I am looking for some function that takes an input array of numbers and adds steps (range) between these numbers. I need to specify the length of the output’s array. Example: Result: Is there something like that, in Numpy for example? I have a prototype of this function that uses dividing input array into pairs ([0,2], [2,5], [5,8]) and filling
Tag: numpy
UFuncTypeError: Cannot cast ufunc ‘det’ input from dtype(‘O’) to dtype(‘float64’) with casting rule ‘same_kind’? How to avoid this issue?
I’m trying to build a PDE in python. I’m new to this and wondering where I have gone wrong. Would appreciate some help. I understand that I have a python object and I’m trying to cast it to a float64 but is there any way around this? Here is my error Here is my code Answer A symbolic calculation like
How to Eliminate for loop in Pandas Dataframe in filling each row values of a column based on multiple if,elif statements
Trying to get rid of for loop to speedup the execution in filling values in Column ‘C’ based on if, elif conditions involving multiple columns and rows. Not able to find a proper solution. tried applying np.where with conditions, choices and default values. But failed to get expected results as i was unable to extract individual values from pandas series
Saving Numpy array using scipy.io.savemat MATLAB does not produce the necessary .mat file
I have written a function to convert a NumPy array into a mat file using scipy.io.savemat() but it produces a generic type of file: File with the same name but not of type .mat as expected. The array I want to save is of type <class ‘numpy.ndarray’> as verified by the print statement. I don’t know what may be the
Curve_Fit returrns error “Result from function Call is not a proper array of floats”
I am trying to call scipy curve_fit(), with the proper: model function xdata (float numpy 1D Array) ydata (float numpy 1D Array) p (float numpy 1D Array, initial values) However I am getting the error: ValueError: Object too deep for desired Array Result from function Call is not a proper array of floats. the model function I am computing is
OpenCV transform image shape transformation into a given contour
Does anyone know whether it’s possible to transform image A into image B contour if their shapes are random, using OpenCV or any other python libraries that work with images? Here is what I have so far with 2 images: I’ve been able to find draw contours of the bulb and insert a fox in it using bitwise_and method, but
Delete characters on python dataframe, the number of characters removed per line varies
i want the first line to remove 0 characters to the right, the second line to remove 8 characters to the right, The resulting data will have the following form Thank you very much everyone. I am a newbie and my English is not very good. Hope everyone can help me Answer You can use Pandas’ string methods on column
Outer product of large vectors exceeds memory
I have three 1D vectors. Let’s say T with 100k element array, f and df each with 200 element array: For each element array, I have to calculate a function such as the following: My first instinct was to use the NumPy outer to find the function with each combination of f and df However, in this case, I am
Vectorized way to construct a block Hankel matrix in numpy (or scipy)
I want to contrsuct the following matrix : where each v(k) is a (ndarray) vector, say from a matrix Using a for loop, I can do something like this for example: And I get : Is there any way to construct this matrix in a vectorized way (which I imagine would be faster than for loops when it comes to
Compare values within a certain timeframe in arrays
I am trying to compare values (0’s and 1’s) in a array. I want to search for each “1” that appears in one column, for another “1” in the other column in a specific timeframe (for example, 5 seconds, 10 seconds, etc.). I will call the 1’s as “signals”. In example, I have an array such as: data1 = [