Implementation of numpy Data Type dtype Conversion
- 2021-11-01 04:12:35
- OfStack
In this article, we play with the numerical data type conversion of numpy
Import numpy
>>> import numpy as np
STEP 1 Have fun
Generate a floating-point array
>>> a = np.random.random(4)
Look at the information
>>> a
array([ 0.0945377 , 0.52199916, 0.62490646, 0.21260126])
>>> a.dtype
dtype('float64')
>>> a.shape
(4,)
Change dtype and find that the array length doubles!
>>> a.dtype = 'float32'
>>> a
array([ 3.65532693e+20, 1.43907535e+00, -3.31994873e-25,
1.75549972e+00, -2.75686653e+14, 1.78122652e+00,
-1.03207532e-19, 1.58760118e+00], dtype=float32)
>>> a.shape
(8,)
Change dtype, and the array length doubles again!
>>> a.dtype = 'float16'
>>> a
array([ -9.58442688e-05, 7.19000000e+02, 2.38159180e-01,
1.92968750e+00, nan, -1.66034698e-03,
-2.63427734e-01, 1.96875000e+00, -1.07519531e+00,
-1.19625000e+02, nan, 1.97167969e+00,
-1.60156250e-01, -7.76290894e-03, 4.07226562e-01,
1.94824219e+00], dtype=float16)
>>> a.shape
(16,)
Change dtype = 'float' and find that the default is float64, and the length is changed back to the original 4
>>> a.dtype = 'float'
>>> a
array([ 0.0945377 , 0.52199916, 0.62490646, 0.21260126])
>>> a.shape
(4,)
>>> a.dtype
dtype('float64')
Change a to an integer and observe its information
>>> a.dtype = 'int64'
>>> a
array([4591476579734816328, 4602876970018897584, 4603803876586077261,
4596827787908854048], dtype=int64)
>>> a.shape
(4,)
Change dtype and find that the array length doubles!
>>> a.dtype = 'int32'
>>> a
array([ 1637779016, 1069036447, -1764917584, 1071690807, -679822259,
1071906619, -1611419360, 1070282372])
>>> a.shape
(8,)
Change dtype and find that the array length doubles again!
>>> a.dtype = 'int16'
>>> a
array([-31160, 24990, 13215, 16312, 32432, -26931, -19401, 16352,
-17331, -10374, -197, 16355, -20192, -24589, 13956, 16331], dtype=int16)
>>> a.shape
(16,)
Change dtype and find that the array length doubles again!
>>> a.dtype = 'int8'
>>> a
array([ 72, -122, -98, 97, -97, 51, -72, 63, -80, 126, -51,
-106, 55, -76, -32, 63, 77, -68, 122, -41, 59, -1,
-29, 63, 32, -79, -13, -97, -124, 54, -53, 63], dtype=int8)
>>> a.shape
(32,)
Change dtype and find the integer default int32!
>>> a = np.random.random(4)
0
2. Change the game
Many times we use numpy to read data from a text file as an array of numpy, and the default dtype is float64.
But there are occasions where we want some data columns to be integers. If you change dtype = 'int' directly, you will make mistakes! For the above reason, the array length has doubled! ! !
The following scenario assumes that we get the imported data. We intended them to be integers, but in fact they were floating-point numbers (float64)
>>> a = np.random.random(4)
1
Use astype (int) to get integers without changing the length of the array
>>> a = np.random.random(4)
2
If you change the dtype of b directly, the length of b will double, which is not what we want (if you want, of course)
>>> b
array([ 1., 2., 3., 4.])
>>> b.dtype = 'int'
>>> b.dtype
dtype('int32')
>>> b
array([ 0, 1072693248, 0, 1073741824, 0,
1074266112, 0, 1074790400])
>>> b.shape
(8,)
3. Conclusion
numpy data type conversion, can not directly change the original data dtype! You can only use the function astype ().