大数据分析及应用 – 实验_2022春

Python基础 – 控制结构

1

break

2

for i in inputlist.split(','):
    student=studentname[count]

3

for score in subjectscore:

4

List = iter(List)

Python基础 – 文件操作与异常处理

1

with open('src/Step1/test.txt') as file_object:
    lines = file_object.readlines()
    i=0
    for line in lines:
        print(line.rstrip())
        i=i+1
        if i==n:
            break

2

with open('src/Step2/test2.txt','w') as example:
    example.write(s)

3

except:
    print("We can't take a root by minus")
else:
    print(answer)

Python基础 – 函数结构

1

def plus(numbers):
   add=0
   for i in numbers:
      add+=i
   return add
d=plus(numbers)

2

def gcd(a,b):
    if a<b:
        t=a
        a=b
        b=t
    while b:
        maxs=a%b
        a=b
        b=maxs
    return a

3

def _gcd(a,b):
    if a < b:
        t=a
        a=b
        b=t
    while b:
        t = a%b
        a = b
        b = t
    return a
 
def lcm(a,b):
    return int(a*b/_gcd(a,b))

Python基础 – 函数调用

1

def prime(n):
    if n <= 1:
        return("False")
    else:
        for i in range(2,n):
            if n % i == 0:
                return("False")
                break
        return("True")

2

def func_call(numbers):
    for i in range(len(numbers) - 1):
        for j in range(len(numbers) - i - 1):
            if numbers[j] > numbers[j + 1]:
                numbers[j], numbers[j + 1] = numbers[j + 1], numbers[j]
    return numbers

func_call(numbers)    
print(numbers)

3

def circle(n):
    return PI * n* n

s = circle(n)
print('%.2f' %s)

Python基础 – 列表

1

a=guests.pop()
guests.insert(2,a)
guests.pop(1)
print(a)
print(guests)

2

source_list.sort()
print(source_list)

3

data_list = list(range(lower,upper,step))
l=-(-(upper-lower)//step)
print(l)
min_value = min(data_list)
max_value = max(data_list)
s=max_value-min_value
print(s)

4

a=my_menu[::3]
print(a)
b=my_menu[-3::]
print(b)

Python基础 – 元组与字典

1

print(tuple(menu_list))
print(max(menu_list))

2

menu_dict['lamb']=50
print(menu_dict['fish'])
menu_dict['fish']=100
del menu_dict['noodles']
print (menu_dict)

3

for key in menu_dict.keys():
    print(''+key)
for value in menu_dict.values():
    print(value)

4

menu2= {}
menu2['fish']=menu1['fish']*2
menu2['pork']=menu1['pork']*2
 
menu_total.append(menu2)

Python 数组与曲线绘制(一)


class Solution:
    def solve(self, s, e):
        """
        :type s, e: int, int
        :rtype: list
        """
        #请在此按照“编程要求”填写代码
        #********** Begin *********#
        import math
        dx = (e-s)/40
        xlist = [s+i*dx for i in range(0,41)]
        def f(x):
            return 1/math.sqrt(2*math.pi)*math.exp(-0.5*x**2)
        ylist = [f(x) for x in xlist]
        return ylist
        ##********** End **********#

第2关 填充数组(循环版本)


class Solution:
        def solve(self, s, e):
                """
                :type s, e: int, int
                :rtype: numpy.ndarray
                """
                #请在此按照“编程要求”填写代码
                #********** Begin *********#
                import numpy as np
 
                xlist = np.zeros(41)
                ylist = np.zeros(41)
                for i in range(41):
                        xlist[i]=s+i*(e - s)/40
                        ylist[i]=1/np.sqrt(2*np.pi)*np.exp(-0.5*xlist[i]**2)
                return ylist
                ##********** End **********#

第3关 填充数组(向量化版本)

class Solution:
    def solve(self, s, e):
        """
        :type s, e: int, int
        :rtype xlist, ylist: numpy.array, numpy.array
        """
        #请在此按照“编程要求”填写代码
        #********** Begin *********#
        import numpy as np
        xlist = np.linspace(s, e, 41)
        ylist = 1/np.sqrt(2*np.pi)*np.exp(-0.5*xlist**2)
        return xlist, ylist
        ##********** End **********#

第4关 绘制函数


class Solution:
    def solve(self, s, e):
        """
        :type s, e: int, int
        :rtype: None
        """
        #请在此按照“编程要求”添加代码
        #********** Begin *********#
        from matplotlib import pyplot as plt
        import math
        dx = (e - s) / 40
        xlist = [s+i*dx for i in range(0,41)]
        def f(x):
            return 1/math.sqrt(2*math.pi)*math.exp(-0.5*x**2)
        ylist = [f(x) for x in xlist]
        plt.plot(xlist, ylist)
        plt.show()
        ##********** End **********#
        plt.savefig("step4/stu_img/student.png")

第5关 函数作用于向量


class Solution:
    def solve_1(self, v):
        """
        :type v: list
        :rtype: list
        """
        #请在此按照“编程要求”添加代码
        #********** Begin *********#
        import math
        def f(x):
            return x**3+x*math.exp(x)+1
        y = [f(a) for a in v]
        return y
        ##********** End **********#
    def solve_2(self, v):
        """
        :type v: list
        :rtype: numpy.array
        """
        #请在此按照“编程要求”添加代码
        #********** Begin *********#
        import numpy as np
        xlist = np.array(v)
        ylist = xlist**3+xlist*np.exp(xlist)+1
        return ylist
        ##********** End **********#

第6关 手工模拟执行向量表达式

class Solution:
    def solve_1(self, x, t):
        """
        :type x, t: list, list
        :rtype: list
        """
        #请在此按照“编程要求:使用math库实现”添加代码
        #********** Begin *********#
        import math
        y = []
        for xi, ti in zip(x, t):
            y.append(math.cos(math.sin(xi)) + math.exp(1/ti))
        return y
        ##********** End **********#
    def solve_2(self, x, t):
        """
        :type x, t: list, list
        :rtype: numpy.array
        """
        #请在此按照“编程要求:使用numpy库实现”添加代码
        #********** Begin *********#
        import numpy as np
        y_1 = np.cos(np.sin(x))+np.exp(1/np.array(t))
        return y_1
        ##********** End **********#

Python 数组与曲线绘制(二)

第1关 展示数组切片

[0.  0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.  1.1 1.2 1.3 1.4 1.5 1.6 1.7
 1.8 1.9 2.  2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3. ]
[0.  0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.  1.1 1.2 1.3 1.4 1.5 1.6 1.7
 1.8 1.9 2.  2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3. ]
[0.  0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.  1.1 1.2 1.3 1.4 1.5 1.6 1.7
 1.8 1.9 2.  2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8]
[0.  0.5 1.  1.5 2.  2.5 3. ]
[0.2 0.8 1.4 2.  2.6]

第2关 绘制公式


class Solution:
    def solve(self, v0, g):
        """
        :type v0, g: int, int
        :rtype: None
        """
        #请在此按照“编程要求”填写代码
        #********** Begin *********#
        import numpy as np
        from matplotlib import pyplot as plt
        #生成横坐标
        x = np.linspace(0.0, 2*v0/g, 50)
        #生成纵坐标
        y = v0*x-1/2*g*x*x
        #描绘函数图像
        plt.plot(x, y)
        #添加横坐坐标信息
        plt.xlabel('time(s)')
        #添加纵坐标信息
        plt.ylabel('height(m)')
        plt.show()
        ##********** End **********#
        plt.savefig("step2/stu_img/student.png")

第3关 绘制多参数公式

class Solution:
    def solve(self, v0):
        """
        :type v0: List[int]
        :rtype: None
        """
        #请在此按照“编程要求”填写代码
        #********** Begin *********#
        import numpy as np
        from matplotlib import pyplot as plt
        g = 9.81
        for v in v0:
            t = np.linspace(0,2.0*v/g,50)
            y = v*t-0.5*g*t**2
            plt.plot(t,y)
        plt.xlabel('time(s)')
        plt.ylabel('height(m)')
        plt.show()
        ##********** End **********#
        plt.savefig("step3/stu_img/student.png")

第4关 指定图中轴的范围

class Solution:
    def solve(self, v0):
        """
        :type v0: List[int]
        :rtype: None
        """
        #请在此按照“编程要求”填写代码
        #********** Begin *********#
        import numpy as np
        from matplotlib import pyplot as plt
 
        g = 9.81
        t1 = 0
        y1 = 0
        for v in v0:
            t = np.linspace(0,2.0*v/g,50)
            if max(t) > t1:
                t1 = max(t)
 
            y = v*t-0.5*g*t**2
            if max(y) > y1:
                y1 = max(y)
            plt.plot(t,y)
 
        plt.axis([0, t1, 0, y1*1.1])
        plt.xlabel('time(s)')
        plt.ylabel('height(m)')
        plt.show()
 
        ##********** End **********#
        plt.savefig("step4/stu_img/student.png")

第5关 绘制精确和不精确的华氏-摄氏转换公式


class Solution:
    def solve(self, s, e):
        """
        :type s, e: int, int
        :rtype: None
        """
        #请在此按照“编程要求”填写代码
        #********** Begin *********#
        import numpy as np
        from matplotlib import pyplot as plt
        f = np.linspace(s, e, 50)
        c1 = (f - 30) / 2
        c2 = (f - 32) * 5 / 9
        plt.plot(f, c1, 'r.', f, c2, 'b-')
        plt.show()
        ##********** End **********#
        plt.savefig("step5/stu_img/student.png")

第6关 绘制球的轨迹


class Solution:
    def solve(self, y0, theta, v0):
        """
        :type y0, theta, v0: int, int, int
        :rtype: None
        """
        #请在此按照“编程要求”填写代码
        #********** Begin *********#
        import numpy as np
        from matplotlib import pyplot as plt
        g = 9.81
        theta = theta/180.0*np.pi
        a = -1/(2*v0**2)*g/(np.cos(theta)**2)
        b = np.tan(theta)
        c = y0
        delta = np.sqrt(b**2-4*a*c)
        x0 = (-b-delta)/(2*a)
        x1 = (-b+delta)/(2*a)
        xmin = min(x0, x1)
        xmax = max(x0, x1)
        x = np.linspace(0,xmax,51)
        y = x*np.tan(theta)-1/(2*v0**2)*g*(x**2)/(np.cos(theta)**2)+y0
        plt.plot(x,y)
        plt.axis([min(x),max(x),0,max(y)*1.1])
        plt.show()
        ##********** End **********#
        plt.savefig("step6/stu_img/student.png")

第7关 绘制文件中的双列数据


class Solution:
    def solve(self, file):
        """
        :type file: str
        :rtype: None
        """
        #请在此按照“编程要求”填写代码
        #********** Begin *********#
        from matplotlib import pyplot as plt
        ifile = open(file, 'r')
        x, y = [], []
        for line in ifile:
            a = line.split()
            x.append(float(a[0]))
            y.append(float(a[1]))
        print(sum(y)/len(y), max(y), min(y))
        plt.plot(x, y)
        plt.show()
        ifile.close()
        ##********** End **********#
        plt.savefig("step7/stu_img/student.png")

Python 数组与曲线绘制(三)

第1关 绘图函数 – 绘制 sin 函数


# 请绘制sin函数曲线
 
import matplotlib
matplotlib.use("Agg") # 设置平台绘图环境,勿删
 
import matplotlib.pyplot as plt
# 请在此添加代码实现函数细节   #
# ********** Begin *********#
x = [0,30,60,90,120,150,180,210,240,270,300,330,360]
y = [0,0.5,0.866,1,0.866,0.5,0,-0.5,-0.866,-1,-0.866,-0.5,0]
plt.plot(x,y,'.')
plt.show()
# ********** End **********#
plt.savefig('picture/step0/fig0.png') #存储输出图像,勿删

第2关 绘图与保存 – 抛物线函数曲线


# 请绘制抛物线曲线
import matplotlib
matplotlib.use("Agg")
 
def f(x):
    # 请在此添加代码实现函数细节   #
    # ********** Begin1 *********#
    x = list(range(0,51,1))
    y = []
    for i in range(0,len(x)):
        y.append(3*(v[i]**2) + 2*(v[i]) + 1)
    return y
    # ********** End1 **********#
 
#   请在此添加代码绘制曲线并存储图像#
# ********** Begin2 *********#
import matplotlib.pyplot as plt
x = list(range(0,51,1))
y = []
for i in range(0,len(x)):
    y.append(3*(x[i]**2) + 2*(x[i]) + 1)
plt.plot(x,y,'r--')
plt.show()
plt.savefig('picture/step1/fig1.png')
# ********** End2 **********#
  • 第3关 数组计算与向量化处理 – 函数曲线绘制与坐标处理

# 请绘制函数曲线
import matplotlib
matplotlib.use("Agg")
#   请在此添加实现代码   #
# ********** Begin *********#
import numpy as np
import matplotlib.pyplot as plt
t = np.linspace(0,3,50)
y = t**2*np.exp(-t**2)
plt.plot(t,y)
plt.show()
plt.savefig('picture/step2/fig2.png')
# ********** End **********#

第4关 图例与坐标设置 – 绘制多条曲线

#请在同一坐标系中绘制两条曲线
import matplotlib
matplotlib.use("Agg")
 
#   请在此添加实现代码   #
# ********** Begin *********#
import numpy as np
import matplotlib.pyplot as plt
t = np.linspace(0,3,50)
y1 = t**2*np.exp(-t**2)
y2 = t**4*np.exp(-t**2)
plt.plot(t,y1,'r--')
plt.plot(t,y2,'b-o')
plt.title('Plotting two curves in the same plot')
plt.xlabel('t')
plt.ylabel('y')
plt.legend(['y1','y2'])
plt.savefig('picture/step3/fig3.png')
# ********** End **********#

第5关 向量化处理 – 绘制函数图形


# 请编写代码实现向量化帽函数并绘制函数曲线
import matplotlib
matplotlib.use("Agg")
#   请在此添加实现代码   #
# ********** Begin *********#
import numpy as np
import matplotlib.pyplot as plt
def H3(x):
    return np.where(x<0,0,(np.where(x<1,x,(np.where(x<2,2-x,0)))))
x = np.linspace(-3,5,1000)
y = H3(x)
plt.title('Plotting hat func in this plot')
plt.plot(x,y,'b-')
plt.show()
plt.savefig('picture/step4/fig4.png')
# ********** End **********#

Python 绘图进阶

第1关 柱状图 – 商品房销售价格统计图

# 请编写代码绘制住宅商品房平均销售价格柱状图
import matplotlib
matplotlib.use("Agg")
#  请在此添加实现代码  #
# ********** Begin *********#
import matplotlib.pyplot as plt
from numpy import *
xstring = '2015 2014 2013 2012 2011     \
           2010 2009 2008 2007 2006     \
           2005 2004 2003 2002 2001    2000'
ystring = '12914 11826 12997 12306.41 12327.28 \
            11406 10608    8378 8667.02 8052.78 \
            6922.52    5744 4196 4336 4588    4751'
y = ystring.split()
y.reverse()
y = [float(e) for e in y]
xlabels = xstring.split()
xlabels.reverse()
x = range(len(xlabels))
plt.xticks(x, xlabels, rotation = 45)
plt.yticks(range(4000,13500,1000))
plt.ylim(4000,13500)
plt.bar(x, y, color = '#800080')
plt.savefig('picture/step1/fig1.png')
# ********** End **********#

第2关 并列柱状图 – 商品房销售价格统计图

# -*- coding: utf-8 -*-
import matplotlib
import re
matplotlib.use("Agg")
 
import matplotlib.pyplot as plt
import numpy as np
 
xstring = '2015 2014 2013 2012 2011     \
           2010 2009 2008 2007 2006     \
           2005 2004 2003 2002 2001    2000' #x轴标签
 
n = 6
ystring = ['']*n #y轴对应的6组数据
ystring[0] = '6793    6324    6237    5790.99    5357.1    5032    4681    3800    3863.9    3366.79    3167.66    2778    2359    2250    2170    2112'
ystring[1] = '6473    5933    5850    5429.93    4993.17    4725    4459    3576    3645.18    3119.25    2936.96    2608    2197    2092    2017    1948'
ystring[2] = '15157    12965    12591    11460.19    10993.92    10934    9662    7801    7471.25    6584.93    5833.95    5576    4145    4154    4348    4288'
ystring[3] = '12914    11826    12997    12306.41    12327.28    11406    10608    8378    8667.02    8052.78    6922.52    5744    4196    4336    4588    4751'
ystring[4] = '9566    9817    9777    9020.91    8488.21    7747    6871    5886    5773.83    5246.62    5021.75    3884    3675.14    3488.57    3273.53    3260.38'
ystring[5] = '4845    5177    4907    4305.73    4182.11    4099    3671    3219    3351.44    3131.31    2829.35    2235    2240.74    1918.83    2033.08    1864.37'
 
labels = ['Commercial housing', 'Residential commercial housing',
          'high-end apartments', 'Office Building', 'Business housing', 'Others'] #图例标签
colors = ['#ff7f50', '#87cefa', '#DA70D6', '#32CD32', '#6495ED', '#FF69B4'] #指定颜色
 
#  请在此添加实现代码  #
# ********** Begin *********#
x_labels=re.findall(r'\b\d+\b',xstring)[::-1]
ylist=[]
for y in ystring:
    ylist.append(list(map(float,re.findall(r'[0-9]+\.?[0-9]*',y)))[::-1]) #或者使用y.split()
 
bar_width = 0.8
xindex=np.arange(1,92,6)
    
fig, ax = plt.subplots()
for i in range(6):
    ax.bar(xindex+bar_width*i, ylist[i], bar_width ,color=colors[i])
    
ax.set_xlim(-1,98) #闭区间
plt.xticks(xindex+bar_width*2.5,x_labels,rotation=45)
ax.set_ylim(1450,15300)
plt.yticks(np.arange(2000,16000,2000))
plt.legend(labels,loc='upper left')
plt.title('Selling Prices of Six Types of Housing')
 
plt.savefig('picture/step2/fig2.png')
 
# ********** End **********#

第3关 饼状图 – 2010 全国人口普查数据分析

# 请绘制育龄妇女的受教育程度分布饼图
import matplotlib
matplotlib.use("Agg")
#  请在此添加实现代码  #
# ********** Begin *********#
import matplotlib.pyplot as plt
labels = ['none', 'primary', 'junior', 'senior', 'specialties', 'bachelor', 'master'] # 标签
colors = ['red','orange','yellow','green','purple','blue','black'] #指定楔形颜色
womenCount = [2052380, 11315444, 20435242, 7456627, 3014264, 1972395, 185028]
explode = [0,0,0.1,0,0,0,0] # 确定突出部分
plt.pie(womenCount, explode=explode, labels=labels, shadow=True,colors=colors)
plt.axis('equal')  # 用于显示为一个长宽相等的饼图
plt.savefig('picture/step3/fig3.png')
# ********** End **********#

第4关 多子图绘制 – 2010 全国人口普查数据分析

import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import numpy as np
labels = ['none', 'primary', 'junior', 'senior', 'specialties', 'bachelor', 'master'] # 标签
womenCount = [2052380, 11315444, 20435242, 7456627, 3014264, 1972395, 185028]
birthMen = [2795259, 12698141, 13982478, 2887164, 903910, 432333, 35915]
birthWomen = [2417485, 11000637, 11897674, 2493829, 786862, 385718, 32270]
liveMen = [2717613, 12477914, 13847346, 2863706, 897607, 429809, 35704]
liveWomen = [2362007, 10854232, 11815939, 2480362, 783225, 384158, 32136]
#  请在此添加实现代码  #
# ********** Begin *********#
x = np.arange(len(labels))
birth = np.array(birthMen) + np.array(birthWomen)
live = np.array(liveMen) + np.array(liveWomen)
plt.figure(figsize=[14,5]) #设置画布大小
plt.subplot(121)
birthrate = (1.0*live) / (1.0*np.array(womenCount))
plt.plot(x, birthrate, 'r')
plt.xticks(x, labels)
plt.subplot(122)
liverate = (1.0*live) / (1.0*birth) * 100
plt.plot(x, liverate, 'b')
plt.xticks(x, labels)
plt.savefig('picture/step4/fig4.png')
# ********** End **********#

Python数据可视化之折线图

第1关 折线图的绘制与优化


# -*- coding: utf-8 -*-
import pandas as pd #用于生成满足绘图要求的数据格式
import numpy as np #用于展示横坐标
from matplotlib import pyplot as plt #用于绘制折线图
 
population = pd.read_csv(r"LineChart/level1/csv/world-population.csv") #返回值为二维标记数据结构 DataFrame
def plot():
    # ********* Begin *********#
    fig,ax=plt.subplots()
    my_x_ticks = np.arange(1960, 2011, 5)
    plt.xticks(my_x_ticks)
    plt.grid(b=True, color='r', linestyle='--', linewidth=1, alpha=0.3, axis='x', which="major") #设置网格
    ax.plot(population["Year"],population["Population"], linewidth=1, c='#00CC88', marker='*', markersize=4) #绘制点和折线
    ax.set_xlabel("Year", fontsize=12)  #设置x轴标签
    ax.set_ylabel("Population", fontsize=12)
    # ********* End *********#
    plt.savefig('LineChart/level1/studentanswer/world-population.png') #保存为png格式
    plt.close() #关闭画布窗口

Python数据可视化之柱形图

  • 第1关 “大胃王”比赛数据柱形图绘制——绘制柱形图的基本步骤
# -*- coding: utf-8 -*-
import pandas as pd
from matplotlib import pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages 
hot_dog = pd.read_csv(r"matplotlib_bar/csv/hot-dog-contest-winners.csv")
 
def plot(): 
    # ********* Begin *********#
    fig, ax = plt.subplots() #subplots返回画布和子图  
    ax.bar(hot_dog["Year"],hot_dog["Dogs eaten"]) #绘制柱形图,第一个参数为x轴变量,第二个参数为y轴变量  
    plt.show()  
 
    # ********* End *********#
    plt.savefig('matplotlib_bar/studentfile/studentanswer/level_1/US.png')
    plt.close()

第2关 “大胃王”比赛数据柱形图绘制——柱形图展示优化


# -*- coding: utf-8 -*-
import pandas as pd
from matplotlib import pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages 
hot_dog = pd.read_csv(r"matplotlib_bar/csv/hot-dog-contest-winners.csv")
 
def plot(): 
    # ********* Begin *********#
    fig, ax = plt.subplots()
    ax.bar(hot_dog["Year"],hot_dog["Dogs eaten"],width=[0.6],color=unitedStatesColor())
    plt.rcParams['figure.figsize'] = (8.0, 4.0)
    ax.set_xlabel("Year")  #设置x轴标签  
    ax.set_ylabel("Dogs Eaten")  #设置y轴标签  
    ax.set_title("Hotdog game scores 1980-2010") #设置标题  
    ax.set_xlim(1979,2011) 
    plt.rcParams['figure.figsize'] = (8.0, 4.0)
    plt.show()   
    # ********* End *********#
    plt.savefig('matplotlib_bar/studentfile/studentanswer/level_2/US.png')
    plt.close()
 
def unitedStatesColor():
    # ********* Begin *********#
    list=[]  
    for i in hot_dog["Country"]:  
        if i=="United States": 
            list.append("#DB7093") #打破记录的年份显示为粉红色  
        else:  
            list.append("#5F9F9F") #其余年份显示为灰绿色  
    return list 
 
    # ********* End *********#

Python数据可视化之散点图

第1关 美国犯罪率数据散点图绘制——散点图的基本绘制步骤


# -*- coding: utf-8 -*-
import pandas as pd #用于生成满足绘图要求的数据格式
from matplotlib import pyplot as plt #用于绘制散点图
import statsmodels.api as sm #用于局部加权回归
from matplotlib.backends.backend_pdf import PdfPages
crime=pd.read_csv(r"matplotlibScatter/csv/crimeRatesByState2005.csv") #返回值为二维标记数据结构 DataFrame
def plot():
    # ********* Begin *********#
    fig,ax=plt.subplots() #subplots返回画布和子图  
    crime2=crime[~crime['state'].isin(['District of Columbia','United States'])] #获取没有全美平均值和华盛顿特区的犯罪率数据  
    ax.plot(crime2["murder"],crime2["burglary"],"*",color="#00CC88") 
    ax.set_xlabel("crime murder", fontsize=12)  #设置x轴标签  
    ax.set_ylabel("crime burglary", fontsize=12) 
    ax.set_xlim(0,10) #x轴范围从0到10  
    ax.set_ylim(0,1200) 
    plt.show()
 
 
    # ********* End *********#
    plt.savefig('matplotlibScatter/studentanswer/level_1/crime.png') #保存为png格式
    plt.close() #关闭画布窗口

第2关 美国犯罪率数据散点图绘制——局部加权回归


# -*- coding: utf-8 -*-
import pandas as pd #用于生成满足绘图要求的数据格式
from matplotlib import pyplot as plt#用于绘制散点图
import statsmodels.api as sm #用于局部加权回归
 
crime=pd.read_csv(r"matplotlibScatter/csv/crimeRatesByState2005.csv") #返回值为二维标记数据结构 DataFrame
def plot():
    # ********* Begin *********#
 
    plt.figure(figsize=(8,4))
    fig,ax=plt.subplots()
    crime2=crime[~crime['state'].isin(['District of Columbia','United States'])]
    lowess = sm.nonparametric.lowess(crime2["burglary"],crime2["murder"])
    ax.plot( lowess[ :,0],lowess[ :,1])
    ax.plot(crime2["murder" ], crime2["burglary"],"*",color="#00CC88")
    ax.set_xlabel("crime murder" ,fontsize=12)
    ax.set_ylabel("crime burglary" ,fontsize=12)
    ax.set_title("美国谋杀率和入室盗窃率",fontproperties="SimHei",fontsize=16)
    ax.set_xlim(0,10) 
    ax.set_ylim(0,1200)
    plt.show()
 
    # ********* End *********#
    plt.savefig('matplotlibScatter/studentanswer/level_2/crime.png') #保存为png格式
    plt.close() #关闭画布窗口

Python数据可视化之多维量法(MDS)

第1关 美国国家教育统计中心数据——降维


# -*- coding: utf-8 -*-
import pandas as pd #用于生成满足绘图要求的数据格式
from sklearn.manifold import MDS #用于MDS降维
import matplotlib.pyplot as plt #用于绘制撒点图
from sklearn.cluster import KMeans #用于Kmeans聚类
from scipy.spatial import distance #用于计算获取距离矩阵
edu=pd.read_csv(r"MDS/csv/education.csv") #读取csv数据,返回值为二维标记数据结构 DataFrame
def plot():
    # ********* Begin *********#
    edu_x=edu.iloc[:,1:7] #选择edu中的第 1 列到第 6 列  
    DM_dist = distance.squareform(distance.pdist(edu_x, metric="euclidean")) #计算距离矩阵 
    clf2 = MDS(n_components=2,dissimilarity="precomputed") 
    edu_t2 = clf2.fit_transform(DM_dist)  
    fig,ax=plt.subplots() 
    ax.scatter(edu_t2[:,0],edu_t2[:,1]) 
    names=list(edu.iloc[:,0]) 
    for i in range(len(names)):  
        plt.annotate(names[i], xy = (edu_t2[:,0][i],edu_t2[:,1][i]), xytext=(-20, 5), textcoords='offset points') 
    # ********* End *********#
    plt.savefig("MDS/studentanswer/level_1/education.png")
    plt.close()

第2关 美国国家教育统计中心数据——分别按特征和聚类结果着色

# -*- coding: utf-8 -*-
import pandas as pd #用于生成满足绘图要求的数据格式
from sklearn.manifold import MDS #用于MDS降维
import matplotlib.pyplot as plt #用于绘制撒点图
from sklearn.cluster import KMeans #用于Kmeans聚类
from scipy.spatial import distance #用于计算获取距离矩阵
edu=pd.read_csv(r"MDS/csv/education.csv") #读取csv数据,返回值为二维标记数据结构 DataFrame
def plot():
    # ********* Begin *********#
    edu_x=edu.iloc[:,1:7] #选择edu中的第 1 列到第 6 列  
    DM_dist = distance.squareform(distance.pdist(edu_x, metric="euclidean")) #计算距离矩阵 
    clf2 = MDS(n_components=2,dissimilarity="precomputed") 
    edu_t2 = clf2.fit_transform(DM_dist)  
    fig,ax=plt.subplots()   
    reading_colors_list=[] 
    average=sum(edu_x["reading"])/len(edu_x["reading"]) #计算阅读平均值  
    for i in range(0,len(edu_x["reading"])):  
        if edu_x["reading"][i] < average:  
            reading_colors_list.append("#DB7093") #小于平均值的数据为粉红色,并添加到颜色列表
        else:  
            reading_colors_list.append("#5F9F9F") #大于平均值的数据为灰绿色,并添加到颜色列
    ax.scatter(edu_t2[:,0],edu_t2[:,1],color=reading_colors_list) 
    names=list(edu.iloc[:,0]) #选择州名这一列数据  
    for i in range(len(names)):  
        plt.annotate(names[i], xy = (edu_t2[:,0][i],edu_t2[:,1][i]), xytext=(-20, 5), textcoords='offset points',color=reading_colors_list[i])
    plt.show()   
    # ********* End *********#
    plt.savefig("MDS/studentanswer/level_2/education.png")
    plt.close()

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