How to "translate" string into an integer in python?

Question

I have the following df:

print(df)
>>>
Marital Status       Income     Education 
  Married             66613       PhD  
  Married             12441       Bachelors 
  Single              52842       Masters Degree
  Relationship        78238       PhD
  Divorced            21242       High School
  Single              47183       Masters Degree

I'd like to convert every "String" to a corresponding number (int). E.g.

"Married" should be 1

"Single" 2

"Relationship" 3

and so on.

I still haven't tried any code yet since I haven't found any reasonable solution after googling for around 1 hour now, but I am sure that the solution is most likely incredibly simple.

Edit: grammar

luangtatipsy · Accepted Answer · 2022-07-18 20:30:15Z

6

This may help you to get what you need.

df['Marital Status'] = df['Marital Status'].astype('category').cat.codes

Reference: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.astype.html

answered Jul 18, 2022 at 20:30

luangtatipsy

3081 silver badge8 bronze badges

Sign up to request clarification or add additional context in comments.

2 Comments

Corralien Over a year ago

Interesting use of category!

AntonioGrissini Over a year ago

This worked well also for every other string column in the dataframe I wanted to convert to int. Thanks a lot for the elegant solution.

Corralien · Accepted Answer · 2022-07-18 20:30:24Z

3

It's exactly what pd.factorize do:

df['Marital Code'] = pd.factorize(df['Marital Status'])[0] + 1
print(df)

# Output

  Marital Status  Income       Education  Marital Code
0        Married   66613             PhD             1
1        Married   12441       Bachelors             1
2         Single   52842  Masters Degree             2
3   Relationship   78238             PhD             3
4       Divorced   21242     High School             4
5         Single   47183  Masters Degree             2

answered Jul 18, 2022 at 20:30

Corralien

121k8 gold badges43 silver badges68 bronze badges

Comments

Andrej Kesely · Accepted Answer · 2022-07-18 20:30:51Z

2

Another solution, using .map:

df["Marital Status"] = df["Marital Status"].map(
    {"Married": 1, "Single": 2, "Relationship": 3, "Divorced": 4, "Single": 5}
)

print(df)

Prints:

   Marital Status  Income       Education
0               1   66613             PhD
1               1   12441       Bachelors
2               5   52842  Masters Degree
3               3   78238             PhD
4               4   21242     High School
5               5   47183  Masters Degree

answered Jul 18, 2022 at 20:30

Andrej Kesely

196k15 gold badges60 silver badges105 bronze badges

1 Comment

Corralien Over a year ago

map is the best way to control the number

ArchAngelPwn · Accepted Answer · 2022-07-18 20:33:36Z

1

Another Map solution for a little more readability and control if you wanted to add more later

df_map = pd.DataFrame({
    'Text' : ['Married', 'Single', 'Relationship'],
    'Int_Conversion' : [1, 2, 3]
})

df['Education'] = df['Marital'].map(df_map.set_index('Text')['Int_Conversion'])

answered Jul 18, 2022 at 20:33

ArchAngelPwn

3,0461 gold badge6 silver badges17 bronze badges

Comments

Dani Mesejo · Accepted Answer · 2022-07-18 20:33:59Z

One approach using categories that will work independent of the data:

categories = pd.CategoricalDtype(categories=["Married", "Single", "Relationship", "Divorced"], ordered=True)
df["result"] = df["Marital Status"].astype(categories).cat.codes + 1
print(df)

Output

  Marital Status  Income       Education  result
0        Married   66613             PhD       1
1        Married   12441       Bachelors       1
2         Single   52842  Masters Degree       2
3   Relationship   78238             PhD       3
4       Divorced   21242     High School       4
5         Single   47183  Masters Degree       2

This approach is suggested by the documentation to control the behavior, quote (emphasis mine):

In the examples above where we passed dtype='category', we used the default behavior:

Categories are inferred from the data.

Categories are unordered.

To control those behaviors, instead of passing 'category', use an instance of CategoricalDtype.

Voided Name · Accepted Answer · 2022-07-18 20:35:46Z

1

By corresponding number. Do you have a specific numbering scheme in mind, or just any number as long as the same string gets the same number assigned?

If the latter, then this code should work.

def replace_words(text):
    next_number = 1
    word_map = {}
    
    def get_number(word):
      nonlocal next_number, word_map
      if word in word_map:
        return word_map[word]
      word_map[word] = next_number
      next_number = next_number + 1
      return next_number - 1
    
    words = text.split(" ")
    replaced_words = [get_number(x) for x in words]
    return " ".join([str(x) for x in replaced_words])
    
print(replace_words("some words some thoughts"))

answered Jul 18, 2022 at 20:35

Voided Name

1285 bronze badges

Collectives™ on Stack Overflow

How to "translate" string into an integer in python?

6 Answers 6

2 Comments

Comments

1 Comment

Comments

Comments

Comments

Your Answer

Hot Network Questions

Collectives™ on Stack Overflow

6 Answers 6

2 Comments

Comments

1 Comment

Comments

Comments

Comments

Your Answer

Sign up or log in

Post as a guest

Related