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Tag: numpy

How to remove numpy columns based on condition?

I have a numpy array which contains the correlation between a label column And also a numpy array containing Can I use a function to determine which columns to keep? Such as It will yield then the resulting numpy array will becomes May I know how to achieve this? Answer You can do boolean indexing along value…

Strictly positive values in normal distribution in Python

Is it possible to define a strictly positive range for normal distribution like I want to have a distribution in the range (0,10) with a certain mu, sigma? Using np.random.normal, sometimes I get negative values which I don’t want. Answer You should try ‘scipy.stats.truncnorm’ – quote …

Create NumPy array from list of tuples

I have data in the following format: And I want use this information to create a NumPy array that has the value 1.0 in position 2, value 2.5 in position 6, etc. All positions not listed in the above should be zeroes. Like this: Answer First reformat the data: And then create the array: Note that you need to c…

Initialize deque efficiently

I am using a deque to store data that is going to be processed. The processing only starts when the deque is full so in a first step I fill my buffer the following way: However, when I do this and I modify one of the elements of my_deque, all elements of my_deque are modified. One alternative I found to

Hiding axes values in Matplotlib

I want to hide the x,y axes values as highlighted in the figure. Is it possible to do it? I also attach the expected representation. The expected representation is Answer You need to empty x and y tick labels from ax variable:

Reshape 3-d array to 2-d

I want to change my array type as pd.DataFrame but its shape is: I’ve tried to reshape by the following code, but it didn’t work: How can I change its shape? Answer NumPy array dimensions can be reduced using various ways; some are: using np.squeeze: using np.reshape: or with using -1 in np.reshap…

Difference of Numpy Dimension Expand code

I got confuse between two Numpy dimension expand code. First code is X[:, np.newaxis]. Second code is X[:, np.newaxis, :]. I ran this code on Jupyter, But it returns same shape and result. What is the difference? Answer Refer numpy documentation for newaxis. https://numpy.org/doc/stable/reference/constants.ht…