有一段时间,我在寻找如何将多个列一次重命名为一个PySparkDF,并遇到了如下情况:def rename_sdf(df, mapper={}, **kwargs_mapper): # return something 我对最后一段感兴趣,其中通过赋值语句将方法添加到pyspar 浏览6提问于2020-07-09得票数 1
You can also use the .alias() method to rename a column you're selecting. So if you wanted to .select() the column duration_hrs (which isn't in your DataFrame) you could do flights.select((flights.air_time/60).alias("duration_hrs")) The equivalent Spark DataFrame method .selectExpr(...
In PySpark, columns can be renamed using thewithColumnRenamedmethod on a DataFrame. This method takes two arguments: the current column name and the new column name. Here is an example of how to rename a column from “age” to “new_age”: df.withColumnRenamed("age","new_age").show()...
data2.with_columns(pl.Series('new1',np.random.randint(0, 10, size=len(data1))).rename({'new1':'new'}) 1. 2. pyspark就是这样子加一列。 data3_with_new = data3.withColumn("new", expr("rand() * 10")) # 使用rand()函数生成随机数列 1. 分组聚合 分组聚合麻烦起来很麻烦,可以写得...
df.columns =new_column_name_list 但是,这在使用 sqlContext 创建的 PySpark 数据帧中不起作用。我能想到的唯一解决方案是: df = sqlContext.read.format("com.databricks.spark.csv").options(header='false', inferschema='true', delimiter='\t').load("data.txt") ...
alias('address_copy') # rename column / create new column df.withColumnRenamed('age', 'birth_age') df.withColumn('age_copy', df['age']).show(1) """ +---+---+---+---+ | address|age|name|age_copy| +---+---+---+---+ |[Nanjing, China]| 12| Li| 12| +---+---...
# Examine the dataairports.show()# Rename the faa columnairports=airports.withColumnRenamed("faa","dest")# Join the DataFramesflights_with_airports=flights.join(airports,on="dest",how="leftouter")# Examine the new DataFrameflights_with_airports.show() ...
Rename columnsTo rename a column, use the withColumnRenamed method, which accepts the existing and new column names:Python Копирај df_customer_flag_renamed = df_customer_flag.withColumnRenamed("balance_flag", "balance_flag_renamed") The alias method is especially helpful when you ...
# rename spark_energy column name 'KWH/hh (per half hour)'new_names=['LCLid','stdorToU','DateTime','KWH_hh','Acorn','Acorn_grouped']spark_energy=spark_energy.toDF(*new_names)spark_energy.printSchema()# rename spark_tariffs column name 'TariffDateTime'new_name=['DateTime','Tariff']...
To rename all columns use toDF with the desired column names in the argument list. This example puts an X in front of all column names. df = auto_df.toDF(*["X" + name for name in auto_df.columns]) # Code snippet result: +---+---+---+---+---+---+---+---+---+ ...