Pandas Display Strings as "Literals" in DataFrame Outputs

EDIT:

I should specify that I want the rich html output to show the escaped characters. It is nice to have the series in the correct form, and @Stu Sztukowski provided a helpful way to do that.

df['type:string'].apply(lambda x: repr(x))

However, what I really want to see is something like this:

Problem

I am wondering if there is a way to force pandas to display strings with their escape characters explicitly shown when the datatype is string. Currently, when displaying a Series, if the datatype is object then the escaped whitespace characters are shown explicitly. However, when that same series is converted to string, then it shows them as generic whitespace (or prints them).

Reproducible example:

Here is some code you can use to reproduce the effect.

import pandas as pd

string_data = ['foo\nbar', 'foo\tbar', '\nfoo\n\tbarbar', 'bar\rbaz', None]

data = {"type:object":string_data, "type:string":string_data}

df = pd.DataFrame(data)

df = df.astype({'type:string':'string'})

Here are the outputs I see when viewing both series on their own:

>>> df['type:object']
0           foo\nbar
1           foo\tbar
2    \nfoo\n\tbarbar
3           bar\rbaz
4               None
Name: type:object, dtype: object

>>> df['type:string']
0         foo
bar
1         foo	bar
2    
foo
	barbar
3         bar
baz
4            <NA>
Name: type:string, dtype: string

I find the utility of the modern pandas>1.0 string datatype and its usage of pd.NA to be very helpful, so I would prefer to use the type:string column from this example. However, this makes it hard to view the strings in an intelligent way with other data, because the true contents of the string are obscured from me.

The problem only gets worse when looking at the DataFrame in .ipynb file outputs such as this view from VSCode:

As you can see, all of the escaped whitespace characters are shown as the same generic whitespace.

Conclusion

Does anyone know of a way to force pandas to display the strings as it does in the type:object column while keeping the datatype as string?

I checked through this pandas documentation thinking that it might be something fixable via pd.set_option() but I didn't see anything there.

Apply repr() as a lambda function.

df['type:string'].apply(lambda x: repr(x))

0           'foo\nbar'
1           'foo\tbar'
2    '\nfoo\n\tbarbar'
3           'bar\rbaz'
4                 <NA>