Input decorators

These decorators are used to wrap the entire content of a cell.

@transformation

@transformation(*objects, display = False, check_duplicate_columns = True)

Used for decorating a function which manipulates with a DataFrame. Runs the decorated function upon declaration.

• *objects : an arbitrary number of objects passed to the decorated function
• display : bool, default False - if True the output DataFrame is displayed
• check_duplicate_columns : bool, default True - if True raises an Exception if there are duplicate columns in the DataFrame

Example:

@transformation(read_table("silver.tbl_loans"), read_table("silver.tbl_repayments"), display=True)
@table_overwrite("silver.tbl_joined_loans_and_repayments", get_joined_schema())
def join_loans_and_repayments(df1: DataFrame, df2: DataFrame):
    return df1.join(df2, "LoanID")

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@notebook_function

@notebook_function(*objects)

Used for decorating any other function which is not decorated with the @transformation decorator. Runs the decorated function upon declaration.

Parameters:

• *objects : an arbitrary number of objects passed to the decorated function
• display: bool, default False - if True the output DataFrame is displayed
• check_duplicate_columns : bool, default True - if True raises an Exception if there are duplicate columns in the DataFrame

Example:

@notebook_function()
def download_data():
    opener = urllib.request.URLopener()
    opener.addheader(
        "User-Agent",
        "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/90.0.4430.93 Safari/537.36",
    )

    opener.retrieve("https://www.bondora.com/marketing/media/LoanData.zip", "/loanData.zip")
    opener.retrieve("https://www.bondora.com/marketing/media/RepaymentsData.zip", "/repaymentsData.zip")

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Objects available in @transformation and @notebook_function

• spark: SparkSession

• dbutils: DBUtils

• logger: Logger

Using Spark and Logger

from logging import Logger
from pyspark.sql.session import SparkSession

@notebook_function()
def customers_table(spark: SparkSession, logger: Logger):
    logger.info('Reading my_crm.customers')

    return spark.read.table('my_crm.customers')

Using DBUtils

from pyspark.dbutils import DBUtils

@notebook_function()
def create_input_widgets(dbutils: DBUtils):
    dbutils.widgets.dropdown("base_year", "2015", list(map(str, range(2009, 2022))), "Base year")

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