Python Pandas: drop rows of a timeserie based on time range

Question

I have the following timeserie:

start = pd.to_datetime(\'2016-1-1\')
end = pd.to_datetime(\'2016-1-15\')
rng = pd.date_range(start, end, freq=\'2h\')
df = pd.Dat         


        

Answer 1

An obscure method is to use slice_indexer on your index by passing your start and end range, this will return a Slice object which you can use to index into your original index and then negate the values using isin:

In [20]:
df.loc[~df.index.isin(df.index[df.index.slice_indexer(start_remove, end_remove)])]

Out[20]:
                     values
timestamp                  
2016-01-01 00:00:00       0
2016-01-01 02:00:00      57
2016-01-01 04:00:00      98
2016-01-01 06:00:00      82
2016-01-01 08:00:00      24
2016-01-01 10:00:00       1
2016-01-01 12:00:00      41
2016-01-01 14:00:00      14
2016-01-01 16:00:00      40
2016-01-01 18:00:00      48
2016-01-01 20:00:00      77
2016-01-01 22:00:00      34
2016-01-02 00:00:00      88
2016-01-02 02:00:00      58
2016-01-02 04:00:00      72
2016-01-02 06:00:00      24
2016-01-02 08:00:00      32
2016-01-02 10:00:00      44
2016-01-02 12:00:00      57
2016-01-02 14:00:00      88
2016-01-02 16:00:00      97
2016-01-02 18:00:00      75
2016-01-02 20:00:00      46
2016-01-02 22:00:00      31
2016-01-03 00:00:00      60
2016-01-03 02:00:00      73
2016-01-03 04:00:00      79
2016-01-03 06:00:00      71
2016-01-03 08:00:00      53
2016-01-03 10:00:00      70
...                     ...
2016-01-12 14:00:00       5
2016-01-12 16:00:00      42
2016-01-12 18:00:00      17
2016-01-12 20:00:00      94
2016-01-12 22:00:00      63
2016-01-13 00:00:00      63
2016-01-13 02:00:00      50
2016-01-13 04:00:00      44
2016-01-13 06:00:00      35
2016-01-13 08:00:00      59
2016-01-13 10:00:00      53
2016-01-13 12:00:00      16
2016-01-13 14:00:00      68
2016-01-13 16:00:00      66
2016-01-13 18:00:00      56
2016-01-13 20:00:00      18
2016-01-13 22:00:00      59
2016-01-14 00:00:00       8
2016-01-14 02:00:00      60
2016-01-14 04:00:00      52
2016-01-14 06:00:00      87
2016-01-14 08:00:00      31
2016-01-14 10:00:00      91
2016-01-14 12:00:00      64
2016-01-14 14:00:00      53
2016-01-14 16:00:00      47
2016-01-14 18:00:00      87
2016-01-14 20:00:00      47
2016-01-14 22:00:00      27
2016-01-15 00:00:00      28

[120 rows x 1 columns]

Here you can see that 49 rows were removed from the original df

In [23]:
df.index.slice_indexer(start_remove, end_remove)

Out[23]:
slice(36, 85, None)

In [24]:
df.index.isin(df.index[df.index.slice_indexer(start_remove, end_remove)])

Out[24]:
array([False, False, False, False, False, False, False, False, False,
       False, False, False, False, False, False, False, False, False,
       False, False, False, False, False, False, False, False, False,
       False, False, False, False, False, False, False, False, False,
        True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True, False, False, False, False, False,
       ........
       False, False, False, False, False, False, False, False, False,
       False, False, False, False, False, False, False, False, False,
       False, False, False, False, False, False, False], dtype=bool)

and then invert the above using ~

Edit Actually you can achieve this without isin:

df.loc[df.index.difference(df.index[df.index.slice_indexer(start_remove, end_remove)])]

will also work.

Timings

Interestingly this is also the fastest method:

In [30]:
%timeit df.loc[df.index.difference(df.index[df.index.slice_indexer(start_remove, end_remove)])]

100 loops, best of 3: 4.05 ms per loop

In [31]:    
%timeit df.query('index < @start_remove or index > @end_remove')

10 loops, best of 3: 15.2 ms per loop

In [32]:    
%timeit df.loc[(df.index < start_remove) | (df.index > end_remove)]

100 loops, best of 3: 4.94 ms per loop