Filter Pyspark dataframe column with None value

Question

I\'m trying to filter a PySpark dataframe that has None as a row value:

df.select(\'dt_mvmt\').distinct().collect()

[Row(dt_mvmt=u\'2016-03-27\         


        

Answer 1

If you want to keep with the Pandas syntex this worked for me.

df = df[df.dt_mvmt.isNotNull()]

Answer 2

To obtain entries whose values in the dt_mvmt column are not null we have

df.filter("dt_mvmt is not NULL")

and for entries which are null we have

df.filter("dt_mvmt is NULL")

Answer 3

If you want to filter out records having None value in column then see below example:

df=spark.createDataFrame([[123,"abc"],[234,"fre"],[345,None]],["a","b"])

Now filter out null value records:

df=df.filter(df.b.isNotNull())

df.show()

If you want to remove those records from DF then see below:

df1=df.na.drop(subset=['b'])

df1.show()

Answer 4

You can use Column.isNull / Column.isNotNull:

df.where(col("dt_mvmt").isNull())

df.where(col("dt_mvmt").isNotNull())

If you want to simply drop NULL values you can use na.drop with subset argument:

df.na.drop(subset=["dt_mvmt"])

Equality based comparisons with NULL won't work because in SQL NULL is undefined so any attempt to compare it with another value returns NULL:

sqlContext.sql("SELECT NULL = NULL").show()
## +-------------+
## |(NULL = NULL)|
## +-------------+
## |         null|
## +-------------+


sqlContext.sql("SELECT NULL != NULL").show()
## +-------------------+
## |(NOT (NULL = NULL))|
## +-------------------+
## |               null|
## +-------------------+

The only valid method to compare value with NULL is IS / IS NOT which are equivalent to the isNull / isNotNull method calls.

Answer 5

There are multiple ways you can remove/filter the null values from a column in DataFrame.

Lets create a simple DataFrame with below code:

date = ['2016-03-27','2016-03-28','2016-03-29', None, '2016-03-30','2016-03-31']
df = spark.createDataFrame(date, StringType())

Now you can try one of the below approach to filter out the null values.

# Approach - 1
df.filter("value is not null").show()

# Approach - 2
df.filter(col("value").isNotNull()).show()

# Approach - 3
df.filter(df["value"].isNotNull()).show()

# Approach - 4
df.filter(df.value.isNotNull()).show()

# Approach - 5
df.na.drop(subset=["value"]).show()

# Approach - 6
df.dropna(subset=["value"]).show()

# Note: You can also use where function instead of a filter.

You can also check the section "Working with NULL Values" on my blog for more information.

I hope it helps.

Answer 6

Try to just use isNotNull function.

df.filter(df.dt_mvmt.isNotNull()).count()