python pandas dataframe slicing by date conditions

Question or problem about Python programming:

I am able to read and slice pandas dataframe using python datetime objects, however I am forced to use only existing dates in index. For example, this works:

>>> data

DatetimeIndex: 252 entries, 2010-12-31 00:00:00 to 2010-04-01 00:00:00
Data columns:
Adj Close    252  non-null values
dtypes: float64(1)

>>> st = datetime.datetime(2010, 12, 31, 0, 0)
>>> en = datetime.datetime(2010, 12, 28, 0, 0)

>>> data[st:en]
            Adj Close
Date                 
2010-12-31     593.97
2010-12-30     598.86
2010-12-29     601.00
2010-12-28     598.92

However if I use a start or end date that is not present in the DF, I get python KeyError.

My Question : How do I query the dataframe object for a date range; even when the start and end dates are not present in the DataFrame. Does pandas allow for range based slicing?

I am using pandas version 0.10.1

How to solve the problem:

Solution 1:

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

Solution 2:

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()

Solution 3:

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.

Solution 4:

I had difficulty with other approaches but I found that the following approach worked for me:

# Set the Index to be the Date
df['Date'] = pd.to_datetime(df['Date_1'], format='%d/%m/%Y')
df.set_index('Date', inplace=True)

# Sort the Data
df = df.sort_values('Date_1')

# Slice the Data
From = '2017-05-07'
To   = '2017-06-07'
df_Z = df.loc[From:To,:]

Hope this helps!