Pyspark: Filter dataframe based on multiple conditions

I want to filter dataframe according to the following conditions firstly (d<5) and secondly (value of col2 not equal its counterpart in col4 if value in col1 equal its counterpart in col3).

If the original dataframe DF is as follows:

+----+----+----+----+---+
|col1|col2|col3|col4|  d|
+----+----+----+----+---+
|   A|  xx|   D|  vv|  4|
|   C| xxx|   D|  vv| 10|
|   A|   x|   A|  xx|  3|
|   E| xxx|   B|  vv|  3|
|   E| xxx|   F| vvv|  6|
|   F|xxxx|   F| vvv|  4|
|   G| xxx|   G| xxx|  4|
|   G| xxx|   G|  xx|  4|
|   G| xxx|   G| xxx| 12|
|   B|xxxx|   B|  xx| 13|
+----+----+----+----+---+

The desired Dataframe is:

+----+----+----+----+---+
|col1|col2|col3|col4|  d|
+----+----+----+----+---+
|   A|  xx|   D|  vv|  4|
|   A|   x|   A|  xx|  3|
|   E| xxx|   B|  vv|  3|
|   F|xxxx|   F| vvv|  4|
|   G| xxx|   G|  xx|  4|
+----+----+----+----+---+

Code I have tried that did not work as expected:

cols=[('A','xx','D','vv',4),('C','xxx','D','vv',10),('A','x','A','xx',3),('E','xxx','B','vv',3),('E','xxx','F','vvv',6),('F','xxxx','F','vvv',4),('G','xxx','G','xxx',4),('G','xxx','G','xx',4),('G','xxx','G','xxx',12),('B','xxxx','B','xx',13)]
df=spark.createDataFrame(cols,['col1','col2','col3','col4','d'])

df.filter((df.d<5)& (df.col2!=df.col4) & (df.col1==df.col3)).show()

+----+----+----+----+---+
|col1|col2|col3|col4|  d|
+----+----+----+----+---+
|   A|   x|   A|  xx|  3|
|   F|xxxx|   F| vvv|  4|
|   G| xxx|   G|  xx|  4|
+----+----+----+----+---+

What should I do to achieve the desired result?

Your logic condition is wrong. IIUC, what you want is:

import pyspark.sql.functions as f

df.filter((f.col('d')<5))\
    .filter(
        ((f.col('col1') != f.col('col3')) | 
         (f.col('col2') != f.col('col4')) & (f.col('col1') == f.col('col3')))
    )\
    .show()

I broke the filter() step into 2 calls for readability, but you could equivalently do it in one line.

Output:

+----+----+----+----+---+
|col1|col2|col3|col4|  d|
+----+----+----+----+---+
|   A|  xx|   D|  vv|  4|
|   A|   x|   A|  xx|  3|
|   E| xxx|   B|  vv|  3|
|   F|xxxx|   F| vvv|  4|
|   G| xxx|   G|  xx|  4|
+----+----+----+----+---+

You can also write like below (without pyspark.sql.functions):

df.filter('d<5 and (col1 <> col3 or (col1 = col3 and col2 <> col4))').show()

Result:

+----+----+----+----+---+
|col1|col2|col3|col4|  d|
+----+----+----+----+---+
|   A|  xx|   D|  vv|  4|
|   A|   x|   A|  xx|  3|
|   E| xxx|   B|  vv|  3|
|   F|xxxx|   F| vvv|  4|
|   G| xxx|   G|  xx|  4|
+----+----+----+----+---+

faster way (without pyspark.sql.functions)

    df.filter((df.d<5)&((df.col1 != df.col3) |
                    (df.col2 != df.col4) & 
                    (df.col1 ==df.col3)))\
    .show()