In [5]:
import numpy as np
import pandas as pd
from pandas import Series, DataFrame

In [8]:
ser1 = Series(np.arange(3), index=['a', 'b', 'c'])

ser1


Out[8]:
a    0
b    1
c    2
dtype: int32

In [9]:
# drop
ser1.drop('b')


Out[9]:
a    0
c    2
dtype: int32

In [14]:
df1 = DataFrame(np.arange(9).reshape((3,3)), index=['SF','LA','NY'], columns=['Pop.', 'Size', 'Year'])

df1


Out[14]:
Pop. Size Year
SF 0 1 2
LA 3 4 5
NY 6 7 8

In [13]:



Out[13]:
Pop. Size Year
SF 0 1 2
LA 3 4 5
NY 6 7 8

In [15]:
df1.drop('LA')


Out[15]:
Pop. Size Year
SF 0 1 2
NY 6 7 8

In [16]:
df1


Out[16]:
Pop. Size Year
SF 0 1 2
LA 3 4 5
NY 6 7 8

In [18]:
# drop columns
df1.drop('Year', axis=1)


Out[18]:
Pop. Size
SF 0 1
LA 3 4
NY 6 7

In [ ]: