awswrangler.s3.to_excel

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

Write EXCEL file on Amazon S3.

Note

This function accepts any Pandas’s read_excel() argument. https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_excel.html

Note

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

Note

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

Parameters:

  • df (DataFrame) – Pandas DataFrame https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html

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

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

  • pyarrow_additional_kwargs – 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 (Any) – 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 awswrangler will accept it. e.g. wr.s3.to_excel(df, path, na_rep=””, index=False) https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.to_excel.html

Return type:

str

Returns:

Written S3 path.

Examples

Writing EXCEL file

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