pandas 为 dataframe 的每一行计算新值的方法是什么？

Question

I have a dataframe like this:

     name   upvotes  posts  
  0  Britt  4        232
  1  Henry  1        152
     ...
  9  Kevin  1        48

I want to create a new column, let's call it clout , that is a function of a user's score and posts.

In standard fare Python, if this was a list of dictionaries, I would approach the problem iteratively as follows:

for row in myListOfDicts:
    row['clout'] = computeClout(row['upvotes'],row['posts'])

But this approach seems wrong in Pandas based off of this answer: https://stackoverflow.com/a/55557758/4382391

So what should I be doing in this case?

Answer 1

You can try

df['clout' ] = df[['upvotes', 'Posts' ]].apply(computeClout, axis=1) 

Answer 2

You can use apply as following

df['clout'] = df.apply(lambda row: computeClout(row['upvotes'],row['posts']), axis=1)