I am working with a large dataset in PySpark that contains numerous missing values across multiple columns. I need to clean this data before passing it into a machine learning pipeline. What is the best practice for replacing these nulls? I’ve seen the fillna() and na.fill() methods—are they interchangeable? Additionally, how can I apply different replacement values for different columns (e.g., filling a "City" column with "Unknown" while filling a "Salary" column with the median) without writing a separate line of code for every single column?
3 answers
In Spark, df.fillna() and df.na.fill() are essentially aliases and perform the exact same operation. The most powerful way to use them is by passing a Python dictionary, which allows you to specify different replacement values for specific columns in a single call. For example: df.na.fill({"City": "Unknown", "Age": 0}). This is much more efficient than chaining multiple calls. If you need to fill nulls with a calculated value like the mean or median, you must calculate that value first using an aggregation function and then pass the result into the fill method. Note that the replacement value must match the data type of the column (e.g., you cannot fill an Integer column with a String).
Are you planning to drop rows with too many nulls entirely before filling the rest, or do you need to preserve all records regardless of how much data is missing?
If you need more complex logic—like replacing nulls based on a condition from another column—you should use pyspark.sql.functions.when(). For example: df.withColumn("Status", when(col("Status").isNull(), "Active").otherwise(col("Status"))). This gives you way more control than a simple fill.
I agree with Elena. While fillna is great for bulk updates, when().otherwise() is the "Swiss Army Knife" for data imputation when the logic depends on the context of the entire row.
That’s a great point, David. I’m actually looking to drop rows only if the "ID" column is null, but for other columns, I need to keep the rows and fill them. Is it better to use df.na.drop(subset=["ID"]) first and then chain it with a fillna()? Also, is there any significant performance overhead when using a large dictionary for column mapping on a cluster with hundreds of executors?