Count number of non-NaN entries in each column of Spark dataframe in PySpark

Question

I have a very large dataset that is loaded in Hive (about 1.9 million rows and 1450 columns). I need to determine the "coverage" of each of the columns, meaning, the fraction of rows that have non-NaN values for each column.

Here is my code:

from pyspark import SparkContext
from pyspark.sql import HiveContext
import string as string

sc = SparkContext(appName="compute_coverages") ## Create the context
sqlContext = HiveContext(sc)

df = sqlContext.sql("select * from data_table")
nrows_tot = df.count()

covgs = sc.parallelize(df.columns)
          .map(lambda x: str(x))
          .map(lambda x: (x, float(df.select(x).dropna().count()) / float(nrows_tot) * 100.))

Trying this out in PySpark shell, if I then do covgs.take(10), it returns a rather large error stack. It says that there's a problem in save in the file /usr/lib64/python2.6/pickle.py. This is the final part of the error:

py4j.protocol.Py4JError: An error occurred while calling o37.__getnewargs__. Trace:
py4j.Py4JException: Method __getnewargs__([]) does not exist
        at py4j.reflection.ReflectionEngine.getMethod(ReflectionEngine.java:333)
        at py4j.reflection.ReflectionEngine.getMethod(ReflectionEngine.java:342)
        at py4j.Gateway.invoke(Gateway.java:252)
        at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:133)
        at py4j.commands.CallCommand.execute(CallCommand.java:79)
        at py4j.GatewayConnection.run(GatewayConnection.java:207)
        at java.lang.Thread.run(Thread.java:745)

Is there a better way to accomplish this? I can't use pandas, though, as it's not currently available on the cluster I work on and I don't have access rights to install it.

Answer 1

For string and numeric columns, summary is convenient.

• Count non-nulls:

  df.summary("count").show()
  

• Count non-NaN:

  df.replace(float("nan"), None).summary("count").show()
  

Note. summary would not return columns of other than string or numeric type (e.g. date type columns would be omitted from the result).

---

Full test:

df = spark.createDataFrame(
    [(0.0, 1, 2, float("Nan")),
     (None, 3, 4, 5.0),
     (None, None, 6, 7.0),
     (float("Nan"), 8, 9, 7.0)],
    ["v", "x", "y", "z"])
df.show()
# +----+----+---+---+
# |   v|   x|  y|  z|
# +----+----+---+---+
# | 0.0|   1|  2|NaN|
# |null|   3|  4|5.0|
# |null|null|  6|7.0|
# | NaN|   8|  9|7.0|
# +----+----+---+---+

df.summary("count").show()
# +-------+---+---+---+---+
# |summary|  v|  x|  y|  z|
# +-------+---+---+---+---+
# |  count|  2|  3|  4|  4|
# +-------+---+---+---+---+

df.replace(float("nan"), None).summary("count").show()
# +-------+---+---+---+---+
# |summary|  v|  x|  y|  z|
# +-------+---+---+---+---+
# |  count|  1|  3|  4|  3|
# +-------+---+---+---+---+

Answer 2

from pyspark.sql import functions as F

z = df.count()
(df.replace(float('nan'), None)
 .agg(*[F.expr(f'count({col})/{z} as {col}') for col in df.columns])
).show()

Answer 3

You may got data type mismatch Exception :

org.apache.spark.sql.AnalysisException: cannot resolve 'isnan(`date_hour`)' due to data type mismatch: argument 1 requires (double or float) type, however, '`date_hour`' is of timestamp type.;

Better select numerical columns at first:

from pyspark.sql.functions import *

def get_numerical_cols(df):
    return [i.name for i in df.schema  if str(i.dataType) in ('IntegerType', 'LongType', 'FloatType', 'DoubleType') ]

numcols = get_numerical_cols(df)
df_nan_rate = df.select([(count(when(isnan(c) | col(c).isNull(), c))/count(lit(1))).alias(c) for c in numcols])

Answer 4

You can use isNotNull() :

df.where(df[YOUR_COLUMN].isNotNull()).select(YOUR_COLUMN).show()

Answer 5

Let's start with a dummy data:

from pyspark.sql import Row

row = Row("v", "x", "y", "z")
df = sc.parallelize([
    row(0.0, 1, 2, 3.0), row(None, 3, 4, 5.0),
    row(None, None, 6, 7.0), row(float("Nan"), 8, 9, float("NaN"))
]).toDF()

## +----+----+---+---+
## |   v|   x|  y|  z|
## +----+----+---+---+
## | 0.0|   1|  2|3.0|
## |null|   3|  4|5.0|
## |null|null|  6|7.0|
## | NaN|   8|  9|NaN|
## +----+----+---+---+

All you need is a simple aggregation:

from pyspark.sql.functions import col, count, isnan, lit, sum

def count_not_null(c, nan_as_null=False):
    """Use conversion between boolean and integer
    - False -> 0
    - True ->  1
    """
    pred = col(c).isNotNull() & (~isnan(c) if nan_as_null else lit(True))
    return sum(pred.cast("integer")).alias(c)

df.agg(*[count_not_null(c) for c in df.columns]).show()

## +---+---+---+---+
## |  v|  x|  y|  z|
## +---+---+---+---+
## |  2|  3|  4|  4|
## +---+---+---+---+

or if you want to treat NaN a NULL:

df.agg(*[count_not_null(c, True) for c in df.columns]).show()

## +---+---+---+---+
## |  v|  x|  y|  z|
## +---+---+---+---+
## |  1|  3|  4|  3|
## +---+---+---+---

You can also leverage SQL NULL semantics to achieve the same result without creating a custom function:

df.agg(*[
    count(c).alias(c)    # vertical (column-wise) operations in SQL ignore NULLs
    for c in df.columns
]).show()

## +---+---+---+
## |  x|  y|  z|
## +---+---+---+
## |  1|  2|  3|
## +---+---+---+

but this won't work with NaNs.

If you prefer fractions:

exprs = [(count_not_null(c) / count("*")).alias(c) for c in df.columns]
df.agg(*exprs).show()

## +------------------+------------------+---+
## |                 x|                 y|  z|
## +------------------+------------------+---+
## |0.3333333333333333|0.6666666666666666|1.0|
## +------------------+------------------+---+

or

# COUNT(*) is equivalent to COUNT(1) so NULLs won't be an issue
df.select(*[(count(c) / count("*")).alias(c) for c in df.columns]).show()

## +------------------+------------------+---+
## |                 x|                 y|  z|
## +------------------+------------------+---+
## |0.3333333333333333|0.6666666666666666|1.0|
## +------------------+------------------+---+

Scala equivalent:

import org.apache.spark.sql.Column
import org.apache.spark.sql.functions.{col, isnan, sum}

type JDouble = java.lang.Double

val df = Seq[(JDouble, JDouble, JDouble, JDouble)](
  (0.0, 1, 2, 3.0), (null, 3, 4, 5.0),
  (null, null, 6, 7.0), (java.lang.Double.NaN, 8, 9, java.lang.Double.NaN)
).toDF()


def count_not_null(c: Column, nanAsNull: Boolean = false) = {
  val pred = c.isNotNull and (if (nanAsNull) not(isnan(c)) else lit(true))
  sum(pred.cast("integer"))
}

df.select(df.columns map (c => count_not_null(col(c)).alias(c)): _*).show
// +---+---+---+---+                                                               
// | _1| _2| _3| _4|
// +---+---+---+---+
// |  2|  3|  4|  4|
// +---+---+---+---+

 df.select(df.columns map (c => count_not_null(col(c), true).alias(c)): _*).show
 // +---+---+---+---+
 // | _1| _2| _3| _4|
 // +---+---+---+---+
 // |  1|  3|  4|  3|
 // +---+---+---+---+