我有以下 pyspark 資料框:
root
|-- tokens: array (nullable = true)
| |-- element: string (containsNull = true)
|-- posTags: array (nullable = true)
| |-- element: string (containsNull = true)
|-- dependencies: array (nullable = true)
| |-- element: string (containsNull = true)
|-- labelledDependencies: array (nullable = true)
| |-- element: string (containsNull = true)
以以下資料為例
------------------------------ --------------------------- ----------------------------------- --------------------------------------------
|tokens |posTags |dependencies |labelledDependencies |
------------------------------ --------------------------- ----------------------------------- --------------------------------------------
|[i, try, to, get, my, balance]|[NNP, VB, TO, VB, PRP$, NN]|[try, ROOT, get, try, balance, get]|[nsubj, root, mark, parataxis, appos, nsubj]|
------------------------------ --------------------------- ----------------------------------- --------------------------------------------
我想將令牌余額的標記依賴性從 nsubj 更改為 dobj。
我的邏輯如下:如果您找到一個標記的依賴項nsubj并且該標記具有POSTagNN并且該標記依賴于具有POS標記VB(get)的標記,則更nsubj改為dobj.
我可以使用以下功能來做到這一點:
def change_things(tokens,posTags,dependencies,labelledDependencies):
for i in range(0,len(labelledDependencies)):
if labelledDependencies[i] == 'nsubj':
if posTags[i] == 'NN':
if posTags[tokens.index(dependencies[i])] == 'VB':
labelledDependencies[i] = 'dobj'
return tokens,posTags,dependencies,labelledDependencies
甚至可能將其注冊為 udf。
但是,我的問題是如何在不使用 udf 而只使用 pyspark 內置方法的情況下做到這一點。
uj5u.com熱心網友回復:
您可以使用 Spark 內置transform函式:
import pyspark.sql.functions as F
df2 = df.withColumn(
"labelledDependencies",
F.expr("""transform(
labelledDependencies,
(x, i) -> CASE WHEN x = 'nsubj'
AND posTags[i] = 'NN'
AND posTags[array_position(tokens, dependencies[i]) - 1] = 'VB'
THEN 'dobj'
ELSE x
END
)
""")
)
df2.show(1, False)
# ------------------------------ --------------------------- ----------------------------------- -------------------------------------------
#|tokens |posTags |dependencies |labelledDependencies |
# ------------------------------ --------------------------- ----------------------------------- -------------------------------------------
#|[i, try, to, get, my, balance]|[NNP, VB, TO, VB, PRP$, NN]|[try, ROOT, get, try, balance, get]|[nsubj, root, mark, parataxis, appos, dobj]|
# ------------------------------ --------------------------- ----------------------------------- -------------------------------------------
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標籤:Python 阿帕奇火花 火花 apache-spark-sql
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