Insert pandas dataframe

async AsyncCogniteClient.time_series.data.insert_dataframe( df: pd.DataFrame, dropna: bool = True, ) → None

Insert a dataframe containing datapoints to one or more time series.

The index of the dataframe must contain the timestamps (pd.DatetimeIndex). The column identifiers must contain the IDs (int), external IDs (str) or instance IDs (NodeId or 2-tuple (space, ext. ID)) of the already existing time series to which the datapoints from that particular column will be written.

Note

The column identifiers must be unique.

Parameters:

df (pd.DataFrame) – Pandas DataFrame object containing the time series.
dropna (bool) – Set to True to ignore NaNs in the given DataFrame, applied per column. Default: True.

Warning

You can not insert datapoints with status codes using this method (insert_dataframe), you’ll need to use the insert() method instead (or insert_multiple())!

Examples

Post a dataframe with white noise to three time series, one using ID, one using external id and one using instance id:

>>> import numpy as np
>>> import pandas as pd
>>> from cognite.client import CogniteClient, AsyncCogniteClient
>>> from cognite.client.data_classes.data_modeling import NodeId
>>> client = CogniteClient()
>>> # async_client = AsyncCogniteClient()  # another option
>>> node_id = NodeId("my-space", "my-ts-xid")
>>> df = pd.DataFrame(
...     {
...         123: np.random.normal(0, 1, 100),
...         "foo": np.random.normal(0, 1, 100),
...         node_id: np.random.normal(0, 1, 100),
...     },
...     index=pd.date_range(start="2018-01-01", periods=100, freq="1d"),
... )
>>> client.time_series.data.insert_dataframe(df)