awswrangler.timestream.write

awswrangler.timestream.write(df: pandas.core.frame.DataFrame, database: str, table: str, time_col: str, measure_col: str, dimensions_cols: List[str], num_threads: int = 32, boto3_session: Optional[boto3.session.Session] = None)List[Dict[str, str]]

Store a Pandas DataFrame into a Amazon Timestream table.

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

  • database (str) – Amazon Timestream database name.

  • table (str) – Amazon Timestream table name.

  • time_col (str) – DataFrame column name to be used as time. MUST be a timestamp column.

  • measure_col (str) – DataFrame column name to be used as measure.

  • dimensions_cols (List[str]) – List of DataFrame column names to be used as dimensions.

  • num_threads (str) – Number of thread to be used for concurrent writing.

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

Returns

Rejected records.

Return type

List[Dict[str, str]]

Examples

Store a Pandas DataFrame into a Amazon Timestream table.

>>> import awswrangler as wr
>>> import pandas as pd
>>> df = pd.DataFrame(
>>>     {
>>>         "time": [datetime.now(), datetime.now(), datetime.now()],
>>>         "dim0": ["foo", "boo", "bar"],
>>>         "dim1": [1, 2, 3],
>>>         "measure": [1.0, 1.1, 1.2],
>>>     }
>>> )
>>> rejected_records = wr.timestream.write(
>>>     df=df,
>>>     database="sampleDB",
>>>     table="sampleTable",
>>>     time_col="time",
>>>     measure_col="measure",
>>>     dimensions_cols=["dim0", "dim1"],
>>> )
>>> assert len(rejected_records) == 0