join(deptDF,empDF.emp_dept_id == deptDF.dept_id,"leftouter") .show(truncate=False) In our dataset, the record with “emp_dept_id” 50 does not have a corresponding entry in the “dept” dataset, resulting in null values in the “dept” columns (dept_name & dept_id). Additionally...
in this article, you will learn how to do aPySpark Join on Two or Multiple DataFramesby applying conditions on the same or different columns. also, you will learn how to eliminate the duplicate columns on the result DataFrame.
You can specify how you would like the DataFrames to be joined in the how (the join type) and on (on which columns to base the join) parameters. Common join types include:inner: This is the join type default, which returns a DataFrame that keeps only the rows where there is a match...
join:对2个rdd执行joi操作,型数据k-v型数据(相当于sql的内连接) rdd1 = sc.parallelize([('name','张三'),('sex','男'),('age',19),('love','足球')]) rdd2 = sc.parallelize([('name','李四'),('sex','女'),('age',12)]) print(rdd1.join(rdd2).collect()) # 输出 ''' [('...
join(probe_cv_df.rdd .map(lambda row: (row['id'], float(row['probability'][1]))) .toDF(['id', probe_prob_col]), 'id') .cache()) print(res_cv_df.count()) print(time() - t0) 25133 6.502754211425781 # Getting probabilities for Test data t0 = time() res_test_df = (res...
# Left join in another datasetdf=df.join(person_lookup_table,'person_id','left')# Match on different columns in left & right datasetsdf=df.join(other_table,df.id==other_table.person_id,'left')# Match on multiple columnsdf=df.join(other_table, ['first_name','last_name'],'left')...
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