# ausweather
Download Australian weather data from the [Bureau of
Meteorology](http://www.bom.gov.au/climate/data/) via
[SILO](https://www.longpaddock.qld.gov.au/silo/) using Python
## Installation
Install from the command line:
```python
python -m pip install -U ausweather
```
## Example of how to use
In a Python interpreter session:
```python
>>> import ausweather
```
To use this package to download annual rainfall data for Kent Town, first you
need to find the station number using the BoM [Weather Station
Directory](http://www.bom.gov.au/climate/data/stations/). Then you can use the
``fetch_bom_station_from_silo(station_number, email_address)`` function to
return a dictionary:
```python
>>> data = ausweather.fetch_bom_station_from_silo(23090, 'kinverarity@hotmail.com')
station #: 23090 name: ADELAIDE (KENT TOWN) title: 23090 ADELAIDE (KENT TOWN) (fetched from SILO on 2020-03-04 16:23:26.395696)
>>> data.keys()
dict_keys(['silo_returned', 'station_no', 'station_name', 'title', 'df', 'annual', 'srn'])
```
The data is stored in this dictionary under the key "df":
```python
>>> data['df'].info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 25677 entries, 1 to 25677
Data columns (total 28 columns):
Date 25677 non-null datetime64[ns]
Day 25677 non-null int32
Date2 25677 non-null object
T.Max 25677 non-null float64
Smx 25677 non-null int32
T.Min 25677 non-null float64
Smn 25677 non-null int32
Rain 25677 non-null float64
Srn 25677 non-null int32
Evap 25677 non-null float64
Sev 25677 non-null object
Radn 25677 non-null float64
Ssl 25677 non-null int32
VP 25677 non-null float64
Svp 25677 non-null int32
RHmaxT 25677 non-null float64
RHminT 25677 non-null float64
FAO56 25677 non-null float64
Mlake 25677 non-null float64
Mpot 25677 non-null float64
Mact 25677 non-null float64
Mwet 25677 non-null float64
Span 25677 non-null float64
Ssp 25677 non-null int32
EvSp 25677 non-null float64
Ses 25677 non-null int32
MSLPres 25677 non-null float64
Sp 25677 non-null int32
dtypes: datetime64[ns](1), float64(16), int32(9), object(2)
memory usage: 4.6+ MB
```
To see annual rainfall, you can group-by the ``dt.year`` accessor of the "Date"
column:
```python
>>> df = data["df"]
>>> df.groupby(df.Date.dt.year).Rain.sum()
Date
1950 426.9
1951 677.9
1952 584.9
1953 601.0
1954 439.6
...
2016 820.8
2017 536.2
2018 429.8
2019 376.3
2020 101.6
Name: Rain, Length: 71, dtype: float64
```
## License
Released under the [MIT License](LICENSE.md).
## Version history
### Version 0.2.1 (3 Mar 2020)
- Fix bug for whitespace in BoM station name
### Version 0.2.0 (3 Mar 2020)
- Update, many changes.
### Version 0.1.0 (11 Feb 2020)
- Initial release
