July 2018
Intermediate to advanced
596 pages
13h 35m
Japanese
Content preview from Pythonによるデータ分析入門 第2版 ―NumPy、pandasを使ったデータ処理
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200
6
章 データの読み込み、書き出しとファイル形式
まざまなファイル形式を試しに使ってみて、それらの速度や、自分の分析への適性を確かめてほしい
と考えています。
pandas
や
NumPy
のデータに使えるその他の保存形式としては、次のようなものがあ
ります。
bcolz（
http://bcolz.blosc.org/
）
Blosc
圧縮ライブラリをベースとした、圧縮可能な列指向のバイナリ形式。
Feather（
http://github.com/wesm/feather
）
著者が
R
プログラミングコミュニティの
Hadley Wickham
（
http://hadley.nz/
）
＊
1
とともに設計
した、言語横断的な列指向のファイル形式。
Feather
では、
Apache Arrow
（
http://apache.
arrow.org
）の列指向のメモリ形式を使っている。
6.2.1
HDF5
形式の使用
HDF5
は、大量の科学的な配列データを保存するための、評判のよいファイル形式です。
HDF5
は
C
で
書かれたライブラリですが、
Java
、
Julia
、
MATLAB
、
Python
など多数の言語向けのインタフェー
スがあります。
HDF5
の「
HDF
」は、「
hierarchical data format
」（階層型データ形式）の意味です。各
HDF5
ファイルには、複数のデータセットや、それらのデータセットの情報を収めるメタデータの保存
が可能です。もっと単純なデータ形式と比べると、
HDF5
はさまざまな圧縮モードでのオンザフライ（中
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.