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for token in doc:
    # check token pos
    if token.pos_=='NOUN':
        # print token
        print(token.text)

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# import spacy
import spacy

# load english language model
nlp = spacy.load('en_core_web_sm',disable=['ner','textcat'])

text = "This is a sample sentence."

# create spacy 
doc = nlp(text)

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import spacy
from spacy import displacy 
doc = nlp('I read blogs')
displacy.render(doc, style='dep',jupyter=True)

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# print second chunk of data
next(df)

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import pandas as pd

# pandas dataframe
df = pd.read_csv('./Black Friday.csv', chunksize=10)

# print first chunk of data
next(df)

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file = "Greetings.txt"
# generator expression
lines = (line for line in open(file))
print(lines)
# print lines
print(next(lines))
print(next(lines))
print(next(lines))

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import sys
# list comprehension
mylist = [i for i in range(10000000)]
print('Size of list in memory',sys.getsizeof(mylist))
# generator expression
mygen = (i for i in range(10000000))
print('Size of generator in memory',sys.getsizeof(mygen))

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for i in squared_gen:
    print(i)

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squared_gen = (x*x for x in range(2,5))
print(squared_gen)

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print(next(gen))