semvec/pypi-packages huggingface dataset:
nosqlmongoautomation 0.0.1
A python package for connecting with database.
License: MIT
Requires Python: >=3.7

# MongoDB Connector

A Python package for connecting to a MongoDB database and performing CRUD operations.

## Table of Contents

- [Introduction](#introduction)
- [Features](#features)
- [Installation](#installation)
- [Usage](#usage)
  - [Initialization](#initialization)
  - [Create Database and Collection](#create-database-and-collection)
  - [Insert Records](#insert-records)
  - [Bulk Insert](#bulk-insert)
- [Testing](#testing)
- [Contributing](#contributing)
- [License](#license)

## Introduction

This package provides an easy-to-use interface for connecting to MongoDB databases, creating collections, and performing insert operations. It supports reading data from `.csv` and `.xlsx` files for bulk insertion.

## Features

- Connect to MongoDB using a connection string
- Create databases and collections
- Insert single or multiple records
- Bulk insert data from `.csv` and `.xlsx` files

## Installation

You can install the package using pip:

```bash
pip install .
KyuuRyuu 0.1
KyuuRyuu is a Python library designed to simplify the process of working with TensorFlow and Keras for machine learning tasks.
License: MIT
Requires Python: >=3.11

# KyuuRyuu
From japanese 急流 meaning "rapid stream" or "swift current" aka "fast (Tensor)flow".

## Overview
**KyuuRyuu** is a Python library designed to simplify the process of working with TensorFlow and Keras for machine learning tasks. It provides convenient utilities for data loading, preprocessing, building models, training, evaluation, and visualization.

## Features
- Seamless integration with TensorFlow and Keras.
- Simplified data loading and preprocessing pipelines.
- Support for various data formats, including CSV, JSON, and image datasets.
- Flexible model creation and customization.
- Built-in visualization tools for monitoring training progress and model performance.
- Extensive documentation and examples for easy usage.

### Dependencies
- TensorFlow
- Keras
- NumPy
- Pandas
- Matplotlib
- Seaborn

## Installation
To install **KyuuRyuu**, simply use pip:

```bash
pip install kyuuryuu
email-utilities 1.0.5
collection of utilities for email-related tasks
License: MIT

# email_utilities

This module is a collection of utility functions for email-related tasks including:
* Tools for gaining insights about emails for data tasks.
* Tools for converting email to text or html-like email to text.

#### Installation
    pip install email_utilities

#### Get started

How to load an email using the **_load_email_** function:

```Python
from email_utilities import EmailConversion
from pathlib import Path

email_conversion = EmailConversion()
file_path = next(Path("tests/Examples/email").iterdir()) # sample file
email_text = email_conversion.load_email(file_path)
```
How to convert an email into a readable text format using the **_convert_email_to_text_** function:
```Python
from email_utilities import EmailConversion
from pathlib import Path

email_conversion = EmailConversion()
file_path = next(Path("tests/Examples/email").iterdir()) # sample file
email_text = email_conversion.load_email(file_path)
email_conversion.convert_email_to_text(email_text)
```
How to get the structures and their frequencies of one or more emails using the **_count_email_structures_** function :
```Python
from email_utilities import EmailConversion
from pathlib import Path
from email_utilities import EmailInsights

email_conversion = EmailConversion()
email_insights = EmailInsights()

# for multiple email files
file_dir = list(Path("Examples/email").iterdir()) + list(Path("Examples/html").iterdir()) # sample files
emails = [email_conversion.load_email(file_) for file_ in file_dir]
email_insights.count_email_structures(emails)

# for a single email file
sample_email = emails[6]
email_insights.count_email_structures(sample_email)
```