100道练习带你玩转Numpy（附答案）
1.导入numpy库并简写为 np (★☆☆)
- (提示: import … as …)
import numpy as np
参考答案
import numpy as np
2. 打印numpy的版本和配置说明 (★☆☆)
- (提示: np.version, np.show_config)
#查看numpy的版本
np.__version__
输出结果
'1.21.1'
#查看numpyd的配置说明：注意np.show_config与np.show_config()区别
print(np.show_config)
print("________________________")
print(np.show_config())
输出结果
<function show at 0x000001B6BFC9DAF0>
________________________
blas_mkl_info:
NOT AVAILABLE
blis_info:
NOT AVAILABLE
openblas_info:
library_dirs = ['D:\\a\\1\\s\\numpy\\build\\openblas_info']
libraries = ['openblas_info']
language = f77
define_macros = [('HAVE_CBLAS', None)]
blas_opt_info:
library_dirs = ['D:\\a\\1\\s\\numpy\\build\\openblas_info']
libraries = ['openblas_info']
language = f77
define_macros = [('HAVE_CBLAS', None)]
lapack_mkl_info:
NOT AVAILABLE
openblas_lapack_info:
library_dirs = ['D:\\a\\1\\s\\numpy\\build\\openblas_lapack_info']
libraries = ['openblas_lapack_info']
language = f77
define_macros = [('HAVE_CBLAS', None)]
lapack_opt_info:
library_dirs = ['D:\\a\\1\\s\\numpy\\build\\openblas_lapack_info']
libraries = ['openblas_lapack_info']
language = f77
define_macros = [('HAVE_CBLAS', None)]
Supported SIMD extensions in this NumPy install:
baseline = SSE,SSE2,SSE3
found = SSSE3,SSE41,POPCNT,SSE42,AVX,F16C,FMA3,AVX2
not found = AVX512F,AVX512CD,AVX512_SKX,AVX512_CLX,AVX512_CNL
None
参考答案
print(np.__version__)
print(np.show_config())
输出结果
1.21.1
blas_mkl_info:
NOT AVAILABLE
blis_info:
NOT AVAILABLE
openblas_info:
library_dirs = ['D:\\a\\1\\s\\numpy\\build\\openblas_info']
libraries = ['openblas_info']
language = f77
define_macros = [('HAVE_CBLAS', None)]
blas_opt_info:
library_dirs = ['D:\\a\\1\\s\\numpy\\build\\openblas_info']
libraries = ['openblas_info']
language = f77
define_macros = [('HAVE_CBLAS', None)]
lapack_mkl_info:
NOT AVAILABLE
openblas_lapack_info:
library_dirs = ['D:\\a\\1\\s\\numpy\\build\\openblas_lapack_info']
libraries = ['openblas_lapack_info']
language = f77
define_macros = [('HAVE_CBLAS', None)]
lapack_opt_info:
library_dirs = ['D:\\a\\1\\s\\numpy\\build\\openblas_lapack_info']
libraries = ['openblas_lapack_info']
language = f77
define_macros = [('HAVE_CBLAS', None)]
Supported SIMD extensions in this NumPy install:
baseline = SSE,SSE2,SSE3
found = SSSE3,SSE41,POPCNT,SSE42,AVX,F16C,FMA3,AVX2
not found = AVX512F,AVX512CD,AVX512_SKX,AVX512_CLX,AVX512_CNL
None
3. 创建一个长度为10的空向量 (★☆☆)
- (提示: np.zeros)
a = np.zeros(10)
a
输出结果
array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])
参考答案
Z = np.zeros(10)
print(Z)
输出结果
[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
4. 如何找到任何一个数组的内存大小？ (★☆☆)
- (提示: size, itemsize)
print(a.size,a.itemsize)
输出结果
10 8
参考答案
Z = np.zeros((10,10))
print("%d bytes" % (Z.size * Z.itemsize))
输出结果
800 bytes
5. 如何从命令行得到numpy中add函数的说明文档? (★☆☆)
- (提示: np.info)
np.info(toplevel='add')
参考答案
np.info(np.add)
输出结果
add(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])
Add arguments element-wise.
Parameters
----------
x1, x2 : array_like
The arrays to be added.
If ``x1.shape != x2.shape``, they must be broadcastable to a common
shape (which becomes the shape of the output).
out : ndarray, None, or tuple of ndarray and None, optional
A location into which the result is stored. If provided, it must have
a shape that the inputs broadcast to. If not provided or None,
a freshly-allocated array is returned. A tuple (possible only as a
keyword argument) must have length equal to the number of outputs.
where : array_like, optional
This condition is broadcast over the input. At locations where the
condition is True, the `out` array will be set to the ufunc result.
Elsewhere, the `out` array will retain its original value.
Note that if an uninitialized `out` array is created via the default
``out=None``, locations within it where the condition is False will
remain uninitialized.
**kwargs
For other keyword-only arguments, see the
:ref:`ufunc docs <ufuncs.kwargs>`.
Returns
-------
add : ndarray or scalar
The sum of `x1` and `x2`, element-wise.
This is a scalar if both `x1` and `x2` are scalars.
Notes
-----
Equivalent to `x1` + `x2` in terms of array broadcasting.
Examples
--------
>>> np.add(1.0, 4.0)
5.0
>>> x1 = np.arange(9.0).reshape((3, 3))
>>> x2 = np.arange(3.0)
>>> np.add(x1, x2)
array([[ 0., 2., 4.],
[ 3., 5., 7.],
[ 6., 8., 10.]])
The ``+`` operator can be used as a shorthand for ``np.add`` on ndarrays.
>>> x1 = np.arange(9.0).reshape((3, 3))
>>> x2 = np.arange(3.0)
>>> x1 + x2
array([[ 0., 2., 4.],
[ 3., 5., 7.],
[ 6., 8., 10.]])
6. 创建一个长度为10并且除了第五个值为1的空向量 (★☆☆)
- (提示: array[4])
array6 = np.zeros(10)
array6[4] = 1
array6
输出结果
array([0., 0., 0., 0., 1., 0., 0., 0., 0., 0.])
参考答案
Z = np.zeros(10)
Z[4] = 1
print(Z)
输出结果
[0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]
7. 创建一个值域范围从10到49的向量(★☆☆)
- (提示: np.arange)
array7 = np.arange(10,50)
array7
输出结果
array([10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,
27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43,
44, 45, 46, 47, 48, 49])
参考答案
Z = np.arange(10,50)
print(Z)
输出结果
[10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33
34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49]
8. 反转一个向量(第一个元素变为最后一个) (★☆☆)
- (提示: array[::-1])
#一维数组情况
array8 = np.arange(10)
print(array8,"\n-----\n",type(array8),"\n-----\n",array8[::-1],"\n-----\n")
print("注意以下区别：")
print("只取向量最后一个元素:\n",array8[-1])
print("------------")
print("取向量第一个元素到倒数第二个:\n",array8[:-1])
print("------------")
print("反转向量:\n",array8[::-1])
输出结果
[0 1 2 3 4 5 6 7 8 9]
-----
<class 'numpy.ndarray'>
-----
[9 8 7 6 5 4 3 2 1 0]
-----
注意以下区别：
只取向量最后一个元素:
9
------------
取向量第一个元素到倒数第二个:
[0 1 2 3 4 5 6 7 8]
------------
反转向量:
[9 8 7 6 5 4 3 2 1 0]
#二维数组情况
array8 = np.arange(12).reshape(3,4)
print(array8,"\n-----\n",type(array8),"\n-----\n",array8[::-1],"\n-----\n")
print("注意以下区别：")
print("只取数组最后一行元素:\n",array8[-1])
print("------------")
print("注意与上一个不同:\n",array8[-1::])
print("------------")
print("取数组第一行元素到倒数第二行:\n",array8[:-1])
print("------------")
print("同上一个:\n",array8[:-1:])
print("------------")
print("数组最后一行到第一行，以此类推:\n",array8[::-1])
输出结果
[[ 0 1 2 3]
[ 4 5 6 7]
[ 8 9 10 11]]
-----
<class 'numpy.ndarray'>
-----
[[ 8 9 10 11]
[ 4 5 6 7]
[ 0 1 2 3]]
-----
注意以下区别：
只取数组最后一行元素:
[ 8 9 10 11]
------------
注意与上一个不同:
[[ 8 9 10 11]]
------------
取数组第一行元素到倒数第二行:
[[0 1 2 3]
[4 5 6 7]]
------------
同上一个:
[[0 1 2 3]
[4 5 6 7]]
------------
数组最后一行到第一行，以此类推:
[[ 8 9 10 11]
[ 4 5 6 7]
[ 0 1 2 3]]
参考答案
Z = np.arange(50)
Z = Z[::-1]
print(Z)
输出结果
[49 48 47 46 45 44 43 42 41 40 39 38 37 36 35 34 33 32 31 30 29 28 27 26
25 24 23 22 21 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2
1 0]
9. 创建一个 3x3 并且值从0到8的矩阵(★☆☆)
- (提示: reshape)
array9 = np.arange(0,9).reshape(3,3)
array9
输出结果
array([[0, 1, 2],
[3, 4, 5],
[6, 7, 8]])
参考答案
Z = np.arange(9).reshape(3,3)
print(Z)
输出结果
[[0 1 2]
[3 4 5]
[6 7 8]]
10. 找到数组[1,2,0,0,4,0]中非0元素的位置索引 (★☆☆)
- (提示: np.nonzero)
array10 = [1,2,0,0,4,0]
np.nonzero(array10)
输出结果
(array([0, 1, 4], dtype=int64),)
参考答案
nz = np.nonzero([1,2,0,0,4,0])
print(nz)
输出结果
(array([0, 1, 4], dtype=int64),)
11. 创建一个 3x3 的单位矩阵 (★☆☆)
- (提示: np.eye)
array11 = np.eye(3,3)
array11
输出结果
array([[1., 0., 0.],
[0., 1., 0.],
[0., 0., 1.]])
参考答案
Z = np.eye(3)
print(Z)
输出结果
[[1. 0. 0.]
[0. 1. 0.]
[0. 0. 1.]]
12. 创建一个 3x3x3的随机数组 (★☆☆)
- (提示: np.random.random)
array12 = np.random.random(27).reshape(3,3,3)
array12
输出结果
array([[[0.82229851, 0.97063002, 0.14368023],
[0.54370589, 0.03199196, 0.85705309],
[0.26821559, 0.15694076, 0.51977488]],
[[0.50242734, 0.29862912, 0.95963966],
[0.4059865 , 0.58694668, 0.82554167],
[0.56819152, 0.34225528, 0.45214493]],
[[0.01517539, 0.89008166, 0.0500397 ],
[0.03236231, 0.19130355, 0.53311733],
[0.09538655, 0.18586758, 0.58278421]]])
参考答案
Z = np.random.random((3,3,3))
print(Z)
输出结果
[[[0.28037142 0.44488439 0.30333044]
[0.1161553 0.82102038 0.25739631]
[0.09821049 0.36713993 0.92724096]]
[[0.2454338 0.87448758 0.50827198]
[0.54874904 0.07023286 0.75978993]
[0.98004792 0.42379336 0.98222663]]
[[0.43035063 0.32212228 0.22881998]
[0.97116268 0.10329178 0.19237496]
[0.89617807 0.79191292 0.51751459]]]
13. 创建一个 10x10 的随机数组并找到它的最大值和最小值 (★☆☆)
- (提示: min, max)
array13 = np.random.random(100).reshape(10,10)
print(array13,"\n最大值为\n",array13.max(),"\n最小值为：\n",array13.min())
输出结果
[[0.99778825 0.58095976 0.34966491 0.61488926 0.22311257 0.4618689
0.17235173 0.70900466 0.98204539 0.16713024]
[0.47931749 0.37411923 0.174987 0.4546529 0.18531676 0.75502785
0.13397944 0.45530322 0.43508141 0.95361024]
[0.17900297 0.93651411 0.87413906 0.73874406 0.52680645 0.64828029
0.24795946 0.88003906 0.62365578 0.80426776]
[0.45185357 0.51363758 0.96072332 0.04641699 0.48586224 0.62842817
0.27009438 0.18875272 0.19874009 0.66220609]
[0.44138034 0.2363918 0.74719564 0.22923504 0.27315476 0.53374481
0.18482755 0.50381315 0.25225246 0.75322919]
[0.35705613 0.51395832 0.51189048 0.09275676 0.75417333 0.06750219
0.43249999 0.62178538 0.46245924 0.65893803]
[0.27734846 0.24961469 0.44867329 0.96181551 0.69065328 0.21640466
0.59641193 0.12161551 0.54862819 0.67923836]
[0.21021391 0.32386752 0.37686985 0.85766783 0.73454876 0.69567777
0.79764775 0.28465536 0.4209324 0.35587512]
[0.15687547 0.69758606 0.3013893 0.53512701 0.99620145 0.22607022
0.11752308 0.54600039 0.9315894 0.63885842]
[0.95544494 0.55211529 0.96081971 0.92697433 0.44943367 0.52602786
0.64950368 0.40008766 0.59628704 0.09932262]]
最大值为
0.9977882475176502
最小值为：
0.046416985065966254
参考答案
Z = np.random.random((10,10))
Zmin, Zmax = Z.min(), Z.max()
print(Zmin, Zmax)
输出结果
0.004121552994131639 0.9861579216981144
14. 创建一个长度为30的随机向量并找到它的平均值 (★☆☆)
- (提示: mean)
array14 = np.random.random(30)
print(array14,"\n-------\n",array14.mean())
输出结果
[0.93012289 0.87846182 0.39524015 0.29766567 0.535985 0.96190709
0.11612866 0.49828173 0.41661926 0.3161008 0.45526546 0.45677439
0.42808616 0.2136142 0.85161725 0.72934575 0.88087434 0.17033377
0.71800939 0.74070516 0.25254667 0.15898121 0.25780968 0.65958564
0.10230427 0.94045116 0.80328989 0.6013053 0.66213565 0.50255439]
-------
0.5310700936558221
参考答案
Z = np.random.random(30)
m = Z.mean()
print(m)
输出结果
0.3515583279019299
15. 创建一个二维数组，其中边界值为1，其余值为0 (★☆☆)
- (提示: array[1:-1, 1:-1])
array15 = np.ones(9).reshape(3,3)
print(array15,"\n------\n")
array15[1:-1, 1:-1] = 0
print(array15)
输出结果
[[1. 1. 1.]
[1. 1. 1.]
[1. 1. 1.]]
------
[[1. 1. 1.]
[1. 0. 1.]
[1. 1. 1.]]
参考答案
Z = np.ones((10,10))
Z[1:-1,1:-1] = 0
print(Z)
输出结果
[[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
[1. 0. 0. 0. 0. 0. 0. 0. 0. 1.]
[1. 0. 0. 0. 0. 0. 0. 0. 0. 1.]
[1. 0. 0. 0. 0. 0. 0. 0. 0. 1.]
[1. 0. 0. 0. 0. 0. 0. 0. 0. 1.]
[1. 0. 0. 0. 0. 0. 0. 0. 0. 1.]
[1. 0. 0. 0. 0. 0. 0. 0. 0. 1.]
[1. 0. 0. 0. 0. 0. 0. 0. 0. 1.]
[1. 0. 0. 0. 0. 0. 0. 0. 0. 1.]
[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]]
16. 对于一个存在在数组，如何添加一个用0填充的边界? (★☆☆)
- (提示: np.pad)
- np.pad用法详解
- np.pad(array, pad_width, mode=‘constant’, **kwargs)
- array：数组
- pad_width
- mode
- constant
- edge
- linear_ramp
- maximum
- mean
- median
- reflect
- symmetric
- wrap
- **kwargs
#用常量填充
a = [1, 2, 3, 4, 5]
np.pad(a, (2, 3), 'constant', constant_values=(4, 6))
输出结果
array([4, 4, 1, 2, 3, 4, 5, 6, 6, 6])
np.pad(a, (2, 3), 'edge')
输出结果
array([1, 1, 1, 2, 3, 4, 5, 5, 5, 5])
np.pad(a, (2, 3), 'linear_ramp', end_values=(5, -4))
输出结果
array([ 5, 3, 1, 2, 3, 4, 5, 2, -1, -4])
np.pad(a, (2,), 'maximum')
输出结果
array([5, 5, 1, 2, 3, 4, 5, 5, 5])
np.pad(a, (2,), 'mean')
输出结果
array([3, 3, 1, 2, 3, 4, 5, 3, 3])
np.pad(a, (2,), 'median')
输出结果
array([3, 3, 1, 2, 3, 4, 5, 3, 3])
a = [[1, 2], [3, 4]]
print(a)
np.pad(a, ((3, 2), (2, 3)), 'minimum')
输出结果
[[1, 2], [3, 4]]
array([[1, 1, 1, 2, 1, 1, 1],
[1, 1, 1, 2, 1, 1, 1],
[1, 1, 1, 2, 1, 1, 1],
[1, 1, 1, 2, 1, 1, 1],
[3, 3, 3, 4, 3, 3, 3],
[1, 1, 1, 2, 1, 1, 1],
[1, 1, 1, 2, 1, 1, 1]])
a = [1, 2, 3, 4, 5]
np.pad(a, (2, 3), 'reflect')
输出结果
array([3, 2, 1, 2, 3, 4, 5, 4, 3, 2])
np.pad(a, (2, 3), 'reflect', reflect_type='odd')
输出结果
array([-1, 0, 1, 2, 3, 4, 5, 6, 7, 8])
np.pad(a, (2, 3), 'symmetric')
输出结果
array([2, 1, 1, 2, 3, 4, 5, 5, 4, 3])
np.pad(a, (2, 3), 'symmetric', reflect_type='odd')
输出结果
array([0, 1, 1, 2, 3, 4, 5, 5, 6, 7])
np.pad(a, (2, 3), 'wrap')
输出结果
array([4, 5, 1, 2, 3, 4, 5, 1, 2, 3])
def pad_with(vector, pad_width, iaxis, kwargs):
pad_value = kwargs.get('padder', 10)
vector[:pad_width[0]] = pad_value
vector[-pad_width[1]:] = pad_value
a = np.arange(6)
a = a.reshape((2, 3))
np.pad(a, 1, pad_with)
输出结果
array([[10, 10, 10, 10, 10],
[10, 0, 1, 2, 10],
[10, 3, 4, 5, 10],
[10, 10, 10, 10, 10]])
np.pad(a, 2, pad_with, padder=100)
输出结果
array([[100, 100, 100, 100, 100, 100, 100],
[100, 100, 100, 100, 100, 100, 100],
[100, 100, 0, 1, 2, 100, 100],
[100, 100, 3, 4, 5, 100, 100],
[100, 100, 100, 100, 100, 100, 100],
[100, 100, 100, 100, 100, 100, 100]])
array16 = np.arange(1,7)
array16 = array16.reshape(2, 3)
print(array16)
np.pad(array16, ((1, 1),(1,1)), 'constant', constant_values=(0, 0))
输出结果
[[1 2 3]
[4 5 6]]
array([[0, 0, 0, 0, 0],
[0, 1, 2, 3, 0],
[0, 4, 5, 6, 0],
[0, 0, 0, 0, 0]])
参考答案
#参考答案
Z = np.ones((5,5))
Z = np.pad(Z, pad_width=1, mode='constant', constant_values=0)
print(Z)
输出结果
[[0. 0. 0. 0. 0. 0. 0.]
[0. 1. 1. 1. 1. 1. 0.]
[0. 1. 1. 1. 1. 1. 0.]
[0. 1. 1. 1. 1. 1. 0.]
[0. 1. 1. 1. 1. 1. 0.]
[0. 1. 1. 1. 1. 1. 0.]
[0. 0. 0. 0. 0. 0. 0.]]
17. 以下表达式运行的结果分别是什么? (★☆☆)
- (提示: NaN = not a number, inf = infinity)
- 0 * np.nan
- np.nan == np.nan
- np.inf > np.nan
- np.nan - np.nan
- 0.3 == 3 * 0.1
参考答案
#0 * np.nan：nan
print("0 * np.nan输出为：",0 * np.nan)
#np.nan == np.nan:False
print("np.nan == np.nan输出为：",np.nan == np.nan)
#np.inf > np.nan:False
print("np.inf > np.nan输出为：",np.inf > np.nan)
#np.nan - np.nan：nan
print("np.nan - np.nan输出为：",np.nan - np.nan)
#0.3 == 3 * 0.1:False
print("0.3 == 3 * 0.1输出为：",0.3 == 3 * 0.1)
输出结果
0 * np.nan输出为： nan
np.nan == np.nan输出为： False
np.inf > np.nan输出为： False
np.nan - np.nan输出为： nan
0.3 == 3 * 0.1输出为： False
18. 创建一个 5x5的矩阵，并设置值1,2,3,4落在其对角线下方位置 (★☆☆)
- (提示: np.diag)
参考答案
#参考答案
#np.diag(v, k=0)
Z = np.diag(1+np.arange(4),k=-1)
print(Z)
输出结果
[[0 0 0 0 0]
[1 0 0 0 0]
[0 2 0 0 0]
[0 0 3 0 0]
[0 0 0 4 0]]
19. 创建一个8x8 的矩阵，并且设置成棋盘样式 (★☆☆)
- (提示: array[::2])
array19 = np.ones((8,8))
array19[1::2,::2]= 0
array19[::2,1::2]= 0
array19
输出结果
array([[1., 0., 1., 0., 1., 0., 1., 0.],
[0., 1., 0., 1., 0., 1., 0., 1.],
[1., 0., 1., 0., 1., 0., 1., 0.],
[0., 1., 0., 1., 0., 1., 0., 1.],
[1., 0., 1., 0., 1., 0., 1., 0.],
[0., 1., 0., 1., 0., 1., 0., 1.],
[1., 0., 1., 0., 1., 0., 1., 0.],
[0., 1., 0., 1., 0., 1., 0., 1.]])
参考答案
#参考答案
Z = np.zeros((8,8),dtype=int)
Z[1::2,::2] = 1
Z[::2,1::2] = 1
print(Z)
输出结果
[[0 1 0 1 0 1 0 1]
[1 0 1 0 1 0 1 0]
[0 1 0 1 0 1 0 1]
[1 0 1 0 1 0 1 0]
[0 1 0 1 0 1 0 1]
[1 0 1 0 1 0 1 0]
[0 1 0 1 0 1 0 1]
[1 0 1 0 1 0 1 0]]
20. 考虑一个 (6,7,8) 形状的数组，其第100个元素的索引(x,y,z)是什么?
- (提示: np.unravel_index)
参考答案
#参考答案
#unravel_index(indices, shape, order='C')
print(np.unravel_index(100,(6,7,8)))
输出结果
(1, 5, 4)
21. 用tile函数去创建一个 8x8的棋盘样式矩阵(★☆☆)
- (提示: np.tile)
- np.tile(A, reps)相关用法
#一维数组情况
a = np.array([0, 1, 2])
print(np.tile(a, 2))
print("-----------")
print(np.tile(a, (2, 2)))
print("-----------")
print(np.tile(a, (2, 2, 2)))
输出结果
[0 1 2 0 1 2]
-----------
[[0 1 2 0 1 2]
[0 1 2 0 1 2]]
-----------
[[[0 1 2 0 1 2]
[0 1 2 0 1 2]]
[[0 1 2 0 1 2]
[0 1 2 0 1 2]]]
c = np.array([1,2,3,4])
print(np.tile(c,(4,1)))
print("-----------")
print(np.tile(c,(1,4)))
输出结果
[[1 2 3 4]
[1 2 3 4]
[1 2 3 4]
[1 2 3 4]]
-----------
[[1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4]]
#二维数组
b = np.array([[1, 2], [3, 4]])
print(np.tile(b, 2))
print("-----------")
print(np.tile(b, (2, 1)))
输出结果
[[1 2 1 2]
[3 4 3 4]]
-----------
[[1 2]
[3 4]
[1 2]
[3 4]]
array21 = np.array([[0,1],[1,0]])
print(array21)
print("-------------")
print(np.tile(array21, (4,4)))
输出结果
[[0 1]
[1 0]]
-------------
[[0 1 0 1 0 1 0 1]
[1 0 1 0 1 0 1 0]
[0 1 0 1 0 1 0 1]
[1 0 1 0 1 0 1 0]
[0 1 0 1 0 1 0 1]
[1 0 1 0 1 0 1 0]
[0 1 0 1 0 1 0 1]
[1 0 1 0 1 0 1 0]]
参考答案
#参考答案
Z = np.tile( np.array([[0,1],[1,0]]), (4,4))
print(Z)
输出结果
[[0 1 0 1 0 1 0 1]
[1 0 1 0 1 0 1 0]
[0 1 0 1 0 1 0 1]
[1 0 1 0 1 0 1 0]
[0 1 0 1 0 1 0 1]
[1 0 1 0 1 0 1 0]
[0 1 0 1 0 1 0 1]
[1 0 1 0 1 0 1 0]]
22. 对一个5x5的随机矩阵做归一化(★☆☆)
- (提示: (x - min) / (max - min))
array22 = np.random.random(25).reshape(5,5)
amin = array22.min()
amax = array22.max()
print(array22,"\n最小值为：\n",amin,"\n最大值为：\n",amax)
array_new = (array22 - amin)/(amax - amin)
array_new
输出结果
[[0.26009871 0.89989705 0.79188441 0.70944836 0.24024913]
[0.13928614 0.97029819 0.07942416 0.20779984 0.7193373 ]
[0.14027008 0.45981306 0.31472733 0.70832382 0.65386747]
[0.57767974 0.37985315 0.8988651 0.80697825 0.74939784]
[0.46605384 0.95553957 0.92271522 0.22827105 0.44096403]]
最小值为：
0.0794241626441996
最大值为：
0.9702981944156541
array([[0.20280594, 0.92097519, 0.79973175, 0.70719784, 0.18052493],
[0.06719466, 1. , 0. , 0.14410082, 0.71829811],
[0.06829912, 0.42698393, 0.2641262 , 0.70593556, 0.64480868],
[0.55928848, 0.33722948, 0.91981684, 0.81667448, 0.75204086],
[0.43398917, 0.98343354, 0.94658844, 0.16707961, 0.40582602]])
参考答案
#参考答案
Z = np.random.random((5,5))
Zmax, Zmin = Z.max(), Z.min()
Z = (Z - Zmin)/(Zmax - Zmin)
print(Z)
输出结果
[[0.12258481 0.48605201 0.72046827 0.68510051 0.77727713]
[1. 0.62709158 0.30209518 0.88260495 0. ]
[0.85075924 0.53774393 0.45084072 0.60056524 0.38530611]
[0.18323325 0.93486688 0.93560248 0.63488952 0.3298871 ]
[0.82238802 0.35623848 0.9348605 0.4193811 0.47054688]]
23. 创建一个将颜色描述为(RGBA)四个无符号字节的自定义dtype？(★☆☆)
- (提示: np.dtype)
参考答案
#参考答案
#np.dtype(self, /, *args, **kwargs)
color = np.dtype([("r", np.ubyte, 1),
("g", np.ubyte, 1),
("b", np.ubyte, 1),
("a", np.ubyte, 1)])
color
输出结果
dtype([('r', 'u1'), ('g', 'u1'), ('b', 'u1'), ('a', 'u1')])
24. 一个5x3的矩阵与一个3x2的矩阵相乘，实矩阵乘积是什么？ (★☆☆)
- (提示: np.dot | @)
array241 = np.random.randint(0,10,(5,3))
array242 = np.random.randint(0,10,(3,2))
print(array241,type(array241),"\n----\n",array242,type(array242))
print("-----------")
array24 = np.dot(array241,array242)
print(array24)
输出结果
[[7 9 8]
[9 5 1]
[8 8 9]
[1 4 6]
[1 7 4]] <class 'numpy.ndarray'>
----
[[7 0]
[0 4]
[0 1]] <class 'numpy.ndarray'>
-----------
[[49 44]
[63 21]
[56 41]
[ 7 22]
[ 7 32]]
from numpy import *
from numpy.linalg import *
array241 = matrix(np.random.randint(0,10,(5,3)))
array242 = matrix(np.random.randint(0,10,(3,2)))
print(array241,type(array241),"\n----\n",array242,type(array242))
print("-----------")
array24 = np.dot(array241,array242)
print(array24)
输出结果
[[1 5 8]
[3 1 6]
[0 4 1]
[9 4 3]
[6 3 8]] <class 'numpy.matrix'>
----
[[4 5]
[0 7]
[6 4]] <class 'numpy.matrix'>
-----------
[[52 72]
[48 46]
[ 6 32]
[54 85]
[72 83]]
参考答案
#参考答案
Z = np.dot(np.ones((5,3)), np.ones((3,2)))
print(Z)
输出结果
[[3. 3.]
[3. 3.]
[3. 3.]
[3. 3.]
[3. 3.]]
25. 给定一个一维数组，对其在3到8之间的所有元素取反 (★☆☆)
- (提示: >, <=)
array25 = np.array([1,2,3,4,5,6,7,8,9,0])
array25[(array25>=3) & (array25 <= 8)] *= -1
array25
输出结果
array([ 1, 2, -3, -4, -5, -6, -7, -8, 9, 0])
参考答案
#参考答案
Z = np.arange(11)
Z[(3 < Z) & (Z <= 8)] *= -1
print(Z)
输出结果
[ 0 1 2 3 -4 -5 -6 -7 -8 9 10]
26. 下面脚本运行后的结果是什么? (★☆☆)
- (提示: np.sum)
- print(sum(range(5),-1))
- from numpy import *
- print(sum(range(5),-1))
参考答案
#参考答案
#求和
print(sum(range(5),-1))
#导入numpy模块
from numpy import *
#求和
print(sum(range(5),-1))
输出结果
10
10
27. 考虑一个整数向量Z,下列表达合法的是哪个? (★☆☆)
- Z**Z
- 2 << Z >> 2
- Z <- Z
- 1j*Z
- Z/1/1
- ZZ
Z = np.array([1,2,3,4])
print(Z**Z)
输出结果
[ 1 4 27 256]
print(2 << Z >> 2)
输出结果
[1 2 4 8]
print(Z <- Z)
输出结果
[False False False False]
print(1j*Z)
输出结果
[0.+1.j 0.+2.j 0.+3.j 0.+4.j]
print(Z/1/1)
输出结果
array([1., 2., 3., 4.])
#不合法
#ZZ
参考答案
#参考答案
Z = np.arange(5)
Z ** Z # legal
输出结果
array([ 1, 1, 4, 27, 256], dtype=int32)
Z = np.arange(5)
2 << Z >> 2 # false
输出结果
array([0, 1, 2, 4, 8], dtype=int32)
Z = np.arange(5)
Z <- Z # legal
输出结果
array([False, False, False, False, False])
Z = np.arange(5)
1j*Z # legal
输出结果
array([0.+0.j, 0.+1.j, 0.+2.j, 0.+3.j, 0.+4.j])
Z = np.arange(5)
Z/1/1 # legal
输出结果
array([0., 1., 2., 3., 4.])
Z = np.arange(5)
#ZZ # false
28. 下列表达式的结果分别是什么?(★☆☆)
- np.array(0) / np.array(0)
- np.array(0) // np.array(0)
- np.array([np.nan]).astype(int).astype(float)
参考答案
#参考答案
np.array(0) / np.array(0)
输出结果
nan
np.array(0) // np.array(0)
输出结果
0
np.array([np.nan]).astype(int).astype(float)
输出结果
array([-2.14748365e+09])
29. 如何从零位对浮点数组做舍入 ? (★☆☆)
- (提示: np.uniform, np.copysign, np.ceil, np.abs)
#补充知识：np.copysign()
print(np.copysign(1.3, -1))
print("-----------")
print(1/np.copysign(0, 1))
print("-----------")
print(1/np.copysign(0, -1))
print("-----------")
print(np.copysign([-1, 0, 1], -1.1))
print("-----------")
print(np.copysign([-1, 0, 1], np.arange(3)-1))
输出结果
-1.3
-----------
inf
-----------
-inf
-----------
[-1. -0. -1.]
-----------
[-1. 0. 1.]
#补充知识：np.ceil()
a = np.array([-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0])
np.ceil(a)
输出结果
array([-1., -1., -0., 1., 2., 2., 2.])
#补充知识：np.abs()
x = np.array([-1.2, 1.2])
print(abs(x))
x = np.array([-1.2, 1.2])
print(np.absolute(x))
print(np.absolute(1.2 + 1j))
输出结果
[1.2 1.2]
[1.2 1.2]
1.5620499351813308
import matplotlib.pyplot as plt
x = np.linspace(start=-10, stop=10, num=101)
plt.plot(x, np.absolute(x))
plt.show()
xx = x + 1j * x[:, np.newaxis]
plt.imshow(np.abs(xx), extent=[-10, 10, -10, 10], cmap='gray')
plt.show()
array29 = np.random.random(5)
print(array29,type(array29))
print("-----------")
print(np.ceil(array29))
输出结果
[0.38875538 0.57606542 0.97401344 0.61982858 0.79914594] <class 'numpy.ndarray'>
-----------
[1. 1. 1. 1. 1.]
参考答案
Z = np.random.uniform(-10,+10,10)
print(Z)
print("-----------")
print(np.copysign(np.ceil(np.abs(Z)), Z))
输出结果
[-9.67597981 -0.36041212 6.07506503 -0.86278789 -5.98091448 6.4237606
1.43246588 3.03546255 -8.31970362 1.94764631]
-----------
[-10. -1. 7. -1. -6. 7. 2. 4. -9. 2.]
30. 如何找到两个数组中的共同元素? (★☆☆)
- (提示: np.intersect1d)
- np.intersect1d(ar1, ar2, assume_unique=False, return_indices=False)用法
np.intersect1d([1, 3, 4, 3], [3, 1, 2, 1])
输出结果
array([1, 3])
from functools import reduce
reduce(np.intersect1d, ([1, 3, 4, 3], [3, 1, 2, 1], [6, 3, 4, 2]))
输出结果
array([3])
x = np.array([1, 1, 2, 3, 4])
y = np.array([2, 1, 4, 6])
xy, x_ind, y_ind = np.intersect1d(x, y, return_indices=True)
xy,x_ind, y_ind
输出结果
(array([1, 2, 4]),
array([0, 2, 4], dtype=int64),
array([1, 0, 2], dtype=int64))
xy, x[x_ind], y[y_ind]
输出结果
(array([1, 2, 4]), array([1, 2, 4]), array([1, 2, 4]))
arr1 = np.array([1,2,3,4])
arr2 = np.array([4,5,6,7])
np.intersect1d(arr1,arr2)
输出结果
array([4])
参考答案
#参考答案
Z1 = np.random.randint(0,10,10)
Z2 = np.random.randint(0,10,10)
print(Z1,"\n-------\n",Z2,"\n-------")
print(np.intersect1d(Z1,Z2))
输出结果
[6 6 9 8 1 0 1 0 9 1]
-------
[1 2 3 2 2 2 0 9 9 2]
-------
[0 1 9]
31. 如何忽略所有的 numpy 警告(尽管不建议这么做)? (★☆☆)
- (提示: np.seterr, np.errstate)
- np.seterr(all=None, divide=None, over=None, under=None, invalid=None)
- np.errstate(*,call=
,**kwargs,)
参考答案
# Suicide mode on
defaults = np.seterr(all="ignore")
Z = np.ones(1) / 0
# Back to sanity
_ = np.seterr(**defaults)
#An equivalent way, with a context manager:
with np.errstate(divide='ignore'):
Z = np.ones(1) / 0
32. 下面的表达式是正确的吗? (★☆☆)
- (提示: imaginary number)
- np.sqrt(-1) == np.emath.sqrt(-1)
参考答案
np.sqrt(-1) == np.emath.sqrt(-1)
输出结果
False
33. 如何得到昨天，今天，明天的日期? (★☆☆)
- (提示: np.datetime64, np.timedelta64)
#np.datetime64(self, /, *args, **kwargs)
print(np.datetime64(10, 'Y'))
print(np.datetime64(10, 'D'))
输出结果
1980
1970-01-11
#np.timedelta64(self, /, *args, **kwargs)
np.timedelta64(10)
输出结果
numpy.timedelta64(10)
参考答案
#参考答案
yesterday = np.datetime64('today', 'D') - np.timedelta64(1, 'D')
today = np.datetime64('today', 'D')
tomorrow = np.datetime64('today', 'D') + np.timedelta64(1, 'D')
print ("Yesterday is " + str(yesterday))
print ("Today is " + str(today))
print ("Tomorrow is "+ str(tomorrow))
输出结果
Yesterday is 2021-08-24
Today is 2021-08-25
Tomorrow is 2021-08-26
34. 如何得到所有与2016年7月对应的日期？ (★★☆)
- (提示: np.arange(dtype=datetime64[‘D’]))
参考答案
#参考答案
Z = np.arange('2016-07', '2016-08', dtype='datetime64[D]')
print(Z)
输出结果
['2016-07-01' '2016-07-02' '2016-07-03' '2016-07-04' '2016-07-05'
'2016-07-06' '2016-07-07' '2016-07-08' '2016-07-09' '2016-07-10'
'2016-07-11' '2016-07-12' '2016-07-13' '2016-07-14' '2016-07-15'
'2016-07-16' '2016-07-17' '2016-07-18' '2016-07-19' '2016-07-20'
'2016-07-21' '2016-07-22' '2016-07-23' '2016-07-24' '2016-07-25'
'2016-07-26' '2016-07-27' '2016-07-28' '2016-07-29' '2016-07-30'
'2016-07-31']
35. 如何直接在位计算(A+B)*(-A/2)(不建立副本)? (★★☆)
- (提示: np.add(out=), np.negative(out=), np.multiply(out=), np.divide(out=))
参考答案
A = np.ones(3)*1
B = np.ones(3)*2
C = np.ones(3)*3
print(A,B,C)
print("-----------")
print(np.add(A,B,out=B))
print("-----------")
print(A,B,C)
输出结果
[1. 1. 1.] [2. 2. 2.] [3. 3. 3.]
-----------
[3. 3. 3.]
-----------
[1. 1. 1.] [3. 3. 3.] [3. 3. 3.]
print(np.divide(A,2,out=A))
print("-----------")
print(A,B,C)
输出结果
[0.5 0.5 0.5]
-----------
[0.5 0.5 0.5] [3. 3. 3.] [3. 3. 3.]
print(np.negative(A,out=A))
print("-----------")
print(A,B,C)
输出结果
[-0.5 -0.5 -0.5]
-----------
[-0.5 -0.5 -0.5] [3. 3. 3.] [3. 3. 3.]
print(np.multiply(A,B,out=A))
print("-----------")
print(A,B,C)
输出结果
[-1.5 -1.5 -1.5]
-----------
[-1.5 -1.5 -1.5] [3. 3. 3.] [3. 3. 3.]
36. 用五种不同的方法去提取一个随机数组的整数部分(★★☆)
- (提示: %, np.floor, np.ceil, astype, np.trunc)
array36 = np.random.uniform(-10,+10,10)
print(array36)
输出结果
[ 6.16472162 -1.05905698 2.13578326 -3.65313804 7.49143151 -4.99765465
-8.27078759 2.35452296 9.55808418 -1.5001378 ]
array36_1 = array36 - array36%1
array36_1
输出结果
array([ 6., -2., 2., -4., 7., -5., -9., 2., 9., -2.])
array36_2 = np.floor(array36)
array36_2
输出结果
array([ 6., -2., 2., -4., 7., -5., -9., 2., 9., -2.])
array36_3 = np.ceil(array36)
array36_3
输出结果
array([ 7., -1., 3., -3., 8., -4., -8., 3., 10., -1.])
#a.astype(dtype, order='K', casting='unsafe', subok=True, copy=True)
array36_4 = array36.astype(int)
array36_4
输出结果
array([ 6, -1, 2, -3, 7, -4, -8, 2, 9, -1])
array36_5 = np.trunc(array36)
array36_5
输出结果
array([ 6., -1., 2., -3., 7., -4., -8., 2., 9., -1.])
参考答案
#参考答案
Z = np.random.uniform(0,10,10)
print (Z - Z%1)
输出结果
[2. 8. 9. 9. 5. 8. 4. 7. 0. 6.]
print (np.floor(Z))
输出结果
[2. 8. 9. 9. 5. 8. 4. 7. 0. 6.]
print (np.ceil(Z)-1)
输出结果
[2. 8. 9. 9. 5. 8. 4. 7. 0. 6.]
print (Z.astype(int))
输出结果
[2 8 9 9 5 8 4 7 0 6]
print (np.trunc(Z))
输出结果
[2. 8. 9. 9. 5. 8. 4. 7. 0. 6.]
37. 创建一个5x5的矩阵，其中每行的数值范围从0到4 (★★☆)
- (提示: np.arange)
参考答案
#参考答案
Z = np.zeros((5,5))
print(Z)
print("---------")
Z += np.arange(5)
print (Z)
输出结果
[[0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0.]]
---------
[[0. 1. 2. 3. 4.]
[0. 1. 2. 3. 4.]
[0. 1. 2. 3. 4.]
[0. 1. 2. 3. 4.]
[0. 1. 2. 3. 4.]]
38. 通过考虑一个可生成10个整数的函数，来构建一个数组(★☆☆)
- (提示: np.fromiter)
iterable = (x for x in np.random.randint(0,100,10))
np.fromiter(iterable, int)
输出结果
array([65, 92, 67, 31, 35, 54, 40, 14, 48, 46])
iterable = (x for x in range(10))
np.fromiter(iterable, int)
输出结果
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
参考答案
#参考答案
def generate():
for x in range(10):
yield x
Z = np.fromiter(generate(),dtype=float,count=-1)
print (Z)
输出结果
[0. 1. 2. 3. 4. 5. 6. 7. 8. 9.]
39. 创建一个长度为10的随机向量，其值域范围从0到1，但是不包括0和1 (★★☆)
- (提示: np.linspace)
array39 = np.linspace(0,1,num=11,endpoint= False)[1:]
array39
输出结果
array([0.09090909, 0.18181818, 0.27272727, 0.36363636, 0.45454545,
0.54545455, 0.63636364, 0.72727273, 0.81818182, 0.90909091])
参考答案
#参考答案
Z = np.linspace(0,1,11,endpoint=False)[1:]
print (Z)
输出结果
[0.09090909 0.18181818 0.27272727 0.36363636 0.45454545 0.54545455
0.63636364 0.72727273 0.81818182 0.90909091]
40. 创建一个长度为10的随机向量，并将其排序 (★★☆)
- (提示: sort)
array40 = np.random.randint(0,100,10)
print("array40向量为：\n",array40,"\n------------")
print("array40排序为：\n",np.sort(array40, axis=-1),"\n------------")
print("array40向量没有发生改变：\n",array40,"\n------------")
array40.sort()
print("array40向量发生改变：\n",array40)
输出结果
array40向量为：
[55 1 31 87 28 55 85 37 71 84]
------------
array40排序为：
[ 1 28 31 37 55 55 71 84 85 87]
------------
array40向量没有发生改变：
[55 1 31 87 28 55 85 37 71 84]
------------
array40向量发生改变：
[ 1 28 31 37 55 55 71 84 85 87]
参考答案
Z = np.random.random(10)
Z.sort()
print(Z)
输出结果
[0.01108845 0.05596719 0.26378488 0.32935733 0.42652857 0.56806563
0.61323955 0.90232693 0.94617123 0.99070713]
41.对于一个小数组，如何用比 np.sum更快的方式对其求和？(★★☆)
- (提示: np.add.reduce)
array41 = np.random.randint(0,100,20).reshape(4,5)
print(array41,"\n------------")
print("np.sum求和：",np.sum(array41,axis = 0))
print("np.add.reduce求和：",np.add.reduce(array41))
输出结果
[[56 89 2 35 41]
[68 30 51 15 61]
[72 50 69 49 96]
[14 27 48 89 69]]
------------
np.sum求和： [210 196 170 188 267]
array([210, 196, 170, 188, 267])
参考答案
#参考答案
Z = np.arange(10)
np.add.reduce(Z)
输出结果
45
42. 对于两个随机数组A和B，检查它们是否相等(★★☆)
- (提示: np.allclose, np.array_equal)
#np.allclose(a, b, rtol=1e-05, atol=1e-08, equal_nan=False)
print(np.allclose([1e10,1e-7], [1.00001e10,1e-8]))
print(np.allclose([1e10,1e-8], [1.00001e10,1e-9]))
print(np.allclose([1e10,1e-8], [1.0001e10,1e-9]))
print(np.allclose([1.0, np.nan], [1.0, np.nan]))
print(np.allclose([1.0, np.nan], [1.0, np.nan], equal_nan=True))
输出结果
False
True
False
False
True
#np.array_equal(a1, a2, equal_nan=False)
print(np.array_equal([1, 2], [1, 2]))
print(np.array_equal(np.array([1, 2]), np.array([1, 2])))
print(np.array_equal([1, 2], [1, 2, 3]))
print(np.array_equal([1, 2], [1, 4]))
a = np.array([1, np.nan])
print(np.array_equal(a, a))
print(np.array_equal(a, a, equal_nan=True))
a = np.array([1 + 1j])
b = a.copy()
a.real = np.nan
b.imag = np.nan
print(np.array_equal(a, b, equal_nan=True))
输出结果
True
True
False
False
False
True
True
A = np.random.randint(0,100,5)
B = np.random.randint(0,100,5)
print(A,"\n------------\n",B,"\n------------")
print("A与B是否相等:",np.allclose(A,B))
print("A与B是否相等:",np.array_equal(A,B))
输出结果
[68 75 89 50 24]
------------
[35 76 1 59 90]
------------
A与B是否相等: False
A与B是否相等: False
参考答案
#参考答案
A = np.random.randint(0,2,5)
B = np.random.randint(0,2,5)
# Assuming identical shape of the arrays and a tolerance for the comparison of values
equal = np.allclose(A,B)
print(equal)
输出结果
True
# 方法2
# Checking both the shape and the element values, no tolerance (values have to be exactly equal)
equal = np.array_equal(A,B)
print(equal)
输出结果
True
43. 创建一个只读数组(read-only) (★★☆)
- (提示: flags.writeable)
#参考答案
Z = np.zeros(10)
Z.flags.writeable = False
Z[0] = 1
44. 将笛卡尔坐标下的一个10x2的矩阵转换为极坐标形式(★★☆)
- (hint: np.sqrt, np.arctan2)
参考答案
#参考答案
Z = np.random.random((10,2))
X,Y = Z[:,0], Z[:,1]
R = np.sqrt(X**2+Y**2)
T = np.arctan2(Y,X)
print (R)
print (T)
输出结果
[0.92221092 1.03210565 1.1226919 1.07363888 0.91806222 0.44833899
0.92030651 0.0995045 0.48589723 0.75119929]
[0.69468262 1.11817969 0.75553459 0.96033932 0.61850187 0.87891577
1.49597306 0.7527101 1.39087811 1.52194917]
45. 创建一个长度为10的向量，并将向量中最大值替换为1 (★★☆)
- (提示: argmax)
array45 = np.random.randint(0,100,10)
print("array45向量为：\n",array45)
array45[array45.argmax()] = 1
print("更改后为：\n",array45)
输出结果
array45向量为：
[96 21 61 33 38 77 72 49 59 54]
更改后为：
[ 1 21 61 33 38 77 72 49 59 54]
参考答案
Z = np.random.random(10)
Z[Z.argmax()] = 1
print (Z)
输出结果
[0.85538406 0.08771085 0.56525424 0.38459612 0.16379397 0.90891447
1. 0.12933557 0.84031549 0.31512573]
46. 创建一个结构化数组，并实现 x 和 y 坐标覆盖 [0,1]x[0,1] 区域 (★★☆)
- (提示: np.meshgrid)
#np.meshgrid(*xi, copy=True, sparse=False, indexing='xy')
nx, ny = (3, 2)
x = np.linspace(0, 1, nx)
y = np.linspace(0, 1, ny)
xv, yv = np.meshgrid(x, y)
print(xv)
print(yv)
xv, yv = np.meshgrid(x, y, sparse=True) # make sparse output arrays
print(xv)
print(yv)
输出结果
[[0. 0.5 1. ]
[0. 0.5 1. ]]
[[0. 0. 0.]
[1. 1. 1.]]
[[0. 0.5 1. ]]
[[0.]
[1.]]
#`meshgrid` is very useful to evaluate functions on a grid.
import matplotlib.pyplot as plt
x = np.arange(-5, 5, 0.1)
y = np.arange(-5, 5, 0.1)
xx, yy = np.meshgrid(x, y, sparse=True)
z = np.sin(xx**2 + yy**2) / (xx**2 + yy**2)
h = plt.contourf(x,y,z)
plt.show()
参考答案
#参考答案
Z = np.zeros((5,5), [('x',float),('y',float)])
print(Z,"\n----------")
Z['x'], Z['y'] = np.meshgrid(np.linspace(0,1,5),
np.linspace(0,1,5))
print(Z)
输出结果
[[(0., 0.) (0., 0.) (0., 0.) (0., 0.) (0., 0.)]
[(0., 0.) (0., 0.) (0., 0.) (0., 0.) (0., 0.)]
[(0., 0.) (0., 0.) (0., 0.) (0., 0.) (0., 0.)]
[(0., 0.) (0., 0.) (0., 0.) (0., 0.) (0., 0.)]
[(0., 0.) (0., 0.) (0., 0.) (0., 0.) (0., 0.)]]
----------
[[(0. , 0. ) (0.25, 0. ) (0.5 , 0. ) (0.75, 0. ) (1. , 0. )]
[(0. , 0.25) (0.25, 0.25) (0.5 , 0.25) (0.75, 0.25) (1. , 0.25)]
[(0. , 0.5 ) (0.25, 0.5 ) (0.5 , 0.5 ) (0.75, 0.5 ) (1. , 0.5 )]
[(0. , 0.75) (0.25, 0.75) (0.5 , 0.75) (0.75, 0.75) (1. , 0.75)]
[(0. , 1. ) (0.25, 1. ) (0.5 , 1. ) (0.75, 1. ) (1. , 1. )]]
47. 给定两个数组X和Y，构造Cauchy矩阵C (Cij =1/(xi - yj))
- (提示: np.subtract.outer)
参考答案
#参考答案
X = np.arange(8)
print(X,"\n----------")
Y = X + 0.5
print(Y,"\n----------")
C = 1.0 / np.subtract.outer(X, Y)
print(C,"\n----------")
print(np.linalg.det(C))
输出结果
[0 1 2 3 4 5 6 7]
----------
[0.5 1.5 2.5 3.5 4.5 5.5 6.5 7.5]
----------
[[-2. -0.66666667 -0.4 -0.28571429 -0.22222222 -0.18181818
-0.15384615 -0.13333333]
[ 2. -2. -0.66666667 -0.4 -0.28571429 -0.22222222
-0.18181818 -0.15384615]
[ 0.66666667 2. -2. -0.66666667 -0.4 -0.28571429
-0.22222222 -0.18181818]
[ 0.4 0.66666667 2. -2. -0.66666667 -0.4
-0.28571429 -0.22222222]
[ 0.28571429 0.4 0.66666667 2. -2. -0.66666667
-0.4 -0.28571429]
[ 0.22222222 0.28571429 0.4 0.66666667 2. -2.
-0.66666667 -0.4 ]
[ 0.18181818 0.22222222 0.28571429 0.4 0.66666667 2.
-2. -0.66666667]
[ 0.15384615 0.18181818 0.22222222 0.28571429 0.4 0.66666667
2. -2. ]]
----------
3638.1636371179666
48. 打印每个numpy标量类型的最小值和最大值？ (★★☆)
- (提示: np.iinfo, np.finfo, eps)
#np.iinfo(int_type)
ii16 = np.iinfo(np.int16)
print(ii16.min,"\n----------\n",ii16.max)
输出结果
-32768
----------
32767
ii32 = np.iinfo(np.int32)
print(ii32.min,"\n----------\n",ii32.max)
输出结果
-2147483648
----------
2147483647
ii32 = np.iinfo(np.int32(10))
print(ii32.min,"\n----------\n",ii32.max)
输出结果
-2147483648
----------
2147483647
#np.finfo(dtype)
参考答案
#参考答案
for dtype in [np.int8, np.int32, np.int64]:
print(np.iinfo(dtype).min,"\n----------")
print(np.iinfo(dtype).max,"\n----------")
for dtype in [np.float32, np.float64]:
print(np.finfo(dtype).min,"\n----------")
print(np.finfo(dtype).max,"\n----------")
print(np.finfo(dtype).eps,"\n----------")
输出结果
-128
----------
127
----------
-2147483648
----------
2147483647
----------
-9223372036854775808
----------
9223372036854775807
----------
-3.4028235e+38
----------
3.4028235e+38
----------
1.1920929e-07
----------
-1.7976931348623157e+308
----------
1.7976931348623157e+308
----------
2.220446049250313e-16
----------
49. 如何打印一个数组中的所有数值? (★★☆)
- (提示: np.set_printoptions)
#np.set_printoptions(precision=None,threshold=None,edgeitems=None,linewidth=None,
#suppress=None,nanstr=None,infstr=None,formatter=None,sign=None,floatmode=None,*,legacy=None,)
#控制输出的小数点个数是4
np.set_printoptions(precision=4)
np.array([1.123456789])
输出结果
array([1.1235])
#Long arrays can be summarised:
#控制输出的值的个数为6，其余以...代替
np.set_printoptions(threshold=5)
np.arange(10)
输出结果
array([0, 1, 2, ..., 7, 8, 9])
参考答案
#控制台输出所有的值，不需要省略号
#但是会报错，后续发现是这个方法应该是没有的
np.set_printoptions(threshold=np.nan)
np.arange(10)
- Python3使用np.set_printoptions(threshold=np.nan)引发错误解决方法
- 目的是把阀值调到足够大，能够显示任意长度的数组，因此只需要借助sys模块就可以实现
import numpy as np
import sys
np.set_printoptions(threshold=6)
Z = np.zeros((16,16))
print(Z)
# 修改阀值
np.set_printoptions(threshold=sys.maxsize)
print(Z)
输出结果
[[0. 0. 0. ... 0. 0. 0.]
[0. 0. 0. ... 0. 0. 0.]
[0. 0. 0. ... 0. 0. 0.]
...
[0. 0. 0. ... 0. 0. 0.]
[0. 0. 0. ... 0. 0. 0.]
[0. 0. 0. ... 0. 0. 0.]]
[[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]]
- precision : int, 可选，float输出的精度，即小数点后维数，默认8。
- threshold : int, 可选，当数组数目过大时，设置显示几个数字，其余用省略号。
- edgeitems : int, 可选，边缘数目，每个维度开始和结束时摘要中的数组项数，默认3
- linewidth : int, 可选，用于确定每行多少字符数后插入换行符，默认为75。
- suppress : bool, 可选，是否压缩由科学计数法表示的浮点数，默认False。
- nanstr : str, 可选，浮点非数字的字符串表示形式，默认nan
- infstr : str, 可选，浮点无穷大的字符串表示形式，默认inf
#Small results can be suppressed:
eps = np.finfo(float).eps
x = np.arange(4.)
x**2 - (x + eps)**2
输出结果
array([-4.9304e-32, -4.4409e-16, 0.0000e+00, 0.0000e+00])
np.set_printoptions(suppress=True)
x**2 - (x + eps)**2
输出结果
array([-0., -0., 0., 0.])
#A custom formatter can be used to display array elements as desired:
np.set_printoptions(formatter={'all':lambda x: 'int: '+str(-x)})
x = np.arange(3)
x
输出结果
array([int: 0, int: -1, int: -2])
# formatter gets reset
np.set_printoptions()
x
输出结果
array([0, 1, 2])
#To put back the default options, you can use:
np.set_printoptions(edgeitems=3, infstr='inf',linewidth=75, nanstr='nan', precision=8,suppress=False, threshold=1000, formatter=None)
#Also to temporarily override options, use `printoptions` as a context manager:
with np.printoptions(precision=2, suppress=True, threshold=5):
np.linspace(0, 10, 10)
50. 给定标量时，如何找到数组中最接近标量的值？(★★☆)
- (提示: argmin)
#np.argmin(a, axis=None, out=None):返回的是最小值的索引
a = np.arange(6).reshape(2,3) + 10
print(a,"\n----------\n",np.argmin(a))
输出结果
[[10 11 12]
[13 14 15]]
----------
0
np.argmin(a, axis=0)
输出结果
array([0, 0, 0], dtype=int64)
np.argmin(a, axis=1)
输出结果
array([0, 0], dtype=int64)
#Indices of the minimum elements of a N-dimensional array:
ind = np.unravel_index(np.argmin(a, axis=None), a.shape)
ind
输出结果
(0, 0)
#返回的是最小值
a[ind]
输出结果
10
b = np.arange(6) + 10
b[4] = 10
b
输出结果
array([10, 11, 12, 13, 10, 15])
# Only the first occurrence is returned.
#多个最小值存在时只会
np.argmin(b)
输出结果
0
x = np.array([[4,2,3], [1,0,3]])
index_array = np.argmin(x, axis=-1)
# Same as np.min(x, axis=-1, keepdims=True)
np.take_along_axis(x, np.expand_dims(index_array, axis=-1), axis=-1)
输出结果
array([[2],
[0]])
# Same as np.max(x, axis=-1)
np.take_along_axis(x, np.expand_dims(index_array, axis=-1), axis=-1).squeeze(axis=-1)
输出结果
array([2, 0])
参考答案
#参考答案
Z = np.arange(100)
print(Z,"\n------")
v = np.random.uniform(0,100)
print(v,"\n------")
index = (np.abs(Z-v)).argmin()
print (Z[index])
输出结果
[ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47
48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71
72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95
96 97 98 99]
------
59.4391273262911
------
59
51. 创建一个表示位置(x,y)和颜色(r,g,b)的结构化数组(★★☆)
- (提示: dtype)
参考答案
#参考答案
Z = np.zeros(10, [ ('position', [ ('x', float, 1),
('y', float, 1)]),
('color', [ ('r', float, 1),
('g', float, 1),
('b', float, 1)])])
print (Z)
输出结果
[((0., 0.), (0., 0., 0.)) ((0., 0.), (0., 0., 0.))
((0., 0.), (0., 0., 0.)) ((0., 0.), (0., 0., 0.))
((0., 0.), (0., 0., 0.)) ((0., 0.), (0., 0., 0.))
((0., 0.), (0., 0., 0.)) ((0., 0.), (0., 0., 0.))
((0., 0.), (0., 0., 0.)) ((0., 0.), (0., 0., 0.))]
52. 对一个表示坐标形状为(100,2)的随机向量，找到点与点的距离(★★☆)
- (提示: np.atleast_2d, T, np.sqrt)
#np.atleast_2d(*arys)
np.atleast_2d(3.0)
输出结果
array([[3.]])
x = np.arange(3.0)
np.atleast_2d(x)
输出结果
array([[0., 1., 2.]])
np.atleast_2d(1, [1, 2], [[1, 2]])
输出结果
[array([[1]]), array([[1, 2]]), array([[1, 2]])]
参考答案
#参考答案
Z = np.random.random((10,2))
X,Y = np.atleast_2d(Z[:,0], Z[:,1])
D = np.sqrt( (X-X.T)**2 + (Y-Y.T)**2)
print (D)
输出结果
[[0. 0.29070406 0.32362647 0.67865678 0.83707011 0.35972606
0.38789831 0.60723344 0.30546822 0.45373561]
[0.29070406 0. 0.3434642 0.73829603 1.03383445 0.08585908
0.64530381 0.8612648 0.31446227 0.73726371]
[0.32362647 0.3434642 0. 0.3984726 0.71940814 0.42539381
0.44012903 0.62091165 0.02902795 0.58394142]
[0.67865678 0.73829603 0.3984726 0. 0.48782049 0.81660089
0.54621201 0.59527603 0.42716232 0.70240421]
[0.83707011 1.03383445 0.71940814 0.48782049 0. 1.11969336
0.48296815 0.33042144 0.74442429 0.55519457]
[0.35972606 0.08585908 0.42539381 0.81660089 1.11969336 0.
0.72641834 0.94358782 0.39654632 0.81184913]
[0.38789831 0.64530381 0.44012903 0.54621201 0.48296815 0.72641834
0. 0.2195894 0.4499221 0.16062628]
[0.60723344 0.8612648 0.62091165 0.59527603 0.33042144 0.94358782
0.2195894 0. 0.63661968 0.23057319]
[0.30546822 0.31446227 0.02902795 0.42716232 0.74442429 0.39654632
0.4499221 0.63661968 0. 0.5896036 ]
[0.45373561 0.73726371 0.58394142 0.70240421 0.55519457 0.81184913
0.16062628 0.23057319 0.5896036 0. ]]
参考答案
# 方法2
# Much faster with scipy
import scipy
# Thanks Gavin Heverly-Coulson (#issue 1)
import scipy.spatial
D = scipy.spatial.distance.cdist(Z,Z)
print (D)
输出结果
[[0. 0.29070406 0.32362647 0.67865678 0.83707011 0.35972606
0.38789831 0.60723344 0.30546822 0.45373561]
[0.29070406 0. 0.3434642 0.73829603 1.03383445 0.08585908
0.64530381 0.8612648 0.31446227 0.73726371]
[0.32362647 0.3434642 0. 0.3984726 0.71940814 0.42539381
0.44012903 0.62091165 0.02902795 0.58394142]
[0.67865678 0.73829603 0.3984726 0. 0.48782049 0.81660089
0.54621201 0.59527603 0.42716232 0.70240421]
[0.83707011 1.03383445 0.71940814 0.48782049 0. 1.11969336
0.48296815 0.33042144 0.74442429 0.55519457]
[0.35972606 0.08585908 0.42539381 0.81660089 1.11969336 0.
0.72641834 0.94358782 0.39654632 0.81184913]
[0.38789831 0.64530381 0.44012903 0.54621201 0.48296815 0.72641834
0. 0.2195894 0.4499221 0.16062628]
[0.60723344 0.8612648 0.62091165 0.59527603 0.33042144 0.94358782
0.2195894 0. 0.63661968 0.23057319]
[0.30546822 0.31446227 0.02902795 0.42716232 0.74442429 0.39654632
0.4499221 0.63661968 0. 0.5896036 ]
[0.45373561 0.73726371 0.58394142 0.70240421 0.55519457 0.81184913
0.16062628 0.23057319 0.5896036 0. ]]
53. 如何将32位的浮点数(float)转换为对应的整数(integer)?
- (提示: astype(copy=False))
参考答案
#参考答案
Z = np.arange(10, dtype=np.float32)
print(Z,type(Z),Z.dtype.name)
Z = Z.astype(np.int32, copy=False)
print(Z,type(Z),Z.dtype.name)
输出结果
[0. 1. 2. 3. 4. 5. 6. 7. 8. 9.] <class 'numpy.ndarray'> float32
[0 1 2 3 4 5 6 7 8 9] <class 'numpy.ndarray'> int32
54. 如何读取以下文件? (★★☆)
- (提示: np.genfromtxt)
- https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.genfromtxt.html
参考答案
from io import StringIO
import numpy as np
#Comma delimited file with mixed dtype
s = StringIO(u"1,1.3,abcde")
data = np.genfromtxt(s, dtype=[('myint','i8'),('myfloat','f8'), ('mystring','S5')], delimiter=",")
data
输出结果
array((1, 1.3, b'abcde'),
dtype=[('myint', '), ('myfloat', '), ('mystring', 'S5')])
#Using dtype = None
_ = s.seek(0) # needed for StringIO example only
data = np.genfromtxt(s, dtype=None,names = ['myint','myfloat','mystring'], delimiter=",")
data
输出结果
array((b'11.3abcde',), dtype=[('myint', 'S9')])
#Specifying dtype and names
_ = s.seek(0)
data = np.genfromtxt(s, dtype="i8,f8,S5",names=['myint','myfloat','mystring'], delimiter=",")
data
输出结果
array((1, 1.3, b'abcde'),
dtype=[('myint', '), ('myfloat', '), ('mystring', 'S5')])
#An example with fixed-width columns
s = StringIO(u"11.3abcde")
data = np.genfromtxt(s, dtype=None, names=['intvar','fltvar','strvar'],delimiter=[1,3,5])
data
输出结果
array((1, 1.3, b'abcde'),
dtype=[('intvar', '), ('fltvar', '), ('strvar', 'S5')])
#An example to show comments
f = StringIO('''text,# of charshello world,11numpy,5''')
np.genfromtxt(f, dtype='S12,S12', delimiter=',')
输出结果
array((b'text', b''), dtype=[('f0', 'S12'), ('f1', 'S12')])
55. 对于numpy数组，enumerate的等价操作是什么？(★★☆)
- (提示: np.ndenumerate, np.ndindex)
#enumerate(iterable, start=0)
array50 = np.random.random(10)
print(array50)
enumerate(array50)
输出结果
[0.83578624 0.16400743 0.52875841 0.26802722 0.91601738 0.26684625
0.18977961 0.80121331 0.47645511 0.9602076 ]
<enumerate at 0x1ad29a6c8c0>
#np.ndenumerate(arr)
a = np.array([[1, 2], [3, 4]])
for index, x in np.ndenumerate(a):
print(index, x)
输出结果
(0, 0) 1
(0, 1) 2
(1, 0) 3
(1, 1) 4
#np.ndindex(*shape)
# dimensions as individual arguments
for index in np.ndindex(3, 2, 1):
print(index)
输出结果
(0, 0, 0)
(0, 1, 0)
(1, 0, 0)
(1, 1, 0)
(2, 0, 0)
(2, 1, 0)
#same dimensions - but in a tuple (3, 2, 1)
for index in np.ndindex((3, 2, 1)):
print(index)
(0, 0, 0)
(0, 1, 0)
(1, 0, 0)
(1, 1, 0)
(2, 0, 0)
(2, 1, 0)
参考答案
#参考答案
Z = np.arange(9).reshape(3,3)
for index, value in np.ndenumerate(Z):
print (index, value)
输出结果
(0, 0) 0
(0, 1) 1
(0, 2) 2
(1, 0) 3
(1, 1) 4
(1, 2) 5
(2, 0) 6
(2, 1) 7
(2, 2) 8
for index in np.ndindex(Z.shape):
print (index, Z[index])
输出结果
(0, 0) 0
(0, 1) 1
(0, 2) 2
(1, 0) 3
(1, 1) 4
(1, 2) 5
(2, 0) 6
(2, 1) 7
(2, 2) 8
56. 生成一个通用的二维Gaussian-like数组 (★★☆)
- (提示: np.meshgrid, np.exp)
参考答案
#参考答案
#np.meshgrid(*xi, copy=True, sparse=False, indexing='xy')
X, Y = np.meshgrid(np.linspace(-1,1,10), np.linspace(-1,1,10))
D = np.sqrt(X*X+Y*Y)
sigma, mu = 1.0, 0.0
G = np.exp(-( (D-mu)**2 / ( 2.0 * sigma**2 ) ) )
print (G)
输出结果
[[0.36787944 0.44822088 0.51979489 0.57375342 0.60279818 0.60279818
0.57375342 0.51979489 0.44822088 0.36787944]
[0.44822088 0.54610814 0.63331324 0.69905581 0.73444367 0.73444367
0.69905581 0.63331324 0.54610814 0.44822088]
[0.51979489 0.63331324 0.73444367 0.81068432 0.85172308 0.85172308
0.81068432 0.73444367 0.63331324 0.51979489]
[0.57375342 0.69905581 0.81068432 0.89483932 0.9401382 0.9401382
0.89483932 0.81068432 0.69905581 0.57375342]
[0.60279818 0.73444367 0.85172308 0.9401382 0.98773022 0.98773022
0.9401382 0.85172308 0.73444367 0.60279818]
[0.60279818 0.73444367 0.85172308 0.9401382 0.98773022 0.98773022
0.9401382 0.85172308 0.73444367 0.60279818]
[0.57375342 0.69905581 0.81068432 0.89483932 0.9401382 0.9401382
0.89483932 0.81068432 0.69905581 0.57375342]
[0.51979489 0.63331324 0.73444367 0.81068432 0.85172308 0.85172308
0.81068432 0.73444367 0.63331324 0.51979489]
[0.44822088 0.54610814 0.63331324 0.69905581 0.73444367 0.73444367
0.69905581 0.63331324 0.54610814 0.44822088]
[0.36787944 0.44822088 0.51979489 0.57375342 0.60279818 0.60279818
0.57375342 0.51979489 0.44822088 0.36787944]]
57. 对一个二维数组，如何在其内部随机放置p个元素? (★★☆)
- (提示: np.put, np.random.choice)
#np.put(a, ind, v, mode='raise')
a = np.arange(5)
np.put(a, [0, 2], [-44, -55])
a
输出结果
array([-44, 1, -55, 3, 4])
a = np.arange(5)
np.put(a, 22, -5, mode='clip')
a
输出结果
array([ 0, 1, 2, 3, -5])
#np.random.choice(a, size=None, replace=True, p=None)
#Generate a uniform random sample from np.arange(5) of size 3:
print(np.random.choice(5, 3))
#等价于np.random.randint(0,5,3)
print(np.random.randint(0,5,3))
输出结果
[4 0 1]
[4 3 0]
#Generate a non-uniform random sample from np.arange(5) of size 3:
np.random.choice(5, 3, p=[0.1, 0, 0.3, 0.6, 0])
array([3, 2, 3])
#Generate a uniform random sample from np.arange(5) of size 3 without
#replacement:
print(np.random.choice(5, 3, replace=False))
#等价于np.random.permutation(np.arange(5))[:3]
print(np.random.permutation(np.arange(5))[:3])
输出结果
[3 4 1]
[4 0 1]
#Generate a non-uniform random sample from np.arange(5) of size
#3 without replacement:
np.random.choice(5, 3, replace=False, p=[0.1, 0, 0.3, 0.6, 0])
输出结果
array([2, 3, 0])
#Any of the above can be repeated with an arbitrary array-like
#instead of just integers. For instance:
aa_milne_arr = ['pooh', 'rabbit', 'piglet', 'Christopher']
np.random.choice(aa_milne_arr, 5, p=[0.5, 0.1, 0.1, 0.3])
输出结果
array(['rabbit', 'Christopher', 'pooh', 'pooh', 'pooh'], dtype=')