使用pandas / python从导入的CSV中挑选日期 [英] Picking dates from an imported CSV with pandas/python

问题描述

我有一个.csv文件包含每日数据，如下所示：

I am having a .csv file with daily data, as follows:

some 19 more header rows
Werte
01.01.1971 07:00:00   ;     0.0
02.01.1971 07:00:00   ;     1.2
...and so on

b
$ b

which I import with:

RainD=pd.read_csv('filename.csv',skiprows=20,sep=';',dayfirst=True,parse_dates=True)

结果，我得到

In [416]: RainD
Out[416]: 
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 14976 entries, 1971-01-01 07:00:00 to 2012-01-01 07:00:00
Data columns:
Werte:    14976  non-null values
dtypes: object(1)

因此它的aa Dataframe，但也许一个Timeseries可能是正确的办法？但是如何导入它呢？ pandas文档列出了 read_csv 中的 dtype 选项，但没有关于我可以/应该指定的信息。

So its a a Dataframe, but maybe a Timeseries might be the right way? But how do I import it as such? The pandas documentation list a dtype option in read_csv, but no info on what I can/should specify.

但另一方面， DatetimeIndex：在我看来似乎熊猫很清楚我处理日期这里，但仍然使它成为一个Dataframe。为此， RainD ['1971'] 只会导致一个 u'no项目命名为1971'错误。

But on the other hand, the DatetimeIndex: seems to me like pandas is quite aware of the fact that i deals with Dates here, but still makes it a Dataframe. And for that, something like RainD['1971'] just results in an u'no item named 1971' Key error.

我有一种感觉，我只是缺少一些非常明显的东西，因为时间序列分析似乎是熊猫是为了。

I have the feeling that I am just missing something really obvious, since time series analysis seems to be THE thing pandas was made for.

我的另一个第一个想法是，熊猫可能会感到困惑的事实，日期是写在正确的（即dd.mm.yyyy;））的方式，但 RainD.head（）显示我可以消化那只是很好。

Another first idea of mine was that pandas might get confused by the fact that the dates are written in the correct (ie dd.mm.yyyy ;) ) way, but a RainD.head() shows me that i could digest that just fine.

JC

推荐答案

EdChum的 df [df.index.year == 1971] 解决了我的问题。

EdChum's df[df.index.year == 1971] solved my issue.

我可能还有其他问题（即过时的pandas版本），但现在，我可以继续工作了。

I might have some other issues (ie outdated version of pandas), but for now, I can continue working.

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