import pandas as pd

s = pd.Series([1,3,6,2])
s
# 0    1
# 1    3
# 2    6
# 3    2
# dtype: int64

s.index
# RangeIndex(start=0, stop=4, step=1)

s.values
# array([1, 3, 6, 2], dtype=int64)

s1 = pd.Series([1,3,6,2], index=['a', 'b', 'c', 'd'])
s1
# a    1
# b    3
# c    6
# d    2
# dtype: int64

s.index
# Index(['a', 'b', 'c', 'd'], dtype='object')

s[s<3]
# a    1
# d    2
# dtype: int64

s*4
# a     4
# b    12
# c    24
# d     8
# dtype: int64

import numpy as np
np.mean(s)   # 3.0
s.mean()      # 3.0
s.max()        # 6
s.index        # Index(['a', 'b', 'c', 'd'], dtype='object')
'e' in s.index  # False
s = pd.Series({'a': 1, 'b': 9, 'c': 4})
s
# a    1
# b    9
# c    4
# dtype: int64
s = pd.Series({'a': 1, 'b': 9, 'c': 4}, index=['a', 'b', 'd'])
s
# a    1
# b    9
# d    NaN
# dtype: int64

s[s.isnull()]
#d   NaN
#dtype: float64

自动按照索引对齐

s
# a    1.0
# b    9.0
# d    NaN
# dtype: float64

s1
# a    1
# b    3
# c    6
# d    2
# dtype: int64

s + s1
# a     2.0
# b    12.0
# c     NaN
# d     NaN
# dtype: float64

s.notnull()
# a     True
# b     True
# d    False
# dtype: bool

s
# a    1.0
# b    9.0
# d    NaN
# dtype: float64

s['a']   
# 1.0

s[['a', 'b']]
# a    1.0
# b    9.0
# dtype: float64

s1
# a    1
# b    3
# c    6
# d    2
# dtype: int64

s1['a': 'c']
# a    1
# b    3
# c    6
# dtype: int64

s['a'] = 1000
s
# a    1000.0
# b       9.0
# d       NaN
# dtype: float64

s
# a    1000.0
# b       9.0
# d       NaN
# dtype: float64

s[s.isnull()]
# d   NaN
# dtype: float64

s[~s.isnull()]
# a    1000.0
# b       9.0
# dtype: float64

s[s.notnull()]
# a    1000.0
# b       9.0
# dtype: float64

取出一列，就是一个series