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CSV版を追加
test
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```
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**### CSV版 ###**
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```Python
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import pandas as pd
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import matplotlib.pyplot as plt
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import matplotlib.dates as mdates
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import io
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dummy_csv = """
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,from,to,value,datetime
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0,0x4e6f10ed3b919cbbd872807f866ded9acdf7eede,,0.0,2019-01-08 10:28:22
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1,0x5a4d185c590c5815a070ed62c278e665d137a0d9,0x6cb4ad504816bd3021ae48286f018ac725239b89,0.024,2019-01-11 02:00:23
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2,0x5a4d185c590c5815a070ed62c278e665d137a0d9,0x6cb4ad504816bd3021ae48286f018ac725239b89,0.024,2019-01-11 07:12:22
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3,0x5a4d185c590c5815a070ed62c278e665d137a0d9,0x6cb4ad504816bd3021ae48286f018ac725239b89,0.024,2019-01-11 07:13:43
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4,0x5a4d185c590c5815a070ed62c278e665d137a0d9,0x6cb4ad504816bd3021ae48286f018ac725239b89,0.024,2019-01-11 07:39:11
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5,0x7efee171ed6e9fd40dff6d7beaeba006d05ba559,0x6cb4ad504816bd3021ae48286f018ac725239b89,0.024,2019-01-11 08:12:38
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6,0x31cdabb1a0b18dd9326f150f352ab2b4597d2bab,0x6cb4ad504816bd3021ae48286f018ac725239b89,0.024,2019-01-11 08:14:46
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7,0x6d603b234a927af2226c0688ad5f8ee7399e984c,0x6cb4ad504816bd3021ae48286f018ac725239b89,0.024,2019-01-11 08:16:14
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8,0x6d603b234a927af2226c0688ad5f8ee7399e984c,0x6cb4ad504816bd3021ae48286f018ac725239b89,0.024,2019-01-11 08:16:58
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9,0xcecddbe88359f6ecebe90b42643b002543f27fe9,0x6cb4ad504816bd3021ae48286f018ac725239b89,0.024,2019-01-11 08:19:33
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10,0x3cd673f4b8e7815f4862222ef0786241f952aeae,0x6cb4ad504816bd3021ae48286f018ac725239b89,0.024,2019-01-11 08:21:24
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11,0x3cd673f4b8e7815f4862222ef0786241f952aeae,0x6cb4ad504816bd3021ae48286f018ac725239b89,0.024,2019-01-11 08:21:32
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12,0x3cd673f4b8e7815f4862222ef0786241f952aeae,0x6cb4ad504816bd3021ae48286f018ac725239b89,0.024,2019-01-11 08:22:05
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13,0x02632569d858370906ac6116b0ae25059fb34a82,0x6cb4ad504816bd3021ae48286f018ac725239b89,0.216,2019-01-11 08:22:05
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14,0x3cd673f4b8e7815f4862222ef0786241f952aeae,0x6cb4ad504816bd3021ae48286f018ac725239b89,0.024,2019-01-11 08:22:22
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15,0x3c5f0629535c755453ad88ef1fd433022f9d306a,0x6cb4ad504816bd3021ae48286f018ac725239b89,0.024,2019-01-11 08:23:32
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16,0xbae25a69a6eb7341ee834be7635e247e22a1e57a,0x6cb4ad504816bd3021ae48286f018ac725239b89,0.024,2019-01-11 08:25:05
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17,0xde9fcd2c2d0f20699b6f08cc4d449499271d8f18,0x6cb4ad504816bd3021ae48286f018ac725239b89,0.024,2019-01-11 08:25:09
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18,0x862244eeca0c73925b5a4daa14815fb81f499c93,0x6cb4ad504816bd3021ae48286f018ac725239b89,0.024,2019-01-11 08:26:08
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19,0xc35a5fec6be6957899e15559be252db882220b37,0x6cb4ad504816bd3021ae48286f018ac725239b89,0.024,2019-01-11 08:27:17
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"""
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df = pd.read_csv(io.StringIO(dummy_csv), parse_dates=['datetime'], index_col='datetime')
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print(df)
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conv_df = df.resample('1d').agg({
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'from': lambda d:d.drop_duplicates().count(),
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'value': 'sum'})
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print(conv_df)
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# value from
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#datetime
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#2019-01-08 0.000, 1
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#2019-01-09 0.000, 0
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#2019-01-10 0.000, 0
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#2019-01-11 0.672,13
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conv_df.plot() #グラフを書く
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plt.show() #表示
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```
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