Filtering Based on the End of a Column Value
Question
While working in an Azure Databricks workspace, you need to filter depending upon the end of a column value utilizing the Column Class.You are specifically looking at a column titled name and filtered by the words ending with "ka"
Which command filters based on the end of a column value as required?
Answers
Explanations
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A. B. C. D.Correct Answer: C
The Column Class supports both the endswith() method and the like() method to filter based on the end of a column value.
Option A is incorrect.
The given command won't help in filtering based on the end of a column value.
Option B is incorrect.
Column Class support is the function to filter based on the end of a column value.
Therefore, the given option is not correct.
Option C is correct.df.filter(col(“name”).endswith(“ka”)) is the right command to filter based on the end of a column value.
Option D is incorrect.
The Column Class supports endswith() method but the given is not the right syntax.
Reference:
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Sure, I'd be happy to provide you with a detailed explanation of the answer options.
A. df.filter("name like '_ka'"): This option uses the SQL-like like operator, which matches patterns in strings. The pattern '_%' matches any string that ends with 'ka'. Therefore, this option will only match rows where the name column is exactly three characters long and the last character is 'ka'. This is not what is required in the question, so this option is incorrect.
B. df.filter("name like '%ka'"): This option also uses the like operator, but the pattern '_%' matches any string that ends with 'ka'. Therefore, this option will match rows where the name column ends with 'ka', which is what is required in the question. This option is correct.
C. df.filter(col("name").endswith("ka")): This option uses the endswith method of the Column class, which returns a boolean expression indicating whether a column ends with a given string. This option will also match rows where the name column ends with 'ka', which is what is required in the question. This option is correct.
D. df.filter( ).col("name").endswith("%ka"): This option is not valid syntax. The filter method expects a boolean expression as an argument, but this option does not provide one. The endswith method also requires a string argument, but this option provides a string with a wildcard character ('%') inside the argument, which is not valid syntax. This option is incorrect.
In summary, options B and C are both correct, but option A is too specific and option D is invalid. Therefore, the correct answer is either option B or option C.
