如何使用Python创建存储气象文件的新目录？附数据获取存储场景

Hey there! Automating directory creation for your weather data is super straightforward with Python's standard libraries. Let me walk you through two common approaches tailored to your use case (4 cities, 48 total files):

Method 1: Use pathlib (Python 3.4+ Recommended) #

pathlib is a modern, object-oriented library that handles cross-platform path separators seamlessly, making it the cleaner choice for recent Python versions.

Basic: Create a Directory per City #

If you want a dedicated folder for each city's weather station (matching your station IDs like KMDLAURE5), use this code:

from pathlib import Path

# Replace with your 4 actual station IDs
station_ids = ["KMDLAURE5", "KNYC123", "LAX456", "CHI789"]

# Loop through each station to create directories
for station_id in station_ids:
    # Build the full path (e.g., ./weather_data/KMDLAURE5)
    dir_path = Path("./weather_data") / station_id
    # Create directory (and parent folders if needed) without errors if it exists
    dir_path.mkdir(parents=True, exist_ok=True)
    print(f"Created/verified directory: {dir_path.resolve()}")

• parents=True: Automatically creates parent directories (like weather_data) if they don't exist
• exist_ok=True: Skips error-throwing if the directory already exists (perfect for rerunning your scraper)

Advanced: Nested Directories by City + Year/Month #

Since you have 48 files (4 cities × 12 months), you might want nested folders to organize data by year and month. Here's how:

from pathlib import Path

station_ids = ["KMDLAURE5", "KNYC123", "LAX456", "CHI789"]
target_year = 2018  # Adjust if you're scraping multiple years
months = range(1, 13)  # Covers 1-12 months

for station_id in station_ids:
    for month in months:
        # Build nested path: ./weather_data/KMDLAURE5/2018/month_02
        dir_path = Path("./weather_data") / station_id / str(target_year) / f"month_{month:02d}"
        dir_path.mkdir(parents=True, exist_ok=True)

This structure will perfectly align with the monthly data you're pulling from Wunderground.

Method 2: Use os Module (Python 2/3 Compatible) #

If you need to support older Python versions, the os module works reliably:

Python 3.2+ Version #

import os

station_ids = ["KMDLAURE5", "KNYC123", "LAX456", "CHI789"]

for station_id in station_ids:
    dir_path = os.path.join("./weather_data", station_id)
    # Create multi-level directories, no error if exists
    os.makedirs(dir_path, exist_ok=True)
    print(f"Created/verified directory: {os.path.abspath(dir_path)}")

Python 2 Compatible Version #

Since exist_ok isn't available in Python 2, add a manual check:

import os

station_ids = ["KMDLAURE5", "KNYC123", "LAX456", "CHI789"]

for station_id in station_ids:
    dir_path = os.path.join("./weather_data", station_id)
    if not os.path.exists(dir_path):
        os.makedirs(dir_path)
    print(f"Created/verified directory: {os.path.abspath(dir_path)}")

Integrate with Your Scraper Workflow #

Add the directory creation code at the start of your scraping script to ensure folders exist before saving cleaned data. Here's a quick example:

from pathlib import Path
import requests

# Define your target parameters
station_id = "KMDLAURE5"
year = 2018
month = 2

# Create the save directory first
save_dir = Path("./weather_data") / station_id / str(year) / f"month_{month:02d}"
save_dir.mkdir(parents=True, exist_ok=True)

# Scrape data (simplified example)
url = f"https://www.wunderground.com/weatherstation/WXDailyHistory.asp?ID={station_id}&year={year}&month={month}&graphspan=month&format=1"
response = requests.get(url)

# --- Add your data cleaning logic here ---

# Save cleaned data to the prepared directory
save_path = save_dir / f"{station_id}_{year}_{month:02d}_cleaned.csv"
with open(save_path, "w", encoding="utf-8") as f:
    f.write(response.text)  # Replace with your cleaned data content

内容的提问来源于stack exchange，提问作者Ma_