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I have a list of 90K Text Lines. I want to find near-duplicates from them and mark them as duplicates. How can I do this using Python?
You need to define what you mean by "near duplicate". If I were to guess, one possible definition of two lines of text being "near duplicates" would be that they have a low Levenshtein distance. One popular Python implementation seems to be this one, but I cannot vouch for it myself.
If that is an acceptable definition, then you can simply compute all pairwise Levenshtein distances between your text lines and mark those below a given threshold.
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I am trying to understand what is Brute Force approach in Python?
I have a Lennard-Jones potential equation and my interatomic distance value is unknown, so my professor told me to use the Brute Force approach.
I don't know how to do so, can you explain it to me with a little simple code example?
Thank you
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Im trying to find a way to compare what students performed consistantly in their InternalAssessment_Performance to their FinalExam_Performance. Essentially i need to find what students have the same answer in both those columns.
How is it possible to compare the values in both commons and have them returned if they are the same?
Any help no matter how small would be great.
If the columns are aligned you can do something like this:
df[df['InternalAssessment_Performance'] == df['FinalExam_Performance']]
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What I am trying to do is to create a Multiple Choice Question (MCQ) generation to our fill in the gap style question generator. I need to generate distracters (Wrong answers) from the Key (correct answer). The MCQ is generated from educational texts that users input. We're trying to tackle this through combining Contextual similarity, similarity of the sentences in which the keys and the distractors occur in and Difference in term frequencies Any help? I was thinking of using big data datasets to generate related distractors such as the ones provided by google vision, I have no clue how to achieve this in python.
This question is way too broad to be answered, though I would do my best to give you some pointers.
If you have a closed set of potential distractors, I would use word/phrase embedding to find the closest distractor to the right answer.
Gensim's word2vec is a good starting point in python
If you want your distractors to follow a template, for example replace a certain word from the right answer with its opposite, I would use nltk's wordnet implementation to find antonyns / synonyms.
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I need to develop a piece of code that converts a number to the corresponding words, e.g. 1 -> "One", 2 -> "Two"
Is there any function in Python to do this task?
The answer to this question is "no". There is no function in Python to do this task.
If you "have to develop code to do it" (your words), then using a builtin wouldn't really be a valid solution, perhaps?
If you have to develop code to do it, you need better specifications. Do you have to be able to just do 0..9, or any cardinal number, or any number at all? (floating point? decimal? negative?). Why do you have to develop this code? Is it homework, or some special purpose?
If you just have to do 0..9, then as mentioned in comments, you should use a dictionary. Take care of case of the input.
If you have to do anything more than that, looking at the implementation of num2word would certainly be educational.
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What is a range query over a kdtree and how is it done by python?
Assuming you are talking about the k-d tree in scipy.spatial, there are a couple of range queries. Which is to say, there are multiple functions that take as their input one or more points and a radius and query the tree for all points within the radius of the query points.
The two most obvious functions are query_ball_point and query_ball_tree.
You can read the source code on github to see how these queries are implemented.