.
print("The content of the given line {",gvn_line,"} is:")
print(rslt)

Output:

Enter some random number = 1
The content of the given line { 1 } is:
Hello this is Python-programs

Python Numpy matrix.cumsum() Function

NumPy Library 

NumPy is a library in python that is created to work efficiently with arrays in python. It is fast, easy to learn, and provides efficient storage. It also provides a better way of handling data for the process. We can create an n-dimensional array in NumPy. To use NumPy simply have to import it in our program and then we can easily use the functionality of NumPy in our program.

NumPy is a Python library that is frequently used for scientific and statistical analysis. NumPy arrays are grids of the same datatype’s values.

Numpy matrix.cumsum() Function: 

Using the matrix.cumsum() function of the NumPy module, we can find the cumulative sum of a given matrix and returns the output as a 1-Dimensional matrix.

Syntax:

 matrix.cumsum()

Return Value:

The cumulative sum of a given matrix is returned by the cumsum() function.

Cumulative sum:

The cumulative sum denotes “how much so far.” The cumulative sum is defined as the sum of a given sequence that grows or increases with more additions.

Numpy matrix.cumsum() Function in Python

For 1-Dimensional (1D) Matrix

Approach:

  • Import numpy module using the import keyword
  • Create a matrix(1-Dimensional) using the matrix() function of numpy module by passing some random 1D matrix as an argument to it and store it in a variable
  • Apply cumsum() function on the given matrix to get the cumulative sum of a given matrix.
  • Store it in another variable
  • Print the cumulative sum of a given matrix.
  • The Exit of the Program.

Below is the implementation:

# Import numpy module using the import keyword
import numpy as np
            
# Create a matrix(1-Dimensional) using the matrix() function of numpy module by passing 
# some random 1D matrix as an argument to it and store it in a variable
gvn_matrx = np.matrix('[1, 4, 2]')
            
# Apply cumsum() function on the given matrix to get the cumulative sum of a given matrix.
# Store it in another variable
rslt = gvn_matrx.cumsum()
# Print the cumulative sum of a given matrix.
print("The cumulative sum of a given matrix:")
print(rslt)

Output:

The cumulative sum of a given matrix:
[[1 5 7]]

Explanation:

Here it prints 1, adds 1+4 = 5, and adds that result to the next element i.e, 5+2=7

For 2-Dimensional (2D) Matrix

Approach:

  • Import numpy module using the import keyword
  • Create a matrix(2-Dimensional) using the matrix() function of numpy module by passing some random 2D matrix as an argument to it and store it in a variable
  • Apply cumsum() function on the given matrix to get the cumulative sum of a given matrix.
  • Store it in another variable
  • Print the cumulative sum of a given matrix.
  • The Exit of the Program.

Below is the implementation:

# Import numpy module using the import keyword
import numpy as np
            
# Create a matrix(2-Dimensional) using the matrix() function of numpy module by passing 
# some random 2D matrix as an argument to it and store it in a variable
gvn_matrx = np.matrix('[1, 2; 4, 5]')
            
# Apply cumsum() function on the given matrix to get the cumulative sum of a given matrix.
# Store it in another variable
rslt = gvn_matrx.cumsum()
# Print the cumulative sum of a given matrix.
print("The cumulative sum of a given matrix:")
print(rslt)

Output:

The cumulative sum of a given matrix:
[[ 1 3 7 12]]

Python html.escape() Function

We must escape special characters that are not markup text but may be misinterpreted as such when saving raw HTML in databases or variables.

 <, >, ", ', and &. are examples of these characters.

If certain characters are not escaped, the browser may display a web page improperly. For example, the quote marks around “Python Programs” in the following HTML content may cause confusion between the end and beginning of a new string.

Hello this is "Python Programs"

Special entity names and entity numbers are essentially escape sequences that replace these characters in HTML. In HTML, escape sequences begin with an ampersand and terminate with a semicolon.

The table below lists the special characters that HTML 4 recommends escaping, as well as their entity names and numbers:

Character                            Entityname                         EntityNumber

>                                                &gt;                                            &#62;

<                                                &lt;                                             &#60;

”                                                 &quot;                                       &#34;

&                                                &amp;                                       &#38;

We can use the html.escape() method in Python to encode your HTML in an ascii string to escape these characters. escape() takes one optional argument quote, which is set to True by default, and an HTML script as an argument. To use html.escape(), you must first import the html module, which is included in Python 3.2 and higher.

Python html.escape() Function:

Using the html.escape() method, we can convert an html script into a string by replacing special characters with ascii characters.

Syntax:

 html.escape(string)

Return Value:

A string of ASCII character script from HTML is returned by the escape() function.

html.escape() Function in Python

Method #1: Using escape() Function (Static Input)

Approach:

  • Import html module using the import keyword
  • Give the HTML script as static input and store it in a variable.
  • Pass the above-given HTML script as an argument to the escape() function to encode the given HTML string in ASCII string and store it in another variable.
  • Print the ASCII string for the given HTML string.
  • The Exit of the Program.

Below is the implementation:

# Import html module using the import keyword
import html

# Give the HTML script as static input and store it in a variable.
gvn_html =  '<html><head></head><body><h1>welcome to "Python-programs"</h1></body></html>'

# Pass the above given HTML script as an argument to the escape() function to
# encode the given HTML string in ascii string and store it in another variable.
rslt = html.escape(gvn_html)

# Print the ascii string for the given html string.
print("The ASCII string for the given html string is:")
print(rslt)

Output:

The ASCII string for the given html string is:
&lt;html&gt;&lt;head&gt;&lt;/head&gt;&lt;body&gt;&lt;h1&gt;welcome to &quot;Python-programs&quot;&lt;/h1&gt;&lt;/body&gt;&lt;/html&gt;

Method #2: Using escape() Function (User Input)

Approach:

  • Import html module using the import keyword
  • Give the HTML script as user input using the input() function and store it in a variable.
  • Pass the above-given HTML script as an argument to the escape() function to encode the given HTML string in ASCII string and store it in another variable.
  • Print the ASCII string for the given HTML string.
  • The Exit of the Program.

Below is the implementation:

# Import html module using the import keyword
import html

# Give the HTML script as user input using the input() function and store it in a variable.
gvn_html =  input("Enter some random HTML string:\n")
print()

# Pass the above given HTML script as an argument to the escape() function to
# encode the given HTML string in ascii string and store it in another variable.
rslt = html.escape(gvn_html)

# Print the ascii string for the given html string.
print("The ASCII string for the given html string is:")
print(rslt)

Output:

Enter some random HTML string:
'<html><body><p>Good morning,&& <all> </p></body></html>'

The ASCII string for the given html string is:
&#x27;&lt;html&gt;&lt;body&gt;&lt;p&gt;Good morning,&amp;&amp; &lt;all&gt; &lt;/p&gt;&lt;/body&gt;&lt;/html&gt;&#x27;

 

12 pip Commands For Python Developers

Pip is a Python package manager tool. If you’ve worked with Python before, you’ve almost certainly used pip as well. Installing packages is the most obvious use of pip, but there are a couple of other useful commands you may not be aware of.
Pip can quickly and efficiently download, install, and manage Python packages.

You’re probably wondering what exactly a Package Manager is and what it accomplishes.

What is a Package Manager?
A package manager is a tool for computer languages that makes it simple to install external dependencies. It installs and uninstalls any package without a problem.

For Example: For package management, JavaScript uses npm, Python uses pip, and.NET uses NuGet.

Package management in Python has grown in popularity and importance to the point where it is now included by default with the Python installation starting with versions 3.4 for Python3 and 2.7.9 for Python2.

12 pip Commands For Python Developers

1)Pip Install and Uninstall Package

This command is essential for installing and uninstalling a package according to your needs. You can use this command on your command prompt to install or uninstall Python by heading to the Python folder.

pip install packageName

pip uninstall packageName

For Example:

If you want to install & uninstall matplotlib module then follow the below commands:

pip install matplotlib - for installation
pip uninstall matplotlib - for Uninstallating

2)Update pip

If the pip is not in its latest version, you can also upgrade it. To check if it has an updated version, simply type pip –version; if it does not, simply update it using the command below. Make sure this command is written in the Python folder.

pip install –upgrade pip

Here the command pip install –upgrade pip,  updates pip to the most recent/latest version.

3)Update Package

pip install -U <packageName>

For Example:
If we want to update the version of numpy, below is the command:

pip install -U numpy

4)Update All Packages

If an older version of the package exists, pip will remove it first. It will then download and install the most recent version from PyPI. If you already have an up-to-date version installed, pip will do nothing.

To update all packages at once, use the command pip freeze > requirements.txt to create a requirements.txt file that contains all of the packages. To update all of the packages, simply update the requirements.txt file.

Packages with updated versions will be excluded in the requirements.txt file, while the remainder will be updated.

pip freeze > requirements.txt
pip install -r requirements.txt –upgrade

For Example:

pip freeze requirements.txt generates a temporary file named requirements.txt that may include all of the packages that need to be installed, and then pip install -r requirements.txt –upgrade updates all of the items in requirements.txt at once.

5)Information about an Installed Package

You can get the information about the package that is installed already using the following command:

pip show packageName

For Example:
You get information about the numpy package that is installed already by:

pip show numpy

pip will return a “Package Not Found” warning if you try to acquire information about a package that isn’t installed in your environment.

5)List all the installed Packages

This is one of the most essential commands for any Python programmer. The pip list command displays the list of packages in the current environment. For each package, it also returns the installed version. The packages are arranged in a case-insensitive manner.

pip list

All packages are listed using pip list. However, we may want to display all of the list of packages that are currently outdated for any reason.
To do so, run the command:

"pip list -o" or
"pip list —outdated,"

which will provide a list of packages with both the currently installed version and the most recent accessible version.

6)Pip search

When you’re working on a project and need a package but don’t know which location we can fing it , then use the pip search packageName command. It returns the name of all matching packages as well as information about them.

pip search packageName

For Example:

pip search pandas

This command helps you navigate to pandas by searching the path where it is kept with pip search pandas.

7) Generating Requirement Files using pip

What is requirements.txt file in a code?

Generally, We create(generate) and share the requirements.txt files to make it easier for other developers to install the righ/correct versions of the required Python libraries (or “packages”) in order to run the Python code that we developed.

We can just execute the below command to generate the requirements.txt file:

pip freeze > requirements.txt

This code will create a requirements.txt file in the working directory after execution.

We can now simply use the following command to install all dependencies on their system.

pip install -r requirements.txt

After you’ve installed these dependencies, you’re ready to go.

8)Activate Editable Mode

If you want to develop a package locally after installing it, you can do so in editable mode. It connects the package to the given location, allowing you to make modifications that are immediately reflected.

The below command installs the package in develop or editable mode, which allows you to alter it.

pip install -e

That means, pip install -e opens the package(whichever was previously installed) in an editable form, allowing you to make modifications locally if necessary.

9)Verifying If the installed Packages have compatible dependencies

It checks if the dependencies of all installed packages in the requirements.txt file are appropriate. It examines all three requirements, namely whether the dependencies are compatible, whether the package has an updated version, and whether a package is missing.

pip check

10)To Remove All the Installed Packages by pip

Instead of deleting packages one by one, you can save time by using the commands

pip freeze > requirements.txt && pip uninstall -e requirements.txt -y.

It creates a requirements.txt file with a list of packages and then uninstalls them all.

11)Using pip to specify the Package Version

In Python, the pip install command always installs the most latest versions of a package. To install a specific version of the package rather than the most recent, we can use the pip command to specify the version directly.

pip install packageName==version

For Example:

pip install pandas==2.7

The command above will download and install the package ‘package’ of the version we specified i.e 2.7 (and not the latest one). It will be installed in your Python installation folder, or if you are using a virtual environment, it will be installed here.

12)To install all the requirements from requirements.txt File

pip install -r requirements.txt

The above command will install all of the dependencies required for project implementation. Dependencies contain all of the package’s information.

Differences Between ExpressJS vs NestJS

What precisely is a web application framework?

You may have heard a lot about Express and how it is a Node web application framework that is used to construct online applications. But, exactly, what is a web application framework?

A web framework is a software framework that is used to standardize and automate processes for building applications via the internet, according to Wikipedia.

These several frameworks help with database access, templating resources, and session management. They aid in the development of dynamic websites.

People look for lightweight frameworks while developing large-scale apps. As a result, NestJS has taken command of ExpressJS. Without a question, ExpressJS has grown in popularity among developers in recent years, and NestJS is on its way to becoming more widely used in the coming years.
Let us now look at what ExpressJS and NestJS are and see their most important features of them.

NestJS:

NestJS is a NodeJS framework for developing scalable server-side applications. NestJS allows for understandable and predictable code, thanks to TypeScript, which is gaining popularity. It is a framework that is adaptable, progressive, and versatile. Despite the fact that the API in ExpressJS is reliable, it cannot handle async/await methods. NestJS was implemented to avoid similar problems.

Furthermore, ExpressJS is a lightweight NodeJS framework for developing online and mobile applications.

ExpressJS:

Express.js is a simple framework that works on top of the Node.js web server functionality to simplify APIs and provide some useful new features. It facilitates the organization of your application’s functionality through the use of middleware and routing. It adds useful utilities to Node.js HTTP objects and makes dynamic HTTP objects easier to render.

Features:

  • Quickly and easily creates Node.js web applications.
  • It is simple to set up and customize.
  • You can use HTTP methods and URLs to define application routes.
  • Contains a number of middleware modules that can be used to carry out additional requests and answers.
  • It’s easy to interface(connect) with a variety of template engines, including Jade, Vash, and EJS.
  • Allows you to provide middleware for error handling.

Differences: ExpressJS vs NestJS

 

             ExpressJs

 

                   NestJS

Because ExpressJS is unopinionated, it does not have a set of pre-defined rules to follow, giving developers the ability to create different options and implement code as needed. NestJS is opinionated, which means it has SOLID Principles to follow, which helps to avoid issues during development and also produces applications that are less prone to error. NestJS is entirely Angular-based, hence TypeScript is its primary programming language. The use of TypeScript ensures that the application is reliable and bug-free.
ExpressJs does not follow  MVC (Model View Controller) architecture NestJS follows MVC architecture.
Because ExpressJS does not follow to MVC, there is no correct structure, making the application inefficient and less optimised. NestJS becomes a better choice for developers because it is explicitly built on an architecture that includes components such as modules, controllers, and providers. You can also break the application into microservices, which simplifies development and makes the code easier to understand.
ExpressJS does not support TypeScript, making ExpressJS less efficient and incompatible with many browsers. Decorators are another aspect of TypeScript NestJS also supports TypeScript, which is a big plus. TypeScript follows to static typing, is reliable, and includes a “type” feature. It is extremely useful when developing large-scale, efficient applications. NestJS is wholly built in TypeScript.
Unit testing with ExpressJS requires the creation of distinct code, which takes time and can reduce the application’s productivity rate. NestJS CLI (Command Line Interface) has a default testing environment that is set up with Jest. When a service, interceptor, or controller is formed, the CLI creates a “spec” file that includes auto-generated testing bed code, removing the need for developers to write extra code for unit testing. As a result, unit testing is significantly easier with NestJS than it is with ExpressJS.
ExpressJS is less efficient and has poor performance compared to NestJS. NestJS has a CLI (command-line interface) to increase productivity. A CLI allows you to give instructions directly without having to write extensive scripts. Dependency Injection is another reason why developers like NestJS since it allows you to add as many dependencies as you need for the application to run smoothly. NestJS provides many developers with an out-of-the-box structure and supports third-party plugins. These are the reasons NestJS can assist you in developing scalable applications, which enhances performance and results in an efficient solution.

Return Statement in Python Function

To return a value from a function, use the Python return statement. In a function, the user can only utilize the return statement. It can only be used within the Python function. A return statement contains the return keyword as well as the value to be returned after that.
We can return a single value or multiple values using the return statement in the python function.

Returning Single Value using return Statement in Python Function

Approach:

  • Create a function say multiply() which accepts two numbers as an argument
  • Inside the function, multiply the above passed two numbers and store it in a variable
  • Return the above multiplication result using the return statement
  • Call the above-created multiply() function by passing some random two numbers as arguments
    and store it in another variable
  • Here it multiplies the two numbers(3*4) that we pass by calling the function and stores the result
  • Print the multiplication of the given two numbers.
  • The Exit of the Program.

Below is the implementation:

# Create a function say multiply() which accepts two numbers as an argument
def multiply(x, y):
  # Inside the function, multiply the above passed two numbers and
  # store it in a variable
  mul = x * y
  # Return the above multiplication result using the return statement
  return mul

# Call the above created multiply() function by passing some random two numbers as arguments
# and store it in another variable
# Here it multiplies the two numbers(3*4) that we pass by calling the function and stores the result
rslt = multiply(3, 4)
# Print the multiplication of given two numbers
print(rslt)

Output:

12

Returning Multiple Values using return Statement in Python Function

Approach:

  • Create a function say multiply() which accepts two numbers as an argument
  • Inside the function, multiply the above passed two numbers and store it in a variable
  • Calculate the average of the given two numbers and store it in another variable
  • Return the above multiplication, and average results using the return statement
  • Here we can observe that we are returning multiple values
  • Call the above-created multiply() function by passing some random two numbers as arguments
    and store it in another variable
  • Here it stores both multiplication and average results of the given two numbers
  • Print the multiplication and average of given two numbers.
  • The Exit of the Program.

Below is the implementation:

# Create a function say multiply() which accepts two numbers as an argument
def multiply(x, y):
  # Inside the function, multiply the above passed two numbers and
  # store it in a variable
  mul = x * y
  # Calculate the average of given two numbers and store it in another variable
  average = (x+y)/2
  # Return the above multiplication, average results using the return statement
  # Here we can observe that we are returning multiple values
  return mul, average

# Call the above created multiply() function by passing some random two numbers as arguments
# and store it in another variable
# Here it stores both multiplication, average results of the given two numbers
rslt = multiply(3, 4)
# Print the multiplication, average of given two numbers
print("The multiplication, average of given two numbers is:")
print(rslt)

Output:

The multiplication, average of given two numbers is:
(12, 3.5)

NOTE:

Not only two values, but you can also return multiple values 
using the return statement in python function

Why Import star(*) in Python is a Bad Idea?

We should not use import * in Python applications since it corrupts our namespace. Importing all functions and classes (whether required or not) into our own namespace using the import * corrupts our namespace.

This may conflict with functions you define or functions from other libraries you import It can sometimes be difficult to determine which library a certain function came from at times.

With the import * practice, there is always the possibility of overriding variables/functions, etc.

Here are some more reasons why import * should not be used:

  • It is difficult to know or recognize what is imported from which module, resulting in poor code readability.
  • The possibility of hidden bugs grows.
  • Because pyflake-like tools cannot be utilized, we cannot discover errors in the source code statically.
  • Python, as we know, allows the user to import any module required. However, in huge projects or with numerous lines of code, we will be unable to detect the user-defined function and different methods.

If you still wish to use it, you should be cautious and strive to keep it in good working order.
All of this is not to say that using import * is always a bad idea;  The only thing you should remember while using import * is that you should always use it with caution and discipline.

Example:

# Import the module sampleModule using import keyword and *
from sampleModule import *

# Create a function say multiply() which accepts two numbers as arguments.
def multiply (a, b):
  # Multiply both the passed numbers and return the result
  return a*b

# Call the above created function by passing two random numbers and print the result.
print (multiply (3, 2))

Explanation:

If there was a ‘multiply’ function in module “sampleModule,” the problem would be that the defined ‘multiply’ function overrides the ‘multiply’ function from module “sampleModule.” As a result, it is recommended that import * not be used in practice.

Correct Approach for the above Code:

sampleModule:

# Create a function say multiply() which accepts two numbers as arguments 
def multiply(a, b):
  # Multiply both the passed numbers and return the result
  print(a*b)

Approach:

  • Import the module sampleModule using import keyword and *
  • Create a function say multiply() which accepts two numbers as arguments.
  • Multiply both the passed numbers and return the result
  • Call the above-created function by passing two random numbers and print the result.
  • The Exit of the Program.

Below is the Implementation:

# Import the module sampleModule using import keyword and *
from sampleModule import *

# Create a function say multiply() which accepts two numbers as arguments.
def multiply (a, b):
  # Multiply both the passed numbers and return the result
  return a*b

# Call the above created function by passing two random numbers and print the result.
print (multiply (3, 2))
print(sampleModule.multiply(4,5)

Output:

6
20

Explanation:

Coding in this manner improves code readability, makes debugging easier, and virtually eliminates the possibility of conflict.(may be 0 chances of conflicts).

How to Compute the Sign and Natural Logarithm of the Determinant of an Array in Python?

Let us see how to use the NumPy module in python to compute the sign and natural logarithm of the determinant of an array here.

Python numpy.linalg.slogdet() Function:
The numpy.linalg.slogdet() method in Python helps us to calculate the sign and natural logarithm of the determinant of an array.
If the determinant of an array is very tiny or very large, a call to slogdet may overflow or underflow. This approach is more resistant to such difficulties since it computes the logarithm of the determinant rather than the determinant of itself.

Syntax:

numpy.linalg.slogdet()

Parameters
x: It is the 2-Dimensional array given as input.

Return Value:
The logarithm of the determinant of an array is returned by this method

The method with a sign returns an integer representing the determinant’s sign. This is 1, 0, or -1 for a real matrix. This is a complex number having an absolute value 1 or 0 for a complex matrix. The method logdet returns the natural log of the determinant’s absolute value.

Python Program to Compute the Sign and Natural Logarithm of the Determinant of an Array

Here the numpy.lib.linalg.slogdet() function is used to calculate the log of the determinant of an array and sign.
It returns the sign and logarithm of the determinant of an array. and logarithm.

Approach:

  • Import numpy module using the import keyword
  • Pass some random 2-dimensional array as an argument to the array() function of the numpy module to create an array.
  • Store it in a variable
  • Print the given array
  • Apply the shape attribute on the given array to get the shape of the given array and print the result
  • Apply ndim attribute on the given array to get the dimension of the given array and print the result
  • Apply dtype attribute on the given array to get the datatype of all the elements of the
    given array and print the result
  • Pass the given array as an argument to the slogdet() function of linalg of the numpy module
    to calculate the sign and logarithm of the determinant of the given input 2D array.
  • Store them in two separate variables.
  • Print the sign of the given 2D array
  • Print the logarithm of the determinant of the given input 2D array.
  • The Exit of the Program.

Below is the implementation:

# Import numpy module using the import keyword
import numpy as np

# Pass some random 2-dimensional array as an argument to the array() function
# of the numpy module to create an array.
# Store it in a variable
gvn_arry = np.array([[5, 12],[8, 10]])

# Print the given array
print("The given array is:")
print(gvn_arry)

# Apply shape attribute on the given array to get the shape of the given array and
# print the result
print("The Shape of the given array = ", gvn_arry.shape)

# Apply ndim attribute on the given array to get the dimension of the given array and
# print the result
print("The dimension of the given array = ",gvn_arry.ndim)

# Apply dtype attribute on the given array to get the datatype of all the elements of the
# given array and print the result
print("The datatype of the given array = ",gvn_arry.dtype)

# Pass the given array as an argument to the slogdet() function of linalg of the numpy module
# to calculate the sign and logarithm of determinant of the given input 2D array
# Store them in two separate variables.
arry_sign, det = (np.linalg.slogdet(gvn_arry))

# Print the sign of the given 2D array
print("The sign of the given 2D array = ", arry_sign)

# Print the logarithm of determinant of the given input 2D array
print("The logarithm determinant of the given input 2D array = ", det)

Output:

The given array is:
[[ 5 12]
[ 8 10]]
The Shape of the given array = (2, 2)
The dimension of the given array = 2
The datatype of the given array = int64
The sign of the given 2D array = -1.0
The logarithm determinant of the given input 2D array = 3.828641396489095

Another Example:

If we additionally wish to find the determinant of an array, we can use sign*exp(logdet). The numpy.lib.linalg.slogdet() method returns sign and logdet.

# Import numpy module using the import keyword
import numpy as np

# Pass some random 2-diemsional array as an argument to the array() function
# of the numpy module to create an array.
# Store it in a variable
gvn_arry = np.array([[4, 8],[3, 1]])

# Print the given array
print("The given array is:")
print(gvn_arry)

# Apply shape attribute on the given array to get the shape of the given array and
# print the result
print("The Shape of the given array = ", gvn_arry.shape)

# Apply ndim attribute on the given array to get the dimension of the given array and
# print the result
print("The dimension of the given array = ",gvn_arry.ndim)

# Apply dtype attribute on the given array to get the datatype of all the elements of the
# given array and print the result
print("The datatype of the given array = ",gvn_arry.dtype)

# Pass the given array as an argument to the slogdet() function of linalg of the numpy module
# to calculate the sign and logarithm of determinant of the given input 2D array
# Store them in two separate variables.
arry_sign, det = (np.linalg.slogdet(gvn_arry))

# Print the sign of the given 2D array
print("The sign of the given 2D array = ", arry_sign)

# Print the logarithm of determinant of the given input 2D array
print("The logarithm determinant of the given input 2D array = ", det)

# Print the determinant of the given array with the np.exp() function
print("The determinant of the given array with the np.exp() function = ", arry_sign*np.exp(det))

Output:

The given array is:
[[4 8]
[3 1]]
The Shape of the given array = (2, 2)
The dimension of the given array = 2
The datatype of the given array = int64
The sign of the given 2D array = -1.0
The logarithm determinant of the given input 2D array = 2.995732273553991
The determinant of the given array with the np.exp() function = -19.999999999999996

How to Compute the logarithm Base 10 with NumPy scimath in Python?

The NumPy package includes numpy.lib.scimath.log10() function, which allows us to compute the logarithm base 10 with scimath in Python.

Syntax:

lib.scimath.log10(x)

This function returns the “primary value” of (see numpy.log10). If real x > 0, this is a real number (log10(0) = -inf, log10(np.inf) = inf). If none of the above requirements are met, the complicated principle value is returned.

Parameters
x: It is the array given as input.

Return Value:

This function returns the log base 10 of the x value. If x was a scalar, out is also a scalar; otherwise, an array object is returned.

Python Program to Compute the logarithm Base 10 with NumPy scimath

Approach:

  • Import numpy module using the import keyword
  • Pass some random list of numbers as an argument to the array() function of the numpy module to create an array.
  • Store it in a variable.
  • Print the given array.
  • Apply the shape attribute on the given array to get the shape of the given array and print the result
  • Apply ndim attribute on the given array to get the dimension of the given array and
    print the result
  • Apply dtype attribute on the given array to get the datatype of all the elements of the given array and print the result
  • Pass the given array as an argument to the log10() function of scimath of the numpy module to calculate the logarithm base 10 values of the given input array and print the result.
  • The Exit of the Program.

Below is the implementation:

# Import numpy module using the import keyword
import numpy as np

# Pass some random list of numbers as an argument to the array() function
# of the numpy module to create an array.
# Store it in a variable
gvn_arry = np.array([5, 10, 15])

# Print the given array
print("The given array is:")
print(gvn_arry)

# Apply the shape attribute on the given array to get the shape of the given array and
# print the result
print("The Shape of the given array ",gvn_arry," = ", gvn_arry.shape)

# Apply ndim attribute on the given array to get the dimension of the given array and
# print the result
print("The dimension of the given array ",gvn_arry," = ",gvn_arry.ndim)

# Apply dtype attribute on the given array to get the datatype of all the elements of the
# given array and print the result
print("The datatype of the given array ",gvn_arry," = ",gvn_arry.dtype)
print()
# Pass the given array as an argument to the log10() function of scimath of the numpy module
# to calculate the logarithm base 10 values of the given input array and print it.
print("The logarithm base 10 values of the given input array:")
print(np.lib.scimath.log10(gvn_arry))

Output:

The given array is:
[ 5 10 15]
The Shape of the given array [ 5 10 15] = (3,)
The dimension of the given array [ 5 10 15] = 1
The datatype of the given array [ 5 10 15] = int64

The logarithm base 10 values of the given input array:
[0.69897 1. 1.17609126]

As previously stated, np.lib.scimath.log10() function returns

  • infinity – when np.inf is passed
  • -inf is returned – when 0 is passed
  • inf – when -inf is passed

Example

# Import numpy module using the import keyword
import numpy as np

# Calculate log10 values when np.inf is passed an an argument
print(np.lib.scimath.log10(np.inf))
# Passing -np.inf as an argument
print(np.lib.scimath.log10(-np.inf))
# Passing 0 as an argument
print(np.lib.scimath.log10(0))

Output:

inf
(inf+1.3643763538418412j)
-inf