Pyspark dataframe operator "IS NOT IN"

In pyspark you can do it like this:

array = [1, 2, 3]
dataframe.filter(dataframe.column.isin(array) == False)

Or using the binary NOT operator:

dataframe.filter(~dataframe.column.isin(array))

Take the operator ~ which means contrary :

df_filtered = df.filter(~df["column_name"].isin([1, 2, 3]))

df_result = df[df.column_name.isin([1, 2, 3]) == False]

slightly different syntax and a "date" data set:

toGetDates={'2017-11-09', '2017-11-11', '2017-11-12'}
df= df.filter(df['DATE'].isin(toGetDates) == False)

* is not needed. So:

list = [1, 2, 3]
dataframe.filter(~dataframe.column.isin(list))

You can use the .subtract() buddy.

Example:

df1 = df.select(col(1),col(2),col(3)) 
df2 = df.subtract(df1)

This way, df2 will be defined as everything that is df that is not df1.

You can also loop the array and filter:

array = [1, 2, 3]
for i in array:
    df = df.filter(df["column"] != i)