This is a very simple and practical question. I have the feeling that it must be a silly detail and that there should be similar questions. I wasn't able to find them tho. If someone does I'll happily delete this one. The closest I found were these: pandas: iterating over DataFrame index with loc How to select rows within a pandas dataframe based on time only when index is date and time anyway, the thing is, I have a datetime indexed panda dataframe as follows: In[81]: y Out[81]: PETR4 CSNA3 VALE5 2008-01-01 0.0 0.0 0.0 2008-01-02 1.0 1.0 1.0 2008-01-03 7.0 7.0 7.0 In[82]: y.index Out[82]: DatetimeIndex(['2008-01-01', '2008-01-02', '2008-01-03'], dtype='datetime64[ns]', freq=None) Oddly enough, I can't access its values using none of the following methods: In[83]: y[datetime.datetime(2008,1,1)] In[84]: y['2008-1-1'] In[85]: y['1/1/2008'] I get the KeyError error. Even more weird is that the following methods DO work: In[86]: y['2008'] Out[86]: PETR4 CSNA3 VALE5 2008-01-01 0.0 0.0 0.0 2008-01-02 1.0 1.0 1.0 2008-01-03 7.0 7.0 7.0 In[87]: y['2008-1'] Out[87]: PETR4 CSNA3 VALE5 2008-01-01 0.0 0.0 0.0 2008-01-02 1.0 1.0 1.0 2008-01-03 7.0 7.0 7.0 I'm fairly new to pandas so maybe I'm missing something here? Continue reading...
