​
1/ Configure and Create a SnowflakeConnection

Create a dedicated plot transform cell that constructs configuration and connection objects. 
from snowflake.connector import connect, SnowflakeConnection
from snowflake.connector.pandas_tools import write_pandas
from dataclasses import dataclass
from contextlib import contextmanager
import logging

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)


@dataclass
class SnowflakeConfig:
    account: str
    user: str
    password: str
    warehouse: str
    database: str
    role: str = "ANALYST"

    @classmethod
    def build(cls) -> 'SnowflakeConfig':
        """Create config from environment variables"""
        return cls(
            account="account-id",
            user="KENNYWORKMAN",
            password="secret",
            warehouse="COMPUTE_WH",
            database="SOME_DB",
            role="ACCOUNTADMIN"
        )

@contextmanager
def snowflake_connection(config: SnowflakeConfig):
    conn = None
    try:
        conn = connect(
            account=config.account,
            user=config.user,
            password=config.password,
            warehouse=config.warehouse,
            database=config.database,
            role=config.role
        )
        yield conn
    except Exception as e:
        logger.error(f"Error connecting to Snowflake: {str(e)}")
        raise
    finally:
        if conn:
            conn.close()

​
2/ Allow scientists to query data with graphical widgets

Once you have established a connection, you can allow scientists to query, manipulate and visualize their own tables of data from Snowflake using graphical query builders. See an example below with a table of synthetic barcode counts. Scientists can retrieve counts and barcodes based on 
from lplots.widgets.text import w_text_input
from lplots.widgets.multiselect import w_multi_select
from datetime import datetime

start_date = w_text_input(
    label="Start Date",
    default="2023-01-01",
    appearance={"help_text": "Enter start date in YYYY-MM-DD format"}
)

end_date = w_text_input(
    label="End Date",
    default="2023-01-31",
    appearance={"help_text": "Enter end date in YYYY-MM-DD format"}
)

sample_ids = w_multi_select(
    label="Sample IDs",
    options=['SAMPLE_001_A1', 'SAMPLE_001_A2', 'SAMPLE_001_A3'],
    appearance={"help_text": "Select one or more sample IDs"}
)

barcode_ids = w_multi_select(
    label="Barcode IDs",
    options=['BC000001', 'BC000002', 'BC000003'],
    appearance={"help_text": "Select one or more barcode IDs"}
)

limit = w_text_input(
    label="Query Limit",
    default="1000",
    appearance={"help_text": "Enter the maximum number of rows to return"}
)

base_query = """
SELECT 
    sample_id,
    barcode_id,
    count,
    run_id,
    experiment_date
FROM barcode_counts
WHERE experiment_date BETWEEN %(start_date)s AND %(end_date)s
"""

params = {
    'start_date': start_date.value,
    'end_date': end_date.value
}

if sample_ids.value:
    base_query += " AND sample_id IN ('" + "','".join(sample_ids.value) + "')"
    
if barcode_ids.value:
    base_query += " AND barcode_id IN ('" + "','".join(barcode_ids.value) + "')"

base_query += """
ORDER BY experiment_date, sample_id
LIMIT %(limit)s
"""
params['limit'] = int(limit.value)
with snowflake_connection(SnowflakeConfig.build()) as conn:
    df = pd.read_sql(base_query, conn, params=params)

df
The above Python code translates to the UI below: Snowflake UI