更新正如@samkart提到的，我们可以使用direct.crossJoin()。更新了解决方案。
PySpark 列的contains(~)方法返回布尔值的Column对象,其中True对应于包含指定子字符串的列值。 参数 1.other|string或Column 用于执行检查的字符串或Column。 返回值 布尔值的Column对象。 例子 考虑以下PySpark DataFrame: df = spark.createDataFrame([["Alex",20], ["Bob",30], ["Cathy",40]], ["name"...
from pyspark.sql import DataFrame, SparkSessionimport pyspark.sql.types as Timport pandera.pyspark as paspark = SparkSession.builder.getOrCreate()class PanderaSchema(DataFrameModel): """Test schema""" id: T.IntegerType() = Field(gt=5) product_name: T.StringType() = Field(str_s...
dataframe_object.where(dataframe_obj.column.contains(value/string)) Where, dataframe_object is the PySpark DataFrame. Parameter: The contains() function takes one parameter. It can be a value or string that the contains() function will check if the specified value is present in the DataFrame co...
如果它只需要处理描述的大小写（因此id的长度不会改变，并且模式将始终相似），则可以添加when/otherwise...
notin: checks if value is not in given list of literals str_contains: checks if value contains string literal str_endswith: checks if value ends with string literal str_length: checks if value length matches str_matches: checks if value matches string literal ...
3.Checking Column Existence Using Schema You can check if a column exists in a PySpark DataFrame using theschemaattribute, which contains the DataFrame’s schema information. By examining the schema, you can verify the presence of a column by checking for its name. Theschemaattribute provides a...
PySpark startswith() and endswith() are string functions that are used to check if a string or column begins with a specified string and if a string or
在这里,我们使用filter()函数过滤了行,并在filter()函数内部指定了text_file_value.contains包含单词"Spark",然后将这些结果放入了lines_with_spark变量中。 我们可以修改上述命令,简单地添加.count(),如下所示: text_file.filter(text_file.value.contains("Spark")).count() ...
你可以和我核对一下rlike并转换为整数：