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"))
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"))
The argument to the mean() operation should be a Column object rather than a string column name. The mean function in PySpark's sql.functions module is designed to operate on a Column object, not a string column name. Therefore, the correct approach is to use the col() function to convert the string column name into a Column object before passing it to the mean function. The code block should be written as storesDF.agg(mean(col('sqft')).alias('sqftMean')).
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.
wrong! A
agg is not required here.
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.
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"))
storesDF.agg(mean("Value").alias("sqftMean")).show() it works
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()
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
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"))
df.agg(mean("amountpaid").alias("amountpaid")).show() df.agg(mean(col("amountpaid")).alias("sqftMean")).show(). Both produces the result
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:
D withColumn() for new calculated column.
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.
A) should be the one considering databricks practice pdf. mean() function should take col object as input.
A is most like correct here