app网站制作要多少费用/百度推广介绍

1. 词云WordCloud——续

①Python中使用open内置函数进行文件读取

②利用函数jieba.lcut(words)进行分词

③过滤重复词和无关词

④给十个人物出现的次数进行排序

⑤输出图片

示例一：三国TOP10人物分析

import jieba

from wordcloud import WordCloud

# 1.读取小说内容

with open('./novel/threekingdom.txt','r',encoding='utf-8') as f:

words = f.read()

counts = {} #{‘曹操’：234，‘回寨’：56}

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",

"孔明曰","玄德曰","刘备","云长"}

# 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] = 去除字典中原来键相应的值 + 1

# counts[word] = counts[word] + 1 # counts[words]如果没有就要报错

# 字典。get(k) 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'] + counts['孔明曰']

counts['玄德'] = counts['玄德'] + counts['玄德曰'] + counts['刘备']

counts['关公'] = counts['关公'] + counts['云长']

for word in excludes:

del counts[word]

# 4.排序[(),()]

items = list(counts.items())

print(items)

def sort_by_count(x):

return x[1]

# items.sort(key=sort_by_count,reverse=True)

items.sort(key=lambda i:i[1],reverse=True)

li = [] #['孔明','','']

# 遍历

for i in range(10):

# 序列解包

role, count = items[i]

print(role, count)

# _ 是告诉看代码的人，循环里面不需要临时变量

for _ in range(count):

li.append(role)

# 5.得出结论

text = ' '.join(li)

WordCloud(

font_path='msyh.ttc',

background_color='white',

width=880,

height=600,

#两个相邻重复词之间的匹配

collocations=False

).generate(text).to_file('TOP10.png')

三国人物TOP10.png

三国人物TOP10.png

示例二：红楼梦TOP10人物分析

# 红楼梦 top1o人物分析

import jieba

from wordcloud import WordCloud

# 1.读取小说内容

with open('./novel/all.txt','r',encoding='utf-8') as f:

words = f.read()

counts = {}

excludes = {'什么','我们','你们','如今','说道','知道','姑娘','起来','这里','出来','众人','那里','奶奶',

'自己','太太','一面','只见','两个','没有','怎么','不是','不知','这个','听见','这样','进来',

'咱们','就是','东西','告诉','回来','回来','只是','大家','老爷','只得','丫头','这些','他们',

'不敢','出去','所以','一个','贾宝玉','王熙凤','老太太','凤姐儿','林黛玉','薛宝钗'}

# 2.分词

words_list = jieba.lcut(words)

print(words_list)

for word in words_list:

if len(word) <= 1:

continue

else:

counts[word] = counts.get(word, 0) + 1

print(counts)

# 3.词语过滤重复词

counts['宝玉'] = counts['宝玉'] + counts['贾宝玉']

counts['黛玉'] = counts['黛玉'] + counts['林黛玉']

counts['宝钗'] = counts['宝钗'] + counts['薛宝钗']

counts['贾母'] = counts['老太太'] + counts['贾母']

counts['凤姐'] = counts['凤姐'] + counts['王熙凤']+ counts['凤姐儿']

#删除无关词

for word in excludes:

del counts[word]

# 4.排序[(),()]

items = list(counts.items())

print(items)

def sort_by_count(x):

return x[1]

# items.sort(key=sort_by_count,reverse=True)

items.sort(key=lambda i:i[1],reverse=True)

li = []

# 遍历

for i in range(10):

# 序列解包

role, count = items[i]

print(role, count)

# _ 是告诉看代码的人，循环里面不需要临时变量

for _ in range(count):

li.append(role)

# 5.得出结论

text = ' '.join(li)

WordCloud(

font_path='msyh.ttc',

background_color='white',

width=880,

height=600,

# 两个相邻重复词之间的匹配

collocations=False

).generate(text).to_file('红楼梦TOP10.png')

红楼梦人物TOP10.png

红楼梦人物TOP10.png

2. 匿名函数

表达式：calc = lambda n: n*n

print(calc(数值))

关键字lambda表示匿名函数，冒号前面的n表示函数参数，可以有多个参数。匿名函数有个限制，就是只能有一个表达式，不用写return，返回值就是该表达式的结果。

sum_num = lambda x1, x2:x1+x2

print(sum_num(2, 3))

# 参数可以是无限多个，但是表达式只有一个

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)

stu_info = [

{"name":'zhangsan',"age":18},

{"name":'lisi',"age":30},

{"name":'wangwu',"age":99},

{"name":'tiaqi',"age":3},

]

stu_info.sort(key=lambda i:i['age'])

print(stu_info)

image.png

3. 列表推导式

1. 列表解析

之前我们使用普通for 创建列表

如：

li = []

for i in range(10):

li.append(i)

print(li)

image.png

使用列表推导式

格式：[表达式 for 临时变量 in 可迭代对象 可以追加条件]

# [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)])

image.png

筛选出列表中所有的偶数

#筛选出列表中所有的偶数

li = []

for i in range(10):

if i%2 == 0:

li.append(i)

print(li)

image.png

使用列表解析

#筛选出列表中所有的偶数

print([i for i in range(10) if i%2 ==0])

image.png

筛选出列表中大于0的数

#筛选出列表中大于0的数

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])

image.png

2. 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)

# 刷选大于60分的所有学生

print({k: v for k, v in stu_grades.items() if v > 60})

image.png

4. 图形绘制

1. 正弦和余弦图

from matplotlib import pyplot as plt

plt.rcParams["font.sans-serif"] = ['SimHei']

plt.rcParams['axes.unicode_minus'] = False

import numpy as np

# 使用100个点，绘制[0, 2∏]正弦曲线图

# .linspace 左闭右闭区间的等差数列

x = np.linspace(0, 2*np.pi, num=100)

print(x)

y = np.sin(x)

# 正弦和余弦在同一坐标系下

cosy = np.cos(x)

plt.plot(x, y,color='g',linestyle='--',label='sin(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的世界')

正弦和余弦图.png

2. 柱状图

# 导入

from matplotlib import pyplot as plt

plt.rcParams["font.sans-serif"] = ['SimHei']

plt.rcParams['axes.unicode_minus'] = False

import numpy as np

# 柱状图

import string

from random import randint

# print(string.ascii_uppercase[0,6])

# ['A','B','C',...]

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()

柱状图.png

3. 饼图

from matplotlib import pyplot as plt

plt.rcParams["font.sans-serif"] = ['SimHei']

plt.rcParams['axes.unicode_minus'] = False

import numpy as np

# # 饼图

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()

饼图.png

4. 散点图

from matplotlib import pyplot as plt

plt.rcParams["font.sans-serif"] = ['SimHei']

plt.rcParams['axes.unicode_minus'] = False

import numpy as np

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()

散点图.png