Functions
featurize_async​
Featurize a DataFrame using asynchronous featurizerParameters
df
(DataFrame
) - The DataFrame to featurizeinput_column
(str
) - The input columnoutput_column
(str
) - The output columnfeaturizer
(Any
) - The async featurizer functionconcurrency
(int
) - The number of concurrent jobs to rundedup
() - Whether to deduplicate the results, defaults to True Default:true
mini_batch_size
() - The mini batch size, defaults to 0 Default:0
Returns
The DataFrame with the output columnget_cache_base_path​
Get the base path for the cacheReturns
Tuple
[pyarrow._fs.FileSystem
, str
]: The filesystem and the path
get_df_schema​
Get the Tecton schema of the DataFrameParameters
df
(DataFrame
) - The DataFrameas_attributes
(bool
) - Whether to return the schema as attributes, defaults to False Default:false
Returns
List
[Any
]: The schema of the DataFrame
make_request_source​
Make a request sourceParameters
fields
() -
Returns
RequestSource
: The request source
run_async​
Run the coroutine asynchronously in both Tecton and Jupyter notebooksParameters
coro
(Any
) - The coroutine to run
Returns
Any
: The result of the coroutine
run_async_jobs​
Run the list of coroutines asynchronously in both Tecton and Jupyter notebooksParameters
jobs
(List
[Any
]) - The list of coroutines to runconcurrency
(int
) - The number of concurrent jobs to run
Returns
List
[Any
]: The results of the coroutines
set_conf​
Parameters
conf
(dict
) -
set_secrets_env​
Set the secrets in the environment variablesParameters
Returns
None
Example
from tecton import Secretsecrets = set_secrets_env({"env_var_name": Secret(scope="", key="")})@batch_feature_view(sources=[your_source, secrets],...)def your_feature_view(your_source, secrets):# no need to do anything with secrets, they are already set in the environment...