In [4]:

    

import pandas as pd
import numpy as np


    

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# Object Creation
#         DataFrame
#         Series
#     Meta Info
#         shape       
#         dtypes
#     Input/Output
#         Read & Write
#     Viewing Data
#         head
#         columns
#         index
#     Getting
#         by Label
#         by Position
#         by index
#         by seq


    

In [5]:

    

WD = '/Users/anand/Documents/consulting/adhyapiyutam/'
df = pd.read_csv(WD+'UniversalBank.csv')#.head()
print df.head()


    

    




   Age  Experience  Income  ZIP.Code  Family  CCAvg  Education  Mortgage  \
0   25           1      49     91107       4    1.6          1         0   
1   45          19      34     90089       3    1.5          1         0   
2   39          15      11     94720       1    1.0          1         0   
3   35           9     100     94112       1    2.7          2         0   
4   35           8      45     91330       4    1.0          2         0   

   class  Securities.Account  CD.Account  Online  CreditCard  
0      0                   1           0       0           0  
1      0                   1           0       0           0  
2      0                   0           0       0           0  
3      0                   0           0       0           0  
4      0                   0           0       0           1  

In [7]:

    

print df.shape
print df.dtypes


    

    




(5000, 13)
Age                     int64
Experience              int64
Income                  int64
ZIP.Code                int64
Family                  int64
CCAvg                 float64
Education               int64
Mortgage                int64
class                   int64
Securities.Account      int64
CD.Account              int64
Online                  int64
CreditCard              int64
dtype: object

In [11]:

    

#Columns
df[['Age','Experience']].head()
df['Age'].head()
df.Age.head()
#Rows


    

    
Out[11]:





0    25
1    45
2    39
3    35
4    35
Name: Age, dtype: int64

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