In [1]:
import numpy as np
import pandas as pd
from pandas import DataFrame, Series

In [4]:
ser1 = Series(np.arange(3), index=['X','Y','Z'])

ser1 = 2*ser1

ser1


Out[4]:
X    0
Y    2
Z    4
dtype: int32

In [6]:
ser1['Y']


Out[6]:
2

In [7]:
ser1[2]


Out[7]:
4

In [8]:
ser1[:1]


Out[8]:
X    0
dtype: int32

In [9]:
# selecting with range by index
ser1[['X','Z']]


Out[9]:
X    0
Z    4
dtype: int32

In [11]:
# selecting by logic
ser1[ser1 > 3]


Out[11]:
Z    4
dtype: int32

In [13]:
# set values
ser1[ser1 > 3] = 10

ser1


Out[13]:
X     0
Y     2
Z    10
dtype: int32

In [19]:
df1 = DataFrame(np.arange(25).reshape(5,5), index=['SF','DC','LA','NY','CH'],
                columns=['Q','W','E','R','T'])

df1


Out[19]:
Q W E R T
SF 0 1 2 3 4
DC 5 6 7 8 9
LA 10 11 12 13 14
NY 15 16 17 18 19
CH 20 21 22 23 24

In [20]:
# selecting by columns name
df1['Q']


Out[20]:
SF     0
DC     5
LA    10
NY    15
CH    20
Name: Q, dtype: int32

In [21]:
df1


Out[21]:
Q W E R T
SF 0 1 2 3 4
DC 5 6 7 8 9
LA 10 11 12 13 14
NY 15 16 17 18 19
CH 20 21 22 23 24

In [22]:
df1[['W','T']]


Out[22]:
W T
SF 1 4
DC 6 9
LA 11 14
NY 16 19
CH 21 24

In [23]:
df1[df1['R'] > 8]


Out[23]:
Q W E R T
LA 10 11 12 13 14
NY 15 16 17 18 19
CH 20 21 22 23 24

In [24]:
# boolean datafarame
df1 > 10


Out[24]:
Q W E R T
SF False False False False False
DC False False False False False
LA False True True True True
NY True True True True True
CH True True True True True

In [25]:
# using 'ix' function
df1.ix['DC']


Out[25]:
Q    5
W    6
E    7
R    8
T    9
Name: DC, dtype: int32

In [26]:
df1.ix[1]


Out[26]:
Q    5
W    6
E    7
R    8
T    9
Name: DC, dtype: int32

In [ ]: