pandas 使用日期时间对象重新索引 DataFrame
声明：本页面是StackOverFlow热门问题的中英对照翻译，遵循CC BY-SA 4.0协议，如果您需要使用它，必须同样遵循CC BY-SA许可，注明原文地址和作者信息，同时你必须将它归于原作者（不是我）：StackOverFlow
原文地址: http://stackoverflow.com/questions/10943478/
Warning: these are provided under cc-by-sa 4.0 license. You are free to use/share it, But you must attribute it to the original authors (not me):
StackOverFlow
pandas reindex DataFrame with datetime objects
提问by BFTM
Is it possible to reindex a pandas DataFrameusing a column made up of datetime objects?
是否可以DataFrame使用由日期时间对象组成的列重新索引熊猫？
I have a DataFrame dfwith the following columns:
我有一个包含df以下列的 DataFrame ：
Int64Index: 19610 entries, 0 to 19609
Data columns:
cntr 19610 non-null values #int
datflt 19610 non-null values #float
dtstamp 19610 non-null values #datetime object
DOYtimestamp 19610 non-null values #float
dtypes: int64(1), float64(2), object(1)
I can reindex the dfeasily along DOYtimestampwith: df.reindex(index=df.dtstamp)and DOYtimestamphas the following values:
我可以df轻松地重新索引DOYtimestamp与：df.reindex(index=df.dtstamp)并DOYtimestamp具有以下值：
>>> df['DOYtimestamp'].values
array([ 153.76252315, 153.76253472, 153.7625463 , ..., 153.98945602,
153.98946759, 153.98947917])
but I'd like to reindex the DataFrame along dtstampwhich is made up of datetime objects so that I generate different timestamps directly from the index. The dtstampcolumn has values which look like:
但我想重新索引dtstamp由 datetime 对象组成的 DataFrame ，以便我直接从索引生成不同的时间戳。该dtstamp列具有如下所示的值：
>>> df['dtstamp'].values
array([2012-06-02 18:18:02, 2012-06-02 18:18:03, 2012-06-02 18:18:04, ...,
2012-06-02 23:44:49, 2012-06-02 23:44:50, 2012-06-02 23:44:51],
dtype=object)
When I try and reindex dfalong dtstampI get the following:
当我尝试重新索引时df，dtstamp我得到以下信息：
>>> df.reindex(index=df.dtstamp)
TypeError: can't compare datetime.datetime to long
I'm just not sure what I need to do get the index to be of a datetime type. Any thoughts?
我只是不确定我需要做什么才能使索引成为日期时间类型。有什么想法吗？
回答by BrenBarn
It sounds like you don't want reindex. Somewhat confusingly reindexis not for defining a new index, exactly; rather, it looks for rows that have the specified indices. So if you have a DataFrame with index [0, 1, 2], then doing a reindex([2, 1, 0])will return the rows in reverse order. Doing something like reindex([8, 9, 10])does not make a new index for the rows; rather, it will return a DataFrame with NaNvalues, since there are no rows with indices 8, 9, or 10.
听起来你不想重新索引。reindex确切地说，有点令人困惑的是不是为了定义新索引；相反，它查找具有指定索引的行。因此，如果您有一个带有 index 的 DataFrame [0, 1, 2]，那么执行 areindex([2, 1, 0])将以相反的顺序返回行。执行类似reindex([8, 9, 10])操作不会为行创建新索引；相反，它将返回一个带有NaN值的 DataFrame ，因为没有索引为 8、9 或 10 的行。
It seems like what you want is to just keep the same rows, but make a totally new index for them. For that you can just assign to the index directly. So try doing df.index = df['dtstamp'].
看起来您想要的只是保留相同的行，但为它们创建一个全新的索引。为此，您可以直接分配给索引。所以尝试做df.index = df['dtstamp']。
