Join Tables Without Shared Keys

Go to futuresearch.ai/app, upload company_info.csv and valuations.csv, and enter:

Merge company_info.csv with valuations.csv. The first table has company names, the second has stock tickers. Match companies to their stock tickers.

437 of 438 companies matched (99.8%). Results take about 11 minutes.

Add the FutureSearch connector if you haven't already. Then upload company_info.csv and valuations.csv and ask Claude:

Merge company_info.csv with valuations.csv. The first table has company names, the second has stock tickers. Match companies to their stock tickers.

437 of 438 companies matched (99.8%). Results take about 11 minutes.

pip install futuresearch
export FUTURESEARCH_API_KEY=your_key_here  # Get one at futuresearch.ai

Download company_info.csv and valuations.csv, or run the full notebook.

import asyncio
import pandas as pd
from futuresearch.ops import merge

# Two tables from different sources - no shared column
companies = pd.read_csv("company_info.csv")    # has: company, price, mkt_cap, shares
valuations = pd.read_csv("valuations.csv")      # has: ticker, fair_value

async def main():
    result = await merge(
        task="Match companies to their stock tickers",
        left_table=companies,
        right_table=valuations,
    )

    # The result is a DataFrame with all columns joined
    print(result.data.head())

    #                company   price      mkt_cap     shares  ticker  fair_value
    # 0                   3M  101.74  61.70678828  606514530     MMM       39.18
    # 1          A. O. Smith   32.38  4.904416495  151464376     AOS        6.59
    # 2  Abbott Laboratories   34.87  51.22933139 1469152033     ABT      119.19

asyncio.run(main())