【数模之数据分析-2】

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-构造一个全零的数模矩阵，并打印其占用的据分内存大小
- 3-打印一个函数的帮助文档，比如numpy.add
- 4-创建一个2~20的数模数组，并将其逆序
- 5-找到一个数组中不为0的据分索引
- - 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的数模矩阵，令其值都为1，据分并在最外层加上一圈0
- 8-构造一个shape为(6,数模 7, 8)的矩阵，并找到第100个元素的据分索引值
- 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、数模明天的据分
- 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-打印大数据的数模部分值
- - 相关程序运行如下：
- 16-找到一个数组中，最接近一个数的据分索引
- 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 K
- 25-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：
- 怎么改变自己的形象
- - 1、不要老是数模笑，特别是据分尴尬的赔笑
  - 2、说话自然一点，数模不要油腔滑调，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：
- - 持续更新中...

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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)

随机模块：

sj = np.random.rand(2, 6)               # 所有的值都是0从1print(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, 0.1fb = 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, 0.1z = np.random.normal(mu, sigma, 8)print(z)

文件读写：

在这里插入图片描述

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)

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)

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)

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：

0.97862378470736970.10837689046425514[[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)

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))

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 = 66print(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)