PySpark: multiple conditions in when clause

Question

I would like to modify the cell values of a dataframe column (Age) where currently it is blank and I would only do it if another column (Survived) has the value 0 for the corresponding row where it is blank for Age. If it is 1 in the Survived column but blank in Age column then I will keep it as null.

I tried to use && operator but it didn't work. Here is my code:

tdata.withColumn("Age",  when((tdata.Age == "" && tdata.Survived == "0"), mean_age_0).otherwise(tdata.Age)).show()

Any suggestions how to handle that? Thanks.

Error Message:

SyntaxError: invalid syntax
  File "<ipython-input-33-3e691784411c>", line 1
    tdata.withColumn("Age",  when((tdata.Age == "" && tdata.Survived == "0"), mean_age_0).otherwise(tdata.Age)).show()
                                                    ^

Answer 1

You get SyntaxError error exception because Python has no && operator. It has and and & where the latter one is the correct choice to create boolean expressions on Column (| for a logical disjunction and ~ for logical negation).

Condition you created is also invalid because it doesn't consider operator precedence. & in Python has a higher precedence than == so expression has to be parenthesized.

(col("Age") == "") & (col("Survived") == "0")
## Column<b'((Age = ) AND (Survived = 0))'>

On a side note when function is equivalent to case expression not WHEN clause. Still the same rules apply. Conjunction:

df.where((col("foo") > 0) & (col("bar") < 0))

Disjunction:

df.where((col("foo") > 0) | (col("bar") < 0))

You can of course define conditions separately to avoid brackets:

cond1 = col("Age") == "" 
cond2 = col("Survived") == "0"

cond1 & cond2