Which of the following code blocks returns a DataFrame containing only the rows from DataFrame storesDF where the value in column sqft is less than or equal to 25,000 AND the value in column customerSatisfaction is greater than or equal to 30?
A.
storesDF.filter(col("sqft") <= 25000 and col("customerSatisfaction") >= 30)
B.
storesDF.filter(col("sqft") <= 25000 or col("customerSatisfaction") >= 30)
C.
storesDF.filter(sqft) <= 25000 and customerSatisfaction >= 30)
D.
storesDF.filter(col("sqft") <= 25000 & col("customerSatisfaction") >= 30)
E.
storesDF.filter(sqft <= 25000) & customerSatisfaction >= 30)
in pyspark, all wrong as the conditions inside the filter should be wrapped inside parentesis. should be: D. storesDF.filter((col("sqft") <= 25000) & (col("customerSatisfaction") >= 30))
It's D:
https://sparkbyexamples.com/spark/spark-and-or-not-operators/
PySpark Logical operations use the bitwise operators:
& for and
| for or
~ for not
No, you do not use and but & in Pyspark
D is correct
upvoted 2 times
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