diff --git a/cookbook/integrations/flytekit_plugins/whylogs_examples/whylogs_example.py b/cookbook/integrations/flytekit_plugins/whylogs_examples/whylogs_example.py index 4fd3d5204..c445174e4 100644 --- a/cookbook/integrations/flytekit_plugins/whylogs_examples/whylogs_example.py +++ b/cookbook/integrations/flytekit_plugins/whylogs_examples/whylogs_example.py @@ -41,9 +41,9 @@ def get_reference_data() -> pd.DataFrame: # %% -# To some extent, we wanted to show kinds of drift in our example -# So in order to reproduce some of what real-life data behaves -# We will take an arbitrary subset of the reference dataset +# To some extent, we wanted to show kinds of drift in our example, +# so in order to reproduce some of what real-life data behaves +# we will take an arbitrary subset of the reference dataset @task def get_target_data() -> pd.DataFrame: diabetes = load_diabetes() @@ -54,9 +54,9 @@ def get_target_data() -> pd.DataFrame: # %% # Now we will define a task that can take in any pandas DataFrame -# and return a `:class:DatasetProfileView, which is our data profile. +# and return a ``DatasetProfileView``, which is our data profile. # With it, users can either visualize and check overall statistics -# Or even run a constraint suite on top of it. +# or even run a constraint suite on top of it. @task def create_profile_view(df: pd.DataFrame) -> DatasetProfileView: result = why.log(df) @@ -65,7 +65,7 @@ def create_profile_view(df: pd.DataFrame) -> DatasetProfileView: # %% # And we will also define a constraints report task -# That will run some checks in our existing profile. +# that will run some checks in our existing profile. @task def constraints_report(profile_view: DatasetProfileView) -> bool: builder = ConstraintsBuilder(dataset_profile_view=profile_view) @@ -84,7 +84,7 @@ def constraints_report(profile_view: DatasetProfileView) -> bool: # %% # This is a representation of a prediction task. Since we are looking -# To take some of the complexity away from our demonstrations, +# to take some of the complexity away from our demonstrations, # our model prediction here will be represented by generating a bunch of # random numbers with numpy. This task will take place if we pass our # constraints suite.