How to Find an Email From a CSV of Names (2026)

Clean the file, match each name to one person, then read that person's checked work email. LeadOcean does the match and the lookup through the API, about 2 records per row that matches.

A CSV with only first and last names will not work. Two Jane Does exist at every large company. You need a second column: a company name or a domain.

TL;DR

• Add a company or domain column, size each row for free, search only the rows that match one person, then call the work-email endpoint.
• A short list takes about 20 minutes end to end. Each matched row costs up to 2 LeadOcean records. A row that matches nobody costs 0.
• Send only to addresses the API returns. It returns verified, catch_all_valid or catch_all work addresses, or null.

Prerequisites

• A LeadOcean API key from app.leadocean.io, exported as $LEADOCEAN_API_KEY. Free gives 1,000 records one-off, no card, at 5 requests a second.
• Python 3 with requests.
• A CSV with the columns first_name, last_name and company (or domain). Use real data in your own file. This guide uses Jane Doe at Acme (acme.com) as the placeholder.

LeadOcean has no upload-a-CSV lookup and no name plus domain lookup in one call. The bulk export takes filters, not a list of names. So you loop the API over your rows.

Step 1: Clean the CSV by hand

Fix the file before you spend a record. Most misses come from the input, not the data.

Check four things:

1. Split names into first_name and last_name. Drop titles such as "Dr." and suffixes such as "Jr.".
2. Put one person per row and remove duplicates.
3. Add a company column. A domain like acme.com matches better than "Acme Inc".
4. Fix spelling and encoding. "Jose" and "José" are different strings to a filter.

A clean file looks like this:

first_name,last_name,company,domain
Jane,Doe,Acme,acme.com

The manual way to a work email is to guess jane.doe@acme.com and test it. That works for a handful of rows. It fails on catch-all domains, where every guess looks valid, so use it only as a fallback.

Step 2: Size one row for free

Count how many people match a row before you search it. A count costs no record.

Send count=true as a query parameter with limit=1. Read meta.total.

curl -s -X POST "https://api.leadocean.io/v1/people/search?count=true&limit=1" \
  -H "x-api-key: $LEADOCEAN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"q": "Jane Doe", "domain": ["acme.com"]}' | jq '.meta.total'

Filters are arrays and the body is flat JSON. Read the total like this:

• 0: nobody matches. Skip the row, or fix the spelling and try again.
• 1: one person. Safe to search.
• 2 or more: ambiguous. Add a title or country column, or review it by hand.

Step 3: Search the match and take the person_id

Search the rows that returned a total of 1. Cap limit at 1 so a row cannot spend more than one record here.

curl -s -X POST https://api.leadocean.io/v1/people/search \
  -H "x-api-key: $LEADOCEAN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"q": "Jane Doe", "domain": ["acme.com"], "limit": 1}' \
  | jq '.data[] | {name: .profile_data.profile_full_name, headline: .profile_data.profile_headline, person_id: .meta.source_ids.person_id}'

Each person returned counts one record. Compare the title and company with what you know before you go on. If you only have a company name, filter with "companyName": ["Acme"] instead of domain.

Step 4: Read the work email

Send the person_id to the work-email endpoint. It returns one address or data: null.

curl -s "https://api.leadocean.io/v2/people/email/work?person_id=1234567890" \
  -H "x-api-key: $LEADOCEAN_API_KEY" | jq '.data'

The address is at the person's current employer's domain. It was verified, catch_all_valid or catch_all. A null means no address qualifies, not that the person has none. The call costs one record, including a miss.

If you want every email and phone on the record, use POST /v1/people/enrich with reveal_email set to true. That route also costs one record.

Step 5: Loop it over the whole file

Wrap steps 2 to 4 in one script. It reads your CSV and writes a new one with the email, so a crash loses nothing.

import csv, os, time, requests

BASE = "https://api.leadocean.io"
H = {"x-api-key": os.environ["LEADOCEAN_API_KEY"]}

def find_email(first, last, domain):
    body = {"q": f"{first} {last}", "domain": [domain]}
    c = requests.post(f"{BASE}/v1/people/search?count=true&limit=1", headers=H, json=body)
    if not c.ok or c.json().get("meta", {}).get("total") != 1:
        return None, "not one match"
    s = requests.post(f"{BASE}/v1/people/search", headers=H, json={**body, "limit": 1})
    rows = s.json().get("data", []) if s.ok else []
    if not rows:
        return None, "no row"
    pid = rows[0]["meta"]["source_ids"]["person_id"]
    w = requests.get(f"{BASE}/v2/people/email/work", headers=H, params={"person_id": pid})
    data = w.json().get("data") if w.ok else None
    if not data:
        return None, "no checked work email"
    return data.get("email"), "ok"

with open("people.csv") as f, open("out.csv", "w", newline="") as g:
    w = csv.writer(g)
    w.writerow(["first_name", "last_name", "domain", "email", "note"])
    for row in csv.DictReader(f):
        email, note = find_email(row["first_name"], row["last_name"], row["domain"])
        w.writerow([row["first_name"], row["last_name"], row["domain"], email, note])
        g.flush()
        time.sleep(0.6)  # 3 calls per row stays under 5 a second

A row that is not a single match costs nothing, because the count is free. A matched row costs at most 2 records. A 500-row file where every row matches costs at most 1,000, which is the whole Free allowance.

On Free the limit is 5 requests a second, and the script makes up to three calls per row. The 0.6-second pause per row keeps it under that. If you run rows in parallel, honour the Retry-After header on a 429.

Past a few hundred rows, check usage with GET /v1/usage before each run. On Solo ($97 a month) or Agency ($497 a month) the work runs at a fair-use pace, with no per-record price. See /pricing.

What you get

The work-email endpoint returns one address in data. This is the response shape from the API reference, with placeholder values.

{
  "success": true,
  "data": {
    "email": "jane.doe@acme.com",
    "status": "verified",
    "verified_batch_date": "2026-09-20"
  }
}

verified_batch_date is the run the check came from. Null means the check was not ours. When nothing qualifies, data is null. Your output CSV ends up like this:

first_name,last_name,domain,email,note
Jane,Doe,acme.com,jane.doe@acme.com,ok
John,Roe,acme.com,,not one match

Review every row marked not one match by hand. Those are where the company, the spelling or a common name needs a second column.

Troubleshooting

FAQ