Python爬虫与数据分析实战

本文将从多个方面详细阐述Python爬虫与数据分析实战，包括爬虫基础、数据获取、数据清洗和数据分析等。

一、爬虫基础

1、了解HTTP请求和响应的基本原理

import requests

url = "https://www.example.com"
response = requests.get(url)
print(response.text)

2、使用BeautifulSoup解析HTML页面

import requests
from bs4 import BeautifulSoup

url = "https://www.example.com"
response = requests.get(url)
soup = BeautifulSoup(response.text, "html.parser")
print(soup.title.text)

3、使用正则表达式提取页面信息

import requests
import re

url = "https://www.example.com"
response = requests.get(url)
pattern = re.compile(r'


二、数据获取
1、使用API获取数据
import requests

url = "https://api.example.com/data"
response = requests.get(url)
data = response.json()
print(data)

2、通过爬虫抓取网页数据
import requests

url = "https://www.example.com"
response = requests.get(url)
data = response.text
print(data)

3、使用第三方库进行数据抓取
import scrapy

class MySpider(scrapy.Spider):
    name = 'example.com'
    start_urls = ['https://www.example.com']

    def parse(self, response):
        # 解析页面数据
        pass

# 运行爬虫
scrapy runspider myspider.py

三、数据清洗
1、去除重复数据
import pandas as pd

data = pd.read_csv("data.csv")
data.drop_duplicates(inplace=True)
print(data)

2、处理缺失值
import pandas as pd

data = pd.read_csv("data.csv")
data.fillna(0, inplace=True)
print(data)

3、数据格式转换
import pandas as pd

data = pd.read_csv("data.csv")
data['date'] = pd.to_datetime(data['date'])
print(data)

四、数据分析
1、数据可视化
import pandas as pd
import matplotlib.pyplot as plt

data = pd.read_csv("data.csv")
plt.plot(data['date'], data['value'])
plt.xlabel('Date')
plt.ylabel('Value')
plt.show()

2、统计指标计算
import pandas as pd

data = pd.read_csv("data.csv")
mean_value = data['value'].mean()
max_value = data['value'].max()
min_value = data['value'].min()
print(f"Mean: {mean_value}, Max: {max_value}, Min: {min_value}")

3、数据分析模型构建
import pandas as pd
from sklearn.linear_model import LinearRegression

data = pd.read_csv("data.csv")
X = data['feature'].values.reshape(-1, 1)
y = data['target'].values.reshape(-1, 1)

model = LinearRegression()
model.fit(X, y)
prediction = model.predict(X)
print(prediction)