Count the number of missing values in a dataframe Spark - dataframe

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

Answer 3

for i in df.columns:
print(i,df.count()-(df.na.drop(subset=i).count()))