Google Maps Data Export to CSV: Fixing Common Export Failures and Formatting Issues

TL;DR: Exporting Google Maps scraped data to CSV looks simple until your file opens as "José" instead of "José," columns shift because of unescaped commas in business names, or Excel mangles UTF-8 characters because you're missing a BOM. I ran into every single one of these. Here's the exact fix for each layer of the CSV nightmare.

google maps data scraping

I spent six hours once trying to figure out why a perfectly scraped list of 847 bakeries in Berlin turned into a spreadsheet full of über characters that looked like someone fed German through a blender. The frustrating part? The CLI that produced the file printed clean text. The CSV file on disk had correct bytes. But the second that file landed in Excel — chaos.

The core problem: scraping Google Maps data is only half the battle. The other half is getting it out of your tool and into something a human (or a CRM) can actually read. According to RFC 4180, CSV has no built-in way to declare its encoding — so when Excel guesses wrong on open, your é becomes é and your ñ becomes ñ (that's UTF-8 bytes decoded as Latin-1, a pattern called mojibake that affects roughly 68% of companies regularly handling CSVs). The fix is almost always either saving with UTF-8 with BOM or using Excel's import wizard with File Origin set to 65001: Unicode (UTF-8).

The data itself is good. The bytes in your CSV are perfectly correct UTF-8. The problem is purely interpretive — the software on the receiving end is reading those bytes with the wrong key. Think of it like ordering a coffee in perfect Spanish at a café in Berlin. The barista speaks German. Your words are fine. The decoder is wrong. And nobody tells you this — you just see é and assume the scraping tool is broken.

best tool to scrape website

Here's the thing I learned the hard way: the best tool to scrape website data isn't the one that extracts the most fields — it's the one that doesn't hand you a corrupted file on the other end. I've cycled through enough scrapers to know that if the export pipeline is broken, the scraper is useless. You can extract 36 data points per listing, but if the CSV opens as a single mangled row, you've got nothing.

A survey across 340 enterprise clients estimated that 68% of companies regularly hit encoding issues during CSV import/export, costing an average of 23 hours per month troubleshooting. That's nearly three full workdays lost, every month, to a problem that three bytes — literally 0xEF 0xBB 0xBF — could solve if you know where to put them.

export map from google maps

When you export map from google maps — whether via Google Takeout or a scraper — you're pulling addresses, business names, phone numbers, and sometimes descriptions that contain every possible special character the Unicode standard covers. I once scraped a list of 300 restaurants and found eleven different special characters across the dataset — em-dashes, smart quotes, copyright symbols, even a degree sign in a bakery's hours field. The CSV parser threw up its hands and split rows into two, shifted columns, and generally made a mess of things.

If you've ever opened a CSV and seen Smith, John split across two columns while the rest of the row shifted sideways, congratulations — you've met the quoting bug. Per RFC 4180, fields containing commas, double quotes, or line breaks must be wrapped in double quotes, and literal quotes inside fields must be escaped by doubling them (""). Half-quoting — where someone only wraps the comma-containing part rather than the whole field — is the most common bug in hand-rolled CSV exporters, and it's everywhere in open-source scraper output.

And then there's the line ending trap. RFC 4180 specifies CRLF between records, but your scraper probably outputs LF (Unix-style). Most modern parsers handle both, but older enterprise tools sometimes read the LF as part of the data. Every time I've had a 5,000-row file that opens as a single row in Excel, this was the culprit.

maps scraper

Every maps scraper I've tested — open-source Python scripts using Selenium, Chrome extensions that scrape the DOM, Apify cloud actors — produces output that needs post-processing before it's CRM-ready. The data inside Google Maps is rich: categories, reviews, hours, websites, coordinates, even owner-claimed status. But the CSV export layer is where that richness turns into garbage if you don't handle encoding, quoting, and delimiter rules explicitly. A tool that extracts 36 data points per listing means nothing if the export can't survive Excel's default import behavior. The value isn't in the scrape — it's in the usable output on the other side.

I've seen a dataset of 12,000 leads destroyed because someone double-clicked the CSV in Excel instead of importing it properly — a problem that compounds when incomplete Google Maps data gets silently corrupted during the handoff. The BOM debate — three bytes (0xEF 0xBB 0xBF) — determines whether Excel recognizes UTF-8 or falls back to Windows-1252. For data going to non-technical team members who will double-click, always use UTF-8 with BOM.

When you're running leadsAgent, the export is handled on the backend — the CSV comes out with proper UTF-8 encoding, correct quoting, and CRM-ready formatting. No manual BOM additions. No last-second encoding detective work. It's exactly what I wish I'd had back when I was wrangling mojibake at 11 PM on a Tuesday, staring at a screen full of é and wondering what I did wrong in life to deserve this. Try leadsAgent for free and skip the CSV headache entirely.

What is the most common CSV export issue with Google Maps data?

Encoding mismatch. Google Maps scrapers output UTF-8, but Excel on Windows defaults to Windows-1252 when a BOM is absent, turning accented characters into mojibake like é instead of é. This affects every non-ASCII character in your dataset — names, addresses, descriptions, everything.

How do I fix garbled characters in my exported CSV from a Google Maps scraper?

Use a text editor to re-save as "UTF-8 with BOM," or use Excel's Data > From Text/CSV import with File Origin set to 65001: Unicode (UTF-8). Never just double-click the file.

Why do my CSV columns get misaligned after scraping Google Maps?

Business names and addresses containing commas need to be wrapped in double quotes per RFC 4180. If your scraper doesn't quote fields containing special characters, columns will shift right on import and everything after the comma gets pushed into the wrong field. This is the most common structural CSV bug I've encountered.

What's the best way to export large Google Maps datasets to CSV?

Use streaming or chunked exports. Most scrapers support limit/offset pagination — fetch 1,000 rows at a time and append incrementally to the file. For datasets over 1M rows, export as JSONL (one JSON object per line) instead of CSV, since JSONL streams efficiently into warehouses like BigQuery and Snowflake and avoids the delimiter headaches entirely. Never try to load 50,000 rows into browser memory before writing — your browser will crash and you'll lose everything.

How do I keep phone numbers with leading zeros in my CSV export?

Wrap the phone field in quotes and format the column as Text before import. Excel strips leading zeros from numeric fields by default. Alternatively, prefix the number with an apostrophe (') in your scraper's output config.

What's the simplest way to avoid all CSV export problems?

Use a tool that handles encoding, quoting, and formatting on the backend so you never touch the raw CSV. leadsAgent does exactly this — agentic extraction with clean, CRM-ready CSV exports out of the box, plus a free tier to test before committing.

Why do Google Takeout CSV exports often lack coordinates?

Google Takeout's "Saved" export path doesn't include latitude/longitude columns — it only exports the Title, Note, URL, and Address. You need a separate geocoding step to add coordinates before the CSV is useful in mapping tools.

Can I recover data that's already been corrupted by a bad CSV save?

If the mojibake is a single layer deep (e.g., UTF-8 bytes read as Latin-1), yes — it's reversible. Re-encode by reading the corrupted text as Latin-1 then decoding as UTF-8: corrupted_text.encode('latin-1').decode('utf-8') in Python. If the file was saved twice with wrong encoding, the data is likely unrecoverable and must be re-exported from the original scraping source. That's why catching encoding issues early matters — every re-save compounds the corruption.