Python on Clarity

1 Array Creation Routines

Sun, 24 May 2026 00:00:00 +0000

title: "Array creation routines" date: 2026-05-24T13:51:21Z

Array creation routines

Ones and zeros

1import numpy as np

Create a new array of 2*2 integers, without initializing entries.

array([[0, 0],
       [0, 0]])

Let X = np.array([1,2,3], [4,5,6], np.int32). Create a new array with the same shape and type as X.

1X = np.array([[1,2,3], [4,5,6]], np.int32)

array([[1, 2, 3],
       [4, 5, 6]])

Create a 3-D array with ones on the diagonal and zeros elsewhere.

array([[ 1.,  0.,  0.],
       [ 0.,  1.,  0.],
       [ 0.,  0.,  1.]])

array([[ 1.,  0.,  0.],
       [ 0.,  1.,  0.],
       [ 0.,  0.,  1.]])

Create a new array of 3*2 float numbers, filled with ones.

1 Array Creation Routines Solution

Sun, 24 May 2026 00:00:00 +0000

title: "Array creation routines" date: 2026-05-24T13:51:24Z

Array creation routines

Ones and zeros

1import numpy as np

Create a new array of 2*2 integers, without initializing entries.

1np.empty([2,2], int)

array([[0, 0],
       [0, 0]])

Let X = np.array([1,2,3], [4,5,6], np.int32). Create a new array with the same shape and type as X.

1X = np.array([[1,2,3], [4,5,6]], np.int32)
2np.empty_like(X)

array([[1, 2, 3],
       [4, 5, 6]])

Create a 3-D array with ones on the diagonal and zeros elsewhere.

1np.eye(3)

array([[ 1.,  0.,  0.],
       [ 0.,  1.,  0.],
       [ 0.,  0.,  1.]])

1np.identity(3)

array([[ 1.,  0.,  0.],
       [ 0.,  1.,  0.],
       [ 0.,  0.,  1.]])

Create a new array of 3*2 float numbers, filled with ones.

1.numpy Beginner

Sun, 24 May 2026 00:00:00 +0000

title: "适合初学者快速入门的Numpy实战全集" date: 2026-05-24T13:49:57Z

适合初学者快速入门的Numpy实战全集

本文由光城同学整理

Numpy是一个用python实现的科学计算的扩展程序库，包括：

1、一个强大的N维数组对象Array；
2、比较成熟的（广播）函数库；
3、用于整合C/C++和Fortran代码的工具包；
4、实用的线性代数、傅里叶变换和随机数生成函数。numpy和稀疏矩阵运算包scipy配合使用更加方便。

NumPy（Numeric Python）提供了许多高级的数值编程工具，如：矩阵数据类型、矢量处理，以及精密的运算库。专为进行严格的数字处理而产生。多为很多大型金融公司使用，以及核心的科学计算组织如：Lawrence Livermore，NASA用其处理一些本来使用C++，Fortran或Matlab等所做的任务。

本文目录

1.Numpy基本操作

1.1 列表转为矩阵

1.2 维度

1.3 行数和列数()

1.4 元素个数

2.Numpy创建array

2.1 一维array创建

2.2 多维array创建

2.3 创建全零数组

2.4 创建全1数据

2.5 创建全空数组

2.6 创建连续数组

2.7 reshape操作

2.8 创建连续型数据

2.9 linspace的reshape操作

3.Numpy基本运算

3.1 一维矩阵运算

3.2 多维矩阵运算

3.3 基本计算

4.Numpy索引与切片

5.Numpy array合并

5.1 数组合并

5.2 数组转置为矩阵

5.3 多个矩阵合并

5.4 合并例子2

6.Numpy array分割

6.1 构造3行4列矩阵

6.2 等量分割

6.3 不等量分割

6.4 其他的分割方式

7.Numpy copy与 =

7.1 =赋值方式会带有关联性

7.2 copy()赋值方式没有关联性

8.广播机制

9.常用函数

1.Numpy基本操作

1.1 列表转为矩阵

1import numpy as np
2array = np.array([
3    [1,3,5],
4    [4,6,9]
5])
6
7print(array)

[[1 3 5]
 [4 6 9]]

1.2 维度

1print('number of dim:', array.ndim)

number of dim: 2

1.3 行数和列数()

1print('shape:',array.shape)

shape: (2, 3)

1.4 元素个数

1print('size:',array.size)

size: 6

2.Numpy创建array

2.1 一维array创建

1import numpy as np
2# 一维array
3a = np.array([2,23,4], dtype=np.int32) # np.int默认为int32
4print(a)
5print(a.dtype)

[ 2 23  4]
int32

2.2 多维array创建

1# 多维array
2a = np.array([[2,3,4],
3              [3,4,5]])
4print(a) # 生成2行3列的矩阵

[[2 3 4]
 [3 4 5]]

2.3 创建全零数组

1a = np.zeros((3,4))
2print(a) # 生成3行4列的全零矩阵

[[0. 0. 0. 0.]
 [0. 0. 0. 0.]
 [0. 0. 0. 0.]]

2.4 创建全1数据

1# 创建全一数据，同时指定数据类型
2a = np.ones((3,4),dtype=np.int)
3print(a)

[[1 1 1 1]
 [1 1 1 1]
 [1 1 1 1]]

2.5 创建全空数组

1# 创建全空数组，其实每个值都是接近于零的数
2a = np.empty((3,4))
3print(a)

[[0. 0. 0. 0.]
 [0. 0. 0. 0.]
 [0. 0. 0. 0.]]

2.6 创建连续数组

1# 创建连续数组
2a = np.arange(10,21,2) # 10-20的数据，步长为2
3print(a)

[10 12 14 16 18 20]

2.7 reshape操作

1# 使用reshape改变上述数据的形状
2b = a.reshape((2,3))
3print(b)

[[10 12 14]
 [16 18 20]]

2.8 创建连续型数据

1# 创建线段型数据
2a = np.linspace(1,10,20) # 开始端1，结束端10，且分割成20个数据，生成线段
3print(a)

[ 1.          1.47368421  1.94736842  2.42105263  2.89473684  3.36842105
  3.84210526  4.31578947  4.78947368  5.26315789  5.73684211  6.21052632
  6.68421053  7.15789474  7.63157895  8.10526316  8.57894737  9.05263158
  9.52631579 10.        ]

2.9 linspace的reshape操作

1# 同时也可以reshape
2b = a.reshape((5,4))
3print(b)

[[ 1.          1.47368421  1.94736842  2.42105263]
 [ 2.89473684  3.36842105  3.84210526  4.31578947]
 [ 4.78947368  5.26315789  5.73684211  6.21052632]
 [ 6.68421053  7.15789474  7.63157895  8.10526316]
 [ 8.57894737  9.05263158  9.52631579 10.        ]]

3.Numpy基本运算

3.1 一维矩阵运算

1import numpy as np
2# 一维矩阵运算
3a = np.array([10,20,30,40])
4b = np.arange(4)
5print(a,b)

[10 20 30 40] [0 1 2 3]

1c = a - b
2print(c)

[10 19 28 37]

1print(a*b) # 若用a.dot(b),则为各维之和

[  0  20  60 120]

1# 在Numpy中，想要求出矩阵中各个元素的乘方需要依赖双星符号 **，以二次方举例，即：
2c = b**2
3print(c)

[0 1 4 9]

1# Numpy中具有很多的数学函数工具
2c = np.sin(a)
3print(c)

[-0.54402111  0.91294525 -0.98803162  0.74511316]

1print(b<2)

[ True  True False False]

1a = np.array([1,1,4,3])
2b = np.arange(4)
3print(a==b)

[False  True False  True]

3.2 多维矩阵运算

1a = np.array([[1,1],[0,1]])
2b = np.arange(4).reshape((2,2))
3print(a)

[[1 1]
 [0 1]]

1print(b)

[[0 1]
 [2 3]]

1# 多维度矩阵乘法
2# 第一种乘法方式:
3c = a.dot(b)
4print(c)

[[2 4]
 [2 3]]

1# 第二种乘法:
2c = np.dot(a,b)
3print(c)

[[2 4]
 [2 3]]

1# 多维矩阵乘法不能直接使用'*'号
2
3a = np.random.random((2,4))
4
5print(np.sum(a))

3.825517216750851

1print(np.min(a))

0.09623355767721398

1print(np.max(a))

0.7420428188342583

1print("a=",a)

a= [[0.48634962 0.74204282 0.09623356 0.69074812]
 [0.60218881 0.52734181 0.41434585 0.26626662]]

如果你需要对行或者列进行查找运算，

1.scipy Intro

Sun, 24 May 2026 00:00:00 +0000

title: "SciPy-数值计算库" date: 2026-05-24T14:11:00Z

1import numpy as np
2import pylab as pl

1import matplotlib as mpl
2mpl.rcParams['font.sans-serif'] = ['SimHei']

SciPy-数值计算库

1import scipy
2scipy.__version__

'1.0.0'

常数和特殊函数

1from scipy import constants as C
2print (C.c) # 真空中的光速
3print (C.h) # 普朗克常数

299792458.0
6.62607004e-34

1C.physical_constants["electron mass"]

(9.10938356e-31, 'kg', 1.1e-38)

1# 1英里等于多少米, 1英寸等于多少米, 1克等于多少千克, 1磅等于多少千克
2print(C.mile)
3print(C.inch)
4print(C.gram)
5print(C.pound)

1609.3439999999998
0.0254
0.001
0.45359236999999997

1import scipy.special as S

1print (1 + 1e-20)
2print (np.log(1+1e-20))
3print (S.log1p(1e-20))

1.0
0.0
1e-20

1m = np.linspace(0.1, 0.9, 4)
2u = np.linspace(-10, 10, 200)
3results = S.ellipj(u[:, None], m[None, :])
4
5print([y.shape for y in results])

[(200, 4), (200, 4), (200, 4), (200, 4)]

1#%figonly=使用广播计算得到的`ellipj()`返回值
2fig, axes = pl.subplots(2, 2, figsize=(12, 4))
3labels = ["$sn$", "$cn$", "$dn$", "$\phi$"]
4for ax, y, label in zip(axes.ravel(), results, labels):
5    ax.plot(u, y)
6    ax.set_ylabel(label)
7    ax.margins(0, 0.1)
8    
9axes[1, 1].legend(["$m={:g}$".format(m_) for m_ in m], loc="best", ncol=2);

10 Minutes To Pandas

Sun, 24 May 2026 00:00:00 +0000

title: "十分钟入门pandas" date: 2026-05-24T14:08:58Z

十分钟入门pandas

1#coding:utf8
2%matplotlib inline

这个一篇针对pandas新手的简短入门，想要了解更多复杂的内容，参阅Cookbook

通常，我们首先要导入以下几个库：

1import pandas as pd
2import numpy as np
3import matplotlib.pyplot as plt

创建对象

通过传递一个list来创建Series，pandas会默认创建整型索引：

1s = pd.Series([1,3,5,np.nan,6,8])
2s

0    1.0
1    3.0
2    5.0
3    NaN
4    6.0
5    8.0
dtype: float64

通过传递一个numpy array，日期索引以及列标签来创建一个DataFrame：

1dates = pd.date_range('20130101', periods=6)
2dates

DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04',
               '2013-01-05', '2013-01-06'],
              dtype='datetime64[ns]', freq='D')

1df = pd.DataFrame(np.random.randn(6,4), index=dates, columns=list('ABCD'))
2df

10 Random Sampling

Sun, 24 May 2026 00:00:00 +0000

title: "Random Sampling" date: 2026-05-24T13:50:59Z

Random Sampling

1import numpy as np

1np.__version__

'1.11.2'

1__author__ = 'kyubyong. longinglove@nate.com'

Simple random data

Q1. Create an array of shape (3, 2) and populate it with random samples from a uniform distribution over [0, 1).

array([[ 0.13879034,  0.71300174],
       [ 0.08121322,  0.00393554],
       [ 0.02349471,  0.56677474]])

Q2. Create an array of shape (1000, 1000) and populate it with random samples from a standard normal distribution. And verify that the mean and standard deviation is close enough to 0 and 1 repectively.

10 Random Sampling Solutions

Sun, 24 May 2026 00:00:00 +0000

title: "Random Sampling" date: 2026-05-24T13:51:01Z

Random Sampling

1import numpy as np

1np.__version__

'1.11.2'

1__author__ = 'kyubyong. longinglove@nate.com'

Simple random data

Q1. Create an array of shape (3, 2) and populate it with random samples from a uniform distribution over [0, 1).

1np.random.rand(3, 2) 
2# Or np.random.random((3,2))

array([[ 0.13879034,  0.71300174],
       [ 0.08121322,  0.00393554],
       [ 0.02349471,  0.56677474]])

10.scipy Spatial

Sun, 24 May 2026 00:00:00 +0000

title: "10.scipy-spatial" date: 2026-05-24T14:11:03Z

1import numpy as np
2import pylab as pl
3from scipy import spatial

1import matplotlib as mpl
2mpl.rcParams['font.sans-serif'] = ['SimHei']

空间算法库-spatial

计算最近旁点

1x = np.sort(np.random.rand(100))
2idx = np.searchsorted(x, 0.5)
3print (x[idx], x[idx - 1]) #距离0.5最近的数是这两个数中的一个

0.5244435681885733 0.4982156075770372

1from scipy import spatial
2np.random.seed(42)
3N = 100
4points = np.random.uniform(-1, 1, (N, 2))
5kd = spatial.cKDTree(points)
6
7targets = np.array([(0, 0), (0.5, 0.5), (-0.5, 0.5), (0.5, -0.5), (-0.5, -0.5)])
8dist, idx = kd.query(targets, 3)

1r = 0.2
2idx2 = kd.query_ball_point(targets, r)
3idx2

array([list([48]), list([37, 78]), list([22, 79, 92]), list([6, 35, 58]),
       list([7, 42, 55, 83])], dtype=object)

1idx3 = kd.query_pairs(0.1) - kd.query_pairs(0.08)
2idx3

{(1, 46),
 (3, 21),
 (3, 82),
 (3, 95),
 (5, 16),
 (9, 30),
 (10, 87),
 (11, 42),
 (11, 97),
 (18, 41),
 (29, 74),
 (32, 51),
 (37, 78),
 (39, 61),
 (41, 61),
 (50, 84),
 (55, 83),
 (73, 81)}

 1#%figonly=用cKDTree寻找近旁点
 2x, y = points.T
 3colors = "r", "b", "g", "y", "k"
 4
 5fig, (ax1, ax2, ax3) = pl.subplots(1, 3, figsize=(12, 4))
 6
 7for ax in ax1, ax2, ax3:
 8    ax.set_aspect("equal")
 9    ax.plot(x, y, "o", markersize=4)
10    
11for ax in ax1, ax2:
12    for i in range(len(targets)):
13        c = colors[i]
14        tx, ty = targets[i]
15        ax.plot([tx], [ty], "*", markersize=10, color=c)
16
17for i in range(len(targets)):
18    nx, ny = points[idx[i]].T
19    ax1.plot(nx, ny, "o", markersize=10, markerfacecolor="None", 
20             markeredgecolor=colors[i], markeredgewidth=1)
21    
22    nx, ny = points[idx2[i]].T
23    ax2.plot(nx, ny, "o", markersize=10, markerfacecolor="None", 
24             markeredgecolor=colors[i], markeredgewidth=1)
25    
26    ax2.add_artist(pl.Circle(targets[i], r, fill=None, linestyle="dashed"))
27    
28for pidx1, pidx2 in idx3:
29    sx, sy = points[pidx1]
30    ex, ey = points[pidx2]
31    ax3.plot([sx, ex], [sy, ey], "r", linewidth=2, alpha=0.6)
32    
33ax1.set_xlabel(u"搜索最近的3个近旁点")
34ax2.set_xlabel(u"搜索距离在0.2之内的所有近旁点")
35ax3.set_xlabel(u"搜索所有距离在0.08到0.1之间的点对");

100 Numpy Exercises

Sun, 24 May 2026 00:00:00 +0000

title: "Alternative solution, in Python 3.5 and above" date: 2026-05-24T13:50:37Z

100 numpy exercises

This is a collection of exercises that have been collected in the numpy mailing list, on stack overflow and in the numpy documentation. The goal of this collection is to offer a quick reference for both old and new users but also to provide a set of exercises for those who teach.

If you find an error or think you've a better way to solve some of them, feel free to open an issue at https://github.com/rougier/numpy-100

100 Numpy Exercises No Solution

Sun, 24 May 2026 00:00:00 +0000

title: "100 numpy exercises" date: 2026-05-24T13:50:39Z

100 numpy exercises

If you find an error or think you've a better way to solve some of them, feel free to open an issue at https://github.com/rougier/numpy-100

100 Numpy Exercises With Hint

Sun, 24 May 2026 00:00:00 +0000

title: "100 numpy exercises with hint" date: 2026-05-24T13:50:42Z

100 numpy exercises with hint

If you find an error or think you've a better way to solve some of them, feel free to open an issue at https://github.com/rougier/numpy-100

11 Set Routines

Sun, 24 May 2026 00:00:00 +0000

title: "Set routines" date: 2026-05-24T13:51:04Z

Set routines

1import numpy as np

1np.__version__

'1.11.2'

1author = 'kyubyong. longinglove@nate.com'

Making proper sets

Q1. Get unique elements and reconstruction indices from x. And reconstruct x.

1x = np.array([1, 2, 6, 4, 2, 3, 2])

unique elements = [1 2 3 4 6]
reconstruction indices = [0 1 4 3 1 2 1]
reconstructed = [1 2 6 4 2 3 2]

Boolean operations

Q2. Create a boolean array of the same shape as x. If each element of x is present in y, the result will be True, otherwise False.

11 Set Routines Solutions

Sun, 24 May 2026 00:00:00 +0000

title: "Set routines" date: 2026-05-24T13:51:07Z

Set routines

1import numpy as np

1np.__version__

'1.11.2'

1author = 'kyubyong. longinglove@nate.com'

Making proper sets

Q1. Get unique elements and reconstruction indices from x. And reconstruct x.

1x = np.array([1, 2, 6, 4, 2, 3, 2])
2out, indices = np.unique(x, return_inverse=True)
3print "unique elements =", out
4print "reconstruction indices =", indices
5print "reconstructed =", out[indices]

unique elements = [1 2 3 4 6]
reconstruction indices = [0 1 4 3 1 2 1]
reconstructed = [1 2 6 4 2 3 2]

Boolean operations

Q2. Create a boolean array of the same shape as x. If each element of x is present in y, the result will be True, otherwise False.

12 Sorting Searching And Counting

Sun, 24 May 2026 00:00:00 +0000

title: "Soring, searching, and counting" date: 2026-05-24T13:51:10Z

Soring, searching, and counting

1import numpy as np

1np.__version__

'1.11.2'

1author = 'kyubyong. longinglove@nate.com'

Sorting

Q1. Sort x along the second axis.

1x = np.array([[1,4],[3,1]])

[[1 4]
 [1 3]]

Q2. Sort pairs of surnames and first names and return their indices. (first by surname, then by name).

1surnames =    ('Hertz',    'Galilei', 'Hertz')
2first_names = ('Heinrich', 'Galileo', 'Gustav')

[1 2 0]

Q3. Get the indices that would sort x along the second axis.

1x = np.array([[1,4],[3,1]])

[[0 1]
 [1 0]]

Q4. Create an array such that its fifth element would be the same as the element of sorted x, and it divide other elements by their value.

12 Sorting Searching And Counting Solutions

Sun, 24 May 2026 00:00:00 +0000

title: "Soring, searching, and counting" date: 2026-05-24T13:51:13Z

Soring, searching, and counting

1import numpy as np

1np.__version__

'1.11.2'

1author = 'kyubyong. longinglove@nate.com'

Sorting

Q1. Sort x along the second axis.

1x = np.array([[1,4],[3,1]])
2out = np.sort(x, axis=1)
3x.sort(axis=1)
4assert np.array_equal(out, x)
5print out

[[1 4]
 [1 3]]

Q2. Sort pairs of surnames and first names and return their indices. (first by surname, then by name).

1surnames =    ('Hertz',    'Galilei', 'Hertz')
2first_names = ('Heinrich', 'Galileo', 'Gustav')
3print np.lexsort((first_names, surnames))

[1 2 0]

Q3. Get the indices that would sort x along the second axis.

13 Statistics

Sun, 24 May 2026 00:00:00 +0000

title: "Statistics" date: 2026-05-24T13:51:15Z

Statistics

1__author__ = "kyubyong. kbpark.linguist@gmail.com"

1import numpy as np

1np.__version__

'1.11.3'

Order statistics

Q1. Return the minimum value of x along the second axis.

1x = np.arange(4).reshape((2, 2))
2print("x=\n", x)

x=
 [[0 1]
 [2 3]]
ans=
 [0 2]

Q2. Return the maximum value of x along the second axis. Reduce the second axis to the dimension with size one.

1x = np.arange(4).reshape((2, 2))
2print("x=\n", x)

x=
 [[0 1]
 [2 3]]
ans=
 [[1]
 [3]]

Q3. Calcuate the difference between the maximum and the minimum of x along the second axis.

13 Statistics Solutions

Sun, 24 May 2026 00:00:00 +0000

title: "Statistics" date: 2026-05-24T13:51:18Z

Statistics

1__author__ = "kyubyong. kbpark.linguist@gmail.com"

1import numpy as np

1np.__version__

'1.11.3'

Order statistics

Q1. Return the minimum value of x along the second axis.

1x = np.arange(4).reshape((2, 2))
2print("x=\n", x)
3print("ans=\n", np.amin(x, 1))

x=
 [[0 1]
 [2 3]]
ans=
 [0 2]

Q2. Return the maximum value of x along the second axis. Reduce the second axis to the dimension with size one.

1x = np.arange(4).reshape((2, 2))
2print("x=\n", x)
3print("ans=\n", np.amax(x, 1, keepdims=True))

x=
 [[0 1]
 [2 3]]
ans=
 [[1]
 [3]]

Q3. Calcuate the difference between the maximum and the minimum of x along the second axis.

2 Array Manipulation Routines

Sun, 24 May 2026 00:00:00 +0000

title: "Array manipulation routines" date: 2026-05-24T13:51:27Z

Array manipulation routines

1import numpy as np

1np.__version__

'1.11.2'

Q1. Let x be a ndarray [10, 10, 3] with all elements set to one. Reshape x so that the size of the second dimension equals 150.

[[ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.
   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.
   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.
   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.
   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.
   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.
   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.
   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.
   1.  1.  1.  1.  1.  1.]
 [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.
   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.
   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.
   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.
   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.
   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.
   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.
   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.
   1.  1.  1.  1.  1.  1.]]

Q2. Let x be array [[1, 2, 3], [4, 5, 6]]. Convert it to [1 4 2 5 3 6].

2 Array Manipulation Routines Solutions

Sun, 24 May 2026 00:00:00 +0000

title: "Array manipulation routines" date: 2026-05-24T13:51:30Z

Array manipulation routines

1import numpy as np

1np.__version__

'1.11.2'

Q1. Let x be a ndarray [10, 10, 3] with all elements set to one. Reshape x so that the size of the second dimension equals 150.

1x = np.ones([10, 10, 3])
2out = np.reshape(x, [-1, 150])
3print out
4assert np.allclose(out, np.ones([10, 10, 3]).reshape([-1, 150]))

[[ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.
   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.
   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.
   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.
   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.
   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.
   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.
   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.
   1.  1.  1.  1.  1.  1.]
 [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.
   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.
   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.
   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.
   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.
   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.
   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.
   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.
   1.  1.  1.  1.  1.  1.]]

Q2. Let x be array [[1, 2, 3], [4, 5, 6]]. Convert it to [1 4 2 5 3 6].

2.numpy

Sun, 24 May 2026 00:00:00 +0000

title: "1.Numpy简易入门" date: 2026-05-24T13:49:59Z

1.Numpy简易入门

1.1 认识NumPy数组对象

Numpy是一个用python实现的科学计算的扩展程序库，包括：

1、一个强大的N维数组对象Array；
2、比较成熟的（广播）函数库；
3、用于整合C/C++和Fortran代码的工具包；
4、实用的线性代数、傅里叶变换和随机数生成函数。numpy和稀疏矩阵运算包scipy配合使用更加方便。

1import numpy as np                     # 导入NumPy工具包

1data = np.arange(12).reshape(3, 4)  # 创建一个3行4列的数组

1data

array([[ 0,  1,  2,  3],
       [ 4,  5,  6,  7],
       [ 8,  9, 10, 11]])

1type(data)

numpy.ndarray

1data.ndim         # 数组维度的个数，输出结果2，表示二维数组

1data.shape        # 数组的维度，输出结果（3，4），表示3行4列

(3, 4)

1data.size         # 数组元素的个数，输出结果12，表示总共有12个元素

1data.dtype # 数组元素的类型，输出结果dtype('int64'),表示元素类型都是int64

dtype('int32')

1.2 创建NumPy数组

1import numpy as np

1data1 = np.array([1, 2, 3])                   # 创建一个一维数组

1data1

array([1, 2, 3])

1data2 = np.array([[1, 2, 3], [4, 5, 6]])   # 创建一个二维数组

1data2

array([[1, 2, 3],
       [4, 5, 6]])

1np.zeros((3, 4))#创建一个全0数组

array([[0., 0., 0., 0.],
       [0., 0., 0., 0.],
       [0., 0., 0., 0.]])

1np.ones((3, 4))#创建全一数组

array([[1., 1., 1., 1.],
       [1., 1., 1., 1.],
       [1., 1., 1., 1.]])

1np.empty((5, 2))# 创建全空数组，其实每个值都是接近于零的数

array([[ 6.95312756e-310,  2.12199579e-314],
       [ 2.12199579e-314,  4.94065646e-324],
       [ 0.00000000e+000, -7.06252554e-311],
       [ 0.00000000e+000, -8.12021073e-313],
       [ 1.29923372e-311,  2.07507571e-322]])

1np.arange(1, 20, 5)

array([ 1,  6, 11, 16])

1np.array([1, 2, 3, 4], float)

array([1., 2., 3., 4.])

1np.ones((2, 3), dtype='float64')

array([[1., 1., 1.],
       [1., 1., 1.]])

1.3 ndarry对象的数据类型

1.3.1 查看数据类型

1data_one = np.array([[1, 2, 3], [4, 5, 6]])

1data_one.dtype.name

'int32'

1.3.2 转换数据类型

1data = np.array([[1, 2, 3], [4, 5, 6]])

1data.dtype

dtype('int32')

1float_data = data.astype(np.float64) # 数据类型转换为float64

1float_data.dtype

dtype('float64')

1float_data = np.array([1.2, 2.3, 3.5])

1float_data

array([1.2, 2.3, 3.5])

1int_data = float_data.astype(np.int64) # 数据类型转换为int64

1int_data

array([1, 2, 3], dtype=int64)

1str_data = np.array(['1', '2', '3'])

1int_data = str_data.astype(np.int64)

1int_data

array([1, 2, 3], dtype=int64)

1.4 数组运算

1.4.1向量化运算

1import numpy as np

1data1 = np.array([[1, 2, 3], [4, 5, 6]])

1data2 = np.array([[1, 2, 3], [4, 5, 6]])

1data1 + data2        # 数组相加

array([[ 2,  4,  6],
       [ 8, 10, 12]])

1data1 * data2        # 数组相乘

array([[ 1,  4,  9],
       [16, 25, 36]])

1data1 - data2        # 数组相减

array([[0, 0, 0],
       [0, 0, 0]])

1data1 / data2       # 数组相除

array([[1., 1., 1.],
       [1., 1., 1.]])

1.4.2 数组广播

numpy数组间的基础运算是一对一，也就是a.shape==b.shape，但是当两者不一样的时候，就会自动触发广播机制，如下例子：

2.scipy Optimize

Sun, 24 May 2026 00:00:00 +0000

title: "f计算方程组的误差，[1,1,1]是未知数的初始值" date: 2026-05-24T14:11:06Z

1import pylab as pl
2import numpy as np

1import matplotlib as mpl
2mpl.rcParams['font.sans-serif'] = ['SimHei']

拟合与优化-optimize

非线性方程组求解

 1from math import sin, cos
 2from scipy import optimize
 3
 4def f(x): #❶
 5    x0, x1, x2 = x.tolist() #❷
 6    return [
 7        5*x1+3,
 8        4*x0*x0 - 2*sin(x1*x2),
 9        x1*x2 - 1.5
10    ]
11
12# f计算方程组的误差，[1,1,1]是未知数的初始值
13result = optimize.fsolve(f, [1,1,1]) #❸
14print (result)
15print (f(result))

[-0.70622057 -0.6        -2.5       ]
[0.0, -9.126033262418787e-14, 5.329070518200751e-15]

 1def j(x):  #❶
 2    x0, x1, x2 = x.tolist()
 3    return [[0, 5, 0],
 4            [8 * x0, -2 * x2 * cos(x1 * x2), -2 * x1 * cos(x1 * x2)],
 5            [0, x2, x1]]
 6
 7
 8result = optimize.fsolve(f, [1, 1, 1], fprime=j)  #❷
 9print(result)
10print(f(result))

[-0.70622057 -0.6        -2.5       ]
[0.0, -9.126033262418787e-14, 5.329070518200751e-15]

最小二乘拟合

 1import numpy as np
 2from scipy import optimize
 3
 4X = np.array([ 8.19,  2.72,  6.39,  8.71,  4.7 ,  2.66,  3.78])
 5Y = np.array([ 7.01,  2.78,  6.47,  6.71,  4.1 ,  4.23,  4.05])
 6
 7def residuals(p): #❶
 8    "计算以p为参数的直线和原始数据之间的误差"
 9    k, b = p
10    return Y - (k*X + b)
11
12# leastsq使得residuals()的输出数组的平方和最小，参数的初始值为[1,0]
13r = optimize.leastsq(residuals, [1, 0]) #❷
14k, b = r[0]
15print ("k =",k, "b =",b)

k = 0.6134953491930442 b = 1.794092543259387

 1#%figonly=最小化正方形面积之和（左），误差曲面（右）
 2scale_k = 1.0
 3scale_b = 10.0
 4scale_error = 1000.0
 5
 6def S(k, b):
 7    "计算直线y=k*x+b和原始数据X、Y的误差的平方和"
 8    error = np.zeros(k.shape)
 9    for x, y in zip(X, Y):
10        error += (y - (k * x + b)) ** 2
11    return error
12
13ks, bs = np.mgrid[k - scale_k:k + scale_k:40j, b - scale_b:b + scale_b:40j]
14error = S(ks, bs) / scale_error
15
16from mpl_toolkits.mplot3d import Axes3D
17from matplotlib.patches import Rectangle
18
19fig = pl.figure(figsize=(12, 5))
20
21ax1 = pl.subplot(121)
22
23ax1.plot(X, Y, "o")
24X0 = np.linspace(2, 10, 3)
25Y0 = k*X0 + b
26ax1.plot(X0, Y0)
27
28for x, y in zip(X, Y):
29    y2 = k*x+b
30    rect = Rectangle((x,y), abs(y-y2), y2-y, facecolor="red", alpha=0.2)
31    ax1.add_patch(rect)
32
33ax1.set_aspect("equal")
34
35
36ax2 = fig.add_subplot(122, projection='3d')
37
38ax2.plot_surface(
39    ks, bs / scale_b, error, rstride=3, cstride=3, cmap="jet", alpha=0.5)
40ax2.scatter([k], [b / scale_b], [S(k, b) / scale_error], c="r", s=20)
41ax2.set_xlabel("$k$")
42ax2.set_ylabel("$b$")
43ax2.set_zlabel("$error$");

3 String Operations

Sun, 24 May 2026 00:00:00 +0000

title: "3_String_operations" date: 2026-05-24T13:51:32Z

String operations

1from __future__ import print_function
2import numpy as np

1author = "kyubyong. https://github.com/Kyubyong/numpy_exercises"

1np.__version__

'1.11.3'

Q1. Concatenate x1 and x2.

1x1 = np.array(['Hello', 'Say'], dtype=np.str)
2x2 = np.array([' world', ' something'], dtype=np.str)

['Hello world' 'Say something']

Q2. Repeat x three time element-wise.

1x = np.array(['Hello ', 'Say '], dtype=np.str)

['Hello Hello Hello ' 'Say Say Say ']

Q3-1. Capitalize the first letter of x element-wise.
Q3-2. Lowercase x element-wise.
Q3-3. Uppercase x element-wise.
Q3-4. Swapcase x element-wise.
Q3-5. Title-case x element-wise.

3 String Operations Solutions

Sun, 24 May 2026 00:00:00 +0000

title: "compare" date: 2026-05-24T13:51:35Z

String operations

1from __future__ import print_function
2import numpy as np

1author = "kyubyong. https://github.com/Kyubyong/numpy_exercises"

1np.__version__

'1.11.3'

Q1. Concatenate x1 and x2.

1x1 = np.array(['Hello', 'Say'], dtype=np.str)
2x2 = np.array([' world', ' something'], dtype=np.str)
3out = np.char.add(x1, x2)
4print(out)

['Hello world' 'Say something']

Q2. Repeat x three time element-wise.

1x = np.array(['Hello ', 'Say '], dtype=np.str)
2out = np.char.multiply(x, 3)
3print(out)

['Hello Hello Hello ' 'Say Say Say ']

Q3-1. Capitalize the first letter of x element-wise.
Q3-2. Lowercase x element-wise.
Q3-3. Uppercase x element-wise.
Q3-4. Swapcase x element-wise.
Q3-5. Title-case x element-wise.

3.scipy Linalg

Sun, 24 May 2026 00:00:00 +0000

title: "3.scipy-linalg" date: 2026-05-24T14:11:09Z

1import pylab as pl
2import numpy as np
3from scipy import linalg

1import matplotlib as mpl
2mpl.rcParams['font.sans-serif'] = ['SimHei']

线性代数-linalg

解线性方程组

 1import numpy as np
 2from scipy import linalg
 3
 4m, n = 500, 50
 5A = np.random.rand(m, m)
 6B = np.random.rand(m, n)
 7X1 = linalg.solve(A, B)
 8X2 = np.dot(linalg.inv(A), B)
 9print (np.allclose(X1, X2))
10%timeit linalg.solve(A, B)
11%timeit np.dot(linalg.inv(A), B)

True
5.38 ms ± 120 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
8.14 ms ± 994 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

1luf = linalg.lu_factor(A)
2X3 = linalg.lu_solve(luf, B)
3np.allclose(X1, X3)

True

 1M, N = 1000, 100
 2np.random.seed(0)
 3A = np.random.rand(M, M)
 4B = np.random.rand(M, N)
 5Ai = linalg.inv(A)
 6luf = linalg.lu_factor(A)   
 7%timeit linalg.inv(A)
 8%timeit np.dot(Ai, B)
 9%timeit linalg.lu_factor(A)    
10%timeit linalg.lu_solve(luf, B)

50.6 ms ± 1.94 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)
3.49 ms ± 306 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
20.1 ms ± 1.42 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)
4.49 ms ± 65 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

最小二乘解

 1from numpy.lib.stride_tricks import as_strided
 2
 3
 4def make_data(m, n, noise_scale):  #❶
 5    np.random.seed(42)
 6    x = np.random.standard_normal(m)
 7    h = np.random.standard_normal(n)
 8    y = np.convolve(x, h)
 9    yn = y + np.random.standard_normal(len(y)) * noise_scale * np.max(y)
10    return x, yn, h
11
12
13def solve_h(x, y, n):  #❷
14    X = as_strided(
15        x, shape=(len(x) - n + 1, n), strides=(x.itemsize, x.itemsize))  #❸
16    Y = y[n - 1:len(x)]  #❹
17    h = linalg.lstsq(X, Y)  #❺
18    return h[0][::-1]  #❻

1x, yn, h = make_data(1000, 100, 0.4)
2H1 = solve_h(x, yn, 120)
3H2 = solve_h(x, yn, 80)
4
5print("Average error of H1:", np.mean(np.abs(h[:100] - h)))
6print("Average error of H2:", np.mean(np.abs(h[:80] - H2)))

Average error of H1: 0.0
Average error of H2: 0.2958422158342371

 1#%figonly=实际的系统参数与最小二乘解的比较
 2fig, (ax1, ax2) = pl.subplots(2, 1, figsize=(6, 4))
 3ax1.plot(h, linewidth=2, label=u"实际的系统参数")
 4ax1.plot(H1, linewidth=2, label=u"最小二乘解H1", alpha=0.7)
 5ax1.legend(loc="best", ncol=2)
 6ax1.set_xlim(0, len(H1))
 7
 8ax2.plot(h, linewidth=2, label=u"实际的系统参数")
 9ax2.plot(H2, linewidth=2, label=u"最小二乘解H2", alpha=0.7)
10ax2.legend(loc="best", ncol=2)
11ax2.set_xlim(0, len(H1));

4 Numpy Specific Help Functions

Sun, 24 May 2026 00:00:00 +0000

title: "4_Numpy-specific_help_functions" date: 2026-05-24T13:51:38Z

1import numpy as np

1np.__version__

'1.11.2'

Q1. Search for docstrings of the numpy functions on linear algebra.

Search results for 'linear algebra'
-----------------------------------
numpy.linalg.solve
    Solve a linear matrix equation, or system of linear scalar equations.
numpy.poly
    Find the coefficients of a polynomial with the given sequence of roots.
numpy.restoredot
    Restore `dot`, `vdot`, and `innerproduct` to the default non-BLAS
numpy.linalg.eig
    Compute the eigenvalues and right eigenvectors of a square array.
numpy.linalg.cond
    Compute the condition number of a matrix.
numpy.linalg.eigh
    Return the eigenvalues and eigenvectors of a Hermitian or symmetric matrix.
numpy.linalg.pinv
    Compute the (Moore-Penrose) pseudo-inverse of a matrix.
numpy.linalg.LinAlgError
    Generic Python-exception-derived object raised by linalg functions.

Q2. Get help information for numpy dot function.

4 Numpy Specific Help Functions Solutions

Sun, 24 May 2026 00:00:00 +0000

title: "4_Numpy-specific_help_functions_Solutions" date: 2026-05-24T13:51:41Z

1import numpy as np

1np.__version__

'1.11.2'

Q1. Search for docstrings of the numpy functions on linear algebra.

1np.lookfor('linear algebra')

Search results for 'linear algebra'
-----------------------------------
numpy.linalg.solve
    Solve a linear matrix equation, or system of linear scalar equations.
numpy.poly
    Find the coefficients of a polynomial with the given sequence of roots.
numpy.restoredot
    Restore `dot`, `vdot`, and `innerproduct` to the default non-BLAS
numpy.linalg.eig
    Compute the eigenvalues and right eigenvectors of a square array.
numpy.linalg.cond
    Compute the condition number of a matrix.
numpy.linalg.eigh
    Return the eigenvalues and eigenvectors of a Hermitian or symmetric matrix.
numpy.linalg.pinv
    Compute the (Moore-Penrose) pseudo-inverse of a matrix.
numpy.linalg.LinAlgError
    Generic Python-exception-derived object raised by linalg functions.

Q2. Get help information for numpy dot function.

4.scipy Stats

Sun, 24 May 2026 00:00:00 +0000

title: "4.scipy-stats" date: 2026-05-24T14:11:12Z

1import numpy as np
2import pylab as pl
3from scipy import stats

1import matplotlib.pyplot as plt
2import matplotlib as mpl
3mpl.rcParams['font.sans-serif'] = ['SimHei']

统计-stats

连续概率分布

1from scipy import stats
2[k for k, v in stats.__dict__.items() if isinstance(v, stats.rv_continuous)]

['ksone',
 'kstwobign',
 'norm',
 'alpha',
 'anglit',
 'arcsine',
 'beta',
 'betaprime',
 'bradford',
 'burr',
 'burr12',
 'fisk',
 'cauchy',
 'chi',
 'chi2',
 'cosine',
 'dgamma',
 'dweibull',
 'expon',
 'exponnorm',
 'exponweib',
 'exponpow',
 'fatiguelife',
 'foldcauchy',
 'f',
 'foldnorm',
 'weibull_min',
 'weibull_max',
 'frechet_r',
 'frechet_l',
 'genlogistic',
 'genpareto',
 'genexpon',
 'genextreme',
 'gamma',
 'erlang',
 'gengamma',
 'genhalflogistic',
 'gompertz',
 'gumbel_r',
 'gumbel_l',
 'halfcauchy',
 'halflogistic',
 'halfnorm',
 'hypsecant',
 'gausshyper',
 'invgamma',
 'invgauss',
 'invweibull',
 'johnsonsb',
 'johnsonsu',
 'laplace',
 'levy',
 'levy_l',
 'levy_stable',
 'logistic',
 'loggamma',
 'loglaplace',
 'lognorm',
 'gilbrat',
 'maxwell',
 'mielke',
 'kappa4',
 'kappa3',
 'nakagami',
 'ncx2',
 'ncf',
 't',
 'nct',
 'pareto',
 'lomax',
 'pearson3',
 'powerlaw',
 'powerlognorm',
 'powernorm',
 'rdist',
 'rayleigh',
 'reciprocal',
 'rice',
 'recipinvgauss',
 'semicircular',
 'skewnorm',
 'trapz',
 'triang',
 'truncexpon',
 'truncnorm',
 'tukeylambda',
 'uniform',
 'vonmises',
 'vonmises_line',
 'wald',
 'wrapcauchy',
 'gennorm',
 'halfgennorm',
 'crystalball',
 'argus']

1stats.norm.stats()

(array(0.), array(1.))

1X = stats.norm(loc=1.0, scale=2.0)
2X.stats()

(array(1.), array(4.))

1x = X.rvs(size=10000) # 对随机变量取10000个值
2np.mean(x), np.var(x) # 期望值和方差

(1.0048352738823323, 3.9372117720073554)

1stats.norm.fit(x) # 得到随机序列期望值和标准差

(1.0048352738823323, 1.984240855341749)

1pdf, t = np.histogram(x, bins=100, normed=True)  #❶
2t = (t[:-1] + t[1:]) * 0.5  #❷
3cdf = np.cumsum(pdf) * (t[1] - t[0])  #❸
4p_error = pdf - X.pdf(t)
5c_error = cdf - X.cdf(t)
6print ("max pdf error: {}, max cdf error: {}".format(
7    np.abs(p_error).max(),
8    np.abs(c_error).max()))

max pdf error: 0.018998755595167102, max cdf error: 0.018503342378306975


C:\ProgramData\Anaconda3\lib\site-packages\ipykernel_launcher.py:1: VisibleDeprecationWarning: Passing `normed=True` on non-uniform bins has always been broken, and computes neither the probability density function nor the probability mass function. The result is only correct if the bins are uniform, when density=True will produce the same result anyway. The argument will be removed in a future version of numpy.
  """Entry point for launching an IPython kernel.

1#%figonly=正态分布的概率密度函数（左）和累积分布函数（右）
2fig, (ax1, ax2) = pl.subplots(1, 2, figsize=(7, 2))
3ax1.plot(t, pdf, label=u"统计值")
4ax1.plot(t, X.pdf(t), label=u"理论值", alpha=0.6)
5ax1.legend(loc="best")
6ax2.plot(t, cdf)
7ax2.plot(t, X.cdf(t), alpha=0.6);

5 Input And Output

Sun, 24 May 2026 00:00:00 +0000

title: "Input and Output" date: 2026-05-24T13:51:44Z

Input and Output

1from __future__ import print_function
2import numpy as np

1author = "kyubyong. https://github.com/Kyubyong/numpy_exercises"

1np.__version__

'1.12.0'

1from datetime import date
2print(date.today())

2017-04-01

NumPy binary files (NPY, NPZ)

Q1. Save x into temp.npy and load it.

1x = np.arange(10)
2...
3
4# Check if there exists the 'temp.npy' file.
5import os
6if os.path.exists('temp.npy'):
7    x2 = ...
8    print(np.array_equal(x, x2))

True

Q2. Save x and y into a single file 'temp.npz' and load it.

1x = np.arange(10)
2y = np.arange(11, 20)
3...
4
5with ... as data:
6    x2 = data['x']
7    y2 = data['y']
8    print(np.array_equal(x, x2))
9    print(np.array_equal(y, y2))

True
True

Text files

Q3. Save x to 'temp.txt' in string format and load it.

5 Input And Output Solutions

Sun, 24 May 2026 00:00:00 +0000

title: "Input and Output" date: 2026-05-24T13:51:46Z

Input and Output

1from __future__ import print_function
2import numpy as np

1author = "kyubyong. https://github.com/Kyubyong/numpy_exercises"

1np.__version__

'1.12.0'

1from datetime import date
2print(date.today())

2017-04-01

NumPy binary files (NPY, NPZ)

Q1. Save x into temp.npy and load it.

1x = np.arange(10)
2np.save('temp.npy', x) # Actually you can omit the extension. If so, it will be added automatically.
3
4# Check if there exists the 'temp.npy' file.
5import os
6if os.path.exists('temp.npy'):
7    x2 = np.load('temp.npy')
8    print(np.array_equal(x, x2))

True

Q2. Save x and y into a single file 'temp.npz' and load it.

5.scipy Integrate

Sun, 24 May 2026 00:00:00 +0000

title: "调用ode对lorenz进行求解, 用两个不同的初始值" date: 2026-05-24T14:11:14Z

1import pylab as pl
2import numpy as np
3from scipy import integrate
4from scipy.integrate import odeint
5import matplotlib as mpl
6mpl.rcParams['font.sans-serif'] = ['SimHei']

数值积分-integrate

球的体积

1def half_circle(x):
2    return (1-x**2)**0.5

1N = 10000
2x = np.linspace(-1, 1, N)
3dx = x[1] - x[0]
4y = half_circle(x)
52 * dx * np.sum(y) # 面积的两倍

3.1415893269307373

1np.trapz(y, x) * 2 # 面积的两倍

3.1415893269315975

1from scipy import integrate
2pi_half, err = integrate.quad(half_circle, -1, 1)
3pi_half * 2

3.141592653589797

1def half_sphere(x, y):
2    return (1-x**2-y**2)**0.5

1volume, error = integrate.dblquad(half_sphere, -1, 1, 
2        lambda x:-half_circle(x), 
3        lambda x:half_circle(x))
4
5print (volume, error, np.pi*4/3/2)

2.094395102393199 1.0002356720661965e-09 2.0943951023931953

解常微分方程组

 1#%fig=洛伦茨吸引子：微小的初值差别也会显著地影响运动轨迹
 2from scipy.integrate import odeint 
 3import numpy as np 
 4
 5def lorenz(w, t, p, r, b): #❶
 6    # 给出位置矢量w，和三个参数p, r, b计算出
 7    # dx/dt, dy/dt, dz/dt的值
 8    x, y, z = w.tolist()
 9    # 直接与lorenz的计算公式对应 
10    return p*(y-x), x*(r-z)-y, x*y-b*z
11
12t = np.arange(0, 30, 0.02) # 创建时间点 
13# 调用ode对lorenz进行求解, 用两个不同的初始值 
14track1 = odeint(lorenz, (0.0, 1.00, 0.0), t, args=(10.0, 28.0, 3.0)) #❷
15track2 = odeint(lorenz, (0.0, 1.01, 0.0), t, args=(10.0, 28.0, 3.0)) #❸
16#%hide
17from mpl_toolkits.mplot3d import Axes3D
18fig = pl.figure()
19ax = Axes3D(fig)
20ax.plot(track1[:,0], track1[:,1], track1[:,2], lw=1)
21ax.plot(track2[:,0], track2[:,1], track2[:,2], lw=1);

6 Linear Algebra

Sun, 24 May 2026 00:00:00 +0000

title: "Linear algebra" date: 2026-05-24T13:51:49Z

Linear algebra

1import numpy as np

1np.__version__

'1.11.2'

Matrix and vector products

Q1. Predict the results of the following code.

1x = [1,2]
2y = [[4, 1], [2, 2]]
3#print np.dot(x, y)
4#print np.dot(y, x)
5#print np.matmul(x, y)
6#print np.inner(x, y)
7#print np.inner(y, x)

Q2. Predict the results of the following code.

1x = [[1, 0], [0, 1]]
2y = [[4, 1], [2, 2], [1, 1]]
3#print np.dot(y, x)
4#print np.matmul(y, x)

Q3. Predict the results of the following code.

1x = np.array([[1, 4], [5, 6]])
2y = np.array([[4, 1], [2, 2]])
3#print np.vdot(x, y)
4#print np.vdot(y, x)
5#print np.dot(x.flatten(), y.flatten())
6#print np.inner(x.flatten(), y.flatten())
7#print (x*y).sum()

Q4. Predict the results of the following code.

6 Linear Algebra Solutions

Sun, 24 May 2026 00:00:00 +0000

title: "Linear algebra" date: 2026-05-24T13:51:52Z

Linear algebra

1import numpy as np

1np.__version__

'1.11.2'

Matrix and vector products

Q1. Predict the results of the following code.

1x = [1,2]
2y = [[4, 1], [2, 2]]
3print np.dot(x, y)
4print np.dot(y, x)
5print np.matmul(x, y)
6print np.inner(x, y)
7print np.inner(y, x)

[8 5]
[6 6]
[8 5]
[6 6]
[6 6]

Q2. Predict the results of the following code.

1x = [[1, 0], [0, 1]]
2y = [[4, 1], [2, 2], [1, 1]]
3print np.dot(y, x)
4print np.matmul(y, x)

[[4 1]
 [2 2]
 [1 1]]
[[4 1]
 [2 2]
 [1 1]]

Q3. Predict the results of the following code.

6.scipy Signal

Sun, 24 May 2026 00:00:00 +0000

title: "设计一个带通滤波器：" date: 2026-05-24T14:11:17Z

1import pylab as pl
2import numpy as np
3from scipy import signal

1import matplotlib as mpl
2mpl.rcParams['font.sans-serif'] = ['SimHei']

信号处理-signal

中值滤波

 1#%fig=使用中值滤波剔除瞬间噪声
 2t = np.arange(0, 20, 0.1)
 3x = np.sin(t)
 4x[np.random.randint(0, len(t), 20)] += np.random.standard_normal(20)*0.6 #❶
 5x2 = signal.medfilt(x, 5) #❷
 6x3 = signal.order_filter(x, np.ones(5), 2)
 7print (np.all(x2 == x3))
 8pl.plot(t, x, label=u"带噪声的信号")
 9pl.plot(t, x2 + 0.5, alpha=0.6, label=u"中值滤波之后的信号")
10pl.legend(loc="best");

True

滤波器设计

 1sampling_rate = 8000.0
 2
 3# 设计一个带通滤波器：
 4# 通带为0.2*4000 - 0.5*4000
 5# 阻带为<0.1*4000, >0.6*4000
 6# 通带增益的最大衰减值为2dB
 7# 阻带的最小衰减值为40dB
 8b, a = signal.iirdesign([0.2, 0.5], [0.1, 0.6], 2, 40) #❶
 9
10# 使用freq计算滤波器的频率响应
11w, h = signal.freqz(b, a) #❷
12
13# 计算增益
14power = 20*np.log10(np.clip(np.abs(h), 1e-8, 1e100)) #❸
15freq = w / np.pi * sampling_rate / 2

 1#%fig=用频率扫描波测量的频率响应
 2# 产生2秒钟的取样频率为sampling_rate Hz的频率扫描信号
 3# 开始频率为0， 结束频率为sampling_rate/2
 4t = np.arange(0, 2, 1/sampling_rate) #❶
 5sweep = signal.chirp(t, f0=0, t1=2, f1=sampling_rate/2) #❷
 6# 对频率扫描信号进行滤波
 7out = signal.lfilter(b, a, sweep) #❸
 8# 将波形转换为能量
 9out = 20*np.log10(np.abs(out)) #❹
10# 找到所有局部最大值的下标
11index = signal.argrelmax(out, order=3)  #❺
12# 绘制滤波之后的波形的增益
13pl.figure(figsize=(8, 2.5))
14pl.plot(freq, power, label=u"带通IIR滤波器的频率响应") 
15pl.plot(t[index]/2.0*4000, out[index], label=u"频率扫描波测量的频谱", alpha=0.6) #❻
16pl.legend(loc="best")
17#%hide
18pl.title(u"频率扫描波测量的滤波器频谱")
19pl.ylim(-100,20)
20pl.ylabel(u"增益(dB)")
21pl.xlabel(u"频率(Hz)");

7 Discrete Fourier Transform

Sun, 24 May 2026 00:00:00 +0000

title: "cf." date: 2026-05-24T13:51:55Z

1from __future__ import print_function
2import numpy as np
3import matplotlib.pyplot as plt
4%matplotlib inline

1from datetime import date
2date.today()

datetime.date(2017, 11, 2)

1author = "kyubyong. https://github.com/Kyubyong/numpy_exercises"

1np.__version__

'1.13.1'

Complex Numbers

Q1. Return the angle of a in radian.

1a = 1+1j
2output = ...
3print(output)

0.785398163397

Q2. Return the real part and imaginary part of a.

1a = np.array([1+2j, 3+4j, 5+6j])
2real = ...
3imag = ...
4print("real part=", real)
5print("imaginary part=", imag)

real part= [ 1.  3.  5.]
imaginary part= [ 2.  4.  6.]

Q3. Replace the real part of a with 9, the imaginary part with [5, 7, 9].

7 Discrete Fourier Transform Solutions

Sun, 24 May 2026 00:00:00 +0000

title: "cf." date: 2026-05-24T13:51:58Z

1from __future__ import print_function
2import numpy as np
3import matplotlib.pyplot as plt
4%matplotlib inline

1from datetime import date
2date.today()

datetime.date(2017, 11, 2)

1author = "kyubyong. https://github.com/Kyubyong/numpy_exercises"

1np.__version__

'1.13.1'

Complex Numbers

Q1. Return the angle of a in radian.

1a = 1+1j
2output = np.angle(a, deg=False)
3print(output)

0.785398163397

Q2. Return the real part and imaginary part of a.

1a = np.array([1+2j, 3+4j, 5+6j])
2real = a.real
3imag = a.imag
4print("real part=", real)
5print("imaginary part=", imag)

real part= [ 1.  3.  5.]
imaginary part= [ 2.  4.  6.]

Q3. Replace the real part of a with 9, the imaginary part with [5, 7, 9].

7.scipy Interpolate

Sun, 24 May 2026 00:00:00 +0000

title: "X-Y轴分为15*15的网格" date: 2026-05-24T14:11:20Z

1import numpy as np
2import pylab as pl
3from scipy import interpolate

1import matplotlib as mpl
2mpl.rcParams['font.sans-serif'] = ['SimHei']

插值-interpolate

一维插值

WARNING

高次interp1d()插值的运算量很大，因此对于点数较多的数据，建议使用后面介绍的UnivariateSpline()。

 1#%fig=`interp1d`的各阶插值
 2from scipy import interpolate
 3
 4x = np.linspace(0, 10, 11)
 5y = np.sin(x)
 6
 7xnew = np.linspace(0, 10, 101)
 8pl.plot(x, y, 'ro')
 9for kind in ['nearest', 'zero', 'slinear', 'quadratic']:
10    f = interpolate.interp1d(x, y, kind=kind)  #❶
11    ynew = f(xnew)  #❷
12    pl.plot(xnew, ynew, label=str(kind))
13
14pl.legend(loc='lower right')

<matplotlib.legend.Legend at 0x274ccb5f6a0>

外推和Spline拟合

 1#%fig=使用UnivariateSpline进行插值：外推（上），数据拟合（下）
 2x1 = np.linspace(0, 10, 20)
 3y1 = np.sin(x1)
 4sx1 = np.linspace(0, 12, 100)
 5sy1 = interpolate.UnivariateSpline(x1, y1, s=0)(sx1)  #❶
 6
 7x2 = np.linspace(0, 20, 200)
 8y2 = np.sin(x2) + np.random.standard_normal(len(x2)) * 0.2
 9sx2 = np.linspace(0, 20, 2000)
10spline2 = interpolate.UnivariateSpline(x2, y2, s=8)  #❷
11sy2 = spline2(sx2)
12
13pl.figure(figsize=(8, 5))
14pl.subplot(211)
15pl.plot(x1, y1, ".", label=u"数据点")
16pl.plot(sx1, sy1, label=u"spline曲线")
17pl.legend()
18
19pl.subplot(212)
20pl.plot(x2, y2, ".", label=u"数据点")
21pl.plot(sx2, sy2, linewidth=2, label=u"spline曲线")
22pl.plot(x2, np.sin(x2), label=u"无噪声曲线")
23pl.legend()

<matplotlib.legend.Legend at 0x274bb27f588>

8 Logic Functions

Sun, 24 May 2026 00:00:00 +0000

title: "Logic functions" date: 2026-05-24T13:52:01Z

Logic functions

1import numpy as np

1np.__version__

'1.11.2'

Truth value testing

Q1. Let x be an arbitrary array. Return True if none of the elements of x is zero. Remind that 0 evaluates to False in python.

1x = np.array([1,2,3])
2#
3
4x = np.array([1,0,3])
5#

True
False

Q2. Let x be an arbitrary array. Return True if any of the elements of x is non-zero.

1x = np.array([1,0,0])
2#
3
4x = np.array([0,0,0])
5#

True
False

Array contents

Q3. Predict the result of the following code.

8 Logic Functions Solutions

Sun, 24 May 2026 00:00:00 +0000

title: "Logic functions" date: 2026-05-24T13:52:03Z

Logic functions

1import numpy as np

1np.__version__

'1.11.2'

Truth value testing

Q1. Let x be an arbitrary array. Return True if none of the elements of x is zero. Remind that 0 evaluates to False in python.

1x = np.array([1,2,3])
2print np.all(x)
3
4x = np.array([1,0,3])
5print np.all(x)

True
False

Q2. Let x be an arbitrary array. Return True if any of the elements of x is non-zero.

1x = np.array([1,0,0])
2print np.any(x)
3
4x = np.array([0,0,0])
5print np.any(x)

True
False

Array contents

Q3. Predict the result of the following code.

8.scipy Sparse

Sun, 24 May 2026 00:00:00 +0000

title: "8.scipy-sparse" date: 2026-05-24T14:11:23Z

1%matplotlib inline
2import numpy as np
3import pylab as pl
4from scipy import sparse
5from scipy.sparse import csgraph

稀疏矩阵-sparse

稀疏矩阵的储存形式

1from scipy import sparse
2a = sparse.dok_matrix((10, 5))
3
4a[2, 3] = 1.0
5a[3, 3] = 2.0
6a[4, 3] = 3.0
7print(a.keys())
8print(a.values())

dict_keys([(2, 3), (3, 3), (4, 3)])
dict_values([1.0, 2.0, 3.0])

1b = sparse.lil_matrix((10, 5))
2b[2, 3] = 1.0
3b[3, 4] = 2.0
4b[3, 2] = 3.0
5print(b.data)
6print(b.rows)

[list([]) list([]) list([1.0]) list([3.0, 2.0]) list([]) list([]) list([])
 list([]) list([]) list([])]
[list([]) list([]) list([3]) list([2, 4]) list([]) list([]) list([])
 list([]) list([]) list([])]

1row = [2, 3, 3, 2]
2col = [3, 4, 2, 3]
3data = [1, 2, 3, 10]
4c = sparse.coo_matrix((data, (row, col)), shape=(5, 6))
5print (c.col, c.row, c.data)
6print (c.toarray())

[3 4 2 3] [2 3 3 2] [ 1  2  3 10]
[[ 0  0  0  0  0  0]
 [ 0  0  0  0  0  0]
 [ 0  0  0 11  0  0]
 [ 0  0  3  0  2  0]
 [ 0  0  0  0  0  0]]

矩阵向量相乘

1 import numpy as np
2from scipy.sparse import csr_matrix
3A = csr_matrix([[1, 2, 0], [0, 0, 3], [4, 0, 5]])
4v = np.array([1, 0, -1])
5A.dot(v)

array([ 1, -3, -1], dtype=int32)

示例1

构造一个1000x1000 lil_matrix并添加值：

9 Mathematical Functions

Sun, 24 May 2026 00:00:00 +0000

title: "Mathematical functions" date: 2026-05-24T13:52:06Z

Mathematical functions

1import numpy as np

1np.__version__

'1.11.2'

1__author__ = "kyubyong. kbpark.linguist@gmail.com. https://github.com/kyubyong"

Trigonometric functions

Q1. Calculate sine, cosine, and tangent of x, element-wise.

1x = np.array([0., 1., 30, 90])

sine: [ 0.          0.84147098 -0.98803162  0.89399666]
cosine: [ 1.          0.54030231  0.15425145 -0.44807362]
tangent: [ 0.          1.55740772 -6.4053312  -1.99520041]

Q2. Calculate inverse sine, inverse cosine, and inverse tangent of x, element-wise.

1x = np.array([-1., 0, 1.])

inverse sine: [ 1.57079633  0.          1.57079633]
inverse cosine: [ 0.          1.57079633  0.        ]
inverse tangent: [ 0.78539816  0.          0.78539816]

Q3. Convert angles from radians to degrees.

9 Mathematical Functions Solutions

Sun, 24 May 2026 00:00:00 +0000

title: "Mathematical functions" date: 2026-05-24T13:52:09Z

Mathematical functions

1import numpy as np

1np.__version__

'1.11.2'

1__author__ = "kyubyong. kbpark.linguist@gmail.com. https://github.com/kyubyong"

Trigonometric functions

Q1. Calculate sine, cosine, and tangent of x, element-wise.

1x = np.array([0., 1., 30, 90])
2print "sine:", np.sin(x)
3print "cosine:", np.cos(x)
4print "tangent:", np.tan(x)

sine: [ 0.          0.84147098 -0.98803162  0.89399666]
cosine: [ 1.          0.54030231  0.15425145 -0.44807362]
tangent: [ 0.          1.55740772 -6.4053312  -1.99520041]

Q2. Calculate inverse sine, inverse cosine, and inverse tangent of x, element-wise.

1x = np.array([-1., 0, 1.])
2print "inverse sine:", np.arcsin(x2)
3print "inverse cosine:", np.arccos(x2)
4print "inverse tangent:", np.arctan(x2)

inverse sine: [ 1.57079633  0.          1.57079633]
inverse cosine: [ 0.          1.57079633  0.        ]
inverse tangent: [ 0.78539816  0.          0.78539816]

Q3. Convert angles from radians to degrees.

9.scipy Ndimage

Sun, 24 May 2026 00:00:00 +0000

title: "9.scipy-ndimage" date: 2026-05-24T14:11:26Z

1import numpy as np
2import pylab as pl

图像处理-ndimage

形态学图像处理

 1import numpy as np
 2
 3def expand_image(img, value, out=None, size = 10):
 4    if out is None:
 5        w, h = img.shape
 6        out = np.zeros((w*size, h*size),dtype=np.uint8)
 7    
 8    tmp = np.repeat(np.repeat(img,size,0),size,1)
 9    out[:,:] = np.where(tmp, value, out)
10    out[::size,:] = 0
11    out[:,::size] = 0
12    return out
13    
14def show_image(*imgs): 
15    for idx, img in enumerate(imgs, 1):
16        ax = pl.subplot(1, len(imgs), idx)
17        pl.imshow(img, cmap="gray")   
18        ax.set_axis_off()    
19    pl.subplots_adjust(0.02, 0, 0.98, 1, 0.02, 0)

膨胀和腐蚀

 1#%fig=四连通和八连通的膨胀运算
 2from scipy.ndimage import morphology
 3
 4def dilation_demo(a, structure=None):
 5    b = morphology.binary_dilation(a, structure)
 6    img = expand_image(a, 255)
 7    return expand_image(np.logical_xor(a,b), 150, out=img)
 8
 9a = pl.imread("scipy_morphology_demo.png")[:,:,0].astype(np.uint8)
10img1 = expand_image(a, 255)
11
12img2 = dilation_demo(a)
13img3 = dilation_demo(a, [[1,1,1],[1,1,1],[1,1,1]])
14show_image(img1, img2, img3)

1#%fig=不同结构元素的膨胀效果
2img4 = dilation_demo(a, [[0,0,0],[1,1,1],[0,0,0]])
3img5 = dilation_demo(a, [[0,1,0],[0,1,0],[0,1,0]])
4img6 = dilation_demo(a, [[0,1,0],[0,1,0],[0,0,0]])
5show_image(img4, img5, img6)

Pandas Beginner

Sun, 24 May 2026 00:00:00 +0000

title: "2天学会Pandas" date: 2026-05-24T14:09:31Z

2天学会Pandas

0.导语

1.Series

2.DataFrame

2.1 DataFrame的简单运用

3.pandas选择数据

3.1 实战筛选

3.2 筛选总结

4.Pandas设置值

4.1 创建数据

4.2 根据位置设置loc和iloc

4.3 根据条件设置

4.4 按行或列设置

4.5 添加Series序列(长度必须对齐)

4.6 设定某行某列为特定值

4.7 修改一整行数据

5.Pandas处理丢失数据

5.1 创建含NaN的矩阵

5.2 删除掉有NaN的行或列

5.3 替换NaN值为0或者其他

5.4 是否有缺失数据NaN

6.Pandas导入导出

6.1 导入数据

6.2 导出数据

7.Pandas合并操作

7.1 Pandas合并concat

7.2.Pandas 合并 merge

*  7.2.1 定义资料集并打印出
*  7.2.2 依据key column合并,并打印
*  7.2.3 两列合并
*  7.2.4 Indicator设置合并列名称
*  7.2.5 依据index合并
*  7.2.6 解决overlapping的问题

8.Pandas plot出图

9.学习来源

0.导语

Pandas是基于Numpy构建的，让Numpy为中心的应用变得更加简单。

Pandas50

Sun, 24 May 2026 00:00:00 +0000

title: "Pandas练习题50题" date: 2026-05-24T14:09:49Z

Pandas练习题50题

作者：王大毛，和鲸社区

出处：https://www.kesci.com/home/project/5ddc974ef41512002cec1dca

修改：黄海广

Pandas 是基于 NumPy 的一种数据处理工具，该工具为了解决数据分析任务而创建。Pandas 纳入了大量库和一些标准的数据模型，提供了高效地操作大型数据集所需的函数和方法。这些练习着重DataFrame和Series对象的基本操作，包括数据的索引、分组、统计和清洗。

基本操作

1.导入 Pandas 库并简写为 pd，并输出版本号

1import pandas as pd
2pd.__version__

'0.22.0'

2. 从列表创建 Series

1arr = [0, 1, 2, 3, 4]
2df = pd.Series(arr) # 如果不指定索引，则默认从 0 开始
3df

0    0
1    1
2    2
3    3
4    4
dtype: int64

3. 从字典创建 Series

1d = {'a':1,'b':2,'c':3,'d':4,'e':5}
2df = pd.Series(d)
3df

a    1
b    2
c    3
d    4
e    5
dtype: int64

4. 从 NumPy 数组创建 DataFrame

1import numpy as np
2dates = pd.date_range('today', periods=6)  # 定义时间序列作为 index
3num_arr = np.random.randn(6, 4)  # 传入 numpy 随机数组
4columns = ['A', 'B', 'C', 'D']  # 将列表作为列名
5df = pd.DataFrame(num_arr, index=dates, columns=columns)
6df

Python打卡Day1

Sun, 24 May 2026 00:00:00 +0000

Question 1

Question:

Write a program which will find all such numbers which are divisible by 7 but are not a multiple of 5, between 2000 and 3200 (both included).The numbers obtained should be printed in a comma-separated sequence on a single line.

Hints:

Consider use range(#begin, #end) method.

Main author's Solution: Python 2

1l=[]
2for i in range(2000, 3201):
3    if (i%7==0) and (i%5!=0):
4        l.append(str(i))
5
6print ','.join(l)

My Solution: Python 3

Using for loops

1for i in range(2000,3201):
2    if i%7 == 0 and i%5!=0:
3        print(i,end=',')
4print("\b")

Using generators and list comprehension

1print(*(i for i in range(2000, 3201) if i%7 == 0 and i%5 != 0), sep=",")

Question 2

Question:

Write a program which can compute the factorial of a given numbers.The results should be printed in a comma-separated sequence on a single line.Suppose the following input is supplied to the program: 8 Then, the output should be:40320

Python打卡Day10

Sun, 24 May 2026 00:00:00 +0000

Question 31

Question:

Define a function which can print a dictionary where the keys are numbers between 1 and 20 (both included) and the values are square of keys.

Hints:

1Use dict[key]=value pattern to put entry into a dictionary.Use ** operator to get power of a number.Use range() for loops.

Main Author's Solution: Python 2

1def printDict():
2	d=dict()
3	for i in range(1,21):
4		d[i]=i**2
5	print d
6
7printDict()

My Solution: Python 3

1def printDict():
2    dict={i:i**2 for i in range(1,21)}   # Using comprehension method and
3    print(dict)
4
5printDict()

Question 32

Question:

Define a function which can generate a dictionary where the keys are numbers between 1 and 20 (both included) and the values are square of keys. The function should just print the keys only.

Python打卡Day11

Sun, 24 May 2026 00:00:00 +0000

Question 38

Question:

With a given tuple (1,2,3,4,5,6,7,8,9,10), write a program to print the first half values in one line and the last half values in one line.

Hints:

Use [n1:n2] notation to get a slice from a tuple.

Main Author's Solution: Python 2

1tp = (1,2,3,4,5,6,7,8,9,10)
2tp1 = tp[:5]
3tp2 = tp[5:]
4print tp1
5print tp2

My Solution: Python 3

1tpl = (1,2,3,4,5,6,7,8,9,10)
2
3for i in range(0,5):
4    print(tpl[i],end = ' ')
5print()
6for i in range(5,10):
7    print(tpl[i],end = ' ')

 1tpl = (1,2,3,4,5,6,7,8,9,10)
 2lst1,lst2 = [],[]
 3
 4for i in range(0,5):
 5    lst1.append(tpl[i])
 6
 7for i in range(5,10):
 8    lst2.append(tpl[i])
 9
10print(lst1)
11print(lst2)

Python打卡Day12

Sun, 24 May 2026 00:00:00 +0000

Question 44

Question:

Write a program which can map() to make a list whose elements are square of numbers between 1 and 20 (both included).

Hints:

Use map() to generate a list. Use lambda to define anonymous functions.

Main Author's Solution: Python 2

1squaredNumbers = map(lambda x: x**2, range(1,21))
2print squaredNumbers

My Solution: Python 3

1def sqr(x):
2    return x*x
3
4squaredNumbers = list(map(sqr, range(1,21)))
5print (squaredNumbers)

Question 45

Question:

Define a class named American which has a static method called printNationality.

Python打卡Day13

Sun, 24 May 2026 00:00:00 +0000

Question 47

Question

Define a class named Circle which can be constructed by a radius. The Circle class has a method which can compute the area.

Hints

Use def methodName(self) to define a method.

Main author's Solution: Python 2

1class Circle(object):
2    def __init__(self, r):
3        self.radius = r
4
5    def area(self):
6        return self.radius**2*3.14
7
8aCircle = Circle(2)
9print aCircle.area()

My Solution: Python 3

 1class Circle():
 2    def __init__(self,r):
 3        self.radius = r
 4
 5    def area(self):
 6        return 3.1416*(self.radius**2)
 7
 8
 9circle = Circle(5)
10print(circle.area())

Question 48

Question

Define a class named Rectangle which can be constructed by a length and width. The Rectangle class has a method which can compute the area.

Python打卡Day14

Sun, 24 May 2026 00:00:00 +0000

Question 51

Question

Write a function to compute 5/0 and use try/except to catch the exceptions.

Hints

Use try/except to catch exceptions.

Main author's Solution: Python 2

 1def throws():
 2    return 5/0
 3
 4try:
 5    throws()
 6except ZeroDivisionError:
 7    print "division by zero!"
 8except Exception, err:
 9    print 'Caught an exception'
10finally:
11    print 'In finally block for cleanup'

My Solution: Python 3

1def divide():
2    return 5/0
3
4try:
5    divide()
6except ZeroDivisionError as ze:
7    print("Why on earth you are dividing a number by ZERO!!")
8except:
9    print("Any other exception")

Question 52

Question

Define a custom exception class which takes a string message as attribute.

Python打卡Day15

Sun, 24 May 2026 00:00:00 +0000

Question 54

Question

Assuming that we have some email addresses in the "username@companyname.com" format, please write program to print the company name of a given email address. Both user names and company names are composed of letters only.

Example: If the following email address is given as input to the program:

1john@google.com

Then, the output of the program should be:

1google

In case of input data being supplied to the question, it should be assumed to be a console input.

Python打卡Day16

Sun, 24 May 2026 00:00:00 +0000

Question 60

Question

Write a program to compute:

1f(n)=f(n-1)+100 when n>0
2and f(0)=0

with a given n input by console (n>0).

Example: If the following n is given as input to the program:

Then, the output of the program should be:

In case of input data being supplied to the question, it should be assumed to be a console input.

Hints

We can define recursive function in Python.

Python打卡Day17

Sun, 24 May 2026 00:00:00 +0000

Question 65

Question

Please write assert statements to verify that every number in the list [2,4,6,8] is even.

Hints

Use "assert expression" to make assertion.

Main author's Solution: Python 2

1li = [2,4,6,8]
2for i in li:
3    assert i%2==0

My Solution: Python 3

1data = [2,4,5,6]
2for i in data:
3    assert i%2 == 0, "{} is not an even number".format(i)

Question 66

Question

Please write a program which accepts basic mathematic expression from console and print the evaluation result.

Example: If the following n is given as input to the program:

Python打卡Day18

Sun, 24 May 2026 00:00:00 +0000

Question 70

Question

Please write a program to output a random even number between 0 and 10 inclusive using random module and list comprehension.

Hints

Use random.choice() to a random element from a list.

Main author's Solution: Python 2

1li = [2,4,6,8]
2import random
3print random.choice([i for i in range(11) if i%2==0])

My Solution: Python 3

1import random
2resp = [i for i in range(0,11,2)]
3print(random.choice(resp))

Question 71

Question

Please write a program to output a random number, which is divisible by 5 and 7, between 10 and 150 inclusive using random module and list comprehension.

Python打卡Day19

Sun, 24 May 2026 00:00:00 +0000

Question 75

Question

Please write a program to randomly print a integer number between 7 and 15 inclusive.

Hints

Use random.randrange() to a random integer in a given range.

Solution:

1import random
2print random.randrange(7,16)

Question 76

Question

Please write a program to compress and decompress the string "hello world!hello world!hello world!hello world!".

Hints

Use zlib.compress() and zlib.decompress() to compress and decompress a string.

Solution:

1import zlib
2s = 'hello world!hello world!hello world!hello world!'
3t = zlib.compress(s)
4print t
5print zlib.decompress(t)

1'''Solution by: anas1434 
2'''
3s = 'hello world!hello world!hello world!hello world!'
4# In Python 3 zlib.compress() accepts only DataType <bytes>
5y = bytes(s, 'utf-8')
6x = zlib.compress(y)
7print(x)
8print(zlib.decompress(x))

Question 77

Question

Please write a program to print the running time of execution of "1+1" for 100 times.

Python打卡Day2

Sun, 24 May 2026 00:00:00 +0000

Question 4

Question:

Write a program which accepts a sequence of comma-separated numbers from console and generate a list and a tuple which contains every number.Suppose the following input is supplied to the program:

134,67,55,33,12,98

Then, the output should be:

1['34', '67', '55', '33', '12', '98']
2('34', '67', '55', '33', '12', '98')

Hints:

In case of input data being supplied to the question, it should be assumed to be a console input.tuple() method can convert list to tuple

Python打卡Day20

Sun, 24 May 2026 00:00:00 +0000

Question 80

Question

Please write a program to print the list after removing even numbers in [5,6,77,45,22,12,24].

Hints

Use list comprehension to delete a bunch of element from a list.

Main author's Solution: Python 2

1li = [5,6,77,45,22,12,24]
2li = [x for x in li if x%2!=0]
3print li

My Solution: Python 3

1def isEven(n):
2    return n%2!=0
3
4li = [5,6,77,45,22,12,24]
5lst = list(filter(isEven,li))
6print(lst)

1li = [5,6,77,45,22,12,24]
2lst = list(filter(lambda n:n%2!=0,li))
3print(lst)

Question 81

Question

By using list comprehension, please write a program to print the list after removing numbers which are divisible by 5 and 7 in [12,24,35,70,88,120,155].

Python打卡Day21

Sun, 24 May 2026 00:00:00 +0000

Question 85

Question

By using list comprehension, please write a program to print the list after removing the 0th,4th,5th numbers in [12,24,35,70,88,120,155].

Hints

Use list comprehension to delete a bunch of element from a list.Use enumerate() to get (index, value) tuple.

Main author's Solution: Python 2

1li = [12,24,35,70,88,120,155]
2li = [x for (i,x) in enumerate(li) if i not in (0,4,5)]
3print li

My Solution: Python 3

1li = [12,24,35,70,88,120,155]
2li = [li[i] for i in range(len(li)) if i not in (0,4,5)]
3print(li)

1'''Solution by: pratikb0501
2'''
3li = [12, 24, 35, 70, 88, 120, 155]
4print(list(j for i, j in enumerate(li) if i != 0 and i != 4 and i != 5))

Question 86

Question

By using list comprehension, please write a program to print the list after removing the value 24 in [12,24,35,24,88,120,155].

Python打卡Day22

Sun, 24 May 2026 00:00:00 +0000

Question 90

Question

Please write a program which count and print the numbers of each character in a string input by console.

Example: If the following string is given as input to the program:

1abcdefgabc

Then, the output of the program should be:

1a,2
2c,2
3b,2
4e,1
5d,1
6g,1
7f,1

Hints

Use dict to store key/value pairs. Use dict.get() method to lookup a key with default value.

Main author's Solution: Python 2

Python打卡Day23

Sun, 24 May 2026 00:00:00 +0000

The extended part of the repository starts from this page. Previous 94 problems were collected from the repository mentioned in intro. The following problems are collected from Hackerrank and other resources from internet.All the given solutions are in python 3.

Question 95

Question

Given the participants' score sheet for your University Sports Day, you are required to find the runner-up score. You are given scores. Store them in a list and find the score of the runner-up.

Python打卡Day24

Sun, 24 May 2026 00:00:00 +0000

Question 100

Question

You are given words. Some words may repeat. For each word, output its number of occurrences. The output order should correspond with the input order of appearance of the word. See the sample input/output for clarification.

If the following string is given as input to the program:
14
2bcdef
3abcdefg
4bcde
5bcdef
Then, the output of the program should be:
13
22 1 1

Hints

Make a list to get the input order and a dictionary to count the word frequency

Python打卡Day3

Sun, 24 May 2026 00:00:00 +0000

Question 10

Question

Write a program that accepts a sequence of whitespace separated words as input and prints the words after removing all duplicate words and sorting them alphanumerically.

Suppose the following input is supplied to the program:

1hello world and practice makes perfect and hello world again

Then, the output should be:

1again and hello makes perfect practice world

Hints:

In case of input data being supplied to the question, it should be assumed to be a console input.We use set container to remove duplicated data automatically and then use sorted() to sort the data.

二维数据结构：DataFrame

Wed, 06 May 2026 00:00:00 +0000

二维数据结构：DataFrame

十分钟上手 Pandas

Wed, 06 May 2026 00:00:00 +0000

十分钟上手 Pandas

一维数据结构：Series

Wed, 06 May 2026 00:00:00 +0000

一维数据结构：Series

01.01 Python 简介