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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.
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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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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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I need to identify the songs by human hum. what are the best methodology and algorithm that i can use for achieve that. I search for code samples. But I couldn't find. Please help me....
You could begin a python program that uses tensorflow to deep-learn the correspondence between humming and songs - it should fall under the umbrella initiative by Google Brain called Magenta.
Of course for Deep-Learning you would need to have a large corpus of examples to learn from.
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Data visualization (python preferred but not essential):
I have some data which fits into daily chunks nicely for a period of a few months, how may I produce an infographic/calender-type graph analogous to this (found online):
My preferred medium is Python (would htmlCalender suffice?) but anything to accomplish this will be fine.
I would suggest that you take a look at D3.js.
If you look at the examples there is a Calendar View that does something closely to what you want to accomplish.
Another example of a similar calendar visualization is Cal-heatmap.
You will find more libraries with similar concepts if you google for "calendar heatmap".
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I want to convert an image of a graph (plot) to corresponding data. Is there any python library to do this or maybe some sample code to learn from?
If not, then how do i approach this problem?
You can also use a digitizing software application, to convert graph images to data, that is to numbers. im2graph is free and available for both Linux and Windows. See http://www.im2graph.co.il.