The code block shown below contains an error. The code block is intended to return a new DataFrame with the mean of column sqft from DataFrame storesDF in column sqftMean. Identify the error. Code block: storesDF.agg(mean("sqft").alias("sqftMean"))
A.
The argument to the mean() operation should be a Column abject rather than a string column name.
B.
The argument to the mean() operation should not be quoted.
C.
The mean() operation is not a standalone function – it’s a method of the Column object.
D.
The agg() operation is not appropriate here – the withColumn() operation should be used instead.
E.
The only way to compute a mean of a column is with the mean() method from a DataFrame.
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
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()
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.
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