In [1]:
#Import the icepython library
#Make sure to read our quick start guide!  Additional support can be reached by contacting [email protected]
import icepython as ice
In [2]:
#Quotes- Latest METAR values for observations
import pandas as pd
data = ice.get_quotes(['KORD-METR','KATL-METR','KNYC-METR'],['2M Hourly Temp','Accum Precip hourly Interpolated','GWDD'])
df = pd.DataFrame(list(data))
print(df)

           0               1                                 2        3
0             2M Hourly Temp  Accum Precip hourly Interpolated     GWDD
1  KORD-METR            12.2                                 0  13.1753
2  KATL-METR            24.4                                 0    3.658
3  KNYC-METR            18.9                             0.254  12.6775
In [3]:
#Quotes - Latest model run data - Hourly values for current model run of GFS
import pandas as pd
data = ice.get_quotes(['KORD FDH0!-GFS','KORD FDH1!-GFS','KORD FDH2!-GFS','KORD FDH3!-GFS','KORD FDH4!-GFS','KORD FDH5!-GFS','KORD FDH6!-GFS','KORD FDH7!-GFS','KORD FDH8!-GFS','KORD FDH9!-GFS','KORD FDH10!-GFS','KORD FDH11!-GFS','KORD FDH12!-GFS','KORD FDH13!-GFS','KORD FDH14!-GFS','KORD FDH15!-GFS','KORD FDH16!-GFS','KORD FDH17!-GFS','KORD FDH18!-GFS','KORD FDH19!-GFS','KORD FDH20!-GFS','KORD FDH21!-GFS','KORD FDH22!-GFS','KORD FDH23!-GFS','KORD FDH24!-GFS','KORD FDH25!-GFS','KORD FDH26!-GFS','KORD FDH27!-GFS','KORD FDH28!-GFS','KORD FDH29!-GFS','KORD FDH30!-GFS','KORD FDH31!-GFS','KORD FDH32!-GFS','KORD FDH33!-GFS','KORD FDH34!-GFS','KORD FDH35!-GFS','KORD FDH36!-GFS','KORD FDH37!-GFS','KORD FDH38!-GFS','KORD FDH39!-GFS','KORD FDH40!-GFS','KORD FDH41!-GFS','KORD FDH42!-GFS','KORD FDH43!-GFS','KORD FDH44!-GFS','KORD FDH45!-GFS','KORD FDH46!-GFS','KORD FDH47!-GFS','KORD FDH48!-GFS','KORD FDH49!-GFS','KORD FDH50!-GFS','KORD FDH51!-GFS','KORD FDH52!-GFS','KORD FDH53!-GFS','KORD FDH54!-GFS','KORD FDH55!-GFS','KORD FDH56!-GFS','KORD FDH57!-GFS','KORD FDH58!-GFS','KORD FDH59!-GFS','KORD FDH60!-GFS','KORD FDH61!-GFS','KORD FDH62!-GFS','KORD FDH63!-GFS','KORD FDH64!-GFS','KORD FDH65!-GFS','KORD FDH66!-GFS','KORD FDH67!-GFS','KORD FDH68!-GFS','KORD FDH69!-GFS','KORD FDH70!-GFS','KORD FDH71!-GFS','KORD FDH72!-GFS','KORD FDH73!-GFS','KORD FDH74!-GFS','KORD FDH75!-GFS','KORD FDH76!-GFS','KORD FDH77!-GFS','KORD FDH78!-GFS','KORD FDH79!-GFS','KORD FDH80!-GFS','KORD FDH81!-GFS','KORD FDH82!-GFS','KORD FDH83!-GFS','KORD FDH84!-GFS','KORD FDH85!-GFS','KORD FDH86!-GFS','KORD FDH87!-GFS','KORD FDH88!-GFS','KORD FDH89!-GFS','KORD FDH90!-GFS','KORD FDH91!-GFS','KORD FDH92!-GFS','KORD FDH93!-GFS','KORD FDH94!-GFS','KORD FDH95!-GFS','KORD FDH96!-GFS','KORD FDH97!-GFS','KORD FDH98!-GFS','KORD FDH99!-GFS','KORD FDH100!-GFS','KORD FDH101!-GFS','KORD FDH102!-GFS','KORD FDH103!-GFS','KORD FDH104!-GFS','KORD FDH105!-GFS','KORD FDH106!-GFS','KORD FDH107!-GFS','KORD FDH108!-GFS','KORD FDH109!-GFS','KORD FDH110!-GFS','KORD FDH111!-GFS','KORD FDH112!-GFS','KORD FDH113!-GFS','KORD FDH114!-GFS','KORD FDH115!-GFS','KORD FDH116!-GFS','KORD FDH117!-GFS','KORD FDH118!-GFS','KORD FDH119!-GFS','KORD FDH120!-GFS','KORD FDH121!-GFS','KORD FDH122!-GFS','KORD FDH123!-GFS','KORD FDH124!-GFS','KORD FDH125!-GFS','KORD FDH126!-GFS','KORD FDH127!-GFS','KORD FDH128!-GFS','KORD FDH129!-GFS','KORD FDH130!-GFS','KORD FDH131!-GFS','KORD FDH132!-GFS','KORD FDH133!-GFS','KORD FDH134!-GFS','KORD FDH135!-GFS','KORD FDH136!-GFS','KORD FDH137!-GFS','KORD FDH138!-GFS','KORD FDH139!-GFS','KORD FDH140!-GFS','KORD FDH141!-GFS','KORD FDH142!-GFS','KORD FDH143!-GFS','KORD FDH144!-GFS','KORD FDH145!-GFS','KORD FDH146!-GFS','KORD FDH147!-GFS','KORD FDH148!-GFS','KORD FDH149!-GFS','KORD FDH150!-GFS','KORD FDH151!-GFS','KORD FDH152!-GFS','KORD FDH153!-GFS','KORD FDH154!-GFS','KORD FDH155!-GFS','KORD FDH156!-GFS','KORD FDH157!-GFS','KORD FDH158!-GFS','KORD FDH159!-GFS','KORD FDH160!-GFS','KORD FDH161!-GFS','KORD FDH162!-GFS','KORD FDH163!-GFS','KORD FDH164!-GFS','KORD FDH165!-GFS','KORD FDH166!-GFS','KORD FDH167!-GFS','KORD FDH168!-GFS','KORD FDH169!-GFS','KORD FDH170!-GFS','KORD FDH171!-GFS','KORD FDH172!-GFS','KORD FDH173!-GFS','KORD FDH174!-GFS','KORD FDH175!-GFS','KORD FDH176!-GFS','KORD FDH177!-GFS','KORD FDH178!-GFS','KORD FDH179!-GFS','KORD FDH180!-GFS','KORD FDH181!-GFS','KORD FDH182!-GFS','KORD FDH183!-GFS','KORD FDH184!-GFS','KORD FDH185!-GFS','KORD FDH186!-GFS','KORD FDH187!-GFS','KORD FDH188!-GFS','KORD FDH189!-GFS','KORD FDH190!-GFS','KORD FDH191!-GFS','KORD FDH192!-GFS','KORD FDH193!-GFS','KORD FDH194!-GFS','KORD FDH195!-GFS','KORD FDH196!-GFS','KORD FDH197!-GFS','KORD FDH198!-GFS','KORD FDH199!-GFS','KORD FDH200!-GFS','KORD FDH201!-GFS','KORD FDH202!-GFS','KORD FDH203!-GFS','KORD FDH204!-GFS','KORD FDH205!-GFS','KORD FDH206!-GFS','KORD FDH207!-GFS','KORD FDH208!-GFS','KORD FDH209!-GFS','KORD FDH210!-GFS','KORD FDH211!-GFS','KORD FDH212!-GFS','KORD FDH213!-GFS','KORD FDH214!-GFS','KORD FDH215!-GFS','KORD FDH216!-GFS','KORD FDH217!-GFS','KORD FDH218!-GFS','KORD FDH219!-GFS','KORD FDH220!-GFS','KORD FDH221!-GFS','KORD FDH222!-GFS','KORD FDH223!-GFS','KORD FDH224!-GFS','KORD FDH225!-GFS','KORD FDH226!-GFS','KORD FDH227!-GFS','KORD FDH228!-GFS','KORD FDH229!-GFS','KORD FDH230!-GFS','KORD FDH231!-GFS','KORD FDH232!-GFS','KORD FDH233!-GFS','KORD FDH234!-GFS','KORD FDH235!-GFS','KORD FDH236!-GFS','KORD FDH237!-GFS','KORD FDH238!-GFS','KORD FDH239!-GFS','KORD FDH240!-GFS','KORD FDH241!-GFS','KORD FDH242!-GFS','KORD FDH243!-GFS','KORD FDH244!-GFS','KORD FDH245!-GFS','KORD FDH246!-GFS','KORD FDH247!-GFS','KORD FDH248!-GFS','KORD FDH249!-GFS','KORD FDH250!-GFS','KORD FDH251!-GFS','KORD FDH252!-GFS','KORD FDH253!-GFS','KORD FDH254!-GFS','KORD FDH255!-GFS','KORD FDH256!-GFS','KORD FDH257!-GFS','KORD FDH258!-GFS','KORD FDH259!-GFS','KORD FDH260!-GFS','KORD FDH261!-GFS','KORD FDH262!-GFS','KORD FDH263!-GFS','KORD FDH264!-GFS','KORD FDH265!-GFS','KORD FDH266!-GFS','KORD FDH267!-GFS','KORD FDH268!-GFS','KORD FDH269!-GFS','KORD FDH270!-GFS','KORD FDH271!-GFS','KORD FDH272!-GFS','KORD FDH273!-GFS','KORD FDH274!-GFS','KORD FDH275!-GFS','KORD FDH276!-GFS','KORD FDH277!-GFS','KORD FDH278!-GFS','KORD FDH279!-GFS','KORD FDH280!-GFS','KORD FDH281!-GFS','KORD FDH282!-GFS','KORD FDH283!-GFS','KORD FDH284!-GFS','KORD FDH285!-GFS','KORD FDH286!-GFS','KORD FDH287!-GFS','KORD FDH288!-GFS','KORD FDH289!-GFS','KORD FDH290!-GFS','KORD FDH291!-GFS','KORD FDH292!-GFS','KORD FDH293!-GFS','KORD FDH294!-GFS','KORD FDH295!-GFS','KORD FDH296!-GFS','KORD FDH297!-GFS','KORD FDH298!-GFS','KORD FDH299!-GFS','KORD FDH300!-GFS'],['2M Hourly Temp'])
df = pd.DataFrame(list(data))
print(df)

                    0               1
0                      2M Hourly Temp
1      KORD FDH0!-GFS            4.87
2      KORD FDH1!-GFS            6.52
3      KORD FDH2!-GFS            8.31
4      KORD FDH3!-GFS            9.82
5      KORD FDH4!-GFS           10.78
6      KORD FDH5!-GFS           11.62
7      KORD FDH6!-GFS           12.16
8      KORD FDH7!-GFS           12.12
9      KORD FDH8!-GFS           11.98
10     KORD FDH9!-GFS           11.37
11    KORD FDH10!-GFS           10.26
12    KORD FDH11!-GFS            9.69
13    KORD FDH12!-GFS            8.96
14    KORD FDH13!-GFS            7.87
15    KORD FDH14!-GFS            7.04
16    KORD FDH15!-GFS            6.58
17    KORD FDH16!-GFS            6.19
18    KORD FDH17!-GFS            5.93
19    KORD FDH18!-GFS            5.65
20    KORD FDH19!-GFS            5.37
21    KORD FDH20!-GFS            5.09
22    KORD FDH21!-GFS             4.7
23    KORD FDH22!-GFS            4.43
24    KORD FDH23!-GFS            4.44
25    KORD FDH24!-GFS            5.96
26    KORD FDH25!-GFS            7.99
27    KORD FDH26!-GFS           10.04
28    KORD FDH27!-GFS           11.93
29    KORD FDH28!-GFS           13.53
..                ...             ...
272  KORD FDH271!-GFS           21.11
273  KORD FDH272!-GFS           21.48
274  KORD FDH273!-GFS           21.84
275  KORD FDH274!-GFS           21.04
276  KORD FDH275!-GFS           20.25
277  KORD FDH276!-GFS           19.45
278  KORD FDH277!-GFS           18.23
279  KORD FDH278!-GFS           17.02
280  KORD FDH279!-GFS            15.8
281  KORD FDH280!-GFS           15.26
282  KORD FDH281!-GFS           14.73
283  KORD FDH282!-GFS           14.19
284  KORD FDH283!-GFS            13.7
285  KORD FDH284!-GFS           13.22
286  KORD FDH285!-GFS           12.73
287  KORD FDH286!-GFS           13.11
288  KORD FDH287!-GFS           13.48
289  KORD FDH288!-GFS           13.86
290  KORD FDH289!-GFS           16.06
291  KORD FDH290!-GFS           18.26
292  KORD FDH291!-GFS           20.46
293  KORD FDH292!-GFS           21.79
294  KORD FDH293!-GFS           23.12
295  KORD FDH294!-GFS           24.45
296  KORD FDH295!-GFS            24.7
297  KORD FDH296!-GFS           24.95
298  KORD FDH297!-GFS            25.2
299  KORD FDH298!-GFS           24.37
300  KORD FDH299!-GFS           23.54
301  KORD FDH300!-GFS           22.71

[302 rows x 2 columns]
In [4]:
#Quotes - Latest model run - Daily Values for current model run of GFS
import pandas as pd
data = ice.get_quotes(['KORD FDD1!-GFS','KORD FDD2!-GFS','KORD FDD3!-GFS','KORD FDD4!-GFS','KORD FDD5!-GFS','KORD FDD6!-GFS','KORD FDD7!-GFS','KORD FDD8!-GFS','KORD FDD9!-GFS','KORD FDD10!-GFS','KORD FDD11!-GFS','KORD FDD12!-GFS','KORD FDD13!-GFS','KORD FDD14!-GFS','KORD FDD15!-GFS','KORD FDD16!-GFS'],['2M Daily Avg Temp','GWDD'])
df = pd.DataFrame(list(data))
print(df)

                  0                  1       2
0                    2M Daily Avg Temp    GWDD
1    KORD FDD1!-GFS               8.64  10.992
2    KORD FDD2!-GFS              10.06   9.657
3    KORD FDD3!-GFS              12.12   8.093
4    KORD FDD4!-GFS              13.73   5.677
5    KORD FDD5!-GFS              13.95   4.333
6    KORD FDD6!-GFS              12.92   3.168
7    KORD FDD7!-GFS              16.58   2.323
8    KORD FDD8!-GFS              12.34   5.302
9    KORD FDD9!-GFS              12.79   6.883
10  KORD FDD10!-GFS              15.66   5.283
11  KORD FDD11!-GFS               14.6   3.492
12  KORD FDD12!-GFS               15.4   5.187
13  KORD FDD13!-GFS              18.83   1.447
14  KORD FDD14!-GFS               19.8   0.861
15  KORD FDD15!-GFS              21.46   2.032
16  KORD FDD16!-GFS              22.53   1.435
In [5]:
#Time Series - Daily values for Model run in a Time Series - Euro Model
import pandas as pd
data = ice.get_timeseries(['KSFO MR0!-ECM'],['2M Daily Avg Temp'],'D','2021-05-10','2021-05-20')
df = pd.DataFrame(list(data))
print(df)

             0                                1
0         Time  KSFO MR0!-ECM.2M DAILY AVG TEMP
1   2021-05-11                               15
2   2021-05-12                             15.2
3   2021-05-13                            13.44
4   2021-05-14                            11.42
5   2021-05-15                            12.65
6   2021-05-16                            13.42
7   2021-05-17                            12.05
8   2021-05-18                            11.74
9   2021-05-19                            12.77
10  2021-05-20                            12.49
In [6]:
#Time Series - Hourly values for Model run in a Time Series - Euro Ensemble Model
import pandas as pd
data = ice.get_timeseries(['KSFO MR0!-ECE'],['Default','2M Hourly Temp Average'],'i60','2021-05-10','2021-05-20')
df = pd.DataFrame(list(data))
print(df)

                       0                      1  \
0                   Time  KSFO MR0!-ECE.DEFAULT   
1    2021-05-11T07:00:00                  11.88   
2    2021-05-11T08:00:00                   10.7   
3    2021-05-11T09:00:00                  10.86   
4    2021-05-11T10:00:00                  11.52   
5    2021-05-11T11:00:00                  12.28   
6    2021-05-11T12:00:00                  12.96   
7    2021-05-11T13:00:00                  13.64   
8    2021-05-11T14:00:00                  14.19   
9    2021-05-11T15:00:00                  14.42   
10   2021-05-11T16:00:00                  14.39   
11   2021-05-11T17:00:00                  14.18   
12   2021-05-11T18:00:00                  14.04   
13   2021-05-11T19:00:00                  13.89   
14   2021-05-11T20:00:00                  13.53   
15   2021-05-11T21:00:00                  13.03   
16   2021-05-11T22:00:00                  12.35   
17   2021-05-11T23:00:00                  11.94   
18   2021-05-12T00:00:00                  11.74   
19   2021-05-12T01:00:00                  11.65   
20   2021-05-12T02:00:00                  11.56   
21   2021-05-12T03:00:00                  11.43   
22   2021-05-12T04:00:00                  11.36   
23   2021-05-12T05:00:00                  11.27   
24   2021-05-12T06:00:00                  11.26   
25   2021-05-12T07:00:00                  11.19   
26   2021-05-12T08:00:00                  11.01   
27   2021-05-12T09:00:00                  11.22   
28   2021-05-12T10:00:00                  11.88   
29   2021-05-12T11:00:00                  12.69   
..                   ...                    ...   
181  2021-05-18T19:00:00                  13.55   
182  2021-05-18T20:00:00                   13.2   
183  2021-05-18T21:00:00                  12.84   
184  2021-05-18T22:00:00                  12.49   
185  2021-05-18T23:00:00                  12.13   
186  2021-05-19T00:00:00                  11.78   
187  2021-05-19T01:00:00                  11.42   
188  2021-05-19T02:00:00                  11.32   
189  2021-05-19T03:00:00                  11.21   
190  2021-05-19T04:00:00                   11.1   
191  2021-05-19T05:00:00                  10.99   
192  2021-05-19T06:00:00                  10.89   
193  2021-05-19T07:00:00                  10.78   
194  2021-05-19T08:00:00                  11.28   
195  2021-05-19T09:00:00                  11.78   
196  2021-05-19T10:00:00                  12.28   
197  2021-05-19T11:00:00                  12.78   
198  2021-05-19T12:00:00                  13.28   
199  2021-05-19T13:00:00                  13.78   
200  2021-05-19T14:00:00                  13.88   
201  2021-05-19T15:00:00                  13.96   
202  2021-05-19T16:00:00                  14.06   
203  2021-05-19T17:00:00                  14.15   
204  2021-05-19T18:00:00                  14.24   
205  2021-05-19T19:00:00                  14.33   
206  2021-05-19T20:00:00                  13.89   
207  2021-05-19T21:00:00                  13.45   
208  2021-05-19T22:00:00                  13.01   
209  2021-05-19T23:00:00                  12.57   
210  2021-05-20T00:00:00                  12.13   

                                        2  
0    KSFO MR0!-ECE.2M HOURLY TEMP AVERAGE  
1                                   11.88  
2                                    10.7  
3                                   10.86  
4                                   11.52  
5                                   12.28  
6                                   12.96  
7                                   13.64  
8                                   14.19  
9                                   14.42  
10                                  14.39  
11                                  14.18  
12                                  14.04  
13                                  13.89  
14                                  13.53  
15                                  13.03  
16                                  12.35  
17                                  11.94  
18                                  11.74  
19                                  11.65  
20                                  11.56  
21                                  11.43  
22                                  11.36  
23                                  11.27  
24                                  11.26  
25                                  11.19  
26                                  11.01  
27                                  11.22  
28                                  11.88  
29                                  12.69  
..                                    ...  
181                                 13.55  
182                                  13.2  
183                                 12.84  
184                                 12.49  
185                                 12.13  
186                                 11.78  
187                                 11.42  
188                                 11.32  
189                                 11.21  
190                                  11.1  
191                                 10.99  
192                                 10.89  
193                                 10.78  
194                                 11.28  
195                                 11.78  
196                                 12.28  
197                                 12.78  
198                                 13.28  
199                                 13.78  
200                                 13.88  
201                                 13.96  
202                                 14.06  
203                                 14.15  
204                                 14.24  
205                                 14.33  
206                                 13.89  
207                                 13.45  
208                                 13.01  
209                                 12.57  
210                                 12.13  

[211 rows x 3 columns]
In [7]:
#Time Series - Daily History of observational CFSR data
import pandas as pd
data = ice.get_timeseries(['KBOS-CFSR'],['2M Hourly Temp Average'],'D','1990-01-01','2021-05-20')
df = pd.DataFrame(list(data))
print(df)

                0                                 1
0            Time  KBOS-CFSR.2M HOURLY TEMP AVERAGE
1      1990-01-01                            0.9363
2      1990-01-02                           -1.4279
3      1990-01-03                            0.3583
4      1990-01-04                            3.6417
5      1990-01-05                            2.3508
6      1990-01-06                            0.1787
7      1990-01-07                           -0.4654
8      1990-01-08                            1.5292
9      1990-01-09                            1.6871
10     1990-01-10                            3.2521
11     1990-01-11                            1.9617
12     1990-01-12                            1.1046
13     1990-01-13                           -3.5867
14     1990-01-14                             -6.29
15     1990-01-15                           -0.6196
16     1990-01-16                            2.8462
17     1990-01-17                            4.2837
18     1990-01-18                            8.1342
19     1990-01-19                           -0.8175
20     1990-01-20                           -0.9004
21     1990-01-21                           -1.5712
22     1990-01-22                           -3.0025
23     1990-01-23                           -0.0846
24     1990-01-24                            3.5871
25     1990-01-25                            4.8288
26     1990-01-26                              5.88
27     1990-01-27                            1.2413
28     1990-01-28                            3.1271
29     1990-01-29                            1.9546
...           ...                               ...
11398  2021-03-31                           11.3479
11399  2021-04-01                            6.3996
11400  2021-04-02                           -0.7425
11401  2021-04-03                            1.3283
11402  2021-04-04                            5.3671
11403  2021-04-08                              5.83
11404  2021-04-09                            9.1904
11405  2021-04-10                           13.7054
11406  2021-04-11                            7.8138
11407  2021-04-12                            6.1117
11408  2021-04-13                            7.6871
11409  2021-04-14                            7.7612
11410  2021-04-15                            7.4096
11411  2021-04-16                            3.6671
11412  2021-04-17                            4.7092
11413  2021-04-22                            3.2454
11414  2021-04-23                            7.7121
11415  2021-04-24                           14.1629
11416  2021-04-25                            9.8254
11417  2021-04-26                            8.0446
11418  2021-04-27                           11.1492
11419  2021-04-28                           12.2642
11420  2021-04-29                            9.1883
11421  2021-04-30                           11.2908
11422  2021-05-01                            9.8279
11423  2021-05-02                           16.1838
11424  2021-05-03                            13.035
11425  2021-05-04                            9.3667
11426  2021-05-05                            8.2125
11427  2021-05-06                            11.865

[11428 rows x 2 columns]
In [8]:
#Time Series - Hourly History of observational CFSR data
import pandas as pd
data = ice.get_timeseries(['KBOS-CFSR'],['2M Hourly Temp Average'],'i60','2021-01-01','2021-05-20')
df = pd.DataFrame(list(data))
print(df)

                        0                                 1
0                    Time  KBOS-CFSR.2M HOURLY TEMP AVERAGE
1     2021-01-01T00:00:00                              -0.6
2     2021-01-01T01:00:00                             -1.18
3     2021-01-01T02:00:00                             -1.63
4     2021-01-01T03:00:00                             -2.07
5     2021-01-01T04:00:00                             -2.19
6     2021-01-01T05:00:00                             -2.41
7     2021-01-01T06:00:00                              -2.4
8     2021-01-01T07:00:00                             -1.98
9     2021-01-01T08:00:00                             -0.62
10    2021-01-01T09:00:00                              0.32
11    2021-01-01T10:00:00                              0.94
12    2021-01-01T11:00:00                               1.5
13    2021-01-01T12:00:00                              2.42
14    2021-01-01T13:00:00                               2.4
15    2021-01-01T14:00:00                               2.1
16    2021-01-01T15:00:00                              0.45
17    2021-01-01T16:00:00                              0.01
18    2021-01-01T17:00:00                              0.25
19    2021-01-01T18:00:00                              0.87
20    2021-01-01T19:00:00                              0.79
21    2021-01-01T20:00:00                              0.91
22    2021-01-01T21:00:00                              1.21
23    2021-01-01T22:00:00                              1.78
24    2021-01-01T23:00:00                              2.32
25    2021-01-02T00:00:00                              2.57
26    2021-01-02T01:00:00                              2.91
27    2021-01-02T02:00:00                              3.14
28    2021-01-02T03:00:00                              3.44
29    2021-01-02T04:00:00                              3.96
...                   ...                               ...
2571  2021-05-05T18:00:00                              7.94
2572  2021-05-05T19:00:00                              7.84
2573  2021-05-05T20:00:00                              7.71
2574  2021-05-05T21:00:00                              7.68
2575  2021-05-05T22:00:00                              7.86
2576  2021-05-05T23:00:00                              8.05
2577  2021-05-06T00:00:00                              8.24
2578  2021-05-06T01:00:00                               8.4
2579  2021-05-06T02:00:00                              8.96
2580  2021-05-06T03:00:00                              9.31
2581  2021-05-06T04:00:00                              8.94
2582  2021-05-06T05:00:00                              8.41
2583  2021-05-06T06:00:00                              8.85
2584  2021-05-06T07:00:00                             10.35
2585  2021-05-06T08:00:00                             11.56
2586  2021-05-06T09:00:00                             13.01
2587  2021-05-06T10:00:00                              14.1
2588  2021-05-06T11:00:00                             15.05
2589  2021-05-06T12:00:00                             15.71
2590  2021-05-06T13:00:00                             16.61
2591  2021-05-06T14:00:00                             17.35
2592  2021-05-06T15:00:00                             17.25
2593  2021-05-06T16:00:00                             16.57
2594  2021-05-06T17:00:00                             15.12
2595  2021-05-06T18:00:00                             12.72
2596  2021-05-06T19:00:00                              10.1
2597  2021-05-06T20:00:00                             10.61
2598  2021-05-06T21:00:00                             10.04
2599  2021-05-06T22:00:00                              8.99
2600  2021-05-06T23:00:00                              8.51

[2601 rows x 2 columns]