2 selectExpr方法 df3 = df2.selectExpr("cast(age as int) age", "cast(isGraduated as string) isGraduated", "cast(jobStartDate as string) jobStartDate") 1 2 3 3 sql方法 df=spark.sql("SELECT STRING(age),BOOLEAN(isGraduated),DATE(jobStartDate) from CastExample") # 这句的bool和date...
还可以使用 selectExpr,它可接受 SQL 表达式:Python 复制 df_customer.selectExpr( "c_custkey as key", "round(c_acctbal) as account_rounded" ) 若要使用字符串字面量选择列,请执行以下操作:Python 复制 df_customer.select( "c_custkey", "c_acctbal" ) 若要从特定 DataFrame 显式选择列,可以...
>>> from pyspark.sql.functions import * >>> df_as1 = df.alias("df_as1") >>> df_as2 = df.alias("df_as2") >>> joined_df = df_as1.join(df_as2, col("df_as1.name") == col("df_as2.name"), 'inner') >>> joined_df.select("df_as1.name", "df_as2.name", "df_as...
df2 = spark.sql("SELECT * from PERSON_DATA") df2.printSchema() df2.show() Usegroup byclause to run aggregate queries. # Using groupby groupDF = spark.sql("SELECT gender, count(*) from PERSON_DATA group by gender") groupDF.show() This yields the below output # Output: +---+--...
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To select a column from the data frame, use the apply method: ageCol = people.age 一个更具体的例子 #To create DataFrame using SQLContextpeople = sqlContext.read.parquet("...") department= sqlContext.read.parquet("...") people.filter(people.age> 30).join(department, people.deptId == ...
result2017_2=group2017.select("Grade", "count").withColumn("precent",group2017['count'] / data2017.count()*100) result2017_2.selectExpr("Grade as grade", "count", "precent").write.format("org.elasticsearch.spark.sql").option("es.nodes","192.168.1.18:9200").mode("overwrite").save("...
a=[('Alice',2),('Bob',5)]df=sqlContext.createDataFrame(a,['name','age'])from pyspark.sql.functionsimport*df_as1=df.alias('df_as1')df_as2=df.alias('sf_as2')joined_df=df_as1.join(df_as2,col('df_as1.name')==col('df_as2.name'),'inner')joined_df.select(col('df_as1....
This is a variant of select() that accepts SQL expressions. >>> df.selectExpr("age * 2","abs(age)").collect()[Row((age * 2)=4, abs(age)=2), Row((age * 2)=10, abs(age)=5)] New in version 1.3. show(n=20, truncate=True, vertical=False)[source] ...
您也可以使用 selectExpr,其接受 SQL 運算式:Python 複製 df_customer.selectExpr( "c_custkey as key", "round(c_acctbal) as account_rounded" ) 若要使用字串常值選取資料行,請執行下列動作:Python 複製 df_customer.select( "c_custkey", "c_acctbal" ) 若要從特定 DataFrame 明確選取資料行,您...