How many keywords are there in Python? There are currently 31 keywords in Python, which can be output and viewed using Python's built-in keyword module.
keyword module
Help on module keyword:
NAME
keyword -Keywords(from"graminit.c")
FILE
/usr/lib64/python2.6/keyword.py
DESCRIPTION
This file is automatically generated; please don't muck it up!
To update the symbols inthis file,'cd' to the top directory of
the python source tree after building the interpreter and run:
python Lib/keyword.py
FUNCTIONS
iskeyword =__contains__(...)
x.__contains__(y) y in x.
DATA
__ all__ =['iskeyword','kwlist']
kwlist =['and','as','assert','break','class','continue','def',...
Get a list of python keywords:
keyword.kwlist
[' and','as','assert','break','class','continue','def','del','elif','else','except','exec','finally','for','from','global','if','import','in','is','lambda','not','or','pass','print','raise','return','try','while','with','yield']
Determine whether the string is a keyword of python
keyword.iskeyword('and')
True
keyword.iskeyword('has')
False
About keyword knowledge point expansion:
TF-IDF
TF-IDF (Term Frequencey-Inverse Document Frequency) refers to term frequency-inverse document frequency, which belongs to the category of numerical statistics. Using TF-IDF, we can learn the importance of a word to a document in the data set.
The concept of TF-IDF
TF-IDF has two parts, term frequency and inverse document frequency. First introduce the word frequency. This word is very intuitive. The word frequency indicates the frequency of each word in the document or data set. The equation is as follows:
TF(t)=The number of times the word t appears in a document / the total number of words in this document
The second part-the inverse document frequency actually tells us the importance of a word to the document. This is because when calculating TF, we assign equal importance to each word. The more it appears, the higher its TF. If it appears 100 times, it may appear less words than other words. , It does not carry so much information, so we need to give them weight to determine the importance of each word. Use the following equation to get IDF:
IDF(t)=(number of log10 documents/number of documents containing word t)
Then, the method of calculating TF-IDF is as follows:
TF * IDF= (the number of times the word t appears in a document / the total number of words in this document) * log10 (the number of documents / the number of documents containing the word t)
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