how to generate column in pandas data frame using other columns and string formatting

Question

I am trying to generate a third column in pandas dataframe using two other columns in dataframe. The requirement is very particular to the scenario for which I need to generate the third column data.

The requirement is stated as:

let the dataframe name be df, first column be 'first_name'. second column be 'last_name'. I need to generate third column in such a manner so that it uses string formatting to generate a particular string and pass it to a function and whatever the function returns should be used as value to third column.

Problem 1

base_string = "my name is {first} {last}"

df['summary'] = base_string.format(first=df['first_name'], last=df['last_name'])

Problem 2

df['summary'] = some_func(base_string.format(first=df['first_name'], last=df['last_name']))

My ultimate goal is to solve problem 2 but for that problem 1 is pre-requisite and as of now I'm unable to solve that. I have tried converting my dataframe values to string but it is not working the way I expected.

Quang Hoang · Accepted Answer · 2021-03-09 21:05:53Z

3

You can do apply:

df.apply(lambda r: base_string.format(first=r['first_name'], last=r['last_name']) ),
         axis=1)

Or list comprehension:

df['summary'] = [base_string.format(first=x,last=y) 
                 for x,y in zip(df['first_name'], df['last_name'])

And then, for general function some_func:

df['summary'] = [some_func(base_string.format(first=x,last=y) )
                 for x,y in zip(df['first_name'], df['last_name'])

edited Mar 9, 2021 at 21:05

answered Mar 9, 2021 at 21:00

Quang Hoang

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Comments

Zeno Dalla Valle · Accepted Answer · 2021-03-09 21:02:42Z

0

You could use pandas.DataFrame.apply with axis=1 so your code will look like this:

def mapping_function(row):
    #make your calculation
    return value
df['summary'] = df.apply(mapping_function, axis=1)

answered Mar 9, 2021 at 21:02

Zeno Dalla Valle

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5 Comments

Caio Castro Over a year ago

It works, but I think passing the entire row to a custom function would be highly inefficient. Would be best to send only the columns necessary.

Zeno Dalla Valle Over a year ago

Look at this question maybe np.vectorize could work for you stackoverflow.com/questions/52673285/…

Caio Castro Over a year ago

Never looked into np.vectorize and my code is plagued with slow.apply(custom_funcs). Really great tip, will definitely checkout :)

Zeno Dalla Valle Over a year ago

Yeah, I found it out just now. It's useful for some of my python notebook too. Cheers.

Quang Hoang Over a year ago

Note that np.vectorize is not vecterization by any mean. It is just a wrapped for loop.

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