In [1]:

    

import pandas as pd
import timeit


    

In [2]:

    

s = pd.Series(['a', 'b', 'c', 'd', 'e'])


    

In [3]:

    

print(s)


    

    




0    a
1    b
2    c
3    d
4    e
dtype: object

In [4]:

    

s_swap = pd.Series(s.index.values, s.values)


    

In [5]:

    

print(s_swap)


    

    




a    0
b    1
c    2
d    3
e    4
dtype: int64

In [6]:

    

print(s.values)


    

    




['a' 'b' 'c' 'd' 'e']

In [7]:

    

print(type(s.values))


    

    




<class 'numpy.ndarray'>

In [8]:

    

print(s.index.values)


    

    




[0 1 2 3 4]

In [9]:

    

print(type(s.index.values))


    

    




<class 'numpy.ndarray'>

In [10]:

    

s_swap = pd.Series(s.index, s)


    

In [11]:

    

print(s_swap)


    

    




a    0
b    1
c    2
d    3
e    4
dtype: int64

In [12]:

    

loop = 10000

result = timeit.timeit(lambda: pd.Series(s.index.values, s.values), number=loop)
print(result / loop)


    

    




8.694580160081386e-05

In [13]:

    

result = timeit.timeit(lambda: pd.Series(s.index, s), number=loop)
print(result / loop)


    

    




0.00010916587258689105

In [14]:

    

s_large = pd.concat([s] * 100000)


    

In [15]:

    

print(len(s_large))


    

    




500000

In [16]:

    

loop = 100

result = timeit.timeit(lambda: pd.Series(s_large.index.values, s_large.values), number=loop)
print(result / loop)


    

    




0.005923357829451561

In [17]:

    

result = timeit.timeit(lambda: pd.Series(s_large.index, s_large), number=loop)
print(result / loop)


    

    




0.006492725329007953