Introducing Mdataset (/em-dataset/): a comprehensive solution tailored for researchers and students seeking a streamlined approach to compiling high-quality datasets and processing.
Why is it necessary?
Dataset compilation and processing can be a formidable challenge in various fields utilizing big data. The complexities lie in either navigating unfamiliar data banks or grappling with the intricacies of downloading specific datasets. Mdataset addresses these hurdles, providing an all-encompassing solution that equips users with the essential tools and methods needed to download existing datasets from renowned sources like Kaggle and Hugging Face. The only limiting factor is your computational power.
What do we offer?
Mdataset delivers a set of wrappers and functions, either wrapping existing tools developed by researchers or providing our solutions. With a simple three-line command, users can effortlessly download and compile datasets while also performing various processing tasks. Our offerings include:
- Downloading datasets from high-quality sources
- Video transcription
- Text-to-audio conversion
- User-friendly web scraping tools
- Secure local-to-internet file transfer on demand
- Scraping popular image boards
- Synthetic generation of tabular and text data
- Secure one-on-one interview method for data extraction by researchers
- YouTube video and audio download
- Powerful Optical Character Recognition for extracting data from PDFs
- Unrestricted on-terminal search engine
- Tor route circuits for secure communication
Choose Mdataset for a comprehensive and efficient solution to your dataset compilation and processing needs.
Quickstart
pip install mdataset
from mdataset import scrape_data
url = "www.example.com'
wanted_list = ['What's the data ca?']
scrape_data(url, wanted_list)
Metadata
Release files for mdatasets 0.1.4
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| mdatasets-0.1.4.tar.gz | 7.9 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| mdatasets-0.1.4-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 16.9 kB
Release files / mdatasets-0.1.4.tar.gz
| Download URL | mdatasets-0.1.4.tar.gz |
|---|---|
| Size | 7.9 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
683919dd3ed58aa09859a2d34dc29d9a01fb15595d6d3042ab42159b9f7d4b56
|
|
BLAKE2b-256 checksum How to use checksums |
bec81db0c6475b3928c07b2606f1a13ae6236b15bf12b7e16cb9eba2b2185916
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.2 CPython/3.8.10
|
Release files / mdatasets-0.1.4-py3-none-any.whl
| Download URL | mdatasets-0.1.4-py3-none-any.whl |
|---|---|
| Size | 9.0 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
1804fc7852c4d722016c301d7007ef17b8961a656b293155276fe0821423a657
|
|
BLAKE2b-256 checksum How to use checksums |
a92b70462f29eb93875de41f4700417d70ebb4e899e66b3a5713537ab8c5747e
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.2 CPython/3.8.10
|