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)

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)

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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)

输出结果

name              object
ID                object
age               object
age02              int64
age03             object
born      datetime64[ns]
sex               object
hobbey            object
money            float64
weight           float64
test01           float64
test02           float64
dtype: object
   name    ID  age  age02 age03       born   sex hobbey  money  weight  0   Bob     1  NaN     14    14        NaT     男    打篮球  200.0   140.5
1  LiSa     2   28     26    26 1990-01-01     女   打羽毛球  240.0   120.8
2  Mary         38     24    24 1980-01-01     女   打乒乓球  290.0   169.4
3  Alan  None           6     6        NaT  None         300.0   155.6   

     test01    test02
0  1.000000  1.000000
1  2.123457  2.123457
2  3.123457  3.123457
3  4.123457  4.123457
   name    ID  age  age02 age03       born   sex hobbey  money  weight  0   Bob     1  NaN     14    14        NaT     男    打篮球  200.0   140.5
1  LiSa     2   28     26    26 1990-01-01     女   打羽毛球  240.0   120.8
2  Mary         38     24    24 1980-01-01     女   打乒乓球  290.0   169.4
3  Alan  None           6     6        NaT  None         300.0   155.6   

     test01             test02  age02_Square
0  1.000000                1.0           196
1  2.123457        2.123456789           676
2  3.123457  3.123456781011126           576
3  4.123457  4.123456789109999            36

实现代码

import pandas as pd
import numpy as np

contents={"name": ['Bob',        'LiSa',                     'Mary',                       'Alan'],
          "ID":   [1,              2,                         ' ',                          None],    # 输出 NaN
          "age":  [np.nan,        28,                           38 ,                          '' ],   # 输出
          "age02":  [14,           26,                           24 ,                          6],
          "age03":  [14,           '26',                      '24' ,                        '6'],
        "born": [pd.NaT,     pd.Timestamp("1990-01-01"),  pd.Timestamp("1980-01-01"),        ''],     # 输出 NaT
          "sex":  ['男',          '女',                        '女',                        None,],   # 输出 None
          "hobbey":['打篮球',     '打羽毛球',                   '打乒乓球',                    '',],   # 输出
          "money":[200.0,                240.0,                   290.0,                     300.0],  # 输出
          "weight":[140.5,                120.8,                 169.4,                      155.6],  # 输出
          "test01":[1,    2.123456789,        3.123456781011126,   4.123456789109999],    # 输出
          "test02":[1,    2.123456789,        3.123456781011126,   4.123456789109999],    # 输出
          }
data_frame = pd.DataFrame(contents)
# data_frame.to_excel("data_Frame.xls")
print(data_frame.dtypes)
print(data_frame)

# 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)
col='test02'
data_frame[col].astype("string")
data_frame[col]=data_frame[col].apply(str)

def ChangeSquare(x):
    return x*x
col='age02'
data_frame[col+'_Square']=data_frame[col].apply(ChangeSquare)
print(data_frame)