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Tag: numpy

Check if two numpy arrays are identical

Suppose I have a bunch of arrays, including x and y, and I want to check if they’re equal. Generally, I can just use np.all(x == y) (barring some dumb corner cases which I’m ignoring now). However this evaluates the entire array of (x == y), which is usually not needed. My arrays are really large, and I have a

Numpy diff inverted operation?

Working with numpy.diff function, suppose this simple case: How can I get easily x back to original scale not differenced? I suppose there is something with numpy.cumsum(). Answer Concatenate with the first element and then use cumsum – For concatenating, we can also use np.hstack, like so – Or with np.concatenate for the concatenation –

check if two numeric values have same sign in numpy (+/-)

currently i am using numpy.logical_or with numpy.logical_and to check if elements of two arrays have same sign. Was wondering if there is already a ufunc or a more effective method that will achieve this. My current solutions is here edit// output Answer One approach with elementwise product and then check for >=0, as same signs (both positive or both negative)

How to automatically annotate maximum value in pyplot

I’m trying to figure out how I can automatically annotate the maximum value in a figure window. I know you can do this by manually entering in x,y coordinates to annotate whatever point you want using the .annotate() method, but I want the annotation to be automatic, or to find the maximum point by itself. Here’s my code so far:

ValueError: The number of classes has to be greater than one; got 1

I am trying to write an SVM following this tutorial but using my own data. https://pythonprogramming.net/preprocessing-machine-learning/?completed=/linear-svc-machine-learning-testing-data/ I keep getting this error: My code is: My array for features which is used for X looks like this: My array for labels used in Y looks like this: I have only used 5 sets of data so far because I knew the

Trying to ignore Nan in csv file throws a typeerror

I’m loading a local csv file that contains data. I’m trying to find the smallest float in a row thats mixed of NaN and numbers. I have tried using the numpy function called np.nanmin, but it throws: Any suggestions to why nanmin might not work? A link to the entire csv file: http://www.sharecsv.com/s/5aea6381d1debf75723a45aacd40abf8/database.csv Here is a sample of my coun_weight:

pandas DataFrame style, highlight nan’s

Say with this DataFrame How can I check which element is nan inside df.applymap? (ie, not using df.isnull) The problem comes from where I want to use the pandas html styling. We have the built-in nan highlighting but it changes the background colour, instead I want “nan” to be displayed in red. So I need to do it myself with

Is numpy.random.choice with replacement equivalent to multinomial sampling for a single trial?

I understand that strictly on concept, they are different. But in a single trial (or experiment) for numpy.random.multinomial, is it sampling the same way as numpy.random.choice though giving a different view of the output? For example: Output gives the identity of what was picked in the array [0,1,2,3,4,5] and Output gives the number of times each choice was picked, but

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