pandas – python data analysis library

import pandas as pd 
from pandas import Series, DataFrame 

data = {"name":["yahoo","google","facebook"], "marks":[200,400,800], "price":[9, 3, 7]} 
f1 = DataFrame(data) 
f1 
     marks  name      price 
0    200    yahoo     9 
1    400    google    3 
2    800    facebook  7 

>>> f2 = DataFrame(data, columns=['name','price','marks']) 
>>> f2 
       name     price  marks 
0     yahoo     9      200 
1    google     3      400 
2  facebook     7      800 

>>> f3 = DataFrame(data, columns=['name', 'price', 'marks', 'debt'], index=['a','b','c']) 
>>> f3 
       name      price  marks  debt 
a     yahoo      9      200     NaN 
b    google      3      400     NaN 
c  facebook      7      800     NaN 

>>> newdata = {"lang":{"firstline":"python","secondline":"java"}, "price":{"firstline":8000}} 
>>> f4 = DataFrame(newdata) 
>>> f4 
              lang     price 
firstline     python   8000 
secondline    java     NaN

>>> import csv 
>>> csv_reader = csv.reader(open("./marks.csv"))
>>> for row in csv_reader:
...     print row
... 
['name', 'physics', 'python', 'math', 'english']
['Google', '100', '100', '25', '12']
['Facebook', '45', '54', '44', '88']
['Twitter', '54', '76', '13', '91']
['Yahoo', '54', '452', '26', '100']

>>> import pandas as pd
>>> marks = pd.read_csv("./marks.csv")
>>> marks
       name  physics  python  math  english
0    Google      100     100    25       12
1  Facebook       45      54    44       88
2   Twitter       54      76    13       91
3     Yahoo       54     452    26      100

>>> pd.read_table("./marks.csv", sep=",")
       name  physics  python  math  english
0    Google      100     100    25       12
1  Facebook       45      54    44       88
2   Twitter       54      76    13       91
3     Yahoo       54     452    26      100