CODEWITHSUNDEEP
pythonapache-sparkpysparkapache-spark-sqlapache-spark-ml
Minh
Minh

Reputation: 2310

Spark: Counting NaNs in a vector column

Suppose I have a Spark DataFrame that looks like this 

+-----------+------------------+
| id        |          features|
+-----------+------------------+
|          1|[57.0,1.0,0.0,0.0]|
|          2|[63.0,NaN,0.0,0.0]|
|          3|[74.0,1.0,3.0,NaN]|
|          4|[67.0,NaN,0.0,0.0]|
|          5|[NaN,1.0,NaN,NaN] |

where each row in the features column is a DenseVector containing a combination of float and NaN datatypes. Is there a way to count the number of NaN in the first column of the DenseVector or any arbitrary column? For instance, I would like something that would return that the first column has 1 NaN, the 2nd has 3, and the 4th has 2.

Upvotes: 0

Views: 1526

Answers (1)

zero323
zero323

Reputation: 330353

As far as I know Spark SQL doesn't provide method like this but it is trivial with RDD and a little bit of NumPy. 

from pyspark.ml.linalg import DenseVector, Vector
import numpy as np

df = sc.parallelize([
    (1, DenseVector([57.0, 1.0, 0.0, 0.0])),
    (2, DenseVector([63.0, float("NaN"), 0.0, 0.0])),
    (3, DenseVector([74.0, 1.0, 3.0, float("NaN")])),
    (4, DenseVector([67.0, float("NaN"), 0.0, 0.0])),
    (5, DenseVector([float("NaN"), 1.0, float("NaN"), float("NaN")])),
]).toDF(["id", "features"])

(df
    .select("features")
    .rdd
    .map(lambda x: np.isnan(x.features.array))
    .sum())

array([1, 2, 1, 2])

You could a similar thing with SQL but it requires significantly more effort. A helper function:

from pyspark.sql.functions import udf
from pyspark.sql.types import ArrayType, DoubleType
from pyspark.sql import Column
from typing import List

def as_array(c: Column) -> Column:
    def as_array_(v: Vector) -> List[float]:
        return v.array.tolist()
    return udf(as_array_, ArrayType(DoubleType()))(c)

Determine size of the vectors:

from pyspark.sql.functions import col, size

(vlen, ) = df.na.drop().select(size(as_array(col("features")))).first()

Create an expression:

from pyspark.sql.functions import col, isnan, sum as sum_

feature_array = as_array("features").alias("features")

Finally select:

(df
    .na.drop(subset=["features"])
    .select([sum_(isnan(feature_array[i]).cast("bigint")) for i in range(vlen)]))

Upvotes: 1

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