In [8]:

    

%matplotlib inline


    

In [9]:

    

import pandas


    

In [21]:

    

df = pandas.read_csv(r'D:\Dev\varsomdata2\localstorage\aval_danger.csv', index_col=0, parse_dates=['date'])
df.info()


    

    




<class 'pandas.core.frame.DataFrame'>
Int64Index: 5 entries, 0 to 4
Data columns (total 57 columns):
avalanche_forecast                           5 non-null object
avalanche_nowcast                            0 non-null float64
avalanche_problem_1_aval_size                5 non-null object
avalanche_problem_1_aval_size_tid            5 non-null int64
avalanche_problem_1_aval_type                5 non-null object
avalanche_problem_1_aval_type_tid            5 non-null int64
avalanche_problem_1_cause_name               5 non-null object
avalanche_problem_1_cause_tid                5 non-null int64
avalanche_problem_1_main_cause               0 non-null float64
avalanche_problem_1_problem                  5 non-null object
avalanche_problem_1_problem_tid              5 non-null int64
avalanche_problem_1_source                   3 non-null object
avalanche_problem_2_aval_size                5 non-null object
avalanche_problem_2_aval_size_tid            5 non-null int64
avalanche_problem_2_aval_type                5 non-null object
avalanche_problem_2_aval_type_tid            5 non-null int64
avalanche_problem_2_cause_name               5 non-null object
avalanche_problem_2_cause_tid                5 non-null int64
avalanche_problem_2_main_cause               0 non-null float64
avalanche_problem_2_problem                  5 non-null object
avalanche_problem_2_problem_tid              5 non-null int64
avalanche_problem_2_source                   0 non-null float64
avalanche_problem_3_aval_size                5 non-null object
avalanche_problem_3_aval_size_tid            5 non-null int64
avalanche_problem_3_aval_type                5 non-null object
avalanche_problem_3_aval_type_tid            5 non-null int64
avalanche_problem_3_cause_name               5 non-null object
avalanche_problem_3_cause_tid                5 non-null int64
avalanche_problem_3_main_cause               0 non-null float64
avalanche_problem_3_problem                  5 non-null object
avalanche_problem_3_problem_tid              5 non-null int64
avalanche_problem_3_source                   0 non-null float64
danger_level                                 5 non-null int64
danger_level_name                            5 non-null object
data_table                                   5 non-null object
date                                         5 non-null datetime64[ns]
main_message_en                              0 non-null float64
main_message_no                              5 non-null object
metadata                                     5 non-null object
mountain_weather_change_hour_of_day_start    5 non-null int64
mountain_weather_change_hour_of_day_stop     5 non-null int64
mountain_weather_change_wind_direction       2 non-null object
mountain_weather_change_wind_speed           2 non-null object
mountain_weather_fl_hour_of_day_start        5 non-null int64
mountain_weather_fl_hour_of_day_stop         5 non-null int64
mountain_weather_freezing_level              5 non-null float64
mountain_weather_precip_most_exposed         5 non-null float64
mountain_weather_precip_region               5 non-null float64
mountain_weather_temperature_elevation       5 non-null float64
mountain_weather_temperature_max             5 non-null float64
mountain_weather_temperature_min             5 non-null float64
mountain_weather_wind_direction              5 non-null object
mountain_weather_wind_speed                  5 non-null object
nick                                         5 non-null object
region_name                                  5 non-null object
region_regobs_id                             5 non-null int64
source                                       5 non-null object
dtypes: datetime64[ns](1), float64(13), int64(18), object(25)
memory usage: 2.3+ KB

In [22]:

    

df.plot(x='date', y='avalanche_problem_1_aval_size_tid')


    

    
Out[22]:





<matplotlib.axes._subplots.AxesSubplot at 0x2b7cff7a860>






    

In [23]:

    

# implement a default - check values for not given at api.nve.no

df.head(5)


    

    
Out[23]:







  

    

      

      
avalanche_forecast
      
avalanche_nowcast
      
avalanche_problem_1_aval_size
      
avalanche_problem_1_aval_size_tid
      
avalanche_problem_1_aval_type
      
avalanche_problem_1_aval_type_tid
      
avalanche_problem_1_cause_name
      
avalanche_problem_1_cause_tid
      
avalanche_problem_1_main_cause
      
avalanche_problem_1_problem
      
...
      
mountain_weather_precip_region
      
mountain_weather_temperature_elevation
      
mountain_weather_temperature_max
      
mountain_weather_temperature_min
      
mountain_weather_wind_direction
      
mountain_weather_wind_speed
      
nick
      
region_name
      
region_regobs_id
      
source
    
  
  

    

      
0
      
De få snøflekkene som ligger igjen høyt til fj...
      
NaN
      
Not given
      
0
      
Not given
      
0
      
Not given
      
0
      
NaN
      
Not given
      
...
      
0.0
      
1400.0
      
-2.0
      
-7.0
      
S
      
Liten kuling
      
Andreas@nve
      
Trollheimen
      
3022
      
Forecast
    
    

      
1
      
De få snøflekkene som ligger igjen høyt til fj...
      
NaN
      
Not given
      
0
      
Not given
      
0
      
Not given
      
0
      
NaN
      
Not given
      
...
      
0.0
      
1400.0
      
-2.0
      
-13.0
      
S
      
Frisk bris
      
Andreas@nve
      
Trollheimen
      
3022
      
Forecast
    
    

      
2
      
Ny nedbør og økende vind fra nordvest kan føre...
      
NaN
      
1 - Små
      
1
      
Tørre flakskred
      
20
      
Nedføyket svakt lag med nysnø
      
10
      
NaN
      
Nysnø - flakskred
      
...
      
4.0
      
1400.0
      
-2.0
      
-6.0
      
SW
      
Bris
      
Andreas@nve
      
Trollheimen
      
3022
      
Forecast
    
    

      
3
      
Vind og nysnø vil føre til danning av ferske n...
      
NaN
      
2 - Middels
      
2
      
Tørre flakskred
      
20
      
Nedføyket svakt lag med nysnø
      
10
      
NaN
      
Nysnø - flakskred
      
...
      
16.0
      
1400.0
      
-6.0
      
-12.0
      
NW
      
Stiv kuling
      
Andreas@nve
      
Trollheimen
      
3022
      
Forecast
    
    

      
4
      
Nysnø og vind vil legge opp fersk fokksnø i le...
      
NaN
      
2 - Middels
      
2
      
Tørre flakskred
      
20
      
Nedføyket svakt lag med nysnø
      
10
      
NaN
      
Nysnø - flakskred
      
...
      
8.0
      
1400.0
      
-6.0
      
-12.0
      
W
      
Liten kuling
      
torolav@obskorps
      
Trollheimen
      
3022
      
Forecast
    
  

5 rows × 57 columns

In [ ]: