使用 Pandas 过滤和比较日期
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Filtering and comparing dates with Pandas
提问by Federico Garza
I would like to know how to filter different dates at all the different time levels, i.e. find dates by year, month, day, hour, minute and/or day. For example, how do I find all dates that happened in 2014 or 2014 in the month of January or only 2nd January 2014 or ...down to the second?
我想知道如何在所有不同的时间级别过滤不同的日期，即按年、月、日、小时、分钟和/或日查找日期。例如，我如何查找 2014 年或 2014 年发生在 1 月份的所有日期，或者仅在 2014 年 1 月 2 日或......直到第二个日期？
So I have my date and time dataframe generated from pd.to_datetime
所以我从pd.to_datetime生成了我的日期和时间数据框
df
timeStamp
0 2014-01-02 21:03:04
1 2014-02-02 21:03:05
2 2016-02-04 18:03:10
So if I filter by the year 2014 then I would have as output:
因此，如果我按 2014 年进行过滤，那么我的输出将是：
timeStamp
0 2014-01-02 21:03:04
1 2014-02-02 21:03:05
Or as a different example I want to know the dates that happened in 2014 and at the 2nd of each month. This would also result in:
或者作为一个不同的例子，我想知道 2014 年和每个月的 2 号发生的日期。这也会导致：
timeStamp
0 2014-01-02 21:03:04
1 2014-02-02 21:03:05
But if I asked for a date that happened on the 2nd of January 2014
但是，如果我要求日期发生在 2014 年 1 月 2 日
timeStamp
0 2014-01-02 21:03:04
How can I achieve this at all the different levels?
我怎样才能在所有不同的层次上做到这一点？
Also how do you compare dates at these different levels to create an array of boolean indices?
另外，您如何比较这些不同级别的日期以创建布尔索引数组？
回答by Andrew L
You can filter your dataframe via boolean indexing like so:
您可以通过布尔索引过滤数据框，如下所示：
df.loc[df['timeStamp'].dt.year == 2014]
df.loc[df['timeStamp'].dt.month == 5]
df.loc[df['timeStamp'].dt.second == 4]
df.loc[df['timeStamp'] == '2014-01-02']
df.loc[pd.to_datetime(df['timeStamp'].dt.date) == '2014-01-02']
... and so on and so forth.
... 等等等等。
回答by Scott Boston
If you set timestamp as index and dtype as datetime to get a DateTimeIndex, then you can use the following Partial String Indexingsyntax:
如果将时间戳设置为索引并将 dtype 设置为日期时间以获取 DateTimeIndex，则可以使用以下Partial String Indexing语法：
df['2014'] # gets all 2014
df['2014-01'] # gets all Jan 2014
df['01-02-2014'] # gets all Jan 2, 2014
回答by Troy D
I would just create a string series, then use str.contains() with wildcards. That will give you whatever granularity you're looking for.
我只会创建一个字符串系列，然后使用带有通配符的 str.contains() 。这将为您提供您正在寻找的任何粒度。
s = df['timeStamp'].map(lambda x: x.strftime('%Y-%m-%d %H:%M:%S'))
print(df[s.str.contains('2014-..-.. ..:..:..')])
print(df[s.str.contains('2014-..-02 ..:..:..')])
print(df[s.str.contains('....-02-.. ..:..:..')])
print(df[s.str.contains('....-..-.. 18:03:10')])
Output:
输出：
timeStamp
0 2014-01-02 21:03:04
1 2014-02-02 21:03:05
timeStamp
0 2014-01-02 21:03:04
1 2014-02-02 21:03:05
timeStamp
1 2014-02-02 21:03:05
2 2016-02-04 18:03:10
timeStamp
2 2016-02-04 18:03:10
I think this also solves your question about boolean indices:
我认为这也解决了您关于布尔索引的问题：
print(s.str.contains('....-..-.. 18:03:10'))
Output:
输出：
0 False
1 False
2 True
Name: timeStamp, dtype: bool
