In [1]:

    

import numpy as np
import pandas as pd
import xarray as xr
import seaborn as sns


    

In [2]:

    

np.random.seed(123)


    

In [3]:

    

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))


    

In [4]:

    

base = 10 + 15 * annual_cycle.reshape(-1, 1)


    

In [31]:

    

tmin_values = base + 3 * np.random.randn(annual_cycle.size, 3)
tmax_values = base + 100 + 3 * np.random.randn(annual_cycle.size, 3)


    

In [35]:

    

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


    

In [7]:

    

ds


    

    
Out[7]:





<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 ...
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 [36]:

    

df = ds.to_dataframe()


    

In [50]:

    

df.head()


    

    
Out[50]:






  

    

      

      

      
tmax
      
tmin
    
    

      
location
      
time
      

      

    
  
  

    

      
IA
      
2000-01-01
      
90.311783
      
-3.554552
    
    

      
2000-01-02
      
89.714741
      
-5.033435
    
    

      
2000-01-03
      
96.416894
      
-4.691945
    
    

      
2000-01-04
      
93.291097
      
-6.824758
    
    

      
2000-01-05
      
94.645452
      
-6.165465
    
  

In [38]:

    

df.describe()


    

    
Out[38]:






  

    

      

      
tmax
      
tmin
    
  
  

    

      
count
      
2193.000000
      
2193.000000
    
    

      
mean
      
109.936650
      
10.049875
    
    

      
std
      
11.162604
      
10.961592
    
    

      
min
      
85.381501
      
-11.903146
    
    

      
25%
      
99.521601
      
0.160592
    
    

      
50%
      
110.250155
      
9.927800
    
    

      
75%
      
120.282290
      
20.190470
    
    

      
max
      
136.964073
      
32.586394
    
  

In [39]:

    

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


    

    
Out[39]:





<matplotlib.axes._subplots.AxesSubplot at 0x22b6b8b0a20>






    

In [26]:

    

import matplotlib.pyplot as plt
%pylab inline
plt.show()


    

    




Populating the interactive namespace from numpy and matplotlib

In [47]:

    

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


    

In [43]:

    

sns.set(style="ticks", color_codes=True)


    

In [48]:

    

ds.data_vars


    

    
Out[48]:





Data variables:
    tmax     (time, location) float64 90.31 98.46 94.54 89.71 94.4 92.19 ...
    tmin     (time, location) float64 -3.555 -0.0124 -1.811 -5.033 -5.386 ...

In [51]:

    

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


    

In [55]:

    

freeze.to_pandas().plot()


    

    
Out[55]:





<matplotlib.axes._subplots.AxesSubplot at 0x22b6d0614a8>






    

In [56]:

    

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


    

In [63]:

    

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


    

    
Out[63]:





<matplotlib.axes._subplots.AxesSubplot at 0x22b6d99d908>






    

In [65]:

    

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


    

In [66]:

    

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


    

In [71]:

    

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


    

    
Out[71]:





<matplotlib.axes._subplots.AxesSubplot at 0x22b6ea4fd30>






    

In [72]:

    

some_missing = ds.tmin.sel(time=ds['time.day'] > 15).reindex_like(ds)


    

In [73]:

    

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


    

In [74]:

    

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


    

In [76]:

    

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


    

In [77]:

    

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


    

    
Out[77]:





<matplotlib.axes._subplots.AxesSubplot at 0x22b6cebfc88>






    

In [ ]: