这里,我们以示例数据创建两个 DataFrame。 data1=[("Alice",1),("Bob",2),("Cathy",3)]columns1=["Name","ID"]data2=[("Alice","F"),("Bob","M"),("David","M")]columns2=["Name","Gender"]df1=spark.createDataFrame(data1,columns1)df2=
frompyspark.sqlimportSparkSession# 创建 Spark 会话spark=SparkSession.builder \.appName("Multiple DataFrames Inner Join Example")\.getOrCreate()# 创建示例数据data1=[("Alice",1),("Bob",2),("Cathy",3)]columns1=["Name","ID"]data2=[("Alice","F"),("Bob","M"),("David","M")]col...
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# Examine the dataprint(airports.show())# Rename the faa column #将faa重命名为destairports=airports.withColumnRenamed('faa','dest')# Join the DataFrames #将flights和airports两张表按列dest进行左连接flights_with_airports=flights.join(airports,on='dest',how='leftouter')# Examine the new DataFra...
operator cannot be used to select columns starting with an integer, or ones that contain a space or special character.) This can be especially helpful when you are joining DataFrames where some columns have the same name.Python Копирај ...
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.
createDataFrame(data, schema) - .groupBy(F.col("age")) - .agg(F.countDistinct(F.col("employee_id")).alias("num_employees")) - .sql() -) - -result = None -for sql in sql_statements: - result = client.query(sql) - -assert result is not None -for row in client.query(result...
Data locality can have a major impact on the performance of Spark jobs. If data and the code that operates on it are together then computation tends to be fast. But if code and data are separated, one must move to the other. Typically it is faster to ship serialized code from place ...
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on –a string for the join column name, a list of column names, a join expression (Column), or a list of Columns. If on is a string or a list of strings indicating the name of the join column(s), the column(s) must exist on both sides, and this performs an equi-join. ...