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.

---

isNull()/isNotNull() will return the respective rows which have dt_mvmt as Null or !Null.

method_1 = df.filter(df['dt_mvmt'].isNotNull()).count()
method_2 = df.filter(df.dt_mvmt.isNotNull()).count()

Both will return the same result

---

if column = None

COLUMN_OLD_VALUE
----------------
None
1
None
100
20
------------------

Use create a temptable on data frame:

sqlContext.sql("select * from tempTable where column_old_value='None' ").show()

So use : column_old_value='None'

---

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

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

---

None/Null is a data type of the class NoneType in PySpark/Python so, below will not work as you are trying to compare NoneType object with the string object

Wrong way of filreting

df[df.dt_mvmt == None].count()

0

df[df.dt_mvmt != None].count()

0

correct

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

returns all records with dt_mvmt as None/Null

---

PySpark provides various filtering options based on arithmetic, logical and other conditions. Presence of NULL values can hamper further processes. Removing them or statistically imputing them could be a choice.

Below set of code can be considered:

# Dataset is df
# Column name is dt_mvmt
# Before filtering make sure you have the right count of the dataset
df.count() # Some number

# Filter here
df = df.filter(df.dt_mvmt.isNotNull())

# Check the count to ensure there are NULL values present (This is important when dealing with large dataset)
df.count() # Count should be reduced if NULL values are present

---

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()

---