def to_parquet( # pylint: disable=too-many-arguments,too-many-locals,too-many-branches,too-many-statements df: pd.DataFrame, path: Optional[str] = None, index: bool = False, compression: Optional[str] = "snappy", pyarrow_additional_kwargs: Optional[Dict[str, Any]] = None, max_rows_by_file: Optional[int] = None, use_threads: Union[bool, int] = True, boto3_session: Optional[boto3.Session] = None, s3_additional_kwargs: Optional[Dict[str, Any]] = None, sanitize_columns: bool = False, dataset: bool = False, filename_prefix: Optional[str] = None, partition_cols: Optional[List[str]] = None, bucketing_info: Optional[Tuple[List[str], int]] = None, concurrent_partitioning: bool = False, mode: Optional[str] = None, catalog_versioning: bool = False, schema_evolution: bool = True, database: Optional[str] = None, table: Optional[str] = None, table_type: Optional[str] = None, transaction_id: Optional[str] = None, dtype: Optional[Dict[str, str]] = None, description: Optional[str] = None, parameters: Optional[Dict[str, str]] = None, columns_comments: Optional[Dict[str, str]] = None, regular_partitions: bool = True, projection_enabled: bool = False, projection_types: Optional[Dict[str, str]] = None, projection_ranges: Optional[Dict[str, str]] = None, projection_values: Optional[Dict[str, str]] = None, projection_intervals: Optional[Dict[str, str]] = None, projection_digits: Optional[Dict[str, str]] = None, catalog_id: Optional[str] = None, ) -> Dict[str, Union[List[str], Dict[str, List[str]]]]: """Write Parquet file or dataset on Amazon S3. The concept of Dataset goes beyond the simple idea of ordinary files and enable more complex features like partitioning and catalog integration (Amazon Athena/AWS Glue Catalog). Note ---- This operation may mutate the original pandas dataframe in-place. To avoid this behaviour please pass in a deep copy instead (i.e. `df.copy()`) Note ---- If `database` and `table` arguments are passed, the table name and all column names will be automatically sanitized using `wr.catalog.sanitize_table_name` and `wr.catalog.sanitize_column_name`. Please, pass `sanitize_columns=True` to enforce this behaviour always. Note ---- On `append` mode, the `parameters` will be upsert on an existing table. Note ---- In case of `use_threads=True` the number of threads that will be spawned will be gotten from os.cpu_count(). Parameters ---------- df: pandas.DataFrame Pandas DataFrame https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html path : str, optional S3 path (for file e.g. ``s3://bucket/prefix/filename.parquet``) (for dataset e.g. ``s3://bucket/prefix``). Required if dataset=False or when dataset=True and creating a new dataset index : bool True to store the DataFrame index in file, otherwise False to ignore it. compression: str, optional Compression style (``None``, ``snappy``, ``gzip``). pyarrow_additional_kwargs : Optional[Dict[str, Any]] Additional parameters forwarded to pyarrow. e.g. pyarrow_additional_kwargs={'coerce_timestamps': 'ns', 'use_deprecated_int96_timestamps': False, 'allow_truncated_timestamps'=False} max_rows_by_file : int Max number of rows in each file. Default is None i.e. dont split the files. (e.g. 33554432, 268435456) 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. 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'} sanitize_columns : bool True to sanitize columns names (using `wr.catalog.sanitize_table_name` and `wr.catalog.sanitize_column_name`) or False to keep it as is. True value behaviour is enforced if `database` and `table` arguments are passed. dataset : bool If True store a parquet dataset instead of a ordinary file(s) If True, enable all follow arguments: partition_cols, mode, database, table, description, parameters, columns_comments, concurrent_partitioning, catalog_versioning, projection_enabled, projection_types, projection_ranges, projection_values, projection_intervals, projection_digits, catalog_id, schema_evolution. filename_prefix: str, optional If dataset=True, add a filename prefix to the output files. partition_cols: List[str], optional List of column names that will be used to create partitions. Only takes effect if dataset=True. bucketing_info: Tuple[List[str], int], optional Tuple consisting of the column names used for bucketing as the first element and the number of buckets as the second element. Only `str`, `int` and `bool` are supported as column data types for bucketing. concurrent_partitioning: bool If True will increase the parallelism level during the partitions writing. It will decrease the writing time and increase the memory usage. https://aws-data-wrangler.readthedocs.io/en/2.13.0/tutorials/022%20-%20Writing%20Partitions%20Concurrently.html mode: str, optional ``append`` (Default), ``overwrite``, ``overwrite_partitions``. Only takes effect if dataset=True. For details check the related tutorial: https://aws-data-wrangler.readthedocs.io/en/2.13.0/stubs/awswrangler.s3.to_parquet.html#awswrangler.s3.to_parquet catalog_versioning : bool If True and `mode="overwrite"`, creates an archived version of the table catalog before updating it. schema_evolution : bool If True allows schema evolution (new or missing columns), otherwise a exception will be raised. True by default. (Only considered if dataset=True and mode in ("append", "overwrite_partitions")) Related tutorial: https://aws-data-wrangler.readthedocs.io/en/2.13.0/tutorials/014%20-%20Schema%20Evolution.html database : str, optional Glue/Athena catalog: Database name. table : str, optional Glue/Athena catalog: Table name. table_type: str, optional The type of the Glue Table. Set to EXTERNAL_TABLE if None. transaction_id: str, optional The ID of the transaction when writing to a Governed Table. dtype : Dict[str, str], optional Dictionary of columns names and Athena/Glue types to be casted. Useful when you have columns with undetermined or mixed data types. (e.g. {'col name': 'bigint', 'col2 name': 'int'}) description : str, optional Glue/Athena catalog: Table description parameters : Dict[str, str], optional Glue/Athena catalog: Key/value pairs to tag the table. columns_comments : Dict[str, str], optional Glue/Athena catalog: Columns names and the related comments (e.g. {'col0': 'Column 0.', 'col1': 'Column 1.', 'col2': 'Partition.'}). regular_partitions : bool Create regular partitions (Non projected partitions) on Glue Catalog. Disable when you will work only with Partition Projection. Keep enabled even when working with projections is useful to keep Redshift Spectrum working with the regular partitions. projection_enabled : bool Enable Partition Projection on Athena (https://docs.aws.amazon.com/athena/latest/ug/partition-projection.html) projection_types : Optional[Dict[str, str]] Dictionary of partitions names and Athena projections types. Valid types: "enum", "integer", "date", "injected" https://docs.aws.amazon.com/athena/latest/ug/partition-projection-supported-types.html (e.g. {'col_name': 'enum', 'col2_name': 'integer'}) projection_ranges: Optional[Dict[str, str]] Dictionary of partitions names and Athena projections ranges. https://docs.aws.amazon.com/athena/latest/ug/partition-projection-supported-types.html (e.g. {'col_name': '0,10', 'col2_name': '-1,8675309'}) projection_values: Optional[Dict[str, str]] Dictionary of partitions names and Athena projections values. https://docs.aws.amazon.com/athena/latest/ug/partition-projection-supported-types.html (e.g. {'col_name': 'A,B,Unknown', 'col2_name': 'foo,boo,bar'}) projection_intervals: Optional[Dict[str, str]] Dictionary of partitions names and Athena projections intervals. https://docs.aws.amazon.com/athena/latest/ug/partition-projection-supported-types.html (e.g. {'col_name': '1', 'col2_name': '5'}) projection_digits: Optional[Dict[str, str]] Dictionary of partitions names and Athena projections digits. https://docs.aws.amazon.com/athena/latest/ug/partition-projection-supported-types.html (e.g. {'col_name': '1', 'col2_name': '2'}) catalog_id : str, optional The ID of the Data Catalog from which to retrieve Databases. If none is provided, the AWS account ID is used by default. Returns ------- Dict[str, Union[List[str], Dict[str, List[str]]]] Dictionary with: 'paths': List of all stored files paths on S3. 'partitions_values': Dictionary of partitions added with keys as S3 path locations and values as a list of partitions values as str. Examples -------- Writing single file >>> import awswrangler as wr >>> import pandas as pd >>> wr.s3.to_parquet( ... df=pd.DataFrame({'col': [1, 2, 3]}), ... path='s3://bucket/prefix/my_file.parquet', ... ) { 'paths': ['s3://bucket/prefix/my_file.parquet'], 'partitions_values': {} } Writing single file encrypted with a KMS key >>> import awswrangler as wr >>> import pandas as pd >>> wr.s3.to_parquet( ... df=pd.DataFrame({'col': [1, 2, 3]}), ... path='s3://bucket/prefix/my_file.parquet', ... s3_additional_kwargs={ ... 'ServerSideEncryption': 'aws:kms', ... 'SSEKMSKeyId': 'YOUR_KMS_KEY_ARN' ... } ... ) { 'paths': ['s3://bucket/prefix/my_file.parquet'], 'partitions_values': {} } Writing partitioned dataset >>> import awswrangler as wr >>> import pandas as pd >>> wr.s3.to_parquet( ... df=pd.DataFrame({ ... 'col': [1, 2, 3], ... 'col2': ['A', 'A', 'B'] ... }), ... path='s3://bucket/prefix', ... dataset=True, ... partition_cols=['col2'] ... ) { 'paths': ['s3://.../col2=A/x.parquet', 's3://.../col2=B/y.parquet'], 'partitions_values: { 's3://.../col2=A/': ['A'], 's3://.../col2=B/': ['B'] } } Writing bucketed dataset >>> import awswrangler as wr >>> import pandas as pd >>> wr.s3.to_parquet( ... df=pd.DataFrame({ ... 'col': [1, 2, 3], ... 'col2': ['A', 'A', 'B'] ... }), ... path='s3://bucket/prefix', ... dataset=True, ... bucketing_info=(["col2"], 2) ... ) { 'paths': ['s3://.../x_bucket-00000.csv', 's3://.../col2=B/x_bucket-00001.csv'], 'partitions_values: {} } Writing dataset to S3 with metadata on Athena/Glue Catalog. >>> import awswrangler as wr >>> import pandas as pd >>> wr.s3.to_parquet( ... df=pd.DataFrame({ ... 'col': [1, 2, 3], ... 'col2': ['A', 'A', 'B'] ... }), ... path='s3://bucket/prefix', ... dataset=True, ... partition_cols=['col2'], ... database='default', # Athena/Glue database ... table='my_table' # Athena/Glue table ... ) { 'paths': ['s3://.../col2=A/x.parquet', 's3://.../col2=B/y.parquet'], 'partitions_values: { 's3://.../col2=A/': ['A'], 's3://.../col2=B/': ['B'] } } Writing dataset to Glue governed table >>> import awswrangler as wr >>> import pandas as pd >>> wr.s3.to_parquet( ... df=pd.DataFrame({ ... 'col': [1, 2, 3], ... 'col2': ['A', 'A', 'B'], ... 'col3': [None, None, None] ... }), ... dataset=True, ... mode='append', ... database='default', # Athena/Glue database ... table='my_table', # Athena/Glue table ... table_type='GOVERNED', ... transaction_id="xxx", ... ) { 'paths': ['s3://.../x.parquet'], 'partitions_values: {} } Writing dataset casting empty column data type >>> import awswrangler as wr >>> import pandas as pd >>> wr.s3.to_parquet( ... df=pd.DataFrame({ ... 'col': [1, 2, 3], ... 'col2': ['A', 'A', 'B'], ... 'col3': [None, None, None] ... }), ... path='s3://bucket/prefix', ... dataset=True, ... database='default', # Athena/Glue database ... table='my_table' # Athena/Glue table ... dtype={'col3': 'date'} ... ) { 'paths': ['s3://.../x.parquet'], 'partitions_values: {} } """ _validate_args( df=df, table=table, database=database, dataset=dataset, path=path, partition_cols=partition_cols, bucketing_info=bucketing_info, mode=mode, description=description, parameters=parameters, columns_comments=columns_comments, ) # Evaluating compression if _COMPRESSION_2_EXT.get(compression, None) is None: raise exceptions.InvalidCompression(f"{compression} is invalid, please use None, 'snappy' or 'gzip'.") compression_ext: str = _COMPRESSION_2_EXT[compression] # Initializing defaults partition_cols = partition_cols if partition_cols else [] dtype = dtype if dtype else {} partitions_values: Dict[str, List[str]] = {} mode = "append" if mode is None else mode commit_trans: bool = False if transaction_id: table_type = "GOVERNED" filename_prefix = filename_prefix + uuid.uuid4().hex if filename_prefix else uuid.uuid4().hex cpus: int = _utils.ensure_cpu_count(use_threads=use_threads) session: boto3.Session = _utils.ensure_session(session=boto3_session) # Sanitize table to respect Athena's standards if (sanitize_columns is True) or (database is not None and table is not None): df, dtype, partition_cols = _sanitize(df=df, dtype=dtype, partition_cols=partition_cols) # Evaluating dtype catalog_table_input: Optional[Dict[str, Any]] = None if database is not None and table is not None: catalog_table_input = catalog._get_table_input( # pylint: disable=protected-access database=database, table=table, boto3_session=session, transaction_id=transaction_id, catalog_id=catalog_id ) catalog_path: Optional[str] = None if catalog_table_input: table_type = catalog_table_input["TableType"] catalog_path = catalog_table_input["StorageDescriptor"]["Location"] if path is None: if catalog_path: path = catalog_path else: raise exceptions.InvalidArgumentValue( "Glue table does not exist in the catalog. Please pass the `path` argument to create it." ) elif path and catalog_path: if path.rstrip("/") != catalog_path.rstrip("/"): raise exceptions.InvalidArgumentValue( f"The specified path: {path}, does not match the existing Glue catalog table path: {catalog_path}" ) if (table_type == "GOVERNED") and (not transaction_id): _logger.debug("`transaction_id` not specified for GOVERNED table, starting transaction") transaction_id = lakeformation.start_transaction(read_only=False, boto3_session=boto3_session) commit_trans = True df = _apply_dtype(df=df, dtype=dtype, catalog_table_input=catalog_table_input, mode=mode) schema: pa.Schema = _data_types.pyarrow_schema_from_pandas( df=df, index=index, ignore_cols=partition_cols, dtype=dtype ) _logger.debug("schema: \n%s", schema) if dataset is False: paths = _to_parquet( df=df, path=path, schema=schema, index=index, cpus=cpus, compression=compression, compression_ext=compression_ext, pyarrow_additional_kwargs=pyarrow_additional_kwargs, boto3_session=session, s3_additional_kwargs=s3_additional_kwargs, dtype=dtype, max_rows_by_file=max_rows_by_file, use_threads=use_threads, ) else: columns_types: Dict[str, str] = {} partitions_types: Dict[str, str] = {} if (database is not None) and (table is not None): columns_types, partitions_types = _data_types.athena_types_from_pandas_partitioned( df=df, index=index, partition_cols=partition_cols, dtype=dtype ) if schema_evolution is False: _utils.check_schema_changes(columns_types=columns_types, table_input=catalog_table_input, mode=mode) if (catalog_table_input is None) and (table_type == "GOVERNED"): catalog._create_parquet_table( # pylint: disable=protected-access database=database, table=table, path=path, # type: ignore columns_types=columns_types, table_type=table_type, partitions_types=partitions_types, bucketing_info=bucketing_info, compression=compression, description=description, parameters=parameters, columns_comments=columns_comments, boto3_session=session, mode=mode, transaction_id=transaction_id, catalog_versioning=catalog_versioning, projection_enabled=projection_enabled, projection_types=projection_types, projection_ranges=projection_ranges, projection_values=projection_values, projection_intervals=projection_intervals, projection_digits=projection_digits, projection_storage_location_template=None, catalog_id=catalog_id, catalog_table_input=catalog_table_input, ) catalog_table_input = catalog._get_table_input( # pylint: disable=protected-access database=database, table=table, boto3_session=session, transaction_id=transaction_id, catalog_id=catalog_id, ) paths, partitions_values = _to_dataset( func=_to_parquet, concurrent_partitioning=concurrent_partitioning, df=df, path_root=path, # type: ignore filename_prefix=filename_prefix, index=index, compression=compression, compression_ext=compression_ext, catalog_id=catalog_id, database=database, table=table, table_type=table_type, transaction_id=transaction_id, pyarrow_additional_kwargs=pyarrow_additional_kwargs, cpus=cpus, use_threads=use_threads, partition_cols=partition_cols, partitions_types=partitions_types, bucketing_info=bucketing_info, dtype=dtype, mode=mode, boto3_session=session, s3_additional_kwargs=s3_additional_kwargs, schema=schema, max_rows_by_file=max_rows_by_file, ) if (database is not None) and (table is not None): try: catalog._create_parquet_table( # pylint: disable=protected-access database=database, table=table, path=path, # type: ignore columns_types=columns_types, table_type=table_type, partitions_types=partitions_types, bucketing_info=bucketing_info, compression=compression, description=description, parameters=parameters, columns_comments=columns_comments, boto3_session=session, mode=mode, transaction_id=transaction_id, catalog_versioning=catalog_versioning, projection_enabled=projection_enabled, projection_types=projection_types, projection_ranges=projection_ranges, projection_values=projection_values, projection_intervals=projection_intervals, projection_digits=projection_digits, projection_storage_location_template=None, catalog_id=catalog_id, catalog_table_input=catalog_table_input, ) if partitions_values and (regular_partitions is True) and (table_type != "GOVERNED"): _logger.debug("partitions_values:\n%s", partitions_values) catalog.add_parquet_partitions( database=database, table=table, partitions_values=partitions_values, bucketing_info=bucketing_info, compression=compression, boto3_session=session, catalog_id=catalog_id, columns_types=columns_types, ) if commit_trans: lakeformation.commit_transaction( transaction_id=transaction_id, boto3_session=boto3_session # type: ignore ) except Exception: _logger.debug("Catalog write failed, cleaning up S3 (paths: %s).", paths) delete_objects( path=paths, use_threads=use_threads, boto3_session=session, s3_additional_kwargs=s3_additional_kwargs, ) raise return {"paths": paths, "partitions_values": partitions_values}
def to_parquet( # pylint: disable=too-many-arguments,too-many-locals df: pd.DataFrame, path: str, index: bool = False, compression: Optional[str] = "snappy", max_rows_by_file: Optional[int] = None, use_threads: bool = True, boto3_session: Optional[boto3.Session] = None, s3_additional_kwargs: Optional[Dict[str, str]] = None, sanitize_columns: bool = False, dataset: bool = False, partition_cols: Optional[List[str]] = None, concurrent_partitioning: bool = False, mode: Optional[str] = None, catalog_versioning: bool = False, database: Optional[str] = None, table: Optional[str] = None, dtype: Optional[Dict[str, str]] = None, description: Optional[str] = None, parameters: Optional[Dict[str, str]] = None, columns_comments: Optional[Dict[str, str]] = None, regular_partitions: bool = True, projection_enabled: bool = False, projection_types: Optional[Dict[str, str]] = None, projection_ranges: Optional[Dict[str, str]] = None, projection_values: Optional[Dict[str, str]] = None, projection_intervals: Optional[Dict[str, str]] = None, projection_digits: Optional[Dict[str, str]] = None, catalog_id: Optional[str] = None, ) -> Dict[str, Union[List[str], Dict[str, List[str]]]]: """Write Parquet file or dataset on Amazon S3. The concept of Dataset goes beyond the simple idea of files and enable more complex features like partitioning, casting and catalog integration (Amazon Athena/AWS Glue Catalog). Note ---- If `dataset=True` The table name and all column names will be automatically sanitized using `wr.catalog.sanitize_table_name` and `wr.catalog.sanitize_column_name`. Please, pass `sanitize_columns=True` to force the same behaviour for `dataset=False`. Note ---- On `append` mode, the `parameters` will be upsert on an existing table. Note ---- In case of `use_threads=True` the number of threads that will be spawned will be gotten from os.cpu_count(). Parameters ---------- df: pandas.DataFrame Pandas DataFrame https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html path : str S3 path (for file e.g. ``s3://bucket/prefix/filename.parquet``) (for dataset e.g. ``s3://bucket/prefix``). index : bool True to store the DataFrame index in file, otherwise False to ignore it. compression: str, optional Compression style (``None``, ``snappy``, ``gzip``). max_rows_by_file : int Max number of rows in each file. Default is None i.e. dont split the files. (e.g. 33554432, 268435456) use_threads : bool True to enable concurrent requests, False to disable multiple threads. If enabled os.cpu_count() will be used as the max number of threads. boto3_session : boto3.Session(), optional Boto3 Session. The default boto3 session will be used if boto3_session receive None. s3_additional_kwargs: Forward to s3fs, useful for server side encryption https://s3fs.readthedocs.io/en/latest/#serverside-encryption sanitize_columns : bool True to sanitize columns names or False to keep it as is. True value is forced if `dataset=True`. dataset : bool If True store a parquet dataset instead of a single file. If True, enable all follow arguments: partition_cols, mode, database, table, description, parameters, columns_comments, . partition_cols: List[str], optional List of column names that will be used to create partitions. Only takes effect if dataset=True. concurrent_partitioning: bool If True will increase the parallelism level during the partitions writing. It will decrease the writing time and increase the memory usage. https://github.com/awslabs/aws-data-wrangler/blob/master/tutorials/022%20-%20Writing%20Partitions%20Concurrently.ipynb mode: str, optional ``append`` (Default), ``overwrite``, ``overwrite_partitions``. Only takes effect if dataset=True. For details check the related tutorial: https://aws-data-wrangler.readthedocs.io/en/latest/stubs/awswrangler.s3.to_parquet.html#awswrangler.s3.to_parquet catalog_versioning : bool If True and `mode="overwrite"`, creates an archived version of the table catalog before updating it. database : str, optional Glue/Athena catalog: Database name. table : str, optional Glue/Athena catalog: Table name. dtype : Dict[str, str], optional Dictionary of columns names and Athena/Glue types to be casted. Useful when you have columns with undetermined or mixed data types. (e.g. {'col name': 'bigint', 'col2 name': 'int'}) description : str, optional Glue/Athena catalog: Table description parameters : Dict[str, str], optional Glue/Athena catalog: Key/value pairs to tag the table. columns_comments : Dict[str, str], optional Glue/Athena catalog: Columns names and the related comments (e.g. {'col0': 'Column 0.', 'col1': 'Column 1.', 'col2': 'Partition.'}). regular_partitions : bool Create regular partitions (Non projected partitions) on Glue Catalog. Disable when you will work only with Partition Projection. Keep enabled even when working with projections is useful to keep Redshift Spectrum working with the regular partitions. projection_enabled : bool Enable Partition Projection on Athena (https://docs.aws.amazon.com/athena/latest/ug/partition-projection.html) projection_types : Optional[Dict[str, str]] Dictionary of partitions names and Athena projections types. Valid types: "enum", "integer", "date", "injected" https://docs.aws.amazon.com/athena/latest/ug/partition-projection-supported-types.html (e.g. {'col_name': 'enum', 'col2_name': 'integer'}) projection_ranges: Optional[Dict[str, str]] Dictionary of partitions names and Athena projections ranges. https://docs.aws.amazon.com/athena/latest/ug/partition-projection-supported-types.html (e.g. {'col_name': '0,10', 'col2_name': '-1,8675309'}) projection_values: Optional[Dict[str, str]] Dictionary of partitions names and Athena projections values. https://docs.aws.amazon.com/athena/latest/ug/partition-projection-supported-types.html (e.g. {'col_name': 'A,B,Unknown', 'col2_name': 'foo,boo,bar'}) projection_intervals: Optional[Dict[str, str]] Dictionary of partitions names and Athena projections intervals. https://docs.aws.amazon.com/athena/latest/ug/partition-projection-supported-types.html (e.g. {'col_name': '1', 'col2_name': '5'}) projection_digits: Optional[Dict[str, str]] Dictionary of partitions names and Athena projections digits. https://docs.aws.amazon.com/athena/latest/ug/partition-projection-supported-types.html (e.g. {'col_name': '1', 'col2_name': '2'}) catalog_id : str, optional The ID of the Data Catalog from which to retrieve Databases. If none is provided, the AWS account ID is used by default. Returns ------- Dict[str, Union[List[str], Dict[str, List[str]]]] Dictionary with: 'paths': List of all stored files paths on S3. 'partitions_values': Dictionary of partitions added with keys as S3 path locations and values as a list of partitions values as str. Examples -------- Writing single file >>> import awswrangler as wr >>> import pandas as pd >>> wr.s3.to_parquet( ... df=pd.DataFrame({'col': [1, 2, 3]}), ... path='s3://bucket/prefix/my_file.parquet', ... ) { 'paths': ['s3://bucket/prefix/my_file.parquet'], 'partitions_values': {} } Writing single file encrypted with a KMS key >>> import awswrangler as wr >>> import pandas as pd >>> wr.s3.to_parquet( ... df=pd.DataFrame({'col': [1, 2, 3]}), ... path='s3://bucket/prefix/my_file.parquet', ... s3_additional_kwargs={ ... 'ServerSideEncryption': 'aws:kms', ... 'SSEKMSKeyId': 'YOUR_KMY_KEY_ARN' ... } ... ) { 'paths': ['s3://bucket/prefix/my_file.parquet'], 'partitions_values': {} } Writing partitioned dataset >>> import awswrangler as wr >>> import pandas as pd >>> wr.s3.to_parquet( ... df=pd.DataFrame({ ... 'col': [1, 2, 3], ... 'col2': ['A', 'A', 'B'] ... }), ... path='s3://bucket/prefix', ... dataset=True, ... partition_cols=['col2'] ... ) { 'paths': ['s3://.../col2=A/x.parquet', 's3://.../col2=B/y.parquet'], 'partitions_values: { 's3://.../col2=A/': ['A'], 's3://.../col2=B/': ['B'] } } Writing dataset to S3 with metadata on Athena/Glue Catalog. >>> import awswrangler as wr >>> import pandas as pd >>> wr.s3.to_parquet( ... df=pd.DataFrame({ ... 'col': [1, 2, 3], ... 'col2': ['A', 'A', 'B'] ... }), ... path='s3://bucket/prefix', ... dataset=True, ... partition_cols=['col2'], ... database='default', # Athena/Glue database ... table='my_table' # Athena/Glue table ... ) { 'paths': ['s3://.../col2=A/x.parquet', 's3://.../col2=B/y.parquet'], 'partitions_values: { 's3://.../col2=A/': ['A'], 's3://.../col2=B/': ['B'] } } Writing dataset casting empty column data type >>> import awswrangler as wr >>> import pandas as pd >>> wr.s3.to_parquet( ... df=pd.DataFrame({ ... 'col': [1, 2, 3], ... 'col2': ['A', 'A', 'B'], ... 'col3': [None, None, None] ... }), ... path='s3://bucket/prefix', ... dataset=True, ... database='default', # Athena/Glue database ... table='my_table' # Athena/Glue table ... dtype={'col3': 'date'} ... ) { 'paths': ['s3://.../x.parquet'], 'partitions_values: {} } """ _validate_args( df=df, table=table, dataset=dataset, path=path, partition_cols=partition_cols, mode=mode, description=description, parameters=parameters, columns_comments=columns_comments, ) # Evaluating compression if _COMPRESSION_2_EXT.get(compression, None) is None: raise exceptions.InvalidCompression( f"{compression} is invalid, please use None, 'snappy' or 'gzip'.") compression_ext: str = _COMPRESSION_2_EXT[compression] # Initializing defaults partition_cols = partition_cols if partition_cols else [] dtype = dtype if dtype else {} partitions_values: Dict[str, List[str]] = {} mode = "append" if mode is None else mode cpus: int = _utils.ensure_cpu_count(use_threads=use_threads) session: boto3.Session = _utils.ensure_session(session=boto3_session) # Sanitize table to respect Athena's standards if (sanitize_columns is True) or (dataset is True): df, dtype, partition_cols = _sanitize(df=df, dtype=dtype, partition_cols=partition_cols) # Evaluating dtype catalog_table_input: Optional[Dict[str, Any]] = None if database is not None and table is not None: catalog_table_input = catalog._get_table_input( # pylint: disable=protected-access database=database, table=table, boto3_session=session, catalog_id=catalog_id) df = _apply_dtype(df=df, dtype=dtype, catalog_table_input=catalog_table_input, mode=mode) schema: pa.Schema = _data_types.pyarrow_schema_from_pandas( df=df, index=index, ignore_cols=partition_cols, dtype=dtype) _logger.debug("schema: \n%s", schema) if dataset is False: paths = _to_parquet( df=df, path=path, schema=schema, index=index, cpus=cpus, compression=compression, compression_ext=compression_ext, boto3_session=session, s3_additional_kwargs=s3_additional_kwargs, dtype=dtype, max_rows_by_file=max_rows_by_file, ) else: paths, partitions_values = _to_dataset( func=_to_parquet, concurrent_partitioning=concurrent_partitioning, df=df, path_root=path, index=index, compression=compression, compression_ext=compression_ext, cpus=cpus, use_threads=use_threads, partition_cols=partition_cols, dtype=dtype, mode=mode, boto3_session=session, s3_additional_kwargs=s3_additional_kwargs, schema=schema, max_rows_by_file=max_rows_by_file, ) if (database is not None) and (table is not None): columns_types, partitions_types = _data_types.athena_types_from_pandas_partitioned( df=df, index=index, partition_cols=partition_cols, dtype=dtype) catalog._create_parquet_table( # pylint: disable=protected-access database=database, table=table, path=path, columns_types=columns_types, partitions_types=partitions_types, compression=compression, description=description, parameters=parameters, columns_comments=columns_comments, boto3_session=session, mode=mode, catalog_versioning=catalog_versioning, projection_enabled=projection_enabled, projection_types=projection_types, projection_ranges=projection_ranges, projection_values=projection_values, projection_intervals=projection_intervals, projection_digits=projection_digits, catalog_id=catalog_id, catalog_table_input=catalog_table_input, ) if partitions_values and (regular_partitions is True): _logger.debug("partitions_values:\n%s", partitions_values) catalog.add_parquet_partitions( database=database, table=table, partitions_values=partitions_values, compression=compression, boto3_session=session, ) return {"paths": paths, "partitions_values": partitions_values}