In [4]:

    

import csv


    

In [11]:

    

with open('coal_plants.csv', 'rb') as csvfile:
    reader = csv.DictReader(csvfile)
    epa = []
    cems = []
    for row in reader:
        epa.append(row['\xef\xbb\xbfORIS Code'])
        cems.append(row[' Facility ID (ORISPL)'])
    print("Things in EPA, not in CEMS")
    for entry in epa:
        if entry not in cems:
            print(entry)
    print("Things in CEMS, not in EPA")
    for entry in cems:
        if entry not in epa:
            print(entry)


    

    




Things in EPA, not in CEMS
6204.2
6204.2
6204.1
50931
10002
50951
10784
10768
10769
10640
10373
54238
2341
2341
Things in CEMS, not in EPA
126
508
6089
6204
6204
6204

In [15]:

    

import sqlite3
import pandas
con = sqlite3.connect('navajo.db')
table = pandas.read_sql('select * from data', con)
table.to_csv('output.csv')


    

In [16]:

    

%load_ext sql


    

    




The sql extension is already loaded. To reload it, use:
  %reload_ext sql

In [18]:

    

%sql sqlite:///navajo.db
%sql select * from data limit 0


    

    




Done.






    
Out[18]:





    

        
state
        
name
        
orispl_code
        
unitid
        
op_date
        
op_hour
        
op_time
        
gload
        
SO2_MASS
        
NOX_MASS
        
CO2_MASS
        
heat_input
        
county
        
latitude
        
longitude
        
fuel_type
    

In [30]:

    

%sql select max(CAST(gload as float)) as global_max_generation, max(CAST(heat_input as float)) as global_max_heat_input, name, orispl_code, unitid from data group by name, unitid


    

    




Done.






    
Out[30]:





    

        
global_max_generation
        
global_max_heat_input
        
name
        
orispl_code
        
unitid
    
    

        
132.0
        
1653.9
        
Cholla
        
113
        
1
    
    

        
306.0
        
3616.46
        
Cholla
        
113
        
2
    
    

        
307.0
        
8860.4
        
Cholla
        
113
        
3
    
    

        
436.0
        
5182.3
        
Cholla
        
113
        
4
    

In [ ]: