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c:T1457,/*@jsxRuntime automatic @jsxImportSource react*/
const {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];
const {useMDXComponents: _provideComponents} = arguments[0];
function _createMdxContent(props) {
  const _components = Object.assign({
    p: "p",
    a: "a",
    h3: "h3",
    img: "img",
    ol: "ol",
    li: "li"
  }, _provideComponents(), props.components);
  return _jsxs(_Fragment, {
    children: [_jsx(_components.p, {
      children: "最近KaggleやSignateを行う際、実行速度がタスクになっていることが多い気がします。"
    }), "\n", _jsx(_components.p, {
      children: "業務でも、大量のデータを処理する際に実行速度は問題視されることが多いです。"
    }), "\n", _jsx(_components.p, {
      children: "以下では、ここ最近で学んだPandasの高速化処理を紹介します。"
    }), "\n", _jsxs(_components.p, {
      children: ["実行コードは", _jsx(_components.a, {
        href: "https://github.com/Shingo425/DataAnalysis/blob/main/src/Prosses_SpeedUp.ipynb",
        children: "こちら"
      })]
    }), "\n", _jsx(_components.h3, {
      children: "Polarsを使用する"
    }), "\n", _jsxs(_components.p, {
      children: ["まずは", _jsx(_components.a, {
        href: "https://www.pola.rs/",
        children: "Polars"
      }), "です。PolarsはRust製で高速な処理が実現可能であり、適宜並列化も行ってくれるそうです。"]
    }), "\n", _jsx(_components.p, {
      children: "また、Pandasと同等の処理を書くことができるかつ、圧倒的に速いです。(下記の例では半分以下の時間で実行できている！)"
    }), "\n", _jsx(_components.p, {
      children: _jsx(_components.img, {
        src: "/images/blog46/image1.png",
        alt: ""
      })
    }), "\n", _jsx(_components.p, {
      children: "Polarsについて覚える必要がありますが、sqlや、Rのdplyrみたいな感じで書けるので、慣れれば書きやすいです。"
    }), "\n", _jsx(_components.p, {
      children: "業務ではPolarsを使う人が多くないのでPandasで書いていますが、 コンペなどでは処理速度向上のためpolarsを使っています。"
    }), "\n", _jsx(_components.h3, {
      children: "並列化手法を使用する"
    }), "\n", _jsx(_components.p, {
      children: "PandasやPolarsのメソッドに実装されていないような複雑な処理でも、 並列化できる処理(特にgroup処理など)は、高速で実行することができます。"
    }), "\n", _jsx(_components.p, {
      children: "並列化手法のうち、おすすめなものを紹介します。"
    }), "\n", _jsxs(_components.ol, {
      children: ["\n", _jsx(_components.li, {
        children: _jsx(_components.a, {
          href: "https://nalepae.github.io/pandarallel/",
          children: "Pandarallel"
        })
      }), "\n"]
    }), "\n", _jsx(_components.p, {
      children: "pandasのapply処理を並列化してくれます。簡単に実装でき、progress barまで出してくれるのでありがたい。"
    }), "\n", _jsxs(_components.ol, {
      start: "3",
      children: ["\n", _jsx(_components.li, {
        children: _jsx(_components.a, {
          href: "https://joblib.readthedocs.io/en/latest/",
          children: "Joblib"
        })
      }), "\n"]
    }), "\n", _jsx(_components.p, {
      children: "listの内包表記を並列化する。進捗をテキストで表示できる。幅が広い。"
    }), "\n", _jsx(_components.p, {
      children: "以下は通常、pandarallel、Joblibそれぞれで実行したときの時間です。"
    }), "\n", _jsx(_components.p, {
      children: _jsx(_components.img, {
        src: "/images/blog46/image2.png",
        alt: ""
      })
    }), "\n", _jsx(_components.p, {
      children: "処理はJoblibを少し変えていますが、ほぼ同じ処理です(0.01s待って集計する処理)"
    }), "\n", _jsx(_components.p, {
      children: "Google Colabで実装しているため、2workerしか使っていませんが、それでもかなり早くなります。 Colab以外でworkerが多いPCで実行すればもっと早くなりそうです。"
    }), "\n", _jsx(_components.p, {
      children: "JoblibとpandarallelはJoblibの方が速そうですが、処理が多くなった時はどうかわかりません。 pandarallelは簡単にかけて可読性もよく、結果も見やすいので最近はpandarallelを使用してます。"
    }), "\n", _jsx(_components.h3, {
      children: "まとめ"
    }), "\n", _jsx(_components.p, {
      children: "高速化できれば、時間が短縮できてより色々な手法が試せるようになります。 処理早いので業務でもPolarsがStandardになってくれないかなぁ(願望)"
    })]
  });
}
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}
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