2.、创建dataframe 代码语言:javascript 代码运行次数:0 运行 AI代码解释 #从pandas dataframe创建spark dataframe colors = ['white','green','yellow','red','brown','pink'] color_df=pd.DataFrame(colors,columns=['color']) color_df['length']=color_df['color'].apply(len) color_df=spark.create...
df = df.groupBy('anchor_id') .agg({"live_score": "sum", "live_comment_count": "sum"}) .withColumnRenamed("sum(live_score)", "total_score") .withColumnRenamed("sum(live_comment_count)", "total_people") 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 现在就获得了...
6.从pandas dataframe创建DataFrame import pandas as pd from pyspark.sql import SparkSession colors = ['white','green','yellow','red','brown','pink'] color_df=pd.DataFrame(colors,columns=['color']) color_df['length']=color_df['color'].apply(len) color_df=spark.createDataFrame(color_df...
import pandas as pd from pyspark.sql import SparkSession spark = SparkSession.builder.getOrCreate()# 初始化spark会话 pandas_df = pd.DataFrame({"name":["ss","aa","qq","ee"],"age":[12,18,20,25]}) spark_df = spark.createDataFrame(pandas_df)#将pandas中的DataFrame转化为pyspark中的Data...
x = sc.parallelize([1,2,3], 2) def f(iterator): yield sum(iterator) #创建一个求和的函数 y = x.mapPartitions(f)#x调用mapPartitions传入函数f glom() flattens elements on the same partition print(x.glom().collect()) print(y.glom().collect()) [[1], [2, 3]] [[1], [5]] map...
Pyspark dataframe列值取决于另一行的值 我有这样一个数据帧: columns = ['manufacturer', 'product_id'] data = [("Factory", "AE222"), ("Sub-Factory-1", "0"), ("Sub-Factory-2", "0"),("Factory", "AE333"), ("Sub-Factory-1", "0"), ("Sub-Factory-2", "0")]...
Parameters: col1 - The name of the first column col2- The name of the second column New in version 1.4. createOrReplaceTempView(name) 根据dataframe创建或者替代一个临时视图 这个视图的生命周期是由创建这个dataframe的SparkSession决定的 >>> df.createOrReplaceTempView("people")>>> df2 = df.filter...
PySpark使用间隔将Dataframe拆分为滑动窗口 我有以下dataframe作为输入 +---+---+ | id | date| +---+---+ | A|2016-03-11 09:00:00| | B|2016-03-11 09:00:07| | C|2016-03-11 09:00:18| | D|2016-03-11 09:00:21| | E|2016-03-11 ...
(col,value)## Collection 函数,return True if the array contains the given value.The collection elements and value must be of the same typedf=spark.createDataFrame([(['a','b','c'],),([],)],['data'])df.select(array_contains(df.data,'a')).collect()[Row(array_contains(data,a)=...
('my_array'))# Unique/Distinct Elements – F.array_distinct(col)df=df.withColumn('unique_elements',F.array_distinct('my_array'))# Map over & transform array elements – F.transform(col, func: col -> col)df=df.withColumn('elem_ids',F.transform(F.col('my_array'),lambdax:x.get...