the problem is to take a black-white image, detect all the places where white borders on black, keep that white, and turn all other white pixels black. I know how to do this using normal for-loops and lists, but I want to do it w/ numpy, which I am not that familiar with. Here is what I have so far:
Tag: numpy
Combine and sort multiple array columns of values A and B where A is the common index
I have a n-long list of arrays. Each array consists of two columns: A) index values between 1-500 B) measured values Each A column is slightly different (i.e. missing or having extra values). I want to create single large array where i) there is single A (index) column consisting of all the index values and ii) all the B (measured
Derive consumption from existing column (Pandas)
Data Desired Doing first create derived column Any suggestion is helpful Answer You aren’t using a correct aggregation function. You should be using sum on both your “used” and “total” columns:
if and else in Convolution function
I have a problem with use if and else statement in convolution function. this code return error: use any() or all() how to can i use condition in convolve? I hope you always good luck. thanks. Answer The name function is called with a numpy array as the parameter. “Is the array less than 300?” isn’t meaningful. Are all the
Is there a numpy function to compress a (k, m, n) matrix to a (k//2, m//2, n//2) matrix by summing neighbors?
This can be accomplished with But I’m wondering if there is a function to accomplish this quickly and cleanly. If not, is there a canonical name for this operation? Answer This sounds like a classical strided-convolution pooling operation. You can do this in many ways, but the most straightforward would probably be to use skimage’s block_reduce functionality, as so –
Vector Calculations in Pandas
I have CSV file with Vector3 values exported from a C# program. I would like to use vector operations (like calculating the distance etc.) in pandas. As far as I have seen, there is no Vector3 type in pandas. np.array offers this kind of operations but it is not available in pandas. What is the easiest way to accomplish vector
The most efficient way rather than using np.setdiff1d and np.in1d, to remove common values of 1D arrays with unique values
I need a much faster code to remove values of an 1D array (array length ~ 10-15) that are common with another 1D array (array length ~ 1e5-5e5 –> rarely up to 7e5), which are index arrays contain integers. There is no duplicate in the arrays, and they are not sorted and the order of the values must be kept
How to assign values to multiple columns using conditions for values from other multiple columns?
Dataset is something like this (there will be duplicate rows in the original): Code: Output should be this: Code: ‘series1′ column values starts row by row as 0, 1, 2, and so on but resets to 0 when: ’email_ID’ column value changes. ‘screen’ column value == ‘rewardapp.PaymentFinalConfirmationActivity’ ‘series2’ column values starts with 0 and increments by 1 whenever ‘series1’ resets.
Optimization problem for S-I-S model using python
I have a susceptible-infectious-susceptible model, to which I’ve written the following python code, And I’m solving it using the following code, This part is fine. I’m having trouble finding the double derivative and optimizing it for the value of the beta parameter. The problem is that the beta is not given and since that parameter is within the exponential function,
AttributeError: ‘numpy.ndarray’ object has no attribute
I am applying selectKbest feature selection technique but it is giving me the following error: here is the portion of my code: (Note: the original data is in CSV format) Answer X is a numpy array, and you can only call the .columns method on a dataframe. You need to convert to a dataframe first, then call the method.