Which of the following code blocks returns a new DataFrame with column storeDescription where the pattern "Description: " has been removed from the beginning of column storeDescription in DataFrame storesDF?
A sample of DataFrame storesDF is below:
Which of the following code blocks returns a new DataFrame with column storeDescription where the pattern "Description: " has been removed from the beginning of column storeDescription in DataFrame storesDF?
A sample of DataFrame storesDF is below:
To remove the pattern 'Description: ' from the beginning of the column storeDescription in the DataFrame storesDF, the correct approach is to use the regexp_replace function. The syntax for this function is regexp_replace(column, pattern, replacement), where the column is specified with the col function, the pattern is the regular expression to look for, and the replacement is what will replace the found pattern. Option E uses this correct syntax, making it the right choice.
The correct answer is option E: storesDF.withColumn("storeDescription", regexp_replace(col("storeDescription"), "^Description: ", "")). This code block uses the withColumn() function to create a new column called storeDescription. It uses the regexp_replace() function to replace the pattern "^Description: " at the beginning of the string in the storeDescription column with an empty string. This effectively removes the pattern from the beginning of the string in each row of the column.
The correct code block that returns a new DataFrame with column storeDescription where the pattern "Description: " has been removed from the beginning of column storeDescription in DataFrame storesDF is: A. storesDF.withColumn("storeDescription", regexp_replace(col("storeDescription"), "^Description: ")) This code uses the regexp_replace function to replace the pattern "^Description: " (which matches the string "Description: " at the beginning of the string) with an empty string in the column storeDescription. The resulting DataFrame will have the modified storeDescription column. Option B has a syntax error because the regexp_replace function should be called on the column using the dot notation instead of passing it as the second argument. Option C uses the regexp_extract function, which extracts a substring matching a regular expression pattern. It doesn't remove the pattern from the string. Option D has a syntax error because the column name is not wrapped in the col function. Option E is the same as option A, except that it uses the col function unnecessarily.
This should actually be D sorry for the wrong answer. refer to this, https://sparkbyexamples.com/pyspark/pyspark-replace-column-values/
Both work: from pyspark.sql import SparkSession from pyspark.sql.functions import regexp_replace,regexp_extract, col spark = SparkSession.builder.appName("test").getOrCreate() data = [ (1, "Description: This is a tech store. Description: This"), (2, "Description: This is a grocery store."), (3, "Description: This is a book store."), ] storesDF = spark.createDataFrame(data, ["storeID", "storeDescription"]) storesDF.show(truncate=False) #Case D print ("Case D") storesDF = storesDF.withColumn("storeDescription", regexp_replace("storeDescription", "^Description: ", "")) storesDF.show(truncate=False) #Case E print ("Case E") storesDF = storesDF.withColumn("storeDescription", regexp_replace(col("storeDescription"), "^Description: ", "")) storesDF.show(truncate=False)
regexp_replace(str, regexp, rep [, position] ) This is what Databricks documentation says. You guys can debate between D and E but actually question clearly says to remove from the begging of the string. And if you take answer D it takes whole only one constant string “storeDescription” to match pattern and will return empty string after Description for each row. So if you have debate between D, E then E is the correct answer.
E is the answer tested
Correct answer is E indeed - According to the pyspark doc, the syntax is regexp_replace(str, pattern, replacement) -> it means that it's not a function of the column object - storeDescription is a String field https://spark.apache.org/docs/3.0.0/api/python/pyspark.sql.html#pyspark.sql.functions.regexp_replace
Correct answer is D. First, regexp_replace/regexp_extract are from sql.functions. They cannot be applied directly after a column Object => B is incorrect. Second, regexp_replace/regexp_extract accept a STRING Object as a first argument to specify the column. Check the documentation there : https://spark.apache.org/docs/3.0.0/api/python/pyspark.sql.html#module-pyspark.sql.functions => A, C, E are incorrects.
Almost right but it's not about "String object" but "String value". So the correct answer is indeed the answer E ;)
Both D and E are correct answer.
Both D and E are correct according to the new version
E is most likely correct in this scenario
Both D and E are correct.
It's between D and E, and D is wrong as there is no replacement string expression (which is a required argument/parameter). Thus, E wins as the correct option.
this is completely wrong explanation. Both D and E has replacement expression, the only difference is how they call the replaced column. Both D and E are correct, but D works for Pyspark 2.0. D and E both work Pyspark 3.0+. Period!
I think what you really mean, "there is no replacement string expression", is for option A. The only difference between A and E, is about the claim of replacement string expression
Option A is correct: storesDF.withColumn("productCategories", explode(col("productCategories"))). Explanation: The explode function is used to transform a column of arrays or maps into multiple rows, one for each element in the array or map. In this case, productCategories is a column with arrays of strings. The withColumn function is used to add a new column or update an existing column. The first argument is the name of the new or existing column, and the second argument is the expression that defines the values for the column.
You got the wrong question :°
The regexp_replace function is used to remove the pattern "Description: " from the beginning of the column storeDescription. The ^ symbol indicates the beginning of the string, and the pattern "Description: " is replaced with an empty string. This results in a new DataFrame with column storeDescription where the pattern "Description: " has been removed from the beginning of each cell in that column.
Option A is incorrect because the regexp_replace function requires two arguments: the column to be transformed and the regular expression pattern to be replaced. In the given code block, only the regular expression pattern is provided, but not the column to be transformed. The correct syntax to use regexp_replace on a DataFrame column is regexp_replace(col(column_name), pattern, replacement), where col(column_name) specifies the DataFrame column to be transformed, pattern specifies the regular expression pattern to be replaced, and replacement specifies the new string to replace the matched pattern. Therefore, the correct code block to remove the pattern "Description: " from the beginning of the storeDescription column in DataFrame storesDF is: storesDF.withColumn("storeDescription", regexp_replace(col("storeDescription"), "^Description: ", ""))