Count the number of missing values in a dataframe Spark

Question

I have a dataset with missing values , I would like to get the number of missing values for each columns. Following is what I did , I got the number of non missing values. How can I use it to get the number of missing values?

df.describe().filter($"summary" === "count").show

+-------+---+---+---+
|summary|  x|  y|  z|
+-------+---+---+---+
|  count|  1|  2|  3|
+-------+---+---+---+

Any help please to get a dataframe in which we'll find columns and number of missing values for each one.

Answer 1

You could count the missing values by summing the boolean output of the isNull() method, after converting it to type integer:

In Scala:

import org.apache.spark.sql.functions.{sum, col}
df.select(df.columns.map(c => sum(col(c).isNull.cast("int")).alias(c)): _*).show

In Python:

from pyspark.sql.functions import col,sum
df.select(*(sum(col(c).isNull().cast("int")).alias(c) for c in df.columns)).show()

Alternatively, you could also use the output of df.describe().filter($"summary" === "count"), and subtract the number in each cell by the number of rows in the data:

In Scala:

import org.apache.spark.sql.functions.lit,

val rows = df.count()
val summary = df.describe().filter($"summary" === "count")
summary.select(df.columns.map(c =>(lit(rows) - col(c)).alias(c)): _*).show

In Python:

from pyspark.sql.functions import lit

rows = df.count()
summary = df.describe().filter(col("summary") == "count")
summary.select(*((lit(rows)-col(c)).alias(c) for c in df.columns)).show()

Answer 2

from pyspark.sql.functions import isnull, when, count, col
nacounts = df.select([count(when(isnull(c), c)).alias(c) for c in df.columns]).toPandas()
nacounts