使用pandas时，为什么会出现AttributeError？

Question

如何根据条件将NaN值转换为分类值。我在尝试转换Nan值时遇到错误。

category           gender     sub-category    title

health&beauty      NaN         makeup         lipbalm

health&beauty      women       makeup         lipstick

NaN                NaN         NaN            lipgloss

我的DataFrame看起来像这样。我将性别中的NaN值转换为分类值的功能如下所示

def impute_gender(cols):
    category=cols[0]
    sub_category=cols[2]
    gender=cols[1]
    title=cols[3]
    if title.str.contains('Lip') and gender.isnull==True:
        return 'women'
df[['category','gender','sub_category','title']].apply(impute_gender,axis=1)

如果我运行代码我会收到错误

----> 7     if title.str.contains('Lip') and gender.isnull()==True:
      8         print(gender)
      9 

AttributeError: ("'str' object has no attribute 'str'", 'occurred at index category')

完成数据集-https://github.com/lakshmipriya04/py-sample

Answer 1

这里要注意的一些事情 -

1. 如果你只使用两列，那么在4列上调用apply是浪费的
2. 一般来说，调用apply是浪费的，因为它很慢并且不会给你带来任何矢量化的好处
3. 在申请中，你正在处理标量，所以你不像.str对象那样使用pd.Series访问器。 title.contains就足够了。或者更热，"lip" in title。
4. gender.isnull是完全错误的，gender是一个标量，它没有isnull属性

---

选项1 np.where

m = df.gender.isnull() & df.title.str.contains('lip')
df['gender'] = np.where(m, 'women', df.gender)

df
        category gender sub-category     title
0  health&beauty  women       makeup   lipbalm
1  health&beauty  women       makeup  lipstick
2            NaN  women          NaN  lipgloss

这不仅快速，而且更简单。如果您担心区分大小写，可以使contains检查不区分大小写 -

m = df.gender.isnull() & df.title.str.contains('lip', flags=re.IGNORECASE)

---

选项2 另一种选择是使用pd.Series.mask / pd.Series.where -

df['gender'] = df.gender.mask(m, 'women')

要么，

df['gender'] = df.gender.where(~m, 'women')

df
        category gender sub-category     title
0  health&beauty  women       makeup   lipbalm
1  health&beauty  women       makeup  lipstick
2            NaN  women          NaN  lipgloss

mask根据提供的掩码隐式地将新值应用于列。

Answer 2

或者只使用loc作为@ COLDSPEED答案的选项3

cond = (df['gender'].isnull()) & (df['title'].str.contains('lip'))
df.loc[cond, 'gender'] = 'women'


    category        gender  sub-category    title
0   health&beauty   women   makeup          lipbalm
1   health&beauty   women   makeup          lipstick
2   NaN             women       NaN         lipgloss

Answer 3

如果我们使用NaN值，fillna可以是方法之一:-)

df.gender=df.gender.fillna(df.title.str.contains('lip').replace(True,'women'))
df
Out[63]: 
        category gender sub-category     title
0  health&beauty  women       makeup   lipbalm
1  health&beauty  women       makeup  lipstick
2            NaN  women          NaN  lipgloss