Data Structure ?
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Data Structure ?
*The above analysis is the result extracted and analyzed by the system, and the specific actual data shall prevail.
README.md
このPythonで地理空間データを使用する簡単な紹介では、私は3種類の異なるデータソースを組み合わせ、それらを1つの地図に実装することができます。この目的のために、まずランダムな住所が記載された.csvファイルを読み込み、GoogleのAPIを使用して地理座標をリクエストし、新しいデータフレームを作成します。続いて、Natural Earthのデータが入ったzipフォルダをPythonに読み込み、最初のデータフレームを幾何学的特性を持つ地理データフレームに変換します。geopandasを使って手動で地理データフレームを構築することも可能です。次に、GeoJSONから地理空間データを読み込むことで、ドイツの行政区画のより正確なポリゴンを取得し、これを前の地理データフレームと一緒にユニークな地図に描画することができます。
2つ目のJupyterノートブックでは、Natural Earthのデータシートを操作して、一人当たりGDPデータに対する凝集型クラスタリングとK-meansクラスタリングを続けました。
次のプロジェクトでは、これらの地理データに対してSVMアルゴリズムを使って作業を始める予定です。
**ファイル「Pythonで地理データを使用する」を表示する**: https://bit.ly/2SN3oTl
**ファイル「凝集型クラスタリングとK-meansクラスタリング」を表示する**: https://bit.ly/2SN3D0H
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