The creation of a dataset requires a lot of operations, such as: 1. importing 2. merging 3. selecting 4. filtering 5. and so on The dplyr library comes with a practical operator, %>%, called the pipeline. The pipeline feature makes the manipulation clean, fast and less prompt to error. This operator is a code which … See more We will begin with the select() verb. We don’t necessarily need all the variables, and a good practice is to select only the variables you find … See more The filter() verb helps to keep the observations following a criteria. The filter() works exactly like select(), you pass the data frame first and then a condition separated by a comma: See more In the previous tutorial, you learn how to sort the values with the function sort(). The library dplyr has its sorting function. It works like a charm … See more WebApr 15, 2024 · The filter function is one of the most straightforward ways to filter rows in a PySpark DataFrame. It takes a boolean expression as an argument and returns a new DataFrame containing only the rows that satisfy the condition. Example: Filter rows with age greater than 30. filtered_df = df.filter(df.age > 29) filtered_df.show()
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