遍历API响应时Pandas DataFrame仅单个列数据异常

问题现象 #

遍历多个邮编调用OpenWeatherMap API获取天气数据时，生成的Pandas DataFrame中「Desc」列所有值均与最后一个邮编的天气描述相同（示例中全为Clouds），但单独运行邮编32303时，能得到正确的Clear结果，说明遍历逻辑存在错误。

错误原因 #

循环内的description = response['weather'][0]['main']是直接对变量赋值，而非像其他数据列那样使用append()将结果添加到列表中。每次循环都会覆盖description变量的内容，最终生成DataFrame时，该变量仅保留最后一次循环的结果，导致整列值重复。

修复后的完整代码 #

import requests
import pandas as pd

api_key = 'a14ac278e4c4fdfd277a5b37e1dbe87a'

# Create a dictionary of zip codes for the team
zip_codes = {
    55446: "You",
    16823: "My Boo",
    94086: "Your Boo",
    32303: "Mr. Manatee",
    95073: "Me"
}

# Create a list of zip codes
zip_list = list(zip_codes.keys())

# Create a list of names
name_list = list(zip_codes.values())

# For team data, create a pandas DataFrame from the dictionary
df1 = pd.DataFrame(list(zip_codes.items()), 
                   columns=['Zip Code', 'Name'])

# Create empty lists to hold the API response data
city_name = []
description = []
weather = []
feels_like = []
wind = []
clouds = []

# Loop through each zip code
for zip_code in zip_list:
  # Make a request to the OpenWeatherMap API
  url = f"http://api.openweathermap.org/data/2.5/weather?zip={zip_code},us&units=imperial&appid={api_key}"
  response = requests.get(url).json()
  # Store the response data in the appropriate empty list
  city_name.append(response['name'])
  # 修复：将赋值改为列表追加操作
  description.append(response['weather'][0]['main'])
  weather.append(response['main']['temp'])
  feels_like.append(response['main']['feels_like']) 
  wind.append(response['wind']['speed'])
  clouds.append(response['clouds']['all'])
#  rain.append(response['humidity']['value'])

# For weather data, create df from lists
df2 = pd.DataFrame({
    
    'City': city_name,
    'Desc': description,
    'Temp (F)': weather,
    'Feels like': feels_like,
    'Wind (mph)': wind,
    'Clouds %': clouds,
#    'Rain (1hr)': rain,
})

# Merge df1 & df2, round decimals, and don't display index or zip.
df3=pd.concat([df1,df2],axis=1,join='inner').drop('Zip Code', axis=1)
df3[['Temp (F)', 'Feels like', 'Wind (mph)', 'Clouds %']] = df3[['Temp (F)', 'Feels like', 'Wind (mph)', 'Clouds %']].astype(int)

# Don't truncate df
pd.set_option('display.width', 150)

# Print the combined DataFrames
display(df3.style.hide_index())

关键修复点 #

将循环内的description = response['weather'][0]['main']修改为description.append(response['weather'][0]['main'])，确保每次循环都把当前邮编对应的天气描述添加到description列表中，与其他数据列表的存储逻辑保持一致，生成DataFrame时就能对应每个邮编的正确天气描述。

内容的提问来源于stack exchange，提问作者Cath Tyner