Exam Certified Associate Developer for Apache Spark topic 1 question 32 discussion

The code block shown is correct and should return a new DataFrame with the mean of column sqft from DataFrame storesDF in column sqftMean. Therefore, the answer is E - none of the options identify a valid error in the code block. Here's an explanation for each option: A. The argument to the mean() operation can be either a Column object or a string column name, so there is no error in using a string column name in this case. E. This option is incorrect because the code block shown is a valid way to compute the mean of a column using PySpark. Another way to compute the mean of a column is with the mean() method from a DataFrame, but that doesn't mean the code block shown is invalid.

The mean() function expects a Column object as an argument, which can be created using col("sqft"). Simply passing the column name as a string will result in an error.

The correct answer is A. The argument to the mean() operation should be a Column object rather than a string column name. In Spark DataFrames, the mean() function takes a Column object as its argument, not a string column name. To create a Column object from a string column name, you can use the col() function.

The error in the code is A. The argument to the mean() operation should be a Column object rather than a string column name. In the provided code block, "sqft" is passed as a string column name to the mean() function. However, the correct approach is to use a Column object. This can be achieved by referencing the column using the storesDF DataFrame and the col() function. Here's the corrected code: storesDF.agg(mean(col("sqft")).alias("sqftMean"))

from pyspark.sql.functions import col, mean students =[ {'rollno':'001','name':'sravan','sqft':23, 'height':5.79,'weight':67,'address':'guntur'}, {'rollno':'002','name':'ojaswi','sqft':16, 'height':3.79,'weight':34,'address':'hyd'}] storesDF = spark.createDataFrame( students) storesDF.agg(mean('sqft').alias('sqftMean')).show() this works as well! not sure which one is wrong then

A is most like correct here

A) should be the one considering databricks practice pdf. mean() function should take col object as input.

it appears that there might be some flexibility in how the mean function can be used with either a string column name or a col() function. However, the most accurate and recommended approach is to use the col() function to create a Column object explicitly. With this in mind, the best choice is: A. The argument to the mean() operation should be a Column object rather than a string column name. The mean function takes a Column object as an argument, not a string column name. To fix the error, the code block should be rewritten as storesDF.agg(mean(col("sqft")).alias("sqftMean")), where the col function is used to create a Column object from the string column name "sqft". While there might be situations where using a string column name works, following the standard practice of creating a Column object with col() ensures compatibility and clarity in code.

Correct answer is A: from pyspark.sql.functions import col, mean students =[ {'rollno':'001','name':'sravan','sqft':23, 'height':5.79,'weight':67,'address':'guntur'}, {'rollno':'002','name':'ojaswi','sqft':16, 'height':3.79,'weight':34,'address':'hyd'}] storesDF = spark.createDataFrame( students) storesDF.agg(mean(col('sqft')).alias('sqftMean')).show()

D withColumn() for new calculated column.

A. A The error in the code block is **A**, the argument to the `mean` operation should be a Column object rather than a string column name. The `mean` function takes a Column object as an argument, not a string column name. To fix the error, the code block should be rewritten as `storesDF.agg(mean(col("sqft")).alias("sqftMean"))`, where the `col` function is used to create a Column object from the string column name `"sqft"`. Here is the correct code storesDF.agg(mean(col("sqft")).alias("sqftMean"))

The correct answer is: B. The argument to the mean() operation should not be quoted. In the context of Apache Spark, the mean function takes a column name as its argument. Therefore, you would write it without quotes. The corrected code line would look something like this:

There's a similar question in the official Databricks samples and the right answer there is: Code block: storesDF.__1__(__2__(__3__).alias("sqftMean")) A. 1. agg 2. mean 3. col("sqft") If we stick to this logic, the answer is A.

df.agg(mean("amountpaid").alias("amountpaid")).show() df.agg(mean(col("amountpaid")).alias("sqftMean")).show(). Both produces the result

agg is not required here.