如何循环不同Series ID从FRED API自动下载数据？报错求助

第一次用Python完成大学作业，需下载全美所有县的全交易房价指数。已从其他文件获取各区县FIPS编码，通过编码生成Series ID后，用API密钥循环获取对应数据，但大量编码返回错误：
Error fetching data for county_id ATNHPIUS01095A: Expecting value: line 1 column 1 (char 0)

import pandas as pd
import requests
df = pd.read_stata(r"D:\Entertaintment\Uni\Seminar Behavioural Finance\Group Assignment\unemployment 12-17.dta")
df = df[['FIPS']]
df['county_id'] = 'ATNHPIUS' + df['FIPS'].astype(str) + 'A'
county_ids = df['county_id'].unique()
print (df.head())
def fetch_data(county_id):
    api_key = "mykey"
    url = f'https://api.stlouisfed.org/fred/series/observations?series_id={county_id}&api_key={api_key}'
    try:
        response = requests.get(url)
        response.raise_for_status()
        data = response.json()
        observations = data['observations']
        df = pd.DataFrame(observations)
        df['date'] = pd.to_datetime(df['date'])
        df.set_index('date', inplace=True)
        df.rename(columns={'value': county_id}, inplace=True)
        return df
    else:
            print(f"No observations found for county_id {county_id}")
            return None
    except requests.exceptions.RequestException as e:
        print(f"Error fetching data for county_id {county_id}: {e}")
        return None  # Return None if data fetching fails for a county_id

dataframes = []
for county_id in county_ids:
   data_df = fetch_data(county_ids)
   if data_df is not None:
       dataframes.append(data_df)
       
combined_df = pd.concat(dataframes, axis=1)
print(combined_df.head())

1. 循环传参错误 #

循环中调用fetch_data(county_ids)时，传入的是整个county_ids数组而非单个county_id，导致请求的series_id是数组字符串，必然返回无效响应。
修复：将循环内的调用改为fetch_data(county_id)。

2. try-except语法错误 #

代码中else块直接跟在try之后，语法非法。else需配合if判断使用，用来检查是否存在观测数据。
修复：在解析data['observations']后添加判断，若为空则返回提示。

3. API响应格式缺失 #

FRED API默认返回XML格式，直接调用response.json()会因解析失败抛出错误。需显式指定返回JSON格式。
修复：在请求URL中添加&file_type=json参数。

4. FIPS编码格式问题 #

部分FIPS编码是5位数字，转换为字符串时可能丢失前导零（如01095变为1095），导致生成的Series ID无效。
修复：使用str.zfill(5)确保FIPS编码为5位带前导零的字符串。

import pandas as pd
import requests

# 读取FIPS数据并生成正确的county_id
df = pd.read_stata(r"D:\Entertaintment\Uni\Seminar Behavioural Finance\Group Assignment\unemployment 12-17.dta")
df = df[['FIPS']]
# 确保FIPS是5位带前导零的字符串
df['county_id'] = 'ATNHPIUS' + df['FIPS'].astype(str).str.zfill(5) + 'A'
county_ids = df['county_id'].unique()
print(df.head())

def fetch_data(county_id):
    api_key = "mykey"
    # 添加file_type=json指定返回JSON格式
    url = f'https://api.stlouisfed.org/fred/series/observations?series_id={county_id}&api_key={api_key}&file_type=json'
    try:
        response = requests.get(url)
        response.raise_for_status()  # 触发HTTP错误异常
        data = response.json()
        observations = data['observations']
        
        # 判断是否有观测数据
        if not observations:
            print(f"No observations found for county_id {county_id}")
            return None
            
        df = pd.DataFrame(observations)
        df['date'] = pd.to_datetime(df['date'])
        df.set_index('date', inplace=True)
        df.rename(columns={'value': county_id}, inplace=True)
        return df
    except requests.exceptions.RequestException as e:
        print(f"Error fetching data for county_id {county_id}: {e}")
        return None

dataframes = []
# 循环传入单个county_id
for county_id in county_ids:
    data_df = fetch_data(county_id)
    if data_df is not None:
        dataframes.append(data_df)
        
combined_df = pd.concat(dataframes, axis=1)
print(combined_df.head())

内容的提问来源于stack exchange，提问作者user24034607