awswrangler.s3.read_excel

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

Read EXCEL file(s) from a received S3 path.

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:

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

• version_id (str | None) – Version id of the object.

• 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 given an int will use the given amount of threads. If integer is provided, specified number is used.

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

• s3_additional_kwargs (dict[str, Any] | None) – Forward to botocore requests, only “SSECustomerAlgorithm” and “SSECustomerKey” arguments will be considered.

• pandas_kwargs (Any) – KEYWORD arguments forwarded to pandas.read_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.read_excel(“s3://bucket/key.xlsx”, na_rep=””, verbose=True) https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_excel.html

Return type:

DataFrame

Returns:

Pandas DataFrame.

Examples

Reading an EXCEL file

>>> import awswrangler as wr
>>> df = wr.s3.read_excel('s3://bucket/key.xlsx')