How to capture dataset structure and statistics

Data scientists can capture their dataset structure and statistics when registering datasets to Vectice using Pandas or Spark (version 3.0 and above) dataframes.

Most common statistics are currently not captured with Spark Dataframes.

Statistics will be captured for the first 100 columns of your dataframe. Statistics are not captured if the numbers of rows are below 100 to keep the data anonymous. The Org Admin can adjust this threshold in organization settings.

Below we have the statistics that are automatically captured based on the column types in your dataframes. If no dataframe is given, the schema columns and rows will be retrieved from the resource (if available). If dataframes are given, the schema columns and rows will be inferred based on the dataframe.

Stats

Dataframe Column Type

Description

Capture Schema without Statistic

By default, both schema and column statistics are captured. Setting capture_schema_only to True captures only schema information, excluding column stats.

my_resource = FileResource(paths, dataframes, capture_schema_only=True)

Column stats computation can impact processing time, so it is recommended to set capture_schema_only to True if performance is a concern or detailed stats are not needed.

Capture Origin and Cleaned Dataset Statistic

To capture your origin and cleaned resource's structure and statistics for each column type, pass a dataframe to your Resource class using the dataframes parameter:

df = dataframe...  # load your pandas or spark dataframe

gcs_resource = GCSResource(
    uris="gs://my_bucket_name/my_folder/my_filename",
    dataframes=df
)

# Register origin or cleaned datasets and its structure and statistics to Vectice
dataset = Dataset.clean(gcs_resource, name="Clean_Dataset")

Capture Modeling Dataset Statistics

Capturing your modeling resources' structure and statistics requires specifying which resource statistics you need. You can collect statistics for all modeling resources (training, testing, and validation) or select the specific resource(s) for statistics capture.

For example, if you only want your testing resource's statistics, use the dataframe parameter in the Resource class to capture statistics of your testing_resource.

Pandas dataframe statistics capture example

# Create your pandas (or spark) dataframes for your modeling datasets  
training_dataframe = pd.DataFrame(pd.read_csv("train_clean.csv"))
testing_dataframe = pd.DataFrame(pd.read_csv("train_reduced.csv"))
validation_dataframe = pd.DataFrame(pd.read_csv("validation.csv"))

# Create your dataset resources to wrap your datasets and collect statistics
training_resource = FileResource(paths="train_clean.csv", dataframes=training_dataframe)
testing_resource = FileResource(paths="train_reduced.csv", dataframes=testing_dataframe)

# Data resource without collecting statistics
validation_resource = FileResource(paths="validation.csv")

# Register all modeling datasets and its structure and statistics to Vectice
modeling_dataset = Dataset.modeling(
                    name = "Modeling",
                    training_resource = training_resource,
                    testing_resource = testing_resource,
                    validation_resource = validation_resource,
                  )

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Stats

Dataframe Column Type

Description

df = dataframe... # load your pandas or spark dataframe gcs_resource = GCSResource( uris="gs://my_bucket_name/my_folder/my_filename", dataframes=df ) # Register origin or cleaned datasets and its structure and statistics to Vectice dataset = Dataset.clean(gcs_resource, name="Clean_Dataset")

# Create your pandas (or spark) dataframes for your modeling datasets training_dataframe = pd.DataFrame(pd.read_csv("train_clean.csv")) testing_dataframe = pd.DataFrame(pd.read_csv("train_reduced.csv")) validation_dataframe = pd.DataFrame(pd.read_csv("validation.csv")) # Create your dataset resources to wrap your datasets and collect statistics training_resource = FileResource(paths="train_clean.csv", dataframes=training_dataframe) testing_resource = FileResource(paths="train_reduced.csv", dataframes=testing_dataframe) # Data resource without collecting statistics validation_resource = FileResource(paths="validation.csv") # Register all modeling datasets and its structure and statistics to Vectice modeling_dataset = Dataset.modeling( name = "Modeling", training_resource = training_resource, testing_resource = testing_resource, validation_resource = validation_resource, )