1. name              object
2. ID                object
3. age               object
4. age02              int64
5. age03             object
6. born      datetime64[ns]
7. sex               object
8. hobbey            object
9. money            float64
10. weight           float64
11. test01           float64
12. test02           float64
13. dtype: object
14.    name    ID  age  age02 age03       born   sex hobbey  money  weight  \
15. 0   Bob     1  NaN     14    14        NaT     男    打篮球  200.0   140.5
16. 1  LiSa     2   28     26    26 1990-01-01     女   打羽毛球  240.0   120.8
17. 2  Mary         38     24    24 1980-01-01     女   打乒乓球  290.0   169.4
18. 3  Alan  None           6     6        NaT  None         300.0   155.6
19. 
20.      test01    test02  
21. 0  1.000000  1.000000
22. 1  2.123457  2.123457
23. 2  3.123457  3.123457
24. 3  4.123457  4.123457
25.    name    ID  age  age02 age03       born   sex hobbey  money  weight  \
26. 0   Bob     1  NaN     14    14        NaT     男    打篮球  200.0   140.5
27. 1  LiSa     2   28     26    26 1990-01-01     女   打羽毛球  240.0   120.8
28. 2  Mary         38     24    24 1980-01-01     女   打乒乓球  290.0   169.4
29. 3  Alan  None           6     6        NaT  None         300.0   155.6
30. 
31.      test01             test02  age02_Square  
32. 0  1.000000                1.0           196
33. 1  2.123457        2.123456789           676
34. 2  3.123457  3.123456781011126           576
35. 3  4.123457  4.123456789109999            36

1. 
2. 
3. import pandas as pd
4. import numpy as np
5. 
6. 
7. 
8. contents={"name": ['Bob',        'LiSa',                     'Mary',                       'Alan'],
9. "ID":   [1,              2,                         ' ',                          None],    # 输出 NaN
10. "age":  [np.nan,        28,                           38 ,                          '' ],   # 输出 
11. "age02":  [14,           26,                           24 ,                          6], 
12. "age03":  [14,           '26',                      '24' ,                        '6'], 
13. "born": [pd.NaT,     pd.Timestamp("1990-01-01"),  pd.Timestamp("1980-01-01"),        ''],     # 输出 NaT
14. "sex":  ['男',          '女',                        '女',                        None,],   # 输出 None
15. "hobbey":['打篮球',     '打羽毛球',                   '打乒乓球',                    '',],   # 输出 
16. "money":[200.0,                240.0,                   290.0,                     300.0],  # 输出
17. "weight":[140.5,                120.8,                 169.4,                      155.6],  # 输出
18. "test01":[1,    2.123456789,        3.123456781011126,   4.123456789109999],    # 输出
19. "test02":[1,    2.123456789,        3.123456781011126,   4.123456789109999],    # 输出
20.           }
21. data_frame = pd.DataFrame(contents)
22. # data_frame.to_excel("data_Frame.xls")
23. print(data_frame.dtypes)
24. print(data_frame)
25. 
26. 
27. 
28. # ML之DS：仅需一行代码实现对某字段下的所有数值实现同一机制的改变或转换(比如全部转为str类型/全部取平方值)
29. col='test02'
30. data_frame[col].astype("string")
31. data_frame[col]=data_frame[col].apply(str)
32. 
33. def ChangeSquare(x):
34. return x*x
35. col='age02'
36. data_frame[col+'_Square']=data_frame[col].apply(ChangeSquare)
37. print(data_frame)