I was reading this article and experimenting on my own data, I found both the examples given in the article and one of my words didn't work as described. You can refer to the article for more information, though the question here has everything to get going.
# stemmed root words: Books, Braveri, Harri, Transpar
from nltk.stem.wordnet import WordNetLemmatizer as Lemmatizer
# the article shared the same lemmatizer initialization.
lem = WordNetLemmatizer()
# returned 'harry' in the example without pos tag
In [269]: lem.lemmatize('harri', pos='n')
Out[269]: 'harri'
In [270]: lem.lemmatize("Books", pos='n')
Out[269]: 'Books'
# returned 'book' in the example with pos tag
In [270]: lem.lemmatize("Books", pos='v')
Out[269]: 'Books'
# my example root word, didn't change at all
[ins] In [278]: lem.lemmatize("Transpar", pos="a")
Out[278]: 'Transpar'
[ins] In [281]: lem.lemmatize("Transpar", pos="n")
Out[281]: 'Transpar'
# returned 'bravery' in the example without pos tag
[ins] In [280]: lem.lemmatize("Braveri", pos="n")
Out[280]: 'Braveri'
the default pos tag for this lemmatizer is just wordnet.NOUN therefore providing pos tag or not wouldn't make a difference.
FYI, transpar was originally transparent.
The only difference was that the author stemmed the words using NLTK stemmer whereas I was using texthero.stem.
Is it that I did it wrong or something has changed in NLTK?