("userPositiveHistory", reverse(F.col("userPositiveHistory"))) \ .withColumn('userRatedMovie1...')[1]) \ .withColumn('userRatedMovie3', F.col('userPositiveHistory')[2]) \ .withColumn...('userRatedMovie4', F.col('userPositiveHistory')[3]) \ .withColumn('userRatedMovie5', F.col('...userPositiveHistory')[4]) \ .withColumn('userRatingCount', F.count(F.lit(....withColumn("userGenre3", F.col("userGenres")[2]) \ .withColumn("userGenre4", F.col("
利用withColumn函数就能实现对dataframe中列的添加。但是由于withColumn这个函数中的第二个参数col必须为原有的某一列。所以默认先选择了个ID。...id| +---+ | 0| | 1| | 2| | 3| | 4| | 5| | 6| | 7| | 8| | 9| +---+ scala> df.withColumn...("bb",col(id)*0) :28: error: not found: value id df.withColumn("bb",col(id)*...0) ^ scala> df.withColumn("bb",col("id")*0) res2: org.apache.spark.sql.DataFrame... 3| 0| | 4| 0| | 5| 0| | 6| 0| | 7| 0| | 8| 0| | 9| 0| +---+---+ scala> res2.withColumn
在 spark 中给 dataframe 增加一列的方法一般使用 withColumn // 新建一个dataFrame val sparkconf = new SparkConf() .setMaster..., (2, "2143"), (3, "rfds") )).toDF("id", "content") // 增加一列 val addColDataframe = tempDataFrame.withColumn...col| +---+-------+---+ |1 |asf |0 | |2 |2143 |0 | |3 |rfds |0 | +---+-------+---+ 可以看到 withColumn...java.lang.String") 1 else 0 } val addCol = udf(code) // 增加一列 val addColDataframe = tempDataFrame.withColumn..."arg1<=arg2").getOrElse("error") } val compareUdf = udf(code) val addColDataframe = tempDataFrame.withColumn
首先使用row_number()给数据进行编号： val windowFun = Window.orderBy(col("feature3").asc) df.withColumn("rank",row_number...("rank",row_number().over(windowFun)) .withColumn("next_feature3",lead(col("feature3"),1).over(windowFun...("rank",row_number().over(windowFun)) .withColumn("next_feature3",lead(col("feature3"),1).over(windowFun...)) .join(q1_index,Seq("rank"),"inner") .withColumn("q1" ,($"float_part" - lit(0)) * $"next_feature3...)) .join(q1_index,Seq("rank"),"inner") .withColumn("q1" ,($"float_part" - lit(0)) * $"next_feature3
Int) => String) = (args:String, k1:Int, k2:Int) => { args.substr(k1,k2)} val sqlfunc = udf(fun) df.withColumn...df.withColumn("column22", sqlfunc(col("column1"), lit(1), lit(3))//只有这样才可以实现。...df.withColumn("column22", sqlfunc(col("column1"), 1,3)
("FRAC_REJECTED_AMT", F.col("TOT_REJECTED_AMT")/F.col("TOT_ORG_PRES_AMT_VALUE")) .withColumn...("FRAC_BEN_TYPE_Spouse", F.col("TOT_is_BEN_TYPE_Spouse")/F.col("NUM_LINES")) .withColumn...("FRAC_BEN_TYPE_Child", F.col("TOT_is_BEN_TYPE_Child")/F.col("NUM_LINES")) .withColumn..."{}_dow".format(col), day_of_week_udf( F.col(col))) .withColumn...( codeField, optUDF(codeEx_df[codeField])) codeEx_df = codeEx_df.withColumn(
20, “gre…| | Mary| 21| blue|[“Mary”, 21, “blue”]| +—–+—+———+——————–+ 1、 增加常数项 frame2 = frame.withColumn...gre…| 10| | Mary| 21| blue|[“Mary”, 21, “blue”]| 10| +—–+—+———+——————–+——-+ 2、简单根据某列进行计算 2.1 使用 withColumn...frame3_1 = frame.withColumn("name_length", functions.length(frame.name)) frame3_1.show() +—–+—+———...| Jane| 4| | Mary| 4| +—–+———–+ 3、定制化根据某列进行计算 比如我想对某列做指定操作，但是对应的函数没得咋办，造，自己造~ frame4 = frame.withColumn...json.loads(obj)))(frame.detail)) # or def length_detail(obj): return len(json.loads(obj)) frame4 = frame.withColumn
("label", lit(0)) one = ImageSchema.readImages("1").withColumn("label", lit(1)) two = ImageSchema.readImages...("2").withColumn("label", lit(2)) three = ImageSchema.readImages("3").withColumn("label", lit(3)) four...= ImageSchema.readImages("4").withColumn("label", lit(4)) five = ImageSchema.readImages("5").withColumn...("7").withColumn("label", lit(7)) eight = ImageSchema.readImages("8").withColumn("label", lit(8)) nine...= ImageSchema.readImages("9").withColumn("label", lit(9)) dataframes = [zero, one, two, three,four,
(value AS STRING)") .as[(String, String)].toDF("id", "data") val transDF: DataFrame = resDF.withColumn...("current_day", split(col("data"), "\t")(0)) .withColumn("ts", split(col("data"), "\t")(1))....withColumn("user_id", split(col("data"), "\t")(2)) .withColumn("page_id", split(col("data"), "\...t")(3)) .withColumn("channel", split(col("data"), "\t")(4)) .withColumn("action", split(col
("rank",rank().over(s2))//生成rank值可以重复但不一定连续 .withColumn("dense_rank",dense_rank().over(s2))//生成rank...值可以重复但是连续 .withColumn("row_number",row_number().over(s2))//生成的rank值不重复但是连续 .show() } ok，...row_number进行过滤，如下，对上面的代码稍加改造即可： val s2=Window.partitionBy("id").orderBy(col("date").desc) df.withColumn...("rank",rank().over(s2))//生成rank值可以重复但不一定连续 .withColumn("dense_rank",dense_rank().over(s2))//生成rank...值可以重复但是连续 .withColumn("row_number",row_number().over(s2))//生成的rank值不重复但是连续 .where("row_number
spark 中，新建一列使用的函数是 withColumn ，首先传入函数名，接下来传入一个 col 对象。...import spark.implicits._ val df_new = df.withColumn("x_new", $"x") 上述代码构造了一个新 df_new 对象，其中有 x_new 列与...{fit, exp, negate} val df_result = df_raw_result .withColumn("x_sig", lit(1.0) / (lit(1.0) + exp...udf((v: org.apache.spark.ml.linalg.DenseVector, i: Int) => v(i)) val df_result = df_raw_result .withColumn
._ dataFrame.withColumn("content", explode(split($"content", "[|]"))).show 方式二 使用 udf ，具体的方式可以看 spark...stringtoArray =org.apache.spark.sql.functions.udf((content : String) => {content.split('|')}) dataFrame.withColumn
', 'PI_SEX').agg(collect_set('PI_AGE').alias('AGE_GROUP')) #cluster ages age_clust = age_gp.withColumn..., explode('AGE_CLUST').alias('AGE_GROUP')) #Assign a PID for each row pid_sdf = clust_expld.withColumn...This will be our final Patient ID soft_df = soft_df.withColumn('PI_ID', monotonically_increasing_id...This will be our final Patient ID soft_df = soft_df.withColumn('PI_ID', monotonically_increasing_id...This will be our final Patient ID soft_df = soft_df.withColumn('PI_ID', monotonically_increasing_id
其描述问题时的重现步骤： val df = spark.range(10000000000L).withColumn(“x”, rand) val resultA = df.withColumn(“r”..., when(”x” < 0.5, lit(1)).otherwise(lit(0))).agg(sum( val resultB = df.withColumn(“r”, expr(“if(x < 0.5
- 1.4 抽样 --- --- 1.5 按条件筛选when / between --- 2、-------- 增、改 -------- --- 2.1 新建数据 --- --- 2.2 新增数据列 withColumn...— 2.2 新增数据列 withColumn— withColumn是通过添加或替换与现有列有相同的名字的列，返回一个新的DataFrame result3.withColumn('label', 0)...或者案例 train.withColumn('Purchase_new', train.Purchase /2.0).select('Purchase','Purchase_new').show(5)...另一种方式通过另一个已有变量： result3 = result3.withColumn('label', df.result*0 ) 修改原有df[“xx”]列的所有值： df = df.withColumn...columns_right = test_right.columns test_right = test_right.withColumn('user_pin_right', test_right
oneHotEncoderExample(movieSamples): # 把movieId的值，转为int直接作为movieIdNumber编号 samplesWithIdNumber = movieSamples.withColumn...genreIndexer.fit(samplesWithGenre) genreIndexSamples = StringIndexerModel.transform(samplesWithGenre).withColumn...genreIndexSamples.groupBy('movieId').agg( F.collect_list('genreIndexInt').alias('genreIndexes')).withColumn...("indexSize", F.lit(indexSize)) # 生成vector finalSample = processedSamples.withColumn("vector"...F.avg("rating").alias("avgRating"))\ .withColumn
show() 样本和变量选择 df.filter(df['mobile']=='Vivo').select('age','ratings','mobile').show() 3.4 增加变量 df.withColumn...",(df["age"]+10)).show(10,False) from pyspark.sql.types import StringType,DoubleType,IntegerType df.withColumn...': return '中档价位' else: return '低档价位' brand_udf=udf(price_range,StringType()) df.withColumn...show(10,False) 匿名函数 age_udf = udf(lambda age: "young" if age <= 30 else "senior", StringType()) df.withColumn...yrs_left=100-age return yrs_left length_udf = pandas_udf(remaining_yrs, IntegerType()) df.withColumn
data-test.json") inputDF.printSchema() // ETL: 一定保留原有的数据 最完整 而且要落地 (理由：要是数据出错好重新计算） val newDF = inputDF.withColumn...("province", MyUDF.getProvince(inputDF.col("ip"))) .withColumn("city", MyUDF.getCity($"ip"))//自定义udf...splits.length == 5){ city = splits(3) } city }) } 调优 ① ETL 落地过程中应该调用coalesce() 防止产生多个小文件 val newDF = inputDF.withColumn...("province", MyUDF.getProvince(inputDF.col("ip"))) .withColumn("city", MyUDF.getCity($"ip")) .coalesce
调用转换函数 val tokenized = tokenizer.transform(sentenceDataFrame) tokenized.select("sentence", "words").withColumn...regexTokenized = regexTokenizer.transform(sentenceDataFrame) regexTokenized.select("sentence", "words").withColumn
pyspark.sql.functions import rand, randn In [2]: # 一个略微不同的方式来生成两个随机的数列 In [3]: df = sqlContext.range(0, 10).withColumn...('uniform', rand(seed=10)).withColumn('normal', randn(seed=27)) In [4]: df.describe().show() +------...的两列的样本协方差可以通过如下方法计算: In [1]: from pyspark.sql.functions import rand In [2]: df = sqlContext.range(0, 10).withColumn...('rand1', rand(seed=10)).withColumn('rand2', rand(seed=27)) In [3]: df.stat.cov('rand1', 'rand2') Out...In [1]: from pyspark.sql.functions import * In [2]: df = sqlContext.range(0, 10).withColumn('uniform'
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