Series的计算
1. Series.add(other, level=None, fill_value=None)
Series加法：+
参数：
other: Series or scalar valuefill_value: None or float value, default None
在计算之前，用于填充缺失值。level: int or name
>>> s = pd.Series(data=np.arange(7), dtype=np.float32, index=list('abcdefg'))
>>> s
a 0.0
b 1.0
c 2.0
d 3.0
e 4.0
f 5.0
g 6.0
dtype: float32
>>> t = pd.Series({'a':1, 'b':1, 'd':np.NaN, 'f':3, 'h':1})
>>> t
a 1.0
b 1.0
d NaN
f 3.0
h 1.0
dtype: float64
>>> s.add(t)
a 1.0
b 2.0
c NaN
d NaN
e NaN
f 8.0
g NaN
h NaN
dtype: float64
>>> s.add(t,fill_value=0)
a 1.0
b 2.0
c 2.0
d 3.0
e 4.0
f 8.0
g 6.0
h 1.0
dtype: float64
2. Series.sub(other, level=None, fill_value=None)
Series减法：-
参数：
other: Series or scalar valuefill_value: None or float value, default None
在计算之前，用于填充缺失值。level: int or name
>>> s = pd.Series(data=np.arange(7), dtype=np.float32, index=list('abcdefg'))
>>> s
a 0.0
b 1.0
c 2.0
d 3.0
e 4.0
f 5.0
g 6.0
dtype: float32
>>> t = pd.Series({'a':1, 'b':1, 'd':np.NaN, 'f':3, 'h':1})
>>> t
a 1.0
b 1.0
d NaN
f 3.0
h 1.0
dtype: float64
>>> s.sub(t)
a -1.0
b 0.0
c NaN
d NaN
e NaN
f 2.0
g NaN
h NaN
dtype: float64
>>> s.sub(t,fill_value=0)
a -1.0
b 0.0
c 2.0
d 3.0
e 4.0
f 2.0
g 6.0
h -1.0
dtype: float64
3. Series.mul(other, level=None, fill_value=None)
Series乘法：*
参数：
other: Series or scalar valuefill_value: None or float value, default None
在计算之前，用于填充缺失值。level: int or name
>>> s = pd.Series(data=np.arange(7), dtype=np.float32, index=list('abcdefg'))
>>> s
a 0.0
b 1.0
c 2.0
d 3.0
e 4.0
f 5.0
g 6.0
dtype: float32
>>> t = pd.Series({'a':1, 'b':1, 'd':np.NaN, 'f':3, 'h':1})
>>> t
a 1.0
b 1.0
d NaN
f 3.0
h 1.0
dtype: float64
>>> s.mul(t)
a 0.0
b 1.0
c NaN
d NaN
e NaN
f 15.0
g NaN
h NaN
dtype: float64
>>> s.mul(t,fill_value=0)
a 0.0
b 1.0
c 2.0
d 3.0
e 4.0
f 15.0
g 6.0
h 1.0
dtype: float64
4. Series.div(other, level=None, fill_value=None)
Series除：/
参数：
other: Series or scalar valuefill_value: None or float value, default None
在计算之前，用于填充缺失值。level: int or name
>>> s = pd.Series(data=np.arange(7), dtype=np.float32, index=list('abcdefg'))
>>> s
a 0.0
b 1.0
c 2.0
d 3.0
e 4.0
f 5.0
g 6.0
dtype: float32
>>> t = pd.Series({'a':1, 'b':1, 'd':np.NaN, 'f':3, 'h':1})
>>> t
a 1.0
b 1.0
d NaN
f 3.0
h 1.0
dtype: float64
>>> s.div(t)
a 0.000000
b 1.000000
c NaN
d NaN
e NaN
f 1.666667
g NaN
h NaN
dtype: float64
>>> s.div(t,fill_value=1)
a 0.000000
b 1.000000
c 2.000000
d 3.000000
e 4.000000
f 1.666667
g 6.000000
h 1.000000
dtype: float64
5. Series.floordiv(other, level=None, fill_value=None)
Series除：//
参数：
other: Series or scalar valuefill_value: None or float value, default None
在计算之前，用于填充缺失值。level: int or name
>>> s = pd.Series(data=np.arange(7), dtype=np.float32, index=list('abcdefg'))
>>> s
a 0.0
b 1.0
c 2.0
d 3.0
e 4.0
f 5.0
g 6.0
dtype: float32
>>> t = pd.Series({'a':1, 'b':1, 'd':np.NaN, 'f':3, 'h':1})
>>> t
a 1.0
b 1.0
d NaN
f 3.0
h 1.0
dtype: float64
>>> s.floordiv(t)
a 0.0
b 1.0
c NaN
d NaN
e NaN
f 1.0
g NaN
h NaN
dtype: float64
>>> s.floordiv(t,fill_value=1)
a 0.0
b 1.0
c 2.0
d 3.0
e 4.0
f 1.0
g 6.0
h 1.0
dtype: float64
6. Series.mod(other, level=None, fill_value=None)
Series求余：%
参数：
other: Series or scalar valuefill_value: None or float value, default None
在计算之前，用于填充缺失值。level: int or name
>>> s = pd.Series(data=np.arange(7), dtype=np.float32, index=list('abcdefg'))
>>> s
a 0.0
b 1.0
c 2.0
d 3.0
e 4.0
f 5.0
g 6.0
dtype: float32
>>> t = pd.Series({'a':1, 'b':1, 'd':np.NaN, 'f':3, 'h':1})
>>> t
a 1.0
b 1.0
d NaN
f 3.0
h 1.0
dtype: float64
>>> s.mod(t)
a 0.0
b 0.0
c NaN
d NaN
e NaN
f 2.0
g NaN
h NaN
dtype: float64
>>> s.mod(t,fill_value=1)
a 0.0
b 0.0
c 0.0
d 0.0
e 0.0
f 2.0
g 0.0
h 0.0
dtype: float64
7. Series.pow(other, level=None, fill_value=None)
Series幂：%
参数：
other: Series or scalar valuefill_value: None or float value, default None
在计算之前，用于填充缺失值。level: int or name
>>> s = pd.Series(data=np.arange(7), dtype=np.float32, index=list('abcdefg'))
>>> s
a 0.0
b 1.0
c 2.0
d 3.0
e 4.0
f 5.0
g 6.0
dtype: float32
>>> t = pd.Series({'a':1, 'b':1, 'd':np.NaN, 'f':3, 'h':1})
>>> t
a 1.0
b 1.0
d NaN
f 3.0
h 1.0
dtype: float64
>>> s.pow(t)
a 0.0
b 1.0
c NaN
d NaN
e NaN
f 125.0
g NaN
h NaN
dtype: float64
>>> s.mod(t,fill_value=1)
a 0.0
b 1.0
c 2.0
d 3.0
e 4.0
f 125.0
g 6.0
h 1.0
dtype: float64
8. Series.combine(other, func, fill_value=None)
使用函数子联合两个Series
参数：
other: Series or scalar valuefun: function
将两个常数所为函数的参数，返回结果fill_value: None or float value, default None
在计算之前，用于填充缺失值。
>>> s = pd.Series(data=np.arange(7), dtype=np.float32, index=list('abcdefg'))
>>> s
a 0.0
b 1.0
c 2.0
d 3.0
e 4.0
f 5.0
g 6.0
dtype: float32
>>> t = pd.Series({'a':1, 'b':1, 'd':np.NaN, 'f':3, 'h':1})
>>> t
a 1.0
b 1.0
d NaN
f 3.0
h 1.0
dtype: float64
>>> s.combine(t, max)
a 1.0
b 1.0
c 2.0
d 3.0
e 4.0
f 5.0
g 6.0
h NaN
dtype: float64
>>> s.mod(t, max, fill_value=1)
a 1.0
b 1.0
c 2.0
d 3.0
e 4.0
f 5.0
g 6.0
h 1.0
dtype: float64
9. Series.round(decimals=0)
使用函数子联合两个Series
参数：
decimals: int, default 0: 要舍入的小数位数。
>>> s = pd.Series([1.2, 2.46, 3.7])
>>> s
0 1.20
1 2.46
2 3.70
dtype: float64
>>> s1.round(decimals=1)
0 1.2
1 2.5
2 3.7
dtype: float64
10. 布尔运算：
Series.lt(other, level, fill_value): <Series.gt(other, level, fill_value): >Series.le(other, level, fill_value): <=Series.ge(other, level, fill_value): >=Series.ne(other, level, fill_value): !=Series.eq(other, level, fill_value): =
参数：
other: Series or scalar valuefill_value: None or float value, default None
在计算之前，用于填充缺失值。level: int or name
>>> s = pd.Series([1, 3, 4, 2, 7, 0, 8], index=list('abcdefg'))
>>> t = pd.Series([1, 2, 4, 3, 6, 1, 8], index=list('abcdefg'))
>>> s.lt(t)
a False
b False
c False
d True
e False
f True
g False
dtype: bool
>>> s.eq(t)
a True
b False
c True
d False
e False
f False
g True
dtype: bool
>>> s.ge(t)
a True
b True
c True
d False
e True
f False
g True
dtype: bool
