[Python Cookbook] Pandas: 3 Ways to define a DataFrame

Using Series (Row-Wise)

import pandas as pd

purchase_1 = pd.Series({'Name': 'Chris',

                        'Item Purchased': 'Dog Food',

                        'Cost': 22.50})

purchase_2 = pd.Series({'Name': 'Kevyn',

                        'Item Purchased': 'Kitty Litter',

                        'Cost': 2.50})

purchase_3 = pd.Series({'Name': 'Vinod',

                        'Item Purchased': 'Bird Seed',

                        'Cost': 5.00})

df = pd.DataFrame([purchase_1, purchase_2, purchase_3], index=['Store 1', 'Store 1', 'Store 2'])

df.head()

Using Series (Column-Wise)

s1 =pd.Series([.25,.5,.75,1],index = ['a','b','c','d']

s2 =pd.Series([.5,.75,1,.25],index = ['a','b','c','d']

df = pd.DataFrame({’s1’:s1,’s2’:s2})

print (df)

Using Dictionary (Columnwise)

data = {'Fruit':['Apple','Pear','Strawberry'],

       'Amount':[3,2,5],

       'Price':[10,9,8]}

df = DataFrame(data)

print(df)

Using Nested Dictionary

The outer dictionary is columnwise and the inner dictionary is rowwise.

data = {'Amount':{'Apple':3,'Pear':2,'Strawberry':5},

       'Price':{'Apple':10,'Pear':9,'Strawberry':8}}

df = DataFrame(data)

print(df)