四、DataFrame API--Spark SQL 代码示例如下: ss = SparkSession.builder.\ master("local[2]").\ appName("Spark SQL Test").\ getOrCreate() # dict df_dict = ss.createDataFrame([ {"Student_ID": 1, "Study_Hours_Per_Day": 6.9,
首先,我们需要创建一个Spark DataFrame: frompyspark.sqlimportSparkSession# 创建Spark会话spark=SparkSession.builder.appName("DataFrame to Dictionary").getOrCreate()# 创建一个简单的DataFramedata=[("Alice",1),("Bob",2),("Cathy",3)]columns=["Name","Id"]df=spark.createDataFrame(data,columns)# ...
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",...
from pyspark.sql import SparkSession from pyspark.sql.types import StructType, StructField, StringType, IntegerType 创建SparkSession对象: 代码语言:txt 复制 spark = SparkSession.builder.appName("NestedDictToDataFrame").getOrCreate() 定义嵌套字典的结构: 代码语言:txt 复制 data = { "name": ["John...
spark.createDataFrame(dataList,schema).show() //第八种:读取数据库(mysql) val options = new util.HashMap[String,String]() options.put("url", "jdbc:mysql://localhost:3306/spark") options.put("driver","com.mysql.jdbc.Driver") options.put("user","root") ...
DataFrame可变性Pandas中DataFrame是可变的Spark中RDDs是不可变的,因此DataFrame也是不可变的 创建从spark_df转换:pandas_df = spark_df.toPandas()从pandas_df转换:spark_df = SQLContext.createDataFrame(pandas_df) 另外,createDataFrame支持从list转换spark_df,其中list元素可以为tuple,dict,rdd ...
将JSON字典转换为Spark DataFrame: 代码语言:txt 复制 df = spark.createDataFrame(list(zip(*json_dict.values())), list(json_dict.keys())) 显示Spark DataFrame的内容: 代码语言:txt 复制 df.show() 这样就可以将JSON字典转换为Spark DataFrame,并显示其内容。 对于这个问题,可以回答如下: 将JSON字典转...
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() ...
deftest_create_dataframe_from_dict_respects_schema(self): df=self.spark.createDataFrame([{'a':1}], ["b"]) self.assertEqual(df.columns, ['b']) Expand Down 26 changes: 17 additions & 9 deletions26python/pyspark/sql/types.py Original file line numberDiff line numberDiff line change ...
data1 = spark.createDataFrame([(1, "Alice"), (2, "Bob"), (3, "Charlie")], ["id", "...