Google.com was the website with the most page views per day in Bolivia in February 2022, according to ranking by Alexa. The website had more than ***** daily page views and was followed by Unitel.bo, with ** page views per day that month. Within Latin America, Mexico was the country where Amazon Alexa contained the largest number of skills.

Traffic analytics, rankings, and competitive metrics for alexa.com as of September 2025

alexa.amazon.com is ranked #4 in US with 680.95K Traffic. Categories: . Learn more about website traffic, market share, and more!

Google.com, youtube.com, and facebook.com were the most visited websites in Ukraine in December 2021. Furthermore, Google's website on the Ukrainian domain, google.com.ua, ranked in the top 10 during that time.

alexa.com is ranked #317632 in US with 31.23K Traffic. Categories: Advertising and Marketing, Computer Software and Development, Information Technology, Online Services. Learn more about website traffic, market share, and more!

Context

Alexa Internet rank websites primarily on tracking a sample set of Internet traffic—users of its toolbar for the Internet Explorer, Firefox and Google Chrome web browsers. The Alexa Toolbar includes a popup blocker (which stops unwanted ads), a search box, links to Amazon.com and the Alexa homepage, and the Alexa ranking of the website that the user is visiting. It also allows the user to rate the website and view links to external, relevant websites. Also, Alexa has prepared a list of information for each site for comparison and ranking with other similar sites for each site.

This dataset is a record of all information on the top websites in each category in Alexa ranking. Source: https://github.com/AshkanGoharfar/Crawler_for_alexa.com

Content

This dataset includes several site data, which were achieved from "alexa.com/siteinfo" (for example alexa.com/siteinfo/facebook.com). Data is included for the top 50 websites for every 550 categories in Alexa ranking. (The dataset was obtained for about 22000 sites.) The data also includes keyword opportunities breakdown fields, which vary between categories. As well as each site has important parameters like all_topics_top_keywords_search_traffic_parameter which represent search traffics in competitor websites to this site. For more details about each site's data, you can find the site's name and site's information in the dataset and you can search alexa.com/siteinfo/SiteName link to understand each parameter and columns in the dataset.

Acknowledgements

This dataset was collected using the selenium library and chrome web driver to crawl alexa.com data with python language.

Provider: Ashkan Goharfar, ashkan_goharfar@aut.ac.ir, Department of Computer Engineering and Information Technology, Amirkabir University of Technology

Citation Request

A. Risheh, A. Goharfar, and N. T. Javan, "Clustering Alexa Internet Data using Auto Encoder Network and Affinity Propagation," 2020 10th International Conference on Computer and Knowledge Engineering (ICCKE), Mashhad, Iran, 2020, pp. 437-443, doi: 10.1109/ICCKE50421.2020.9303705.

Inspiration

Possible uses for this dataset could include:

Sentiment analysis in a variety of forms. Categorizing websites based on their competitor websites, daily time on the website and Keyword opportunities.

Analyzing what factors affect on Comparison metrics search traffic, Comparison metrics data, Audience overlap sites overlap scores, top keywords share of voice, top keywords search traffic, optimization opportunities organic share of voice, Optimization opportunities search popularity, Buyer keywords organic competition, Buyer keywords Avg traffic, Easy to rank keywords search pop, Easy to rank keywords relevance to site, Keyword gaps search popularity, Keyword gaps Avg traffic and Keywords search traffic.

Training ML algorithms like RNNs to generate a probability for each site in each category to being SEO by Google.

Use NLP for columns like keyword gaps name, Easy to rank keywords name, Buyer keywords name, optimization opportunities name, Top keywords name and Audience overlap similar sites to this site.

QAP regressions for popular websites (Alexa)/ videos (YouTube)/ topics (Twitter) similarity across countries (Final block, September).

Descriptive statistics for matrices of Alexa, YouTube, and Twitter (September).

This statistic shows the ten most popular web shops in Sweden in 2017, by Alexa global traffic rank. In first place was zalando.se, ranked ***** by Alexa, followed by adlibris.com, which was ranked ******. Komplett.se came in third place at ******.

Dataset

This dataset was created by DNS_dataset

Contents

We are publishing a dataset we created for the HTTPS traffic classification.

Since the data were captured mainly in the real backbone network, we omitted IP addresses and ports. The datasets consist of calculated from bidirectional flows exported with flow probe Ipifixprobe. This exporter can export a sequence of packet lengths and times and a sequence of packet bursts and time. For more information, please visit ipfixprobe repository (Ipifixprobe).

During our research, we divided HTTPS into five categories: L -- Live Video Streaming, P -- Video Player, M -- Music Player, U -- File Upload, D -- File Download, W -- Website, and other traffic.

We have chosen the service representatives known for particular traffic types based on the Alexa Top 1M list and Moz's list of the most popular 500 websites for each category. We also used several popular websites that primarily focus on the audience in our country. The identified traffic classes and their representatives are provided below:

Live Video Stream Twitch, Czech TV, YouTube Live

Video Player DailyMotion, Stream.cz, Vimeo, YouTube

Music Player AppleMusic, Spotify, SoundCloud

File Upload/Download FileSender, OwnCloud, OneDrive, Google Drive

Website and Other Traffic Websites from Alexa Top 1M list

The people from Czech are publishing a dataset for the HTTPS traffic classification.

Since the data were captured mainly in the real backbone network, they omitted IP addresses and ports. The datasets consist of calculated from bidirectional flows exported with flow probe Ipifixprobe. This exporter can export a sequence of packet lengths and times and a sequence of packet bursts and time. For more information, please visit ipfixprobe repository (Ipifixprobe).

During research, they divided HTTPS into five categories: L -- Live Video Streaming, P -- Video Player, M -- Music Player, U -- File Upload, D -- File Download, W -- Website, and other traffic.

They have chosen the service representatives known for particular traffic types based on the Alexa Top 1M list and Moz's list of the most popular 500 websites for each category. They also used several popular websites that primarily focus on the audience in Czech. The identified traffic classes and their representatives are provided below:

Live Video Stream Twitch, Czech TV, YouTube Live Video Player DailyMotion, Stream.cz, Vimeo, YouTube Music Player AppleMusic, Spotify, SoundCloud File Upload/Download FileSender, OwnCloud, OneDrive, Google Drive Website and Other Traffic Websites from Alexa Top 1M list

alexia.fr is ranked #3711 in FR with 443.58K Traffic. Categories: . Learn more about website traffic, market share, and more!

In November 2021, Agt.se was the most popular website in Sweden based on user engagement with an average session length of about ** minutes and ** seconds. Facebook.com was ranked second with users spending approximately ** minutes and ** seconds per visit to the platform.

Context

This dataset includes some of the basic information of the websites we daily use. While scrapping this info, I learned quite a lot in R programming, system speed, memory usage etc. and developed my niche in Web Scrapping. It took about 4-5 hrs for scrapping this data through my system (4GB RAM) and nearly about 4-5 days working out my idea through this project.

Content

The dataset contains Top 50 ranked sites from each 191 countries along with their traffic (global) rank. Here, country_rank represent the traffic rank of that site within the country, and traffic_rank represent the global traffic rank of that site.

Since most of the columns meaning can be derived from their name itself, its pretty much straight forward to understand this dataset. However, there are some instances of confusion which I would like to explain in here:

1) most of the numeric values are in character format, hence, contain spaces which you might need to clean on.

2) There are multiple instances of same website. for.e.g. Yahoo. com is present in 179 rows within this dataset. This is due to their different country rank in each country.

3)The information provided in this dataset is for the top 50 websites in 191 countries as on 25th May 2017 and is subjected to change in future time due to the dynamic structure of ranking.

4) The dataset inactual contains 9540 rows instead of 9550(50*191 rows). This was due to the unavailability of information for 10 websites.

PS: in case if there are anymore queries, comment on this, I'll add an answer to that in above list.

Acknowledgements

I wouldn't have done this without the help of others. I've scrapped this information from publicly available (open to all) websites namely: 1) http://data.danetsoft.com/ 2) http://www.alexa.com/topsites , of which i'm highly grateful. I truly appreciate and thanks the owner of these sites for providing us with the information that I included today in this dataset.

Inspiration

I feel that there this a lot of scope for exploring & visualization this dataset to find out the trends in the attributes of these websites across countries. Also, one could try predicting the traffic(global) rank being a dependent factor on the other attributes of the website. In any case, this dataset will help you find out the popular sites in your area.

These are similarity matrices of countries based on dfferent modalities of web use. Alexa website traffic, trending vidoes on Youtube and Twitter trends. Each matrix is a month of data aggregated

In November 2024, the online marketplace Finn.no was the website with the most pages per visit in Norway, with an average of 16.91 pages visited per session. Additionally, visitors to Meta's social platforms Facebook.com and Instagram.com accessed approximately 9.81 and 9.21 pages per visit, respectively. Google.no ranked fourth with 8.5 pages per visit.

Content

I collected data from here by country and with the help of a little bit of data wrangling, I could convert the data into the JSON and CSV format. The dataset contains 2 files:

countries.json: The top 50 most popular websites by each country, the ranking order is stored by indexes. sites.csv: General information about every website on the list, such as: * Daily Minutes on Site: Estimated daily minutes on site per visitor to the site * Daily Pageviews per Visitor: Estimated daily unique pageviews per visitor on the site * Ratio of Traffic From Search: The ratio of all referrals that came from Search engines over the trailing month * Total Sites Linking In: The total number of sites that are linked to this website

Acknowledgements

Source: Alexa.com.

The Top Websites in the World

How They Change Over Time

About this dataset

This dataset consists of the top 50 most visited websites in the world, as well as the category and principal country/territory for each site. The data provides insights into which sites are most popular globally, and what type of content is most popular in different parts of the world

How to use the dataset

This dataset can be used to track the most popular websites in the world over time. It can also be used to compare website popularity between different countries and categories

Research Ideas

• To track the most popular websites in the world over time
• To see how website popularity changes by region
• To find out which website categories are most popular

Acknowledgements

Dataset by Alexa Internet, Inc. (2019), released on Kaggle under the Open Data Commons Public Domain Dedication and License (ODC-PDDL)

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: df_1.csv | Column name | Description | |:--------------------------------|:---------------------------------------------------------------------| | Site | The name of the website. (String) | | Domain Name | The domain name of the website. (String) | | Category | The category of the website. (String) | | Principal country/territory | The principal country/territory where the website is based. (String) |