i asked question yesterday beautifully answered @jezrael
in used:
df = pd.dataframe([[1, 0, 1], [1, 1, 0], [0, 1, 1], [0, 0, 1]]) print (df.t.dot(1 << np.arange(df.shape[0] - 1, -1, -1))) 0 12 1 6 2 11 dtype: int64 the 1 << np.arange(df.shape[0]) works great df.shape[0] less 30 blows after that. understandable considering data type of int32 or int64. there limit. however, when perform left shift << int operand, returns long , keeps accuracy. how use numpy same result when use 1 << 60?
here's ran:
import numpy np n in range(0, 61, 10): = np.arange(n + 1, dtype=int) pstr = "for n = {:<5d}; 1 << a[-1] = {:<12d}; 1 << n = {:<12d}" print pstr.format(n, 1 << a[-1], 1 << n) n = 0 ; 1 << a[-1] = 1 ; 1 << n = 1 n = 10 ; 1 << a[-1] = 1024 ; 1 << n = 1024 n = 20 ; 1 << a[-1] = 1048576 ; 1 << n = 1048576 n = 30 ; 1 << a[-1] = 1073741824 ; 1 << n = 1073741824 n = 40 ; 1 << a[-1] = 256 ; 1 << n = 1099511627776 n = 50 ; 1 << a[-1] = 262144 ; 1 << n = 1125899906842624 n = 60 ; 1 << a[-1] = 268435456 ; 1 << n = 1152921504606846976
you have convert array of int32 array of python objects:
numbers = np.arange(100,dtype=int) longs = 1 << np.arange(100).astype(object) 