通过对每行的最大值进行比较,我们可以得出每一行的最大列名。 max_columns=[]forrowindf.collect():max_value=max(row[1:])max_index=row[1:].index(max_value)+1# +1 因为第一列是 Productmax_columns.append(df.columns[max_index])df_with_max_column=df.withColumn("Max_Column",spark_max(max_c...
#5.1读取hive数据 spark.sql("CREATE TABLE IF NOT EXISTS src (key INT, value STRING) USING hive")spark.sql("LOAD DATA LOCAL INPATH 'data/kv1.txt' INTO TABLE src")df=spark.sql("SELECT key, value FROM src WHERE key < 10 ORDER BY key")df.show(5)#5.2读取mysql数据 url="jdbc:mysql:/...
Row(value='# Apache Spark') 现在,我们可以通过以下方式计算包含单词Spark的行数: lines_with_spark = text_file.filter(text_file.value.contains("Spark")) 在这里,我们使用filter()函数过滤了行,并在filter()函数内部指定了text_file_value.contains包含单词"Spark",然后将这些结果放入了lines_with_spark变量...
first_row = df.first() numAttrs = len(first_row['score'].split(" ")) print("新增列的个数", numAttrs) attrs = sc.parallelize(["score_" + str(i) for i in range(numAttrs)]).zipWithIndex().collect() print("列名:", attrs) for name, index in attrs: df_split = df_split.wit...
df.select(df.age.alias('age_value'),'name') 查询某列为null的行: 1 2 frompyspark.sql.functionsimportisnull df=df.filter(isnull("col_a")) 输出list类型,list中每个元素是Row类: 1 list=df.collect()#注:此方法将所有数据全部导入到本地,返回一个Array对象 ...
first_row = df.first() numAttrs = len(first_row['score'].split(" ")) print("新增列的个数", numAttrs) # 利用zipWithIndex给每一个元素生成索引 attrs = sc.parallelize(["score_" + str(i) for i in range(numAttrs)]).zipWithIndex().collect() print("列名:", attrs) for name, ...
select(['address.city']) # DataFrame[city: string] # Filter column with value df.filter(df.age == 12).show() """ +---+---+---+ | address|age| name| +---+---+---+ |[Nanjing, China]| 12| Li| | [Paris, France]| 12| Jacob| | [London, UK]| 12|Manuel| +---+-...
from pyspark.sql import Row def rowwise_function(row): # convert row to dict: row_dict = row.asDict() # Add a new key in the dictionary with the new column name and value. row_dict['Newcol'] = math.exp(row_dict['rating']) ...
.config("spark.driver.maxResultSize","5g")\ .config ("spark.sql.execution.arrow.enabled", "true")\ .getOrCreate() 想了解SparkSession每个参数的详细解释,请访问pyspark.sql.SparkSession。 3、创建数据框架 一个DataFrame可被认为是一个每列有标题的分布式列表集合,与关系数据库的一个表格类似。在这篇...
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