awswrangler.timestream.write¶
- awswrangler.timestream.write(df: DataFrame, database: str, table: str, time_col: str, measure_col: str | List[str], dimensions_cols: List[str], version: int = 1, num_threads: int = 32, measure_name: str | None = None, boto3_session: Session | None = 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 (Union[str, List[str]]) – DataFrame column name(s) to be used as measure.
dimensions_cols (List[str]) – List of DataFrame column names to be used as dimensions.
version (int) – Version number used for upserts. Documentation https://docs.aws.amazon.com/timestream/latest/developerguide/API_WriteRecords.html.
measure_name (Optional[str]) – Name that represents the data attribute of the time series. Overrides
measure_col
if specified.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