如何从DataFrame中选择确切数量的随机行

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?

如何有效地从DataFrame中选择确切数量的随机行？数据包含可以使用的索引列。如果我必须使用最大大小，那么索引列上的效率，count（）或max（）更高效？

1 个解决方案

#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.

一种可能的方法是使用.count（）计算行数，然后使用python随机库中的sample（）从该范围生成任意长度的随机序列。最后使用结果数字列表val来对索引列进行子集化。

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:

例：

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|
+---+---+