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DataFrame Transformation

API str, cache: bool = True) -> DataFrame

This method applies a transformation to a provided Spark DataFrame, the specifics of which are determined by the desc parameter:

  • param desc: A natural language string that outlines the specific transformation to be applied on the DataFrame.
  • param cache: If True, fetches cached data, if available. If False, retrieves fresh data and updates cache.
  • return: Returns a new Spark DataFrame that is the result of applying the specified transformation on the input DataFrame.


Given the following DataFrame df:

df = spark_ai._spark.createDataFrame(
        ("Normal", "Cellphone", 6000),
        ("Normal", "Tablet", 1500),
        ("Mini", "Tablet", 5500),
        ("Mini", "Cellphone", 5000),
        ("Foldable", "Cellphone", 6500),
        ("Foldable", "Tablet", 2500),
        ("Pro", "Cellphone", 3000),
        ("Pro", "Tablet", 4000),
        ("Pro Max", "Cellphone", 4500)
    ["product", "category", "revenue"]

You can write English to perform transformations. For example:"What are the best-selling and the second best-selling products in every category?").show()

product category revenue
Foldable Cellphone 6500
Nromal Cellphone 6000
Mini Tablet 5500
Pro Tablet 4000"Pivot the data by product and the revenue for each product").show()
Category Normal Mini Foldable Pro Pro Max
Cellphone 6000 5000 6500 3000 4500
Tablet 1500 5500 2500 4000 null

For a detailed walkthrough of the transformations, please refer to our transform_dataframe.ipynb notebook.