WebDec 4, 2013 · Suppose a is your numpy matrix, use b = (a == 0) or b = (a != 0) to get the boolean value matrix. In some case, since the value maybe sufficiently small but non-zero, you may use abs (a) < TH, where TH is the numerical threshold you set. Share Follow answered Dec 4, 2013 at 10:50 Ray 2,472 17 22 Add a comment 3 .astype (dtype) WebThe default is 'None', indicating the NumPy default of C-ordered. Cannot be specified in conjunction with the `out` argument. out : ndarray, 2-D, optional If specified, uses this array (or `numpy.matrix`) as the output buffer instead of allocating a new array to return.
Tensorflow - ValueError: Failed to convert a NumPy array to a …
WebJul 8, 2024 · This is my data, id label tweet 0 1 0 @user when a father is dysfunctional and is so selfish he drags his kids into his dysfunction. #run which is in text format, I have pre-processed it and then I want to fit a PyTorch LSTM model in it. To fit the model I have to split the dataset into train and test set, and as PyTorch has a very interesting module called … china roaches food waste disposal
Numpy Boolean Array - Easy Guide for Beginners - AskPython
WebJul 4, 2024 · My attempt is: result ['bdate'] = pd.to_datetime (result ['dte']) + BMonthEnd (0) result ['bdaterange'] = pd.bdate_range (pd.to_datetime (result ['dte'], unit='ns').values, pd.to_datetime (result ['bdate'], unit='ns').values) print (result ['bdaterange']) Not sure how to solve the error though. python pandas Share Improve this question Follow WebNov 11, 2013 · If you want to use a boolean array a to select rows of b, then, as Joran Beasley states, just keep a as a 1-dimensional boolean array: import numpy as np a = np.array ( [True, False]) b = np.array ( [1, 2, 3, 4]) b.shape = (2,2) print (b [a]) # [ [1 2]] Share Improve this answer Follow edited Nov 11, 2013 at 23:19 answered Nov 11, 2013 at 21:10 WebYou can try numpy.asscalar import numpy as np x = np.zeros (100).astype (np.bool) z = [np.asscalar (x_i) for x_i in x] print (type (z)) You can also use item () which is a better option since asscalar is depreceted. import numpy as np x = np.zeros (100).astype (np.bool) z = [x_i.item () for x_i in x] print (type (z)) print (z) china road and bridge corporation address