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, "Sleep_Hours_Per_Day": 8.7, "Stress_Level": "Moderate...
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",...
首先,我们需要创建一个Spark DataFrame: AI检测代码解析 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(da...
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...
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...
29: 5, 30: 6} def f(x): x=x.asDict() try: x['日期']=placedict[x['日期']] pass except: x['日期']=-1 pass return list(x.values()) pass # 保存原来的头 StructType = data.schema # 转换 datardd = data.rdd.map(f) # 转换完成 data = spark.createDataFrame(datardd, StructTyp...
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 ...
simple_dict=[{'name':'id1','old':21}]spark.createDataFrame(simple_dict).collect() [Row(name='id1', old=21)] rdd = sc.parallelize(simple)spark.createDataFrame(rdd).collect() [Row(_1='杭州', _2='40')] schema参数代码运用: ...
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字典转换为Spark DataFrame的步骤如上所述。首先,需要导入必要的...
["a"] + majors}) reshaped2 = sqlContext.createDataFrame(grouped.map(make_row)) reshaped2.show() ## +---+---+---+---+---+---+ ## | a| m1| m2| m3| m4| m5| ## +---+---+---+---+---+---+ ## | a| 1| 1| 2| 3| 0| ## | e| 4| 5| 1| 1| 1|...