nodes_cust = edges.select('tx_ccl_id','cust_id')# 客户编号nodes_cp = edges.select('tx_ccl_id','cp_cust_id')# 交易对手编号nodes_cp = nodes_cp.withColumnRenamed('cp_cust_id','cust_id')# 统一节点列名nodes = nodes_cust.union(n
union合并+去重: nodes_cust = edges.select('tx_ccl_id', 'cust_id') # 客户编号 nodes_cp = edges.select('tx_ccl_id', 'cp_cust_id') # 交易对手编号 nodes_cp = nodes_cp.withColumnRenamed('cp_cust_id', 'cust_id') # 统一节点列名 nodes = nodes_cust.union(nodes_cp).dropDuplicates(...
(2)SparkSession创建RDD frompyspark.sql.sessionimportSparkSessionif__name__=="__main__":spark=SparkSession.builder.master("local")\.appName("My test")\.config("spark.some.config.option","some-value")\.getOrCreate()sc=spark.sparkContextdata=[1,2,3,4,5,6,7,8,9]rdd=sc.parallelize(d...
包含在df1但不在df2的行,去重df1.subtract(df2).show()#新DataFrame中包含只存在于df1和df2中的行,去重df1.intersect(df2).sort(df1.C1.desc()).show()#与intersect相同,但保留duplicatedf1.intersectAll(df2).sort("C1","C2").show()#将两个DataFrame进行union,union不去重,可用distinct跟后...
leftOuterJoin-左连接 leftOuterJoin(other, numPartitions) 官方文档:pyspark.RDD.leftOuterJoin 以“左侧”的RDD...2.Union-集合操作 2.1 union union(other) 官方文档:pyspark.RDD.union 转化操作union()把一个RDD追加到另一个RDD后面,两个RDD的结构并不一定要相同...2.2 intersection intersection(other) 官方...
join data using broadcasting 流水线式处理数据 删除无效得行 划分数据集 Split the content of _c0 on the tab character (aka, '\t') Add the columns folder, filename, width, and height Add split_cols as a column spark 分布式存储 # Don't change this query query = "FROM flights SELECT * ...
# View the row count of df1 and df2 print("df1 Count: %d" % df1.count()) print("df2 Count: %d" % df2.count()) # Combine the DataFrames into one df3 = df1.union(df2) # 等价于r里面的rbind,就是按行拼接 # Save the df3 DataFrame in Parquet format df3.write.parquet('AA_DFW...
df_appended_rows = df_that_one_customer.union(df_filtered_customer) display(df_appended_rows) Напомена You can also combine DataFrames by writing them to a table and then appending new rows. For production workloads, incremental processing of data sources to a target table can drast...
In this post, I will use a toy data to show some basic dataframe operations that are helpful in working with dataframes in PySpark or tuning the performance of Spark jobs.
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