回答編集履歴
2
サンプル追加
test
CHANGED
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@@ -1,3 +1,91 @@
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``df.groupby('race').apply(lambda d: d[['馬番','能力1','能力2','着順(y)']].stack().reset_index(level=0, drop=True))``
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でどうでしょうか。
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```Python
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import pandas as pd
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import io
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data = """
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race,馬番,能力1,能力2,着順(y)
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1,1,0.112115797,0.980271793,0.5
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1,2,0.10156389,0.15123039,0.25
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1,3,0.141461107,0.889222429,0.75
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1,4,0.364152658,0.504141353,1
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2,1,0.232178085,0.811654683,0.25
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2,2,0.490554328,0.271948642,0.75
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2,3,0.912273302,0.545971675,1
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2,4,0.306427195,0.219543843,0.5
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3,1,0.84874005,0.225376628,0.75
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3,2,0.980524014,0.518634533,1
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3,3,0.793379468,0.707376409,0.5
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3,4,0.337912623,0.340699924,0.25
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4,1,0.865030143,0.54997774,1
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4,2,0.743468075,0.717770634,0.75
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4,3,0.175989149,0.219996247,0.5
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4,4,0.032296776,0.140063011,0.25
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5,1,0.108151134,0.01417521,0.25
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5,2,0.812937171,0.969057406,0.75
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5,3,0.869512736,0.196469624,1
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5,4,0.380255494,0.544955296,0.5
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6,1,0.265640962,0.367447423,0.25
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6,2,0.606604272,0.149497873,0.5
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6,3,0.748067103,0.704372874,0.75
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6,4,0.896956794,0.22287917,1
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"""
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df = pd.read_csv(io.StringIO(data))
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ret = df.groupby('race').apply(lambda d: d[['馬番','能力1','能力2','着順(y)']].stack().reset_index(level=0, drop=True))
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# 馬番 能力1 能力2 着順(y) 馬番 ... 着順(y) 馬番 能力1 能力2 着順(y)
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#race ...
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#1 1.0 0.112116 0.980272 0.50 2.0 ... 0.75 4.0 0.364153 0.504141 1.00
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80
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81
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#2 1.0 0.232178 0.811655 0.25 2.0 ... 1.00 4.0 0.306427 0.219544 0.50
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82
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#3 1.0 0.848740 0.225377 0.75 2.0 ... 0.50 4.0 0.337913 0.340700 0.25
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84
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#4 1.0 0.865030 0.549978 1.00 2.0 ... 0.50 4.0 0.032297 0.140063 0.25
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86
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#5 1.0 0.108151 0.014175 0.25 2.0 ... 1.00 4.0 0.380255 0.544955 0.50
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#6 1.0 0.265641 0.367447 0.25 2.0 ... 0.75 4.0 0.896957 0.222879 1.00
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```
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1
ちょっと修正
test
CHANGED
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@@ -1,3 +1,3 @@
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1
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-
``df.groupby('race').apply(lambda d: d[['馬番','能力
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``df.groupby('race').apply(lambda d: d[['馬番','能力1','能力2','着順(y)']].stack().reset_index(level=0, drop=True))``
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2
2
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3
3
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でどうでしょうか。
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