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https://github.com/betodealmeida/gsheets-db-api

A Python DB-API and SQLAlchemy dialect to Google Spreasheets
https://github.com/betodealmeida/gsheets-db-api

api db google python spreadsheet spreadsheets sql

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A Python DB-API and SQLAlchemy dialect to Google Spreasheets

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[![Build Status](https://travis-ci.org/betodealmeida/gsheets-db-api.svg?branch=master)](https://travis-ci.org/betodealmeida/gsheets-db-api) [![codecov](https://codecov.io/gh/betodealmeida/gsheets-db-api/branch/master/graph/badge.svg)](https://codecov.io/gh/betodealmeida/gsheets-db-api)



**Note:** [shillelagh](https://github.com/betodealmeida/shillelagh/) is a drop-in replacement for `gsheets-db-api`, with many additional features. You should use it instead. If you're using SQLAlchemy all you need to do:



```bash

$ pip uninstall gsheetsdb

$ pip install shillelagh

```



If you're using the DB API:



```bash

# from gsheetsdb import connect

from shillelagh.backends.apsw.db import connect

```



# A Python DB API 2.0 for Google Spreadsheets #



This module allows you to query Google Spreadsheets using SQL.



Using [this spreadsheet](https://docs.google.com/spreadsheets/d/1_rN3lm0R_bU3NemO0s9pbFkY5LQPcuy1pscv8ZXPtg8/) 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:



```python

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](https://developers.google.com/chart/interactive/docs/querylanguage). This module will transpile the SQL query into a simpler query that the API understands. Eg, the query above would be translated to:



```sql

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:



```sql

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

```



Gets translated to:



```sql

SELECT COUNT(A), COUNT(B)

```



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



### 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 ##



```bash

$ 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`:



```bash

$ 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:



```python

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](https://docs.google.com/spreadsheets/d/1AAqVVSpGeyRZyrr4n--fb_IxhLwwKtLbjfu4h6MyyYA/edit#gid=0):



|| 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](https://developers.google.com/api-client-library/python/auth/service-accounts#creatinganaccount). 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:



```python

>>> auth = {'service_account_file': '/path/to/certificate.json', 'subject': '[email protected]'}

>>> conn = connect(auth)

```