A Python DB API 2.0 for Google Spreadsheets (0.2.0)

This version (0.2.0) allows you to query, insert, update, and delete Google Spreadsheets using SQL and SQLAlchemy. The version (0.2.0) removes the limitations in version (<= 0.1.12) for insert, update, and delete operations.

Examples using SQLAlchemy grammar

INSERT:

engine.execute(table.insert(), column1=value1, column2=value2, column3=value3, ...)

UPDATE:

engine.execute(table.update().where(table.c.column == value).values(column1=value1, column2=value2, column3=value3,...))

DELETE:

engine.execute(table.delete().where(table.c.column <= value))

A Python DB API 2.0 for Google Spreadsheets

This module allows you to query Google Spreadsheets using SQL.

Using this spreadsheet as an example:

	A	B
1	country	cnt
2	BR	1
3	BR	3
4	IN	5

Here's a simple query using the Python API:

from gsheetsdb import connect

conn = connect()
result = conn.execute("""
    SELECT
        country
      , SUM(cnt)
    FROM
        "https://docs.google.com/spreadsheets/d/1_rN3lm0R_bU3NemO0s9pbFkY5LQPcuy1pscv8ZXPtg8/"
    GROUP BY
        country
""", headers=1)
for row in result:
    print(row)

This will print:

Row(country='BR', sum_cnt=4.0)
Row(country='IN', sum_cnt=5.0)

How it works

Transpiling

Google spreadsheets can actually be queried with a very limited SQL API. This module will transpile the SQL query into a simpler query that the API understands. Eg, the query above would be translated to:

SELECT A, SUM(B) GROUP BY A

Processors

In addition to transpiling, this module also provides pre- and post-processors. The pre-processors add more columns to the query, and the post-processors build the actual result from those extra columns. Eg, COUNT(*) is not supported, so the following query:

SELECT COUNT(*) FROM "https://docs.google.com/spreadsheets/d/1_rN3lm0R_bU3NemO0s9pbFkY5LQPcuy1pscv8ZXPtg8/"

Gets translated to:

SELECT COUNT(A), COUNT(B)

And then the maximum count is returned. This assumes that at least one column has no NULLs.

SQLite

When a query can't be expressed, the module will issue a SELECT *, load the data into an in-memory SQLite table, and execute the query in SQLite. This is obviously inneficient, since all data has to be downloaded, but ensures that all queries succeed.

Installation

$ pip install gsheetsdb
$ pip install gsheetsdb[cli]         # if you want to use the CLI
$ pip install gsheetsdb[sqlalchemy]  # if you want to use it with SQLAlchemy

CLI

The module will install an executable called gsheetsdb:

$ gsheetsdb --headers=1
> SELECT * FROM "https://docs.google.com/spreadsheets/d/1_rN3lm0R_bU3NemO0s9pbFkY5LQPcuy1pscv8ZXPtg8/"
country      cnt
---------  -----
BR             1
BR             3
IN             5
> SELECT country, SUM(cnt) FROM "https://docs.google.com/spreadsheets/d/1_rN3lm0R_bU3NemO0s9pbFkY5LQPcuy1
pscv8ZXPtg8/" GROUP BY country
country      sum cnt
---------  ---------
BR                 4
IN                 5
>

SQLAlchemy support

This module provides a SQLAlchemy dialect. You don't need to specify a URL, since the spreadsheet is extracted from the FROM clause:

from sqlalchemy import *
from sqlalchemy.engine import create_engine
from sqlalchemy.schema import *

engine = create_engine('gsheets://')
inspector = inspect(engine)

table = Table(
    'https://docs.google.com/spreadsheets/d/1_rN3lm0R_bU3NemO0s9pbFkY5LQPcuy1pscv8ZXPtg8/edit#gid=0',
    MetaData(bind=engine),
    autoload=True)
query = select([func.count(table.columns.country)], from_obj=table)
print(query.scalar())  # prints 3.0

Alternatively, you can initialize the engine with a "catalog". The catalog is a Google spreadsheet where each row points to another Google spreadsheet, with URL, number of headers and schema as the columns. You can see an example here:

	A	B	C
1	https://docs.google.com/spreadsheets/d/1_rN3lm0R_bU3NemO0s9pbFkY5LQPcuy1pscv8ZXPtg8/edit#gid=0	1	default
2	https://docs.google.com/spreadsheets/d/1_rN3lm0R_bU3NemO0s9pbFkY5LQPcuy1pscv8ZXPtg8/edit#gid=1077884006	2	default

This will make the two spreadsheets above available as "tables" in the default schema.

Authentication

You can access spreadsheets that are shared only within an organization. In order to do this, first create a service account. Make sure you select "Enable G Suite Domain-wide Delegation". Download the key as a JSON file.

Next, you need to manage API client access at https://admin.google.com/${DOMAIN}/AdminHome?chromeless=1#OGX:ManageOauthClients. Add the "Unique ID" from the previous step as the "Client Name", and add https://spreadsheets.google.com/feeds as the scope.

Now, when creating the connection from the DB API or from SQLAlchemy you can point to the JSON file and the user you want to impersonate:

>>> auth = {'service_account_file': '/path/to/certificate.json', 'subject': 'user@domain.com'}
>>> conn = connect(auth)

JagritiG/gsheet-db-api-plus