数据分析之Numpy
四则运算:相关程序运行如下：
随机模块：相关程序运行如下：
文件读写：相关程序运行如下：
数组保存：相关程序运行如下：
Numpy练习题：1-打印当前Numpy版本2-构造一个全零的矩阵，并打印其占用的内存大小3-打印一个函数的帮助文档，比如numpy.add4-创建一个2~20的数组，并将其逆序5-找到一个数组中不为0的索引相关程序运行如下：
6-随机构造一个3*3矩阵，并打印其中最大与最小值7-构造一个5*5的矩阵，令其值都为1，并在最外层加上一圈08-构造一个shape为(6, 7, 8)的矩阵，并找到第100个元素的索引值9-对一个5*5的矩阵做归一化操作10-找到两个数组中相同的值相关程序运行如下：
11-得到昨天、今天、明天的12-得到一个月中所有的天13-得到一个数的整数部分14-构造一个数组，让它不能被改变--只读15-打印大数据的部分值相关程序运行如下：
16-找到一个数组中，最接近一个数的索引17-32位float类型和32位int类型转换18-打印数组元素位置坐标与数值19-按照数组的某一列进行排序20-统计数组中每个数值出现的次数相关程序运行如下：
21-如何对一个四维数组的最后两维求和22-交换矩阵中的两行23-找到一个数组中最常出现的数字24-快速查找TOP K25-去除掉一个数组中所有元素都相同的数据相关程序运行如下：
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import numpy as np # 导入Numpy库
四则运算:
x = np.array([3, 5])
y = np.array([6, 2])
# 列相乘
xc = np.multiply(x, y)
print(xc)
# 列乘后相加
qxc = np.dot(x, y)
print(qxc)
print(x.shape)
print(y.shape)
# 一维与二维相乘
x = np.array([2, 3, 4])
y = np.array([
[1, 2, 3],
[2, 3, 4]
])
print(x * y)
# 辨别x和y2是否一样
y2 = np.array([2, 4, 9])
print(x == y2)
# 与
yy = np.logical_and(x, y2)
print(yy)
# 或
hh = np.logical_or(x, y2)
print(hh)
# 非
ff = np.logical_not(x, y2)
print(ff)
相关程序运行如下：
[18 10]
28
(2,)
(2,)
[[ 2 6 12]
[ 4 9 16]]
[ True False False]
[ True True True]
[ True True True]
[0 0 0]
print()
随机模块：
sj = np.random.rand(2, 6) # 所有的值都是0从1
print(sj)
yx = np.random.randint(8, size=(5, 3)) # 返回的是随机的整数，左闭右开
print(yx)
# 随机数
s = np.random.rand()
print(s)
# 随机样本
yb = np.random.random_sample()
print(yb)
# 区间内的随机数
qjs = np.random.randint(0, 10, 6)
print(qjs)
# 高斯分布
mu, sigma = 0, 0.1
fb = np.random. normal(mu, sigma, 8)
print(fb)
# 指定精度
zd = np.set_printoptions(precision=3)
print(fb)
# 洗牌
xps = np.arange(10)
np.random.shuffle(xps)
print(xps)
# 随机的种子
np.random.seed(100)
mu, sigma = 0, 0.1
z = np.random.normal(mu, sigma, 8)
print(z)
相关程序运行如下：
[[0.63334441 0.85097104 0.59019264 0.310542 0.90493224 0.64755 ]
[0.26229661 0.22710308 0.8936011 0.42837496 0.06484865 0.01209753]]
[[3 5 4]
[6 4 0]
[5 3 5]
[4 2 7]
[2 0 3]]
0.5814122350900927
0.37162507133518075
[1 0 1 4 6 2]
[ 0.04351687 -0.02026214 0.02332794 -0.09842403 0.06876269 0.02239188
-0.06339656 0.11343825]
[ 0.044 -0.02 0.023 -0.098 0.069 0.022 -0.063 0.113]
[6 2 4 3 7 0 1 5 8 9]
[-0.175 0.034 0.115 -0.025 0.098 0.051 0.022 -0.107]
print()
文件读写：
data = []
with open('np2.txt') as f:
for line in f:
fil = line.split()
f_data = [float(i) for i in fil]
data.append(f_data)
data = np.array(data)
print(data)
# 法二--简便
# delimiter 分隔符 | skiprows=1 去掉几行 | usecols = (0, 1, 4) 指定使用哪几列
data = np.loadtxt('np2.txt', delimiter=' ', skiprows=1)
print(data)
相关程序运行如下：
[[1. 2. 3. 4. 5. 6.]
[4. 5. 6. 7. 8. 9.]]
[4. 5. 6. 7. 8. 9.]
print()
数组保存：
xr = np.array([
[1, 2, 3],
[6, 7, 8]
])
np.savetxt('np2_1.txt', xr)
np.savetxt('np2_2.txt', xr, fmt='%d')
np.savetxt('np2_3.txt', xr, fmt='%d', delimiter=',')
np.savetxt('np2_4.txt', xr, fmt='%.2f', delimiter=' ')
# 读写array结构
dx_array = np.array([
[5, 2, 0],
[1, 4, 9]
])
np.save('np2_1.npy', dx_array)
dx = np.load('np2_1.npy')
print(dx)
相关程序运行如下：
[[5 2 0]
[1 4 9]]
Numpy练习题：
import numpy as np # 导入Numpy库
1-打印当前Numpy版本
print(np.__version__)
2-构造一个全零的矩阵，并打印其占用的内存大小
ojz = np.zeros((5, 5))
print(ojz)
print("%d bytes" % (ojz.size*ojz.itemsize))
3-打印一个函数的帮助文档，比如numpy.add
bz = help(np.info(np.add))
print(bz)
4-创建一个2~20的数组，并将其逆序
sz = np.arange(2, 21, 1)
print(sz)
sz = sz[::-1]
print(sz)
5-找到一个数组中不为0的索引
sy = np.nonzero([2, 53, 12, 43, 0, 0, 0, 23, 90])
print(sy)
相关程序运行如下：
1.22.3
[[0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0.]]
200 bytes
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
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.]])
Help on NoneType object:
class NoneType(object)
| Methods defined here:
|
| __bool__(self, /)
| self != 0
|
| __repr__(self, /)
| Return repr(self).
|
| ----------------------------------------------------------------------
| Static methods defined here:
|
| __new__(*args, **kwargs) from builtins.type
| Create and return a new object. See help(type) for accurate signature.
None
[ 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20]
[20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2]
(array([0, 1, 2, 3, 7, 8], dtype=int32),)
6-随机构造一个3*3矩阵，并打印其中最大与最小值
zz = np.random.random((3, 3))
print(zz.max())
print(zz.min())
7-构造一个5*5的矩阵，令其值都为1，并在最外层加上一圈0
jz = np.ones((5, 5))
jz = np.pad(jz, pad_width=1, mode='constant', constant_values=0)
print(jz)
print(help(np.pad)) # 帮助文档
8-构造一个shape为(6, 7, 8)的矩阵，并找到第100个元素的索引值
sy8 = np.unravel_index(100, (6, 7, 8))
print(sy8)
9-对一个5*5的矩阵做归一化操作
cz = np.random.random((5, 5))
cz_max = cz.max()
cz_min = cz.min()
cz = (cz-cz_min)/(cz_max-cz_min)
print(cz)
10-找到两个数组中相同的值
sz1 = np.random.randint(0, 20, 8)
sz2 = np.random.randint(0, 20, 8)
print(sz1)
print(sz2)
print(np.intersect1d(sz1, sz2))
相关程序运行如下：
0.9786237847073697
0.10837689046425514
[[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.]]
Help on function pad in module numpy:
pad(array, pad_width, mode='constant', **kwargs)
Pad an array.
Parameters
----------
array : array_like of rank N
The array to pad.
pad_width : {sequence, array_like, int}
Number of values padded to the edges of each axis.
((before_1, after_1), ... (before_N, after_N)) unique pad widths
for each axis.
((before, after),) yields same before and after pad for each axis.
(pad,) or int is a shortcut for before = after = pad width for all
axes.
mode : str or function, optional
One of the following string values or a user supplied function.
'constant' (default)
Pads with a constant value.
'edge'
Pads with the edge values of array.
'linear_ramp'
Pads with the linear ramp between end_value and the
array edge value.
'maximum'
Pads with the maximum value of all or part of the
vector along each axis.
'mean'
Pads with the mean value of all or part of the
vector along each axis.
'median'
Pads with the median value of all or part of the
vector along each axis.
'minimum'
Pads with the minimum value of all or part of the
vector along each axis.
'reflect'
Pads with the reflection of the vector mirrored on
the first and last values of the vector along each
axis.
'symmetric'
Pads with the reflection of the vector mirrored
along the edge of the array.
'wrap'
Pads with the wrap of the vector along the axis.
The first values are used to pad the end and the
end values are used to pad the beginning.
'empty'
Pads with undefined values.
.. versionadded:: 1.17
Padding function, see Notes.
stat_length : sequence or int, optional
Used in 'maximum', 'mean', 'median', and 'minimum'. Number of
values at edge of each axis used to calculate the statistic value.
((before_1, after_1), ... (before_N, after_N)) unique statistic
lengths for each axis.
((before, after),) yields same before and after statistic lengths
for each axis.
(stat_length,) or int is a shortcut for before = after = statistic
length for all axes.
Default is ``None``, to use the entire axis.
constant_values : sequence or scalar, optional
Used in 'constant'. The values to set the padded values for each
axis.
``((before_1, after_1), ... (before_N, after_N))`` unique pad constants
for each axis.
``((before, after),)`` yields same before and after constants for each
axis.
``(constant,)`` or ``constant`` is a shortcut for ``before = after = constant`` for
all axes.
Default is 0.
end_values : sequence or scalar, optional
Used in 'linear_ramp'. The values used for the ending value of the
linear_ramp and that will form the edge of the padded array.
``((before_1, after_1), ... (before_N, after_N))`` unique end values
for each axis.
``((before, after),)`` yields same before and after end values for each
axis.
``(constant,)`` or ``constant`` is a shortcut for ``before = after = constant`` for
all axes.
Default is 0.
reflect_type : {'even', 'odd'}, optional
Used in 'reflect', and 'symmetric'. The 'even' style is the
default with an unaltered reflection around the edge value. For
the 'odd' style, the extended part of the array is created by
subtracting the reflected values from two times the edge value.
Returns
-------
pad : ndarray
Padded array of rank equal to `array` with shape increased
according to `pad_width`.
Notes
-----
.. versionadded:: 1.7.0
For an array with rank greater than 1, some of the padding of later
axes is calculated from padding of previous axes. This is easiest to
think about with a rank 2 array where the corners of the padded array
are calculated by using padded values from the first axis.
The padding function, if used, should modify a rank 1 array in-place. It
has the following signature::
padding_func(vector, iaxis_pad_width, iaxis, kwargs)
where
vector : ndarray
A rank 1 array already padded with zeros. Padded values are
vector[:iaxis_pad_width[0]] and vector[-iaxis_pad_width[1]:].
iaxis_pad_width : tuple
A 2-tuple of ints, iaxis_pad_width[0] represents the number of
values padded at the beginning of vector where
iaxis_pad_width[1] represents the number of values padded at
the end of vector.
iaxis : int
The axis currently being calculated.
kwargs : dict
Any keyword arguments the function requires.
Examples
--------
>>> a = [1, 2, 3, 4, 5]
>>> np.pad(a, (2, 3), 'constant', constant_values=(4, 6))
array([4, 4, 1, ..., 6, 6, 6])
>>> np.pad(a, (2, 3), 'edge')
array([1, 1, 1, ..., 5, 5, 5])
>>> np.pad(a, (2, 3), 'linear_ramp', end_values=(5, -4))
array([ 5, 3, 1, 2, 3, 4, 5, 2, -1, -4])
>>> np.pad(a, (2,), 'maximum')
array([5, 5, 1, 2, 3, 4, 5, 5, 5])
>>> np.pad(a, (2,), 'mean')
array([3, 3, 1, 2, 3, 4, 5, 3, 3])
>>> np.pad(a, (2,), 'median')
array([3, 3, 1, 2, 3, 4, 5, 3, 3])
>>> a = [[1, 2], [3, 4]]
>>> np.pad(a, ((3, 2), (2, 3)), 'minimum')
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')
array([3, 2, 1, 2, 3, 4, 5, 4, 3, 2])
>>> np.pad(a, (2, 3), 'reflect', reflect_type='odd')
array([-1, 0, 1, 2, 3, 4, 5, 6, 7, 8])
>>> np.pad(a, (2, 3), 'symmetric')
array([2, 1, 1, 2, 3, 4, 5, 5, 4, 3])
>>> np.pad(a, (2, 3), 'symmetric', reflect_type='odd')
array([0, 1, 1, 2, 3, 4, 5, 5, 6, 7])
>>> np.pad(a, (2, 3), 'wrap')
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, 2, pad_with)
array([[10, 10, 10, 10, 10, 10, 10],
[10, 10, 10, 10, 10, 10, 10],
[10, 10, 0, 1, 2, 10, 10],
[10, 10, 3, 4, 5, 10, 10],
[10, 10, 10, 10, 10, 10, 10],
[10, 10, 10, 10, 10, 10, 10]])
>>> np.pad(a, 2, pad_with, padder=100)
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]])
None
(1, 5, 4)
[[0.275 0.437 0.958 0.833 0.339]
[0.174 0.376 0. 0.253 0.81 ]
[0.01 0.608 0.613 0.102 0.386]
[0.032 0.907 1. 0.056 0.907]
[0.586 0.756 0.64 0.591 0.015]]
[19 14 0 13 12 10 3 6]
[ 3 15 10 15 3 9 16 11]
[ 3 10]
11-得到昨天、今天、明天的
yes = np.datetime64('today', 'D') - np.timedelta64(1, 'D')
tod = np.datetime64('today', 'D')
tom = np.datetime64('today', 'D') + np.timedelta64(1, 'D')
print(f"昨天是{yes}")
print(f"今天是{tod}")
print(f"明天是{tom}")
12-得到一个月中所有的天
tt = np.arange('2022-08', '2022-09', dtype='datetime64[D]')
print(tt)
13-得到一个数的整数部分
xs = np.random.uniform(0, 20, 8)
print(xs)
print(np.floor(xs))
14-构造一个数组，让它不能被改变–只读
# zz = np.zeros(5)
# zz.flags.writeable = False
# zz[0] = 2
# print(zz[0])
15-打印大数据的部分值
np.set_printoptions(threshold=5)
bq = np.zeros((20, 20))
print(bq)
相关程序运行如下：
昨天是2022-08-29
今天是2022-08-30
明天是2022-08-31
['2022-08-01' '2022-08-02' '2022-08-03' '2022-08-04' '2022-08-05'
'2022-08-06' '2022-08-07' '2022-08-08' '2022-08-09' '2022-08-10'
'2022-08-11' '2022-08-12' '2022-08-13' '2022-08-14' '2022-08-15'
'2022-08-16' '2022-08-17' '2022-08-18' '2022-08-19' '2022-08-20'
'2022-08-21' '2022-08-22' '2022-08-23' '2022-08-24' '2022-08-25'
'2022-08-26' '2022-08-27' '2022-08-28' '2022-08-29' '2022-08-30'
'2022-08-31']
[16.229 12.806 12.496 2.91 11.404 1.302 6.268 4.341]
[16. 12. 12. 2. 11. 1. 6. 4.]
[[0. 0. 0. ... 0. 0. 0.]
[0. 0. 0. ... 0. 0. 0.]
[0. 0. 0. ... 0. 0. 0.]
...
[0. 0. 0. ... 0. 0. 0.]
[0. 0. 0. ... 0. 0. 0.]
[0. 0. 0. ... 0. 0. 0.]]
16-找到一个数组中，最接近一个数的索引
zd = np.arange(100)
vv = np.random.uniform(0, 100)
print(vv)
index = (np.abs(zd-vv)).argmin()
print(zd[index])
17-32位float类型和32位int类型转换
lx = np.arange(10, dtype=np.int32)
print(lx.dtype)
lx = lx.astype(np.float32)
print(lx.dtype)
18-打印数组元素位置坐标与数值
dy = np.arange(12).reshape(3, 4)
for i, val in np.ndenumerate(dy):
print(i, val)
19-按照数组的某一列进行排序
px = np.random.randint(0, 10, (3, 3))
print(px)
print(px[px[:, 0].argsort()])
20-统计数组中每个数值出现的次数
cs = np.array([3, 5, 23, 5, 2, 5, 6, 7, 2, 3, 5])
print(np.bincount(cs))
相关程序运行如下：
52.69503887473037
53
int32
float32
(0, 0) 0
(0, 1) 1
(0, 2) 2
(0, 3) 3
(1, 0) 4
(1, 1) 5
(1, 2) 6
(1, 3) 7
(2, 0) 8
(2, 1) 9
(2, 2) 10
(2, 3) 11
[[6 0 7]
[2 3 5]
[4 2 4]]
[[2 3 5]
[4 2 4]
[6 0 7]]
[0 0 2 ... 0 0 1]
21-如何对一个四维数组的最后两维求和
szzz = np.random.randint(0, 10, [4, 4, 4, 4])
qh = szzz.sum(axis=(-2, -1))
print(qh)
22-交换矩阵中的两行
sz = np.arange(16).reshape(4, 4)
sz[[0, 1]] = sz[[1, 0]]
print(sz)
23-找到一个数组中最常出现的数字
sz = np.random.randint(0, 20, 20)
print(np.bincount(sz).argmax())
24-快速查找TOP K
sz = np.arange(1000)
np.random.shuffle(sz)
x = 66
print(sz[np.argpartition(-sz, x)[:x]])
25-去除掉一个数组中所有元素都相同的数据
np.set_printoptions(threshold=6)
sz = np.random.randint(0, 5, (10, 3))
print(sz)
sj = np.all(sz[:, 1:] == sz[:, :-1], axis=1)
print(sj)
sj2 = np.any(sz[:, 1:] == sz[:, :-1], axis=1)
print(sj2)
相关程序运行如下：
[[81 81 71 54]
[78 60 38 63]
[63 81 74 80]
[67 58 69 76]]
[[ 4 5 6 7]
[ 0 1 2 3]
[ 8 9 10 11]
[12 13 14 15]]
3
[982 977 979 ... 948 952 934]
[[4 3 3]
[0 3 1]
[1 4 1]
...
[0 2 0]
[0 0 1]
[0 4 3]]
[False False False ... False False False]
[ True False False ... False True False]
————————————————————————————————————————————————————————
怎么改变自己的形象
1、不要老是笑，特别是尴尬的赔笑
2、说话自然一点，不要油腔滑调，语气平和稳重，语速不紧不慢。
3、大大方方拒绝别人，理由简短。过多的解释反而让你称为错误的异方，记住，拒绝别人不是你的错。
4、不要主动帮助别人。如果别人没主动请求你，不要主动提供帮助。帮上了它不会感激，帮不上反而会成为背锅的一方。
5、不要拿自己的糗事逗别人开心，不要说贬低自己抬高别人的话。说话宁可保守也不要夸奖，否则也是适得其反，过犹不及。
6、和别人在一起时，让别人找话题，不要非把自己当成活跃气氛的那一个。
7、小动作不要太多，容易体现出自己的不自信，行为举止要正常放松。
8、不要做别人情绪的垃圾桶，别人向你抱怨时请保持安静。记住，你不用讨好任何一个人。
9、比起被人喜欢，你的尊严和你的原则更加重要。他们是你作为一个独立完整的人格的验证，不容侵犯。
10、学会拒绝，做好自己分内的事，责任分工要明确。
每日一言：
成年人最好的自律是及时止损，人都有执念，但不能执迷不悟！
持续更新中…
点赞，你的认可是我创作的动力！ 收藏，你的青睐是我努力的方向！ 评论，你的意见是我进步的财富！ 关注，你的喜欢是我长久的坚持！
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