将 3D xarray 数据集转换为数据框

Question

I have imported a xarray dataset like this and extracted the values at coordinates defined by zones from a csv file, and a time period defined by a date range (30 days of a (lon,lat) grid with some environmental values for every coordinates).

from xgrads import open_CtlDataset
ds_Snow = open_CtlDataset(path + 'file')
ds_Snow = ds_Snow.sel(lat = list(set(zones['lat'])), lon = list(set(zones['lon'])), 
time = period, method = 'nearest')

When i look for the information of ds_Snow, this is what I get :

Dimensions:  (lat: 12, lon: 12, time: 30)
Coordinates:
  * time     (time) datetime64[ns] 2000-09-01 2000-09-02 ... 2000-09-30
  * lat      (lat) float32 3.414e+06 3.414e+06 3.414e+06 ... 3.414e+06 3.414e+06
  * lon      (lon) float32 6.873e+05 6.873e+05 6.873e+05 ... 6.873e+05 6.873e+05
Data variables:
    spre     (time, lat, lon) float32 dask.array<chunksize=(1, 12, 12), meta=np.ndarray>
Attributes:
    title:    SnowModel
    undef:    -9999.0 type : <class 'xarray.core.dataset.Dataset'>

I would like to make it a dataframe, respecting the initial dimensions (time, lat, lon). So I did this :

df_Snow = ds_Snow.to_dataframe()

But here are the dimensions of the dataframe :

print(df_Snow)
lat       lon        time            
3414108.0 687311.625 2000-09-01   0.0
                     2000-09-02   0.0
                     2000-09-03   0.0
                     2000-09-04   0.0
                     2000-09-05   0.0
...                               ...
                     2000-09-26   0.0
                     2000-09-27   0.0
                     2000-09-28   0.0
                     2000-09-29   0.0
                     2000-09-30   0.0

[4320 rows x 1 columns]

It looks like all the data just got put in a single column. I have tried giving the dimensions orders as some documentation explained :

df_Snow = ds_Snow.to_dataframe(dim_order = ['time', 'lat', 'lon'])

But it does not change anything, and I can't seem to find an answer in forums or the documentation. I would like to know a way to keep the array configuration in the dataframe.

EDIT : I found a solution

Instead of converting the xarray, I chose to build my dataframe with pd.Series of each attributes like this :

ds_Snow = ds_Snow.sel(lat = list(set(station_list['lat_utm'])),lon = list(set(station_list['lon_utm'])), time = Ind_Run_ERA5_Land, method = 'nearest')
time = pd.Series(ds_Snow.coords["time"].values)
lon = pd.Series(ds_Snow.coords["lon"].values)
lat = pd.Series(ds_Snow.coords["lat"].values)
spre = pd.Series(ds_Snow['spre'].values[:,0,0])
frame = { 'spre': spre, 'time': time, 'lon' : lon,  'lat' : lat}
df_Snow = pd.DataFrame(frame)

Answer 1

This is the expected behaviour. From the docs :

The DataFrame is indexed by the Cartesian product of index coordinates (in the form of a pandas.MultiIndex ). Other coordinates are included as columns in the DataFrame.

There is only one variable, spre , in the dataset. The other properties, the 'coordinates' have become the index. Since there were several coordinates ( lat , lon , and time ), the DataFrame has a hierarchical MultiIndex .

You can either get the index data through tools like get_level_values or, if you want to change how the DataFrame is indexed, you can use reset_index() .