Querying

read() filters support these operators: $eq $ne $gt $gte $lt $lte $in $nin $contains $regex.

await db.read({"age": {"$gte": 18}})
await db.read({"plan": {"$in": ["pro", "team"]}})
await db.read({"email": {"$contains": "@"}})

For server-side power — filtering, sorting and aggregation that run on Google's side, not in Python — use query() with the Google Visualization query language. Columns are referenced by letter; column() maps a field name to its letter.

rows = await db.query("SELECT A, D WHERE D = 'pro' ORDER BY A DESC LIMIT 10")
db.column("plan")   # -> "D"

query() returns dicts keyed by the sheet's header labels. Columns that map to a schema field come back in that field's Python type (and decrypted), matching read(); aggregates like SUM(D) stay gviz-native.

Server-side filtering on DATE columns isn't supported yet — filter dates in Python via read().

pandas bridge

await db.from_dataframe(df)            # insert every row in bulk; returns count
out = await db.to_dataframe()          # read into a DataFrame
out = await db.to_dataframe({"plan": "pro"})

Requires pip install "gsab[pandas]".