Livescraper
Sign inGet Started
Lowes Products Scraper

Lowes product pages,
as fifteen columns.

Paste a Lowes product URL - the platform's own example is lowes.com/pd/625493716 - or a category or search URL, and the products come back as rows: name, price and currency, availability, brand, the identifier columns, rating and reviews, an image, a link and a description.

Start free See pricing
one-time 500 free rows$0.002 per row after15 columnsCSV · XLSX · JSON
How it works

Six steps,
one of them a proxy.

A list of links and a limit - plus the one piece of setup this retailer needs and most of its siblings do not.

  1. STEP 1Sign in to the platform.
  2. STEP 2Open the Lowes Products Scraper.
  3. STEP 3Paste Lowes product URLs or category and search URLs, one per line - or upload a CSV, XLSX or TXT file.
  4. STEP 4Configure a residential proxy - Lowes is one of the four retailers the tool names as blocking datacenter IPs or rendering products client-side.
  5. STEP 5Set a limit per query, or set it to zero to take everything, then click Get Data.
  6. STEP 6Download the result as CSV, XLSX or JSON.

The limit is per query, so five category URLs with a limit of two hundred is two hundred products from each - not two hundred altogether.

Why teams use it

A catalogue you can sort,
not a grid you scroll.

A product link or a whole category

A line can be a single product page or a category or search URL, and a category comes back expanded into its products, one row each. That is how a browse page becomes something you can sort by price or group by brand.

One setup step, stated up front

Lowes is one of the four retailers on this pipeline that needs a residential exit of your own - the others are Grainger, Zalando and Fastenal. We would rather you knew that before the first run than discovered it in an empty file.

Identifier columns, kept separate

sku, mpn and gtin arrive as their own columns rather than buried in a title, and currency sits beside price instead of inside it. That is what makes an export joinable to a catalogue that is not Lowes's - where the page declares those fields at all.

What this page can and cannot tell you

The names are confirmed.
The contents are not.

Worth being straight about, because it changes how you should read the dictionary below.

The fifteen column names are solid: they are confirmed twice over, by the published column array for this service and by the header row of every workbook we hold from the shared pipeline behind it - identical on the populated one and the empty ones alike. Build a schema around those names with confidence.

What we cannot do is tell you what Lowes puts in them, because we have never run this against Lowes. Not one of the exports we hold is a Lowes run. Almost all of them came back with a header and no rows, and the single populated one is a different retailer entirely. Rather than dress that up, this page prints no example values and no formats - with no Lowes run, we would be inventing them.

The proxy requirement is worth planning around rather than discovering. The tool names four retailers on this pipeline that need a residential exit of your own, and Lowes is one of them; the reason it gives is that such retailers block datacenter addresses or render their products client-side. It does not say which of the two applies here, so neither do we - what matters practically is the same either way: without that proxy configured, expect the run to come back thin or empty.

Try one category free
Data dictionary

Fifteen columns,
and an honest ceiling.

The names are exact and doubly confirmed. The descriptions say what each column is for - not what it will contain, because that is the retailer's decision rather than ours, and the note below explains why.

query
The URL you submitted for this row - the product, category or search page it came from. Written by the scraper, so it is there on every row.
service
Which retailer this row came from. This scraper shares one pipeline with several other product services, and the one you picked is stamped onto every row - useful if you keep more than one retailer in the same table.
name
The product name as the page declares it.
price
The price the page declares.
currency
The currency that price is in, as a separate column - so you are not parsing a symbol out of the price field.
availability
The stock status the page declares.
brand
The brand the page declares.
sku
The retailer's own stock code for the item.
mpn
The manufacturer part number. Along with gtin, this is what makes an export joinable to a catalogue that is not Lowes's.
gtin
The global trade item number - the barcode identifier.
rating
The product rating, if the page publishes one. No scale is stated here; we have not seen a value.
reviews
The review count. A number of reviews, not the review text - there is no review body in this export.
image
The product image the page declares.
url
The product link.
description
The product description the page declares.

Two things about the evidence behind this page. First, the mechanism: products are read from the JSON-LD a page publishes about itself - the structured data retailers embed for search engines. That sets a ceiling this page will not pretend past. Every column after service exists only if Lowes declares that field, so a blank means not published, not run failed. Second, the gap: we have never run this against Lowes. We hold forty-five exports from the shared pipeline behind this service and not one is a Lowes run - forty-four came back empty and the only populated one is a different retailer. That export is worth one honest data point about the tool rather than about Lowes: query and service filled on every row, because the scraper writes them itself - and six of the fifteen were empty on every single row. Expect gaps here too, expect them to fall differently because a different retailer publishes different structured data, and let your first free-tier run tell you which. And configure the residential proxy first: on this retailer an empty file is more likely to mean the proxy than the markup.

Run one category free
Run controls

Two inputs,
and one piece of setup.

A list of URLs and a limit - plus the residential proxy this retailer needs. No sort and no filter; there is nothing else to set.

https://www.lowes.com/pd/625493716 A product URL, or a category / search URL One per line CSV · XLSX · TXT upload Residential proxy required Limit per query, or 0 for everything One row per product Read from the page's JSON-LD
Where the fields come from

The page declares it,
we report it.

This scraper reads the structured data a product page publishes about itself - the JSON-LD block retailers embed so search engines can show a price and a stock status. That is why the columns read like they do: sku, mpn, gtin, brand, availability and currency are the standard vocabulary for describing a product, not names we invented.

The practical consequence is the one worth planning around. A column is only as good as the retailer's own markup. If a field is missing from the page's structured data it comes back blank, and no amount of re-running changes that - it is not a failure, it is an absence. In the one populated export we hold from this shared tool, six of the fifteen columns were blank on every row.

If you need something the markup does not carry, that is a different job - the Universal AI Scraper reads the page itself and returns the attributes you name, rather than only what was declared for search engines.

livescraper.app · the ceiling
Read from the page's JSON-LD✓
query and service: written by us✓
The other thirteen: declared by the page✓
Blank = not published, not failed!
Check the gaps on your first run✓
Structured data is a retailer's choice, and it differs between them.
Handing over the list

Uploads add,
and don't de-duplicate.

An uploaded file does not clear the box - the lines are appended to whatever you had already typed, and duplicates are not removed. Upload the same file twice and those URLs really will be fetched twice, and billed twice. That is specific to this pipeline: some of our other scrapers de-duplicate on upload and this one does not, so do not carry the habit across.

For a CSV or TXT the whole line is taken as one URL, so a spreadsheet with extra columns is not what you want here. Only an XLSX is read column by column, and there it takes column A.

The picker lists Parquet alongside CSV, XLSX and TXT, but it cannot actually read one in the browser and will tell you so - use one of the other three.

livescraper.app · the upload
CSV · XLSX · TXT✓
Duplicates are NOT removed!
CSV and TXT: the whole line✓
XLSX: column A✓
Appended to what you typed✓
Check the box before you run - a doubled list is a doubled bill.
Common workflows

Three jobs people
most often run here.

A few examples of what a home-improvement catalogue is worth once it is a table.

Assortment

See a category as a list rather than a grid

Hand it a category page and you get every product on it as a row, with the brand and identifier columns beside the name. Sorting a browse page by anything other than what the site offers is the whole point.

Merchandising · Research
Comparison

Line a retailer up against the others you track

Because service is stamped on every row, one table can hold Lowes alongside the other retailers on this pipeline and still split cleanly. Where mpn or gtin are declared, the same item can be matched across suppliers.

Sourcing · Procurement
Monitoring

Re-run a category and diff it

The same category URL on a schedule gives you two comparable files. What appeared, what disappeared and what changed is a diff rather than an afternoon of clicking - and sku is the key to diff on where it is published.

Analysis · Reporting
Get started
Pricing

Pay only for the products
you actually pull.

No subscription, no minimum, no recurring bill. Your first 500 rows are on us - after that, pay-as-you-go at the same flat rate as every other scraper here.

Free tier

500 free rows - $0

Every new account, one-time. No credit card required. Per-query limits, file upload and every export format included. Note that the residential proxy this retailer needs is a separate arrangement of your own.

$0 forever
Pay-as-you-go

$0.002 per row, after the free tier

The same flat rate as every other scraper on the platform. The pre-flight estimator shows the row count and credit cost before a run starts - worth a glance here, because the limit is per query and a list of category URLs multiplies.

Most popular
Volume

Custom · high volume

Volume pricing, dedicated workers and an SLA for tracking a catalogue on a schedule. Tell us your numbers and we will quote.

Talk to us
Start free See full pricing
10% off your first paid run.Use code LIVESCRAPER10 at checkout.
Sign up
Pairs well with

The same pipeline,
and the way past it.

Two siblings on the same shared tool, and one for when the structured data does not carry what you need.

Fastenal Products Scraper

The same fifteen columns and the same JSON-LD ceiling on a different catalogue - and the other page in this family that needs a residential proxy of your own, for the same stated reason.

Explore

Quill Products Scraper

Same pipeline again, with one difference that matters: Quill is not one of the four, so it needs no proxy of your own. Worth knowing before you assume every page in this family runs the same way.

Explore

Universal AI-Powered Scraper

When the field you want was never put in the structured data, this one reads the page itself and returns the attributes you name. Slower and less predictable than a fixed schema - but it is not limited to what was declared for search engines.

Explore
The legal bit

Is it legal to scrape
Lowes?

Short answer: this is public catalogue data with no personal data in it - and the structured data is published to be read by machines in the first place.

Everything in these fifteen columns is shown on a public Lowes product page to any visitor, signed in or not. No login is used, no paywall is crossed and no account is touched. The particular thing being read is the JSON-LD block - structured data a retailer embeds precisely so that automated readers can pick up the name, the price and the availability.

No person is named anywhere in this export. There is no customer, no reviewer and no seller column - reviews is a count, not review text, so not even a review body comes back. It is products and prices, which puts it at the lighter end of the range for data-protection purposes.

Lowes's own terms govern automated collection from their site, so read them before you scale rather than relying on the pages being public. Note too that this is one of the retailers where the run goes out through a residential proxy you arrange, rather than our shared pool - so the terms question is one you are closer to than usual. And if you republish product descriptions or images, remember those are somebody else's copy and photography. We run no third-party trackers on the data layer, and your exports auto-delete after 30 days.

Sign up now
livescraper.app · principles
Public product pages only✓
No logins, no accounts touched✓
Structured data, published for machines✓
Products and prices - nobody is named✓
Exports auto-delete (30 days)✓
Check Lowes's own terms before scaling.
Common questions

Things people
ask before signing up.

The questions we hear most. Anything else? Talk to us - humans, not bots, write the answers.

How do I scrape Lowes products?+
Using the Lowes Products Scraper: sign in to the platform, open the Lowes Products Scraper, paste Lowes product URLs or category and search URLs one per line (or upload a CSV, XLSX or TXT file), configure a residential proxy, set a limit per query or set it to zero to take everything, then click Get Data and download the result as CSV, XLSX or JSON.
Do I need a residential proxy for this one?+
Yes. Lowes is one of exactly four retailers on this shared pipeline that the tool names as needing a residential exit of your own - the others are Grainger, Zalando and Fastenal. The reason it gives is that such retailers block datacenter IPs or render their products client-side; it does not say which of the two applies to Lowes, so neither will we. Practically it comes to the same thing: configure the proxy before your first run, or expect a thin or empty file.
What can I paste in as input?+
A Lowes product URL, or a category or search URL - both work, one per line. The platform's own example for this service is a product page, https://www.lowes.com/pd/625493716. A category URL returns the products on it, one row each, up to whatever limit you set.
What columns will I get?+
Fifteen, in this order: query, service, name, price, currency, availability, brand, sku, mpn, gtin, rating, reviews, image, url and description. Those names are confirmed twice over - by the published column array for this service and by the header row of every workbook we hold from the shared pipeline behind it. The JSON adds a sixteenth field, position; the CSV and XLSX stop at fifteen.
Why does this page not show example values?+
Because we have never run this against Lowes, and we would rather say so than invent them. Of the forty-five exports we hold from the shared pipeline behind this service, not one is a Lowes run: forty-four came back with a header and no rows, and the only populated one is a different retailer. The column names are solid; the cell contents are not something we can honestly describe yet.
Why might a column come back blank?+
Two different reasons on this retailer, and it is worth telling them apart. If the proxy is not configured, the run itself is likely to come back thin or empty - that is the first thing to check here. Otherwise a blank means the page never declared that field: products are read from the JSON-LD a page publishes about itself, so every column after service exists only if the retailer put it in their structured data, and re-running will not change it. In the one populated export we hold from this shared tool, six of the fifteen columns were blank on every row.
Does uploading a file replace what I typed?+
No - it appends to it, and it does not remove duplicates. Upload the same file twice and those URLs will be fetched twice and billed twice, so check the box before you run. For CSV and TXT the whole line is taken as one URL; only XLSX is read column by column, and there it takes column A. Parquet is listed in the picker but cannot be read in the browser.
How much does it cost?+
The first 500 rows are free and one-time, with no credit card. After that it is $0.002 per row - the same flat rate as every other scraper on the platform. The residential proxy this retailer needs is a separate arrangement of your own and is not part of that rate. The estimator shows the cost of a run before it starts, which is worth a glance here because the limit is per query and a list of category URLs multiplies.

Turn a category page
into a table you can sort.

Your first 500 rows are free - no card, no subscription. After that it is $0.002 per row, flat.

Start free See full pricing
Activates instantly · no card required

Scrape Lowes product pages into rows

The Lowes Products Scraper turns product and category pages on lowes.com into rows. Paste a product URL - the platform's own example is lowes.com/pd/625493716 - or a category or search URL, one per line, or upload a CSV, XLSX or TXT file, set a limit per query, and every product comes back as a record with the query it came from, the service that produced it, the name, price and currency, availability, brand, sku, mpn and gtin, a rating and review count, an image, a link and a description. Fifteen columns in the file and sixteen in the JSON, one row per product.

One piece of setup separates this page from most of its siblings. Lowes is one of exactly four retailers on this shared pipeline that the tool names as needing a residential proxy of your own - the others are Grainger, Zalando and Fastenal. The reason given is that such retailers block datacenter addresses or render their products client-side, and the tool does not say which applies to Lowes, so this page does not guess. Either way the practical consequence is the same: configure the proxy before the first run, because on this retailer an empty file is more likely to mean the proxy than the markup.

Two things are worth understanding before you build on the export. Products are read from the JSON-LD a page publishes about itself - the structured data retailers embed so search engines can display a price and a stock status. That is why the columns carry the names they do, and it sets a ceiling: every column after service exists only if the retailer declared that field, so a blank cell means not published rather than run failed. And this page does not print example values, because we have never run it against Lowes. Of the forty-five exports we hold from the shared pipeline behind this service, not one is a Lowes run - forty-four returned a header and no rows, and the single populated one is a different retailer. That export is worth exactly one honest observation, and it is about the tool rather than about Lowes: query and service filled on every row because the scraper writes them itself, and six of the fifteen columns were blank on every row.

On the legal side this is among the lighter exports in our catalogue. Everything collected is shown on a public product page to any visitor, no login or paywall is involved, and no person is named anywhere in the fifteen columns - reviews is a count rather than review text. It is products and prices. Lowes's own terms still govern automated collection from their site, and because the run goes out through a proxy you arrange rather than our shared pool, that is a question you are closer to than usual - read them before you scale, and remember that product descriptions and images are somebody else's copy and photography if you intend to republish them. Start free: your first 500 rows cost nothing and need no credit card, and after that it is $0.002 per row, flat.