How to Capitalize Locations in a List Python

What you're looking for is Named Entity Recognition (NER). NLTK does support a named entity function: ne_chunk, which can be used for this purpose. I'll give a demonstration:

from nltk import word_tokenize, pos_tag, ne_chunk

sentence = "In the wake of a string of abuses by New York police officers in the 1990s, Loretta E. Lynch, the top federal prosecutor in Brooklyn, spoke forcefully about the pain of a broken trust that African-Americans felt and said the responsibility for repairing generations of miscommunication and mistrust fell to law enforcement."

# Tokenize str -> List[str]
tok_sent = word_tokenize(sentence)
# Tag List[str] -> List[Tuple[str, str]]
pos_sent = pos_tag(tok_sent)
print(pos_sent)
# Chunk this tagged data
tree_sent = ne_chunk(pos_sent)
# This returns a Tree, which we pretty-print
tree_sent.pprint()

locations = []
# All subtrees at height 2 will be our named entities
for named_entity in tree_sent.subtrees(lambda t: t.height() == 2):
    # Extract named entity type and the chunk
    ne_type = named_entity.label()
    chunk = " ".join([tagged[0] for tagged in named_entity.leaves()])
    print(ne_type, chunk)
    if ne_type == "GPE":
        locations.append(chunk)

print(locations)

This outputs (with my comments added):

# pos_tag output:
[('In', 'IN'), ('the', 'DT'), ('wake', 'NN'), ('of', 'IN'), ('a', 'DT'), ('string', 'NN'), ('of', 'IN'), ('abuses', 'NNS'), ('by', 'IN'), ('New', 'NNP'), ('York', 'NNP'), ('police', 'NN'), ('officers', 'NNS'), ('in', 'IN'), ('the', 'DT'), ('1990s', 'CD'), (',', ','), ('Loretta', 'NNP'), ('E.', 'NNP'), ('Lynch', 'NNP'), (',', ','), ('the', 'DT'), ('top', 'JJ'), ('federal', 'JJ'), ('prosecutor', 'NN'), ('in', 'IN'), ('Brooklyn', 'NNP'), (',', ','), ('spoke', 'VBD'), ('forcefully', 'RB'), ('about', 'IN'), ('the', 'DT'), ('pain', 'NN'), ('of', 'IN'), ('a', 'DT'), ('broken', 'JJ'), ('trust', 'NN'), ('that', 'IN'), ('African-Americans', 'NNP'), ('felt', 'VBD'), ('and', 'CC'), ('said', 'VBD'), ('the', 'DT'), ('responsibility', 'NN'), ('for', 'IN'), ('repairing', 'VBG'), ('generations', 'NNS'), ('of', 'IN'), ('miscommunication', 'NN'), ('and', 'CC'), ('mistrust', 'NN'), ('fell', 'VBD'), ('to', 'TO'), ('law', 'NN'), ('enforcement', 'NN'), ('.', '.')]
# ne_chunk output:
(S
  In/IN
  the/DT
  wake/NN
  of/IN
  a/DT
  string/NN
  of/IN
  abuses/NNS
  by/IN
  (GPE New/NNP York/NNP)
  police/NN
  officers/NNS
  in/IN
  the/DT
  1990s/CD
  ,/,
  (PERSON Loretta/NNP E./NNP Lynch/NNP)
  ,/,
  the/DT
  top/JJ
  federal/JJ
  prosecutor/NN
  in/IN
  (GPE Brooklyn/NNP)
  ,/,
  spoke/VBD
  forcefully/RB
  about/IN
  the/DT
  pain/NN
  of/IN
  a/DT
  broken/JJ
  trust/NN
  that/IN
  African-Americans/NNP
  felt/VBD
  and/CC
  said/VBD
  the/DT
  responsibility/NN
  for/IN
  repairing/VBG
  generations/NNS
  of/IN
  miscommunication/NN
  and/CC
  mistrust/NN
  fell/VBD
  to/TO
  law/NN
  enforcement/NN
  ./.)
# All entities found
GPE New York
PERSON Loretta E. Lynch
GPE Brooklyn
# All GPE (Geo-Political Entity)
['New York', 'Brooklyn']

However, it should be noted that the performance of this ne_chunk seems to fall significantly if we remove all capitalisation from the sentence.

We can perform similar stuff with spaCy:

import spacy
import en_core_web_sm
from pprint import pprint

sentence = "In the wake of a string of abuses by New York police officers in the 1990s, Loretta E. Lynch, the top federal prosecutor in Brooklyn, spoke forcefully about the pain of a broken trust that African-Americans felt and said the responsibility for repairing generations of miscommunication and mistrust fell to law enforcement."
nlp = en_core_web_sm.load()

doc = nlp(sentence)
pprint([(X.text, X.label_) for X in doc.ents])
# Then, we can take only `GPE`:
print([X.text for X in doc.ents if X.label_ == "GPE"])

Which outputs:

[('New York', 'GPE'),
 ('the 1990s', 'DATE'),
 ('Loretta E. Lynch', 'PERSON'),
 ('Brooklyn', 'GPE'),
 ('African-Americans', 'NORP')]
['New York', 'Brooklyn']

This output (for GPE's) is identical to NLTK's, but the reason I mention spaCy is because unlike NLTK, it also works on fully lower-case sentences. If I lower-case my test sentence, then the output becomes:

[('new york', 'GPE'),
 ('the 1990s', 'DATE'),
 ('loretta e. lynch', 'PERSON'),
 ('brooklyn', 'GPE'),
 ('african-americans', 'NORP')]
['new york', 'brooklyn']

This allows you to title-case these words in an otherwise lower-case sentence.