如果这些值的一部分在 pandas 的预定义列表中，则替换列中的值的有效方法

Question

So I've actually solved this, but the way I'm doing it may not be the most efficient.

For a column in my database - Industry - I want to replace values. If a value contains the word "tech", "technology" or something similar, I want to replace that value with just the word "technology".

I've followed a basic algorithm below using apply which basically loops through a predefined list (eg science ) and checks whether any of the values are present in the current Industry cell, and replaces if they are.

It then does the same for the next list. I only have two lists so far, but I'll likely have over a dozen once I'm finished.

def industry_convert(row):
    
    science = ["research", "science", "scientific", "scientist", "academia", "education", "academic"]
    tech = ["technology", "tech", "software"]

    for v in science:
        if v.lower() in row.Industry.lower():
            row.Industry = "Research, Science, & Education"
            
    for v in tech:
        if v.lower() in row.Industry.lower():
            row.Industry = "Technology"
            
    return row

df = df.apply(industry_convert, axis = 1)

I'm just wondering if this is the best way to do this, or if there is a more pythonic or pandas way of doing it?

EDIT:

This is what some of the Industry column looks like:

Industry
Research Scientist
Science: Education
Tech
Technical Assistance
Technology
Medical
Hospitality

This what it would look like after applying the code:

Industry            
Research, Science, & Education
Research, Science, & Education
Technology
Technology
Technology
Medical
Hospitality

Answer 1

Tell me if this works i updated the for loop, in your function

science = list(map(lambda x:x.lower(),["research", "science", "scientific", "scientist", "academia", "education", "academic"]))
tech = list(map(lambda x:x.lower(),["technology", "tech", "software"]))
def industry_convert(row):
    global science,tech
    


  
     if row.Industry.lower() in science:
          row.Industry = "Research, Science, & Education"
            
    
     if row.Industry.lower() in science:
          row.Industry = "Technology"
            
    return row

df = df.apply(industry_convert, axis = 1)

I computed the lists to lower only once, so that it is not recomputed and the computation of the for loop is saved Hope it works Happy coding ^-^

Answer 2

Personally, I would use str.contains and .loc to assign new values.

this will work a number of times faster than looping over each row individually to check. (Which is an anti pattern with regards to the pandas API)

science = ["research", "science", "scientific", "scientist", "academia", "education", "academic"]
tech = ["technology", "tech", "software"]

---

df.loc[df['Industry'].str.contains(f"{'|'.join(science)}",regex=True,case=False),
                         'industry_new'] = "Research, Science, & Education"

df.loc[df['Industry'].str.contains(f"{'|'.join(tech)}",regex=True,case=False),
                         'industry_new'] = "Technology"


df['industry_new'] = df['industry_new'].fillna(df['Industry'])  

---

print(df)

               Industry                    industry_new
0    Research Scientist  Research, Science, & Education
1    Science: Education  Research, Science, & Education
2                  Tech                      Technology
3  Technical Assistance                      Technology
4            Technology                      Technology
5               Medical                         Medical
6           Hospitality                     Hospitality