Filter Pyspark dataframe column with None value

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.

Try to just use isNotNull function.

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

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")

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.