Re-posting the resolution in the comments above as a community wiki for better visibility: The numpy typing module was introduced in numpy 1.20 Make sure that you have the correct numpy version by running the following at the beginning of your notebook: %pip install -U numpy
There is a built in lookup function that can handle this type of situation (looks up by row/column). I don’t know how optimized it is, but may be faster than the apply solution. In [9]: df[‘value’] = df.lookup(df.index, df[‘best’]) In [10]: df Out[10]: Date best a b c d value 0 1990 a 5 4 … Read more
Vectorized solution with rand+argsort trick We could generate unique indices along the specified axis and index into the the input array with advanced-indexing. To generate the unique indices, we would use random float generation + sort trick, thus giving us a vectorized solution. We would also generalize it to cover generic n-dim arrays and along … Read more
*.npy files are binary files to store numpy arrays. They are created with import numpy as np data = np.random.normal(0, 1, 100) np.save(‘data.npy’, data) And read in like import numpy as np data = np.load(‘data.npy’)
I don’t think pandas offers a way to do this in read_csv. Perhaps the neatest (in one pass) is to use collections.deque: from collections import deque from StringIO import StringIO with open(fname, ‘r’) as f: q = deque(f, 2) # replace 2 with n (lines read at the end) In [12]: q Out[12]: deque([‘7,8,9\n’, ‘10,11,12’], … Read more
Go to ctrl-alt-s click “project:projet name” click project interperter double click pip search numpy from the top bar click on numpy click install package button if it doesnt work this can help you: https://www.jetbrains.com/help/pycharm/installing-uninstalling-and-upgrading-packages.html
There must be something in your Q that i do not understand… Anyway In [2]: from numpy import array In [3]: a = array((1,0,0,1,1,0,0)) In [4]: b = 1-a In [5]: print a ; print b [1 0 0 1 1 0 0] [0 1 1 0 0 1 1] In [6]:
You can set the alpha argument in your imshow command. In your example, img3 = plt.imshow(zvals2, interpolation=’nearest’, cmap=cmap2, origin=’lower’, alpha=0.6) EDIT: Thanks for the clarification. Here is a description of what you can do: First, choose a matplotlib colormap object (in your case, for white and black, you can take the ‘binary’ colormap). Or create … Read more
Numpy provides logical_and() for that purpose: a = np.array([ True, False, False, True, False], dtype=bool) b = np.array([False, True, True, True, False], dtype=bool) c = np.logical_and(a, b) # array([False, False, False, True, False], dtype=bool) More at Numpy Logical operations.
Here’s a way to clear exactly 10% of cells (or rather, as close to 10% as can be achieved with the existing data frame’s size). import random ix = [(row, col) for row in range(df.shape[0]) for col in range(df.shape[1])] for row, col in random.sample(ix, int(round(.1*len(ix)))): df.iat[row, col] = np.nan Here’s a way to clear cells … Read more