python pandas dataframe slicing by date conditions
You can use a simple mask to accomplish this:
date_mask = (data.index > start) & (data.index < end)
dates = data.index[date_mask]
data.ix[dates]
By the way, this works for hierarchical indexing as well. In that case data.index
would be replaced with data.index.levels[0]
or similar.
Short answer: Sort your data (data.sort()
) and then I think everything will work the way you are expecting.
Yes, you can slice using datetimes not present in the DataFrame. For example:
In [12]: df
Out[12]:
0
2013-04-20 1.120024
2013-04-21 -0.721101
2013-04-22 0.379392
2013-04-23 0.924535
2013-04-24 0.531902
2013-04-25 -0.957936
In [13]: df['20130419':'20130422']
Out[13]:
0
2013-04-20 1.120024
2013-04-21 -0.721101
2013-04-22 0.379392
As you can see, you don't even have to build datetime objects; strings work.
Because the datetimes in your index are not sequential, the behavior is weird. If we shuffle the index of my example here...
In [17]: df
Out[17]:
0
2013-04-22 1.120024
2013-04-20 -0.721101
2013-04-24 0.379392
2013-04-23 0.924535
2013-04-21 0.531902
2013-04-25 -0.957936
...and take the same slice, we get a different result. It returns the first element inside the range and stops at the first element outside the range.
In [18]: df['20130419':'20130422']
Out[18]:
0
2013-04-22 1.120024
2013-04-20 -0.721101
2013-04-24 0.379392
This is probably not useful behavior. If you want to select ranges of dates, would it make sense to sort it by date first?
df.sort_index()
Use searchsorted
to find the nearest times first, and then use it to slice.
In [15]: df = pd.DataFrame([1, 2, 3], index=[dt.datetime(2013, 1, 1), dt.datetime(2013, 1, 3), dt.datetime(2013, 1, 5)])
In [16]: df
Out[16]:
0
2013-01-01 1
2013-01-03 2
2013-01-05 3
In [22]: start = df.index.searchsorted(dt.datetime(2013, 1, 2))
In [23]: end = df.index.searchsorted(dt.datetime(2013, 1, 4))
In [24]: df.iloc[start:end]
Out[24]:
0
2013-01-03 2