如何在Intrinio中基于指定标签获取全量企业数据并导出至CSV列

It sounds like you already have a base CSV of full company details (with unique identifiers like company_id or ticker), but need to enrich it with specific tag values (apturnover, 52_week_high, capex, ceo, address) as dedicated columns. Here's a practical, step-by-step solution using Python—perfect for automating data fetching and merging:

Step 1: Prep Your Base Company Data #

First, load your existing full company CSV into a dataframe to isolate the unique identifiers you’ll use to fetch tag data. Let’s assume your CSV has a company_id column (swap with ticker if that’s your preferred identifier):

import pandas as pd

# Load your base company details file
company_df = pd.read_csv('your_full_company_details.csv')
# Extract unique company IDs to target for tag data
company_ids = company_df['company_id'].unique().tolist()

Step 2: Fetch Tag Data via Intrinio API #

You’ll need your Intrinio API key (found in your account settings). We’ll loop through each target tag and company to pull the relevant data, then structure it for easy merging:

import requests

INTRINIO_API_KEY = 'your_api_key_here'
BASE_URL = 'https://api.intrinio.com'
TARGET_TAGS = ['apturnover', '52_week_high', 'capex', 'ceo', 'address']

# Initialize a dictionary to store tag values per company
tag_data = {company_id: {} for company_id in company_ids}

# Fetch each tag for every company
for tag in TARGET_TAGS:
    for company_id in company_ids:
        try:
            # Call Intrinio's API to get the tag value for the company
            response = requests.get(
                f"{BASE_URL}/companies/{company_id}/data_point/{tag}",
                params={'api_key': INTRINIO_API_KEY}
            )
            response.raise_for_status()  # Trigger error for HTTP issues
            data = response.json()
            tag_data[company_id][tag] = data['value']
        except Exception as e:
            print(f"Failed to fetch {tag} for company {company_id}: {str(e)}")
            tag_data[company_id][tag] = None  # Mark missing data for later cleaning

# Convert the tag data dictionary to a dataframe
tag_df = pd.DataFrame.from_dict(tag_data, orient='index').reset_index().rename(columns={'index': 'company_id'})

Step 3: Merge and Export the Final CSV #

Combine your original company details with the fetched tag data, then export to a new CSV with all your desired columns:

# Merge the two dataframes using company_id as the key
final_df = pd.merge(company_df, tag_df, on='company_id', how='left')

# Export the enriched data to CSV
final_df.to_csv('enriched_company_data.csv', index=False)

Key Tips to Avoid Headaches: #

• Rate Limits: Intrinio enforces API rate limits—check your plan and add small delays (time.sleep(1)) between requests if you’re working with a large number of companies.
• Identifier Adjustments: If you use tickers instead of company_id, update the API endpoint to /securities/{ticker}/data_point/{tag}.
• Special Cases: Some tags like address might live under the company profile endpoint (/companies/{company_id}) instead of a data point—adjust the fetch logic if you hit missing data for these fields.
• Missing Data: The script marks unavailable tag values as None (which becomes NaN in the CSV), making it easy to clean or filter later.

内容的提问来源于stack exchange，提问作者griez007