from StringIO import StringIO
import sqlite3
import csv
import operator #from operator import itemgetter, attrgetter

data = open('sal.csv', 'r').read()
string = ''.join(data)
f = StringIO(string)
reader = csv.reader(f)
conn = sqlite3.connect(':memory:')
c = conn.cursor()
c.execute('''create table data (City text, Job text, Salary real)''')
conn.commit()
count = 0

for e in reader:
    if count==0:
        print""
    else:
        e[0]=str(e[0])
        e[1]=str(e[1])
        e[2] = float(e[2])
        c.execute("""insert into data values (?,?,?)""", e)
        count=count+1
        conn.commit()

labels = []
counts = []
count = 0
c.execute('''select count(Salary),Job from data group by Job''')

for row in c:
      for i in row:
            if count==0:
               counts.append(i)
               count=count+1
           else:
                count=0
      labels.append(i)

c.execute('''select Salary,Job from data order by Job''')

count = 1
count1 = 1
temp = 0
pri = 0
lis = []

for row in c:
      lis.append(row)
for cons in counts:
      if cons%2 == 0:
         pri = cons/2
     else:
         pri = (cons+1)/2
     if count1 == 1:
        for li in lis:
              if count == pri:
                  print"Median is",li
        count = count + 1
        count = 0
        temp = pri+cons
     else:
        for li in lis:
              if count == temp:
                  print"Median is",li
              count = count+1
              count = 0
              temp = temp + pri
       count1 = count1 + 1

IndentationError('expected an indented block', ('', 28, 2, 'if count==0:
'))

import csv
from collections import defaultdict

with open("C:/Users/jimenez/Desktop/a.csv","r") as f:
    d = defaultdict(list)
    reader = csv.reader(f)
    reader.next()
    for row in reader:
        d[row[1]].append(float(row[2]))  

for k,v in d.iteritems():
    print"{} median is {}".format(k,sorted(v)[len(v) // 2])
    print"{} average is {}".format(k,sum(v)/len(v))

Plumbers median is 500.0
Plumbers average is 475.0
Lawyers median is 700.0
Lawyers average is 628.571428571
Dog catchers median is 400.0
Dog catchers average is 400.0
Doctors median is 800.0
Doctors average is 787.5

import pandas as pd
df = pd.read_csv('test.csv', names=['City', 'Job', 'Salary'])
df.groupby('Job').median()

#               Salary
# Job                
# Doctors          800
# Dog catchers     400
# Lawyers          700
# Plumbers         450

df.groupby('Job').mean()

#                   Salary
# Job                    
# Doctors       787.500000
# Dog catchers  400.000000
# Lawyers       628.571429
# Plumbers      475.000000

import csv
professions = {}

with open("sal.csv") as data:
    for city, profession, salary in csv.reader(data):
        professions.setdefault(profession.strip(), []).append(int(salary.strip()))

for profession, salaries in sorted(professions.items()):
    print ("{}: {}".format(profession, sorted(salaries)[len(salaries//2)] ))