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# Loading a TCPD time series in Python
The ``load_dataset.py`` file contains example code to load a time series as a
``TimeSeries`` object.
```python
>>> from load_dataset import TimeSeries
>>> ts = TimeSeries.from_json('../../datasets/ozone/ozone.json')
```
To export the time series as a [pandas
DataFrame](https://pandas.pydata.org/pandas-docs/stable/getting_started/dsintro.html#dataframe),
simply use:
```python
>>> ts.df
t Total Emissions
0 0 380000.0
1 1 400000.0
2 2 440000.0
3 3 480000.0
4 4 510000.0
5 5 540000.0
...
```
The ``TimeSeries`` instance ``ts`` has an integer time axis at ``ts.t`` and
the observations at ``ts.y``. The time axis is zero-based by default. If you
prefer to use a one-based indexing, simply run:
```python
>>> ts.make_one_based()
>>> ts.df
t Total Emissions
0 1 380000.0
1 2 400000.0
2 3 440000.0
3 4 480000.0
4 5 510000.0
5 6 540000.0
...
```
Many of the time series in TCPD have date or datetime labels for the time
axis. This axis can be retrieved using:
```python
>>> ts.datestr
array(['1961', '1962', '1963', '1964', '1965', '1966', '1967', '1968',
...
'2009', '2010', '2011', '2012', '2013', '2014'], dtype='<U4')
```
which uses the date format stored in ``ts.datefmt``.
```python
>>> ts.datefmt
'%Y'
```
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