Pandas庫學習筆記(5) Pandas中Series和DataFrame數據類型的運算

參考連接: Python數據分析與展現
參考連接: Pandas官網
參考連接: User Guide
參考連接: Getting started tutorialsjavascript

算術運算:html

算術運算法則:
算術運算根據行列索引，補齊後運算，運算默認產生浮點數
補齊時缺項填充NaN (空值)
二維和一維、一維和零維間爲廣播運算
採用+ ‐ * /符號進行的二元運算產生新的對象

實驗演示1:java

Microsoft Windows [版本 10.0.18363.1198]
(c) 2019 Microsoft Corporation。保留全部權利。

C:\Users\chenxuqi>python
Python 3.7.4 (tags/v3.7.4:e09359112e, Jul  8 2019, 20:34:20) [MSC v.1916 64 bit (AMD64)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> import pandas as pd
>>> import numpy as np
>>> a = pd.DataFrame(np.arange(12).reshape(3,4))
>>> a
   0  1   2   3
0  0  1   2   3
1  4  5   6   7
2  8  9  10  11
>>> b = pd.DataFrame(np.arange(20).reshape(4,5))
>>> b
    0   1   2   3   4
0   0   1   2   3   4
1   5   6   7   8   9
2  10  11  12  13  14
3  15  16  17  18  19
>>> a + b
      0     1     2     3   4
0   0.0   2.0   4.0   6.0 NaN
1   9.0  11.0  13.0  15.0 NaN
2  18.0  20.0  22.0  24.0 NaN
3   NaN   NaN   NaN   NaN NaN
>>> a * b
      0     1      2      3   4
0   0.0   1.0    4.0    9.0 NaN
1  20.0  30.0   42.0   56.0 NaN
2  80.0  99.0  120.0  143.0 NaN
3   NaN   NaN    NaN    NaN NaN
>>>
>>>

算術運算的使用方法:python

方法	說明
.add(d, **argws)	類型間加法運算，可選參數
.sub(d, **argws)	類型間減法運算，可選參數
.mul(d, **argws)	類型間乘法運算，可選參數
.div(d, **argws)	類型間除法運算，可選參數

實驗演示2:web

>>>
>>> a = pd.DataFrame(np.arange(12).reshape(3,4))
>>> a
   0  1   2   3
0  0  1   2   3
1  4  5   6   7
2  8  9  10  11
>>> b = pd.DataFrame(np.arange(20).reshape(4,5))
>>> b
    0   1   2   3   4
0   0   1   2   3   4
1   5   6   7   8   9
2  10  11  12  13  14
3  15  16  17  18  19
>>> b.add(a)
      0     1     2     3   4
0   0.0   2.0   4.0   6.0 NaN
1   9.0  11.0  13.0  15.0 NaN
2  18.0  20.0  22.0  24.0 NaN
3   NaN   NaN   NaN   NaN NaN
>>> b.add(a,fill_value=20200910)
            0           1           2           3           4
0         0.0         2.0         4.0         6.0  20200914.0
1         9.0        11.0        13.0        15.0  20200919.0
2        18.0        20.0        22.0        24.0  20200924.0
3  20200925.0  20200926.0  20200927.0  20200928.0  20200929.0
>>>
>>> a.mul(b)
      0     1      2      3   4
0   0.0   1.0    4.0    9.0 NaN
1  20.0  30.0   42.0   56.0 NaN
2  80.0  99.0  120.0  143.0 NaN
3   NaN   NaN    NaN    NaN NaN
>>> a.mul(b,fill_value=20200910)
             0            1            2            3            4
0          0.0          1.0          4.0          9.0   80803640.0
1         20.0         30.0         42.0         56.0  181808190.0
2         80.0         99.0        120.0        143.0  282812740.0
3  303013650.0  323214560.0  343415470.0  363616380.0  383817290.0
>>>
>>> c = pd.Series(np.arange(4))
>>> c
0    0
1    1
2    2
3    3
dtype: int32
>>>
>>> # 一維和零維之間的廣播
...
>>> c - 20200910
0   -20200910
1   -20200909
2   -20200908
3   -20200907
dtype: int32
>>>
>>> # 二維與一維之間的廣播,默認在1軸上進行廣播
...
>>> b - c
      0     1     2     3   4
0   0.0   0.0   0.0   0.0 NaN
1   5.0   5.0   5.0   5.0 NaN
2  10.0  10.0  10.0  10.0 NaN
3  15.0  15.0  15.0  15.0 NaN
>>> b.sub(c,axis=0)
    0   1   2   3   4
0   0   1   2   3   4
1   4   5   6   7   8
2   8   9  10  11  12
3  12  13  14  15  16
>>> b.sub(c)
      0     1     2     3   4
0   0.0   0.0   0.0   0.0 NaN
1   5.0   5.0   5.0   5.0 NaN
2  10.0  10.0  10.0  10.0 NaN
3  15.0  15.0  15.0  15.0 NaN
>>>
>>>

比較運算:算法

比較運算法則:
比較運算只能比較相同索引的元素，不進行補齊
二維和一維、一維和零維間爲廣播運算
採用> < >= <= == !=等符號進行的二元運算產生布爾對象

實驗演示3:ide

>>>
>>> # 比較運算
... # 比較運算只能比較相同索引的元素，不進行補齊
... # 二維和一維、一維和零維間爲廣播運算
... # 採用> < >= <= == !=等符號進行的二元運算產生布爾對象
...
>>> a = pd.DataFrame(np.arange(12).reshape(3,4))
>>> a
   0  1   2   3
0  0  1   2   3
1  4  5   6   7
2  8  9  10  11
>>> d = pd.DataFrame(np.arange(12,0,-1).reshape(3,4))
>>> d
    0   1   2  3
0  12  11  10  9
1   8   7   6  5
2   4   3   2  1
>>> a > d
       0      1      2      3
0  False  False  False  False
1  False  False  False   True
2   True   True   True   True
>>> a == d
       0      1      2      3
0  False  False  False  False
1  False  False   True  False
2  False  False  False  False
>>>
>>>
>>>
>>>
>>> a = pd.DataFrame(np.arange(12).reshape(3,4))
>>> c = pd.Series(np.arange(4))
>>> a
   0  1   2   3
0  0  1   2   3
1  4  5   6   7
2  8  9  10  11
>>> c
0    0
1    1
2    2
3    3
dtype: int32
>>> a > c
       0      1      2      3
0  False  False  False  False
1   True   True   True   True
2   True   True   True   True
>>> c > 0
0    False
1     True
2     True
3     True
dtype: bool
>>>
>>>

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