更新时间:2023-11-18 23:09:52
collect_list
仅在1.6中出现.
我将研究基础的RDD.方法如下:
I'd go through the underlying RDD. Here's how:
data_df.show()
+--------+---+------+----+
|username|qid|row_no|text|
+--------+---+------+----+
| d| 2| 2|ball|
| a| 1| 1|this|
| a| 1| 3|text|
| a| 1| 2| is|
| d| 2| 1| the|
+--------+---+------+----+
然后这个
reduced = data_df\
.rdd\
.map(lambda row: ((row[0], row[1]), [(row[2], row[3])]))\
.reduceByKey(lambda x,y: x+y)\
.map(lambda row: (row[0], sorted(row[1], key=lambda text: text[0]))) \
.map(lambda row: (
row[0][0],
row[0][1],
','.join([str(e[0]) for e in row[1]]),
' '.join([str(e[1]) for e in row[1]])
)
)
schema_red = typ.StructType([
typ.StructField('username', typ.StringType(), False),
typ.StructField('qid', typ.IntegerType(), False),
typ.StructField('row_no', typ.StringType(), False),
typ.StructField('text', typ.StringType(), False)
])
df_red = sqlContext.createDataFrame(reduced, schema_red)
df_red.show()
上面产生了以下内容:
+--------+---+------+------------+
|username|qid|row_no| text|
+--------+---+------+------------+
| d| 2| 1,2| the ball|
| a| 1| 1,2,3|this is text|
+--------+---+------+------------+
在大熊猫中
df4 = pd.DataFrame([
['a', 1, 1, 'this'],
['a', 1, 2, 'is'],
['d', 2, 1, 'the'],
['a', 1, 3, 'text'],
['d', 2, 2, 'ball']
], columns=['username', 'qid', 'row_no', 'text'])
df_groupped=df4.sort_values(by=['qid', 'row_no']).groupby(['username', 'qid'])
df3 = pd.DataFrame()
df3['row_no'] = df_groupped.apply(lambda row: ','.join([str(e) for e in row['row_no']]))
df3['text'] = df_groupped.apply(lambda row: ' '.join(row['text']))
df3 = df3.reset_index()