getOrCreate() ss.stop() # 提交任务 bin\spark-submit.cmd --conf "spark.pyspark.python=C:\Users\Administrator\.virtualenvs\spark-install-PTQa4YhU\Scripts\python.exe" D:\Administrator\Data\spark-install\02-dataframe\dataframe.py 不同方式创建DataFrame # list df_list = ss.createDataFrame([ [1,...
object CreateDataFrame { def main(args: Array[String]): Unit = { val spark = SparkSession.builder() .master("local[*]") .appName("CreateDataFrame") .getOrCreate() import spark.implicits._ //通过toDF方法创建 val df1 = Seq( (1, "Karol", 19), (2, "Abby", 20), (3, "Zena",...
如果想从一个 Python 字典生成 DataFrame,可以这样做: # 创建一个示例数据data=[{"name":"Alice","age":30},{"name":"Bob","age":25},{"name":"Cathy","age":27}]# 从数据生成 DataFramedf_dict=spark.createDataFrame(data)# 展示 DataFrame 的内容df_dict.show() 1. 2. 3. 4. 5. 6. 7....
pd.read_sql(“SELECT name, age FROM people WHERE age >= 13 AND age <= 19″)表格注册:把DataFrame结构注册成SQL语句使用类型 df.registerTempTable(“people”) 或者 sqlContext.registerDataFrameAsTable(df, “people”) sqlContext.sql(“SELECT name, age FROM people WHERE age >= 13 AND age <= 1...
spark.createDataFrame(nestedRdd) self.assertEqual(Row(f1=[Row(payment=200.5, name='A')], f2=[1, 2]), df.first()) df = self.spark.createDataFrame(data) self.assertEqual(Row(f1=[Row(payment=200.5, name='A')], f2=[1, 2]), df.first()) def test_create_dataframe_from_dict_...
将列表转换为Spark DataFrame。 代码语言:python 代码运行次数:0 复制 df=spark.createDataFrame(data) 使用select函数将DataFrame中的dict列转换为Spark Map类型的列。 代码语言:python 代码运行次数:0 复制 frompyspark.sql.functionsimportcol df=df.select(col("name"),col("age"),col("city").alias("map_co...
spark = SparkSession.builder.appName("Convert Struct/Dict to Array").getOrCreate() # 定义结构(或字典)数据 data = [ {"name": "Alice", "age": 25}, {"name": "Bob", "age": 30}, {"name": "Charlie", "age": 35} ] # 将结构(或字典)数据转换为DataFrame ...
type DataFrame = Dataset[Row] /** * 元数据键,用于在以下情况下写入Spark版本: * - Parquet文件元数据 * - ORC文件元数据 * - Avro文件元数据 * * 需要注意的是,Hive表属性`spark.sql.create.version`也包含了Spark版本。 */ private[sql] val SPARK_VERSION_METADATA_KEY = "org.apache.spark.version...
使用spark.createDataFrame和以前保存的 OLTP 配置将示例数据添加到目标容器。 Python # Ingest sample dataspark.createDataFrame(products) \ .toDF("id","category","name","quantity","price","clearance") \ .write \ .format("cosmos.oltp") \ .options(**config) \ .mode("APPEND") \ .save() ...
dict_dataframe = sqlContext.createDataFrame(dicts) print(dict_dataframe.show()) print("---dict end---") lists = [['a',1], ['b',2]] list_dataframe = sqlContext.createDataFrame(lists, ['col1','col2']) print(list_dataframe.show()) print(...