接下来,我们想筛选出分数在80分以上的学生。我们将结合使用filter和array_contains来实现这一点。 frompyspark.sql.functionsimportarray_contains# 使用 filter 筛选分数大于80的学生filtered_df=grouped_df.filter(array_contains(grouped_df.scores,85))filtered_df.show() 1. 2. 3. 4. 5. 这段代码会过滤出成...
过滤指定数组包含的条件 获取ctr 大于等于 0.2 或者 content 数组中包含 'person' 的数据,包含关系的操作可以使用 spark 1.5 时新增的array_contains函数,具体代码如下: df.filter("ctr >= 0.2 or array_contains(content, 'person')").show() 输出如下: +---+---+---+---+---+---+ | id|impressi...
spark.sql("select * from t1 where array_contains(a['col1'],1)").show() #另外一种方式展开:先行列变换,然后按条件过滤 def lg_to_number(string): return unidecode(string) udf_lg_to_number =udf(lg_to_number,returnType=StringType()) df1.select(F.col('c1'),F.explode(F.col('a'))....
PySpark Filter on array values in column How to PySpark filter with custom function PySpark filter with SQL Example PySpark filtering array based columns In SQL Further Resources PySpark filter By Example Setup To run our filter examples, we need some example data. As such, we will load some e...
我正在尝试使用pyspark来实现一个点产品,以学习pyspark的语法。我目前已经实现了如下所示的点产品:from functools import reduce return(rdd.zip(rdd2) .reduce(lambda x,y: x + y)我的解决方案感觉不 浏览0提问于2016-01-20得票数 3 回答已采纳 1回答 对seq的每个元素应用约简 、 我有列表的...
PySpark SQL contains() function is used to match a column value contains in a literal string (matches on part of the string), this is mostly used to
8. Filtering Array column To filter DataFrame rows based on the presence of a value within an array-type column, you can employ the first syntax. The following example uses array_contains() fromPySpark SQL functions. This function examines whether a value is contained within an array. If the...
#如果想根据列的值进行过滤,可以使用array_contains spark.sql("select * from t1 where array_contains(a['col1'],1)").show() #另外一种方式展开:先行列变换,然后按条件过滤 def lg_to_number(string): return unidecode(string) udf_lg_to_number =udf(lg_to_number,returnType=StringType()) ...
["Spark","Python"])]# Filter Pandas DataFrame of Multiple columns.df2=df.apply(lambdacol:col.str.contains('Spark|Python',na=False),axis=1)# Join multiple terms.terms=['Spark','PySpark']df2=df[df['Courses'].str.contains('|'.join(terms))]# Using re.escape() function to get ...