import numpy as np
import pandas as pd
s1 = pd.Series([1,2,3,4])
s1
0 1
1 2
2 3
3 4
dtype: int64
s1.values
array([1, 2, 3, 4])
s1.index
RangeIndex(start=0, stop=4, step=1)
s2 = pd.Series(np.arange(10))
s2
0 0
1 1
2 2
3 3
4 4
5 5
6 6
7 7
8 8
9 9
dtype: int64
s3 = pd.Series({'1':'1','2':'2','3':'3'})
s3
1 1
2 2
3 3
dtype: object
s3.values
array(['1', '2', '3'], dtype=object)
s3.index
Index(['1', '2', '3'], dtype='object')
s4 = pd.Series([1,2,3,4],index=['A','B','C','D'])
s4
A 1
B 2
C 3
D 4
dtype: int64
s4.index
Index(['A', 'B', 'C', 'D'], dtype='object')
s4['A']
1
s4[s4>2]
C 3
D 4
dtype: int64
s4.to_dict()
{'A': 1, 'B': 2, 'C': 3, 'D': 4}
index_1 = ['A', 'B', 'C', 'D', 'E']
s5 = pd.Series(s4, index=index_1)
s5
A 1.0
B 2.0
C 3.0
D 4.0
E NaN
dtype: float64
pd.isnull(s5)
A False
B False
C False
D False
E True
dtype: bool
pd.notnull(s5)
A True
B True
C True
D True
E False
dtype: bool
s5.name = 'demo'
s5
A 1.0
B 2.0
C 3.0
D 4.0
E NaN
Name: demo, dtype: float64
s5.index.name = 'demo index'
s5.index
Index(['A', 'B', 'C', 'D', 'E'], dtype='object', name='demo index')
s5.values
array([ 1., 2., 3., 4., nan])