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Is there any difference in semantics between df.na().drop() and df.filter(df.col("onlyColumnInOneColumnDataFrame").isNotNull() && 

!df.col("onlyColumnInOneColumnDataFrame").isNaN()) where df is Apache Spark Dataframe?

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With df.na.drop() you actually drop the rows containing any null or NaN values.

And With df.filter(df.col("onlyColumnInOneColumnDataFrame").isNotNull()) you drop those rows which have null only in the column onlyColumnInOneColumnDataFrame.


In order to achieve the same thing with df.na.drop() , you can do:


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