1. Derive a column
withColumn adds a new "adult" column computed from the col("age") >= 18 expression.
df.withColumn("adult", col("age") >= 18)Typorithm · medium
withColumn
df-withcolumnsparkwithColumnrenameimmutable
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Step 1 of 8 · intro card
Learn withColumn
DataFrames
withColumn derives a new column from an expression; withColumnRenamed changes a column's name. Both return a brand-new DataFrame — the original stays untouched, because DataFrames are immutable.
withColumn("adult", col("age") >= 18) adds a column whose value comes from a per-row expression; if the name already exists it replaces that column.
withColumnRenamed("name", "fullName") takes the old name then the new name and returns a DataFrame with the column relabelled.
Crucially, both methods are immutable: they return new DataFrames rather than mutating the original, so chaining them composes cleanly.
01
withColumn derives or replaces a column from an expression.
02
withColumnRenamed relabels a column (old, new).
03
Both return new DataFrames; the original is unchanged.
df.withColumn("adult", col("age") >= 18).withColumnRenamed("name", "fullName");Read the implementation
Connect each part of the code to the concept before you type.
2. Column expression
The expression produces a boolean per row, becoming the value of the new column.
col("age") >= 183. Rename a column
withColumnRenamed changes a column's name, returning a new DataFrame.
.withColumnRenamed("name", "fullName")4. Old then new
The first argument is the existing name, the second is the new name.
"name", "fullName"A complete learning loop
01
Understand
Read the concept map and follow the code breakdown.
02
Reconstruct
Type the implementation, repair mistakes and work through every recall exercise.
03
Verify
Use the final check without hints; revisit weak concepts in short practice sessions.