具有多个日期范围的 Pandas.DataFrame 切片

Question

I have a datetime-indexed dataframe object with 100,000+ rows. I was wondering if there was a convenient way using pandas to get a subset of this dataframe that is within multiple date ranges.

For example, let us say that we have two date ranges:

(datetime.datetime(2016,6,27,0,0,0), datetime.datetime(2016,6,27,5,0,0)

and

(datetime.datetime(2016,6,27,15,0,0), datetime.datetime(2016,6,27,23,59,59)

Let us say we want to get all rows of a dataframe object that is in either the first date range or the second date range, where the dataframe object has rows for every second from 2016-06-27 00:00:00 to 2016-06-27 23:59:59 . Is there an easy way in pandas to do this?

Answer 1

There are two main ways to slice a DataFrame with a DatetimeIndex by date.

• by slices: df.loc[start:end] . If there are multiple date ranges, the single slices may be concatenated with pd.concat .

• by boolean selection mask: df.loc[mask]

---

Using pd.concat and slices :

import numpy as np
import pandas as pd
np.random.seed(2016)

N = 10**2
df = pd.DataFrame(np.random.randint(10, size=(N, 2)), 
                  index=pd.date_range('2016-6-27', periods=N, freq='45T'))

result = pd.concat([df.loc['2016-6-27':'2016-6-27 5:00'],
                    df.loc['2016-6-27 15:00':'2016-6-27 23:59:59']])

yields

                     0  1
2016-06-27 00:00:00  0  2
2016-06-27 00:45:00  5  5
2016-06-27 01:30:00  9  6
2016-06-27 02:15:00  8  4
2016-06-27 03:00:00  5  0
2016-06-27 03:45:00  4  8
2016-06-27 04:30:00  7  0
2016-06-27 15:00:00  2  5
2016-06-27 15:45:00  6  7
2016-06-27 16:30:00  6  8
2016-06-27 17:15:00  5  1
2016-06-27 18:00:00  2  9
2016-06-27 18:45:00  9  1
2016-06-27 19:30:00  9  7
2016-06-27 20:15:00  3  6
2016-06-27 21:00:00  3  5
2016-06-27 21:45:00  0  8
2016-06-27 22:30:00  5  6
2016-06-27 23:15:00  0  8

---

Note that unlike most slicing syntaxes used in Python,

df.loc['2016-6-27':'2016-6-27 5:00']

is inclusive on both ends -- the slice defines a closed interval, is not a half-open interval.

---

Using a boolean selection mask:

mask = (((df.index >= '2016-6-27') & (df.index <= '2016-6-27 5:00')) 
        | ((df.index >= '2016-6-27 15:00') & (df.index < '2016-6-28')))
result2 = df.loc[mask]
assert result.equals(result2)

Answer 2

I feel the best option will be to use the direct checks rather than using loc function:

df = df[((df.index >= '2016-6-27') & (df.index <= '2016-6-27 5:00')) 
    | ((df.index >= '2016-6-27 15:00') & (df.index < '2016-6-28'))]

It works for me.

Major issue with loc function with a slice is that the limits should be present in the actual values, if not this will result in KeyError.