Python學習第三天

可視化 繪制正弦余弦曲線

案例:

import numpy as np
import matplotlib.pyplot as plt
from matplotlib import pyplot as plt
plt.rcParams["font.sans-serif"] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
#可視化
#折線圖
#繪制正弦曲線
#[0,2π]閉區間的等間距100個點
x=np.linspace(0,2*np.pi,num=100)
print(x)

siny=np.sin(x)
cosy=np.cos(x)
plt.xlabel('時間(s)')
plt.ylabel('電壓(v)')
plt.title('正弦電壓曲線')

plt.plot(x, siny,color='g',linestyle='--',marker='+',label='sin(x)')
plt.plot(x, cosy,color='r',label='cos(x)')
plt.legend()
plt.show()

輸出結果:


image.png

餅狀圖

案例:

from matplotlib import pyplot as plt
plt.rcParams["font.sans-serif"] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
from random import randint
import string
#餅圖
counts=[randint(3500,10000) for i in range(5)]
print(counts)
labels=['員工{}'.format(i) for i in string.ascii_uppercase[:5]]
print(labels)
#距離圓心的距離
#autopct百分比
explode=[0.2,0,0,0,0]
colors=['red','yellow','blue','purple','green']
plt.pie(counts,explode=explode,labels=labels,shadow=True,colors=colors,autopct='%1.1f%%')
plt.legend(loc=3)
plt.title('員工工資占比圖')
plt.show()

輸出結果:


image.png

散點圖

案例:

from matplotlib import pyplot as plt
plt.rcParams["font.sans-serif"] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
x=np.random.normal(0,1,100000)
y=np.random.normal(0,1,100000)
#alpha為透明度
plt.scatter(x,y,alpha=0.1)
plt.show()

輸出結果:


字典解析

和集合很像
和列表推導式很像
案例:

from random import randint
stu_grade={'student{}'.format(i):randint(50,100) for i in range(1,101)}
for k,v in stu_grade.items():
    print(k,v)

#篩選出及格的學生
res_dict={k:v for k,v in stu_grade.items() if v>60}
for k,v in res_dict.items():
    print(k,v)

輸出結果:


image.png

集合解析

案例:

from random import randint
set1={randint(50,100) for i in range(1,101)}
print(set1)
#篩選能被3整除的
res={x for x in set1 if x%3==0}
for x in res:
    print(x)

輸出結果:


image.png

輸出三國TOP10餅狀圖

案例:

import jieba
from wordcloud import WordCloud
#1、讀取文件
with open('threekingdom.txt','r',encoding='UTF-8')as f:
    words=f.read()
    word_list=jieba.lcut(words)
    excludes = {"將軍", "卻說", "丞相", "二人", "不可", "荊州", "不能", "如此", "商議",
                "如何", "主公", "軍士", "軍馬", "左右", "次日", "引兵", "大喜", "天下",
                "東吳", "于是", "今日", "不敢", "魏兵", "陛下", "都督", "人馬", "不知",
                '孔明曰', '玄德曰', '劉備', '云長'}
    # print(word_list)
    # print(len(word_list))
    #定義一個字典{‘夏侯淵’:788,‘不來’:56}
    counts={}
    for word in word_list:

    #刪除靠前與人名無關的詞匯
        if len(word)==1:
            continue
        else:
    #往字典里添加元素
    # count[word]=取出字典中原來的計數+1
    # count[word]=count[word]+1
            counts[word]=counts.get(word,0)+1
    # print(counts)
    counts['孔明']=counts['孔明曰'] + counts['孔明']
    counts['玄德']=counts['玄德曰'] + counts['玄德'] + counts['劉備']
    counts['關公']=counts['關公'] + counts['云長']
    #將counts轉化成列表
    for word in excludes:
        del counts[word]
    items=list(counts.items())

    def sort_by_count(x):
        return x[1]
    items.sort(key=sort_by_count,reverse=True)
    # print(items)
    # 顯示計數前20詞語
    role_list=[]
    for i in  range(10):
        #拆包 序列解包
        role_name,count=items[i]
        print(role_name,count)
        #給讀代碼的人看的,_代表并沒有使用臨時變量
    #     for _ in range(1):
    #         role_list.append(role_name)
    # # print(role_list)
    # text=' '.join(role_list)
    # WordCloud(
    #     background_color='white',
    #     width=800,
    #     height=600,
    #     font_path='msyh.ttc',
    #     #相同匹配詞的處理
    #     collocations=False
    # ).generate((text)).to_file('top10.png')

    import  matplotlib.pyplot as plt
    plt.rcParams["font.sans-serif"] = ['SimHei']
    plt.rcParams['axes.unicode_minus'] = False
    # print(items)
    print(items[0:10:1])
    dict1=dict(items[0:10:1])
    print(dict1)

    x = ["{}".format(i) for i in dict1.keys()]
    print(x)
    y = [i for i in dict1.values()]
    print(y)
    # 繪制 條形圖
    explode=[0.2,0,0,0,0,0,0,0,0,0]
    plt.pie(y,explode=explode,labels=x,shadow=True,autopct='%1.1f%%')
    plt.legend(loc=1)
    plt.title('三國人物出場次數占比圖')
    plt.show()

輸出結果:


image.png

Python爬蟲

案例(爬取豆瓣TOP250電影):

# -*- coding: utf-8 -*-
# @Time    : 2019/7/24 15:22
# @Author  : Eric Lee
# @Email   : li.yan_li@neusoft.com
# @File    : 豆瓣top250爬蟲.py
# @Software: PyCharm
import requests
from lxml import etree
def parse():
    """豆瓣網top250爬蟲"""
    # 1、獲取url地址
    # for i in range(0, 226, 25):
    #     url = 'https://movie.douban.com/top250?start={}&filter='.format(i)
    #     print(url)
    #     # 獲取 byte的類型的響應
    #     resp = requests.get(url)
    #     data = resp.content
    headers = {"User-Agent": "ozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/74.0.3729.131 Safari/537.36"}

    url = 'https://movie.douban.com/top250?start=0&filter='
    # 獲取 byte的類型的響應
    resp = requests.get(url,headers=headers)
    data = resp.content
    # 調用etree.HTML獲取html對象,然后調用html的xpath語法
    html = etree.HTML(data)

    movie_list = html.xpath('//div[@id="content"]//ol/li')
    print(len(movie_list))
    for movie in movie_list:
        # 獲取電影序號
        serial_number = movie.xpath('./div[@class="item"]/div[@class="pic"]/em/text()')
        serial_number = '' if len(serial_number)==0 else serial_number[0]
        print(serial_number)

        movie_name = movie.xpath('./div[@class="item"]/div[@class="info"]/div[@class="hd"]/a/span[1]/text()')

        movie_name = '' if len(movie_name) == 0 else movie_name[0]
        print(movie_name)


    # 電影介紹
    # introduce =
        introduce = movie.xpath('./div[@class="item"]/div[@class="info"]/div[@class="bd"]/p/text()')
        introduce = '' if len(introduce) == 0 else introduce[0]
        print(introduce)

    # 電影星級
    # star =
        star = movie.xpath('./div[@class="item"]/div[@class="info"]/div[@class="bd"]/div[@class="star"]/span[2]/text()')
        star = '' if len(star) == 0 else star[0]
        print(star)

    # 電影的評價
    # evalute =
        evalute = movie.xpath('./div[@class="item"]/div[@class="info"]/div[@class="bd"]/div[@class="star"]/span[4]/text()')
        evalute = '' if len(evalute) == 0 else evalute[0]
        print(evalute)
    # 電影的描述
    # describe =
        describe = movie.xpath('./div[@class="item"]/div[@class="info"]/div[@class="bd"]/p[@class="quote"]/span[1]/text()')
        describe = '' if len(describe) == 0 else describe[0]
        print(describe)


parse()

輸出結果:


image.png
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