# Import spaCy and load the language library
import spacy
nlp = spacy.load('en_core_web_sm')


# Create a string that includes opening and closing quotation marks
mystring = '"We\'re moving to L.A.!"'
print(mystring)


# Create a Doc object and explore tokens
doc = nlp(mystring)

for token in doc:
    print(token.text, end=' | ')














doc2 = nlp(u"We're here to help! Send snail-mail, email support@oursite.com or visit us at http://www.oursite.com!")

for t in doc2:
    print(t)





doc3 = nlp(u'A 5km NYC cab ride costs $10.30')

for t in doc3:
    print(t)








doc4 = nlp(u"Let's visit St. Louis in the U.S. next year.")

for t in doc4:
    print(t)








len(doc)





len(doc.vocab)








doc5 = nlp(u'It is better to give than to receive.')

# Retrieve the third token:
doc5[2]


# Retrieve three tokens from the middle:
doc5[2:5]


# Retrieve the last four tokens:
doc5[-4:]





doc6 = nlp(u'My dinner was horrible.')
doc7 = nlp(u'Your dinner was delicious.')


# Try to change "My dinner was horrible" to "My dinner was delicious"
doc6[3] = doc7[3]





doc8 = nlp(u'Apple to build a Hong Kong factory for $6 million')

for token in doc8:
    print(token.text, end=' | ')

print('\n----')

for ent in doc8.ents:
    print(ent.text+' - '+ent.label_+' - '+str(spacy.explain(ent.label_)))





len(doc8.ents)








doc9 = nlp(u"Autonomous cars shift insurance liability toward manufacturers.")

for chunk in doc9.noun_chunks:
    print(chunk.text)


doc10 = nlp(u"Red cars do not carry higher insurance rates.")

for chunk in doc10.noun_chunks:
    print(chunk.text)


doc11 = nlp(u"He was a one-eyed, one-horned, flying, purple people-eater.")

for chunk in doc11.noun_chunks:
    print(chunk.text)











from spacy import displacy

doc = nlp(u'Apple is going to build a U.K. factory for $6 million.')
displacy.render(doc, style='dep', jupyter=True, options={'distance': 110})








doc = nlp(u'Over the last quarter Apple sold nearly 20 thousand iPods for a profit of $6 million.')
displacy.render(doc, style='ent', jupyter=True)





doc = nlp(u'This is a sentence.')
displacy.serve(doc, style='dep')