如何添加火花数据框中的火花数据框的列（使用Pyspark）？

Question

我有两个火花数据框，我想从一个火花数据框添加一列到另一个。

我的代码是：

new = df.withColumn("prob", tr_df.prob) 

在这里，我想列RESULT2这是tr_df添加到我的数据帧DF命名为概率。我搜索了这一点，但没有什么工作对我来说，我得到一个error--

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模式 -

tr_df.printSchema() 
root 
|-- prob: float (nullable = true) 

DF模式 -

df.printSchema() 
root 
|-- test_id: long (nullable = true) 

请帮助！提前致谢。

Answer 1

由于错误信息中明确指出需要spark.sql.crossJoin.enabled = true设置为您的火花配置

您可以设置相同类似如下：

val sparkConf = new SparkConf().setAppName("Test") 
sparkConf.set("spark.sql.crossJoin.enabled", "true") 

然后得到或通过将这种SparkConf

val sparkSession = SparkSession.builder().config(sparkConf).getOrCreate() 

创建SparkSession然后做你的加入...

来源：How to enable Cartesian join in Spark 2.0?