如何在 Python dataframe 中同时替换多行？

Question

I have a dataset with the following unique values in one of its columns.

   df['Gender'].unique()

   array(['Female', 'M', 'Male', 'male', 'm', 'Male-ish', 'maile',
   'Trans-female', 'Cis Female', 'something kinda male?', 'Cis Male',
   'queer/she/they', 'non-binary', 'Make', 'Nah', 'All', 'Enby',
   'fluid', 'Genderqueer', 'Androgyne', 'Agender', 'Guy (-ish) ^_^',
   'male leaning androgynous', 'Male ', 'Man', 'msle', 'Neuter',
   'queer', 'A little about you', 'Malr',
   'ostensibly male, unsure what that really means')]

As you can see, there are obvious cases where a row should be listed as 'Male' (I'm referring to the cases where 'Male' is misspelled, of course). How can I replace these values with 'Male' without calling the replace function ten times? This is the code I have tried:

x=0
while x<=11:
for i in df['Gender']:
    if i[0:2]=='Ma':
        print('Male')
    elif i[0]=='m':
        print('Male')
x+=1

However, I just get a print of a bunch of "Male".

Edit: I want to convert the following values to 'Male': 'M', 'male', 'm', 'maile', 'Make', 'Man', 'msle', 'Malr', 'Male '

Answer 1

Create a list with all the nicknames of Male:

males_list = ['M', 'male', 'm', 'maile', 'Make', 'Man', 'msle', 'Malr', 'Male ']

And then replace them with:

df.loc[df['Gender'].isin(males_list), 'Gender'] = 'Male'

btw: There is always a better solution than looping the rows in pandas , not just in cases like this.

Answer 2

I would use the map function as it allows you to create any custom logic. So for instance, by looking at your code, something like this would do the trick:

def correct_gender(text):

    if text[0:2]=='Ma' or text[0]=='m':
        return "Male"

    return text

df["Gender"] = df["Gender"].map(correct_gender)

Answer 3

If I understand you correctly, you want a more generalized approach. We can use regex to check if the word starts with M or has the letters Ma preceded by a whitespace, so we dont catch Female :

• (?i) : stands for ignore case sensitivity
• ?<=\s : means all the words which start with ma and are preceded by a whitespace

df.loc[df['Gender'].str.contains('(?i)^M|(?<=\s)ma'), 'Gender'] = 'Male'

Output

                Gender
0               Female
1                 Male
2                 Male
3                 Male
4                 Male
5                 Male
6                 Male
7         Trans-female
8           Cis Female
9                 Male
10                Male
11      queer/she/they
12          non-binary
13                Male
14                 Nah
15                 All
16                Enby
17               fluid
18         Genderqueer
19           Androgyne
20             Agender
21      Guy (-ish) ^_^
22                Male
23                Male
24                Male
25                Male
26              Neuter
27               queer
28  A little about you
29                Male
30                Male