pandas中 transform 函数和 apply 函数的区别

爱被打了一巴掌 2022-05-19 12:40 215阅读 0赞

> There are two major differences between the `transform` and `apply` groupby methods.
> 
>  *  `apply` implicitly passes all the columns for each group as a **DataFrame** to the custom function, while `transform` passes each column for each group as a **Series** to the custom function
>  *  The custom function passed to `apply` can return a scalar, or a Series or DataFrame (or numpy array or even list). The custom function passed to `transform` must return a sequence (a one dimensional Series, array or list) the same length as the group.（transform必须返回与组合相同长度的序列(一维的序列、数组或列表)）
> 
> So, `transform` works on just one Series at a time and `apply` works on the entire DataFrame at once.

from :[https://stackoverflow.com/questions/27517425/apply-vs-transform-on-a-group-object\#][https_stackoverflow.com_questions_27517425_apply-vs-transform-on-a-group-object]

### transform 函数： ###

> 1.只允许在同一时间在一个Series上进行一次转换，如果定义列‘a’ 减去列‘b’， 则会出现异常；
> 
> 2.必须返回与 group相同的单个维度的序列（行）
> 
> 3. 返回单个标量对象也可以使用，如 . transform(sum)

### apply函数： ###

> 1. 不同于transform只允许在Series上进行一次转换， apply对整个DataFrame 作用
> 
> 2.apply隐式地将group 上所有的列作为自定义函数

### 栗子： ###

#coding=gbk
    import numpy as np
    import pandas as pd
    data  = pd.DataFrame({'state':['Florida','Florida','Texas','Texas'],
                          'a':[4,5,1,3],
                          'b':[6,10,3,11]
                          })
    print(data)
    #    a   b    state
    # 0  4   6  Florida
    # 1  5  10  Florida
    # 2  1   3    Texas
    # 3  3  11    Texas
    def sub_two(X):
        return X['a'] - X['b']
    data1 = data.groupby(data['state']).apply(sub_two) # 此处使用transform 则会出现错误
    print(data1)
    # state     
    # Florida  0   -2
    #          1   -5
    # Texas    2   -2
    #          3   -8
    # dtype: int64

返回单个标量可以使用transform：

：我们可以看到使用transform 和apply 的输出结果形式是不一样的，transform返回与数据同样长度的行，而apply则进行了聚合

此时，使用apply说明的信息更明确

def group_sum(x):
        return x.sum()
    data3 = data.groupby(data['state']).transform(group_sum)    #返回与数据一样的 行
    print(data3)
    #    a   b
    # 0  9  16
    # 1  9  16
    # 2  4  14
    # 3  4  14
    #但是使用apply时
    data4 = data.groupby(data['state']).apply(group_sum)
    print(data4)
    #          a   b           state
    # state                         
    # Florida  9  16  FloridaFlorida
    # Texas    4  14      TexasTexas

> The other difference is that `transform` must return a single dimensional sequence the same size as the group. In this particular instance, each group has two rows, so `transform` must return a sequence of two rows. If it does not then an error is raised:

栗子2：

np.random.seed(666)
    df = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar',
                              'foo', 'bar', 'foo', 'foo'],
                       'B' : ['one', 'one', 'two', 'three',
                             'two', 'two', 'one', 'three'],
                       'C' : np.random.randn(8), 'D' : np.random.randn(8)})
    print(df)
    #      A      B         C         D
    # 0  foo    one  0.824188  0.640573
    # 1  bar    one  0.479966 -0.786443
    # 2  foo    two  1.173468  0.608870
    # 3  bar  three  0.909048 -0.931012
    # 4  foo    two -0.571721  0.978222
    # 5  bar    two -0.109497 -0.736918
    # 6  foo    one  0.019028 -0.298733
    # 7  foo  three -0.943761 -0.460587
    def zscore(x):
        return (x - x.mean())/ x.var()  
    print(df.groupby('A').transform(zscore))  #自动识别CD列
    print(df.groupby('A')['C','D'].apply(zscore))   #此种形式则两种输出数据是一样的
    # df.groupby('A').apply(zscore)  此种情况则会报错，apply对整个dataframe作用
    
    df['sum_c'] = df.groupby('A')['C'].transform(sum)   #先对A列进行分组， 计算C列的和
    df = df.sort_values('A')
    print(df)
    #      A      B         C         D     sum_c
    # 1  bar    one  0.479966 -0.786443  1.279517
    # 3  bar  three  0.909048 -0.931012  1.279517
    # 5  bar    two -0.109497 -0.736918  1.279517
    # 0  foo    one  0.824188  0.640573  0.501202
    # 2  foo    two  1.173468  0.608870  0.501202
    # 4  foo    two -0.571721  0.978222  0.501202
    # 6  foo    one  0.019028 -0.298733  0.501202
    # 7  foo  three -0.943761 -0.460587  0.501202
    print(df.groupby('A')['C'].apply(sum))
    # A
    # bar    1.279517
    # foo    0.501202
    # Name: C, dtype: float64

> The function passed to `transform` must return a number, a row, or the same shape as the argument. if it's a number then the number will be set to all the elements in the group, if it's a row, it will be broadcasted to all the rows in the group. 
> 
> 函数传递给`transform`必须返回一个数字，一行，或者与参数相同的形状。 如果是一个数字，那么数字将被设置为组中的所有元素，如果是一行，它将会被广播到组中的所有行。

--------------------

### 项目推荐： ###

**[2000多G的计算机各行业电子资源分享（持续更新）][2000_G]**

**[2020年微信小程序全栈项目之喵喵交友【附课件和源码】][2020]**

**[Spring Boot开发小而美的个人博客【附课件和源码】][Spring Boot]**

**[Java微服务实战296集大型视频-谷粒商城【附代码和课件】][Java_296_-]**

**[Java开发微服务畅购商城实战【全357集大项目】-附代码和课件][Java_357_-]**

**[最全最详细数据结构与算法视频-【附课件和源码】][-]**

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQwNTg3NTc1_size_16_color_FFFFFF_t_70_pic_center]

参考：[https://stackoverflow.com/questions/27517425/apply-vs-transform-on-a-group-object\#][https_stackoverflow.com_questions_27517425_apply-vs-transform-on-a-group-object]

[https_stackoverflow.com_questions_27517425_apply-vs-transform-on-a-group-object]: https://stackoverflow.com/questions/27517425/apply-vs-transform-on-a-group-object#
[2000_G]: https://mp.weixin.qq.com/s/sP4JgGWkCzpgwKr9sAV2_Q
[2020]: https://mp.weixin.qq.com/s?__biz=MzIyNTI3NDQ4NQ==&mid=2247487704&idx=1&sn=5f4b2127c4d49fd07ae072a0721424a2&chksm=e8036fc2df74e6d489c4aa9b06f917ef7cee6027f13e150fca53cf79d5d188d4ccc1af49e098&scene=21#wechat_redirect
[Spring Boot]: https://mp.weixin.qq.com/s?__biz=MzIyNTI3NDQ4NQ==&mid=2247487798&idx=2&sn=ac0293b996521b872a9dba5fbb3e65e6&chksm=e8036e2cdf74e73aba104a9a994a5b2e31483e8dcbe0f1d9936f6d5173b887e1560f59d2819c&scene=21#wechat_redirect
[Java_296_-]: https://mp.weixin.qq.com/s?__biz=MzIyNTI3NDQ4NQ==&mid=2247487674&idx=1&sn=7aff0bdf2bb727303f3d3618995aef21&chksm=e8036fa0df74e6b6d872c7e6ece179c524ed463a4a6b74c96875475c9a3d5ddb903427dd993b&scene=21#wechat_redirect
[Java_357_-]: https://mp.weixin.qq.com/s?__biz=MzIyNTI3NDQ4NQ==&mid=2247486376&idx=1&sn=d1fef270c463ea8ac663f6fbfedd70a0&chksm=e80374b2df74fda4d3bafba878a106a19e18c5fcda266008f4f37975847a21bc612ffcd5ff39&scene=21#wechat_redirect
[-]: https://mp.weixin.qq.com/s?__biz=MzIyNTI3NDQ4NQ==&mid=2247487750&idx=1&sn=747bccbb5f5ea6b58915198de40da777&chksm=e8036e1cdf74e70ae97a5e8e265b49d7236d904d291203309159d07ba1724033062c0e370843&scene=21#wechat_redirect
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQwNTg3NTc1_size_16_color_FFFFFF_t_70_pic_center]: /images/20220519/765749e1c6e14ee08bfec8cfbb9693bb.png