我有两个spark数据帧，我想从一个spark数据帧添加一个列到另一个。
我的代码是：
new = df.withColumn("prob", tr_df.prob)
在这里，我想将 tr_df 中的 result2 列添加到名为 prob 的数据框 df 中。我搜索了这个，但没有任何对我有用，我收到了错误 -
AnalysisException: u'resolved attribute(s) prob#579 missing from q1_n_words#388L,prediction#510,res1#390,q2_n_words#389L,tfidf_word_match#384,Average#379,prob#385,probability#485,Cosine#381,word_m#383,rawPrediction#461,features#438,res2#391,question1#373,Jaccard#382,test_id#372L,raw_pred#377,question2#374,q2len#376,Common#378L,result2#387,q1len#375,result1#386,Percentage#380 in operator !Project [test_id#372L, question1#373, question2#374, q1len#375, q2len#376, raw_pred#377, Common#378L, Average#379, Percentage#380, Cosine#381, Jaccard#382, word_m#383, tfidf_word_match#384, prob#579 AS prob#634, result1#386, result2#387, q1_n_words#388L, q2_n_words#389L, res1#390, res2#391, features#438, rawPrediction#461, probability#485, prediction#510];;\n!Project [test_id#372L, question1#373, question2#374, q1len#375, q2len#376, raw_pred#377, Common#378L, Average#379, Percentage#380, Cosine#381, Jaccard#382, word_m#383, tfidf_word_match#384, prob#579 AS prob#634, result1#386, result2#387, q1_n_words#388L, q2_n_words#389L, res1#390, res2#391, features#438, rawPrediction#461, probability#485, prediction#510]\n+- Project [test_id#372L, question1#373, question2#374, q1len#375, q2len#376, raw_pred#377, Common#378L, Average#379, Percentage#380, Cosine#381, Jaccard#382, word_m#383, tfidf_word_match#384, prob#385, result1#386, result2#387, q1_n_words#388L, q2_n_words#389L, res1#390, res2#391, features#438, rawPrediction#461, probability#485, UDF(rawPrediction#461) AS prediction#510]\n +- Project [test_id#372L, question1#373, question2#374, q1len#375, q2len#376, raw_pred#377, Common#378L, Average#379, Percentage#380, Cosine#381, Jaccard#382, word_m#383, tfidf_word_match#384, prob#385, result1#386, result2#387, q1_n_words#388L, q2_n_words#389L, res1#390, res2#391, features#438, rawPrediction#461, UDF(rawPrediction#461) AS probability#485]\n +- Project [test_id#372L, question1#373, question2#374, q1len#375, q2len#376, raw_pred#377, Common#378L, Average#379, Percentage#380, Cosine#381, Jaccard#382, word_m#383, tfidf_word_match#384, prob#385, result1#386, result2#387, q1_n_words#388L, q2_n_words#389L, res1#390, res2#391, features#438, UDF(features#438) AS rawPrediction#461]\n +- Project [test_id#372L, question1#373, question2#374, q1len#375, q2len#376, raw_pred#377, Common#378L, Average#379, Percentage#380, Cosine#381, Jaccard#382, word_m#383, tfidf_word_match#384, prob#385, result1#386, result2#387, q1_n_words#388L, q2_n_words#389L, res1#390, res2#391, UDF(struct(q1len#375, q2len#376, cast(q1_n_words#388L as double) AS q1_n_words_double_VectorAssembler_4158baa8e5b4f3aced2b#435, cast(q2_n_words#389L as double) AS q2_n_words_double_VectorAssembler_4158baa8e5b4f3aced2b#436, cast(Common#378L as double) AS Common_double_VectorAssembler_4158baa8e5b4f3aced2b#437, Average#379, Percentage#380, Cosine#381, Jaccard#382, word_m#383, prob#385, raw_pred#377, res1#390, res2#391)) AS features#438]\n +- LogicalRDD [test_id#372L, question1#373, question2#374, q1len#375, q2len#376, raw_pred#377, Common#378L, Average#379, Percentage#380, Cosine#381, Jaccard#382, word_m#383, tfidf_word_match#384, prob#385, result1#386, result2#387, q1_n_words#388L, q2_n_words#389L, res1#390, res2#391]\n'
tr_df架构 -
tr_df.printSchema()
root
|-- prob: float (nullable = true)
df Schema -
df.printSchema()
root
|-- test_id: long (nullable = true)
请帮忙！提前谢谢。
答案 0 :(得分：0)
由于错误消息明确指出您需要将spark.sql.crossJoin.enabled = true设置为您的spark配置
您可以设置如下相同的内容：
val sparkConf = new SparkConf().setAppName("Test")
sparkConf.set("spark.sql.crossJoin.enabled", "true")
然后通过传递此SparkConf
来获取或创建SparkSessionval sparkSession = SparkSession.builder().config(sparkConf).getOrCreate()
然后加入你...
答案 1 :(得分：0)
在pyspark中，您可以按照以下方式进行操作。希望它会有用。
>>> spark.conf.set("spark.sql.crossJoin.enabled", True)
>>> df1.show()
+----+
|col1|
+----+
| 23|
| 56|
| 78|
| 31|
+----+
>>> df2.show()
+----+
|col2|
+----+
| 87|
| 45|
| 23|
| 11|
+----+
>>> final = df1.crossJoin(df2)
>>> final.withColumnRenamed('col2', 'result').show()
+----+------+
|col1|result|
+----+------+
| 23| 87|
| 23| 45|
| 23| 23|
| 23| 11|
| 56| 87|
| 56| 45|
| 56| 23|
| 56| 11|
| 78| 87|
| 78| 45|
| 78| 23|
| 78| 11|
| 31| 87|
| 31| 45|
| 31| 23|
| 31| 11|
+----+------+