如何重新采样具有应用于每列的不同函数的数据框?

2022-01-11 00:00:00 python numpy pandas time-series

问题描述

我在 pandas dataframe 中有温度和辐射的时间序列.时间分辨率为 1 分钟，以常规步长.

I have a times series with temperature and radiation in a pandas dataframe. The time resolution is 1 minute in regular steps.

import datetime
import pandas as pd
import numpy as np

date_times = pd.date_range(datetime.datetime(2012, 4, 5, 8, 0),
                           datetime.datetime(2012, 4, 5, 12, 0),
                           freq='1min')
tamb = np.random.sample(date_times.size) * 10.0
radiation = np.random.sample(date_times.size) * 10.0
frame = pd.DataFrame(data={'tamb': tamb, 'radiation': radiation},
                     index=date_times)
frame
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 241 entries, 2012-04-05 08:00:00 to 2012-04-05 12:00:00
Freq: T
Data columns:
radiation    241  non-null values
tamb         241  non-null values
dtypes: float64(2)

如何将此 dataframe 下采样到一小时的分辨率，计算温度的每小时 mean 和每小时 sum辐射?

How can I down-sample this dataframe to a resolution of one hour, computing the hourly mean for the temperature and the hourly sum for radiation?

解决方案

在 pandas 0.18 中，重采样 API 发生了变化(参见 文档).所以对于 pandas >= 0.18 的答案是:

With pandas 0.18 the resample API changed (see the docs). So for pandas >= 0.18 the answer is:

In [31]: frame.resample('1H').agg({'radiation': np.sum, 'tamb': np.mean})
Out[31]: 
                         tamb   radiation
2012-04-05 08:00:00  5.161235  279.507182
2012-04-05 09:00:00  4.968145  290.941073
2012-04-05 10:00:00  4.478531  317.678285
2012-04-05 11:00:00  4.706206  335.258633
2012-04-05 12:00:00  2.457873    8.655838

<小时>

旧答案:

Old Answer:

我正在回答我的问题以反映 pandas >= 0.8 中与时间序列相关的变化(所有其他答案都已过时).

I am answering my question to reflect the time series related changes in pandas >= 0.8 (all other answers are outdated).

使用 pandas >= 0.8 答案是:

Using pandas >= 0.8 the answer is:

In [30]: frame.resample('1H', how={'radiation': np.sum, 'tamb': np.mean})
Out[30]: 
                         tamb   radiation
2012-04-05 08:00:00  5.161235  279.507182
2012-04-05 09:00:00  4.968145  290.941073
2012-04-05 10:00:00  4.478531  317.678285
2012-04-05 11:00:00  4.706206  335.258633
2012-04-05 12:00:00  2.457873    8.655838

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