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
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.
agg is not required here.
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
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"))
storesDF.agg(mean("Value").alias("sqftMean")).show() it works
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.
D withColumn() for new calculated column.
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:
df.agg(mean("amountpaid").alias("amountpaid")).show() df.agg(mean(col("amountpaid")).alias("sqftMean")).show(). Both produces the result