import numpy as np
import pandas as pd

data = {'Mon':'Monday',
        'Tues':'Tuesday',
        'Wed':'Wednesday',
        'Thurs':'Thursday',
        }
s = pd.Series(data)
s

s.index

s = pd.Series(np.random.randint(5, 15, 7), ('Mon', 'Tues', 'Wed', 'Thur', 'Fri', 'Sat',
            'Sun'), name='Temperature')

s.index.name = "Day of the Week"

s

s['Tues']

'Mon' in s

'Son' in s

s['Thur':'Sun']

s.max()

s + 2*s #Vectorized operation


s[1] #Accessing a value by position

s[2:5] #Slicing the Series by position

s[:1]

s - np.random.randint(5, 15, 7)

for x in s: print x #iterating over values

for pos, value in enumerate(s): print pos, ':', value

for key, value in s.iteritems(): print key, ':', value

import datetime

base = datetime.datetime.today()
days = 20
date_list = [base - datetime.timedelta(days=x) for x in range(0, days)]
date_list = [datetime.date(x.year, x.month, x.day) for x in date_list]
date_list.reverse()
data = {'date':date_list, 
        'Chennai':np.random.randint(25,35,days), 
        'Mumbai':np.random.randint(15,25,days), 
        'Delhi':np.random.randint(5,15,days)}
df = pd.DataFrame(data)

type(df)

df.head()

df = df.set_index('date')

df.head()

df.median()

df.mean()

df.diff().head()

titanic = pd.read_csv('data/titanic.csv')

titanic = titanic.set_index('PassengerId')

titanic.head()

len(titanic)

titanic.Fare.sum()

titanic.Survived.value_counts()

titanic.Pclass.value_counts()