I have a 2D image with the shape (M, N), and would like to reshape it into (M//m * N//n, m, n). That is, to stack small patches of images into a 3D array. Currently, I used two for-loop to achieve that Is there any other faster way to do this? Thanks a lot! Answer Use skimage.util.view_as_blocks: Output: NB. Be
Tag: reshape
Pandas dataframe manipulation/re-sizing of a single-column count file
I have a file that looks like this: I want to read this into a pandas dataframe and re-shape it so that it looks like this: Is this possible? If so, how? Notes: it will not always be this size, so the solution needs to be size-independent. The input file will be max ~200gRNAs x 20genes. There will be gRNA_somelettercombos,
bypandas shape (n,1) to shape (n,)
I have a pandas dataframe and I want to convert it from (n,1) to shape (n,). Probably I have to use squeeze but can’t figure out, How to. squeeze documentation I also tried z[‘0’]=z[‘0’].squeeze() but it didn’t help. How can I convert? Answer z=z.squeeze() works the best and keeps the result dataframe. of course maybe its because I just had
Reshape Pandas DatafRames by binary columns value
Can’t figure out how to reshape my DataFrame into new one by several binary columns value. Input: I want to reshape by binary values, i.e. column a/b/c, if their value == 1, I need every time new column with all data. Expected output: Stucked here from the morning, will appreciate help very much ! Answer Use DataFrame.melt with filtering 1
Reshape 5-dimensional tiled image to 3-dimensional normal image
I’m creating a program that takes use of an RGB image that is tiled of the shape (n, n, 3, l, l). n is the number of each tile on each side of the image, and l is the length of each tile in pixels. I am trying to reshape this into a (3, l * n, l * n)
supposedly incorrect output np.reshape function
I have an array called “foto_dct” with shape (16,16,8,8) which means 16×16 matrices of 8×8. When I print foto_dct[0,15], being the last matrix of the first row I get: when i do foto_dct_big = np.reshape(foto_dct,(128,128)) and print foto_dct_big I get this: As you can see is the top righter corner( which is supposed to be the matrix above with all