kaggle-st-connection 1.3.3

st.connection implementation for Kaggle Public API

KaggleStConnection Documentation

KaggleStConnection is a Streamlit connection class that connects to Kaggle API. Better than the default Kaggle API, KaggleStConnection automates the reading of a specific data file from your desired dataset found on Kaggle.

You can view the source GitHub repository for the connection class and the demo app in the links below:

• KaggleStConnection
• KaggleStConnectionDemo

You may access the app at this link.

Package Installation

To install KaggleStConnection in your local machine, install the package with the code below in the terminal and you are ready to go.

pip install kaggle-st-connection

Initializing KaggleStConnection

After you have installed the KaggleStConnection package in the step above, you can start to initialize a connection to the Kaggle API and obtain a connection object with the following code.

# Package imports
from KaggleAPIConnection import KaggleAPIConnection
import streamlit as st

# Initialize connection
conn = st.connection("kaggle", type=KaggleAPIConnection)

# Obtain connection object
cursor = conn.cursor()

You can use the connection object cursor to call some KaggleAPI methods like dataset_list() and many more depending on your use cases.

Querying Data From Kaggle Datasets

We can use the query() method and specify the Kaggle dataset reference in the format of '[owner_slug]/[dataset_slug]' as the method argument.

We can additionally use the file keyword argument to specify the dataset file to query from. By default, the first file in the dataset will be queried.

# Using the previous initialized connection object `conn`
data = conn.query(
    'kritikseth/us-airbnb-open-data',   # Dataset ref
    file='AB_US_2023.csv'               # Optional file to open
)

In this case, we set the parameter file to 'AB_US_2023.csv' so that we will obtain the desired latest data for our sample visualization shown below.

Configuring Kaggle API Key

Before the above code can work, we will need to specify the API key during the initialization of the connection. The following quote the steps to obtain your API key on kaggle.com from the Kaggle Documentation.

🗨 Kaggle's Public API Documentation (Read Here)
In order to use Kaggle's public API, you must first authenticate using an API token. Go to the 'Account' tab of your user profile and select 'Create New Token'. This will trigger the download of kaggle.json, a file containing your API credentials.

To use your API keys in Streamlit, you can either:

• Go to your Project Root (~) and create a .kaggle folder and place your kaggle.json inside the folder.
• Go to your Workspace Root and create a .streamlit folder and create a secrets.toml file, then enter the following in the file:

  [connections.kaggle]
  kaggle_username='<USERNAME>'
  kaggle_key='<API_KEY>'
  

  Replace the <USERNAME> and the <API_KEY> with that in your kaggle.json file.
• [NOT RECOMMENDED FOR PUBLIC DEPLOYMENT] Specify the kaggle_username and kaggle_key optional parameter during the initialization of the connection with st.connection. An example of this use case is shown below:

  conn = st.connection(
      'kaggle', 
      type=KaggleAPIConnection,
      kaggle_username='<USERNAME>',
      kaggle_key='<API_KEY>'
  )
  

  Similarly to the previous bullet, replace the <USERNAME> and the <API_KEY> with that in your kaggle.json file.

You can read more about secrets management in Streamlit's secrets management documentation.