df.withColumn("rounded", round(col("value"), 2)) # 向下/向上取整 df.withColumn("floored", floor(col("value"))) df.withColumn("ceiled", ceil(col("value"))) # 取绝对值 df.withColumn("absolute", abs(col("value"))) # 平方根
import mathfrom pyspark.sql import Row defrowwise_function(row):# convert row to dict:row_dict = row.asDict()# Add a new key in the dictionary with the new column name and value.row_dict['Newcol'] = math.exp(row_dict['rating'])# convert dict to row:newrow = Row(**row_dict)#...
# Select column address_df = df.select(['address.city']) # DataFrame[city: string] # Filter column with value df.filter(df.age == 12).show() """ +---+---+---+ | address|age| name| +---+---+---+ |[Nanjing, China]| 12| Li| | [Paris, France]| 12| Jacob| | [...
在这个示例中,查询 table_name 视图中 column_name 列值大于 100 的所有记录。 25510 PySpark 读写 JSON 文件到 DataFrame 本文中,云朵君将和大家一起学习了如何将具有单行记录和多行记录的 JSON 文件读取到 PySpark DataFrame 中,还要学习一次读取单个和多个文件以及使用不同的保存选项将 JSON 文件写回......
from pyspark.sql import functions as f def generate_udf(constant_var): def test(col1, col2): if col1 == col2: return col1 else: return constant_var return f.udf(test, StringType()) df.withColumn('new_column',generate_udf('default_value')(f.col('userID'), f.col('movieID'))...
value – 一个文字值或一个Column表达式 >>> df.select(when(df['age'] == 2, 3).otherwise(4).alias("age")).collect() [Row(age=3), Row(age=4)] >>> df.select(when(df.age == 2, df.age + 1).alias("age")).collect() [Row(age=3), Row(age=None)] df3 = df.withColumn(...
schema = StructType([ StructField('id', IntegerType(), Tru.e), StructField('name', StringType(), True), StructField('age', IntegerType(), True), StructField('gender', StringType(), True), StructField('country', StringType(), True)]) df = df.withColumn('value', from_json(col(...
根据给定的SparkConf设置配置选项列表。2、config(Stringkey,booleanvalue) 设置配置项,针对值为boolean的3、config(Stringkey,doublevalue) 设置配置项,针对值为double的4、config(Stringkey,longvalue) 设置配置项,针对值为long的5、config(Stringkey,Stringvalue) ...
Now that we have adjusted the values in medianHouseValue, we will now add the following columns to the data set: Rooms per household which refers to the number of rooms in households per block group; Population per household, which basically gives us an indication of how many people live in...
To create a new column, use the withColumn method. The following example creates a new column that contains a boolean value based on whether the customer account balance c_acctbal exceeds 1000:Python Копирај df_customer_flag = df_customer.withColumn("balance_flag", col("c_acct...