# Author: OMKAR PATHAK # Data type Description # bool_ Boolean (True or False) stored as a byte # int_ Default integer type (same as C long; normally either int64 or int32) # intc Identical to C int (normally int32 or int64) # intp Integer used for indexing (same as C ssize_t; normally either int32 or int64) # int8 Byte (-128 to 127) # int16 Integer (-32768 to 32767) # int32 Integer (-2147483648 to 2147483647) # int64 Integer (-9223372036854775808 to 9223372036854775807) # uint8 Unsigned integer (0 to 255) # uint16 Unsigned integer (0 to 65535) # uint32 Unsigned integer (0 to 4294967295) # uint64 Unsigned integer (0 to 18446744073709551615) # float_ Shorthand for float64. # float16 Half precision float: sign bit, 5 bits exponent, 10 bits mantissa # float32 Single precision float: sign bit, 8 bits exponent, 23 bits mantissa # float64 Double precision float: sign bit, 11 bits exponent, 52 bits mantissa # complex_ Shorthand for complex128. # complex64 Complex number, represented by two 32-bit floats (real and imaginary components) # complex128 Complex number, represented by two 64-bit floats (real and imaginary components) import numpy as np # while creating a numpy array, any data type from above can be explicitly specified. myArray = np.arange(10) print(myArray) # [0 1 2 3 4 5 6 7 8 9] myArray = np.array(myArray, dtype = np.float32) print(myArray) # [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9.] myArray = np.array(myArray, dtype = np.complex64) print(myArray) # [ 0.+0.j  1.+0.j  2.+0.j  3.+0.j  4.+0.j  5.+0.j  6.+0.j  7.+0.j  8.+0.j 9.+0.j]