however, we still need to create a DataFrame manually with the same column names we expect. If we don’t create with the same column names, our operations/transformations (like unions) on DataFrame fail as we r
考虑一下示例dataframe (“value”列在这里没有意义):我想要的是一个列,只计算'key‘列的唯一值,警告是值计数将递增,只有在前几行没有出现单元格值的情况下,计数才会上升。在这里,"A“将被赋 浏览2提问于2020-11-25得票数 0 回答已采纳 1回答 pyspark withColumnRenamed,drop函数,u‘’Reference歧义错误...
create_table only accepts a str and drop_table accepts a tuple. If I set the catalog and database via pyspark, create_table works as excepted, but I can't figure out a way to do so in my create_table, I had to do it through the pyspark session directly: from pyspark.sql import ...
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from pyspark.sql import SparkSession spark = SparkSession.builder \ .appName('so')\ .getOrCreate() sc= spark.sparkContext map = {'a':3,'b':44} data = sc.parallelize([(k,v) for k,v in map.items()]).toDF(['A','B']) ...