(py)Spark中分组数据的模式 [英] Mode of grouped data in (py)Spark
问题描述
我有一个带有多个列的spark DataFrame.我想基于一列对行进行分组,然后为每个组找到第二列的模式.使用pandas DataFrame,我会做这样的事情:
I have a spark DataFrame with multiple columns. I would like to group the rows based on one column, and then find the mode of the second column for each group. Working with a pandas DataFrame, I would do something like this:
rand_values = np.random.randint(max_value,
size=num_values).reshape((num_values/2, 2))
rand_values = pd.DataFrame(rand_values, columns=['x', 'y'])
rand_values['x'] = rand_values['x'] > max_value/2
rand_values['x'] = rand_values['x'].astype('int32')
print(rand_values)
## x y
## 0 0 0
## 1 0 4
## 2 0 1
## 3 1 1
## 4 1 2
def mode(series):
return scipy.stats.mode(series['y'])[0][0]
rand_values.groupby('x').apply(mode)
## x
## 0 4
## 1 1
## dtype: int64
在pyspark中,我能够找到单列的模式
Within pyspark, I am able to find the mode of a single column doing
df = sql_context.createDataFrame(rand_values)
def mode_spark(df, column):
# Group by column and count the number of occurrences
# of each x value
counts = df.groupBy(column).count()
# - Find the maximum value in the 'counts' column
# - Join with the counts dataframe to select the row
# with the maximum count
# - Select the first element of this dataframe and
# take the value in column
mode = counts.join(
counts.agg(F.max('count').alias('count')),
on='count'
).limit(1).select(column)
return mode.first()[column]
mode_spark(df, 'x')
## 1
mode_spark(df, 'y')
## 1
我不知道该如何将该功能应用于分组数据.如果不可能直接将此逻辑应用于DataFrame,是否可以通过其他方法达到相同的效果?
I'm at a loss for how to apply that function to grouped data. If it's not possible to directly apply this logic to a DataFrame, is it possible to achieve the same effect by some other means?
提前谢谢!
推荐答案
zero323建议的解决方案.
Solution suggested by zero323.
原始解决方案: https://stackoverflow.com/a/35226857/1560062
首先,计算每个(x,y)组合的出现次数.
First, count the occurances of each (x, y) combination.
counts = df.groupBy(['x', 'y']).count().alias('counts')
counts.show()
## +---+---+-----+
## | x| y|count|
## +---+---+-----+
## | 0| 1| 2|
## | 0| 3| 2|
## | 0| 4| 2|
## | 1| 1| 3|
## | 1| 3| 1|
## +---+---+-----+
解决方案1:按"x"分组,通过取每组中计数的最大值进行汇总.最后,删除计数"列.
Solution 1: Group by 'x', aggregate by taking the maximum value of the counts in each group. Finally, Drop the 'count' column.
result = (counts
.groupBy('x')
.agg(F.max(F.struct(F.col('count'),
F.col('y'))).alias('max'))
.select(F.col('x'), F.col('max.y'))
)
result.show()
## +---+---+
## | x| y|
## +---+---+
## | 0| 4|
## | 1| 1|
## +---+---+
解决方案2:使用窗口,按"x"进行分区,并按"count"列进行排序.现在,在每个分区中选择第一行.
Solution 2: Using a window, partition by 'x', and order by the 'count' column. Now, pick the first row in each of the partitions.
win = Window().partitionBy('x').orderBy(F.col('count').desc())
result = (counts
.withColumn('row_num', F.rowNumber().over(win))
.where(F.col('row_num') == 1)
.select('x', 'y')
)
result.show()
## +---+---+
## | x| y|
## +---+---+
## | 0| 1|
## | 1| 1|
## +---+---+
由于行的排序方式,两个结果有不同的结果.如果没有联系,则两种方法给出的结果相同.
The two results have a different outcome because of the way the rows are sorted. If there are no ties, the two methods give the same result.
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