Author name: Vikram Chiluka

Magic 8 Ball Program In Python

random module in Python:
The random module in Python can be used to generate random numbers.
These are pseudo-random numbers since the seed determines the sequence of numbers generated.
This module can be used to do tasks like generating random numbers, outputting a random value for a list or string, and so on.

Given a list of 8 sentences and the game is to select any random sentence out of the given 8 sentences. This program/code is also known as the magic 8 ball Program.

Python Program for Magic 8 Ball Code Implementation

Approach:

  • Import random module using the import keyword.
  • Create a function say codeMagic_8ball() (Which is used to Implement the main logic of the magic 8 ball program)
  • Give the response you want as user input using the input() function and store it in a variable.
  • Give the list of all eight ball answers and store it in another variable.
  • Check if the obtained/input response is “quit” using the if conditional statement.
  • If it is true then print Some Random Acknowledgement indicating the Game is over.
  • Else print some random answer from the above given list of answers using the choice() function of the random module by Passing the about Eight answers list to the choice() Function.
  • Here \n is used as a separator to Print the new line.
  • Call the above-created codeMagic_8ball() function Recursively to Play the Game Again.
  • Call the above created codeMagic_8ball() function to start the game.
  • The Exit of the Program.

Below is the Implementation:

# Import random module using the import keyword
import random

# Create a function say codeMagic_8ball() (Which is used to Implement the main logic of the magic 8 ball program)
def codeMagic_8ball():
    # Give the response you want as user input using the input() function and 
    # store it in a variable
    User_response = input("Enter any key for answer and 'quit' to exit:")
    # Give the list of all eight ball answers and store it in another variable
    Magic_8ball_answers = [
        "BtechGeeks platform for all type of articles",
        "Python Programs for in depth articles about Python Language",
        "SheetTips for Articles about excel and google sheets",
        "PythonArray",
        "Hello Good Morning",
        "This is BtechGeeks",
        "This is PythonPrograms",
        "Refer Excel Articles on SheetTips"
        ]
    # Check if the obtained/input response is "quit" using the if conditional statement
    if User_response == "quit":
        # If it is true then print Some Random Acknowledgement indicating the Game is over.
        print("The Game is Ended <!!>")
    else:
        # Else print some random answer from the above given list of answers using the 
        # choice() function of the random module by Passing the about Eight answers list to choice Function
        # Here \n is used a separator to Print the new line
        print(random.choice(Magic_8ball_answers), "\n")
        # Call the above created codeMagic_8ball() function Recursively to Play the Game Again.
        codeMagic_8ball()

# Call the above created codeMagic_8ball() function to start the game.
codeMagic_8ball()

Output:

Enter any key for answer and 'quit' to exit:v
This is PythonPrograms

Enter any key for answer and 'quit' to exit:r
This is PythonPrograms

Enter any key for answer and 'quit' to exit:f
Refer Excel Articles on SheetTips

Enter any key for answer and 'quit' to exit:u
Python Programs for in depth articles about Python Language

Enter any key for answer and 'quit' to exit:w
Hello Good Morning

Enter any key for answer and 'quit' to exit:k
This is PythonPrograms

Enter any key for answer and 'quit' to exit:z
Hello Good Morning

Enter any key for answer and 'quit' to exit:q
PythonArray

Enter any key for answer and 'quit' to exit:e
SheetTips for Articles about excel and google sheets

Enter any key for answer and 'quit' to exit:d
SheetTips for Articles about excel and google sheets

Enter any key for answer and 'quit' to exit:m
Python Programs for in depth articles about Python Language

Enter any key for answer and 'quit' to exit:l
Python Programs for in depth articles about Python Language

Enter any key for answer and 'quit' to exit:l
PythonArray

Enter any key for answer and 'quit' to exit:c
SheetTips for Articles about excel and google sheets

Enter any key for answer and 'quit' to exit:quit
The Game is Ended <!!>

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How to Convert base64 String to Image in Python

Have you ever thought about how images are saved and transferred without being corrupted? When we open the photographs in their raw format, we sometimes see that they are encoded in unusual characters. Those characters represent the Base64 string data. It is necessary to convert them back to their original format.
Let us see how to convert a Base64 string to an image in Python.

Python Program to Convert base64 String to Image

Python base64 module:
Base64 is a Python package used for data encoding and decoding. The binary form of data is expressed in printable ASCII text format by translating to radix-64 representation in Base64 encoded data. Data decoding is the inverse of data encoding. The data in ASCII format is converted back to binary data. This binary data is transformed into byte-sized pieces before being returned to its original format.

Installation:

pip install base64

What is Base64 Encoding & Decoding?
Base64 encoding is a sort of byte-to-ASCII character conversion. The basis of a number system in mathematics refers to how many different characters represent numbers. This encoding gets its name from the mathematical definition of bases – we have 64 letters that represent numbers.

The Base64 character set includes the following characters:
uppercase letters – 26
lowercase letters – 26
numbers – 10
For new lines, + and / (some implementations may use different characters).
Each Base64 character represents 6 bits of information when converted to binary by the computer.

Why to convert Base64 String to an Image and ViceVersa?
There are several reasons to convert a Base64 string to an Image and vice versa. The following principles explain why images must be encoded and decoded.

Base64 is a format for converting photos into data that may be included in other forms such as HTML, CSS, JSON, and so on. For example, because the image data is already embedded in the text, the browser does not need to perform a separate web request to retrieve the file. We can utilize base64 decoding to obtain the photos from the embedded data.
Base64 can also be used to encode images, allowing them to be stored and transferred without being corrupted. When the pictures arrive at their destination, they can be decoded and returned to their original format.

Sample Base64 String:

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base64 string file image

Approach:

  • Import base64 module using the import keyword
  • Open some random text file containing base64 string data in read-binary mode using the open() function and store it in a variable.
  • Read the above given file using the read() function. Here it is the encoded data.
  • Close the above opened given file using the close() function.
  • Pass the above encoded data as an argument to the b64decode() function of the base64 module to decode the encoded string data.
  • Open some random file in write-binary mode and store it in another variable(Here it decodes the base64 string)
  • Apply the write() function on the above opened file by passing the decoded data an an argument to the to write the decoded data back to its original format in the above opened file
  • Close the above opened file using the close() function.
  • The Exit of the Program.

Below is the Implementation:

# Import base64 module using the import keyword
import base64
 
# Open some random text file containing base64 string data in read-binary
# mode using the open() function and store it in a variable.
gvn_file = open('base64String.txt', 'rb')
# Read the above given file using the read() function. Here it is the encoded data.
encoded_data = gvn_file.read()
# Close the above opened given file using the close() function.
gvn_file.close()

# Pass the above encoded data as an argument to the b64decode() function of the base64 module
# to decode the encoded string data.
decoded_data = base64.b64decode(encoded_data)

# Open some random file in write-binary mode and  store it in another variable(Here it decodes the base64 string)
outputimage_file = open('Outputimage.jpeg', 'wb')
# Apply write() function on the above opened file by passing the decoded data
# an an argument to the to write the decoded data back to original format in the 
# above opened file
outputimage_file.write(decoded_data)
# Close the above opened file using the close() function.
outputimage_file.close()

Output:

Base 64 Image Output

How to Convert base64 String to Image in Python Read More »

How To Generate a Random Password Using Python

Let us see how to generate a random password in this article. Here we generate a strong password using a combination of alphabets, numbers, and symbols.

For this purpose we use the below modules in this project:

  • random module
  • string module

Python Program to Generate a Random Password

random module in Python:
The random module in Python can be used to generate random numbers.
These are pseudo-random numbers since the seed determines the sequence of numbers generated.
This module can be used to do tasks like generating random numbers, outputting a random value for a list or string, and so on.

string module:

The Python String module contains string manipulation constants, utility functions, and classes.

string.ascii_letters:
It represents all the letters(alphabets)
“abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ”

string.punctuation :
It represents all the symbols that are shown below
” !”#$%&'()*+,-./:;<=>?@[]^_`{|}~”

string.digits:
It represents all the digits(numbers)
“0123456789”

We concatenate all the above three methods and generate a random password.

random.choice(char):
It is used to select a random character from all the characters.

random.randint(6, 16):
It is used to generate a random integer in the given range.

Approach:

  • Import string module using the import keyword
  • Import random module using the import keyword
  • Concatenate all the alphabets, symbols, and digits using the string.ascii_letters, string. punctuation , string.digits to create a set of all possible characters and store it in a variable.
  • Generate a random password in a given range(6, 25) using the random.choice(), for loop, and random.randint() functions and convert it into a string using the join() function.
  • Print the randomly generated password.
  • The Exit of the Program.

Below is the Implementation:

# Import string module using the import keyword
import string
# Import random module using the import keyword
import random

# Concatenate all the alphabets, symbols and digits using the string.ascii_letters,
# string.punctuation ,string.digits to create a set of all possible characters
# and store it in a variable.
charcters = string.ascii_letters + string.punctuation + string.digits

# Generate a random password in a given range(6, 25) using the random.choice(), for loop,
# and random.randint() functions and convert it into a string using the join() function.
rndm_password = "".join(random.choice(charcters) for i in range(random.randint(6, 25)))
# Print the randomly genarated password.
print('Random Password : ',rndm_password)

Output:

Random Password : [FkdR]6dTw4#XSfx_

Explanation:

Now you must construct a password that is both random and strong. We’ll make it a random length, with a random selection of characters. Using random. choice, we select characters at random (char). This random selection occurs 6 – 25 times, resulting in a password length of 6 to 25 characters. This random length is determined by random. randint(6, 25), which returns a random integer within the given range.

We create an empty string using “” and then use. join to add this string of random characters to it, resulting in a string password. Using a for loop, we add characters one by one. This loop executes the value returned by the randint() function.

We now use print to display the password (random password).

How To Generate a Random Password Using Python Read More »

How to Install openpyxl Library in Python?

openpyxl Library in Python:

Openpyxl is a Python library for reading and writing Excel files with the xlsx, xlsm, xltx, and xltm extensions. It comes with a number of different modules that allow you to work with Excel files without having to use any third-party software.

Let us use the Python package manager pip to install the openpyxl module here. The pip package manager supports in the installation and management of additional Python packages that are not included in the standard library.

Installation:

pip install package_name

Output:

Collecting package_name
Downloading package_name-0.1.tar.gz (782 bytes)
Building wheels for collected packages: package-name
Building wheel for package-name (setup.py) ... done
Created wheel for package-name: filename=package_name-0.1-py3-none-any.whl size=1254 
sha256=5704546ee196cfea91be74986fddb721d38e9f6063d138b8e1eb00d6f7b3699b
Stored in directory: /root/.cache/pip/wheels/aa/56/2f/2bf8ec875b1c71660b2692b4aab073132abc
78ac076140489b
Successfully built package-name
Installing collected packages: package-name
Successfully installed package-name-0.1

Installation of openpyxl in windows

Open the command prompt and type the below command for the installation of the openpyxl package.

pip install openpyxl

Output:

Collecting openpyxl
Downloading openpyxl-3.0.9-py2.py3-none-any.whl (242 kB)
|████████████████████████████████| 242 kB 1.6 MB/s
Collecting et-xmlfile
Downloading et_xmlfile-1.1.0-py3-none-any.whl (4.7 kB)
Installing collected packages: et-xmlfile, openpyxl
Successfully installed et-xmlfile-1.1.0 openpyxl-3.0.9

Installation of openpyxl in Windows
The openpyxl package is installed successfully on your Windows system

Another Way to Install openpyxl Library in Windows

We can install the openpyxl package using the pip package manager; however, if the above technique fails to install the package, we may still install the package manually from the official website on Windows.

Steps to be followed:

  1. To install the Mahotas package, go to the official website (https://pypi.org/project/openpyxl/)
  2. Navigate to the Download Files option and click on the openpyxl openpyxl.x.x.x.tar.gz file link to download the openpyxl package. (The version number of the package is denoted by x.x.x here)
  3. Installation of openpyxl library on Windows
  4. Go to the Downloads folder and extract the openpyxl TAR file there.
  5. The package should be extracted to the Python folder which is on the C:\ disk.
  6. Set the path to the extracted openpyxl package file in the CMD terminal to modify the directory.
  7. Finally, for package installation, run the setup file contained within the openpyxl package file.
  8. py setup.py install

Output:

installing openpyxl

Installation of  functools32 Library in Linux

We may also use the pip package manager to install the openpyxl package on a Linux system.

Steps to be followed:

  • Open the Linux terminal using Ctrl+Alt+T 
  • Type the below pip command for the installation of the openpyxl library.
$ sudo pip install openpyxl

The package openpyxl was successfully installed on the Linux system.

Installation of  functools32 Library using conda

Use the below command to install the openpyxl package using conda:

conda install -c anaconda openpyxl

How to Install openpyxl Library in Python? Read More »

Python playsound Module

playsound Module in Python:

We can play sound using the playsound module. This playsound is a cross-platform module for playing audio files.

The single function in the playsound module is the playsound() function. It has only one function.

This module has been tested to play .wav and .mp3 files only and is available for both Python 2 and Python 3.

Syntax:

playsound(path, block)

Parameters:

path: This is required. It is the path to the sound file we want to play. It could be a local file or a URL.

block: This is optional. It is set to True by default. If we set it to False, the function will run asynchronously.

Before we work with this module we use should first install it on our system as shown below.

Installation:

pip install playsound
Output:
Collecting playsound
Downloading playsound-1.3.0.tar.gz (7.7 kB)
Building wheels for collected packages: playsound
Building wheel for playsound (setup.py) ... done
Created wheel for playsound: filename=playsound-1.3.0-py3-none-any.whl size=7035 
sha256=4e4b206f1c64dd86e591df5decf7285c23048d33f4e8c291dd8f63d7226fe91f
Stored in directory: /root/.cache/pip/wheels/ba/f8/bb/ea57c0146b664dca3a0ada4199
b0ecb5f9dfcb7b7e22b65ba2
Successfully built playsound
Installing collected packages: playsound
Successfully installed playsound-1.3.0

playsound Module  in Python

1)For .mp3 File

Approach:

  • Import playsound from playsound module using the import keyword
  • Pass some random .mp3 file path/location to the playsound() function to play the sound of the given .mp3 file.
  • Print some random text for acknowledgment.
  • The Exit of the Program.

Below is the implementation:

# Import playsound from playsound module using the import keyword
from playsound import playsound
# Pass some random .mp3 file path/location to the playsound() function to play the 
# sound of the given .mp3 file
playsound("demofile.mp3")
# Print some random text for acknowledgment
print("The .mp3 file music is playing!!!!")

Output:

The .mp3 file music is playing!!!!

2)For .wav File

Approach:

  • Import playsound from playsound module using the import keyword
  • Pass some random .wav file path/location to the playsound() function to play the sound of given .wav file
  • Print some random text for acknowledgment.
  • The Exit of the Program.

Below is the implementation:

# Import playsound from playsound module using the import keyword
from playsound import playsound
# Pass some random .wav file path/location to the playsound() function to play the 
# sound of given .wav file
playsound("samplefile.wav")
# Print some random text for acknowledgment
print("The .wav file music is playing!!!!")

Output:

The .wav file music is playing!!!!

 

 

Python playsound Module Read More »

How to Install SymPy Library in Python?

SymPy Library in Python:

Python has a number of libraries, each of which contains functions and methods. You may quickly import these built-in processes into your programs to execute various functions.

SymPy is a Python library that includes symbolic mathematics methods. It is a fantastic tool for calculus that also includes computer algebra features and functionalities. It also includes tools for simplifying expressions, computing derivatives, performing integrations, and solving equations, among other things.

We must first install SymPy before we can begin studying and working with it. The SymPy library can be installed on any Python-enabled machine (version 2.6 and above).

Installation of SymPy Library in Anaconda

Anaconda is a free, open-source Python distribution that includes a Python interpreter as well as many tools and packages.
It is one of the most widely used Python programming platforms.

Anaconda will automatically install the SymPy library, as well as other major libraries and packages, on your machine.
If you don’t have the Anaconda Python distribution, you can install SymPy by running the following command in the Anaconda Prompt:

conda install sympy

Installing using the pip command:

pip install sympy

Checking if the sympy library is installed or Not:

import sympy
sympy.__version__

Output:

'1.7.1'

Command for Updating:

If you already have the SymPy package installed but want to update it, run the below command in the Anaconda Prompt:

conda update sympy

Installation of SymPy Library Using Git

If you’re a GitHub contributor who wants to help develop the SymPy library or stay up to date on improvements/updates, you can use git to install SymPy.
Go to the command prompt (CMD) and run the following command:

git clone https://github.com/sympy/sympy.git

Command for Updating:

If you’ve already completed this installation and want to update your SymPy version, run the following command:

git pull origin master

Another command which allows us to install SymPy and yet use the git version:

python setupegg.py develop

Checking with some Examples If the SymPy library is installed or Not

Example1

# Import all functions from sympy module using the import keyword
from sympy import *
numb = 8
# Getting square root of the given number
sqrt(numb)

Output:

2√2

Example2

# Import all functions from sympy module using the import keyword
from sympy import *
numb = 13
# Getting square root of the given number
sqrt(numb)

Output:

√13

The mpmath library is normally installed in the background if you installed SymPy with Anaconda. This helps SymPy in a variety of numerical calculations.
If that is not the case, you can install mpmath using one of the following two commands:

conda install mpmath
pip install mpmath

You can also get SymPy straight from its source code by visiting https://github.com/sympy/sympy/releases

How to Install SymPy Library in Python? Read More »

How to Clamp Floating Numbers in Python?

In this article, let us see how to clamp or clip floating-point numbers in Python.

What do you mean by clamping a Number?

The clamp function in python limits a number between two numbers. If a number is clamped, it retains its value if it falls inside the specified range. It takes the lower value if it is less than the min value, and the higher value if it is greater than the max value.

Example:

Input:

Given Numbers : 25 150 30

Output:

30

Explanation:

As 30 is between the 25 and 150

Clamping Floating Numbers in Python

The clamping of floating numbers in python can be done in the below ways:

Method #1: Using user-defined Function

In Python, there is no built-in clamping function. The clamping function can be defined as follows:

def clamp(number, min, max):
    return min if number < min else max if number> max else number

Approach:

  • Create a function say clamping which accepts a number, minimum value, maximum value passed
  • as the arguments to it.
  • Inside the function, check if the given number is less than the minimum value if it is true then return the minimum value
  • Else check if the given number is more than maximum value if it is true then return maximum value
  • Else return given number
  • Pass the number, minimum value, maximum value as the arguments to the above created user-defined clamping() function and print the result
  • Similarly, check out for the other numbers.
  • The Exit of the Program.

Below is the implementation:

# Create a function say clamping which accepts a number, minimum value, maximum value passed
# as the arguments 
def clamping(gvn_number, minimumval, maximumval):
    # Check if the given number is less than minimumval if it is true then return minimumval
    # Else check if the given number is more than maximumval if it is true then return maximumval
    # Else return given number
    return minimumval if gvn_number < minimumval else maximumval if gvn_number > maximumval else gvn_number
# Pass the number, minimum value, maximum value as the arguments to the above created clamping() function
# and print the result
print(clamping(1.5, 2, 4))
# Similarly check out for the other numbers
print(clamping(1.23, 1.15, 1.31))
print(clamping(2.35, 1.10, 1.25))

Output:

2
1.23
1.25

Method #2: Using numpy.clip() Function

Also, we can we the numpy.clip() function to clamp the numbers.

Syntax:

numpy.clip(number, min, max)

Approach:

  • Import numpy module using the import keyword
  • Pass some random number, minimum value, maximum value as the arguments to the clip() function of the numpy module to clip/clamp the given number.
  • The Exit of the Program.

Below is the implementation:

# Import numpy module using the import keyword
import numpy
# Pass some random number, minimum value, maximum value as the arguments to the clip() function
# of the numpy module to clip/clamp the given number.
numpy.clip(3.5, 2, 4)

Output:

3.5

Similarly, check for the other numbers

# Import numpy module using the import keyword
import numpy
# Pass some random number, minimum value, maximum value as the arguments to the clip() function
# of the numpy module to clip/clamp the given number.
numpy.clip(1.23, 1.15, 1.31)

Output:

1.23

Method #3: Using PyTorch clamp() Function

We can clamp the input element in the given range using the torch.clamp() method from the PyTorch library.

Syntax:

torch.clamp(number, min, max, out=None)

Below is the implementation:

import torch

print(ex1 = torch.clamp(0.5, min = 5, max = 10))
print(ex2 = torch.clamp(0.27, min = 0.11, max = 0.35))

Output:

5
0.23 
0.27

 

 

How to Clamp Floating Numbers in Python? Read More »

How to Install functools32 in Python?

functools32 module:

The functools32 is a backport of Python3.2.3’s functools module for use with Python2.7 and PyPy. Here, let us see how to install the functools32 Python package on various systems.

For this purpose, we will use the Python package manager pip to install the functools32 module. The pip package manager is used to install and manage extra packages that are not part of the Python standard library.

NOTE: It is important to note that we can only install the functools32 package on Python2.7; otherwise, Python3 would throw an error.

Error that occurs if the version doesn't support

Installation of functools32 in windows

Open the command prompt and type the below command for the installation of the functools32 package.

pip install functools32

Note:

It is only for lower version Python like 2.7

Output for Python 3.7 will be like:

Collecting functools32
Downloading functools32-3.2.3-2.zip (34 kB)
WARNING: Discarding https://files.pythonhosted.org/packages/5e/1a/0aa2c8195a204a9f51284018562dea77e25511f02fe924fac202fc012172/functools32-3.2.3-2.zip#sha256=89d824aa6c358c421a234d7f9ee0bd75933a67c29588ce50aaa3acdf4d403fa0 (from https://pypi.org/simple/functools32/). Command errored out with exit status 1: python setup.py egg_info Check the logs for full command output.
Downloading functools32-3.2.3-2.tar.gz (31 kB)
WARNING: Discarding https://files.pythonhosted.org/packages/c5/60/6ac26ad05857c601308d8fb9e87fa36d0ebf889423f47c3502ef034365db/functools32-3.2.3-2.tar.gz#sha256=f6253dfbe0538ad2e387bd8fdfd9293c925d63553f5813c4e587745416501e6d (from https://pypi.org/simple/functools32/). Command errored out with exit status 1: python setup.py egg_info Check the logs for full command output.
Downloading functools32-3.2.3-1.zip (34 kB)
WARNING: Discarding https://files.pythonhosted.org/packages/bb/02/8d75ecdec125bed75e414bd9c52160c42c7fdbbc11b01435e46d8859651b/functools32-3.2.3-1.zip#sha256=3c0752c677a8b5169ac7afb194847db53d4ece88681951c6ca6e9fdd1c12ed62 (from https://pypi.org/simple/functools32/). Command errored out with exit status 1: python setup.py egg_info Check the logs for full command output.
Downloading functools32-3.2.3-1.tar.gz (31 kB)
WARNING: Discarding https://files.pythonhosted.org/packages/e9/07/09abd35aaaef501a669f1b0bc39004e512dce3abaa1846450a952a09f9eb/functools32-3.2.3-1.tar.gz#sha256=6a3b7420432b0817ef192aef341cd766e199801a90ca7fe319f9d5fca40edda2 (from https://pypi.org/simple/functools32/). Command errored out with exit status 1: python setup.py egg_info Check the logs for full command output.
ERROR: Could not find a version that satisfies the requirement functools32 (from versions: 3.2.3.post1, 3.2.3.post2)
ERROR: No matching distribution found for functools32

The functools32 package is installed successfully on your Windows system

Another Way to Install functools32 Library in Windows

We can install the functools32 package using the pip package manager; however, if the above technique fails to install the package, we may still install the package manually from the official website on Windows.

Steps to be followed:

  1. To install the Mahotas package, go to the official website (https://pypi.org/project/functools32/)
  2. Navigate to the Download Files option and click on the e functools32.x.x.x.tar.gz link to download the functools32 package. (The version number of the package is denoted by x.x.x here)
  3. Installation of functools32 package for windows
  4. Go to the Downloads folder and extract the functools32 TAR file there.
  5. The package should be extracted to the Python folder which is on the C:\ disk.
  6. Set the path to the extracted functools32 package file in the CMD terminal to modify the directory.
  7. Finally, for package installation, run the setup file contained within the functools32 package file.
  8. > python setup.py install

Output for python 3.7 version:

Installing functools32

Installation of  functools32 Library in Linux

We may also use the pip package manager to install the functools32 package on a Linux system.

Steps to be followed:

  • Open the Linux terminal using Ctrl+Alt+T 
  • Type the below pip command for the installation of the functools32 library.
$ sudo pip install functools32

The package functools32 was successfully installed on the Linux system.

Installation of  functools32 Library using conda

Use the below command to install the functools32 package using conda:

conda install -c anaconda functools32

How to Install functools32 in Python? Read More »

How to Find Country Name of a Phone Number from Country Code in Python?

In this post, let us see how to use Python to determine the country of a phone number based on its country code. It’s pretty simple and straightforward, with only a few lines of code.

Here we will use the python phonenumbers module to identify the country corresponding to their phone number attached with the phone code. The phonenumbers module is used to obtain basic information about the phone number, carrier, region, and so on.

Before we work with this phonenumbers module we must first install it in our system.

Installation:

pip install phonenumbers

Find Country Name of a Phone Number from Country Code in Python

Below are the ways to find the country name of the given phone number using the country code:

Method #1: Using phonenumbers Module

Approach:

  • Import phonenumbers module using the import keyword
  • Import geocoder from phonenumbers module using the import keyword
  • Give some random phone number along with the country code as user input using the input() function and store it in a variable.
  • Pass the above-given phone number as an argument to the parse() function to process the given phone number and store it in the same variable.
  • Print the corresponding country name for the given phone number using the description_for_number() function by passing phone number, “en” as arguments to it.
  • The Exit of the Program.

Below is the implementation:

# Import phonenumbers module using the import keyword
import phonenumbers as pn
# Import geocoder from phonenumbers module using the import keyword
from phonenumbers import geocoder

# Give some random phone number along with the country code as user input
# using the input() function and store it in a variable.
gvn_phonenum = input("Give some random phone number along with the country code: ")
# Pass the above given phone number as an argument to the parse() function
# to process the given phone number and store it in the same variable.
gvn_phonenum = pn.parse(gvn_phonenum)

# Print the corresponding country name for the given phone number using the 
# description_for_number() function by passing phone number, "en" as arguments to it.
print(geocoder.description_for_number(gvn_phonenum, "en"))

Output:

Give some random phone number along with the country code: +919494580181
India

Method #2: Using phone-iso3166 module

Install the phone-iso3166 module using the below syntax:

pip install phone-iso3166

Approach:

  • Import the phone-iso3166 module using the import keyword.
  • Pass the phone number to the phone_country() function and print the result
  • The Exit of the Program.

Below is the Implementation:

#Import the phone-iso3166 module using the import keyword.
from phone_iso3166.country import *
#Pass the phone number to the phone_country() function and print the result
res = phone_country('+91 9578457739')
print(res)

Output:

IN

How to Find Country Name of a Phone Number from Country Code in Python? Read More »

How to Install Mahotas Library in Python?

Mahotas Library in Python:

Mahotas is a Python library for computer vision, image processing, and manipulation. A library is a set of functions and methods that allow you to do a variety of tasks without writing hundreds of lines of code. Mahotas incorporates several array-based algorithms. Mahotas now has over 100 functions for image processing and computer vision and is constantly expanding.

Mahotas is a good tool for discovering patterns in images. For example, the “Where’s Wally Problem” can be simply performed with Mahotas.

It also contains plenty of algorithms for working with numpy arrays. Thresholding, Convolution, Watershed, Spline interpolation, and so on are examples.

The Python’s pip package manager can be used to install the Mahotas library on both Windows and Linux platforms.

Installation of  Mahotas Library in windows

Open the command prompt and type the below command for the installation of the Mahotas library

pip install mahotas

Output:

Collecting mahotas
Downloading mahotas-1.4.12-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (5.7 MB)
|████████████████████████████████| 5.7 MB 3.9 MB/s 
Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages
(from mahotas) (1.21.5)
Installing collected packages: mahotas
Successfully installed mahotas-1.4.12

Another Way to Install Mahotas Library in Windows

We can install the mahotas package using the pip package manager; however, if the above technique fails to install the package, we may still install the package manually from the official website on Windows.

Steps to be followed:

  1. To install the Mahotas package, go to the official website (https://pypi.org/project/mahotas/)
  2. Navigate to the Download Files option and click on the mahotas-x.x.x-win amd64.whl link to download the package. (The version number of the package is denoted by x.x.x here)
  3. Downloading mahotas package from official website
  4. Open the command prompt(CMD )console and change the working directory to the Downloads directory.
  5. Install the .whl Mahotas package file that we downloaded from the website just a little time ago using the pip command.
  6. pip install mahotas-1.4.12-cp310-cp310-win_amd64.whl

We successfully installed the mahotas package on our Windows PC using this way.

Installing Mahotas from web

Installation of  Mahotas Library in Linux

We may also use the pip package manager to install the Mahotas package on a Linux system.

Steps to be followed:

  • Open the Linux terminal using Ctrl+Alt+T 
  • Type the below pip command for the installation of the Mahotas library.
sudo pip install mahotas

The package was successfully installed on the Linux system.

Installation of  Mahotas Library using conda

Use any of the below commands:

conda install -c conda-forge mahotas
conda install -c conda-forge/label/gcc7 mahotas

 

How to Install Mahotas Library in Python? Read More »