Its output is as follows −

           B           D

1   0.890791    0.631615

3  -1.284314   -0.026251

5  -0.512888   -0.518930

           A           B           C           D

1  -0.685354    0.890791   -0.813012    0.631615

2  -0.783192   -0.531378    0.025070    0.230806

           B           C

0   0.256239   -1.270702

1   0.890791   -0.813012

2  -0.531378    0.025070

3  -1.284314    0.826977

4  -0.460729    1.423332

5  -0.512888    0.581409

6  -1.204853    0.098060

7  -0.947857    0.641358

.ix():

Besides pure label based and integer based, Pandas provides a hybrid method for selections and subsetting the object using the .ix() operator.

Example 1:

import pandas as pd

import numpy as np

df = pd. DataFrame ( np. random. randn( 8 , 4 ), columns = [ 'A' , 'B' , 'C' , 'D' ])

# Integer slicing

print df. ix[: 4 ]

Its output is as follows −

           A          B           C           D

0   0.699435   0.256239   -1.270702   -0.645195

1  -0.685354   0.890791   -0.813012    0.631615

2  -0.783192  -0.531378    0.025070    0.230806

3   0.539042  -1.284314    0.826977   -0.026251

Example 2:

import pandas as pd

import numpy as np

df = pd. DataFrame ( np. random. randn( 8 , 4 ), columns = [ 'A' , 'B' , 'C' , 'D' ])

# Index slicing

print df. ix[:, 'A' ]

Its output is as follows −

0   0.699435

1  -0.685354

2  -0.783192

3   0.539042

4  -1.044209

5  -1.415411

6   1.062095

7   0.994204

Name: A, dtype: float64

Use of Notations:

Getting values from the Pandas object with Multi-axes indexing uses the following notation −

Object	Indexers	Return Type
Series	s.loc[indexer]	Scalar value
DataFrame	df.loc[row_index,col_index]	Series object
Panel	p.loc[item_index,major_index, minor_index]	p.loc[item_index,major_index, minor_index]

Note − .iloc() & .ix()  applies the same indexing options and Return value.

Let us now see how each operation can be performed on the DataFrame object. We will use the basic indexing operator '[ ]' −

Example 1:

import pandas as pd

import numpy as np

df = pd. DataFrame ( np. random. randn( 8 , 4 ), columns = [ 'A' , 'B' , 'C' , 'D' ])

print df[ 'A' ]

Its output is as follows −

0  -0.478893

1   0.391931

2   0.336825

3  -1.055102

4  -0.165218

5  -0.328641

6   0.567721

7  -0.759399

Name: A, dtype: float64

Note  − We can pass a list of values to [ ] to select those columns.

Example 2:

import pandas as pd

import numpy as np

df = pd. DataFrame ( np. random. randn( 8 , 4 ), columns = [ 'A' , 'B' , 'C' , 'D' ])

print df[[ 'A' , 'B' ]]

Its output is as follows −

           A           B

0  -0.478893   -0.606311

1   0.391931   -0.949025

2   0.336825    0.093717

3  -1.055102   -0.012944

4  -0.165218    1.550310

5  -0.328641   -0.226363

6   0.567721   -0.312585

7  -0.759399   -0.372696

Example 3:

import pandas as pd

import numpy as np

df = pd. DataFrame ( np. random. randn( 8 , 4 ), columns = [ 'A' , 'B' , 'C' , 'D' ])

print df[ 2 : 2 ]

Its output is as follows −

Columns: [A, B, C, D]

Index: []

Attribute Access:

Columns can be selected using the attribute operator '.'.

Example:

import pandas as pd

import numpy as np

df = pd. DataFrame ( np. random. randn( 8 , 4 ), columns = [ 'A' , 'B' , 'C' , 'D' ])

print df. A

Its output is as follows −

0   -0.478893

1    0.391931

2    0.336825

3   -1.055102

4   -0.165218

5   -0.328641

6    0.567721

7   -0.759399

Name: A, dtype: float64