如何将BeautifulSoup提取的Python数据输出转换为DataFrame行列格式

Got it, let's tweak your code to turn that line-by-line output into a structured pandas DataFrame. Here's a straightforward approach:

First, make sure you've got pandas installed and imported. Instead of printing each entry right away, we'll collect the scraped data into lists, then convert those lists into a DataFrame for easier analysis.

Modified Code #

import requests
from bs4 import BeautifulSoup
import pandas as pd  # Import pandas for DataFrame functionality

response = requests.get(source_data)
soup = BeautifulSoup(response.text, "html.parser")
States = soup.find_all('div', class_='card bg-darker p-3 mb-3')

# Initialize empty lists to store our scraped data
state_list = []
vaccine_rate_list = []

for item in States:
    # Clean up text with .strip() to remove extra whitespace from HTML
    state_name = item.find(class_='fw-bold fs-5 mb-2').text.strip()
    vaccinated_per = item.find(class_='col-3 text-end fs-5 ff-s text-success').text.strip()
    
    # Add each entry to our lists
    state_list.append(state_name)
    vaccine_rate_list.append(vaccinated_per)

# Create the DataFrame with descriptive column names
vaccination_data = pd.DataFrame({
    "State": state_list,
    "Vaccination Rate": vaccine_rate_list
})

# Optional: Convert percentage strings to numeric values (for calculations)
vaccination_data["Vaccination Rate (Numeric)"] = vaccination_data["Vaccination Rate"].str.replace("%", "").astype(float)

# View the final DataFrame
print(vaccination_data)

What This Does: #

• We use empty lists to gather all state names and vaccination rates as we scrape them, instead of printing immediately.
• The pd.DataFrame() function turns our lists into a table with clear column headers.
• The optional step converts percentage strings (like "80.24%") to float values (80.24), which lets you run numerical operations (like averages or sorting) on the data.

Example Output DataFrame: #

State Vaccination Rate  Vaccination Rate (Numeric)
0      Flanders           80.24%                        80.24
1      Wallonia           70.00%                        70.00
2     Brussels           56.73%                        56.73
3  Ostbelgien            65.11%                        65.11

Now you've got a structured dataset you can export to CSV, filter, or use for further analysis!

内容的提问来源于stack exchange，提问作者zzstar