Closed
Description
How do I go about calculating cumsums on a groupby object?
I have a Dataset that looks as the following:
lat = np.linspace(-5.175003, 5.9749985, 224)
lon = np.linspace(33.524994, 42.274994, 176)
time = pd.date_range(start='1981-01-31', end='2019-04-30', freq='M')
data = np.random.randn(len(time), len(lat), len(lon))
dims = ['time', 'lat', 'lon']
coords = {'time': time, 'lat': lat, 'lon': lon}
ds = xr.Dataset({'precip': (dims, data)}, coords=coords)
Out[]:
<xarray.Dataset>
Dimensions: (lat: 224, lon: 176, time: 460)
Coordinates:
* time (time) datetime64[ns] 1981-01-31 1981-02-28 ... 2019-04-30
* lat (lat) float64 -5.175 -5.125 -5.075 -5.025 ... 5.875 5.925 5.975
* lon (lon) float64 33.52 33.57 33.62 33.67 ... 42.12 42.17 42.22 42.27
Data variables:
precip (time, lat, lon) float64 0.006328 0.2969 1.564 ... 0.6675 2.32I need to groupby year and calculate the cumsum for each year. That way I will have a value for each month (timestep) and each pixel (lat - lon pair).
But the cumsum operation doesn't work on a groupby object
ds.groupby('time.year').cumsum(dim='time')
Out[]:
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-12-dceee5f5647c> in <module>
9 display(ds_)
10
---> 11 ds_.groupby('time.year').cumsum(dim='time')
AttributeError: 'DatasetGroupBy' object has no attribute 'cumsum'Is there a work around?
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.0 | packaged by conda-forge | (default, Nov 12 2018, 12:34:36)
[Clang 4.0.1 (tags/RELEASE_401/final)]
python-bits: 64
OS: Darwin
OS-release: 18.2.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
libhdf5: 1.10.4
libnetcdf: 4.6.2
xarray: 0.12.2
pandas: 0.24.2
numpy: 1.16.4
scipy: 1.3.0
netCDF4: 1.5.1.2
pydap: None
h5netcdf: None
h5py: 2.9.0
Nio: None
zarr: None
cftime: 1.0.3.4
nc_time_axis: None
PseudonetCDF: None
rasterio: 1.0.17
cfgrib: 0.9.7
iris: None
bottleneck: 1.2.1
dask: 1.2.2
distributed: 1.28.1
matplotlib: 3.1.0
cartopy: 0.17.0
seaborn: 0.9.0
numbagg: None
setuptools: 41.0.1
pip: 19.1
conda: None
pytest: 4.5.0
IPython: 7.1.1
sphinx: 2.0.1