gspread-pandas 4.1.0

Optional: better matching with a model

Each of the three falls back to exact matching, similarity and strict type inference, which covers most sheets on its own. Set an API key and they can also handle renames that don’t look alike (revenue to Sales Total), stacked headers, and columns whose type only makes sense from the name:

export GSPREAD_PANDAS_AI_API_KEY=...
export GSPREAD_PANDAS_AI_BASE_URL=https://api.deepseek.com/v1  # default
export GSPREAD_PANDAS_AI_MODEL=deepseek-chat                   # default

Any OpenAI-compatible endpoint works, so DeepSeek, OpenAI, OpenRouter, LiteLLM and a local Ollama are all reachable by changing the base URL. No extra packages are needed.

The model is only ever asked for a small structured answer: a column-to-header mapping, a header row number, a type name from a fixed list. Nothing it returns is executed, and anything naming a column, header or type that isn’t really there is discarded. A wrong answer costs you a match, never a wrong value in a cell. Only column names and a bounded sample of rows are ever sent.

Client Credentials

To allow a script to use Google Drive API we need to authenticate our self towards Google. To do so, we need to create a project, describing the tool and generate credentials. Please use your web browser and go to Google console and :

• Choose Create Project in popup menu on the top.

• A dialog box appears, so give your project a name and click on Create button.

• On the left-side menu click on API Manager.

• A table of available APIs is shown. Switch Drive API and click on Enable API button. Do the same for Sheets API. Other APIs might be switched off, for our purpose.

• On the left-side menu click on Credentials.

• In section OAuth consent screen select your email address and give your product a name. Then click on Save button.

• In section Credentials click on Add credentials and switch OAuth client ID (if you want to use your own account or enable the use of multiple accounts) or Service account key (if you prefer to have a service account interacting with spreadsheets).

• If you select OAuth client ID:

  • Select Application type item as Desktop app and give it a name.

  • Click on Create button.

  • Click on Download JSON icon on the right side of created OAuth client IDs and store the downloaded file on your file system.

• If you select Service account key

  • Click on Service account dropdown and select New service account

  • Give it a Service account name and ignore the Role dropdown (unless you know you need this for something else, it’s not necessary for working with spreadsheets)

  • Note the Service account ID as you might need to give that user permission to interact with your spreadsheets

  • Leave Key type as JSON

  • Click Create and store the downloaded file on your file system.

• Please be aware, the file contains your private credentials, so take care of the file in the same way you care of your private SSH key; Move the downloaded JSON to ~/.config/gspread_pandas/google_secret.json (or you can configure the directory and file name by directly calling gspread_pandas.conf.get_config

Thanks to similar project df2gspread for this great description of how to get the client credentials.

You can read more about it in the configuration docs including how to change the default behavior.

EOFError in Rodeo

If you’re trying to use gspread_pandas from within Rodeo you might get an EOFError: EOF when reading a line error when trying to pass in the verification code. The workaround for this is to first verify your account in a regular shell. Since you’re just doing this to get your Oauth token, the spreadsheet doesn’t need to be valid. Just run this in shell:

python -c "from gspread_pandas import Spread; Spread('<user_key>','')"

Then follow the instructions to create and store the OAuth creds.

This action would increase the number of cells in the workbook above the limit of 10000000 cells.

IMO, Google sheets is not the right tool for large datasets. However, there’s probably good reaons you might have to use it in such cases. When uploading a large DataFrame, you might run into this error.

By default, Spread.df_to_sheet will add rows and/or columns needed to accomodate the DataFrame. Since a new sheet contains a fairly large number of columns, if you’re uploading a DF with lots of rows you might exceed the max number of cells in a worksheet even if your data does not. In order to fix this you have 2 options:

1. The easiest is to pass replace=True, which will first resize the worksheet and clear out all values.

2. Another option is to first resize to 1x1 using Spread.sheet.resize(1, 1) and then do df_to_sheet

There’s a strange caveat with resizing, so going to 1x1 first is recommended (replace=True already does this). To read more see this issue