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text1 = "Ethics are built right into the ideals and objectives of the United Nations "
len(text1) # The length of text1
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text2 = text1.split(' ') # Return a list of the words in text2, separating by ' '.
len(text2)
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text2
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List comprehension allows us to find specific words:
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[w for w in text2 if len(w) > 3] # Words that are greater than 3 letters long in text2
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[w for w in text2 if w.istitle()] # Capitalized words in text2
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[w for w in text2 if w.endswith('s')] # Words in text2 that end in 's'
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We can find unique words using set().
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text3 = 'To be or not to be'
text4 = text3.split(' ')
len(text4)
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len(set(text4))
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set(text4)
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len(set([w.lower() for w in text4])) # .lower converts the string to lowercase.
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set([w.lower() for w in text4])
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text5 = '"Ethics are built right into the ideals and objectives of the United Nations" \
#UNSG @ NY Society for Ethical Culture bit.ly/2guVelr'
text6 = text5.split(' ')
text6
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Finding hastags:
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[w for w in text6 if w.startswith('#')]
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Finding callouts:
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[w for w in text6 if w.startswith('@')]
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text7 = '@UN @UN_Women "Ethics are built right into the ideals and objectives of the United Nations" \
#UNSG @ NY Society for Ethical Culture bit.ly/2guVelr'
text8 = text7.split(' ')
We can use regular expressions to help us with more complex parsing.
For example '@[A-Za-z0-9_]+' will return all words that:
'@' and are followed by at least one: 'A-Z')'a-z') '0-9')'_')
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import re # import re - a module that provides support for regular expressions
[w for w in text8 if re.search('@[A-Za-z0-9_]+', w)]
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