Labelbox enables teams to maximize the value of their unstructured data with its enterprise-grade training data platform. For ML use cases, Labelbox has tools to deploy labelers to annotate data at massive scale, diagnose model performance to prioritize labeling, and plug in existing ML models to speed up labeling. For non-ML use cases, Labelbox has a powerful catalog with auto-computed similarity scores that users can use add metadata tags to large amounts of data with a couple clicks.
Pandas stands as the premier open-source Python library for handling CSV and tabluar data and as one of the most widely used Python libraries in the world.
This GitHub repo stands as an open-source Python library, moderated by the Labelbox Solutions team, in facilitating Labelbox users in uploading data to Labelbox and retreiving data from Labelbox in tabular / CSV format using Pandas.
We strongly encourage collaboration - please free to fork this repo and tweak the code base to work for you own data, and make pull requests if you have suggestions on how to enhance the overall experience, add new features, or improve general performance.
Please report any issues/bugs via Github Issues.
Set up LabelPandas with the following lines of code:
!pip install labelpandas -q
import labelpandas as lp
api_key = "" # Insert your Labelbox API key here
client = lp.Client(api_key)
Once set up, you can run the following core functions:
-
client.create_data_rows_from_table(): Creates Labelbox data rows (and metadata) given a Pandas table -
client.export_to_table(): Exports labels (and metadata) from a given Labelbox project and creates a Pandas DataFrame
| Notebook | Github | Google Colab |
|---|---|---|
| Basics: Data Rows from URLs |  |
 |
| Data Rows from Raw Text* | |
|
| Data Rows from Local Files | |
|
| Data Rows with Metadata | |
|
| Data Rows with Attachments | |
|
| Data Rows with Annotations | |
|
| Putting it all Together | |
|
| Notebook | Github | Google Colab |
|---|---|---|
| Exporting Data to a CSV | |
|
- = Coming soon