Replace missing values with mean - Spark Dataframe

Question

I have a Spark Dataframe with some missing values. I would like to perform a simple imputation by replacing the missing values with the mean for that column. I am very new to Spark, so I have been struggling to implement this logic. This is what I have managed to do so far:

a) To do this for a single column (let's say Col A), this line of code seems to work:

df.withColumn("new_Col", when($"ColA".isNull, df.select(mean("ColA"))
  .first()(0).asInstanceOf[Double])
  .otherwise($"ColA"))

b) However, I have not been able to figure out, how to do this for all the columns in my dataframe. I was trying out the Map function, but I believe it loops through each row of a dataframe

c) There is a similar question on SO - here. And while I liked the solution (using Aggregated tables and coalesce), I was very keen to know if there is a way to do this by looping through each column (I come from R, so looping through each column using a higher order functional like lapply seems more natural to me).

Thanks!

Answer 1

Spark >= 2.2

You can use org.apache.spark.ml.feature.Imputer (which supports both mean and median strategy).

Scala :

import org.apache.spark.ml.feature.Imputer

val imputer = new Imputer()
  .setInputCols(df.columns)
  .setOutputCols(df.columns.map(c => s"${c}_imputed"))
  .setStrategy("mean")

imputer.fit(df).transform(df)

Python:

from pyspark.ml.feature import Imputer

imputer = Imputer(
    inputCols=df.columns, 
    outputCols=["{}_imputed".format(c) for c in df.columns]
)
imputer.fit(df).transform(df)

Spark < 2.2

Here you are:

import org.apache.spark.sql.functions.mean

df.na.fill(df.columns.zip(
  df.select(df.columns.map(mean(_)): _*).first.toSeq
).toMap)

where

df.columns.map(mean(_)): Array[Column] 

computes an average for each column,

df.select(_: *).first.toSeq: Seq[Any]

collects aggregated values and converts row to Seq[Any] (I know it is suboptimal but this is the API we have to work with),

df.columns.zip(_).toMap: Map[String,Any] 

creates aMap: Map[String, Any] which maps from the column name to its average, and finally:

df.na.fill(_): DataFrame

fills the missing values using:

fill: Map[String, Any] => DataFrame 

from DataFrameNaFunctions.

To ingore NaN entries you can replace:

df.select(df.columns.map(mean(_)): _*).first.toSeq

with:

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


df.select(df.columns.map(
  c => mean(when(!isnan(col(c)), col(c)))
): _*).first.toSeq

Answer 2

For PySpark, this is the code I used:

mean_dict = { col: 'mean' for col in df.columns }
col_avgs = df.agg( mean_dict ).collect()[0].asDict()
col_avgs = { k[4:-1]: v for k,v in col_avgs.iteritems() }
df.fillna( col_avgs ).show()

The four steps are:

1. Create the dictionary mean_dict mapping column names to the aggregate operation (mean)
2. Calculate the mean for each column, and save it as the dictionary col_avgs
3. The column names in col_avgs start with avg( and end with ), e.g. avg(col1). Strip the parentheses out.
4. Fill the columns of the dataframe with the averages using col_avgs

Answer 3

For imputing the median (instead of the mean) in PySpark < 2.2

## filter numeric cols
num_cols = [col_type[0] for col_type in filter(lambda dtype: dtype[1] in {"bigint", "double", "int"}, df.dtypes)]
### Compute a dict with <col_name, median_value>
median_dict = dict()
for c in num_cols:
   median_dict[c] = df.stat.approxQuantile(c, [0.5], 0.001)[0]

Then, apply na.fill

df_imputed = df.na.fill(median_dict)