df("columnName") // On a specific DataFrame. col("columnName") // A generic column no yet associated with a DataFrame. col("columnName.field") // Extracting a struct field col("`a.column.with.dots`") // Escape `.` in column names. $"columnName" // Scala short hand for a nam...
SQLContext上的sql函数使应用程序以编程方式运行SQL查询,并将结果作为DataFrame返回。 SQLContext sqlContext = ...//An existing SQLContextDataFrame df = sqlContext.sql("SELECT * FROM table") Data Sources Spark SQL支持通过DataFrame界面对各种数据源进行操作。DataFrame可以作为普通RDD操作,也可以注册为临时表。
As you can see from the above, we got a column name of Series at the time of creation. Thenameattribute is set to ‘Technology’. When you later convert this Series to a DataFrame, the name will be used as the column name in the DataFrame. 3. Add Column Names to Existing Series Al...
Renaming columns with a list modifies the DataFrame in place if using the.columnsattribute orset_axis()withinplace=True. The new column names should be unique to avoid confusion when accessing columns later. Theset_axis()method allows you to rename columns by specifying the axis parameter, givin...
df("columnName")//On a specific DataFrame.col("columnName")//A generic column no yet associated with a DataFrame.col("columnName.field")//Extracting a struct fieldcol("`a.column.with.dots`")//Escape `.` in column names.$"columnName"//Scala short hand for a named column.expr("a ...
Create DataFrame Create Example DataFrame Show Original DataFrame Filter Columns Filter Age > 30 Show Filtered DataFrame Filter Column in Spark DataFrame 结语 通过上述步骤,我们成功地对 Spark DataFrame 进行了列过滤。你可以根据自己的数据集和需求,调整过滤条件。这种能力在处理大数据时尤为重要,可以有效提高数...
Apache Spark 联接 Dataframe 并以相同名称重命名结果列只重命名相交列的另一种方法
importcom.databricks.spark.xml.functions.from_xmlimportcom.databricks.spark.xml.schema_of_xmlimportspark.implicits._valdf = .../// DataFrame with XML in column 'payload'valpayloadSchema = schema_of_xml(df.select("payload").as[String])valparsed = df.withColumn("parsed", from_xml($"payload...
SQLContext.sql可以执行一个SQL查询,并返回DataFrame结果。 Scala Java Python R val sqlContext = ... // 已有一个 SQLContext 对象 val df = sqlContext.sql("SELECT * FROM table") 创建Dataset Dataset API和RDD类似,不过Dataset不使用Java序列化或者Kryo,而是使用专用的编码器(Encoder)来序列化对象和跨网络...
write.parquet("data/test_table/key=1") // Create another DataFrame in a new partition directory, // adding a new column and dropping an existing column val cubesDF = spark.sparkContext.makeRDD(6 to 10).map(i => (i, i * i * i)).toDF("value", "cube") cubesDF.write.parquet("...