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NEW QUESTION 23
The code block shown below should return an exact copy of DataFrame transactionsDf that does not include rows in which values in column storeId have the value 25. Choose the answer that correctly fills the blanks in the code block to accomplish this.
Answer: C
Explanation:
Explanation
transactionsDf.where(transactionsDf.storeId!=25)
Correct. DataFrame.where() is an alias for the DataFrame.filter() method. Using this method, it is straightforward to filter out rows that do not have value 25 in column storeId.
transactionsDf.select(transactionsDf.storeId!=25)
Wrong. The select operator allows you to build DataFrames column-wise, but when using it as shown, it does not filter out rows.
transactionsDf.filter(transactionsDf.storeId==25)
Incorrect. Although the filter expression works for filtering rows, the == in the filtering condition is inappropriate. It should be != instead.
transactionsDf.drop(transactionsDf.storeId==25)
No. DataFrame.drop() is used to remove specific columns, but not rows, from the DataFrame.
transactionsDf.remove(transactionsDf.storeId==25)
False. There is no DataFrame.remove() operator in PySpark.
More info: pyspark.sql.DataFrame.where - PySpark 3.1.2 documentation
Static notebook | Dynamic notebook: See test 3
NEW QUESTION 24
The code block displayed below contains an error. The code block should write DataFrame transactionsDf as a parquet file to location filePath after partitioning it on column storeId. Find the error.
Code block:
transactionsDf.write.partitionOn("storeId").parquet(filePath)
Answer: A
Explanation:
Explanation
No method partitionOn() exists for the DataFrame class, partitionBy() should be used instead.
Correct! Find out more about partitionBy() in the documentation (linked below).
The operator should use the mode() option to configure the DataFrameWriter so that it replaces any existing files at location filePath.
No. There is no information about whether files should be overwritten in the question.
The partitioning column as well as the file path should be passed to the write() method of DataFrame transactionsDf directly and not as appended commands as in the code block.
Incorrect. To write a DataFrame to disk, you need to work with a DataFrameWriter object which you get access to through the DataFrame.writer property - no parentheses involved.
Column storeId should be wrapped in a col() operator.
No, this is not necessary - the problem is in the partitionOn command (see above).
The partitionOn method should be called before the write method.
Wrong. First of all partitionOn is not a valid method of DataFrame. However, even assuming partitionOn would be replaced by partitionBy (which is a valid method), this method is a method of DataFrameWriter and not of DataFrame. So, you would always have to first call DataFrame.write to get access to the DataFrameWriter object and afterwards call partitionBy.
More info: pyspark.sql.DataFrameWriter.partitionBy - PySpark 3.1.2 documentation Static notebook | Dynamic notebook: See test 3
NEW QUESTION 25
In which order should the code blocks shown below be run in order to read a JSON file from location jsonPath into a DataFrame and return only the rows that do not have value 3 in column productId?
1. importedDf.createOrReplaceTempView("importedDf")
2. spark.sql("SELECT * FROM importedDf WHERE productId != 3")
3. spark.sql("FILTER * FROM importedDf WHERE productId != 3")
4. importedDf = spark.read.option("format", "json").path(jsonPath)
5. importedDf = spark.read.json(jsonPath)
Answer: B
Explanation:
Explanation
Correct code block:
importedDf = spark.read.json(jsonPath)
importedDf.createOrReplaceTempView("importedDf")
spark.sql("SELECT * FROM importedDf WHERE productId != 3")
Option 5 is the only correct way listed of reading in a JSON in PySpark. The option("format", "json") is not the correct way to tell Spark's DataFrameReader that you want to read a JSON file. You would do this through format("json") instead. Also, you can communicate the specific path of the JSON file to the DataFramReader using the load() method, not the path() method.
In order to use a SQL command through the SparkSession spark, you first need to create a temporary view through DataFrame.createOrReplaceTempView().
The SQL statement should start with the SELECT operator. The FILTER operator SQL provides is not the correct one to use here.
Static notebook | Dynamic notebook: See test 2
NEW QUESTION 26
Which of the following code blocks prints out in how many rows the expression Inc. appears in the string-type column supplier of DataFrame itemsDf?
2.
3.def count(x):
4. if 'Inc.' in x['supplier']:
5. counter = counter + 1
6.
7.itemsDf.foreach(count)
8.print(counter)B. 1.counter = 0
2.
3.for index, row in itemsDf.iterrows():
4. if 'Inc.' in row['supplier']:
5. counter = counter + 1
6.
7.print(counter)C. print(itemsDf.foreach(lambda x: 'Inc.' in x).sum())D. 1.accum=sc.accumulator(0)
2.
3.def check_if_inc_in_supplier(row):
4. if 'Inc.' in row['supplier']:
5. accum.add(1)
6.
7.itemsDf.foreach(check_if_inc_in_supplier)
8.print(accum.value)E. print(itemsDf.foreach(lambda x: 'Inc.' in x))
Answer: D
Explanation:
Explanation
Correct code block:
accum=sc.accumulator(0)
def check_if_inc_in_supplier(row):
if 'Inc.' in row['supplier']:
accum.add(1)
itemsDf.foreach(check_if_inc_in_supplier)
print(accum.value)
To answer this question correctly, you need to know both about the DataFrame.foreach() method and accumulators.
When Spark runs the code, it executes it on the executors. The executors do not have any information about variables outside of their scope. This is whhy simply using a Python variable counter, like in the two examples that start with counter = 0, will not work. You need to tell the executors explicitly that counter is a special shared variable, an Accumulator, which is managed by the driver and can be accessed by all executors for the purpose of adding to it.
If you have used Pandas in the past, you might be familiar with the iterrows() command. Notice that there is no such command in PySpark.
The two examples that start with print do not work, since DataFrame.foreach() does not have a return value.
More info: pyspark.sql.DataFrame.foreach - PySpark 3.1.2 documentation
Static notebook | Dynamic notebook: See test 3
NEW QUESTION 27
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