importpyspark # importing sparksession from # pyspark.sql module frompyspark.sqlimportSparkSession # creating sparksession and giving # an app name spark=SparkSession.builder.appName('sparkdf').getOrCreate() # list of college data with two lists data=[["node.js","dbms","integration"], ["...
2. Create DataFrame from List Collection ''' # 2.1 Using createDataFrame() from SparkSession dfFromData2 = spark.createDataFrame(data).toDF(*columns) dfFromData2.printSchema() dfFromData2.show() # 2.2 Using createDataFrame() with the Row type # 需要将list对象[(), (), ...],转换成[...
df.select(df.customerID.alias(“customer_ID”)).show() #取别名 from pyspark.sql.functions import isnull df = df.filter(isnull(“Churn”)) df.show() #查询某列为null的行 df_list = df.collect() print(df_list) #将数据以python的列表格式输出 df[“Partner”,“gender”].describe().show...
1、直接创建Dataframe spark.createDataFrame(data, schema=None, samplingRatio=None),直接创建,其中:data是行或元组或列表或字典的RDD、list、pandas.DataFrame: df = spark.createDataFrame([ (1, 144.5, 5.9, 33, 'M'), (2, 167.2, 5.4, 45, 'M'), (3, 124.1, 5.2, 23, 'F'), (4, 144.5,...
pyspark.sql.SparkSession.createDataFrame方法可以通过scheme参数指定DataFrame的模式。当省略该参数时,PySpark会通过从数据中取样来推断相应的模式。 首先,可以从一组行创建一个PySpark DataFrame: fromdatetimeimportdatetime,dateimportpandasaspdfrompyspark.sqlimportRowdf=spark.createDataFrame([Row(a=1,b=2.,c='...
from pyspark.sql.functions import monotonically_increasing_id dfWithIndex = df.withColumn(“id”,monotonically_increasing_id()) 第二步,筛选特定行。 1 dfWithIndex.select(dfWithIndex.name, dfWithIndex.id.between(50, 100)).show() 2.增、改 2.1 新建数据 有这么两种常规的新建数据方式:createDa...
过滤操作personDF.filter(personDF['age']>21).show()# 4.1.3.6 统计操作personDF.groupBy("age").count().show()# 5-SQL操作 创建临时试图# Creates a temporary view using the DataFramepersonDF.createOrReplaceTempView("people")# 5.1.1 查看DataFrame中的内容spark.sql("SELECT * FROM people").show...
result_df=pd.DataFrame([1,2,3],columns=['a'])save_table="tmp.samshare_pyspark_savedata"# 获取DataFrame的schema c1=list(result_df.columns)# 转为SparkDataFrame result=hc.createDataFrame(result_df.astype(str),c1)result.write.format("hive").mode("overwrite").saveAsTable(save_table)# 或者...
from pyspark.sql import Row class SparkContext: def __init__(self, name="cleaner"): self.spark = ( SparkSession.builder.appName(name) .config("hive.exec.dynamic.partition", True) .config("hive.exec.dynamic.partition.mode", "nonstrict") .enableHiveSupport() .getOrCreate() ) self.spark...
将Python函数转换为UDF:sum_list_udf = udf(sum_list, IntegerType()) 使用UDF将列表作为参数应用于DataFrame:df = spark.createDataFrame([(1, [1, 2, 3]), (2, [4, 5, 6])], ["id", "list_col"]) df.withColumn("sum", sum_list_udf(df["list_col"])).show() ...