gsheetstables 1.0.1

Access the Tables of your Google Sheets as Pandas Dataframes and write them to a database

Google Sheets Tables extraction

Use Google Spreadsheet Tables (only Tables) as Pandas Dataframes or save them to a SQL database.

pip install gsheetstables

Command Line tool

The tool does one thing and does it well: Makes database tables of all the Google Sheets Tables (only Tables) found on the spreadsheet. On any database. Just make sure you have the correct SQLAlchemy driver installed. Simplest example with SQLite:

gsheetstables2db -s 1zYR...tT8

This will create the SQLite database on file tables.sqlite with all tables from GSheet 1zYR...tT8.

Execute some SQL queries after (or before, with --sql-pre) the tables were loaded/created:

gsheetstables2db -s 1zYR...tT8 \
    --sql-split-char § \
    --sql-post "{% for table in tables %}create index if not exists idx_snapshot_{{table}} on {{table}} (_GSheetsTables_utc_timestamp) § {% endfor %}"

Keep up to 6 snapshots of each table (after running it multiple times) and save a column with the row numbers that users see in GSpread:

gsheetstables2db -s 1zYR...tT8 \
    --append \
    --keep-snapshots 6 \
    --row-numbers

Write it to a MariaDB/MySQL database accessible through local socket:

pip install mysql-connector-python
gsheetstables2db -s 1zYR...tT8 --db mariadb://localhost/marketing_db

Here are SQLAlchemy URL examples along with drivers required for connectors (table provided by ChatGPT):

Database	Example SQLAlchemy URL	Driver / Package to install	Notes
MariaDB	mariadb+mariadbconnector://dbuser:[e-mail]:3306/sales_db	pip install mariadb	Native MariaDB driver
MariaDB (alt)	mysql+pymysql://dbuser:[e-mail]:3306/sales_db?charset=utf8mb4	pip install pymysql	Pure Python
PostgreSQL	postgresql+psycopg://dbuser:[e-mail]:5432/analytics_db	pip install psycopg[binary]	Recommended
PostgreSQL (legacy)	postgresql+psycopg2://dbuser:[e-mail]:5432/analytics_db	pip install psycopg2-binary	Legacy
Oracle	oracle+oracledb://dbuser:[e-mail]:1521/?service_name=ORCLPDB1	pip install oracledb	Thin mode (no Oracle Client)
AWS Athena	awsathena+rest://AWS_ACCESS_KEY_ID:[e-mail]:443/my_schema?s3_staging_dir=s3://my-athena-results/&work_group=primary	pip install sqlalchemy-athena	Uses REST API
Databricks SQL	databricks+connector://token:[e-mail]:443/default?http_path=/sql/1.0/warehouses/abc123	pip install databricks-sql-connector sqlalchemy-databricks	Token-based auth

API Usage

Initialize and bring all tables (only tables) from a Google Sheet:

import gsheetstables

account_file = "account.json"
gsheetid = "1zYR7Hlo7EtmY6...tT8"

tables = gsheetstables.GSheetsTables(
    gsheetid             = gsheetid,
    service_account_file = account_file,
    slugify              = True
)

This is done very efficiently, doing exactly 2 calls to Google’s API. One for table discovery and second one to retrieve all tables data at once.

See bellow how to get the service account file

Tables retrieved:

>>> tables.tables
[
    'products',
    'clients',
    'sales'
]

Use the tables as Pandas Dataframes.

tables.t('products')

ID	Name	Price
1	Laptop	999.99
2	Smartphone	699.00
3	Headphones	149.50
4	Keyboard	89.90

Sheet rows that are completeley empty will be removed from resulting dataframe. But the index will always match the Google Sheet row number as seen by spreadsheet users. So you can use loc method to get a specific sheet row number:

tables.t('products').loc[1034]

Another example using data and time columns:

tables.t('clients')

ID	Name	birthdate	affiliated
1	Alice Silva	1990-05-12T00:00:00-03:00	2021-03-15T10:45:00-03:00
2	Bruno Costa	1985-11-23T00:00:00-03:00	2019-08-02T14:20:00-03:00
3	Carla Mendes	1998-02-07T00:00:00-03:00	2022-01-10T09:00:00-03:00
4	Daniel Rocha	1976-09-30T00:00:00-03:00	2015-06-25T16:35:00-03:00

Notice that Google Sheets Table columns of type DATE (which may contain also time) will be converted to pandas.Timestamps and the spreadsheet timezone will be associated to it, aiming at minimum loss of data. If you want just naive dates, as they are probably formated in your sheets, use Pandas like this:

(
    tables.t('clients')
    .assign(
        birthdate  = lambda table: table.birthdate.dt.normalize().dt.tz_localize(None),
        affiliated = lambda table: table.affiliated.dt.normalize().dt.tz_localize(None),
    )
)

ID	Name	birthdate	affiliated
1	Alice Silva	1990-05-12	2021-03-15
2	Bruno Costa	1985-11-23	2019-08-02
3	Carla Mendes	1998-02-07	2022-01-10
4	Daniel Rocha	1976-09-30	2015-06-25

Remember that the complete concept of universal and portable Time always includes date, time and timezone. Displaying as just the date is an abbreviation that assumes interpretation by the reader. Information that seems to contain just a date, is actually stored as the starting midnight of that day, in the timezone of the spreadsheet. If that date is describing a business transaction, it probably didn't happen at that moment, most likely closer to the mid of the day. Your spreadsheet must display timestamps as date and time to reduce ambiguity. Example of ambiguity is Alices‘s birthday as it is actually stored by your spreadsheet: 1990-05-12T00:00:00-03:00. This timestamp is a different day in other timezones, for example, it is the same moment in Time as timestamp 1990-05-11T23:00:00-04:00 (late night of the previous day).

Column names normalization

People that edit spreadsheets can get creative when naming columns. Pass slugify=True (the default) to:

• transliterate accents and international characters with unidecode
• convert spaces, /, : to _
• lowercase all characters

In addition, you can pass a dict for custom column renaming as:

tables = gsheetstables.GSheetsTables(
    ...
    column_rename_map = {
        "table_1": {
            "Column with strange chars/letters": "short_name",
            "Other crazy column name": "other_short_name",
        },
        "table_2": {
            "Column with strange chars/letters": "short_name",
            "Other crazy column name": "other_short_name",
        }
    },
    ...
)

Pass only the columns you want to rename. Combine with slugify=True to have a complete service. Your column_rename_map dict will have priority over slugification.

What are Google Sheets Tables

Tables feature was introduced in 2024-05 and they look like this:

More than looks, Tables have structure:

• table names are unique
• columns have names
• columns have types as number, date, text, dropdown (kind of categories)
• cells have validation and can reference data in other tables and sheets

These are features that make data entry by humans less susceptible to errors, yet as easy and well known as editing a spreadsheet.

This Python module closes the gap of bringing all that nice and structured human-generated data back to the database or to your app.

Get a Service Account file for authorization

1. Go to https://console.cloud.google.com/projectcreate, make sure you are under correct Google account and create a project named My Project (or reuse a previously existing project)
2. On same page, edit the Project ID to make it smaller and more meanigfull (or leave defaults); this will be part of an e-mail address that we’ll use later
3. Go to https://console.cloud.google.com/apis/library/sheets.googleapis.com and activate the API
4. Go to https://console.cloud.google.com/apis/credentials, make sure you are in the correct project and select Create Credentials → Service account. This is like creating an operator user that will access your Google Spreadsheet; and as a user, it has an e-mail address that appears on the screen. Copy this e-mail address.
5. After service account created, go into its details and create a keypair (or upload the public part of an existing one).
6. Download the JSON file generated for this keypair, it contains the private part of the key, required to identify the program as your service account.
7. Go to the Google Sheet your program needs to extract tables, hit Share button on top right and add the virtual e-mail address of the service account you just created and copied. This is an e-mail address that looks like [e-mail]