回答編集履歴
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サンプル追加
test
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@@ -21,3 +21,115 @@
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---
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**動作サンプルコード**
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```Python
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import pandas as pd
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import io
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data_csv = """
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id,id2,cost
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1,10,5
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2,10,3
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3,10,1
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1,13,10
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2,13,5
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3,13,4
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"""
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df = pd.read_csv(io.StringIO(data_csv))
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print(df)
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# id id2 cost
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#0 1 10 5
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#1 2 10 3
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#2 3 10 1
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#3 1 13 10
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#4 2 13 5
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#5 3 13 4
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conv = df.groupby('id2')['cost'].min()
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print(conv)
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#id2
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#10 1
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#13 4
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list_csv = """
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id2,min
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10,
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13,
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"""
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df1 = pd.read_csv(io.StringIO(list_csv))
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print(df1)
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# id2 min
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#0 10 NaN
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#1 13 NaN
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ret = df1['id2'].map(conv)
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print(ret)
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#0 1
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#1 4
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#Name: id2, dtype: int64
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df1['min'] = ret
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print(df1)
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# id2 min
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#0 10 1
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#1 13 4
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```
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1
説明追加
test
CHANGED
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@@ -5,3 +5,19 @@
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df1['min'] = df1['id2'].map(df.groupby('id2')['cost'].min())
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```
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少し説明を加えると
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1. ``df.groupby('id2')['cost'].min()`` にて df(``data.csv``)のデータフレームを ``id2``の値ごとに``cost``列が最小の値を導く
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2. ``df1['id2'].map(...)`` にて上記の結果を df1(``list.csv``)の``id2``列の値で引き当てる
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3. 結果をdf1の``min``列に格納する
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を行っております。
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