python学习的第三天

三国Top10人物

import jieba
from wordcloud import WordCloud
import imageio

#1.读取小说内容
with open('./novel/novel/threekingdom.txt','r',encoding='utf-8') as f:
    words = f.read()

    counts = {}     #{'曹操'：234，'玄德'：51}  //字典
    excludes = {"将军", "却说", "丞相", "二人", "不可", "荆州", "不能", "如此", "商议",
                "如何", "主公", "军士", "军马", "左右", "次日", "引兵", "大喜", "天下",
                "东吴", "于是", "今日", "不敢", "魏兵", "陛下", "都督", "人马", "不知"}

    #2.分词
    words_list = jieba.lcut(words)
    # print(words_list)

    for word in words_list:
        if len(word) <= 1:
            continue
        else:
            #更新字典中的值
            # counts[word] = 取出字典中原来键的值+1
            #counts[word] = counts[word]+1  #count[word]如果没有就要报错
            #字典.get()  如果字典中没有这个键，返回NONE
            counts[word] = counts.get(word,0)+1
    print(counts)
#3.词语过滤 删除无关词，重复词
    counts['孔明'] = counts['孔明曰'] + counts['孔明']
    counts['玄德'] = counts['玄德曰'] + counts['玄德']+ counts['刘备']
    counts['云长'] = counts['云长'] + counts['关公']
    excludes = {"将军", "却说", "丞相", "二人", "不可", "荆州", "不能", "如此", "商议",
                "如何", "主公", "军士", "军马", "左右", "次日", "引兵", "大喜", "天下",
                "东吴", "于是", "今日", "不敢", "魏兵", "陛下", "都督", "人马", "不知",
                "关公","玄德曰","刘备","孔明曰"}
    for word in excludes:
        del counts[word]
    #4.排序 [(),()]
    Items = list(counts.items())
    print(Items)
    # 这是第一种方法
    # def sort_by_count(x):
    #     return x[1]

    Items.sort(key=lambda x:x[1],reverse=True) # 运用了匿名函数
    print(Items)
    li = []
    for i in range(10):
        #序列解包
        role,count = Items[i]
        print(role, count)
        # _是告诉看代码的人，循环里面不需要使用循环变量
        for _ in range(count):
            li.append(role)
#5.得出结论
    mask = imageio.imread('china.jpg')
    text = ' '.join(li)
    WordCloud(
        font_path='msyh.ttc',
        background_color='white',
        width=550,
        height=520,
        collocations = False,
        mask = mask
    ).generate(text).to_file('TOP10.png')

在上个实例中我们运用了匿名函数，那么匿名函数到底是什么

匿名函数

结构

lambda x1,x2,....xn:表达式

sum_num = lambda x1,x2:x1+x2

print(sum_num(2,3))

注意 参数可以是无限多个，但是表达式只有一个

实例

name_info_list = [
    ('张三',4500),
    ('李四',9900),
    ('王五',2000),
    ('赵六',5500),
]
name_info_list.sort(key=lambda x:x[1],reverse=True)
print(name_info_list)

使用列表推导式

[表达式 for 临时变量 in 可迭代对象 可以追加条件]

print([i for i in range(10)])

实例

from random import randint
 num_list = [randint(-10,10) for _ in range(10)]
 print(num_list)
 print([i for i in num_list if i>0])

#字典解析
#生成100个学生的成绩
from random import randint
stu_grades = {
    'student{}'.format(i):randint(50,100) for i in range(1,101)
}
print(stu_grades)

#筛选出大于60分的学生
print({k:v for k,v in stu_grades() if v > 60})

图形

from matplotlib import pyplot as plt
import numpy as np
plt.rcParams["font.sans-serif"] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
#使用100个点绘制[0,2π]正弦曲线图
x = np.linspace(0,2*np.pi,num=100)
print(x)
y=np.sin(x)
plt.plot(x,y,color='g',linestyle='--',label='sin(x)')
#   正弦与余弦在同一坐标系下
cosy = np.cos(x)
plt.plot(x,cosy,color='r',label='cos(x)')
plt.xlabel('时间（S）')
plt.ylabel('电压（V）')
plt.title('欢迎来到Python世界')
#图例
plt.legend()
plt.show()

#柱状图
from matplotlib import pyplot as plt
import numpy as np
plt.rcParams["font.sans-serif"] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
import string
from random import randint
# print(string.ascii_uppercase[0:6])
x= ['口红{}'.format(x) for x in string.ascii_uppercase[:5]]
y= [randint(200,500) for _ in range(5)]
print(x)
print(y)
plt.xlabel('口红品牌')
plt.ylabel('价格（元）')
plt.bar(x,y)
plt.show()

#饼图
from matplotlib import pyplot as plt
import numpy as np
plt.rcParams["font.sans-serif"] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
from random import randint
import string
counts = [randint(3500, 9000) for _ in range(6)]
labels = ['员工{}'.format(x) for x in string.ascii_lowercase[:6] ]
# 距离圆心点距离
explode = [0.1,0,0, 0, 0,0]
colors = ['red', 'purple','blue', 'yellow','gray','green']
plt.pie(counts,explode = explode,shadow=True, labels=labels, autopct = '%1.1f%%',colors=colors)
plt.legend(loc=2)
plt.axis('equal')
plt.show()

#散点图
from matplotlib import pyplot as plt
import numpy as np
plt.rcParams["font.sans-serif"] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
#均值为 0 标准差为1 的正太分布数据
x = np.random.normal(0, 1, 100)
y = np.random.normal(0, 1, 100)
plt.scatter(x, y)
plt.show()

x = np.random.normal(0, 1, 1000000)
y = np.random.normal(0, 1, 1000000)
# alpha透明度
plt.scatter(x, y, alpha=0.1)
plt.show()