[英]Python: Indexing in Pandas with Datetime indices
I've got problems regarding to indexing in Pandas and hope you can help me: 我在熊猫索引方面遇到了问题,希望您能为我提供帮助:
rng = pd.date_range('2015-12-31 21:00:00', periods=7, freq='H')
df = pd.DataFrame({ 'Val' : np.random.randn(len(rng)) }, index=rng)
first_value_of_year = df['2016'].first('1H').index
returns the first value of the year as DatetimeIndex: 返回年份的第一个值作为DatetimeIndex:
DatetimeIndex(['2016-01-01'], dtype='datetime64[ns]', freq='H')
When I call the dataframe with this index, everything seems to be working fine: 当我使用该索引调用数据帧时,一切似乎都工作正常:
df.loc[first_value_of_year]
returns 回报
+------------------------+-----------+
| | Val |
+------------------------+-----------+
| 2016-01-01 | 0.144044 |
So, everything is OK up to here! 因此,到目前为止一切正常! But if I want to get all values greater than this value, it doesn't work anymore:
但是,如果我想获得所有大于此值的值,它将不再起作用:
df.loc[df.index >= first_value_of_year]
and gives ValueError (lenghts must match): 并给出ValueError(长度必须匹配):
but if I take the same command with the timestamp itself as string it does what it should do 但是如果我将时间戳本身作为字符串使用相同的命令,它将执行应做的事情
df.loc[df.index >= '2016-01-01 00:00:00']
returns 回报
+------------------------+-----------+
| | Val |
+------------------------+-----------+
| 2016-01-01 01:00:00 | 1.454274 |
| 2016-01-01 02:00:00 | 0.761038 |
| 2016-01-01 03:00:00 | 0.121675 |
so, what am I missing here? 所以,我在这里想念什么? How do I correctly access all values greater than the DatetimeIndex variable?
如何正确访问所有大于DatetimeIndex变量的值?
Thanks! 谢谢!
我相信您需要通过索引- [0]
索引的第一个值选择为标量:
df.loc[df.index >= first_value_of_year[0]]
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