使用Python进行数据分析

```bash
pip install requests beautifulsoup4
```

```python
import requests
from bs4 import BeautifulSoup
# 发送HTTP请求获取网页内容
url = 'https://example.com'
response = requests.get(url)
# 使用BeautifulSoup解析网页
soup = BeautifulSoup(response.text, 'html.parser')
# 提取所需数据，例如提取所有标题
titles = soup.find_all('h1')
for title in titles:
    print(title.text)
```

```bash
pip install pandas numpy matplotlib
```
接下来，我们可以使用`pandas`库加载和处理数据。以下是一个简单的示例：
```python
import pandas as pd
# 假设我们有一个CSV文件包含收集到的数据
data_file = 'data.csv'
# 加载数据到DataFrame中
df = pd.read_csv(data_file)
# 查看数据的前几行
print(df.head())
# 进行数据清洗和转换操作
# 例如，删除缺失值、转换数据类型等
df = df.dropna()  # 删除包含缺失值的行
df['column_name'] = pd.to_datetime(df['column_name'])  # 将某列转换为日期类型
# 进行统计分析
mean = df['column_name'].mean()  # 计算某列的平均值
print('平均值：', mean)
# 数据可视化
import matplotlib.pyplot as plt
# 绘制柱状图
df['column_name'].value_counts().plot(kind='bar')
plt.title('Column Name Distribution')
plt.xlabel('Value')
plt.ylabel('Count')
plt.show()
```

```python
from selenium import webdriver
from selenium.webdriver.common.by import By
# 初始化WebDriver
driver = webdriver.Chrome()
# 打开网页
driver.get('https://example.com')
# 动态等待某个元素出现（例如，等待页面加载完毕）
element = WebDriverWait(driver, 10).until(
    EC.presence_of_element_located((By.ID, "myElement"))
# 提取所需数据，例如通过XPath或CSS选择器
data = driver.find_element(By.XPATH, '//div[@class="myData"]').text
print(data)
# 关闭WebDriver
driver.quit()
```

```python
import requests
from bs4 import BeautifulSoup
base_url = 'https://example.com/page/'
num_pages = 10  # 假设有10页数据
for page in range(1, num_pages + 1):
    url = f"{base_url}{page}"
    response = requests.get(url)
    soup = BeautifulSoup(response.text, 'html.parser')
    
    # 提取当前页的数据...
    # 例如，提取所有链接
    links = soup.find_all('a')
    for link in links:
        print(link.get('href'))
```

```python
# 根据某个列对数据进行分组，并计算每组的平均值
grouped = df.groupby('category')['value'].mean()
print(grouped)
```

```python
# 计算相关性矩阵
correlation_matrix = df.corr()
print(correlation_matrix)
```

```python
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn import metrics
# 假设我们有一个包含特征和目标变量的数据集
X = df[['feature1', 'feature2']]  # 特征列
y = df['target']  # 目标列
# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
# 创建线性回归模型并训练
regressor = LinearRegression()
regressor.fit(X_train, y_train)
# 进行预测并评估模型性能
y_pred = regressor.predict(X_test)
print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred))
```

```python
import plotly.graph_objects as go
# 假设我们有一个包含数据点的DataFrame
df = pd.DataFrame({
    'x': [1, 2, 3, 4, 5],
    'y': [2, 3, 5, 7, 11],
    'label': ['A', 'B', 'C', 'D', 'E']
})
# 创建散点图
fig = go.Figure(data=go.Scatter(
    x=df['x'],
    y=df['y'],
    mode='markers',
    marker=dict(
        size=12,
        color=df['label'],
        colorscale='Viridis',
        line=dict(
            color='rgba(255, 255, 255, 0.8)',
            width=0.5
        ),
        symbol='circle'
    )
))
# 添加交互功能
fig.update_layout(hovermode='closest')
# 显示图表
fig.show()
```

```python
import geopandas as gpd
import folium
# 读取地理空间数据（例如，GeoJSON或Shapefile）
gdf = gpd.read_file('path_to_your_geospatial_data.geojson')
# 创建一个Folium地图对象
m = folium.Map(location=[gdf.geometry.y.mean(), gdf.geometry.x.mean()], zoom_start=10)
# 将地理空间数据添加到地图上
folium.GeoJson(gdf).add_to(m)
# 显示地图
m
```