import numpy as np
import pandas as pd
import xarray as xr
data = xr.DataArray(np.random.randn(2,3),
dims=('x', 'y'),
coords={'x': [10,20]})
data
array([[ 0.34345939, -0.46292499, -1.950736 ], [-0.80822203, 0.39672384, -0.97251945]])
array([10, 20])
data.values
array([[ 0.34345939, -0.46292499, -1.950736 ], [-0.80822203, 0.39672384, -0.97251945]])
type(data.values)
numpy.ndarray
data.dims
('x', 'y')
data.coords
Coordinates: * x (x) int64 10 20
data.attrs
{}
# positional and by integer label, like numpy
data[0, :]
array([ 0.34345939, -0.46292499, -1.950736 ])
array(10)
# loc or "location": positional and coordinate label, like pandas
data.loc[10]
array([ 0.34345939, -0.46292499, -1.950736 ])
array(10)
# isel or "integer select": by dimension name and integer label
data.isel(x=0)
array([ 0.34345939, -0.46292499, -1.950736 ])
array(10)
# sel or "select": by dimension name and coordinate label
data.sel(x=10)
array([ 0.34345939, -0.46292499, -1.950736 ])
array(10)
When setting up a DataArray, it's a good idea to set metadata attributes.
data.attrs['long_name'] = 'random velocity'
data.attrs['units'] = 'meters/sec'
data.attrs['description'] = 'A random variable created as an example'
data.attrs['random_attribute'] = 123
data
array([[ 0.34345939, -0.46292499, -1.950736 ], [-0.80822203, 0.39672384, -0.97251945]])
array([10, 20])
np.sin(data)
array([[ 0.33674645, -0.44656715, -0.92868701], [-0.72306012, 0.38639872, -0.82630734]])
array([10, 20])
data.T
array([[ 0.34345939, -0.80822203], [-0.46292499, 0.39672384], [-1.950736 , -0.97251945]])
array([10, 20])
data.sum()
array(-3.45421924)
labels = xr.DataArray(['E', 'F', 'E'], [data.coords['y']], name='labels')
labels
array(['E', 'F', 'E'], dtype='<U1')
array([0, 1, 2])
data.groupby(labels).mean('y')
array([[-0.80363831, -0.46292499], [-0.89037074, 0.39672384]])
array([10, 20])
array(['E', 'F'], dtype=object)
data.plot()
<matplotlib.collections.QuadMesh at 0x7f2e4e275190>
ds = xr.Dataset({'foo': data,
'bar': ('x', [1, 2]),
'baz': np.pi})
ds
array([10, 20])
array([[ 0.34345939, -0.46292499, -1.950736 ], [-0.80822203, 0.39672384, -0.97251945]])
array([1, 2])
array(3.14159265)
NetCDF is the recommended format.
ds.to_netcdf('example.nc')
x = xr.open_dataset('example.nc')
x
array([10, 20])
array([[ 0.343459, -0.462925, -1.950736], [-0.808222, 0.396724, -0.972519]])
array([1, 2])
array(3.141593)
?xr.DataArray
Init signature: xr.DataArray( data: Any = <NA>, coords: Union[Sequence[Tuple], Mapping[Hashable, Any], NoneType] = None, dims: Union[Hashable, Sequence[Hashable], NoneType] = None, name: Hashable = None, attrs: Mapping = None, indexes: Dict[Hashable, pandas.core.indexes.base.Index] = None, fastpath: bool = False, ) Docstring: N-dimensional array with labeled coordinates and dimensions. DataArray provides a wrapper around numpy ndarrays that uses labeled dimensions and coordinates to support metadata aware operations. The API is similar to that for the pandas Series or DataFrame, but DataArray objects can have any number of dimensions, and their contents have fixed data types. Additional features over raw numpy arrays: - Apply operations over dimensions by name: ``x.sum('time')``. - Select or assign values by integer location (like numpy): ``x[:10]`` or by label (like pandas): ``x.loc['2014-01-01']`` or ``x.sel(time='2014-01-01')``. - Mathematical operations (e.g., ``x - y``) vectorize across multiple dimensions (known in numpy as "broadcasting") based on dimension names, regardless of their original order. - Keep track of arbitrary metadata in the form of a Python dictionary: ``x.attrs`` - Convert to a pandas Series: ``x.to_series()``. Getting items from or doing mathematical operations with a DataArray always returns another DataArray. Init docstring: Parameters ---------- data : array_like Values for this array. Must be an ``numpy.ndarray``, ndarray like, or castable to an ``ndarray``. If a self-described xarray or pandas object, attempts are made to use this array's metadata to fill in other unspecified arguments. A view of the array's data is used instead of a copy if possible. coords : sequence or dict of array_like objects, optional Coordinates (tick labels) to use for indexing along each dimension. The following notations are accepted: - mapping {dimension name: array-like} - sequence of tuples that are valid arguments for xarray.Variable() - (dims, data) - (dims, data, attrs) - (dims, data, attrs, encoding) Additionally, it is possible to define a coord whose name does not match the dimension name, or a coord based on multiple dimensions, with one of the following notations: - mapping {coord name: DataArray} - mapping {coord name: Variable} - mapping {coord name: (dimension name, array-like)} - mapping {coord name: (tuple of dimension names, array-like)} dims : hashable or sequence of hashable, optional Name(s) of the data dimension(s). Must be either a hashable (only for 1D data) or a sequence of hashables with length equal to the number of dimensions. If this argument is omitted, dimension names default to ``['dim_0', ... 'dim_n']``. name : str or None, optional Name of this array. attrs : dict_like or None, optional Attributes to assign to the new instance. By default, an empty attribute dictionary is initialized. File: ~/anaconda3/envs/fastai/lib/python3.8/site-packages/xarray/core/dataarray.py Type: type Subclasses:
data = np.random.randn(4,3)
locs = ['IA', 'IL', 'IN']
times = pd.date_range('2000-01-01', periods=4)
da = xr.DataArray(data, coords=[times, locs], dims=['time', 'space'])
da
array([[ 1.23897824, -0.50397658, 0.52884644], [ 0.28392284, 0.89488413, 1.15145111], [ 0.35966487, -0.74219201, 0.42086507], [ 0.91730451, 0.90061316, 0.36556488]])
array(['2000-01-01T00:00:00.000000000', '2000-01-02T00:00:00.000000000', '2000-01-03T00:00:00.000000000', '2000-01-04T00:00:00.000000000'], dtype='datetime64[ns]')
array(['IA', 'IL', 'IN'], dtype='<U2')
xr.DataArray(data)
array([[ 1.23897824, -0.50397658, 0.52884644], [ 0.28392284, 0.89488413, 1.15145111], [ 0.35966487, -0.74219201, 0.42086507], [ 0.91730451, 0.90061316, 0.36556488]])
# It will return the coords
da['time']
array(['2000-01-01T00:00:00.000000000', '2000-01-02T00:00:00.000000000', '2000-01-03T00:00:00.000000000', '2000-01-04T00:00:00.000000000'], dtype='datetime64[ns]')
array(['2000-01-01T00:00:00.000000000', '2000-01-02T00:00:00.000000000', '2000-01-03T00:00:00.000000000', '2000-01-04T00:00:00.000000000'], dtype='datetime64[ns]')
da.coords['time']
array(['2000-01-01T00:00:00.000000000', '2000-01-02T00:00:00.000000000', '2000-01-03T00:00:00.000000000', '2000-01-04T00:00:00.000000000'], dtype='datetime64[ns]')
array(['2000-01-01T00:00:00.000000000', '2000-01-02T00:00:00.000000000', '2000-01-03T00:00:00.000000000', '2000-01-04T00:00:00.000000000'], dtype='datetime64[ns]')
temp = xr.DataArray(data, dims=('time', 'space'))
temp['time']
array([0, 1, 2, 3])
latitude = [f'lat_{x}' for x in range(5)]
longitude = [f'long_{x}' for x in range(7)]
time_stamps = [f'time_{x}' for x in range(10)]
temperature = np.random.randn(5, 7, 10) # lat, long, time
percipitation = np.random.randn(5, 7, 10) # lat, long, time
# We can combine all the above data into a a DataArray where we would need to concatenate the arrays
da = xr.DataArray(data = np.stack((temperature, percipitation)),
coords = {
'type': ['temperature', 'percipitation'],
'latitude': latitude,
'longitude': longitude,
'time': time_stamps,
},
dims = ('type', 'latitude', 'longitude', 'time'))
da
array([[[[ 4.47082280e-01, 1.88325936e-01, -1.65918865e+00, 1.96480075e+00, 6.98552249e-01, 2.10955846e-02, 5.31266582e-01, -2.64638148e-01, 7.87175419e-02, 7.03485520e-01], [ 7.85183837e-02, 1.72373696e+00, -9.65377936e-01, 5.98948995e-01, -1.27011106e+00, -7.63483334e-01, 1.37769508e-01, 7.87209551e-01, -2.54826616e-01, -1.53759980e+00], [ 8.57916718e-01, -3.90329906e-01, -1.54942226e-01, -8.53896468e-01, -1.92076695e-01, 1.15669759e+00, -5.49514847e-01, -1.60112365e+00, -8.44298420e-01, -1.26316434e+00], [ 1.81813781e+00, 1.42282242e+00, -1.48482163e+00, 1.82004470e-01, -1.39025368e+00, 5.29204619e-01, 7.49478325e-01, 1.28561092e+00, 1.47027226e+00, 9.43837720e-01], [ 8.42901554e-01, -2.91785660e-01, -7.14993131e-01, 7.20818304e-01, 1.58972556e+00, -8.33146239e-01, 2.51293524e+00, 4.13376553e-01, -1.15030723e+00, 7.58735368e-01], [-2.12996285e+00, -2.40198756e-01, -2.12210377e+00, 9.65198272e-01, 1.24345220e+00, 1.06780432e+00, -1.05646966e+00, -3.44437988e-01, -9.58907488e-01, -1.50462941e-01], [ 6.32816549e-01, 1.99637604e+00, 3.23199055e-02, 6.39374009e-02, 7.72283669e-01, 3.37877926e-01, -5.78279517e-03, 1.13145345e+00, -3.03170748e-01, 1.43652658e+00]], [[-1.46075130e-02, 1.08242456e+00, 5.67060448e-01, -2.86864954e-01, 5.66171025e-01, 3.76389567e-01, -2.66608668e-01, 2.12104874e+00, 8.08504260e-01, -1.01268815e+00], [ 4.16540149e-01, -1.00675499e-02, 4.97299842e-01, -2.33661833e-01, 6.32385643e-01, 4.97859116e-01, -3.92504378e-01, -3.27737871e-01, -4.91882335e-02, 1.53466583e+00], [ 2.12841515e-02, -1.32696704e+00, -6.02513124e-01, -8.56689905e-01, -9.21479647e-01, -5.44468247e-01, 3.61380503e-01, 1.64537304e+00, -8.97327640e-01, 3.36621871e-02], [ 1.34930009e-01, 1.56130366e+00, -7.33810358e-01, -5.30501942e-01, -1.29241566e+00, 4.81763921e-01, 3.04834855e-01, 1.84299323e+00, 1.29405763e+00, -3.60854347e-01], [ 1.70391353e+00, 5.20875619e-01, -9.39673086e-02, -1.41955578e-01, -6.40285755e-01, 1.36969125e+00, 6.49649564e-01, -5.60591897e-01, 1.79905082e+00, -5.77566757e-01], [-1.10154643e+00, 7.72232113e-01, -3.39117082e-01, -1.66640288e+00, -2.05469398e-01, 1.40308338e+00, -7.61560096e-01, 8.80326266e-01, -1.13936494e+00, -5.63570449e-01], [-1.44480760e+00, 4.63246613e-01, 5.81894693e-01, -7.54153399e-01, -6.87779723e-02, -9.20962186e-01, 7.93750677e-02, -1.22943674e+00, 1.23499065e+00, -6.74342807e-01]], [[-8.36659114e-01, -1.29182103e+00, 1.88148694e+00, -6.53348563e-01, -8.46004026e-01, -4.41520977e-02, -2.76054883e-01, -8.46625688e-01, -2.60585400e-01, 1.29195018e+00], [-5.77618276e-01, 1.69487356e-01, -6.44966796e-01, 4.84038798e-01, -4.64464691e-01, 1.06838558e+00, 2.79462948e-01, -4.29643808e-02, 4.08872780e-02, -1.25191673e+00], [ 5.37682611e-01, 7.37622047e-01, 3.23784140e-01, -5.87974666e-01, 3.05068113e-01, -4.69658112e-01, -5.70152918e-01, -1.53927468e+00, -3.99153429e-02, 1.21876832e+00], [ 1.13971663e+00, 2.10148952e+00, -7.53603391e-01, -4.28045255e-02, -9.71322011e-01, 9.27275786e-01, 5.46093109e-01, -3.08377839e-01, -1.39184711e+00, 4.34415911e-01], [-6.41474500e-01, 6.51804949e-01, -6.03503320e-01, -1.88860212e+00, -6.53414353e-01, 8.68952115e-01, -1.42410059e+00, 3.27591694e-01, -9.83548722e-02, -3.04908843e-01], [ 1.69887077e+00, 4.72866697e-01, -5.60552876e-01, 6.78849932e-01, 1.79923003e-01, 1.47708576e+00, -5.96143983e-01, 5.74009397e-01, -7.72183640e-01, -5.64594485e-01], [ 3.29787928e-01, -5.85506516e-01, -1.08064827e-02, 1.51993864e+00, -8.68891696e-01, -1.24491560e+00, -3.82781340e-01, 6.49207727e-01, 2.16470147e+00, -1.09380609e+00]], [[-1.13840594e+00, 1.43940136e+00, -1.52970061e+00, 5.54932858e-01, -1.38118679e+00, 5.21925881e-01, 1.03409791e-01, -8.00557350e-01, 1.79107828e-01, 1.54439893e+00], [-6.13141539e-01, 8.14198750e-02, -1.20365819e+00, 2.18639878e+00, 1.88222602e-01, 1.10757875e+00, 1.11402036e+00, 1.19893194e+00, -9.09108050e-01, -1.03917429e+00], [ 3.13309623e-01, -2.45999367e-01, -5.39580241e-01, 1.81800001e+00, 2.47654134e-02, -6.36250151e-01, -6.31603376e-01, 1.80472465e+00, -1.51253475e+00, 1.35065707e+00], [ 6.41437142e-01, 6.73775307e-01, 4.23653617e-01, -8.59234436e-01, 1.28709535e+00, -5.24715025e-01, 3.02887411e-01, -3.78819290e-01, -6.56721031e-01, 1.54957492e+00], [ 2.04571944e-01, -1.08270571e+00, 1.12990285e+00, 7.36778261e-01, -2.34989521e+00, -2.96735952e-01, 2.76801759e-01, 1.39717217e+00, 2.28054678e+00, -8.19688725e-01], [ 1.90507914e-02, -3.53468491e-01, 1.30640256e+00, 2.24974458e-02, -7.84420950e-02, 1.33528962e+00, 6.99245318e-01, 7.28682355e-01, -4.73279038e-01, -1.71499183e+00], [ 3.71737813e-01, -8.38707318e-01, -4.52707968e-01, -4.84228595e-01, 3.60778328e-01, -7.93612742e-01, 6.37497719e-01, -1.61516681e+00, 4.08658646e-01, 1.11670202e+00]], [[-9.46153034e-02, -9.02874500e-01, -4.47838404e-01, 7.27205096e-01, -1.57359623e+00, -1.64194481e-03, 1.54513822e-01, -7.51058128e-01, 4.76101569e-01, -5.62799932e-01], [-1.35422420e-01, -3.80745497e-01, -1.32484712e-01, 1.22308235e+00, -1.42887261e+00, -1.24119903e+00, -5.33782502e-01, 4.28762130e-01, 7.44995384e-01, -2.14342985e-01], [-2.93174403e+00, -9.78230282e-02, -7.22133526e-01, -1.24206554e+00, -1.56613559e+00, 1.36722555e-02, 1.81516126e-01, -9.62803521e-01, -7.09752494e-01, -2.65044789e-01], [-8.93038923e-01, -4.44854652e-01, 2.14106328e+00, -6.82083853e-01, 3.34656869e-01, 3.60197001e-01, 2.30352860e+00, 1.13177694e+00, 1.33328048e+00, -1.21632057e+00], [-7.84637836e-01, 1.25186685e+00, 9.87137134e-01, 4.44238419e-01, -5.40098366e-01, 1.43930086e+00, 1.98211477e-01, -4.81647996e-01, -7.30037498e-01, -3.41858850e-01], [-1.13102542e+00, 6.92883650e-01, -9.66154570e-01, -3.40482739e-01, -3.39012159e-01, -6.04437420e-01, -4.65921539e-01, -1.10440763e+00, -9.93330871e-01, 3.23721627e-01], [-6.32289765e-01, 1.12559954e+00, 9.12726508e-01, 9.66262908e-01, -1.60492717e+00, -3.64185114e-02, 6.19490414e-01, 6.08557370e-01, 2.31685529e+00, 3.66790179e-01]]], [[[ 1.46230068e+00, 8.07572287e-02, -6.01856051e-01, 7.18512761e-02, 3.77263118e-01, -1.16271485e+00, -3.87722297e-02, 3.66381096e-01, -1.31578227e+00, -5.85342801e-01], [-2.68171573e-01, 2.16536304e+00, -4.72914440e-01, 1.71781749e+00, 1.26667557e+00, -8.54666485e-01, 1.58786820e+00, 9.35442051e-01, -6.32194173e-01, -1.53763104e+00], [ 4.52895448e-02, -1.25167871e+00, 6.24486418e-02, -7.92381600e-01, -5.67580200e-01, 1.02598199e+00, -4.06233617e-01, 2.71447646e-01, 8.57238463e-02, 1.24019192e+00], [ 9.49614350e-01, -4.78221622e-01, -7.43613505e-01, 1.96037367e+00, -3.80524633e-01, 8.42647892e-01, -1.83463225e+00, -2.19009596e-01, -1.08708616e-01, -1.74945238e+00], [ 6.63305609e-01, 1.54482485e+00, 8.98084961e-01, -7.20663486e-01, -1.25839118e+00, 9.77754133e-01, -4.30710401e-01, -1.06085281e+00, 3.95528107e-01, 6.96899423e-01], [-1.81517643e+00, 1.10109033e+00, -1.05124025e+00, -9.10892968e-01, -3.23287290e-01, -3.85680324e-01, 4.96756579e-01, -1.04497196e+00, 8.82877770e-01, -3.65229864e-01], [-1.26437510e+00, 6.13302049e-01, -5.89131967e-01, 4.88827088e-01, -9.21412019e-01, 3.21553348e-01, 8.34864841e-01, -1.68967767e+00, -3.07493785e-01, 2.34355405e+00]], [[ 1.27385793e+00, -1.61258906e+00, 1.08569596e+00, -1.69236001e+00, 4.62035529e-02, -9.92038913e-01, -1.65980953e+00, -2.20419960e+00, -2.77654590e-01, 6.92119551e-01], [-6.11906798e-01, 1.58983017e+00, 5.00987229e-01, -1.20221807e-01, -1.48229470e+00, -7.13229569e-03, -7.67535223e-03, -5.18371127e-01, -1.19055986e+00, -3.09270500e-01], [-5.69787714e-01, 3.78760369e-01, -5.80627453e-02, 6.61683511e-01, 1.47655368e+00, 5.27595058e-01, 7.96106236e-01, 7.82868192e-01, 7.62108396e-02, 4.10999101e-01], [-2.42864578e+00, 4.27300146e-01, 1.15223280e-02, -1.68764933e+00, 1.43505537e+00, 1.16015151e-02, 1.96201761e+00, 1.58115371e-02, -4.92779191e-02, -4.06677499e-02], [-4.29540013e-01, -9.53899561e-01, -3.60073480e-01, -8.32266923e-01, 1.99016556e-01, -5.26238858e-01, -9.73570853e-01, -1.24645317e+00, -4.52543371e-01, 3.72234369e-02], [-5.26247576e-01, 1.05224734e-01, 2.98075234e-01, -9.87077305e-01, -5.92301572e-02, 1.62688709e+00, -1.91621779e-01, 3.16132210e-01, 4.35452909e-01, 7.64903098e-01], [-9.79479654e-01, -2.88970458e+00, -2.41051357e-01, 1.89022189e-01, 4.52298477e-01, -3.33496052e-01, 5.15203057e-01, 2.26426813e-01, -2.62564218e-01, 1.57058919e-01]], [[-4.50406229e-01, 9.20388181e-01, 1.55770479e-01, -1.03033403e+00, 2.61701618e-01, 5.02320869e-01, -5.60095650e-01, 5.75639541e-01, -4.23664088e-01, -1.45820035e+00], [ 2.86409112e-01, -1.63079375e+00, -4.01629321e-01, 5.28533019e-01, 4.06141367e-01, -5.16891243e-01, 1.80348153e-02, -3.06741567e-01, 7.27861862e-01, -7.44540990e-01], [-1.67870655e+00, 1.54444464e+00, -2.30235435e+00, -3.90642581e-01, -1.30590838e+00, -5.33501133e-02, -5.61602279e-01, -6.79607546e-01, 8.60888875e-01, -1.58768599e+00], [-5.44507443e-01, 6.24262606e-01, -1.14937628e+00, 5.25618878e-01, -2.54515977e+00, -1.04121584e+00, -2.62767403e-01, -7.26376946e-01, 6.17967222e-01, -1.51217010e+00], [-8.05848357e-01, 5.31075568e-01, -6.44610044e-01, -1.54620828e-01, -1.06253060e+00, 2.42521975e+00, 2.25140477e-01, -1.52382558e+00, 3.76486085e-01, -7.20610561e-01], [ 5.94848057e-01, 9.77069286e-02, 2.48044006e-01, -4.12393871e-01, 1.21240643e+00, 4.25979873e-01, -5.87437164e-01, -8.49302210e-01, 3.80656478e-01, 3.38152110e-01], [-1.01970801e+00, 3.36556893e-01, 8.60829193e-01, 2.03690881e+00, 1.31452152e+00, -3.92999345e-01, -7.90134583e-01, 1.62128439e-01, -2.58516249e+00, -9.28245789e-01]], [[-1.14065104e+00, -7.12746481e-01, -5.78295227e-01, 2.13911270e+00, 1.61357513e+00, -3.51740975e-01, -6.80329460e-01, -1.00752466e+00, -1.59198324e+00, 3.40984799e-01], [ 4.33145126e-01, 8.99822016e-01, 5.28390844e-01, 2.54118174e+00, 9.94719982e-01, 3.41163521e-02, -1.32437203e+00, -2.46588374e+00, 2.01431624e-01, 1.31095910e+00], [-1.87932697e+00, 2.07708805e+00, -6.70885586e-01, -1.01601021e+00, -8.12864167e-01, -4.97487524e-01, 2.19963565e-02, -8.07188731e-01, 7.54789574e-01, 5.17694381e-01], [-1.48188794e+00, 4.83879876e-01, -3.56599707e-01, -2.77060491e+00, -1.83075444e+00, 5.49369992e-01, 1.09758068e+00, -2.14753948e-01, -5.11673616e-01, -2.08557638e-01], [ 8.61501141e-01, 1.09441177e+00, 2.44869625e-01, -9.33962281e-01, 6.28576149e-01, -9.84086178e-01, 9.69727920e-01, 1.34662108e+00, 1.04519818e+00, -1.40531462e+00], [-3.52923630e-01, -2.16347463e+00, -8.23557734e-01, 4.40372583e-01, 8.15664878e-01, 1.12985747e+00, -3.71730696e-01, 4.99399333e-01, -9.90729774e-01, 9.14705730e-01], [-4.81908267e-01, -6.94978724e-01, 5.93688013e-01, 1.52606800e+00, -7.81466504e-01, 1.02327100e+00, -2.10411241e-01, -4.00298347e-01, 1.13495693e-01, -5.32857582e-01]], [[-5.45379506e-01, 2.29447697e+00, -5.31319184e-01, -7.14600844e-01, -7.09983961e-01, -1.27071907e+00, 1.65168217e-01, 1.58337941e+00, 1.60992550e+00, 5.24714878e-01], [-1.07240908e-02, -1.42283233e+00, 1.96468816e-01, 7.89873504e-02, -3.53931710e-01, -4.92560088e-01, -1.96181228e+00, 1.90969687e+00, 1.12984050e+00, 1.39374732e+00], [ 7.89699460e-01, 7.04643401e-01, -1.30268574e+00, 1.52042164e+00, -1.82487175e+00, -5.58028973e-01, -8.74832450e-01, -2.15608885e-01, 5.29189101e-01, -5.23404318e-01], [-1.02119985e-01, -6.67684945e-01, -2.59287171e-02, 1.17551531e+00, -1.04645355e+00, 1.25068060e+00, 1.77309369e-01, 4.78210865e-01, -8.38697579e-01, -5.19175710e-01], [ 1.77708317e+00, -1.87136985e-01, 1.52219934e-02, 2.61370272e-01, -6.62074503e-01, 9.13160085e-02, -8.16252330e-02, 2.14630398e-01, 1.97106240e+00, 5.34741587e-02], [-7.25739965e-01, 9.00902158e-01, 3.79942271e-01, -4.09120297e-02, 8.48890253e-01, -2.10322690e-01, 1.66423184e+00, -5.88162228e-01, 1.21990084e+00, -6.86654272e-01], [ 4.35066796e-01, 2.50076863e-02, 3.30692644e-01, -6.77229842e-01, 8.18379613e-01, -3.19063040e-01, 5.15499828e-01, -5.26555109e-01, 3.69881695e-01, 6.16667813e-01]]]])
array(['temperature', 'percipitation'], dtype='<U13')
array(['lat_0', 'lat_1', 'lat_2', 'lat_3', 'lat_4'], dtype='<U5')
array(['long_0', 'long_1', 'long_2', 'long_3', 'long_4', 'long_5', 'long_6'], dtype='<U6')
array(['time_0', 'time_1', 'time_2', 'time_3', 'time_4', 'time_5', 'time_6', 'time_7', 'time_8', 'time_9'], dtype='<U6')
In the above example we kind of introduced complexity as we had to stack two arrays and introduce a new dimension named type
. We can see that in the above example for both the arrays (temperature and percipitation) they share the same dims. Dataset
can help in this case.
# First let's create temperature and percipitation DataArrays
temp_da = xr.DataArray(temperature,
coords = {
'latitude': latitude,
'longitude': longitude,
'time': time_stamps,
},
dims = ('latitude', 'longitude', 'time'))
percip_da = xr.DataArray(percipitation,
coords = {
'latitude': latitude,
'longitude': longitude,
'time': time_stamps,
},
dims = ('latitude', 'longitude', 'time'))
# Create Dataset
ds = xr.Dataset({
'temperature': temp_da,
'percipitation': percip_da,
})
ds
array(['lat_0', 'lat_1', 'lat_2', 'lat_3', 'lat_4'], dtype='<U5')
array(['long_0', 'long_1', 'long_2', 'long_3', 'long_4', 'long_5', 'long_6'], dtype='<U6')
array(['time_0', 'time_1', 'time_2', 'time_3', 'time_4', 'time_5', 'time_6', 'time_7', 'time_8', 'time_9'], dtype='<U6')
array([[[ 4.47082280e-01, 1.88325936e-01, -1.65918865e+00, 1.96480075e+00, 6.98552249e-01, 2.10955846e-02, 5.31266582e-01, -2.64638148e-01, 7.87175419e-02, 7.03485520e-01], [ 7.85183837e-02, 1.72373696e+00, -9.65377936e-01, 5.98948995e-01, -1.27011106e+00, -7.63483334e-01, 1.37769508e-01, 7.87209551e-01, -2.54826616e-01, -1.53759980e+00], [ 8.57916718e-01, -3.90329906e-01, -1.54942226e-01, -8.53896468e-01, -1.92076695e-01, 1.15669759e+00, -5.49514847e-01, -1.60112365e+00, -8.44298420e-01, -1.26316434e+00], [ 1.81813781e+00, 1.42282242e+00, -1.48482163e+00, 1.82004470e-01, -1.39025368e+00, 5.29204619e-01, 7.49478325e-01, 1.28561092e+00, 1.47027226e+00, 9.43837720e-01], [ 8.42901554e-01, -2.91785660e-01, -7.14993131e-01, 7.20818304e-01, 1.58972556e+00, -8.33146239e-01, 2.51293524e+00, 4.13376553e-01, -1.15030723e+00, 7.58735368e-01], [-2.12996285e+00, -2.40198756e-01, -2.12210377e+00, 9.65198272e-01, 1.24345220e+00, 1.06780432e+00, -1.05646966e+00, -3.44437988e-01, -9.58907488e-01, -1.50462941e-01], [ 6.32816549e-01, 1.99637604e+00, 3.23199055e-02, 6.39374009e-02, 7.72283669e-01, 3.37877926e-01, -5.78279517e-03, 1.13145345e+00, -3.03170748e-01, 1.43652658e+00]], [[-1.46075130e-02, 1.08242456e+00, 5.67060448e-01, -2.86864954e-01, 5.66171025e-01, 3.76389567e-01, -2.66608668e-01, 2.12104874e+00, 8.08504260e-01, -1.01268815e+00], [ 4.16540149e-01, -1.00675499e-02, 4.97299842e-01, -2.33661833e-01, 6.32385643e-01, 4.97859116e-01, -3.92504378e-01, -3.27737871e-01, -4.91882335e-02, 1.53466583e+00], [ 2.12841515e-02, -1.32696704e+00, -6.02513124e-01, -8.56689905e-01, -9.21479647e-01, -5.44468247e-01, 3.61380503e-01, 1.64537304e+00, -8.97327640e-01, 3.36621871e-02], [ 1.34930009e-01, 1.56130366e+00, -7.33810358e-01, -5.30501942e-01, -1.29241566e+00, 4.81763921e-01, 3.04834855e-01, 1.84299323e+00, 1.29405763e+00, -3.60854347e-01], [ 1.70391353e+00, 5.20875619e-01, -9.39673086e-02, -1.41955578e-01, -6.40285755e-01, 1.36969125e+00, 6.49649564e-01, -5.60591897e-01, 1.79905082e+00, -5.77566757e-01], [-1.10154643e+00, 7.72232113e-01, -3.39117082e-01, -1.66640288e+00, -2.05469398e-01, 1.40308338e+00, -7.61560096e-01, 8.80326266e-01, -1.13936494e+00, -5.63570449e-01], [-1.44480760e+00, 4.63246613e-01, 5.81894693e-01, -7.54153399e-01, -6.87779723e-02, -9.20962186e-01, 7.93750677e-02, -1.22943674e+00, 1.23499065e+00, -6.74342807e-01]], [[-8.36659114e-01, -1.29182103e+00, 1.88148694e+00, -6.53348563e-01, -8.46004026e-01, -4.41520977e-02, -2.76054883e-01, -8.46625688e-01, -2.60585400e-01, 1.29195018e+00], [-5.77618276e-01, 1.69487356e-01, -6.44966796e-01, 4.84038798e-01, -4.64464691e-01, 1.06838558e+00, 2.79462948e-01, -4.29643808e-02, 4.08872780e-02, -1.25191673e+00], [ 5.37682611e-01, 7.37622047e-01, 3.23784140e-01, -5.87974666e-01, 3.05068113e-01, -4.69658112e-01, -5.70152918e-01, -1.53927468e+00, -3.99153429e-02, 1.21876832e+00], [ 1.13971663e+00, 2.10148952e+00, -7.53603391e-01, -4.28045255e-02, -9.71322011e-01, 9.27275786e-01, 5.46093109e-01, -3.08377839e-01, -1.39184711e+00, 4.34415911e-01], [-6.41474500e-01, 6.51804949e-01, -6.03503320e-01, -1.88860212e+00, -6.53414353e-01, 8.68952115e-01, -1.42410059e+00, 3.27591694e-01, -9.83548722e-02, -3.04908843e-01], [ 1.69887077e+00, 4.72866697e-01, -5.60552876e-01, 6.78849932e-01, 1.79923003e-01, 1.47708576e+00, -5.96143983e-01, 5.74009397e-01, -7.72183640e-01, -5.64594485e-01], [ 3.29787928e-01, -5.85506516e-01, -1.08064827e-02, 1.51993864e+00, -8.68891696e-01, -1.24491560e+00, -3.82781340e-01, 6.49207727e-01, 2.16470147e+00, -1.09380609e+00]], [[-1.13840594e+00, 1.43940136e+00, -1.52970061e+00, 5.54932858e-01, -1.38118679e+00, 5.21925881e-01, 1.03409791e-01, -8.00557350e-01, 1.79107828e-01, 1.54439893e+00], [-6.13141539e-01, 8.14198750e-02, -1.20365819e+00, 2.18639878e+00, 1.88222602e-01, 1.10757875e+00, 1.11402036e+00, 1.19893194e+00, -9.09108050e-01, -1.03917429e+00], [ 3.13309623e-01, -2.45999367e-01, -5.39580241e-01, 1.81800001e+00, 2.47654134e-02, -6.36250151e-01, -6.31603376e-01, 1.80472465e+00, -1.51253475e+00, 1.35065707e+00], [ 6.41437142e-01, 6.73775307e-01, 4.23653617e-01, -8.59234436e-01, 1.28709535e+00, -5.24715025e-01, 3.02887411e-01, -3.78819290e-01, -6.56721031e-01, 1.54957492e+00], [ 2.04571944e-01, -1.08270571e+00, 1.12990285e+00, 7.36778261e-01, -2.34989521e+00, -2.96735952e-01, 2.76801759e-01, 1.39717217e+00, 2.28054678e+00, -8.19688725e-01], [ 1.90507914e-02, -3.53468491e-01, 1.30640256e+00, 2.24974458e-02, -7.84420950e-02, 1.33528962e+00, 6.99245318e-01, 7.28682355e-01, -4.73279038e-01, -1.71499183e+00], [ 3.71737813e-01, -8.38707318e-01, -4.52707968e-01, -4.84228595e-01, 3.60778328e-01, -7.93612742e-01, 6.37497719e-01, -1.61516681e+00, 4.08658646e-01, 1.11670202e+00]], [[-9.46153034e-02, -9.02874500e-01, -4.47838404e-01, 7.27205096e-01, -1.57359623e+00, -1.64194481e-03, 1.54513822e-01, -7.51058128e-01, 4.76101569e-01, -5.62799932e-01], [-1.35422420e-01, -3.80745497e-01, -1.32484712e-01, 1.22308235e+00, -1.42887261e+00, -1.24119903e+00, -5.33782502e-01, 4.28762130e-01, 7.44995384e-01, -2.14342985e-01], [-2.93174403e+00, -9.78230282e-02, -7.22133526e-01, -1.24206554e+00, -1.56613559e+00, 1.36722555e-02, 1.81516126e-01, -9.62803521e-01, -7.09752494e-01, -2.65044789e-01], [-8.93038923e-01, -4.44854652e-01, 2.14106328e+00, -6.82083853e-01, 3.34656869e-01, 3.60197001e-01, 2.30352860e+00, 1.13177694e+00, 1.33328048e+00, -1.21632057e+00], [-7.84637836e-01, 1.25186685e+00, 9.87137134e-01, 4.44238419e-01, -5.40098366e-01, 1.43930086e+00, 1.98211477e-01, -4.81647996e-01, -7.30037498e-01, -3.41858850e-01], [-1.13102542e+00, 6.92883650e-01, -9.66154570e-01, -3.40482739e-01, -3.39012159e-01, -6.04437420e-01, -4.65921539e-01, -1.10440763e+00, -9.93330871e-01, 3.23721627e-01], [-6.32289765e-01, 1.12559954e+00, 9.12726508e-01, 9.66262908e-01, -1.60492717e+00, -3.64185114e-02, 6.19490414e-01, 6.08557370e-01, 2.31685529e+00, 3.66790179e-01]]])
array([[[ 1.46230068, 0.08075723, -0.60185605, 0.07185128, 0.37726312, -1.16271485, -0.03877223, 0.3663811 , -1.31578227, -0.5853428 ], [-0.26817157, 2.16536304, -0.47291444, 1.71781749, 1.26667557, -0.85466649, 1.5878682 , 0.93544205, -0.63219417, -1.53763104], [ 0.04528954, -1.25167871, 0.06244864, -0.7923816 , -0.5675802 , 1.02598199, -0.40623362, 0.27144765, 0.08572385, 1.24019192], [ 0.94961435, -0.47822162, -0.74361351, 1.96037367, -0.38052463, 0.84264789, -1.83463225, -0.2190096 , -0.10870862, -1.74945238], [ 0.66330561, 1.54482485, 0.89808496, -0.72066349, -1.25839118, 0.97775413, -0.4307104 , -1.06085281, 0.39552811, 0.69689942], [-1.81517643, 1.10109033, -1.05124025, -0.91089297, -0.32328729, -0.38568032, 0.49675658, -1.04497196, 0.88287777, -0.36522986], [-1.2643751 , 0.61330205, -0.58913197, 0.48882709, -0.92141202, 0.32155335, 0.83486484, -1.68967767, -0.30749379, 2.34355405]], [[ 1.27385793, -1.61258906, 1.08569596, -1.69236001, 0.04620355, -0.99203891, -1.65980953, -2.2041996 , -0.27765459, 0.69211955], [-0.6119068 , 1.58983017, 0.50098723, -0.12022181, -1.4822947 , -0.0071323 , -0.00767535, -0.51837113, -1.19055986, -0.3092705 ], [-0.56978771, 0.37876037, -0.05806275, 0.66168351, 1.47655368, 0.52759506, 0.79610624, 0.78286819, 0.07621084, 0.4109991 ], [-2.42864578, 0.42730015, 0.01152233, -1.68764933, 1.43505537, 0.01160152, 1.96201761, 0.01581154, -0.04927792, -0.04066775], [-0.42954001, -0.95389956, -0.36007348, -0.83226692, 0.19901656, -0.52623886, -0.97357085, -1.24645317, -0.45254337, 0.03722344], [-0.52624758, 0.10522473, 0.29807523, -0.9870773 , -0.05923016, 1.62688709, -0.19162178, 0.31613221, 0.43545291, 0.7649031 ], [-0.97947965, -2.88970458, -0.24105136, 0.18902219, 0.45229848, -0.33349605, 0.51520306, 0.22642681, -0.26256422, 0.15705892]], [[-0.45040623, 0.92038818, 0.15577048, -1.03033403, 0.26170162, 0.50232087, -0.56009565, 0.57563954, -0.42366409, -1.45820035], [ 0.28640911, -1.63079375, -0.40162932, 0.52853302, 0.40614137, -0.51689124, 0.01803482, -0.30674157, 0.72786186, -0.74454099], [-1.67870655, 1.54444464, -2.30235435, -0.39064258, -1.30590838, -0.05335011, -0.56160228, -0.67960755, 0.86088887, -1.58768599], [-0.54450744, 0.62426261, -1.14937628, 0.52561888, -2.54515977, -1.04121584, -0.2627674 , -0.72637695, 0.61796722, -1.5121701 ], [-0.80584836, 0.53107557, -0.64461004, -0.15462083, -1.0625306 , 2.42521975, 0.22514048, -1.52382558, 0.37648609, -0.72061056], [ 0.59484806, 0.09770693, 0.24804401, -0.41239387, 1.21240643, 0.42597987, -0.58743716, -0.84930221, 0.38065648, 0.33815211], [-1.01970801, 0.33655689, 0.86082919, 2.03690881, 1.31452152, -0.39299935, -0.79013458, 0.16212844, -2.58516249, -0.92824579]], [[-1.14065104, -0.71274648, -0.57829523, 2.1391127 , 1.61357513, -0.35174098, -0.68032946, -1.00752466, -1.59198324, 0.3409848 ], [ 0.43314513, 0.89982202, 0.52839084, 2.54118174, 0.99471998, 0.03411635, -1.32437203, -2.46588374, 0.20143162, 1.3109591 ], [-1.87932697, 2.07708805, -0.67088559, -1.01601021, -0.81286417, -0.49748752, 0.02199636, -0.80718873, 0.75478957, 0.51769438], [-1.48188794, 0.48387988, -0.35659971, -2.77060491, -1.83075444, 0.54936999, 1.09758068, -0.21475395, -0.51167362, -0.20855764], [ 0.86150114, 1.09441177, 0.24486963, -0.93396228, 0.62857615, -0.98408618, 0.96972792, 1.34662108, 1.04519818, -1.40531462], [-0.35292363, -2.16347463, -0.82355773, 0.44037258, 0.81566488, 1.12985747, -0.3717307 , 0.49939933, -0.99072977, 0.91470573], [-0.48190827, -0.69497872, 0.59368801, 1.526068 , -0.7814665 , 1.023271 , -0.21041124, -0.40029835, 0.11349569, -0.53285758]], [[-0.54537951, 2.29447697, -0.53131918, -0.71460084, -0.70998396, -1.27071907, 0.16516822, 1.58337941, 1.6099255 , 0.52471488], [-0.01072409, -1.42283233, 0.19646882, 0.07898735, -0.35393171, -0.49256009, -1.96181228, 1.90969687, 1.1298405 , 1.39374732], [ 0.78969946, 0.7046434 , -1.30268574, 1.52042164, -1.82487175, -0.55802897, -0.87483245, -0.21560888, 0.5291891 , -0.52340432], [-0.10211998, -0.66768495, -0.02592872, 1.17551531, -1.04645355, 1.2506806 , 0.17730937, 0.47821087, -0.83869758, -0.51917571], [ 1.77708317, -0.18713699, 0.01522199, 0.26137027, -0.6620745 , 0.09131601, -0.08162523, 0.2146304 , 1.9710624 , 0.05347416], [-0.72573997, 0.90090216, 0.37994227, -0.04091203, 0.84889025, -0.21032269, 1.66423184, -0.58816223, 1.21990084, -0.68665427], [ 0.4350668 , 0.02500769, 0.33069264, -0.67722984, 0.81837961, -0.31906304, 0.51549983, -0.52655511, 0.3698817 , 0.61666781]]])
ds['temperature']
array([[[ 4.47082280e-01, 1.88325936e-01, -1.65918865e+00, 1.96480075e+00, 6.98552249e-01, 2.10955846e-02, 5.31266582e-01, -2.64638148e-01, 7.87175419e-02, 7.03485520e-01], [ 7.85183837e-02, 1.72373696e+00, -9.65377936e-01, 5.98948995e-01, -1.27011106e+00, -7.63483334e-01, 1.37769508e-01, 7.87209551e-01, -2.54826616e-01, -1.53759980e+00], [ 8.57916718e-01, -3.90329906e-01, -1.54942226e-01, -8.53896468e-01, -1.92076695e-01, 1.15669759e+00, -5.49514847e-01, -1.60112365e+00, -8.44298420e-01, -1.26316434e+00], [ 1.81813781e+00, 1.42282242e+00, -1.48482163e+00, 1.82004470e-01, -1.39025368e+00, 5.29204619e-01, 7.49478325e-01, 1.28561092e+00, 1.47027226e+00, 9.43837720e-01], [ 8.42901554e-01, -2.91785660e-01, -7.14993131e-01, 7.20818304e-01, 1.58972556e+00, -8.33146239e-01, 2.51293524e+00, 4.13376553e-01, -1.15030723e+00, 7.58735368e-01], [-2.12996285e+00, -2.40198756e-01, -2.12210377e+00, 9.65198272e-01, 1.24345220e+00, 1.06780432e+00, -1.05646966e+00, -3.44437988e-01, -9.58907488e-01, -1.50462941e-01], [ 6.32816549e-01, 1.99637604e+00, 3.23199055e-02, 6.39374009e-02, 7.72283669e-01, 3.37877926e-01, -5.78279517e-03, 1.13145345e+00, -3.03170748e-01, 1.43652658e+00]], [[-1.46075130e-02, 1.08242456e+00, 5.67060448e-01, -2.86864954e-01, 5.66171025e-01, 3.76389567e-01, -2.66608668e-01, 2.12104874e+00, 8.08504260e-01, -1.01268815e+00], [ 4.16540149e-01, -1.00675499e-02, 4.97299842e-01, -2.33661833e-01, 6.32385643e-01, 4.97859116e-01, -3.92504378e-01, -3.27737871e-01, -4.91882335e-02, 1.53466583e+00], [ 2.12841515e-02, -1.32696704e+00, -6.02513124e-01, -8.56689905e-01, -9.21479647e-01, -5.44468247e-01, 3.61380503e-01, 1.64537304e+00, -8.97327640e-01, 3.36621871e-02], [ 1.34930009e-01, 1.56130366e+00, -7.33810358e-01, -5.30501942e-01, -1.29241566e+00, 4.81763921e-01, 3.04834855e-01, 1.84299323e+00, 1.29405763e+00, -3.60854347e-01], [ 1.70391353e+00, 5.20875619e-01, -9.39673086e-02, -1.41955578e-01, -6.40285755e-01, 1.36969125e+00, 6.49649564e-01, -5.60591897e-01, 1.79905082e+00, -5.77566757e-01], [-1.10154643e+00, 7.72232113e-01, -3.39117082e-01, -1.66640288e+00, -2.05469398e-01, 1.40308338e+00, -7.61560096e-01, 8.80326266e-01, -1.13936494e+00, -5.63570449e-01], [-1.44480760e+00, 4.63246613e-01, 5.81894693e-01, -7.54153399e-01, -6.87779723e-02, -9.20962186e-01, 7.93750677e-02, -1.22943674e+00, 1.23499065e+00, -6.74342807e-01]], [[-8.36659114e-01, -1.29182103e+00, 1.88148694e+00, -6.53348563e-01, -8.46004026e-01, -4.41520977e-02, -2.76054883e-01, -8.46625688e-01, -2.60585400e-01, 1.29195018e+00], [-5.77618276e-01, 1.69487356e-01, -6.44966796e-01, 4.84038798e-01, -4.64464691e-01, 1.06838558e+00, 2.79462948e-01, -4.29643808e-02, 4.08872780e-02, -1.25191673e+00], [ 5.37682611e-01, 7.37622047e-01, 3.23784140e-01, -5.87974666e-01, 3.05068113e-01, -4.69658112e-01, -5.70152918e-01, -1.53927468e+00, -3.99153429e-02, 1.21876832e+00], [ 1.13971663e+00, 2.10148952e+00, -7.53603391e-01, -4.28045255e-02, -9.71322011e-01, 9.27275786e-01, 5.46093109e-01, -3.08377839e-01, -1.39184711e+00, 4.34415911e-01], [-6.41474500e-01, 6.51804949e-01, -6.03503320e-01, -1.88860212e+00, -6.53414353e-01, 8.68952115e-01, -1.42410059e+00, 3.27591694e-01, -9.83548722e-02, -3.04908843e-01], [ 1.69887077e+00, 4.72866697e-01, -5.60552876e-01, 6.78849932e-01, 1.79923003e-01, 1.47708576e+00, -5.96143983e-01, 5.74009397e-01, -7.72183640e-01, -5.64594485e-01], [ 3.29787928e-01, -5.85506516e-01, -1.08064827e-02, 1.51993864e+00, -8.68891696e-01, -1.24491560e+00, -3.82781340e-01, 6.49207727e-01, 2.16470147e+00, -1.09380609e+00]], [[-1.13840594e+00, 1.43940136e+00, -1.52970061e+00, 5.54932858e-01, -1.38118679e+00, 5.21925881e-01, 1.03409791e-01, -8.00557350e-01, 1.79107828e-01, 1.54439893e+00], [-6.13141539e-01, 8.14198750e-02, -1.20365819e+00, 2.18639878e+00, 1.88222602e-01, 1.10757875e+00, 1.11402036e+00, 1.19893194e+00, -9.09108050e-01, -1.03917429e+00], [ 3.13309623e-01, -2.45999367e-01, -5.39580241e-01, 1.81800001e+00, 2.47654134e-02, -6.36250151e-01, -6.31603376e-01, 1.80472465e+00, -1.51253475e+00, 1.35065707e+00], [ 6.41437142e-01, 6.73775307e-01, 4.23653617e-01, -8.59234436e-01, 1.28709535e+00, -5.24715025e-01, 3.02887411e-01, -3.78819290e-01, -6.56721031e-01, 1.54957492e+00], [ 2.04571944e-01, -1.08270571e+00, 1.12990285e+00, 7.36778261e-01, -2.34989521e+00, -2.96735952e-01, 2.76801759e-01, 1.39717217e+00, 2.28054678e+00, -8.19688725e-01], [ 1.90507914e-02, -3.53468491e-01, 1.30640256e+00, 2.24974458e-02, -7.84420950e-02, 1.33528962e+00, 6.99245318e-01, 7.28682355e-01, -4.73279038e-01, -1.71499183e+00], [ 3.71737813e-01, -8.38707318e-01, -4.52707968e-01, -4.84228595e-01, 3.60778328e-01, -7.93612742e-01, 6.37497719e-01, -1.61516681e+00, 4.08658646e-01, 1.11670202e+00]], [[-9.46153034e-02, -9.02874500e-01, -4.47838404e-01, 7.27205096e-01, -1.57359623e+00, -1.64194481e-03, 1.54513822e-01, -7.51058128e-01, 4.76101569e-01, -5.62799932e-01], [-1.35422420e-01, -3.80745497e-01, -1.32484712e-01, 1.22308235e+00, -1.42887261e+00, -1.24119903e+00, -5.33782502e-01, 4.28762130e-01, 7.44995384e-01, -2.14342985e-01], [-2.93174403e+00, -9.78230282e-02, -7.22133526e-01, -1.24206554e+00, -1.56613559e+00, 1.36722555e-02, 1.81516126e-01, -9.62803521e-01, -7.09752494e-01, -2.65044789e-01], [-8.93038923e-01, -4.44854652e-01, 2.14106328e+00, -6.82083853e-01, 3.34656869e-01, 3.60197001e-01, 2.30352860e+00, 1.13177694e+00, 1.33328048e+00, -1.21632057e+00], [-7.84637836e-01, 1.25186685e+00, 9.87137134e-01, 4.44238419e-01, -5.40098366e-01, 1.43930086e+00, 1.98211477e-01, -4.81647996e-01, -7.30037498e-01, -3.41858850e-01], [-1.13102542e+00, 6.92883650e-01, -9.66154570e-01, -3.40482739e-01, -3.39012159e-01, -6.04437420e-01, -4.65921539e-01, -1.10440763e+00, -9.93330871e-01, 3.23721627e-01], [-6.32289765e-01, 1.12559954e+00, 9.12726508e-01, 9.66262908e-01, -1.60492717e+00, -3.64185114e-02, 6.19490414e-01, 6.08557370e-01, 2.31685529e+00, 3.66790179e-01]]])
array(['lat_0', 'lat_1', 'lat_2', 'lat_3', 'lat_4'], dtype='<U5')
array(['long_0', 'long_1', 'long_2', 'long_3', 'long_4', 'long_5', 'long_6'], dtype='<U6')
array(['time_0', 'time_1', 'time_2', 'time_3', 'time_4', 'time_5', 'time_6', 'time_7', 'time_8', 'time_9'], dtype='<U6')
latitude = [f'lat_{x}' for x in range(2)]
longitude = [f'long_{x}' for x in range(3)]
temperature = np.random.randn(2, 3) # lat, long, time
da = xr.DataArray(data = temperature,
coords = {
'latitude': latitude,
'longitude': longitude,
},
dims = ('latitude', 'longitude'))
da
array([[-1.19488346, -0.37699944, -0.83201202], [ 0.55794168, -0.7171424 , -0.50806347]])
array(['lat_0', 'lat_1'], dtype='<U5')
array(['long_0', 'long_1', 'long_2'], dtype='<U6')
da.values
array([[-1.19488346, -0.37699944, -0.83201202], [ 0.55794168, -0.7171424 , -0.50806347]])
da[0,2].values
array(-0.83201202)
da.loc['lat_0', 'long_1'].values
array(-0.37699944)
da.isel(latitude=0).values
array([-1.19488346, -0.37699944, -0.83201202])
# The av=bove is same as
da[dict(latitude=0)].values
array([-1.19488346, -0.37699944, -0.83201202])
da.sel(latitude='lat_1').values
array([ 0.55794168, -0.7171424 , -0.50806347])
# The above is same as
da.loc[dict(latitude='lat_1')].values
array([ 0.55794168, -0.7171424 , -0.50806347])
latitude = [f'lat_{x}' for x in range(2)]
longitude = [f'long_{x}' for x in range(3)]
time_stamps = [f'time_{x}' for x in range(5)]
temperature = np.random.randn(2, 3, 5) # lat, long, time
da = xr.DataArray(data = temperature,
coords = {
'latitude': latitude,
'longitude': longitude,
'time': time_stamps,
},
dims = ('latitude', 'longitude', 'time'))
da.values
array([[[-2.19999848, 0.60035589, 0.3138689 , -1.34548462, 0.5856584 ], [-1.08251151, 0.08987359, -0.61163246, 0.78603542, -2.32751104], [ 0.20233695, 0.02227249, -0.71950543, 0.36714685, 0.60950142]], [[-0.25423416, 1.41147933, -0.23429217, 1.13515727, 0.61161487], [ 0.78062272, -0.40739367, -0.1340985 , 2.08598546, 0.09458488], [ 1.03933086, 0.7279347 , 0.3559329 , -0.51714241, -0.68835698]]])
da.isel(latitude=0, time=slice(None, 2)).values
array([[-2.19999848, 0.60035589], [-1.08251151, 0.08987359], [ 0.20233695, 0.02227249]])
da.sel(latitude='lat_0', time=slice(None, 'time_1')).values
array([[-2.19999848, 0.60035589], [-1.08251151, 0.08987359], [ 0.20233695, 0.02227249]])
da = xr.DataArray([1, 2, 3], [('x', [0, 1, 2])])
da
array([1, 2, 3])
array([0, 1, 2])
da.sel(x=[1.1, 1.9], method='nearest').values
array([2, 3])
da.sel(x=0.1, method='backfill').values
array(2)
da.sel(x=[0.5, 1, 1.9, 2, 2.5, 1.3], method='pad').values
array([1, 2, 2, 3, 3, 2])
da.reindex(x=[1.1, 1.5], method='nearest', tolerance=0.2).values
array([ 2., nan])