1.3.3. 更复杂的数组¶
Section contents
1.3.3.1. More data types¶
1.3.3.1.1. 类型转换¶
“Bigger” type wins in mixed-type operations:
>>> np.array([1, 2, 3]) + 1.5
array([ 2.5, 3.5, 4.5])
赋值不会改变类型!
>>> a = np.array([1, 2, 3])
>>> a.dtype
dtype('int64')
>>> a[0] = 1.9 # <-- float is truncated to integer
>>> a
array([1, 2, 3])
Forced casts:
>>> a = np.array([1.7, 1.2, 1.6])
>>> b = a.astype(int) # <-- truncates to integer
>>> b
array([1, 1, 1])
Rounding:
>>> a = np.array([1.2, 1.5, 1.6, 2.5, 3.5, 4.5])
>>> b = np.around(a)
>>> b # still floating-point
array([ 1., 2., 2., 2., 4., 4.])
>>> c = np.around(a).astype(int)
>>> c
array([1, 2, 2, 2, 4, 4])
1.3.3.1.2. 不同数据类型的大小¶
Integers (signed):
int8 |
8 bits |
int16 |
16 bits |
int32 |
32 bits (same as int on 32-bit platform) |
int64 |
64 bits (same as int on 64-bit platform) |
>>> np.array([1], dtype=int).dtype
dtype('int64')
>>> np.iinfo(np.int32).max, 2**31 - 1
(2147483647, 2147483647)
Unsigned integers:
uint8 |
8 bits |
uint16 |
16 bits |
uint32 |
32 bits |
uint64 |
64 bits |
>>> np.iinfo(np.uint32).max, 2**32 - 1
(4294967295, 4294967295)
浮点数:
float16 |
16 bits |
float32 |
32 bits |
float64 |
64 bits (same as float ) |
float96 |
96 bits, platform-dependent (same as np.longdouble ) |
float128 |
128 bits, platform-dependent (same as np.longdouble ) |
>>> np.finfo(np.float32).eps
1.1920929e-07
>>> np.finfo(np.float64).eps
2.2204460492503131e-16
>>> np.float32(1e-8) + np.float32(1) == 1
True
>>> np.float64(1e-8) + np.float64(1) == 1
False
Complex floating-point numbers:
complex64 |
two 32-bit floats |
complex128 |
two 64-bit floats |
complex192 |
two 96-bit floats, platform-dependent |
complex256 |
two 128-bit floats, platform-dependent |
Smaller data types
如果你不知道你需要特殊的数据类型,那么你只是可能不需要。
Comparison on using float32
instead of float64
:
在内存中和磁盘上大小相差一半
需要内存带宽相差一半(在某些操作中可能会快一点)
In [1]: a = np.zeros((1e6,), dtype=np.float64) In [2]: b = np.zeros((1e6,), dtype=np.float32) In [3]: %timeit a*a 1000 loops, best of 3: 1.78 ms per loop In [4]: %timeit b*b 1000 loops, best of 3: 1.07 ms per loop
但是:舍入误差更大 —— 有时令人惊讶(不要使用它们,除非你真的需要它们)
1.3.3.2. Structured data types¶
sensor_code |
(4字符字符串) |
position |
(float) |
value |
(float) |
>>> samples = np.zeros((6,), dtype=[('sensor_code', 'S4'),
... ('position', float), ('value', float)])
>>> samples.ndim
1
>>> samples.shape
(6,)
>>> samples.dtype.names
('sensor_code', 'position', 'value')
>>> samples[:] = [('ALFA', 1, 0.37), ('BETA', 1, 0.11), ('TAU', 1, 0.13),
... ('ALFA', 1.5, 0.37), ('ALFA', 3, 0.11), ('TAU', 1.2, 0.13)]
>>> samples
array([('ALFA', 1.0, 0.37), ('BETA', 1.0, 0.11), ('TAU', 1.0, 0.13),
('ALFA', 1.5, 0.37), ('ALFA', 3.0, 0.11), ('TAU', 1.2, 0.13)],
dtype=[('sensor_code', 'S4'), ('position', '<f8'), ('value', '<f8')])
字段访问通过字段名索引:
>>> samples['sensor_code']
array(['ALFA', 'BETA', 'TAU', 'ALFA', 'ALFA', 'TAU'],
dtype='|S4')
>>> samples['value']
array([ 0.37, 0.11, 0.13, 0.37, 0.11, 0.13])
>>> samples[0]
('ALFA', 1.0, 0.37)
>>> samples[0]['sensor_code'] = 'TAU'
>>> samples[0]
('TAU', 1.0, 0.37)
一次多个字段:
>>> samples[['position', 'value']]
array([(1.0, 0.37), (1.0, 0.11), (1.0, 0.13), (1.5, 0.37), (3.0, 0.11),
(1.2, 0.13)],
dtype=[('position', '<f8'), ('value', '<f8')])
花式索引像往常一样工作:
>>> samples[samples['sensor_code'] == 'ALFA']
array([('ALFA', 1.5, 0.37), ('ALFA', 3.0, 0.11)],
dtype=[('sensor_code', 'S4'), ('position', '<f8'), ('value', '<f8')])
1.3.3.3. maskedarray
:处理丢失数据(的传播) ¶
For floats one could use NaN’s, but masks work for all types:
>>> x = np.ma.array([1, 2, 3, 4], mask=[0, 1, 0, 1]) >>> x masked_array(data = [1 -- 3 --], mask = [False True False True], fill_value = 999999) >>> y = np.ma.array([1, 2, 3, 4], mask=[0, 1, 1, 1]) >>> x + y masked_array(data = [2 -- -- --], mask = [False True True True], fill_value = 999999)
常用函数的掩码版本:
>>> np.ma.sqrt([1, -1, 2, -2]) masked_array(data = [1.0 -- 1.41421356237... --], mask = [False True False True], fill_value = 1e+20)
Note
还有其他有用的数组的类似类型
虽然这是一个关于numpy的章节的主题,让我们花一点时间回忆良好的编码实践,这从长远来看真的有回报:
Good practices
显式变量名(不需要注释来解释变量中是什么)
样式:逗号、
=
等后面带空格。在Python代码样式指南和文档字符串约定页面(以管理帮助字符串)中提供了一定数量的用于编写“漂亮”代码的规则(更重要的是,使用与其他人相同的约定)。are given in the Style Guide for Python Code and the Docstring Conventions page (to manage help strings).
除了一些罕见的情况,变量名和注释用英语。