Showing posts with label Python. Show all posts

Cannot import name 'layer_utils' from 'keras.utils'

This error occurs because the layer_utils module is not part of the standalone keras library anymore or is being accessed incorrectly. Instead, it is available in tensorflow.keras.utils if you are using TensorFlow.

ImportError: cannot import name 'layer_utils' from 'keras.utils'

cannot import name 'layer_utils' from 'keras.utils'

Solution:

Upgrade the Tensorflow and retry import.

pip install --upgrade tensorflow

Above is the command to upgrade the trensorflow in python.

How to export data and store in any file using Python

In the below Python code syntax, we are storing the test_data values in a csv file "my_file_v1.csv" under data folder.

import pandas as pd

pd.DataFrame({'user_data':test_data}).to_csv('data/my_file_v1.csv',index=False)

Difference between function and method in Python

Let’s understand the difference between Python methods and functions and when and how to use function or method:

Functions:
- Functions are standalone blocks of code that can be called by their name.
- They are defined independently and are not associated with any specific class or object.
- Functions can have zero or more parameters.
- They may or may not return any data.
- Example of a user-defined function:
```
def subtract(a, b):
    return a - b

print(subtract(10, 12))  # Output: -2
```
Methods:
- Methods are special types of functions associated with a specific class or object.
- They are defined within a class and are dependent on that class.
- A method always includes a self parameter (for instance methods) or a cls parameter (for class methods).
- Methods operate on the data (instance variables) contained by the corresponding class.
- Example of a user-defined method:
```
class ABC:
    def method_abc(self):
        print("I am in method_abc of ABC class.")

class_ref = ABC()  # Create an object of ABC class
class_ref.method_abc()  # Output: "I am in method_abc of ABC class." 
```

In final conclusion, functions are independent, while methods are associated with classes or objects. Functions can be called directly by their name, but methods require invoking the class or object they belong to.

Different ways of writting loops in Python

Lets see we have a list of inetgers name x.

x = [3,3,3,4,4,4,3,3,5,5,6,6,7,8,9,9,9]

Now iterate over this list using differnet loops.

For loop

#To read all the elements from list

for elem in x:
print(elem)

#To read element from specific index using range(startindex, stopindex)

for i in range(2, len(x)):

print(x[i])

While loop

i = 4

while i < len(x):
print(x[i])
i+=1

Pyhton Function sample code

Different ways to manage fucntion parameter in Python.

def paragraph(font,background,fontSize,color):
    print('Font:',font,', Background',background,', Font Size',fontSize,', Font Color',color)
    
paragraph('Ariel','red','18px','white')


# Fucntion parameters with default value
def paragraph(font='Monoview',background='grey',fontSize='18px',color='blue'):
    print('Font:',font,', Background:',background,', Font Size:',fontSize,', Font Color:',color)
    
paragraph('Ariel','red','white')


# Fucntion parameters with following the sequence of paramters
def paragraph(font='Monoview',background='grey',fontSize='18px',color='blue'):
    print('Font:',font,', Background:',background,', Font Size:',fontSize,', Font Color:',color)
    
paragraph(color='red',background='orange')

How to create virtual environment in python

A Python virtual environment, often referred to as "virtualenv," is a tool that allows Python developers to create isolated and self-contained environments for their Python projects. Each virtual environment acts as a sandbox, providing a separate space with its own Python interpreter and package dependencies, isolated from the system-wide Python installation.

The primary purpose of using virtual environments is to manage project dependencies efficiently. With Python virtual environments, we can install Python packages in a separate and isolated location, distinct from your system-wide installations. Different projects may require specific versions of Python packages, and conflicts can arise when installing packages globally on the system. Virtual environments help avoid these conflicts by creating separate environments for each project, ensuring that the project's dependencies do not interfere with one another.

Key features and benefits of Python virtual environments include:

1. Isolation: Each virtual environment contains its own Python interpreter and library dependencies, isolating it from the system's Python installation and other virtual environments.

2. Dependency Management: Virtual environments allow developers to install and manage project-specific dependencies without affecting the system-wide Python installation.

3. Version Compatibility: Different projects may require specific versions of Python packages. With virtual environments, you can easily set up the required versions for each project.

4. Reproducibility: By using virtual environments, you can ensure that other developers working on the project can replicate the exact environment to maintain consistency and avoid compatibility issues.

Steps to create virtual environment

Creating a virtual environment is straightforward. In Python 3 and above, you can use the built-in module `venv` to create a new virtual environment. Here's a simple example of creating and activating a virtual environment:

1. Open a terminal or command prompt.

2. Navigate to your project directory.

3. Create the virtual environment:

python -m venv myenv

4. Activate the virtual environment:

- On Windows:

myenv\Scripts\activate

- On macOS and Linux:

source myenv/bin/activate

Once activated, any Python packages installed using `pip` will be isolated within the virtual environment. When you are done working on your project, you can deactivate the virtual environment using the command `deactivate`.

Using Python virtual environments is a best practice in Python development, as it promotes a clean and organized approach to managing project dependencies and ensures a smooth and hassle-free development experience.

A quick video tutorial of creating python virtual environment.

References

- PythonLand virtual environments

Why we need Python to run Node.js?

Node.js is written on c++, and is build on GYP. GYP is a Meta-Build system: a build system that generates other build systems and help us to build the large project that needs multiple platform to build. GYP is developed and written using Python, so to build and run few modules of Node.js we need Python.

Therefore wherever we need node-gyp to build any node module, Python will be required.

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