cleaning data with dropna in Pyspark

Question

I'm still relatively new to Pyspark. I use version 2.1.0. I'm trying to clean some data on a much larger data set. I've successfully used several techniques such as "dropDuplicates" along with subsets and sql functions (distinct, count etc).

I then came across dropna, which I thought might simplify matters. But I do not understand why rows 3 and 6 are still present after using dropna. For example:

df = spark.createDataFrame([(1, 'Peter', 1.79, 28,'M', 'Tiler'),
                            (2, 'Fritz', 1.78, 45,'M', None),
                            (3, 'Florence', 1.75, None, None, None),
                            (4, 'Nicola',1.6, 33,'F', 'Dancer'),
                            (5, 'Gregory', 1.8, 54,'M', 'Teacher'),
                            (6, 'Steven', 1.82, None, 'M', None),
                            (7, 'Dagmar', 1.7, 42,'F', 'Nurse'),]
                           , ['id', 'Name', 'Height', 'Age', 'Gender', 'Occupation'])

df.show()

df.dropna(thresh=2)

df.show()

Output:

+---+--------+------+----+------+----------+
| id|    Name|Height| Age|Gender|Occupation|
+---+--------+------+----+------+----------+
|  1|   Peter|  1.79|  28|     M|     Tiler|
|  2|   Fritz|  1.78|  45|     M|      null|
|  3|Florence|  1.75|null|  null|      null|
|  4|  Nicola|   1.6|  33|     F|    Dancer|
|  5| Gregory|   1.8|  54|     M|   Teacher|
|  6|  Steven|  1.82|null|     M|      null|
|  7|  Dagmar|   1.7|  42|     F|     Nurse|
+---+--------+------+----+------+----------+

+---+--------+------+----+------+----------+
| id|    Name|Height| Age|Gender|Occupation|
+---+--------+------+----+------+----------+
|  1|   Peter|  1.79|  28|     M|     Tiler|
|  2|   Fritz|  1.78|  45|     M|      null|
|  3|Florence|  1.75|null|  null|      null|
|  4|  Nicola|   1.6|  33|     F|    Dancer|
|  5| Gregory|   1.8|  54|     M|   Teacher|
|  6|  Steven|  1.82|null|     M|      null|
|  7|  Dagmar|   1.7|  42|     F|     Nurse|
+---+--------+------+----+------+----------+

Could somebody suggest why the rows aren't removed?

The pyspark examples show correct counts as per the usage I assumed.

# threshold
        self.assertEqual(self.spark.createDataFrame(
            [(u'Alice', None, 80.1)], schema).dropna(thresh=2).count(),
            1)
        self.assertEqual(self.spark.createDataFrame(
            [(u'Alice', None, None)], schema).dropna(thresh=2).count(),
            0)

Answer 1

First thing is, na creates a new dataframe, so assign it to new df name, and 2nd, specify subset to check which columns to check for null values

df2 = df.dropna(thresh=2,subset=('Age','Gender','Occupation'))

df2.show()

output :

+---+-------+------+---+------+----------+
| id|   Name|Height|Age|Gender|Occupation|
+---+-------+------+---+------+----------+
|  1|  Peter|  1.79| 28|     M|     Tiler|
|  2|  Fritz|  1.78| 45|     M|      null|
|  4| Nicola|   1.6| 33|     F|    Dancer|
|  5|Gregory|   1.8| 54|     M|   Teacher|
|  7| Dagmar|   1.7| 42|     F|     Nurse|
+---+-------+------+---+------+----------+

edit : by the way, thresh=2 alone doesnt work because thresh means drop rows that have less than thresh (i.e. 2 in this case) non-null values, but 3rd row has id,name and height i.e total 3 non-nulls and 6th row has 4 non-nulls, so they dont satisfy thresh=2 criteria. You can try thresh=5