如何从DataFrame中选择确切数量的随机行 [英] How to select an exact number of random rows from DataFrame
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问题描述
如何有效地从DataFrame中选择确切个随机行? 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count()或max()效率更高?
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?
推荐答案
一种可行的方法是使用.count()
计算行数,然后使用python
的
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))
示例:
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|
+---+---+
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