它是 immutable, partitioned collection of elements 周小董 2021/07/14 1.4K0 PySpark SQL——SQL和pd.DataFrame的结合体 sqlsparkpython数据结构 昨日推文PySpark环境搭建和简介,今天开始介绍PySpark中的第一个重要组件SQL/DataFrame,实际上从名字便可看出这是关系型
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...
第一种,将pandas中的DataFrame转为spark中的DataFrame 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(...
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...
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...
df = spark.createDataFrame(data = simpleData, schema = columns) df.printSchema() df.show(truncate=False) Yields below output # Output: root |-- employee_name: string (nullable = true) |-- department: string (nullable = true) |-- salary: long (nullable = true) ...
(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)=...
wordCounts= pairs.reduceByKey(lambdax, y: x +y)#Print the first ten elements of each RDD generated in this DStream to the consolewordCounts.pprint() ssc.start()#Start the computationssc.awaitTermination()#Wait for the computation to terminate ...
('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...