The Python library pandas provides a wealth of time and data functionality that make a Python developer's life much easier, including
- Conversion between a variety of time formats, see pandas.to_datetime()
- Creation of a datetime index over a specified period and having a speified frequency, see pandas.date_range()
- Use of datetime indexes as the primary index for a Series or DataFrame
- Resampling of timeseries data using a new datetime index
import pandas as pd ts = pd.date_range('05-05-2015 16:00', periods=30, freq='S') ts[:5]
with output:
<class 'pandas.tseries.index.DatetimeIndex'> [2015-05-05 16:00:00, ..., 2015-05-05 16:00:04] Length: 5, Freq: S, Timezone: None
Note the timezone is not specified so these timestamps are naive, they don't know their timezone, this information must be added separately using pytz
Using DatetimeIndex
A DatetimeIndex can be used as the index of a DataFrame or Series object:
import pandas as pd ts = pd.date_range('05-05-2015 16:00', periods=30, freq='S') df = pd.DataFrame(np.random.randn(30), index=ts)
The advantage of this is you can select the data df using a human readable timestamp or one that is constructed programmatically.
df['2015-05-05 16:00:09':'2015-05-05 16:00:15']
which outputs this (or something like it):
2015-05-05 16:00:09 -0.358329 2015-05-05 16:00:10 -1.253738 2015-05-05 16:00:11 -0.070274 2015-05-05 16:00:12 1.384043 2015-05-05 16:00:13 2.049855 2015-05-05 16:00:14 0.377063 2015-05-05 16:00:15 -0.346427