Pandas Time Series: Indexing by Time

Where the Pandas time series tools really become useful is when you begin to index data by timestamps.

For example, we can construct a Series object that has time indexed data:

index = pd.DatetimeIndex(['2014-07-04', '2014-08-04',
                          '2015-07-04', '2015-08-04'])
data = pd.Series([0, 1, 2, 3], index=index)
data

2014-07-04    0
2014-08-04    1
2015-07-04    2
2015-08-04    3
dtype: int64

Now that we have this data in a Series, we can make use of any of the Series indexing patterns we discussed in previous sections, passing values that can be coerced into dates:

data['2014-07-04':'2015-07-04']

2014-07-04    0
2014-08-04    1
2015-07-04    2
dtype: int64

There are additional special date-only indexing operations, such as passing a year to obtain a slice of all data from that year:

data['2015']

2015-07-04    2
2015-08-04    3
dtype: int64