# 执行聚合 agg_data = data.groupBy("customerID").agg({"totalAmt": "sum"}).orderBy(desc("sum(totalAmt)")) 返回agg_data 打印(no_salting(df)) 高效— 使用加盐偏移键来聚合数据 from pyspark.sql.functions import col, lit, concat, rand, split, desc @time_decorator def 进行加盐处理(data)...
rdd1 = rdd.map(lambda x: x.split("|#$")) # 按指定分隔符进行分割 # print(rdd1.collect()) # [['POD9_6ec8794bd3297048d6ef7b6dff7b8be1', '2023-10-24', '0833', '#', '#', '99999999999', '#', '12345678912'], ['POD9_352858578708f144bb166a77bad743f4', '2023-10-24',...
# 提取出所有自变量名称 predictors = sports.columns[4:] # 构建自变量矩阵 x = sports.loc[:, predictors] # 提取y变量值 y = sports.activity # 将数据拆分成训练集和测试集 x_train, x_test, y_train, y_test = model_selection.train_test_split(x, y, test_size=0.25, random_state=1234) # ...
StringType, IntegerType, FloatType from pyspark.sql.types import StructField from pyspark.sql.types import StructType from pyspark.sql.functions import date_format, to_timestamp from pyspark.sql.functions import split, reg
# Give regex expression to split your string based on anticipated delimiters (this could be dangerous # if those delimiter occur as part of value. e.g.: 2021-12-31 is a single value in reality. # But this a price we have to pay for not having good data). ...
pyspark.sql.functions.split(str, pattern, limit=-1) The split() function takes the DataFrame column of type String as the first argument and string delimiter as the second argument you want to split on. You can also use the pattern as a delimiter. This function returnspyspark.sql.Columnof...
py_val = [str(x) for x in line.split (',')] if (py_val[3] > py_val[2]): hot = 1.0 else: hot = 0.0 After creating the function now in this step we are loading the dataset file name as pyspark.txt are as follows.
>>> df.select(split(df.s,'[0-9]+').alias('s')).collect()[Row(s=[u'ab', u'cd'])] 9.132 pyspark.sql.functions.sqrt(col):New in version 1.3. 计算指定浮点值的平方根 9.133 pyspark.sql.functions.stddev(col):New in version 1.6. ...
Apache Spark支持Java、Scala、Python和R语言，并提供了相应的API。而在数据科学领域，Python是应用最广的...
sql_context=SQLContext(spark)gzfile=main_dir+'\\*.gz'%base_weeksc_file=spark.textFile(gzfile)csv=sc_file.map(lambdax:x.split("\t"))rows=csv.map(lambdap:Row(ID=p[0],Category=p[1],FIPS=p[2],date_idx=p[3]))All_device_list=sql_context.createDataFrame(rows) ...