In [34]:

    

import sqlite3
import pandas
con = sqlite3.connect('2016.db')
table = pandas.read_sql("select avg(heat_input), avg(gload), op_hour from data where fuel_actual is 'NG' and state is 'CA' and substr(op_date, 1, 2) is '06' group by op_hour", con)
table.to_csv('ca_ng_june.csv')


    

In [ ]:

    

import sqlite3
import pandas
con = sqlite3.connect('2016.db')
table = pandas.read_sql("select substr(op_date, 1, 2) as month, name, orispl_code, unitid, avg(heat_input) as avg_heat, avg(gload) as avg_gen, op_hour from data where fuel_actual is 'NG' and state is 'CA'  group by op_hour, substr(op_date, 1, 2), orispl_code, unitid ", con)
table.to_csv('ca_ng_june.csv')


    

In [11]:

    

import sqlite3
import pandas
con = sqlite3.connect('2016.db')
table = pandas.read_sql("select * from ( \
                        select avg(gload), op_hour from data where fuel_actual is 'NG' and state is 'CA' and substr(op_date, 1, 2) is '06' group by op_hour ) as a \
                        , data as ", con)
table.to_csv('ca_ng_june.csv')


    

In [12]:

    

%sql sqlite:///2016.db


    

    
Out[12]:





u'Connected: None@2016.db'

In [20]:

    

%sql select gload, heat_input, unitid, op_date, op_hour from data where name is 'Colstrip' and substr(op_date, 1, 2) is '06' order by unitid limit 5


    

    




Done.






    
Out[20]:





    

        
gload
        
heat_input
        
unitid
        
op_date
        
op_hour
    
    

        
175.0
        
1910.5
        
1
        
06-01-2016
        
0
    
    

        
161.0
        
1831.7
        
1
        
06-01-2016
        
1
    
    

        
162.0
        
1849.3
        
1
        
06-01-2016
        
2
    
    

        
162.0
        
1859.0
        
1
        
06-01-2016
        
3
    
    

        
162.0
        
1843.4
        
1
        
06-01-2016
        
4
    

In [ ]: