Saving to a Spark / Pandas DataFrame
Rather than saving data to a lakehouse table, you can get it in a dataframe
import bifabrik as bif
df = bif.fromCsv("Files/CsvFiles/dimBranch.csv").delimiter(';').decimal(',').toSparkDf().run()
This can be useful when you only need the source functionality - whether it be JSON, CSV or something else - and want to take care of data transformations in Spark.
Once you are done with transforming the dataframe, you can again use bifabrik to save the data from the DF to a table using a dataframe source
If you prefer pandas, you can similarly save data to a pandas DataFrame
import bifabrik as bif
pandas_df = bif.fromSql('SELECT * FROM SomeTable').toPandasDf().run()
Also, have a look at DataFrame transformations using lambda functions