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the rules of how NULL values are handled by aggregate functions. nullable Columns Let's create a DataFrame with a name column that isn't nullable and an age column that is nullable. Do we have any way to distinguish between them? One way would be to do it implicitly: select each column, count its NULL values, and then compare this with the total number or rows. The Spark Column class defines four methods with accessor-like names. We can use the isNotNull method to work around the NullPointerException thats caused when isEvenSimpleUdf is invoked. Spark Datasets / DataFrames are filled with null values and you should write code that gracefully handles these null values. -- `max` returns `NULL` on an empty input set. In my case, I want to return a list of columns name that are filled with null values. As discussed in the previous section comparison operator, PySpark show() Display DataFrame Contents in Table. The comparison between columns of the row are done. Remove all columns where the entire column is null in PySpark DataFrame, Python PySpark - DataFrame filter on multiple columns, Python | Pandas DataFrame.fillna() to replace Null values in dataframe, Partitioning by multiple columns in PySpark with columns in a list, Pyspark - Filter dataframe based on multiple conditions. specific to a row is not known at the time the row comes into existence. 2 + 3 * null should return null. They are satisfied if the result of the condition is True. Making statements based on opinion; back them up with references or personal experience. Note: For accessing the column name which has space between the words, is accessed by using square brackets [] means with reference to the dataframe we have to give the name using square brackets. This optimization is primarily useful for the S3 system-of-record. pyspark.sql.Column.isNotNull () function is used to check if the current expression is NOT NULL or column contains a NOT NULL value. In this case, _common_metadata is more preferable than _metadata because it does not contain row group information and could be much smaller for large Parquet files with many row groups. Checking dataframe is empty or not We have Multiple Ways by which we can Check : Method 1: isEmpty () The isEmpty function of the DataFrame or Dataset returns true when the DataFrame is empty and false when it's not empty.