Contents

Some tips with xarray and pandas

Some tips with xarray and pandas

  • We have massively different levels here

  • Try to make some aims for technical skills you can learn!

  • If you are beginning with python –> learn the basics

  • If you are good at basic python –> learn new packages

  • If you know all the packages –> improve your skills with producing your own software etc.

  • If you don’t know git and github –> get better at this!

What are pandas and xarray?

  • Pandas –> like a spreadsheet 2D data with columns and rows

  • xarray –> like pandas, but in N dimensions

!wget 'https://zenodo.org/record/5639504/files/OsloAeroSec2011-3_subset2.nc'

--2021-11-02 19:12:35--  https://zenodo.org/record/5639504/files/OsloAeroSec2011-3_subset2.nc
Resolving zenodo.org (zenodo.org)... 137.138.76.77
Connecting to zenodo.org (zenodo.org)|137.138.76.77|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 177021566 (169M) [application/octet-stream]
Saving to: ‘OsloAeroSec2011-3_subset2.nc’

OsloAeroSec2011-3_s 100%[===================>] 168.82M  12.9MB/s    in 6.3s    

2021-11-02 19:12:42 (26.7 MB/s) - ‘OsloAeroSec2011-3_subset2.nc’ saved [177021566/177021566]

Some examples with xarray and pandas:

import xarray as xr
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import datetime
import seaborn as sns

Reading in the data:

path='OsloAeroSec2011-3_subset2.nc'
ds = xr.open_dataset(path)

Opening multiple files:


list_of_files = [
    'file1.nc',
    'file2.nc'
]
xr.open_mfdataset(list_of_files, concat_dim='time',combine='by_coords')

Check how your dataset looks

Different types of information/data:

  • Coordinates

  • Data variables

  • Global attributes

  • Variable attributes

ds

<xarray.Dataset>
Dimensions:  (lat: 96, lev: 32, lon: 144, time: 36)
Coordinates:
  * lat      (lat) float64 -90.0 -88.11 -86.21 -84.32 ... 84.32 86.21 88.11 90.0
  * lon      (lon) float64 0.0 2.5 5.0 7.5 10.0 ... 350.0 352.5 355.0 357.5
  * lev      (lev) float64 3.643 7.595 14.36 24.61 ... 936.2 957.5 976.3 992.6
  * time     (time) datetime64[ns] 2011-02-01 2011-03-01 ... 2014-01-01
Data variables:
    hyam     (lev) float64 0.003643 0.007595 0.01436 ... 0.006255 0.001989 0.0
    hybm     (lev) float64 0.0 0.0 0.0 0.0 0.0 ... 0.9251 0.9512 0.9743 0.9926
    P0       float64 1e+05
    N_AER    (time, lev, lat, lon) float32 ...
    PS       (time, lat, lon) float32 ...
    T        (time, lev, lat, lon) float32 ...
    U        (time, lev, lat, lon) float32 ...
    V        (time, lev, lat, lon) float32 ...
Attributes:
    Conventions:       CF-1.0
    source:            CAM
    case:              OsloAeroSec_intBVOC_f19_f19
    logname:           x_sarbl
    host:              
    initial_file:      OsloAero_intBVOC_f19_f19_spinup.cam.i.2011-01-01-00000.nc
    topography_file:   /proj/cesm_input-data/inputdata/noresm-only/inputForNu...
    model_doi_url:     https://doi.org/10.5065/D67H1H0V
    time_period_freq:  month_1
    history:           Tue Nov  2 08:04:41 2021: ncrcat -v T,N_AER,V,U atm/hi...
    NCO:               netCDF Operators version 4.7.9 (Homepage = http://nco....

Sometimes we want to do some nice tweaks before we start:

ds['T']

<xarray.DataArray 'T' (time: 36, lev: 32, lat: 96, lon: 144)>
[15925248 values with dtype=float32]
Coordinates:
  * lat      (lat) float64 -90.0 -88.11 -86.21 -84.32 ... 84.32 86.21 88.11 90.0
  * lon      (lon) float64 0.0 2.5 5.0 7.5 10.0 ... 350.0 352.5 355.0 357.5
  * lev      (lev) float64 3.643 7.595 14.36 24.61 ... 936.2 957.5 976.3 992.6
  * time     (time) datetime64[ns] 2011-02-01 2011-03-01 ... 2014-01-01
Attributes:
    mdims:         1
    units:         K
    long_name:     Temperature
    cell_methods:  time: mean

Assign attributes! Nice for plotting and to keep track of what is in your dataset (especially ‘units’ and ‘standard_name’/’long_name’ will be looked for by xarray.

ds['T_C'] = ds['T']-273.15

ds['T_C'] = ds['T_C'].assign_attrs({'units': '$^\circ$C'})

May always be small things you need to adjust:

ds['time']

<xarray.DataArray 'time' (time: 36)>
array(['2011-02-01T00:00:00.000000000', '2011-03-01T00:00:00.000000000',
       '2011-04-01T00:00:00.000000000', '2011-05-01T00:00:00.000000000',
       '2011-06-01T00:00:00.000000000', '2011-07-01T00:00:00.000000000',
       '2011-08-01T00:00:00.000000000', '2011-09-01T00:00:00.000000000',
       '2011-10-01T00:00:00.000000000', '2011-11-01T00:00:00.000000000',
       '2011-12-01T00:00:00.000000000', '2012-01-01T00:00:00.000000000',
       '2012-02-01T00:00:00.000000000', '2012-03-01T00:00:00.000000000',
       '2012-04-01T00:00:00.000000000', '2012-05-01T00:00:00.000000000',
       '2012-06-01T00:00:00.000000000', '2012-07-01T00:00:00.000000000',
       '2012-08-01T00:00:00.000000000', '2012-09-01T00:00:00.000000000',
       '2012-10-01T00:00:00.000000000', '2012-11-01T00:00:00.000000000',
       '2012-12-01T00:00:00.000000000', '2013-01-01T00:00:00.000000000',
       '2013-02-01T00:00:00.000000000', '2013-03-01T00:00:00.000000000',
       '2013-04-01T00:00:00.000000000', '2013-05-01T00:00:00.000000000',
       '2013-06-01T00:00:00.000000000', '2013-07-01T00:00:00.000000000',
       '2013-08-01T00:00:00.000000000', '2013-09-01T00:00:00.000000000',
       '2013-10-01T00:00:00.000000000', '2013-11-01T00:00:00.000000000',
       '2013-12-01T00:00:00.000000000', '2014-01-01T00:00:00.000000000'],
      dtype='datetime64[ns]')
Coordinates:
  * time     (time) datetime64[ns] 2011-02-01 2011-03-01 ... 2014-01-01
Attributes:
    long_name:  time
    bounds:     time_bnds

This data in particular has an issue that the date is the end of the month, which gets read as the first of the next month. So I usually just to a quick fix and subtract roughly 15 days (half a month)

t_corrected= pd.to_datetime( ds['time'].values)- datetime.timedelta(days=15)
ds['time'] = t_corrected

ds['time']

<xarray.DataArray 'time' (time: 36)>
array(['2011-01-17T00:00:00.000000000', '2011-02-14T00:00:00.000000000',
       '2011-03-17T00:00:00.000000000', '2011-04-16T00:00:00.000000000',
       '2011-05-17T00:00:00.000000000', '2011-06-16T00:00:00.000000000',
       '2011-07-17T00:00:00.000000000', '2011-08-17T00:00:00.000000000',
       '2011-09-16T00:00:00.000000000', '2011-10-17T00:00:00.000000000',
       '2011-11-16T00:00:00.000000000', '2011-12-17T00:00:00.000000000',
       '2012-01-17T00:00:00.000000000', '2012-02-15T00:00:00.000000000',
       '2012-03-17T00:00:00.000000000', '2012-04-16T00:00:00.000000000',
       '2012-05-17T00:00:00.000000000', '2012-06-16T00:00:00.000000000',
       '2012-07-17T00:00:00.000000000', '2012-08-17T00:00:00.000000000',
       '2012-09-16T00:00:00.000000000', '2012-10-17T00:00:00.000000000',
       '2012-11-16T00:00:00.000000000', '2012-12-17T00:00:00.000000000',
       '2013-01-17T00:00:00.000000000', '2013-02-14T00:00:00.000000000',
       '2013-03-17T00:00:00.000000000', '2013-04-16T00:00:00.000000000',
       '2013-05-17T00:00:00.000000000', '2013-06-16T00:00:00.000000000',
       '2013-07-17T00:00:00.000000000', '2013-08-17T00:00:00.000000000',
       '2013-09-16T00:00:00.000000000', '2013-10-17T00:00:00.000000000',
       '2013-11-16T00:00:00.000000000', '2013-12-17T00:00:00.000000000'],
      dtype='datetime64[ns]')
Coordinates:
  * time     (time) datetime64[ns] 2011-01-17 2011-02-14 ... 2013-12-17

Convert longitude:

this data comes in 0–360 degrees, but often -180 to 180 is more convenient. So we can convert:

NOTE: Maybe you want to put this in a module? Or a package..

ds

<xarray.Dataset>
Dimensions:  (lat: 96, lev: 32, lon: 144, time: 36)
Coordinates:
  * lat      (lat) float64 -90.0 -88.11 -86.21 -84.32 ... 84.32 86.21 88.11 90.0
  * lon      (lon) float64 0.0 2.5 5.0 7.5 10.0 ... 350.0 352.5 355.0 357.5
  * lev      (lev) float64 3.643 7.595 14.36 24.61 ... 936.2 957.5 976.3 992.6
  * time     (time) datetime64[ns] 2011-01-17 2011-02-14 ... 2013-12-17
Data variables:
    hyam     (lev) float64 0.003643 0.007595 0.01436 ... 0.006255 0.001989 0.0
    hybm     (lev) float64 0.0 0.0 0.0 0.0 0.0 ... 0.9251 0.9512 0.9743 0.9926
    P0       float64 1e+05
    N_AER    (time, lev, lat, lon) float32 ...
    PS       (time, lat, lon) float32 ...
    T        (time, lev, lat, lon) float32 268.3 268.3 268.3 ... 250.4 250.4
    U        (time, lev, lat, lon) float32 ...
    V        (time, lev, lat, lon) float32 ...
    T_C      (time, lev, lat, lon) float32 -4.89 -4.89 -4.89 ... -22.78 -22.78
Attributes:
    Conventions:       CF-1.0
    source:            CAM
    case:              OsloAeroSec_intBVOC_f19_f19
    logname:           x_sarbl
    host:              
    initial_file:      OsloAero_intBVOC_f19_f19_spinup.cam.i.2011-01-01-00000.nc
    topography_file:   /proj/cesm_input-data/inputdata/noresm-only/inputForNu...
    model_doi_url:     https://doi.org/10.5065/D67H1H0V
    time_period_freq:  month_1
    history:           Tue Nov  2 08:04:41 2021: ncrcat -v T,N_AER,V,U atm/hi...
    NCO:               netCDF Operators version 4.7.9 (Homepage = http://nco....
def convert360_180(_ds):
    """
    convert longitude from 0-360 to -180 -- 180 deg
    """
    # check if already 
    attrs = _ds['lon'].attrs
    if _ds['lon'].min() >= 0:
        with xr.set_options(keep_attrs=True): 
            _ds.coords['lon'] = (_ds['lon'] + 180) % 360 - 180
        _ds = _ds.sortby('lon')
    return _ds

(migth want to move this to a module!)

ds = convert360_180(ds)

ds['lon']

<xarray.DataArray 'lon' (lon: 144)>
array([-180. , -177.5, -175. , -172.5, -170. , -167.5, -165. , -162.5, -160. ,
       -157.5, -155. , -152.5, -150. , -147.5, -145. , -142.5, -140. , -137.5,
       -135. , -132.5, -130. , -127.5, -125. , -122.5, -120. , -117.5, -115. ,
       -112.5, -110. , -107.5, -105. , -102.5, -100. ,  -97.5,  -95. ,  -92.5,
        -90. ,  -87.5,  -85. ,  -82.5,  -80. ,  -77.5,  -75. ,  -72.5,  -70. ,
        -67.5,  -65. ,  -62.5,  -60. ,  -57.5,  -55. ,  -52.5,  -50. ,  -47.5,
        -45. ,  -42.5,  -40. ,  -37.5,  -35. ,  -32.5,  -30. ,  -27.5,  -25. ,
        -22.5,  -20. ,  -17.5,  -15. ,  -12.5,  -10. ,   -7.5,   -5. ,   -2.5,
          0. ,    2.5,    5. ,    7.5,   10. ,   12.5,   15. ,   17.5,   20. ,
         22.5,   25. ,   27.5,   30. ,   32.5,   35. ,   37.5,   40. ,   42.5,
         45. ,   47.5,   50. ,   52.5,   55. ,   57.5,   60. ,   62.5,   65. ,
         67.5,   70. ,   72.5,   75. ,   77.5,   80. ,   82.5,   85. ,   87.5,
         90. ,   92.5,   95. ,   97.5,  100. ,  102.5,  105. ,  107.5,  110. ,
        112.5,  115. ,  117.5,  120. ,  122.5,  125. ,  127.5,  130. ,  132.5,
        135. ,  137.5,  140. ,  142.5,  145. ,  147.5,  150. ,  152.5,  155. ,
        157.5,  160. ,  162.5,  165. ,  167.5,  170. ,  172.5,  175. ,  177.5])
Coordinates:
  * lon      (lon) float64 -180.0 -177.5 -175.0 -172.5 ... 172.5 175.0 177.5
Attributes:
    long_name:  longitude
    units:      degrees_east

Let’s pick out only the surface layer. It’s the last one:

Selecting data and super quick plotting:

xarray loads data only when it needs to (it’s lazy, Anne can explain), and you might want to early on define the subset of data you want to look at so that you don’t end up loading a lot of extra data.

See here for nice overview

isel, sel

ds_s = ds.isel(lev=-1)

ds_s

<xarray.Dataset>
Dimensions:  (lat: 96, lon: 144, time: 36)
Coordinates:
  * lat      (lat) float64 -90.0 -88.11 -86.21 -84.32 ... 84.32 86.21 88.11 90.0
  * lon      (lon) float64 -180.0 -177.5 -175.0 -172.5 ... 172.5 175.0 177.5
    lev      float64 992.6
  * time     (time) datetime64[ns] 2011-01-17 2011-02-14 ... 2013-12-17
Data variables:
    hyam     float64 0.0
    hybm     float64 0.9926
    P0       float64 1e+05
    N_AER    (time, lat, lon) float32 ...
    PS       (time, lat, lon) float32 ...
    T        (time, lat, lon) float32 249.1 249.1 249.1 ... 250.4 250.4 250.4
    U        (time, lat, lon) float32 ...
    V        (time, lat, lon) float32 ...
    T_C      (time, lat, lon) float32 -24.04 -24.04 -24.04 ... -22.78 -22.78
Attributes:
    Conventions:       CF-1.0
    source:            CAM
    case:              OsloAeroSec_intBVOC_f19_f19
    logname:           x_sarbl
    host:              
    initial_file:      OsloAero_intBVOC_f19_f19_spinup.cam.i.2011-01-01-00000.nc
    topography_file:   /proj/cesm_input-data/inputdata/noresm-only/inputForNu...
    model_doi_url:     https://doi.org/10.5065/D67H1H0V
    time_period_freq:  month_1
    history:           Tue Nov  2 08:04:41 2021: ncrcat -v T,N_AER,V,U atm/hi...
    NCO:               netCDF Operators version 4.7.9 (Homepage = http://nco....
ds_s['T_C'].isel(time=0).plot()

<matplotlib.collections.QuadMesh at 0x7feada143040>
../../_images/some-xarray-pandas-presentation_Sara_35_1.png

Notice how the labels use both the attribute “standard_name” and “units” from the dataset.

ds['T_C'].sel(lev=1000., lon=0, method='nearest').plot(x='time')

<matplotlib.collections.QuadMesh at 0x7fead9fc6430>
../../_images/some-xarray-pandas-presentation_Sara_37_1.png

Slice:

ds_s['T_C'].sel(lat=slice(0,90)).isel(time=0).plot()

<matplotlib.collections.QuadMesh at 0x7fead9f22ee0>
../../_images/some-xarray-pandas-presentation_Sara_39_1.png

Super quick averaging etc

da_T = ds['T_C']

Mean:

da_T.mean(['time','lon']).plot(ylim=[1000,100], yscale='log')

<matplotlib.collections.QuadMesh at 0x7fead9e4cfd0>
../../_images/some-xarray-pandas-presentation_Sara_43_1.png

Standard deviation

da_T.isel(lev=-1).std(['time']).plot()

<matplotlib.collections.QuadMesh at 0x7fead9d0e520>
../../_images/some-xarray-pandas-presentation_Sara_45_1.png

Temperature change much stronger over land than ocean…

Seasonal average

month = (ds['time.month']==7) | (ds['time.month']==8)

ds_sum = ds.where(month).mean('time')

ds_sum

<xarray.Dataset>
Dimensions:  (lat: 96, lev: 32, lon: 144)
Coordinates:
  * lat      (lat) float64 -90.0 -88.11 -86.21 -84.32 ... 84.32 86.21 88.11 90.0
  * lon      (lon) float64 -180.0 -177.5 -175.0 -172.5 ... 172.5 175.0 177.5
  * lev      (lev) float64 3.643 7.595 14.36 24.61 ... 936.2 957.5 976.3 992.6
Data variables:
    hyam     (lev) float64 0.003643 0.007595 0.01436 ... 0.006255 0.001989 0.0
    hybm     (lev) float64 0.0 0.0 0.0 0.0 0.0 ... 0.9251 0.9512 0.9743 0.9926
    P0       float64 1e+05
    N_AER    (lev, lat, lon) float32 0.01799 0.01799 0.01799 ... 13.3 13.3 13.31
    PS       (lat, lon) float32 6.869e+04 6.869e+04 ... 1.012e+05 1.012e+05
    T        (lev, lat, lon) float32 202.9 202.9 202.9 ... 272.2 272.2 272.2
    U        (lev, lat, lon) float32 7.391 7.34 7.276 ... -0.4215 -0.394 -0.3657
    V        (lev, lat, lon) float32 1.004 1.325 1.644 ... 0.6221 0.6399 0.6565
    T_C      (lev, lat, lon) float32 -70.26 -70.26 -70.26 ... -0.9582 -0.9582
ds_season = ds.groupby('time.season').mean()

ds_season

<xarray.Dataset>
Dimensions:  (lat: 96, lev: 32, lon: 144, season: 4)
Coordinates:
  * lat      (lat) float64 -90.0 -88.11 -86.21 -84.32 ... 84.32 86.21 88.11 90.0
  * lon      (lon) float64 -180.0 -177.5 -175.0 -172.5 ... 172.5 175.0 177.5
  * lev      (lev) float64 3.643 7.595 14.36 24.61 ... 936.2 957.5 976.3 992.6
  * season   (season) object 'DJF' 'JJA' 'MAM' 'SON'
Data variables:
    hyam     (season, lev) float64 0.003643 0.007595 0.01436 ... 0.001989 0.0
    hybm     (season, lev) float64 0.0 0.0 0.0 0.0 ... 0.9512 0.9743 0.9926
    P0       (season) float64 1e+05 1e+05 1e+05 1e+05
    N_AER    (season, lev, lat, lon) float32 0.006281 0.006281 ... 16.53 16.53
    PS       (season, lat, lon) float32 6.922e+04 6.922e+04 ... 1.011e+05
    T        (season, lev, lat, lon) float32 263.9 263.9 263.9 ... 259.2 259.2
    U        (season, lev, lat, lon) float32 0.6781 0.7017 ... 0.4854 0.5219
    V        (season, lev, lat, lon) float32 -0.5561 -0.526 ... 0.8473 0.8253
    T_C      (season, lev, lat, lon) float32 -9.245 -9.245 ... -13.91 -13.91
ds_season['T_C'].isel(lev=-1).plot(col='season')

<xarray.plot.facetgrid.FacetGrid at 0x7fead9cac040>
../../_images/some-xarray-pandas-presentation_Sara_53_1.png

Controle the plot visuals:

# lets plot the wind fields
_ds = ds_s[['V','U']]
_da = np.sqrt(_ds['V']**2 + _ds['U']**2)

#_da.attrs['long_name'] = 'Wind speed'
#_da.attrs['units'] = 'm/s'

f,ax = plt.subplots(dpi=100)
_dm = _da.isel(time=0)
_dm.plot(cmap = plt.get_cmap('Reds'),ax=ax,
                   cbar_kwargs={'label':'Wind Speed [m/s]'})


_ds = ds_s.isel(time=0,lon =slice(0,None,2), lat=slice(0,None,2))
ax.quiver(_ds['lon'],_ds['lat'],_ds['U'],_ds['V'], scale=300, )

#ax.set_title('Wind strength and pattern')
#ax.set_xlabel('Longitude [$^\circ$E]')

<matplotlib.quiver.Quiver at 0x7fead9dc7fd0>
../../_images/some-xarray-pandas-presentation_Sara_56_1.png

Plotting with cartopy

import cartopy as cy
import cartopy.crs as ccrs

f,ax = plt.subplots(dpi=100,subplot_kw={'projection':ccrs.PlateCarree()})


_dm.plot.pcolormesh(
    cmap = plt.get_cmap('Reds'),ax=ax,
    cbar_kwargs={
        'label':'Wind Speed [m/s]', 
        'orientation':'horizontal',
        
    },
    transform=ccrs.PlateCarree(), 
    x='lon',y='lat',
    levels = 6
)
ax.set_title('ilev:0; Mean over Time')
ax.coastlines()

gl = ax.gridlines(draw_labels=True)
gl.xlabels_top = False
gl.ylabels_right = False

ax.add_feature(cy.feature.BORDERS);

/opt/conda/lib/python3.8/site-packages/cartopy/mpl/gridliner.py:307: UserWarning: The .xlabels_top attribute is deprecated. Please use .top_labels to toggle visibility instead.
  warnings.warn('The .xlabels_top attribute is deprecated. Please '
/opt/conda/lib/python3.8/site-packages/cartopy/mpl/gridliner.py:343: UserWarning: The .ylabels_right attribute is deprecated. Please use .right_labels to toggle visibility instead.
  warnings.warn('The .ylabels_right attribute is deprecated. Please '
/opt/conda/lib/python3.8/site-packages/cartopy/io/__init__.py:260: DownloadWarning: Downloading: https://naciscdn.org/naturalearth/110m/cultural/ne_110m_admin_0_boundary_lines_land.zip
  warnings.warn('Downloading: {}'.format(url), DownloadWarning)
../../_images/some-xarray-pandas-presentation_Sara_59_1.png

Convert to pandas & do some random fun stuff:

Maybe we e.g. want to compare with a station, or just use some of the considerable functionalities available from pandas. It’s easy to convert back and forth between xarray and pandas:

Pick out station:

Lets pick out Tjärnö research station!

lat_tjarno = 58.9
lon_tjarno = 11.1
# pick out surface
ds_surf =ds.isel(lev=-1)
ds_tjarno = ds_surf.sel(lat=lat_tjarno, lon = lon_tjarno, method ='nearest')

Resample:

df_tjarno = ds_tjarno.to_dataframe()

df_tjarno.head()
lat lon lev hyam hybm P0 N_AER PS T U V T_C
time
2011-01-17 59.684211 10.0 992.556095 0.0 0.992556 100000.0 708.058899 96880.921875 269.207245 1.728664 0.475478 -3.942749
2011-02-14 59.684211 10.0 992.556095 0.0 0.992556 100000.0 830.089844 97367.218750 266.607880 -0.424161 1.017061 -6.542114
2011-03-17 59.684211 10.0 992.556095 0.0 0.992556 100000.0 585.032104 97448.093750 271.787231 2.352789 0.320621 -1.362762
2011-04-16 59.684211 10.0 992.556095 0.0 0.992556 100000.0 906.228210 97496.757812 279.498474 1.632975 -0.223941 6.348480
2011-05-17 59.684211 10.0 992.556095 0.0 0.992556 100000.0 872.936096 97293.382812 281.770081 1.449824 1.465539 8.620087 
df_yearly = df_tjarno.resample('Y' ).mean()#.plot()

df_yearly[['U','V']].plot()

<AxesSubplot:xlabel='time'>
../../_images/some-xarray-pandas-presentation_Sara_69_1.png

Using pandas specific tools:

ds_s['Wind_speed'] = np.sqrt(ds_s['U']**2 + ds_s['V']**2)

df = ds_s.to_dataframe()
df.head()
lev hyam hybm P0 N_AER PS T U V T_C Wind_speed
lat lon time
-90.0 -180.0 2011-01-17 992.556095 0.0 0.992556 100000.0 29.523746 69700.375000 249.113022 1.537032 4.258970 -24.036972 4.527835
2011-02-14 992.556095 0.0 0.992556 100000.0 44.145832 69311.914062 239.829407 2.053496 4.899427 -33.320587 5.312366
2011-03-17 992.556095 0.0 0.992556 100000.0 21.503481 68972.187500 223.339386 4.478918 5.187434 -49.810608 6.853479
2011-04-16 992.556095 0.0 0.992556 100000.0 7.794546 68879.500000 219.738770 4.953977 5.242508 -53.411224 7.212889
2011-05-17 992.556095 0.0 0.992556 100000.0 2.681895 68319.210938 218.717712 4.364126 5.071507 -54.432281 6.690724 
df[['U','V']].plot.hist(alpha=0.5, bins=200)

<AxesSubplot:ylabel='Frequency'>
../../_images/some-xarray-pandas-presentation_Sara_73_1.png 
df_ri = df.reset_index()
df_ri.head()
lat lon time lev hyam hybm P0 N_AER PS T U V T_C Wind_speed
0 -90.0 -180.0 2011-01-17 992.556095 0.0 0.992556 100000.0 29.523746 69700.375000 249.113022 1.537032 4.258970 -24.036972 4.527835
1 -90.0 -180.0 2011-02-14 992.556095 0.0 0.992556 100000.0 44.145832 69311.914062 239.829407 2.053496 4.899427 -33.320587 5.312366
2 -90.0 -180.0 2011-03-17 992.556095 0.0 0.992556 100000.0 21.503481 68972.187500 223.339386 4.478918 5.187434 -49.810608 6.853479
3 -90.0 -180.0 2011-04-16 992.556095 0.0 0.992556 100000.0 7.794546 68879.500000 219.738770 4.953977 5.242508 -53.411224 7.212889
4 -90.0 -180.0 2011-05-17 992.556095 0.0 0.992556 100000.0 2.681895 68319.210938 218.717712 4.364126 5.071507 -54.432281 6.690724

Check out the tradewinds (skip in presentation):

trops = (-20<df_ri['lat']) & (df_ri['lat']<20)
df_ri[['U','V']][trops].plot.hist(alpha=0.5, bins=200)

<AxesSubplot:ylabel='Frequency'>
../../_images/some-xarray-pandas-presentation_Sara_76_1.png

lets do something unnecesarily complicated :D

qcut, cut

qcut splits the data into quantile ranges

df_ri['wind_speed_cat'] = pd.qcut(df_ri['Wind_speed'], 
                                  q=[0.05,0.17, 0.34,0.66, 0.83,0.95], 
                                  labels=['very low','low','med','high','very high'])

Cut cuts into categories

df_ri['lat_cat'] = pd.cut(df_ri['lat'], [-90,-60,-30,0,30,60,90], 
                          labels=['S polar','S mid','S tropics', 'N tropic', 'N mid','N polar'])

df_ri.groupby('lat_cat').mean()
lat lon lev hyam hybm P0 N_AER PS T U V T_C Wind_speed
lat_cat
S polar -74.842105 -1.25 992.556095 0.0 0.992556 100000.0 37.134502 84496.015625 247.586441 -0.925003 1.516052 -25.563553 6.033710
S mid -45.473684 -1.25 992.556095 0.0 0.992556 100000.0 244.884354 100566.250000 281.765289 5.021757 -0.741656 8.615295 5.929924
S tropics -15.157895 -1.25 992.556095 0.0 0.992556 100000.0 913.148193 99746.179688 295.955811 -3.451257 1.499390 22.805801 4.714082
N tropic 15.157895 -1.25 992.556095 0.0 0.992556 100000.0 1587.196167 99328.976562 297.289948 -2.338687 -0.537982 24.139965 4.485467
N mid 45.473684 -1.25 992.556095 0.0 0.992556 100000.0 1425.352539 97057.734375 281.913513 1.253338 0.129856 8.763531 2.898627
N polar 75.789474 -1.25 992.556095 0.0 0.992556 100000.0 225.529175 98979.195312 262.373474 0.150430 -0.033408 -10.776512 2.685246 
sns.boxenplot(x="lat_cat", y="U",
              color="b",
              scale="linear", data=df_ri)

<AxesSubplot:xlabel='lat_cat', ylabel='U'>
../../_images/some-xarray-pandas-presentation_Sara_85_1.png 
sns.boxenplot(x="wind_speed_cat", y="N_AER",
              color="b",
              scale="linear", data=df_ri,
             )

<AxesSubplot:xlabel='wind_speed_cat', ylabel='N_AER'>
../../_images/some-xarray-pandas-presentation_Sara_86_1.png 
sns.displot(x="N_AER",hue = 'lat_cat',log_scale=True,kind='kde',
            data=df_ri,  multiple="stack")

<seaborn.axisgrid.FacetGrid at 0x7feac837ce20>
../../_images/some-xarray-pandas-presentation_Sara_87_1.png

Convert back to xarray if we need:

ds_new = df_ri.set_index(['time','lat','lon']).to_xarray()

ds_new

<xarray.Dataset>
Dimensions:         (lat: 96, lon: 144, time: 36)
Coordinates:
  * time            (time) datetime64[ns] 2011-01-17 2011-02-14 ... 2013-12-17
  * lat             (lat) float64 -90.0 -88.11 -86.21 ... 86.21 88.11 90.0
  * lon             (lon) float64 -180.0 -177.5 -175.0 ... 172.5 175.0 177.5
Data variables: (12/13)
    lev             (time, lat, lon) float64 992.6 992.6 992.6 ... 992.6 992.6
    hyam            (time, lat, lon) float64 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0
    hybm            (time, lat, lon) float64 0.9926 0.9926 ... 0.9926 0.9926
    P0              (time, lat, lon) float64 1e+05 1e+05 1e+05 ... 1e+05 1e+05
    N_AER           (time, lat, lon) float32 29.52 29.52 29.52 ... 29.87 29.87
    PS              (time, lat, lon) float32 6.97e+04 6.97e+04 ... 1.012e+05
    ...              ...
    U               (time, lat, lon) float32 1.537 1.35 1.161 ... 3.829 3.874
    V               (time, lat, lon) float32 4.259 4.322 4.377 ... 1.122 0.9543
    T_C             (time, lat, lon) float32 -24.04 -24.04 ... -22.78 -22.78
    Wind_speed      (time, lat, lon) float32 4.528 4.528 4.528 ... 3.99 3.99
    wind_speed_cat  (time, lat, lon) object 'med' 'med' 'med' ... 'med' 'med'
    lat_cat         (time, lat, lon) object nan nan nan ... 'N polar' 'N polar'

Groupby

ds_new.where(ds_new['wind_speed_cat']=='low').mean('time')['N_AER'].plot()

<matplotlib.collections.QuadMesh at 0x7feac80d2b80>
../../_images/some-xarray-pandas-presentation_Sara_92_1.png 
ds['wind_speed'] = np.sqrt(ds['U']**2+ ds['V']**2)

ds_new.groupby('wind_speed_cat').mean()

<xarray.Dataset>
Dimensions:         (wind_speed_cat: 5)
Coordinates:
  * wind_speed_cat  (wind_speed_cat) object 'high' 'low' ... 'very low'
Data variables:
    lev             (wind_speed_cat) float64 992.6 992.6 992.6 992.6 992.6
    hyam            (wind_speed_cat) float64 0.0 0.0 0.0 0.0 0.0
    hybm            (wind_speed_cat) float64 0.9926 0.9926 0.9926 0.9926 0.9926
    P0              (wind_speed_cat) float64 1e+05 1e+05 1e+05 1e+05 1e+05
    N_AER           (wind_speed_cat) float32 329.0 1.117e+03 ... 237.5 1.437e+03
    PS              (wind_speed_cat) float32 9.642e+04 9.782e+04 ... 9.712e+04
    T               (wind_speed_cat) float32 279.2 279.3 279.2 273.2 280.1
    U               (wind_speed_cat) float32 -0.7335 0.06986 ... -0.1049 0.02193
    V               (wind_speed_cat) float32 0.4115 0.04852 ... 0.3027 -0.01771
    T_C             (wind_speed_cat) float32 6.061 6.121 6.028 0.01729 6.922
    Wind_speed      (wind_speed_cat) float32 6.476 2.127 3.922 8.538 1.218
df[['U','V']].plot.hist(alpha=0.5, bins=200)

<AxesSubplot:ylabel='Frequency'>
../../_images/some-xarray-pandas-presentation_Sara_95_1.png 
df_tjarno.head()
lat lon lev hyam hybm P0 N_AER PS T U V T_C
time
2011-01-17 59.684211 10.0 992.556095 0.0 0.992556 100000.0 708.058899 96880.921875 269.207245 1.728664 0.475478 -3.942749
2011-02-14 59.684211 10.0 992.556095 0.0 0.992556 100000.0 830.089844 97367.218750 266.607880 -0.424161 1.017061 -6.542114
2011-03-17 59.684211 10.0 992.556095 0.0 0.992556 100000.0 585.032104 97448.093750 271.787231 2.352789 0.320621 -1.362762
2011-04-16 59.684211 10.0 992.556095 0.0 0.992556 100000.0 906.228210 97496.757812 279.498474 1.632975 -0.223941 6.348480
2011-05-17 59.684211 10.0 992.556095 0.0 0.992556 100000.0 872.936096 97293.382812 281.770081 1.449824 1.465539 8.620087

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