In Pyspark we can use theF.whenstatement or aUDF.This allows us to achieve the same result as above. 在Pyspark中,我们可以使用F.when语句或UDF这使我们可以获得与上述相同的结果。 from pyspark.sql import functions as Fdf = df.withColumn('is_police',\ F.when(\ F.lower(\ F.col('local_sit...
data=data.withColumn('population', when(col('population') < 2, lit(None)).otherwise(col('population'))) 按照district_code列,对数据在磁盘上进行划分 w = Window.partitionBy(data['district_code']) data = data.withColumn('population', when(col('population').isNull(), avg(data['population']...
False)# truncate=False表示不压缩显示spark_df.describe().show()# 均值/最值等描述# **dataframe操作**# 取'age','mobile'两列spark_df.select('age','mobile').show(5)# 新增一列:age_after_10_yrsspark_df.withColumn("age_after_10_yrs",(spark_df["age...
.withColumn('dimCategoryId', lit(None)) \ .select('dimCategoryId','categoryName', col(category_name).alias('categoryValue')) \ .alias('cm') If I check the results of the above without the.selectstatement, I get 9 rows with no nulls in thecol(category_name)column but once I add th...
I've tried wrapping a when statement within and after the .withColumn statementdf = df.withColumn('total_new_load', col('existing_load') * (5 - col('tot_reduced_load'))) Basically I need to add an if-statement of some sort in a pyspark syntax relating to my dataframe code, such...
)# gooddf.withColumn('days_open', (F.coalesce(F.unix_timestamp('closed_at'),F.unix_timestamp())-F.unix_timestamp('created_at'))/86400) Avoid including columns in the select statement if they are going to remain unused and choose instead an explicit set of columns - this is a prefe...
We can also use operators with the When statement and create a condition within a DataFrame. From the above example, we saw the use of the When function with Pyspark Note: When can be used in select operation as well as withColumn function?
// insertStatement.setString(6, record.Date) // insertStatement.executeUpdate() // insertStatement.close() // }) // connection.close() // }) // println("数据写入成功") //插入数据速度较慢,用批处理 import spark.implicits._ if(!order1.isEmpty()) { ...
spark_df = spark_df.withColumn("ingestion_date_time", current_timestamp()) spark_df.show() Phase 3: SQL Server Configuration and Data Load After the transformation process is complete, we need to load the transformed data into a table in the SQL Server database. We can achieve this by ...
The Import statement is to be used for defining the pre-defined function over the column. b.withColumn("Applied_Column",lower(col("Name"))).show() The with Column function is used to create a new column in a Spark data model, and the function lower is applied that takes up the column...