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']) # convert dict to row: newrow = Row(**row_dict) # return new row return newrow # convert ratings dataframe to RDD ratings_rdd =...
frompyspark.sqlimportRowdefrowwise_function(row):#convert row to dict:row_dict =row.asDict()#设置新列的值row_dict['NameReverse'] = row_dict['name'][::-1]#convert dict to row:newrow = Row(**row_dict)returnnewrow#dataframe convert to RDDdf_rdd =df.rdd#apply function to RDDdf_name...
# convert ratings dataframe to RDDratings_rdd = ratings.rdd# apply our function to RDD ratings_rdd_new = ratings_rdd.map(lambda row: rowwise_function(row)) # Convert RDD Back to DataFrameratings_new_df = sqlContext.createDataFrame(ratings_rdd_new)ratings_new_df.show() 1. 1. 1. 1. 1...
这里我们使用 PySpark 和SparkSession来创建一个简单的 DataFrame。 # 引入必要的库frompyspark.sqlimportSparkSessionfrompyspark.sqlimportRow# 创建 SparkSessionspark=SparkSession.builder.appName("AddColumnExample").getOrCreate()# 创建一个示例 DataFramedata=[Row(name='Alice',age=34),Row(name='Bob',age=...
You shouldn't need to use exlode, that will create a new row for each value in the array. The reason max isn't working for your dataframe is because it is trying to find the max for that column for every row in you dataframe and not just the max in the array. ...
1.DataFrame的组成 在结构层面: StructType对象描述整个DataFrame的表结构 StructField对象描述一个列的信息 在数据层面 Row对象记录一行数据 Column对象记录一列数据并包含列的信息 2.DataFrame之DSL """ 1. agg: 它是GroupedData对象的API, 作用是 在里面可以写多个聚合 2. alias: 它是Column对象的API, 可以针对...
Pyspark dataframe位运算及按行运算 >>> from pyspark.sql import Row >>> df = spark.createDataFrame([Row(a=170, b=75)]) >>> df.select(df.a.bitwiseOR(df.b)).show() +---+ |(a | b)| +---+ | 235| +---+ >>> df.select(...
from pyspark.sql import Rowdf = spark.createDataFrame([ Row(name='Alice', age=5, height=80), Row(name='Alice', age=5, height=80), Row(name='Alice', age=10, height=80)])df.show()+---+---+---+| name|age|height|+---+---+---+|Alice| 5| 80||Alice| 5| 80||Alice|...
itertuples(): 按行遍历，将DataFrame的每一行迭代为元祖，可以通过row[name]对元素进行访问，比iterrows...
创建不输入schema格式的DataFramefrom datetime import datetime, date import pandas as pd from pyspark.sql import Row df = spark.createDataFrame([ Row(a=1, b=2., c='string1', d=date(2000, 1, 1), e=datetime(2000, 1, 1, 12, 0)), Row(a=2, b=3., c='string2', d=date(2000,...