semvec/pypi-packages huggingface dataset:
mutahar-automation-framework 1.0.0
Automation framework using Playwright Python
Requires Python: >=3.6

# Automation Framework using Playwright Python

This project contains a custom automation framework built using Playwright and Python for web automation.

## Features

- **Base Page**: Contains base functionalities and configurations for tests.
- **Utilities**: Various utility functions for common operations.
- **Conftest**: Configuration settings for pytest.
- **Requirements**: List of dependencies required by the framework.
- **pytest.ini**: Configuration file for pytest.

## Installation

To install and use this framework in your projects, follow these steps:


```bash
pip install mutahar_automation_framework
sheetconnect 1.0.2
Simple get data from Google sheet in form of Pandas dataframe
Requires Python: >=3.6

# Google Sheets to DataFrame

This Python module provides a simple interface to connect to Google Sheets and retrieve data as a pandas DataFrame. It uses the `gspread` library to handle Google Sheets API interactions and `pandas` for data manipulation.

## Prerequisites

Ensure you have the following Python libraries installed:

- `gspread`
- `pandas`
- `oauth2client`

You can install them using pip:

```bash
pip install gspread pandas oauth2client
```
# Google Sheets API Setup
1. Go to the Google Cloud Console.
2. Create a new project (or select an existing one).
3. Enable the Google Sheets API and Google Drive API for the project.
4. Create credentials for a Service Account and download the JSON key file.
5. Share your Google Sheet with the email address in the JSON key file.

# Usage
1. Save your Service Account credential JSON file in your project directory.

2. Use the Connect class to fetch data from your Google Sheet:

```bash
from sheetconnect import Connect

credential = 'path/to/your/credential.json'
sheet = 'Your Google Sheet Name'
sheet_name = 'Sheet1'

dataframe = Connect.GetDF_Sheet(credential, sheet, sheet_name)
print(dataframe)
```
Replace 'path/to/your/credential.json', 'Your Google Sheet Name', and 'Sheet1' with your actual file path, Google Sheet name, and worksheet name.

## Acknowledgements

- [gspread](https://github.com/burnash/gspread) - Google Sheets Python API
- [pandas](https://pandas.pydata.org/) - Data analysis and manipulation library
- [oauth2client](https://github.com/googleapis/oauth2client) - OAuth 2.0 client library
mdgrad 0.3
Tensor-based autdiff engine and neural network API
Requires Python: >=3.8
Keywords: python, tensors, neural networks, automatic differentiation

# mdgrad

A small autograd engine that implements backpropagation (reverse-mode autodiff). Heavily inspired by karpathy's [micrograd](https://github.com/karpathy/micrograd/tree/master), and extended to support operations on tensors instead of scalars. Includes a small neural network api for building and training neural networks. Has a PyTorch-like API.

Hopefully useful as an educational resource.

## Installation

``` bash
pip install mdgrad
```

## Example Usage

A dumb example showing supported operations

```python

import mdgrad
import mdgrad.nn as nn

a = 3 * mdgrad.randn(3, 2)
b = mdgrad.ones(shape=(2, 2))
c = a @ b
d = c * 3 / 2
e = d ** 2
f = e.sum()
print(f.data) 
f.backward()
print(a.grad) 
```

An example showing how to define and run a neural network. See the files in `examples/` for more details on building and training models.

```python

import mdgrad
import mdgrad.nn as nn

# Define the model and loss function
model = nn.Sequential(
    nn.Linear(2, 20),
    nn.ReLU(),
    nn.Linear(20, 50), 
    nn.ReLU(),
    nn.Linear(50, 15),
    nn.ReLU(),
    nn.Linear(15, 1),
    nn.Sigmoid()
)
loss_fn = nn.MSELoss()

# Create dummy data
X = mdgrad.randn(100, 2)
target = mdgrad.randn(100, 1)

# Compute output and loss
out = model(X)
loss = loss_fn(out, target)

# Compute gradients of parameters
loss.backward()
```