

In [2]:

    
%matplotlib inline

Toy weather data

Here is an example of how to easily manipulate a toy weather dataset using xarray and other recommended Python libraries:

Examine a dataset with pandas and seaborn
Probability of freeze by calendar month
Monthly averaging
Calculate monthly anomalies
Fill missing values with climatology

Shared setup:



In [3]:

    
import xarray as xr
import numpy as np
import pandas as pd
import seaborn as sns # pandas aware plotting library

np.random.seed(123)

times = pd.date_range('2000-01-01', '2001-12-31', name='time')
annual_cycle = np.sin(2 * np.pi * (times.dayofyear / 365.25 - 0.28))

base = 10 + 15 * annual_cycle.reshape(-1, 1)
tmin_values = base + 3 * np.random.randn(annual_cycle.size, 3)
tmax_values = base + 10 + 3 * np.random.randn(annual_cycle.size, 3)

ds = xr.Dataset({'tmin': (('time', 'location'), tmin_values),
                 'tmax': (('time', 'location'), tmax_values)},
                {'time': times, 'location': ['IA', 'IN', 'IL']})

Examine a dataset with pandas and seaborn



In [40]:

    
ds









    Out[40]:





<xarray.Dataset>
Dimensions:   (location: 3, time: 731)
Coordinates:
  * time      (time) datetime64[ns] 2000-01-01 2000-01-02 2000-01-03 ...
  * location  (location) <U2 'IA' 'IN' 'IL'
Data variables:
    tmax      (time, location) float64 12.98 3.31 6.779 0.4479 6.373 4.843 ...
    tmin      (time, location) float64 -8.037 -1.788 -3.932 -9.341 -6.558 ...



In [5]:

    
df = ds.to_dataframe()



In [6]:

    
df.head()



In [7]:

    
df.describe()









    Out[7]:






  
    
      
      tmax
      tmin
    
  
  
    
      count
      2193.000000
      2193.000000
    
    
      mean
      20.108232
      9.975426
    
    
      std
      11.010569
      10.963228
    
    
      min
      -3.506234
      -13.395763
    
    
      25%
      9.853905
      -0.040347
    
    
      50%
      19.967409
      10.060403
    
    
      75%
      30.045588
      20.083590
    
    
      max
      43.271148
      33.456060



In [8]:

    
ds.mean(dim='location').to_dataframe().plot()









    Out[8]:





<matplotlib.axes._subplots.AxesSubplot at 0x141373ae710>



In [9]:

    
sns.pairplot(df.reset_index(), vars=ds.data_vars)









    Out[9]:





<seaborn.axisgrid.PairGrid at 0x14137318780>

Probability of freeze by calendar month



In [10]:

    
freeze = (ds['tmin'] <= 0).groupby('time.month').mean('time')



In [11]:

    
freeze









    Out[11]:





<xarray.DataArray 'tmin' (month: 12, location: 3)>
array([[ 0.9516129 ,  0.88709677,  0.93548387],
       [ 0.84210526,  0.71929825,  0.77192982],
       [ 0.24193548,  0.12903226,  0.16129032],
       [ 0.        ,  0.        ,  0.        ],
       [ 0.        ,  0.        ,  0.        ],
       [ 0.        ,  0.        ,  0.        ],
       [ 0.        ,  0.        ,  0.        ],
       [ 0.        ,  0.        ,  0.        ],
       [ 0.        ,  0.        ,  0.        ],
       [ 0.        ,  0.01612903,  0.        ],
       [ 0.33333333,  0.35      ,  0.23333333],
       [ 0.93548387,  0.85483871,  0.82258065]])
Coordinates:
  * location  (location) <U2 'IA' 'IN' 'IL'
  * month     (month) int64 1 2 3 4 5 6 7 8 9 10 11 12



In [12]:

    
freeze.to_pandas().plot()









    Out[12]:





<matplotlib.axes._subplots.AxesSubplot at 0x14138f639b0>

Monthly averaging



In [13]:

    
monthly_avg = ds.resample('1MS', dim='time', how='mean')



In [14]:

    
monthly_avg.sel(location='IA').to_dataframe().plot(style='s-')









    Out[14]:





<matplotlib.axes._subplots.AxesSubplot at 0x14138f59860>

Note that MS here refers to Month-Start; M labels Month-End (the last day of the month).

Calculate monthly anomalies

In climatology, “anomalies” refer to the difference between observations and typical weather for a particular season. Unlike observations, anomalies should not show any seasonal cycle.



In [15]:

    
climatology = ds.groupby('time.month').mean('time')



In [16]:

    
anomalies = ds.groupby('time.month') - climatology



In [17]:

    
anomalies.mean('location').to_dataframe()[['tmin', 'tmax']].plot()









    Out[17]:





<matplotlib.axes._subplots.AxesSubplot at 0x14139021780>

Fill missing values with climatology

The fillna() method on grouped objects lets you easily fill missing values by group:



In [18]:

    
# throw away the first half of every month
some_missing = ds.tmin.sel(time=ds['time.day'] > 15).reindex_like(ds)



In [19]:

    
filled = some_missing.groupby('time.month').fillna(climatology.tmin)



In [20]:

    
both = xr.Dataset({'some_missing': some_missing, 'filled': filled})



In [21]:

    
both









    Out[21]:





<xarray.Dataset>
Dimensions:       (location: 3, time: 731)
Coordinates:
  * location      (location) <U2 'IA' 'IN' 'IL'
  * time          (time) datetime64[ns] 2000-01-01 2000-01-02 2000-01-03 ...
    month         (time) int32 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ...
Data variables:
    filled        (time, location) float64 -5.163 -4.216 -4.681 -5.163 ...
    some_missing  (time, location) float64 nan nan nan nan nan nan nan nan ...



In [22]:

    
df = both.sel(time='2000').mean('location').reset_coords(drop=True).to_dataframe()



In [23]:

    
df[['filled', 'some_missing']].plot()









    Out[23]:





<matplotlib.axes._subplots.AxesSubplot at 0x14139044908>



In [ ]:

		tmax	tmin
location	time
IA	2000-01-01	12.980549	-8.037369
	2000-01-02	0.447856	-9.341157
	2000-01-03	5.322699	-12.139719
	2000-01-04	1.889425	-7.492914
	2000-01-05	0.791176	-0.447129

	tmax	tmin
count	2193.000000	2193.000000
mean	20.108232	9.975426
std	11.010569	10.963228
min	-3.506234	-13.395763
25%	9.853905	-0.040347
50%	19.967409	10.060403
75%	30.045588	20.083590
max	43.271148	33.456060