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How do I store a dataframe as an XLS in CDF files? I have to perform this within CDF functions.
How do I perform upsert using this dataframe. I also need to associate the timeseries against a specified asset. Please advise. How to use this dataframe and perform associating to an asset id using this line: client.time_series.data.insert_dataframe(df) I have a dataframe that looks like this as below: DATE LP_Crude API_Meter LP_FRN_KERO_SW_CUTPT LP_KERO_DSL_SW_CUTPT LP_DSL_AGO_SW_CUTPT LP_MVG_HVGO_SW_CUTPT 1/1/2023 28.75472705 271.8662 440.9032 680.3416 928.875 1/2/2023 28.21111702 269.3863 466.2317 686.5167 924.1292 1/3/2023 27.78340123 268.8638 484.6189 684.5542 919.9917 1/4/2023 27.5781529 269.3117 506.7792 686.6708 921.9375 1/5/2023 28.03229217 268.1567 500.2208 688.2208 917.575
I have a set of timeseries objects t1,t2,t3,t4,t5…..etc. Now I would like to create a derived timeseries objects based on the existing timeseries objects. For instance T1 = (t1/1000) *141.5T2 = (t2/100 - t3)T3 = (t3/100 - 14)T4 = t4 * t2/100T5 = t5/100How do I perform these steps using cognite SDK. Is there a way to perform these operations?Please advise. Retrieving dataframe from data-points and then create new timeseries objects (T1,T2….) will be challenging and hence wanted if there is an optimal way to handle this in a better way. Is this possible using synthetic time-series?IS there some examples and code snippets that can be shared so that I can grasp them better?
hello Community,Can someone please share the approach (along with code) for extracting files from sharepoint online (xls , with multiple worksheets) and extract the content and load them as RAW tables in CDF. Is there a direct feature available in Sharepoint file extractor that does this job? Should we use SDK to extend the file extraction and then read the content and insert into tables?. If we have a large set of files and each containing multiple sheets, it can be hard to process all of them dynamically. Please advise.
I want to retrieve data frame based on external IDs for the past 24 hours. So, if current date = 08/15/2023, it should try to get the previous day’s datapoints for 24 hours i.e, from 08/14/2023 00:00:00 to 08/14/2023 23:59:59. Please share with the actual code on how to put the parameters. from cognite.client import CogniteClientclient = CogniteClient()df = client.time_series.data.retrieve_dataframe(... id=12345,... start=<What to fill here>,... end=<what to fill here>)
While trying to setup the Google - colab for CDF environment and authentication, i am getting an error. Unable to trace the rootcause of this error. This code is a part of Notebook setup given in the Hands-On course Link to notebook - Data processing and analysis for IDA course.ipynb - Colaboratory (google.com) TypeError Traceback (most recent call last)Cell In [2], line 57 55 def get_token(): 56 return authenticate_device_code(app)['access_token']---> 57 client = CogniteClient( 58 ## token_url=f'{AUTHORITY_URI}/v2.0', 59 token=get_token, 60 token_client_id=CLIENT_ID, 61 project=COGNITE_PROJECT, 62 base_url=f'https://{CDF_CLUSTER}.cognitedata.com', 63 client_name='cognite-python-dev', 64 ) 65 print(client.iam.token.inspect())TypeError: __init__() got an unexpected keyword argument 'token'
How do I log errors and activities while working with the Cognite SDK. I need to keep track of what the code is doing, diagnose issues, and improve the overall reliability of the application. If I am designing a function, how to handle all these logging in my code to better log errors and validations within the application. Please advise. Also share practical code samples from SDK
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