Create DataFrame Objects with Functions | DP-203 Exam Question

Correct Answers: A and D

First, introduce a variable name and then equate it tomyDataFrameDF =” it is the right way to create the DataFrame objects.

Also, DataFrame object can be created using createDataFrame() function.

Option A is correct.

Introduce a variable name and then equate it tomyDataFrameDF =” it is the right way to create the DataFrame objects.

Option B is incorrect.

createOrReplaceObject() won't help in creating the DataFrame objects.

Option C is incorrect.

The given function is not the right way to create a DataFrame object.

Option D is correct.

DataFrame object can be created using createDataFrame() function.

Option E is incorrect.

Not E, A and D are the correct options.

To know more about dataframes, please visit the below given links:

The correct answers are A and E, as both these options are valid functions that can be used to create a DataFrame object.

A) Introduce a variable name and assign it to something like myDataFrameDF= In this option, you create a DataFrame object by assigning it to a variable name. This is a common method of creating DataFrames in Python using popular libraries such as Pandas and PySpark. For example, you could create a DataFrame in PySpark using the following code:

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from pyspark.sql import SparkSession from pyspark.sql.types import StructType, StructField, StringType, IntegerType  spark = SparkSession.builder.appName("example").getOrCreate()  data = [("Alice", 1), ("Bob", 2), ("Charlie", 3)] schema = StructType([StructField("Name", StringType(), True), StructField("Age", IntegerType(), True)])  df = spark.createDataFrame(data, schema)  df.show() 

In this code, we first create a SparkSession object named "spark", and then create a list of tuples containing the data we want to include in our DataFrame. We then create a StructType object that defines the schema of our DataFrame, which consists of two fields: "Name" and "Age". Finally, we create the DataFrame by calling the createDataFrame() function on the SparkSession object, passing in our data and schema as arguments. We assign the resulting DataFrame to the variable "df".

E) Use function createDataFrame() The createDataFrame() function is a built-in function in PySpark that can be used to create a DataFrame object from a variety of data sources. This function can take many different types of input data, including lists, dictionaries, and Pandas DataFrames. For example, you could create a DataFrame in PySpark using the following code:

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from pyspark.sql import SparkSession import pandas as pd  spark = SparkSession.builder.appName("example").getOrCreate()  data = pd.DataFrame({'Name': ['Alice', 'Bob', 'Charlie'], 'Age': [1, 2, 3]})  df = spark.createDataFrame(data)  df.show() 

In this code, we first create a SparkSession object named "spark", and then create a Pandas DataFrame object named "data" containing the data we want to include in our DataFrame. We then create the DataFrame by calling the createDataFrame() function on the SparkSession object, passing in our Pandas DataFrame as an argument. We assign the resulting DataFrame to the variable "df".

B, C, and D are not valid functions for creating DataFrame objects. The createOrReplaceObject() function is not a valid function in PySpark or Pandas, and the create() function is not a valid function for creating DataFrames in PySpark (it is used for creating tables in SQL databases).