colors = ['white','green','yellow','red','brown','pink'] color_df=pd.DataFrame(colors,columns=['color']) color_df['length']=color_df['color'].apply(len) # 抽样 sample1 = color_df.sample( withReplacement=False, # 无放回抽样 fraction=0.6, seed=1000) sample1.show() 1. 2. 3...
8)使用pandas聚合数据(类似SQL中的GROUP BY 或HAVING): data_obj['用户标识'].groupby(data_obj['支局_维护线']) data_obj.groupby('支局_维护线')['用户标识'] #上面的简单写法 adsl_obj.groupby('支局_维护线')['用户标识'].agg([('ADSL','count')])#按支局进行汇总对用户标识进行计数,并将计数...
data.select('columns').distinct().show() 跟py中的set一样,可以distinct()一下去重,同时也可以.count()计算剩余个数 随机抽样 随机抽样有两种方式,一种是在HIVE里面查数随机;另一种是在pyspark之中。 HIVE里面查数随机 代码语言:javascript 复制 sql="select * from data order by rand() limit 2000" py...
stddevfrompyspark.sql.functionsimportformat_numberspark=SparkSession.builder.appName('aggs').getOrCreate()df=spark.read.csv('EcommerceCustomers.csv',inferSchema=True,header=True)df.show()df.printSchema()# 根据字段分组df.groupBy('Avatar')# 分组求平均df.groupBy('Avatar').mean().show()df.groupBy...
data.select('columns').distinct().show() 1 跟py中的set一样,可以distinct()一下去重,同时也可以.count()计算剩余个数 随机抽样 随机抽样有两种方式,一种是在HIVE里面查数随机;另一种是在pyspark之中。 HIVE里面查数随机 sql = "select * from data order by rand() limit 2000" ...
data.groupBy("Descript")\.count()\.orderBy(col("count").desc())\.show() 流水线(Model Pipeline) 我们的流程和scikit-learn版本的很相似,包含3个步骤: 1.regexTokenizer:利用正则切分单词 2.stopwordsRemover:移除停用词 3.countVectors:构建词频向量 ...
aggcols = ['sales1','sales2','sales3'] df.groupBy('group').agg(*[sum(c).alias(c) for c in aggcols]).show() 多列求和 from functools import reduce from operator import add df.withColumn('result', reduce(add, [col(x) for x in df.columns])).show()...
dataframe.columns # Counts the number of rows in dataframe dataframe.count() # Counts the number of distinct rows in dataframe dataframe.distinct().count() # Prints plans including physical and logical dataframe.explain(4) 8、“GroupBy”操作 ...
dataframe.columns # Counts the number of rows in dataframe dataframe.count() # Counts the number of distinct rows in dataframe dataframe.distinct().count() # Prints plans including physical and logical dataframe.explain(4) 8、“GroupBy”操作 ...
.groupBy("userId", "memberId") .agg(max_("datetime")) #注意事项 1 filter (命名) test = a.groupBy('USER_NM').agg(F.count('USER_NM').alias('count')).sort(desc('count')) test.filter(test.count > 1).show() 会报错:'>' not supported between instances of 'method' and 'int' ...