回答編集履歴
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回答が間違っていたので追加修正
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#5 logoff 2018-10-24 11:50:00
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【追記】
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反対の処理は ``DataFrame.pivot()`` または ``DataFrame.pivot_table()`` にて行うことができます。
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[https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.pivot.html](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.pivot.html)
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[https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.pivot_table.html](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.pivot_table.html)
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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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2018/10/17 13:29,logon
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2018/10/19 17:51,logoff
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2018/10/19 18:04,logon
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2018/10/19 18:11,logoff
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2018/10/24 10:18,logon
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2018/10/24 11:50,logoff
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"""
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df = pd.read_csv(io.StringIO(data), header=None, parse_dates=[0])
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# 0 1
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#0 2018-10-17 13:29:00 logon
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#1 2018-10-19 17:51:00 logoff
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#2 2018-10-19 18:04:00 logon
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#3 2018-10-19 18:11:00 logoff
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#4 2018-10-24 10:18:00 logon
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#5 2018-10-24 11:50:00 logoff
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result = df.pivot(columns=1, index=df.index//2)
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# 0
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#1 logoff logon
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#0 2018-10-19 17:51:00 2018-10-17 13:29:00
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#1 2018-10-19 18:11:00 2018-10-19 18:04:00
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#2 2018-10-24 11:50:00 2018-10-24 10:18:00
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result = df.pivot_table(values=0, columns=1, aggfunc='first', index=df.index//2)
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#1 logoff logon
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#0 2018-10-19 17:51:00 2018-10-17 13:29:00
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#1 2018-10-19 18:11:00 2018-10-19 18:04:00
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#2 2018-10-24 11:50:00 2018-10-24 10:18:00
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```
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