How to select an exact number of random rows from DataFrame

Question

How can I select an exact number of random rows from a DataFrame efficiently? The data contains an index column that can be used. If I have to use maximum size, what is more efficient, count() or max() on the index column?

Answer 1

A possible approach is to calculate the number of rows using .count(), then use sample() from python's random library to generate a random sequence of arbitrary length from this range. Lastly use the resulting list of numbers vals to subset your index column.

import random 
def sampler(df, col, records):

  # Calculate number of rows
  colmax = df.count()

  # Create random sample from range
  vals = random.sample(range(1, colmax), records)

  # Use 'vals' to filter DataFrame using 'isin'
  return df.filter(df[col].isin(vals))

Example:

df = sc.parallelize([(1,1),(2,1),
                     (3,1),(4,0),
                     (5,0),(6,1),
                     (7,1),(8,0),
                     (9,0),(10,1)]).toDF(["a","b"])

sampler(df,"a",3).show()
+---+---+
|  a|  b|
+---+---+
|  3|  1|
|  4|  0|
|  6|  1|
+---+---+