Why do I get an AttributeError when using pandas apply?

Question

How should I convert NaN value into categorical value based on condition. I am getting error while trying to convert Nan value.

category           gender     sub-category    title

health&beauty      NaN         makeup         lipbalm

health&beauty      women       makeup         lipstick

NaN                NaN         NaN            lipgloss

My DataFrame looks like this. And my function to convert NaN values in gender to categorical value looks like

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)

If I run the code I am getting error

----> 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')

Complete Dataset -https://github.com/lakshmipriya04/py-sample

Answer 1

Or simply use loc as an option 3 to @COLDSPEED's answer

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 2

If we are due with NaN values , fillna can be one of the method:-)

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

Answer 3

Some things to note here -

1. If you're using only two columns, calling apply over 4 columns is wasteful
2. Calling apply is wasteful and inefficient, because it is slow, uses a lot of memory, and offers no vectorisation benefits to you
3. In apply, you're dealing with scalars, so you do not use the .str accessor as you would a pd.Series object. title.contains would be enough. Or more pythonically, "lip" in title.
4. gender.isnull sounds completely wrong to the interpreter because gender is a scalar, it has no isnull attribute

---

Option 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

Which is not only fast, but simpler as well. If you're worried about case sensitivity, you can make your contains check case insensitive -

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

---

Option 2
Another alternative is using pd.Series.mask/pd.Series.where -

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

Or,

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

The mask implicitly applies the new value to the column based on the mask provided.