[In]: spark=SparkSession.builder.appName('data_processing').getOrCreate() [In]: df=spark.read.csv('sample_data.csv',inferSchema=True,header=True) 我们需要确保数据文件位于我们打开 PySpark 的同一个文件夹中,或者我们可以指定数据所在文件夹的路径
AI代码解释 object PythonEvalsextendsStrategy{override defapply(plan:LogicalPlan):Seq[SparkPlan]=plan match{caseArrowEvalPython(udfs,output,child,evalType)=>ArrowEvalPythonExec(udfs,output,planLater(child),evalType)::NilcaseBatchEvalPython(udfs,output,child)=>BatchEvalPythonExec(udfs,output,planLater(...
{ "schema":"PanderaSchema", "column":"description", "check":"dtype('ArrayType(StringType(), True)')", "error":"expected column 'description' to have type ArrayType(StringType(), True), got ArrayType(StringType(), False)" }, { "schema":"PanderaSchema", "...
...rows = self.ws.max_row columns = self.ws.max_column return rows, columns # 获取指定单元格的值...cellvalue = self.ws.cell(row=row, column=column).value return cellvalue # 修改指定单元格值...mytest.getCellValue(row, 4) # 获取所有选项 Selects = mytest.getCellValue(row, 5) ...
import pyspark from pyspark.sql import SparkSession from pyspark.sql.types import StructType,StructField, StringType, IntegerType spark = SparkSession.builder.master("local[1]") \ .appName('SparkByExamples.com') \ .getOrCreate() data = [("James","","Smith","36636","M",3000), ("Micha...
StructField('B',ArrayType(elementType=IntegerType())), StructField('C', DecimalType())]) spark=SparkSession.builder.appName("jsonRDD").getOrCreate() df=spark.createDataFrame(data,schema) 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.
# To convert the type of a column using the .cast() method, you can write code like this:dataframe=dataframe.withColumn("col",dataframe.col.cast("new_type"))# Cast the columns to integersmodel_data=model_data.withColumn("arr_delay",model_data.arr_delay.cast("integer"))model_data=model...
value – 一个文字值或一个Column表达式 >>> df.select(when(df['age'] == 2, 3).otherwise(4).alias("age")).collect() [Row(age=3), Row(age=4)] >>> df.select(when(df.age == 2, df.age + 1).alias("age")).collect() [Row(age=3), Row(age=None)] df3 = df.withColumn(...
sc = SparkSession.builder.master("local[*]").getOrCreate() feature = sc.read.csv("features.csv", inferSchema=True, header=True) label = sc.read.csv("labels.csv", inferSchema=True, header=True) data = feature.join(label, on=("id")) 任务2 修改column数据类型,去掉空白符和去掉重复行 ...
sql.types import StructType, StructField, IntegerType, StringType from pyspark.sql import SparkSession 创建session sparkSession = SparkSession.builder.appName("datasource-redis").getOrCreate() 设置连接参数 host = "192.168.4.199" port = "6379" table = "person" auth = "@@@" 创建DataFr...