awswrangler.s3.to_excel(df: DataFrame, path: str, boto3_session: Optional[Session] = None, s3_additional_kwargs: Optional[Dict[str, Any]] = None, use_threads: Union[bool, int] = True, **pandas_kwargs: Any) str

Write EXCEL file on Amazon S3.


This function accepts any Pandas’s read_excel() argument.


Depending on the file extension (‘xlsx’, ‘xls’, ‘odf’…), an additional library might have to be installed first (e.g. xlrd).


In case of use_threads=True the number of threads that will be spawned will be gotten from os.cpu_count().

  • df (pandas.DataFrame) – Pandas DataFrame

  • path (str) – Amazon S3 path (e.g. s3://bucket/filename.xlsx).

  • boto3_session (boto3.Session(), optional) – Boto3 Session. The default boto3 Session will be used if boto3_session receive None.

  • s3_additional_kwargs (Optional[Dict[str, Any]]) – Forwarded to botocore requests. e.g. s3_additional_kwargs={‘ServerSideEncryption’: ‘aws:kms’, ‘SSEKMSKeyId’: ‘YOUR_KMS_KEY_ARN’}

  • use_threads (bool, int) – True to enable concurrent requests, False to disable multiple threads. If enabled os.cpu_count() will be used as the max number of threads. If integer is provided, specified number is used.

  • pandas_kwargs – KEYWORD arguments forwarded to pandas.DataFrame.to_excel(). You can NOT pass pandas_kwargs explicit, just add valid Pandas arguments in the function call and Wrangler will accept it. e.g. wr.s3.to_excel(df, path, na_rep=””, index=False)


Written S3 path.

Return type



Writing EXCEL file

>>> import awswrangler as wr
>>> import pandas as pd
>>> wr.s3.to_excel(df, 's3://bucket/filename.xlsx')