基于日期的Pythonic方式过滤DataFrame示例

Question

In [90]: list_dates = [datetime.date(2014,2,2),datetime.date(2015,2,2), datetime.date(2013,4,5)]

In [91]: df = DataFrame(list_dates, columns=['Date'])

In [92]: df
Out[92]: 
         Date
0  2014-02-02
1  2015-02-02
2  2013-04-05

现在，我想获得一个仅包含2014年和2013年日期的新DataFrame：

In [93]: result = DataFrame([date for date in df.Date if date.year in (2014,2013)])

In [94]: result
Out[94]: 
            0
0  2014-02-02
1  2013-04-05

那行得通，给了我想要的DataFrame。 为什么不起作用：

In [95]: result1 = df[df.Date.map(lambda x: x.year) == 2014 or p.Date.map(lambda x: x.year) == 2013]
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-95-86f01906c89b> in <module>()
----> 1 result1 = df[df.Date.map(lambda x: x.year) == 2014 or p.Date.map(lambda x: x.year) == 2013]

/home/marcos/anaconda/lib/python2.7/site-packages/pandas/core/generic.pyc in __nonzero__(self)
    690         raise ValueError("The truth value of a {0} is ambiguous. "
    691                          "Use a.empty, a.bool(), a.item(), a.any() or a.all()."
--> 692                          .format(self.__class__.__name__))
    693 
    694     __bool__ = __nonzero__

ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

或以下内容：

In [96]: df['year'] = df.Date.map(lambda x: x.year)

In [97]:     result2 = df[df.year in (2014, 2013)]
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-97-814358a4edff> in <module>()
----> 1 result2 = df[df.year in (2014, 2013)]

/home/marcos/anaconda/lib/python2.7/site-packages/pandas/core/generic.pyc in __nonzero__(self)
    690         raise ValueError("The truth value of a {0} is ambiguous. "
    691                          "Use a.empty, a.bool(), a.item(), a.any() or a.all()."
--> 692                          .format(self.__class__.__name__))
    693 
    694     __bool__ = __nonzero__

ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

我认为问题是当我使用'in'命令时，我试图检查整个Series是否在一个元组中。 但是，如何使评估成为元素分类，以便获得所需的结果？

Answer 1

我使用to_datetime将日期转换为datetime对象，然后允许您使用dt访问器访问year属性，然后我们可以调用isin并传递感兴趣的年份列表以过滤df：

In [68]:

df['Date'] = pd.to_datetime(df['Date'])
In [69]:

df[df['Date'].dt.year.isin([2013,2014])]
Out[69]:
        Date
0 2014-02-02
2 2013-04-05