Spark算子 -python

### Python Spark Operators Documentation and Examples In the context of Apache Spark, operations can be performed using a variety of APIs tailored for different programming languages including Python. For Python users, PySpark provides an interface that allows interaction with RDDs (Resilient Distributed Datasets), DataFrames, and Datasets. The `DataFrame` API is widely used due to its rich set of functionalities which include reading from various data sources through methods like those mentioned in SQLContext.read[^1]. This enables loading structured data into DataFrame objects within Python applications easily. For instance, when working with text files where each file represents one record, newer versions have introduced convenient functions such as `wholeTextFiles`, enhancing usability by treating entire contents of smaller files as single entries during processing tasks[^2]. Moreover, while full support might not yet cover all aspects available in other supported languages like Scala or Java under Dataset API at present time point specified here[^3], there are still numerous built-in transformations and actions provided specifically designed for manipulating large-scale datasets efficiently via simple commands written directly inside your scripts without requiring deep knowledge about underlying mechanisms involved behind scenes regarding memory management concerns raised previously elsewhere[^4]. Below demonstrates how some common operators work: #### Example Code Using Basic Transformations & Actions ```python from pyspark.sql import SparkSession spark = SparkSession.builder.appName("OperatorsExample").getOrCreate() data = [("James", "Smith"), ("Michael", "Rose")] df = spark.createDataFrame(data).toDF("firstname", "lastname") # Show function displays top n rows. df.show() # Select only firstname column first_names_df = df.select("firstname") first_names_df.show() ```