在PySpark中,为DataFrame新增一列是一个常见的操作。以下是完成此任务的详细步骤,包括代码示例: 导入PySpark库并初始化SparkSession: 首先,需要导入PySpark库并创建一个SparkSession对象。SparkSession是PySpark的入口点,用于与Spark进行交互。 python from pyspark.sql import SparkSession # 初始化SparkSession spark = Sp...
步骤1:创建或加载一个 DataFrame 在进行任何操作之前,首先需要创建或者加载一个 DataFrame。这里我们使用 PySpark 和SparkSession来创建一个简单的 DataFrame。 # 引入必要的库frompyspark.sqlimportSparkSessionfrompyspark.sqlimportRow# 创建 SparkSessionspark=SparkSession.builder.appName("AddColumnExample").getOrCreate...
frompyspark.sqlimportSparkSessionfrompyspark.sql.functionsimportcol,lit# 创建 Spark 会话spark=SparkSession.builder \.appName("Add Column Example")\.getOrCreate()# 创建示例 DataFramedata=[("Alice",25),("Bob",30),("Cathy",29)]columns=["Name","Age"]df=spark.createDataFrame(data,columns)# ...
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) # return new row return newrow # convert ratings dataframe to RDD ratings_rdd =...
pyspark dataframe Column alias 重命名列(name) df = spark.createDataFrame( [(2, "Alice"), (5, "Bob")], ["age", "name"])df.select(df.age.alias("age2")).show()+---+|age2|+---+| 2|| 5|+---+ astype alias cast 修改列类型 data.schemaStructType([StructField('name', String...
from pyspark.sql import SparkSession # 创建SparkSession对象 spark = SparkSession.builder.getOrCreate() # 创建示例DataFrame data = [("Alice", 25), ("Bob", 30), ("Charlie", 35)] df = spark.createDataFrame(data, ["Name", "Age"]) # 添加新列 df_with_new_column = df.withColumn("Gen...
在PySpark中,对DataFrame新增一列有几种写法: df=spark.createDataFrame([('p1',56),('p2',23),('p3',11),('p4',40),('p5',29)],['name','age']) df.show() ===>> +---+---+ |name|age| +---+---+ | p1| 56| |
createDataFrame(data, schema=['id', 'date']) >>> df.show() +---+---+ | id| date| +---+---+ | 1|2016-12-31| | 2|2016-01-01| | 3|2016-01-02| | 4|2016-01-03| | 5|2016-01-04| +---+---+ >>> df.withColumn("new_column",expr("date_add(date,id)"))....
itertuples(): 按行遍历，将DataFrame的每一行迭代为元祖，可以通过row[name]对元素进行访问，比iterrows...
DataFrame[a: bigint, b: double, c: string, d: date, e: timestamp] 从Pandas DataFrame创建 pandas_df = pd.DataFrame({'a': [1, 2, 3],'b': [2., 3., 4.],'c': ['string1', 'string2', 'string3'],'d': [date(2000, 1, 1), date(2000, 2, 1), date(2000, 3, 1)]...