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How do you drop columns with null values in PySpark?

Spark/Big Datamedium0.6 min readPremium
Frequency
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Asked at 2 companies
Category
452
questions in Spark/Big Data
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88E|81M|283H
in this category
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1,863
across 7 categories
Asked at these companies
DatameticaGlobant
Key Concepts Tested
partitionspark
Expert AnswerPremium
119 wordsInterview-ready
Two distinct operations: (1) **Drop columns that are entirely null** (no non-null values): null_cols = [c for c in df.columns if df.filter(col(c).isNotNull()).count() == 0]; df = df.drop(*null_cols). **Caveat**: count() triggers a full scan—expensive on large tables. (2) **Drop rows with null in specified columns**: df.dropna(subset=["col1", "col2"]). **Scalability**: The column-null check is O(partitions × columns) and can be costly; consider sampling or inferring from schema/sample....
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