代码语言:txt 复制 from pyspark.sql.functions import regexp_replace df = df.withColumn("text", regexp_replace(df["value"], r"(.*)\|", "$1")) 上述代码中,df["value"]表示DataFrame中的文本列,r"(.*)\|"是一个正则表达式,匹配最后一个分隔符(这里以竖线符号"
如果另一列中的行值为空,我想用固定值填充一列。因此,在customer_df中,如果customer_address为null,则将城市列填充为“unknown” 我在试这个 customer_df = customer_df.withColumn('city',when(customer_< 浏览172提问于2020-09-26得票数 0 回答已采纳 1回答 如何使用Python在中处理NullType? 、、、 我正在...
其结果如下: 除了这两个排序方法之外,asc_nulls_first()、asc_nulls_last()、desc_nulls_first()、desc_nulls_last()方法规定了空值的位置。 cast()、astype()方法修改数据类型,这两个方法作用相同 df_1=df.withColumn('str_age',df['age'].cast("string")) print(df_1.dtypes) 1. 2. 其结果如下:...
F.whenis actually useful for a lot of different things. In fact you can even do a chainedF.when: F.when实际上可用于许多不同的事物。 实际上,您甚至可以在以下情况下执行链式F.when: df = df.withColumn('rating', F.when(F.lower(F.col('local_site_name')).contains('police'), F.lit('...
from pyspark.sql.functions import col df_casted = df_customer.withColumn("c_custkey", col("c_custkey").cast(StringType())) print(type(df_casted)) Remove columnsTo remove columns, you can omit columns during a select or select(*) except or you can use the drop method:Python Копи...
DoubleType())) # Replace all nulls with a specific value df = df.fillna({ 'first_name': 'Tom', 'age': 0, }) # Take the first value that is not null df = df.withColumn('last_name', F.coalesce(df.last_name, df.surname, F.lit('N/A'))) # Drop duplicate rows in a ...
I can create new columns in Spark using .withColumn(). I have yet found a convenient way to create multiple columns at once without chaining multiple .withColumn() methods. df2.withColumn('AgeTimesFare', df2.Age*df2.Fare).show() +---+---+---+---+---+ |PassengerId|Age|Fare|...
withColumn("spend_dollars", money_convert(df.spend_dollars)) # Code snippet result: +---+---+---+ | date|customer_id|spend_dollars| +---+---+---+ |2020-01-31| 0| 0.0700| |2020-01-31| 1| 0.9800| |2020-01-31| 2| 0.0600| |2020-01-31| 3| 0.6500| |2020-01-31| 4| ...
pyspark 日期范围间隔的左联接你需要 1.将列名包含在F.col('')中 1.简化条件语句（if-else子句）...
deletes=list(map(lambdarow:(row[0],row[1]),ds.collect()))hard_delete_df=spark.sparkContext.parallelize(deletes).toDF(['uuid','partitionpath']).withColumn('ts',lit(0.0))hard_delete_df.write.format("hudi").\ options(**hudi_hard_delete_options).\ ...