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Python tool set for interacting with Google Sheets data.

Project description


(Formerly googlesheet_tools, GoogleSheetAPITools)

Basic Python functions for interacting with data in Google Sheets. See for more setup details. See API documentation: This library has been heavily used in Columbia University Libraries' archival data migrations and other activites; a case study involving its use can be found in If you are using a lot of CSVs or Excel files for your data pipelines, Google Sheets integration may be a worthwhile alternative.


  • Python 3.4 or higher.
  • A Google Apps account.
  • Python packages:
    • requests
    • google-api-python-client
    • oauth2client
    • httplib2


This library is bundled as an installable package from

  1. Installation

    There are several ways to use sheetFeeder, depending how you want to manage dependencies like authentication credentials. Three options are described here: system installation, installation in a virtual environment, and stand-alone module use. For testing and portability, the virtual-environment option is most recommended.

    A. System installation

    To install into your default Python 3 environment, use the version of pip assocated with that environment (usually pip3).

    pip3 install sheetFeeder

    NOTE: You may need to prepend sudo to the above to install at the system level. If you do not have su permissions to install Python packages, you may do better to use a virtual environment (see below).

    You will need to note the location where the package is installed for step 2 below. It will be something like:


    B. Virtual environment installation

    The command venv is used to create a virtual Python environment. See (Commands below are for a bash shell in Linux or Mac OS; your use of venv may vary,see the venv documentation linked above.)

    • Use venv to create a new virtual Python 3 environment in a convenient location with an appropriate name such as "sfvenv":

      python3 -m venv sfvenv
    • Activate the virtual environment to which dependencies will be added:

      source sfvenv/bin/activate

      (To deactivate the environment use the command deactivate.)

    • Install sheetFeeder using pip:

      pip install sheetFeeder

      This will install into the activated virtual environment and only be available while the environment is active. Note the location where the library was installed for step 2 below. It will be something like:


    C. Stand-alone installation

    If you prefer not to install the module as a package but rather wish to use it as a standalone Python module, you will need to install a few dependencies yourself, either in a virtual environment or in your default Python 3 environment. In this case, download to your working directory and import it from your scripts in the same directory.

    Dependencies to install into environment:

    • pip install requests
    • pip install --upgrade google-api-python-client
    • pip install oauth2client

    In this scenario, you will place the credentials.json file from step 2 below in the same working directory as

  2. Obtain API credentials. To begin using the Google Sheets API you need to obtain credentials specific to your Google account and make them available to sheetFeeder.

    Note: Google has significanlty changed the way that API access is enabled, now requiring a more complicated but still very doable process. You now must have a project with API enabled in Google Cloud Console. This should be available to any Google Apps user. This is a one-time setup, so don't worry about the details much.

    A more detailed walk-through of this process can be found in the tutorial here.

    • Follow Google's instructions to Create a project and enable the API. If you already have a project, you can simply select it and edit it to enable the API.
    • Follow the directions in the documentation to configure the OAuth consent screen for the project and generate an OAuth client ID credential.
      • You must set up the credentials for a Desktop application.
    • Download the file (API and Services > Credentials) for the Oauth client ID credentials.
    • Important: You must rename the downloaded file as credentials.json.
    • Place credentials.json in the sheetFeeder package location as identified in step 1 above (will be different depending on which type of installation you opted for).
  3. Authenticate and authorize access to your Google account's API (Quickstart).

    • Download and run in your working directory.
    • The first time you use the API you will be asked to select the Google identity to use (if more than one are detected) and to verify access. Note that you may see a warning that the application is not verified by Google. You can go to the "advanced" option and proceed with the "Quickstart" authentication process from there.
    • Click through to grant read/write permission to your Google Sheets account. If successful you will see a message saying "The authentication flow has completed." 8 If successful, a token.json file should be created in the same folder as the credentials.json file (see step 1 above for location), and a brief readout of sample table data will appear. Once the credentials and token are in place, you be able to access sheets via the API without additional steps; you can verify this by running again—you should get the read-out without the authentication steps. 

Reusing and revoking API credentials

Note that your API credentials (credentials.json and token.json) can be reused in other environments where sheetFeeder is installed without repeating steps 2–3 above. You may copy them to the appropriate location per step 1 above. To disallow API access and reset to the initial state, simply delete the files. You may also manage API access via the Google API console.

Using sheetFeeder

The dataSheet() class

The core class is dataSheet(id,range). Define a dataSheet to operate on using the id string of a Google Sheet (the long string between "" and "/edit#gid=0" or the like), and a range including a tab name. Example:

from sheetFeeder import dataSheet

my_sheet = dataSheet('1YzM1diaFchenQnchemgogyU2menGxv5Gme','Sheet1!A:Z')

This enables several methods on the dataSheet class, as outlined below.


  • clear()

    • Empty the contents of range, as defined by dataSheet.
    • Example: my_sheet.clear():
  • getData()

    • Return the contents of dataSheet in a list of lists.
    • Optional query to filter results, see syntax of matchingRows() below.
    • Example: my_sheet.getData()
    • Result: [['head1', 'head2'],['a', 'b'],['one', 'two']]
    • Example with filter: my_sheet.getData(filter_queries=[['head2', 'b']])
    • Result: [['a', 'b']]
  • getDataColumns()

    • Return the contents of dataSheet rotated as columns, in a list of lists.
    • Example: my_sheet.getDataColumns()
    • Result: [['head1', 'a', 'one'],['head2', 'b','two']]
  • getDataSeries()

    • Return the contents of dataSheet as a dict with each column a series. Assumes that the first row is heads.
    • Example: my_sheet.getDataSeries()
    • Result: {'head1: ['a', 'c'], 'head2': ['b', 'd']}
  • appendData(data)

    • Append rows of data to sheet. Note: the range is only used to identify a table; values will be appended as rows at the end of table, not at end of range.
    • Example: my_sheet.appendData([[5,"e", 'xx'],[6,"f"],[7,"g"]])
    • Result: add some rows.
  • lookup(search_str,col_search,col_result)

    • Provide string to match, the column to match in, and col(s) to return. The col_result can either be an integer or a list of integers, e.g., col_search=0, col_result=[1,2], which will return an array of results. Will return multiple matches in a list.
    • Example: my_sheet.lookup('Smith',2,[3,4])
    • Result: Return values of columns 3 and 4 for any row where column 2 equals "Smith".
  • matchingRows(queries,regex=True,operator='or')

    • Return a list of rows for which at least one queried column matches regex query. Assumes the first row contains heads. Queries are pairs of column heads and matching strings, e.g., [['ID','123'],['Author','Yeats']]. They are regex by default and can be joined by either 'and' or 'or' logic.
    • Example: my_sheet.matchingRows([['ID', '123'], ['Title', '.*Humph.*']])
    • Result: Return all rows where ID = 123 or Title matches the regex expression .*Humph.*.
    • Example: my_sheet.matchingRows([['ID', '123'], ['Title', '.*Humph.*']], operator='and')
    • Result: Return all rows where ID = 123 and Title matches the regex expression .*Humph.*.
  • importCSV(csv,delim=',',quote='NONE')

    • Import a CSV file into a designated sheet range, overwriting what is there. Can be either local or remote. If string begins with "http" it will be treated as a URL and requested via urllib3. Otherwise it will treat it as a local file path. Delimeter is comma by default, but can be any character, e.g., pipe ('|').
    • Example: my_sheet.importCSV(my_file_path,delim='|')
    • Result: Import contents of pipe-delimited text file into dataSheet.

Additional subclasses

  • .id: Returns id part of dataSheet
  • .range: Returns range part of dataSheet
  • .initInfo: Returns dictionary of metadata about sheet (all tabs, not just the one defined in 2nd arg of dataSheet).
  • .initTabs: Returns a list of names of tabs in spreadsheet.
  • .url: Returns public url of sheet of form{sheet_id}/edit#gid={tab_id}

Using sheetFeeder with Pandas

You can easily use sheetFeeder in Pandas projects. Data retrieved from Google Sheets is easily translated into Pandas dataframes. Pandas outputs can be posted to Google Sheets as well. The file demonstrates some use cases. The default output from sheetFeeder is a 2-dimensional list array. So, assuming the first row contains the heads, you only need to pop that row into the columns parameter when creating a DataFrame object.

def datasheet_to_dataframe(_sheet_id, _sheet_range):
  the_data = dataSheet(_sheet_id, _sheet_range).getData()
  heads = the_data.pop(0)  # assumes the first row is column heads.
  return pd.DataFrame(the_data, columns=heads)

df = datasheet_to_dataframe(sheet_id, sheet_range)

Using the sample table in the example, the df object will look like:

    Col A Col B Col C Col D
0     1     2     3     4
1     5     6     7     8

The reverse process converts a Pandas dataframe back into a 2-dimensional array for use in sheetFeeder:

def dataframe_to_datasheet(_df):
  heads = list(_df.columns.values)
  ds = _df.values.tolist()
  ds.insert(0, heads)
  return ds


>>> [['Col A', 'Col B', 'Col C', 'Col D'], ['1', '2', '3', '4'], ['5', '6', '7', '8']]

The getDataSeries() method also gets data as series such that they can be fed into a Pandas dataframe easily:

ds = dataSheet(sheet_id, sheet_range)
ds_series = ds.getDataSeries()

>>> {'Col A': ['1', '5'], 'Col B': ['2', '6'], 'Col C': ['3', '7'], 'Col D': ['4', '8']}


  Col A Col B Col C Col D
0     1     2     3     4
1     5     6     7     8

Run the included to demonstrate the transformations to and from Pandas.

Error handling and recovery

Occasionally the API returns an HTTP error of one kind or another (googleapiclient.errors.HttpError) and refuses a request. Assuming credentials are in order, this is likely an intermittent problem on the server side, and can be addressed by recovering and retrying the request after a short interval. Several defaults are defined as global variables in to apply to all API calls:

  • retry_default = True
  • interval_default = 0.5
  • max_tries_default = 5

A backoff function will double the retry interval with each try until the max number is reached. If the request still cannot be executed, an exception is raised of the class sheetFeederError. The optimal interval between retries is subject to speculation.


This is a work in progress. Comments/suggestions very welcome.

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