python - 如何在Python中按天对时间序列数据求和？ resample.sum() 没有效果

Question

我是 Python 新手。如何根据日期对数据求和并绘制结果？

我有一个 Series 对象，其数据如下:

2017-11-03 07:30:00      NaN
2017-11-03 09:18:00      NaN
2017-11-03 10:00:00      NaN
2017-11-03 11:08:00      NaN
2017-11-03 14:39:00      NaN
2017-11-03 14:53:00      NaN
2017-11-03 15:00:00      NaN
2017-11-03 16:00:00      NaN
2017-11-03 17:03:00      NaN
2017-11-03 17:42:00    800.0
2017-11-04 07:27:00    600.0
2017-11-04 10:10:00      NaN
2017-11-04 11:48:00      NaN
2017-11-04 12:58:00    500.0
2017-11-04 13:40:00      NaN
2017-11-04 15:15:00      NaN
2017-11-04 16:21:00      NaN
2017-11-04 17:37:00    500.0
2017-11-04 21:37:00      NaN
2017-11-05 03:00:00      NaN
2017-11-05 06:30:00      NaN
2017-11-05 07:19:00      NaN
2017-11-05 08:31:00    200.0
2017-11-05 09:31:00    500.0
2017-11-05 12:03:00      NaN
2017-11-05 12:25:00    200.0
2017-11-05 13:11:00    500.0
2017-11-05 16:31:00      NaN
2017-11-05 19:00:00    500.0
2017-11-06 08:08:00      NaN

我有以下代码:

# load packages
import pandas as pd
import matplotlib.pyplot as plt

# import painkiller data
df = pd.read_csv('/Users/user/Documents/health/PainOverTime.csv',delimiter=',')

# plot bar graph of date and painkiller amount
times = pd.to_datetime(df.loc[:,'Time'])

ts = pd.Series(df.loc[:,'acetaminophen'].values, index = times,
               name = 'Painkiller over Time')
ts.plot()

这给了我以下线(？)图:

这是一个开始；现在我想按日期汇总剂量。但是，此代码无法实现任何更改:生成的图是相同的。怎么了？

ts.resample('D',closed='left', label='right').sum()
ts.plot()

我还尝试过ts.resample('D').sum()、ts.resample('1d').sum()、 ts.resample('1D').sum()，但绘图没有变化。

.resample 是正确的函数吗？我理解重采样是从数据中采样，例如每天随机取一个点，而我想对每天的值进行求和。

也就是说，我希望得到一些结果(基于上述数据)，例如:

2017-11-03 800
2017-11-04 1600
2017-11-05 1900
2017-11-06 NaN

Answer 1

使用 pandas groupby 函数。

import io
import pandas as pd

data = io.StringIO('''
2017-11-03 07:30:00,NaN
2017-11-03 09:18:00,NaN
2017-11-03 10:00:00,NaN
2017-11-03 11:08:00,NaN
2017-11-03 14:39:00,NaN
2017-11-03 14:53:00,NaN
2017-11-03 15:00:00,NaN
2017-11-03 16:00:00,NaN
2017-11-03 17:03:00,NaN
2017-11-03 17:42:00,800.0
2017-11-04 07:27:00,600.0
2017-11-04 10:10:00,NaN
2017-11-04 11:48:00,NaN
2017-11-04 12:58:00,500.0
2017-11-04 13:40:00,NaN
2017-11-04 15:15:00,NaN
2017-11-04 16:21:00,NaN
2017-11-04 17:37:00,500.0
2017-11-04 21:37:00,NaN
2017-11-05 03:00:00,NaN
2017-11-05 06:30:00,NaN
2017-11-05 07:19:00,NaN
2017-11-05 08:31:00,200.0
2017-11-05 09:31:00,500.0
2017-11-05 12:03:00,NaN
2017-11-05 12:25:00,200.0
2017-11-05 13:11:00,500.0
2017-11-05 16:31:00,NaN
2017-11-05 19:00:00,500.0
2017-11-06 08:08:00,NaN
''')
column_names = ['date', 'val']
df = pd.read_csv(data, sep=',', header = None, names = column_names)
df['date'] = pd.to_datetime(df['date'])
df = df.groupby(df['date'].dt.date)[['val']].sum()
df.plot()