python - Replace empty strings with None/null values in DataFrame

Question

I have a Spark 1.5.0 DataFrame with a mix of null and empty strings in the same column. I want to convert all empty strings in all columns to null (None, in Python). The DataFrame may have hundreds of columns, so I'm trying to avoid hard-coded manipulations of each column.

See my attempt below, which results in an error.

from pyspark.sql import SQLContext
sqlContext = SQLContext(sc)

## Create a test DataFrame
testDF = sqlContext.createDataFrame([Row(col1='foo', col2=1), Row(col1='', col2=2), Row(col1=None, col2='')])
testDF.show()
## +----+----+
## |col1|col2|
## +----+----+
## | foo|   1|
## |    |   2|
## |null|null|
## +----+----+

## Try to replace an empty string with None/null
testDF.replace('', None).show()
## ValueError: value should be a float, int, long, string, list, or tuple

## A string value of null (obviously) doesn't work...
testDF.replace('', 'null').na.drop(subset='col1').show()
## +----+----+
## |col1|col2|
## +----+----+
## | foo|   1|
## |null|   2|
## +----+----+

Answer 1

It is as simple as this:

from pyspark.sql.functions import col, when

def blank_as_null(x):
    return when(col(x) != "", col(x)).otherwise(None)

dfWithEmptyReplaced = testDF.withColumn("col1", blank_as_null("col1"))

dfWithEmptyReplaced.show()
## +----+----+
## |col1|col2|
## +----+----+
## | foo|   1|
## |null|   2|
## |null|null|
## +----+----+

dfWithEmptyReplaced.na.drop().show()
## +----+----+
## |col1|col2|
## +----+----+
## | foo|   1|
## +----+----+

If you want to fill multiple columns you can for example reduce:

to_convert = set([...]) # Some set of columns

reduce(lambda df, x: df.withColumn(x, blank_as_null(x)), to_convert, testDF)

or use comprehension:

exprs = [
    blank_as_null(x).alias(x) if x in to_convert else x for x in testDF.columns]

testDF.select(*exprs)

If you want to specifically operate on string fields please check the answer by robin-loxley.

Answer 2

UDFs are not terribly efficient. The correct way to do this using a built-in method is:

df = df.withColumn('myCol', when(col('myCol') == '', None).otherwise(col('myCol')))

Answer 3

My solution is much better than all the solutions I'v seen so far, which can deal with as many fields as you want, see the little function as the following:

  // Replace empty Strings with null values
  private def setEmptyToNull(df: DataFrame): DataFrame = {
    val exprs = df.schema.map { f =>
      f.dataType match {
        case StringType => when(length(col(f.name)) === 0, lit(null: String).cast(StringType)).otherwise(col(f.name)).as(f.name)
        case _ => col(f.name)
      }
    }

    df.select(exprs: _*)
  }

You can easily rewrite the function above in Python.

I learned this trick from @liancheng

Answer 4

Simply add on top of zero323's and soulmachine's answers. To convert for all StringType fields.

from pyspark.sql.types import StringType
string_fields = []
for i, f in enumerate(test_df.schema.fields):
    if isinstance(f.dataType, StringType):
        string_fields.append(f.name)

Answer 5

If you are using python u can check the following.

+----+-----+----+
|  id| name| age|
+----+-----+----+
|null|name1|  50|
|   2|     |    |
|    |name3|null|
+----+-----+----+

def convertToNull(dfa):
   for i in dfa.columns:
    dfa = dfa.withColumn(i , when(col(i) == '', None ).otherwise(col(i)))
  return dfa

convertToNull(dfa).show()

+----+-----+----+
|  id| name| age|
+----+-----+----+
|null|name1|  50|
|   2| null|null|
|null|name3|null|
+----+-----+----+

Answer 6

This is a different version of soulmachine's solution, but I don't think you can translate this to Python as easily:

def emptyStringsToNone(df: DataFrame): DataFrame = {
  df.schema.foldLeft(df)(
    (current, field) =>
      field.dataType match {
        case DataTypes.StringType =>
          current.withColumn(
            field.name,
            when(length(col(field.name)) === 0, lit(null: String)).otherwise(col(field.name))
          )
        case _ => current
      }
  )
}