1.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)
根据关键字排序
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)
2. 列表推导式
列表解析个字典解析。之前我们使用普通for创建列表
li=[]
for i in range(10):
li.append(i)
print(li)
print([i for i in range(10)])
列表推导式 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)
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个学生成绩
stu_grade = {'student{}'.format(i):randint(50,100) for i in range(1,101)}
print(stu_info)
# 筛选出大于60分的所有学生
print({k: v for k,v in stu_grade.items()if v>60})
3.matplotlib
曲线图
#导入
from matplotlib import pyplot as plt
from matplotlib import pyplot as plt
plt.rcParams["font.sans-serif"] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
import numpy as np
# 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)
#`正余玄函数在同一坐标下
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世界")
plt.legend()
plt.show()
柱状图
from random import randint
from matplotlib import pyplot as plt
plt.rcParams["font.sans-serif"] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
import string
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("口红价格")
plt.bar(x,y)
plt.show()
饼图
from random import randint
from matplotlib import pyplot as plt
plt.rcParams["font.sans-serif"] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
import string
counts = [randint(3500,9000) for _ in range(6)]
labels = ['员工{}'.format(x) for x in string.ascii_uppercase[:6]]
explode = [0.1,0,0,0,0,0]
plt.pie(counts,labels=labels,autopct='%1.1f%%',shadow=True ,explode=explode)
plt.legend(loc=2)
plt.axis("equal")
plt.show()
散点图
import numpy as np
from matplotlib import pyplot as plt
from random import randint
x = np.random.normal(0,1,100)
y = np.random.normal(0,1,100)
plt.scatter(x,y,alpha = 1)
plt.show()
三国人物分析
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] = 取出字典中原来键对应的值 + 1
# counts[word] = counts[word] + 1 # counts[word]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(len(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)
li = [] # ['孔明'， 孔明， 孔明，孔明...., '曹操'。。。。。]
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=800,
height=600,
# 相邻两个重复词之间的匹配
collocations=False
).generate(text).to_file('TOP10.png')
红楼梦
import jieba
from wordcloud import WordCloud
with open('all.txt', 'r', encoding='utf-8') as f:
words = f.read()
#print(words)
counts = {}
excludes = {"什么", "一个", "我们", "你们", "如今", "说道", "知道", "起来", "这里",
"出来", "众人", "那里", "自己", "一面", "只见", "太太", "两个", "没有",
"怎么", "不是", "不知", "这个", "听见", "这样", "进来", "咱们", "就是",
"老太太", "东西", "告诉", "回来", "只是", "大家", "姑娘", "奶奶", "凤姐儿","分节"}
words_list = jieba.lcut(words)
for word in words_list:
if len(word) <= 1:
continue
else:
# 更新字典中的值
# counts[word] = 取出字典中原来键对应的值 + 1
# counts[word] = counts[word] + 1 # counts[word]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(len(counts))
counts['贾母'] = counts['贾母'] + counts['老太太']
counts['宝钗'] = counts['宝钗'] + counts['薛宝钗']
counts['凤姐'] = counts['凤姐儿'] + counts['王熙凤'] + counts['凤姐']
counts['宝玉'] = counts['贾宝玉'] + counts['宝玉']
counts['王夫人'] = counts['王夫人'] + counts['太太']
counts['黛玉'] = counts['黛玉'] + counts['林黛玉']
counts['贾政'] = counts['贾政'] + counts['老爷']
for word in excludes:
del counts[word]
items = list(counts.items())
# print(items)
items.sort(key=lambda i: i[1], reverse=True)
li = []
peo_li = []
for i in range(10):
# 序列解包
role, count = items[i]
a = {'name': '', 'count': 0}
a['name'] = role
a['count'] = count
peo_li.append(a)
print(role, count)
for _ in range(count): # _是告诉看代码的人循环里不需要使用临时变量
li.append(role)
text = ' '.join(li)
WordCloud(
font_path='msyh.ttc',
background_color='white',
width=800,
height=600,
#mask=mask,
# 相邻两个值的重复
collocations=False
).generate(text).to_file('红楼Top10.png')
# 用饼图显示人物
from random import randint
import string
from matplotlib import pyplot as plt
plt.rcParams["font.sans-serif"] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
counts = []
labels = []
for i in range(len(peo_li)):
counts.append(peo_li[i]['count'])
labels.append(peo_li[i]['name'])
# 距离圆心点距离
explode = [0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0]
# colors = ['red', 'purple', 'blue', 'yellow', 'gray', 'green']
plt.pie(counts, explode=explode, shadow=True, labels=labels, autopct='%1.1f%%')
plt.legend(loc=2)
plt.axis('equal')
plt.show()
