首先,我们需要初始化 PySpark 环境并创建一个示例数据框。 frompyspark.sqlimportSparkSessionfrompyspark.sql.functionsimportcollect_list,col# 初始化 Spark 会话spark=SparkSession.builder \.appName("Collect List Example")\.getOrCreate()# 创建示例数据data=[("Alice",3000),("Bob",4000),("Charlie",3000...
步骤3: 使用groupBy和collect_list 在数据准备好之后,我们将使用groupBy按学生的姓名分组,然后使用collect_list来收集每位学生的分数列表。 frompyspark.sql.functionsimportcollect_list# 按学生姓名分组,并收集列表grouped_df=df.groupBy("student").agg(collect_list("score").alias("scores"))grouped_df.show() 1...
介绍pyspark.sql.functions中的常用函数。 官方链接https://spark.apache.org/docs/latest/api/python/reference/index.html SparkSession配置,导入pyspark包 spark.stop()spark=SparkSession\.builder\.appName('pyspark_test')\.config('spark.sql.broadcastTimeout',36000)\.config('spark.executor.memory','2G')...
4.pyspark.sql.functions.array_contains(col, value) 5.pyspark.sql.functions.ascii(col) 6.pyspark.sql.functions.avg(col) 7.pyspark.sql.functions.cbrt(col) 9.pyspark.sql.functions.coalesce(*cols) 10.pyspark.sql.functions.col(col) 11.pyspark.sql.functions.collect_list(col) 12.pyspark.sql.funct...
如果需要collect_list多列,那么可以这么写: frompyspark.sqlimportSparkSessionfrompyspark.sql.functionsimportcollect_set# 初始化spark会话spark = SparkSession \ .builder \ .appName("test") \ .master("local") \ .getOrCreate() df = spark.createDataFrame([('abcd','123','456'),('xyz','123',...
sql import SparkSession from pyspark.sql.types import IntegerType, ArrayType, StringType, FloatType from pyspark.sql.functions import * import numpy as np from sparkdl.transformers.tf_text import CategoricalBinaryTransformer, CombineBinaryColumnTransformer, \ TextAnalysisTransformer, TextEmbeddingSequence...
frompyspark.sqlimportfunctionsasF frompyspark.sql.typesimportIntegerType, DateType frompyspark.sql.windowimportWindow frompyspark.ml.featureimportCountVectorizer, IDF, CountVectorizerModel frompyspark.ml.featureimportOneHotEncoder, VectorAssembler frompyspark.ml.classificationimportRandomForestClassifier, GBTClassifier...
[Row(value=1)]>>> spark.createDataFrame(rdd,"boolean").collect() Traceback (most recent call last): ... Py4JJavaError: ... SparkSession.sql: 使用sql 方法返回的是 df 例如: >>> df.createOrReplaceTempView("table1")>>> df2 = spark.sql("SELECT field1 AS f1, field2 as f2 from ta...
sql.functions import col, lit, udf from pyspark.sql.types import StringType, MapType import pandas as pd conf = SparkConf() \ .setAppName("your_appname") \ .set("hive.exec.dynamic.partition.mode", "nonstrict") sc = SparkContext(conf=conf) hc = HiveContext(sc) """ your code ""...
# coding: UTF-8from__future__importdivisionfrompyspark.sqlimportfunctionsasFfrompyspark.sqlimporttypesasTfrompysparkimportSparkConf,SparkContextfrompyspark.sqlimportSparkSessionimportjsonimportpandasaspdimportnumpyasnpimportosfrompyspark.sqlimportSQLContextfrompyspark.sqlimportRowfro...