Google Sheets Import & Export

Why use Google Sheets Import and Export?

If you're looking for an easy way to import and export data from datasets across multiple Google sheets, Google Sheets Import & Export is the Apify automation tool for you.

It can process data in your current spreadsheet, import new data from Apify datasets, or a raw JSON file. It can be run both on the Apify platform or locally.

You can use this actor with any programming language of your choice by calling Apify API.

Input settings

For a complete description of all settings, see input specification.

Limits

Google limits how many sheet reads or updates (one Actor run is generally one read or update) you can do per minute. If you exceed these limits, the actor run will fail, and no data will be imported.

• Maximum sheet reads/updates: 60 per minute.
• Maximum cells in a spreadsheet: 5 million. - If you try to import data over this limit, the actor will throw an error and not import anything. In this case, use more spreadsheets.

Authentication and authorization

If you are using this actor for the first time, you have to connect your Google account via the Connect Google Account to your Actor input field. This will allow you to pick a spreadsheet from that account. You can have multiple Google accounts connected, and you can switch between them in the Actor input.

Important tips

• No matter which mode you choose, the actor recalculates how the data should be positioned in the sheet, updates all the cells, and then trims the exceeding rows and columns. This ensures that the sheet always has the optimal number of rows and columns and there is enough space for the newly generated data.

• The actor parsing follows the default Google Sheets parsing. Therefore, depending on the configuration of your system, constructions such as "1.1" and "1,1" can be interpreted either as a number or a string (text). For this reason, it is recommended that you always use valid JSON numbers (e.g. 1.1).

Modes

This actor can be run in multiple different modes. Each run must have only one specific mode. Mode also affects how other options work (details are explained in the specific options).

• replace: If there's any old data in the sheet, it is cleaned, and then new data is imported.

• append: This mode adds new data as additional rows below the old rows already present in the sheet. Keep in mind that the columns are recalculated so some of them may move to different cells if new columns are added in the middle.

• modify: This mode doesn't import anything. It only loads the data from your sheets and applies any of the processing you set in the options.

• read: This mode simply loads the data from the spreadsheet, optionally can process them, and saves them as 'OUTPUT' JSON file to the default key-value store.

• load backup: This mode simply loads any backup rows from previous runs (look at the backup option for details) and imports it to a sheet in the replace mode.

Raw data import

If you want to send data in raw JSON format, you need to pass the data to the rawData input parameter. You will also need to have an Apify account so we can properly store your Google authentication tokens (you can opt out at anytime).

Important! - Raw data cannot exceed 9MB, as this the default limit for Apify actor inputs. If you want to upload more data, you can easily split it into more runs (they're fast and cheap).

Raw data table format (array of arrays)

rawData should be an array of arrays where each of the arrays represents one row in the sheet. The first row should be a header row where the field names are defined. Every other row is a data row.

It is important to have a proper order in each array. If the field is null for any row, the array should contain an empty string in that index. Data rows can have a smaller length than the header row but if they are longer the extra data will be trimmed off.

Arrays cannot contain nested structures like objects or other arrays! You have to flatten them in a format where / is a delimiter. E.g. personal/hobbies/0.

"rawData": [
  ["name", "occupation", "email", "hobbies/0", "hobbies/1"],
  ["John Doe", "developer", "john@google.com", "sport", "movies with Leonardo"],
  ["Leonardo DiCaprio", "actor", "leonardo@google.com", "being rich", "climate change activism"]
]

Dataset format (array of objects)

rawData should be an array of objects where each object represents one row in the sheet. The keys of the objects will be transformed to a header row and the values will be inserted into the data rows. Objects don't need to have the same keys. If an object doesn't have a key that another object has, the row will have an empty cell in that field.

The object can contain nested structures (objects and arrays) but in that case, it will call Apify API to flatten the data which can take a little more time on large uploads so try to prefer flattened data.

Nested:

"rawData": [
  {
    "name": "John Doe",
    "email": "john@google.com",
    "hobbies": ["sport", "movies with Leonardo", "dog walking"]
  },
  {
    "name": "Leonardo DiCaprio",
    "email": "leonardo@google.com",
    "hobbies": ["being rich", "climate change activism"]
  }
]

Flattened:

"rawData": [
  {
    "name": "John Doe",
    "email": "john@google.com",
    "hobbies/0": "sport",
    "hobbies/1": "movies with Leonardo",
    "hobbies/2": "dog walking"
  },
  {
    "name": "Leonardo DiCaprio",
    "email": "leonardo@google.com",
    "hobbies/0": "being rich",
    "hobbies/1": "climate change activism"
  }
]