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Scraper2

Streamlined web crawler for rental listings

What it does

The scraper2 directory defines a generalized Craigslist rental listing crawler, and scripts like test_scraper.py and scrape_prior_day.py instantiate it with particular lists of search domains, time parameters, and other settings.

Functionality is similar to ClistRentScraper (developed by Geoff in 2014), but the operations are now defined explicitly rather than as Scrapy projects. This should make the crawler easier to maintain and debug. We use the Requests library for HTTP requests and Lxml to parse webpage content.

The crawler begins by loading a page of Craigslist search results, for example http://sfbay.craigslist.org/search/apa. From that page, it parses each listing's title, url, price, characteristics, and timestamp. Next, it visits each listing url to extract addresses and lat-lon coordinates. Then it moves on to the next page of search results and repeats the process.

The data is saved to a CSV file, and diagnostics and errors are saved to a log file.

ClistRentScraper only kept lat-lon coordinates, but Craigslist listings now also include a location accuracy indicator and an address or cross streets, when users have provided it. Scraper2 saves this data for future analysis.

Status

This project is on hold while we look into more official access to Craigslist data.

Task list (higher priority)

  • Figure out what Craigslist's throttle thresholds are, and put rate limits into our scripts. On 5/6 they blocked my IP after requesting about 10,000 URLs over a few hours.

  • Add a filter to avoid requesting URLs for listings that we know from the results page are missing necessary variables like price or number of bedrooms.

  • Add docstrings and other proper code templating.

  • Deploy on Linux server.

  • Add option to upload data to S3.

  • Add Slack status notification.

  • Write unit tests.

  • Craigslist returns a maximum of 2500 search results per query, which in high-traffic regions is much less than a full day of listings. For example, it's approx 6 daytime hours in the "sfbay" region. Here are some options: (a) run the scraper once a day at midnight and accept that we are getting an incomplete sample (current approach), (b) run the scraper more frequently, (c) try switching to sub-region searches.

Task list (lower priority)

  • Improve log messages.

  • Improve and test the fail conditions to make sure we won't get into recursive loops, and exit quickly when Craigslist throttles our requests.

  • Is the list of data fields we're collecting optimal?

  • Check the master list of Craigslist domains that we're crawling, which was compiled by Geoff in 2014. Is there a way to assemble the list programmatically, to make sure it's always up to date?

  • Should we put the rental listings directly into a database as well as flat files?

  • Develop a separate set of scripts for data cleaning, for example to remove duplicate listings.

About

Streamlined web crawler for rental listings

Resources

Readme
Activity
Custom properties

Stars

3 stars

Watchers

8 watching

Forks

5 forks
Report repository

Releases

Packages

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

Skip to content
BranchesTags
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Scraper2
Streamlined web crawler for rental listings
What it does
The scraper2 directory defines a generalized Craigslist rental listing crawler, and scripts like test_scraper.py and scrape_prior_day.py instantiate it with particular lists of search domains, time parameters, and other settings.
Functionality is similar to ClistRentScraper (developed by Geoff in 2014), but the operations are now defined explicitly rather than as Scrapy projects. This should make the crawler easier to maintain and debug. We use the Requests library for HTTP requests and Lxml to parse webpage content.
The crawler begins by loading a page of Craigslist search results, for example http://sfbay.craigslist.org/search/apa. From that page, it parses each listing's title, url, price, characteristics, and timestamp. Next, it visits each listing url to extract addresses and lat-lon coordinates. Then it moves on to the next page of search results and repeats the process.
The data is saved to a CSV file, and diagnostics and errors are saved to a log file.
ClistRentScraper only kept lat-lon coordinates, but Craigslist listings now also include a location accuracy indicator and an address or cross streets, when users have provided it. Scraper2 saves this data for future analysis.
Status
This project is on hold while we look into more official access to Craigslist data.
Task list (higher priority)
  • Figure out what Craigslist's throttle thresholds are, and put rate limits into our scripts. On 5/6 they blocked my IP after requesting about 10,000 URLs over a few hours.
  • Add a filter to avoid requesting URLs for listings that we know from the results page are missing necessary variables like price or number of bedrooms.
  • Add docstrings and other proper code templating.
  • Deploy on Linux server.
  • Add option to upload data to S3.
  • Add Slack status notification.
  • Write unit tests.
  • Craigslist returns a maximum of 2500 search results per query, which in high-traffic regions is much less than a full day of listings. For example, it's approx 6 daytime hours in the "sfbay" region. Here are some options: (a) run the scraper once a day at midnight and accept that we are getting an incomplete sample (current approach), (b) run the scraper more frequently, (c) try switching to sub-region searches.
Task list (lower priority)
  • Improve log messages.
  • Improve and test the fail conditions to make sure we won't get into recursive loops, and exit quickly when Craigslist throttles our requests.
  • Is the list of data fields we're collecting optimal?
  • Check the master list of Craigslist domains that we're crawling, which was compiled by Geoff in 2014. Is there a way to assemble the list programmatically, to make sure it's always up to date?
  • Should we put the rental listings directly into a database as well as flat files?
  • Develop a separate set of scripts for data cleaning, for example to remove duplicate listings.
About
Streamlined web crawler for rental listings
Resources
Readme
Activity
Custom properties
Stars
3 stars
Watchers
8 watching
Forks
5 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Scraper2
Streamlined web crawler for rental listings
What it does
The scraper2 directory defines a generalized Craigslist rental listing crawler, and scripts like test_scraper.py and scrape_prior_day.py instantiate it with particular lists of search domains, time parameters, and other settings.
Functionality is similar to ClistRentScraper (developed by Geoff in 2014), but the operations are now defined explicitly rather than as Scrapy projects. This should make the crawler easier to maintain and debug. We use the Requests library for HTTP requests and Lxml to parse webpage content.
The crawler begins by loading a page of Craigslist search results, for example http://sfbay.craigslist.org/search/apa. From that page, it parses each listing's title, url, price, characteristics, and timestamp. Next, it visits each listing url to extract addresses and lat-lon coordinates. Then it moves on to the next page of search results and repeats the process.
The data is saved to a CSV file, and diagnostics and errors are saved to a log file.
ClistRentScraper only kept lat-lon coordinates, but Craigslist listings now also include a location accuracy indicator and an address or cross streets, when users have provided it. Scraper2 saves this data for future analysis.
Status
This project is on hold while we look into more official access to Craigslist data.
Task list (higher priority)
  • Figure out what Craigslist's throttle thresholds are, and put rate limits into our scripts. On 5/6 they blocked my IP after requesting about 10,000 URLs over a few hours.
  • Add a filter to avoid requesting URLs for listings that we know from the results page are missing necessary variables like price or number of bedrooms.
  • Add docstrings and other proper code templating.
  • Deploy on Linux server.
  • Add option to upload data to S3.
  • Add Slack status notification.
  • Write unit tests.
  • Craigslist returns a maximum of 2500 search results per query, which in high-traffic regions is much less than a full day of listings. For example, it's approx 6 daytime hours in the "sfbay" region. Here are some options: (a) run the scraper once a day at midnight and accept that we are getting an incomplete sample (current approach), (b) run the scraper more frequently, (c) try switching to sub-region searches.
Task list (lower priority)
  • Improve log messages.
  • Improve and test the fail conditions to make sure we won't get into recursive loops, and exit quickly when Craigslist throttles our requests.
  • Is the list of data fields we're collecting optimal?
  • Check the master list of Craigslist domains that we're crawling, which was compiled by Geoff in 2014. Is there a way to assemble the list programmatically, to make sure it's always up to date?
  • Should we put the rental listings directly into a database as well as flat files?
  • Develop a separate set of scripts for data cleaning, for example to remove duplicate listings.
About
Streamlined web crawler for rental listings
Resources
Readme
Activity
Custom properties
Stars
3 stars
Watchers
8 watching
Forks
5 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Scraper2
Streamlined web crawler for rental listings
What it does
The scraper2 directory defines a generalized Craigslist rental listing crawler, and scripts like test_scraper.py and scrape_prior_day.py instantiate it with particular lists of search domains, time parameters, and other settings.
Functionality is similar to ClistRentScraper (developed by Geoff in 2014), but the operations are now defined explicitly rather than as Scrapy projects. This should make the crawler easier to maintain and debug. We use the Requests library for HTTP requests and Lxml to parse webpage content.
The crawler begins by loading a page of Craigslist search results, for example http://sfbay.craigslist.org/search/apa. From that page, it parses each listing's title, url, price, characteristics, and timestamp. Next, it visits each listing url to extract addresses and lat-lon coordinates. Then it moves on to the next page of search results and repeats the process.
The data is saved to a CSV file, and diagnostics and errors are saved to a log file.
ClistRentScraper only kept lat-lon coordinates, but Craigslist listings now also include a location accuracy indicator and an address or cross streets, when users have provided it. Scraper2 saves this data for future analysis.
Status
This project is on hold while we look into more official access to Craigslist data.
Task list (higher priority)
  • Figure out what Craigslist's throttle thresholds are, and put rate limits into our scripts. On 5/6 they blocked my IP after requesting about 10,000 URLs over a few hours.
  • Add a filter to avoid requesting URLs for listings that we know from the results page are missing necessary variables like price or number of bedrooms.
  • Add docstrings and other proper code templating.
  • Deploy on Linux server.
  • Add option to upload data to S3.
  • Add Slack status notification.
  • Write unit tests.
  • Craigslist returns a maximum of 2500 search results per query, which in high-traffic regions is much less than a full day of listings. For example, it's approx 6 daytime hours in the "sfbay" region. Here are some options: (a) run the scraper once a day at midnight and accept that we are getting an incomplete sample (current approach), (b) run the scraper more frequently, (c) try switching to sub-region searches.
Task list (lower priority)
  • Improve log messages.
  • Improve and test the fail conditions to make sure we won't get into recursive loops, and exit quickly when Craigslist throttles our requests.
  • Is the list of data fields we're collecting optimal?
  • Check the master list of Craigslist domains that we're crawling, which was compiled by Geoff in 2014. Is there a way to assemble the list programmatically, to make sure it's always up to date?
  • Should we put the rental listings directly into a database as well as flat files?
  • Develop a separate set of scripts for data cleaning, for example to remove duplicate listings.
About
Streamlined web crawler for rental listings
Resources
Readme
Activity
Custom properties
Stars
3 stars
Watchers
8 watching
Forks
5 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
BranchesTags
Repository files navigation
Scraper2
Streamlined web crawler for rental listings
What it does
The scraper2 directory defines a generalized Craigslist rental listing crawler, and scripts like test_scraper.py and scrape_prior_day.py instantiate it with particular lists of search domains, time parameters, and other settings.
Functionality is similar to ClistRentScraper (developed by Geoff in 2014), but the operations are now defined explicitly rather than as Scrapy projects. This should make the crawler easier to maintain and debug. We use the Requests library for HTTP requests and Lxml to parse webpage content.
The crawler begins by loading a page of Craigslist search results, for example http://sfbay.craigslist.org/search/apa. From that page, it parses each listing's title, url, price, characteristics, and timestamp. Next, it visits each listing url to extract addresses and lat-lon coordinates. Then it moves on to the next page of search results and repeats the process.
The data is saved to a CSV file, and diagnostics and errors are saved to a log file.
ClistRentScraper only kept lat-lon coordinates, but Craigslist listings now also include a location accuracy indicator and an address or cross streets, when users have provided it. Scraper2 saves this data for future analysis.
Status
This project is on hold while we look into more official access to Craigslist data.
Task list (higher priority)
  • Figure out what Craigslist's throttle thresholds are, and put rate limits into our scripts. On 5/6 they blocked my IP after requesting about 10,000 URLs over a few hours.
  • Add a filter to avoid requesting URLs for listings that we know from the results page are missing necessary variables like price or number of bedrooms.
  • Add docstrings and other proper code templating.
  • Deploy on Linux server.
  • Add option to upload data to S3.
  • Add Slack status notification.
  • Write unit tests.
  • Craigslist returns a maximum of 2500 search results per query, which in high-traffic regions is much less than a full day of listings. For example, it's approx 6 daytime hours in the "sfbay" region. Here are some options: (a) run the scraper once a day at midnight and accept that we are getting an incomplete sample (current approach), (b) run the scraper more frequently, (c) try switching to sub-region searches.
Task list (lower priority)
  • Improve log messages.
  • Improve and test the fail conditions to make sure we won't get into recursive loops, and exit quickly when Craigslist throttles our requests.
  • Is the list of data fields we're collecting optimal?
  • Check the master list of Craigslist domains that we're crawling, which was compiled by Geoff in 2014. Is there a way to assemble the list programmatically, to make sure it's always up to date?
  • Should we put the rental listings directly into a database as well as flat files?
  • Develop a separate set of scripts for data cleaning, for example to remove duplicate listings.
About
Streamlined web crawler for rental listings
Resources
Readme
Activity
Custom properties
Stars
3 stars
Watchers
8 watching
Forks
5 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Scraper2
Streamlined web crawler for rental listings
What it does
The scraper2 directory defines a generalized Craigslist rental listing crawler, and scripts like test_scraper.py and scrape_prior_day.py instantiate it with particular lists of search domains, time parameters, and other settings.
Functionality is similar to ClistRentScraper (developed by Geoff in 2014), but the operations are now defined explicitly rather than as Scrapy projects. This should make the crawler easier to maintain and debug. We use the Requests library for HTTP requests and Lxml to parse webpage content.
The crawler begins by loading a page of Craigslist search results, for example http://sfbay.craigslist.org/search/apa. From that page, it parses each listing's title, url, price, characteristics, and timestamp. Next, it visits each listing url to extract addresses and lat-lon coordinates. Then it moves on to the next page of search results and repeats the process.
The data is saved to a CSV file, and diagnostics and errors are saved to a log file.
ClistRentScraper only kept lat-lon coordinates, but Craigslist listings now also include a location accuracy indicator and an address or cross streets, when users have provided it. Scraper2 saves this data for future analysis.
Status
This project is on hold while we look into more official access to Craigslist data.
Task list (higher priority)
  • Figure out what Craigslist's throttle thresholds are, and put rate limits into our scripts. On 5/6 they blocked my IP after requesting about 10,000 URLs over a few hours.
  • Add a filter to avoid requesting URLs for listings that we know from the results page are missing necessary variables like price or number of bedrooms.
  • Add docstrings and other proper code templating.
  • Deploy on Linux server.
  • Add option to upload data to S3.
  • Add Slack status notification.
  • Write unit tests.
  • Craigslist returns a maximum of 2500 search results per query, which in high-traffic regions is much less than a full day of listings. For example, it's approx 6 daytime hours in the "sfbay" region. Here are some options: (a) run the scraper once a day at midnight and accept that we are getting an incomplete sample (current approach), (b) run the scraper more frequently, (c) try switching to sub-region searches.
Task list (lower priority)
  • Improve log messages.
  • Improve and test the fail conditions to make sure we won't get into recursive loops, and exit quickly when Craigslist throttles our requests.
  • Is the list of data fields we're collecting optimal?
  • Check the master list of Craigslist domains that we're crawling, which was compiled by Geoff in 2014. Is there a way to assemble the list programmatically, to make sure it's always up to date?
  • Should we put the rental listings directly into a database as well as flat files?
  • Develop a separate set of scripts for data cleaning, for example to remove duplicate listings.
About
Streamlined web crawler for rental listings
Resources
Readme
Activity
Custom properties
Stars
3 stars
Watchers
8 watching
Forks
5 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Scraper2
Streamlined web crawler for rental listings
What it does
The scraper2 directory defines a generalized Craigslist rental listing crawler, and scripts like test_scraper.py and scrape_prior_day.py instantiate it with particular lists of search domains, time parameters, and other settings.
Functionality is similar to ClistRentScraper (developed by Geoff in 2014), but the operations are now defined explicitly rather than as Scrapy projects. This should make the crawler easier to maintain and debug. We use the Requests library for HTTP requests and Lxml to parse webpage content.
The crawler begins by loading a page of Craigslist search results, for example http://sfbay.craigslist.org/search/apa. From that page, it parses each listing's title, url, price, characteristics, and timestamp. Next, it visits each listing url to extract addresses and lat-lon coordinates. Then it moves on to the next page of search results and repeats the process.
The data is saved to a CSV file, and diagnostics and errors are saved to a log file.
ClistRentScraper only kept lat-lon coordinates, but Craigslist listings now also include a location accuracy indicator and an address or cross streets, when users have provided it. Scraper2 saves this data for future analysis.
Status
This project is on hold while we look into more official access to Craigslist data.
Task list (higher priority)
  • Figure out what Craigslist's throttle thresholds are, and put rate limits into our scripts. On 5/6 they blocked my IP after requesting about 10,000 URLs over a few hours.
  • Add a filter to avoid requesting URLs for listings that we know from the results page are missing necessary variables like price or number of bedrooms.
  • Add docstrings and other proper code templating.
  • Deploy on Linux server.
  • Add option to upload data to S3.
  • Add Slack status notification.
  • Write unit tests.
  • Craigslist returns a maximum of 2500 search results per query, which in high-traffic regions is much less than a full day of listings. For example, it's approx 6 daytime hours in the "sfbay" region. Here are some options: (a) run the scraper once a day at midnight and accept that we are getting an incomplete sample (current approach), (b) run the scraper more frequently, (c) try switching to sub-region searches.
Task list (lower priority)
  • Improve log messages.
  • Improve and test the fail conditions to make sure we won't get into recursive loops, and exit quickly when Craigslist throttles our requests.
  • Is the list of data fields we're collecting optimal?
  • Check the master list of Craigslist domains that we're crawling, which was compiled by Geoff in 2014. Is there a way to assemble the list programmatically, to make sure it's always up to date?
  • Should we put the rental listings directly into a database as well as flat files?
  • Develop a separate set of scripts for data cleaning, for example to remove duplicate listings.
About
Streamlined web crawler for rental listings
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3 stars
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8 watching
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5 forks
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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Scraper2
Streamlined web crawler for rental listings
What it does
The scraper2 directory defines a generalized Craigslist rental listing crawler, and scripts like test_scraper.py and scrape_prior_day.py instantiate it with particular lists of search domains, time parameters, and other settings.
Functionality is similar to ClistRentScraper (developed by Geoff in 2014), but the operations are now defined explicitly rather than as Scrapy projects. This should make the crawler easier to maintain and debug. We use the Requests library for HTTP requests and Lxml to parse webpage content.
The crawler begins by loading a page of Craigslist search results, for example http://sfbay.craigslist.org/search/apa. From that page, it parses each listing's title, url, price, characteristics, and timestamp. Next, it visits each listing url to extract addresses and lat-lon coordinates. Then it moves on to the next page of search results and repeats the process.
The data is saved to a CSV file, and diagnostics and errors are saved to a log file.
ClistRentScraper only kept lat-lon coordinates, but Craigslist listings now also include a location accuracy indicator and an address or cross streets, when users have provided it. Scraper2 saves this data for future analysis.
Status
This project is on hold while we look into more official access to Craigslist data.
Task list (higher priority)
  • Figure out what Craigslist's throttle thresholds are, and put rate limits into our scripts. On 5/6 they blocked my IP after requesting about 10,000 URLs over a few hours.
  • Add a filter to avoid requesting URLs for listings that we know from the results page are missing necessary variables like price or number of bedrooms.
  • Add docstrings and other proper code templating.
  • Deploy on Linux server.
  • Add option to upload data to S3.
  • Add Slack status notification.
  • Write unit tests.
  • Craigslist returns a maximum of 2500 search results per query, which in high-traffic regions is much less than a full day of listings. For example, it's approx 6 daytime hours in the "sfbay" region. Here are some options: (a) run the scraper once a day at midnight and accept that we are getting an incomplete sample (current approach), (b) run the scraper more frequently, (c) try switching to sub-region searches.
Task list (lower priority)
  • Improve log messages.
  • Improve and test the fail conditions to make sure we won't get into recursive loops, and exit quickly when Craigslist throttles our requests.
  • Is the list of data fields we're collecting optimal?
  • Check the master list of Craigslist domains that we're crawling, which was compiled by Geoff in 2014. Is there a way to assemble the list programmatically, to make sure it's always up to date?
  • Should we put the rental listings directly into a database as well as flat files?
  • Develop a separate set of scripts for data cleaning, for example to remove duplicate listings.
About
Streamlined web crawler for rental listings
Resources
Readme
Activity
Custom properties
Stars
3 stars
Watchers
8 watching
Forks
5 forks
Report repository
Releases
Packages
Contributors
Languages