Pandas 在特定开始时间后使用增量时间重新采样

Question

将 CSV 读入数据框后，我尝试将我的“值”列重新采样为 5 秒，从时间值的第一个舍入秒开始。我想在接下来的 5 秒内获得所有值的平均值，从 46:19.6 开始（格式 %M:%S:%f）。所以代码会给我 46:20 的平均值，然后是 46:25，依此类推...有人知道该怎么做吗？谢谢！

输入：

df = pd.DataFrame({'Time': {0: '46:19.6',
  1: '46:20.7',
  2: '46:21.8',
  3: '46:22.9',
  4: '46:24.0',
  5: '46:25.1',
  6: '46:26.2',
  7: '46:27.6',
  8: '46:28.7',
  9: '46:29.8',
  10: '46:30.9',
  11: '46:32.0',
  12: '46:33.2',
  13: '46:34.3',
  14: '46:35.3',
  15: '46:36.5',
  16: '46:38.8',
  17: '46:40.0'},
 'Value': {0: 0,
  1: 1,
  2: 2,
  3: 3,
  4: 4,
  5: 5,
  6: 6,
  7: 8,
  8: 9,
  9: 10,
  10: 11,
  11: 12,
  12: 13,
  13: 14,
  14: 15,
  15: 17,
  16: 19,
  17: 20}})

Answer 1

假设您的 Time 字段采用 datetime64[ns] 格式，您只需使用 pd.Grouper 并传递 freq=5S:

# next line of code is optional to transform to datetime format if the `Time` field is an `object` i.e. string.
# df['Time'] = pd.to_datetime('00:'+df['Time'])

df1 = df.groupby(pd.Grouper(key='Time', freq='5S'))['Value'].mean().reset_index()

#Depending on what you want to do, you can also replace the above line of code with one of two below:
#df1 = df.groupby(pd.Grouper(key='Time', freq='5S'))['Value'].mean().reset_index().iloc[1:]
#df1 = df.groupby(pd.Grouper(key='Time', freq='5S', base=4.6))['Value'].mean().reset_index()
#In the above line of code 4.6s can be adjusted to whatever number between 0 and 5.
df1

输出：

    Time                 Value
0   2020-07-07 00:46:15  0.0
1   2020-07-07 00:46:20  2.5
2   2020-07-07 00:46:25  7.6
3   2020-07-07 00:46:30  12.5
4   2020-07-07 00:46:35  17.0
5   2020-07-07 00:46:40  20.0

我创建的示例 DataFrame 中的完整可重现代码：

import re
import pandas
df = pd.DataFrame({'Time': {0: '46:19.6',
  1: '46:20.7',
  2: '46:21.8',
  3: '46:22.9',
  4: '46:24.0',
  5: '46:25.1',
  6: '46:26.2',
  7: '46:27.6',
  8: '46:28.7',
  9: '46:29.8',
  10: '46:30.9',
  11: '46:32.0',
  12: '46:33.2',
  13: '46:34.3',
  14: '46:35.3',
  15: '46:36.5',
  16: '46:38.8',
  17: '46:40.0'},
 'Value': {0: 0,
  1: 1,
  2: 2,
  3: 3,
  4: 4,
  5: 5,
  6: 6,
  7: 8,
  8: 9,
  9: 10,
  10: 11,
  11: 12,
  12: 13,
  13: 14,
  14: 15,
  15: 17,
  16: 19,
  17: 20}})
df['Time'] = pd.to_datetime('00:'+df['Time'])
df1 = df.groupby(pd.Grouper(key='Time', freq='5S'))['Value'].mean().reset_index()
df1

Pandas 在特定开始时间后使用增量时间重新采样

Pandas Resampling with delta time after specific starting time

python

python-3.x

pandas

pandas-groupby

resampling