Hello. I have been using np.arrange for a long time but only now I have noticed that this built-in function doesn’t include the last maximum value. Like for example: step = 2 print(np.arange(0, 26, step)) results in [0 2 4 6 8 10 12 14 16 18 20 22 24] So how do we make … Read more
Unfortunately you can use ternary operator like this a if x>y else b on pandas dataframe logic. With that said you can use numpy.where instead: df[‘result’] = np.where(df1[‘col1’] > df1[‘col2′], 1, 0) There you go. It’s also much faster.
This example is perfect for showing how fast Numba can be with when put right with Nopython mode (@jit(nopython=True) or@njit) import numpy as np import numba as nb from numba import jit, njit import time start_time = time.time() # @nb.jit(nb.float64[:](nb.float64[:])) @njit def f(arr): res = np.zeros(len(arr)) for i in range(len(arr)): res[i] = (arr[i]) ** 2 … Read more