2. Create DataFrame from List Collection ''' # 2.1 Using createDataFrame() from SparkSession dfFromData2 = spark.createDataFrame(data).toDF(*columns) dfFromData2.printSchema() dfFromData2.show() # 2.2 Using createDataFrame() with the Row type # 需要将list对象[(), (), ...],转换成[...
列表达式必须是此DataFrame上的表达式;列只能引用此数据集提供的属性。添加引用其他数据集的列是错误的。 可以使用lit设置常量作为列 可以使用表达式设置列 df = spark.createDataFrame([(2, "Alice"), (5, "Bob")], schema=["age", "name"])df.withColumns({'age2': df.age + 2, 'age3': df.age...
schema 显示dataframe结构 将此DataFrame的架构作为pyspark.sql.types返回 df.schemaStructType([StructField('id', LongType(), False)])df.printSchema()root |-- id: long (nullable = false) select 查询 查询并返回新dataframe,可结合多方法使用是。 df = spark.createDataFrame([ (2, "Alice"), (5, ...
StringType,IntegerType# 创建SparkSessionspark=SparkSession.builder \.appName("SchemaRedefinition")\.getOrCreate()# 原始数据data=[("Alice","34"),("Bob","45"),("Cathy","19")]schema=StructType([StructField("Name",StringType(),True),StructField("Age",StringType(),True)])# 创建DataFramedf...
df_rdd2 = spark.createDataFrame(rdd,['name', 'age']) df_rdd2.show() +---+---+ | name|age| +---+---+ |Alice| 1| +---+---+ ## with scheme from pyspark.sql.types import * schema = StructType([ StructField("name", StringType(), True), StructField...
df= spark.createDataFrame(rdd_, schema=schema)#working when the struct of data is same.print(df.show()) 其中,DataFrame和hive table的相互转换可见:https://www.cnblogs.com/qi-yuan-008/p/12494024.html 4. RDD数据的保存:saveAsTextFile,如下 repartition 表示使用一个分区,后面加上路径即可 ...
data = [[295,"South Bend","Indiana","IN",101190,112.9]] columns = ["rank","city","state","code","population","price"] df1 = spark.createDataFrame(data, schema="rank LONG, city STRING, state STRING, code STRING, population LONG, price DOUBLE") display(df1) ...
PySpark SQL 提供 read.json("path") 将单行或多行（多行）JSON 文件读取到 PySpark DataFrame 并 ...
df = spark.createDataFrame([{'name':'Alice','age':1}, {'name':'Polo','age':1}]) (3)指定schema创建 schema = StructType([ StructField("id", LongType(),True), StructField("name", StringType(),True), StructField("age", LongType(),True), ...
SparkSession.createDataFrame(data,schema=None,samplingRatio=None,verifySchema=True) 功能 从一个RDD、列表或pandas dataframe转换创建为一个Spark DataFrame。 参数说明 data:接受类型为[pyspark.rdd.RDD[Any], Iterable[Any], PandasDataFrameLike]。任何类型的SQL数据表示(Row、tuple、int、boolean等)、列表或pandas...