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所以我有一个名为“df”的熊猫数据框，我想删除秒数，只使用 YYYY-MM-DD HH:MM 格式的索引。但也会对分钟进行分组，并显示该分钟的平均值。

所以我想把这个dataFrame

                        value
2015-05-03 00:00:00     61.0
2015-05-03 00:00:10     60.0
2015-05-03 00:00:25     60.0
2015-05-03 00:00:30     61.0
2015-05-03 00:00:45     61.0
2015-05-03 00:01:00     61.0
2015-05-03 00:01:10     60.0
2015-05-03 00:01:25     60.0
2015-05-03 00:01:30     61.0
2015-05-03 00:01:45     61.0
2015-05-03 00:02:00     61.0
2015-05-03 00:02:10     60.0
2015-05-03 00:02:25     60.0
2015-05-03 00:02:40     60.0
2015-05-03 00:02:55     60.0
2015-05-03 00:03:00     59.0
2015-05-03 00:03:15     59.0
2015-05-03 00:03:20     59.0
2015-05-03 00:03:35     59.0
2015-05-03 00:03:40     60.0

进入这个数据框

                        value
2015-05-03 00:00        60.6
2015-05-03 00:01        60.6
2015-05-03 00:02        60.2
2015-05-03 00:03        59.2

我试过像这样的代码

df['value'].resample('1Min').mean()

或者

df.index.resample('1Min').mean()

但这似乎不起作用。有任何想法吗？

4

1 回答 1

7

您需要首先将索引转换为DatetimeIndex：

df.index = pd.DatetimeIndex(df.index)
#another solution
#df.index = pd.to_datetime(df.index)

print (df['value'].resample('1Min').mean())
#another same solution
#print (df.resample('1Min')['value'].mean())
2015-05-03 00:00:00    60.6
2015-05-03 00:01:00    60.6
2015-05-03 00:02:00    60.2
2015-05-03 00:03:00    59.2
Freq: T, Name: value, dtype: float64

另一种将索引中的秒值设置为0by的解决方案astype：

print (df.groupby([df.index.values.astype('<M8[m]')])['value'].mean())
2015-05-03 00:00:00    60.6
2015-05-03 00:01:00    60.6
2015-05-03 00:02:00    60.2
2015-05-03 00:03:00    59.2
Name: value, dtype: float64
于 2016-10-10T07:05:21.850 回答