学习Python的第三天
三国TOP10人物分析
读取小说内容
with open('./novel/threekingdom.txt','r',encoding='utf-8' as f):
words = f.read()
counts={}
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"}
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(len(counts))
排序
items=list(counts.items())
print(items)
def sort_by_count(x)
return x[1]
items.sort(key=sort_by_count,reverse=True)
for i in range(20):
role,count=items[i]
print(role,count)
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]
得出结论
text = ' '.join(li)
WordCloud(
font_path='msyh.ttc',
background_color='white',
width=800,
height=600,
# 相邻两个重复词之间的匹配
collocations=False
).generate(text).to_file('TOP10.png')
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))
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)
列表
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)
# 使用列表解析
print([i for i in range(10) if i%2 == 0])
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])
字典解析
生成100个学生的成绩
stu_grades = {'student{}'.format(i):randint(50, 100) for i in range(1, 101)}
print(stu_grades)
筛选大于 60分的所有学生
print({k: v for k, v in stu_grades.items() if v >60})
导入matplotlib
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 , 2π]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)
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世界')
plt.legend()
plt.show()
柱状图
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 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] ]
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
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)
plt.scatter(x, y, alpha=0.1)
plt.show()