import numpy as np
import pandas as pd

!head eng-hourly-08012013-08312013.csv

!head -20 eng-hourly-08012013-08312013.csv

!tail eng-hourly-08012013-08312013.csv

data = pd.read_csv('eng-hourly-08012013-08312013.csv', skiprows=16)
print data[0:4]

print data.tail(1)

!tail -1 eng-hourly-08012013-08312013.csv

data = pd.read_csv('eng-hourly-08012013-08312013.csv', skiprows=16, encoding="ISO-8859-1")
print data.columns

temps = data[u'Temp (°C)']
print 'max:', temps.max(), 'on', data['Date/Time'][temps.argmax()]
print 'min:', temps.min(), 'on', data['Date/Time'][temps.argmin()]
print 'mean:', temps.mean()
print 'std dev:', temps.std()

temps = data[u'Temp (°C)']
for day in range(1, 32):
    mask = data['Day']==day
    max_temp = temps[mask].max()
    date = data[mask]['Date/Time'][temps[mask].argmax()][:11]
    hour = data[mask]['Time'][temps[mask].argmax()]    
    print 'max temperature on',date, 'was', max_temp, 'at', hour

import requests
url = 'http://climate.weather.gc.ca/climateData/bulkdata_e.html'
params = {
    'timeframe': 1,
    'stationID': 51442,
    'Year': 2013,
    'Month': 7,
    'Day': 1,
    'format': 'csv',
}
response = requests.get(url, params=params)

response.headers

from StringIO import StringIO
fakefile = StringIO(response.content)
datajul = pd.read_csv(fakefile, skiprows=16, encoding="ISO-8859-1")

print datajul.head(2)