DataElevate 1.0.2

DataElevate is a Python library designed to simplify and enhance data management and analysis workflows. It offers tools for seamless data access, transformation, and integration with external services like Google Drive, ensuring security and ease of use.

DataElevate

DataElevate is a Python library designed to streamline the process of downloading, loading, and extracting data from various sources such as Google Drive, Kaggle, and local archives. It simplifies working with data files, enabling developers and analysts to focus on their data analysis tasks.

---

Features

• Download Data:
  • Easily download files from Google Drive and Kaggle.
• Load Data:
  • Load data directly from CSV, Excel, text, or other supported file formats.
• Extract Archives:
  • Extract files from compressed archives (e.g., .zip, .tar, .gz).

---

Installation

Install the package using pip:

pip install DataElevate

---

Quickstart

Here's how to get started with DataElevate:

1. Import the Library

from DataElevate import Download_data, Load_data, Extractor

2. Download Data

GoogleDrive

Downloads a files/ folders from Google Drive.

File download

Download_data.GoogleDrive.download_file(url/ file_id: str, destination: str) #destination: Optional

Folder download

Download_data.GoogleDrive.download_folder(url/ file_id: str, destination: str) #destination: Optional

Check FileName

Download_data.GoogleDrive.check_filename(url/ file_id: str)

Get Id of file

Download_data.GoogleDrive.get_file_id(url : str)

Kaggle

# Provide Kaggle dataset path and destination
Download_data.Kaggle.from_kaggle(dataset="kaggle-dataset-URL", destination="path/to/save") #destination: Optional

3. Load Data

From Local (CSV, Text, Excel)

data = Load_data.from_local("path/to/your_file")

From Kaggle

data = Load_data.from_kaggle(url = "kaggle-dataset-URL")

From Drive (Under Maintenance)

data = Load_data.from_drive(url = "dataset url from drive")

From Database (Under Maintenance)

Supported Databases

The following databases are supported:

• PostgreSQL
• MySQL
• Microsoft SQL Server (MSSQL)
• Oracle
• SQLite
• MariaDB
• Amazon RDS
• Azure SQL

Usage Examples

Loading Data from PostgreSQL (Under Maintenance)

To load data from a PostgreSQL database, use the from_postgresql method:

data = Load_data.Database.from_postgresql(
    db_name='your_database_name',
    table_name='your_table_name',
    username='your_username',
    password='your_password',
    host='your_host',
    port='your_port'
)

Loading Data from SQLite (Under Maintenance)

To load data from an SQLite database, use the sqlite method:

data = Load_data.Database.sqlite(
    db_name='your_database_name',
    table_name='your_table_name'
)

Generalized Syntax for Other Databases (Under Maintenance)

data = Load_data.Database.DataBase_Type(
    db_name='your_database_name',
    table_name='your_table_name',
    username='your_username',
    password='your_password',
    host='your_host',
    port='your_port'
)

Example

data = Load_data.Database.from_mysql(
    db_name='your_database_name',
    table_name='your_table_name',
    username='your_username',
    password='your_password',
    host='your_host',
    port='your_port'
)

4. Extract Archives

Extractor.extract_archive("path/to/your_archive.zip", destination="path/to/extract") destination: Optional

---

Contributing

Contributions are welcome! Feel free to submit a pull request or raise issues for any bugs or feature requests.

---

License

This project is licensed under the MIT License. See the LICENSE file for more details.

---

Author

Name: Moanl Bhiwgade Email: [e-mail]