November 2017
Intermediate to advanced
328 pages
6h 51m
Japanese
Content preview from 入門 PySpark ―PythonとJupyterで活用するSpark 2エコシステム
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38
3
章
DataFrame
3.4
DataFrame
の生成
通常、DataFrame はSparkSession（あるいは PySparkのシェルなら
spark
）を使ってデータをイ
ンポートすることによって生成します
Spark 1.x
では、通常
sqlContext
を使わなければなりませんでした。
この先の章では、ローカルファイルシステム、Hadoop Distributed File System（HDFS）、あるい
はその他のクラウドのストレージシステム（たとえば S3 や WASB からデータをインポートする方法を
述べていきます。本章では、Sparkの中で直接 DataFrameのデータを生成する方法や、Databricks
Community Editionで利用できるようになっているデータソースの活用方法に焦点を当てていきます。
Databricks
の
Community Edition
へのサインアップ方法については付録
B
を参照してください。
まずはファイルシステムにアクセスする代わりに、データを生成してそこからDataFrameを作って
みましょう。ここではまず
stringJSONRDD
RDDを生成し、それをDataFrameに変換します。この
コードは、JSON 形式の水泳選手のデータ（ID、名前、年齢、目の色）から RDDを生成します。
3.4.1
JSON
データの生成
まず次のコードで
stringJSONRDD
RDDを生成します。
stringJSONRDD = ...
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