We all turn to Google when we have a question that needs an immediate answer. But sometimes, we need more than answers, we want solutions! And tech enthusiasts turn to ProductHunt.com to find products to help them accomplish their tasks. Product Hunt is a great online community for tech enthusiasts to explore popular apps and online services.
But that is not the only use case of the Product Hunt website. It is a perfect platform for tech companies and start-ups to take their products in front of users who are more than willing to try new products. That also makes ProductHunt.com an excellent resource for data analysts and marketers. Data from the website can help us explore popular products and online services and analyze consumer behavior.
In this blog, we will use Gigasheet, a free online CSV viewer, to explore a Product Hunt data download to find what consumers look for on the website, what products are popular, and more. So, let us dive straight in.
We are using Product Hunt data download from 2020, 2021, and 2022 stored in three separate CSV files. The three datasets combined have details of more than 35,000 products across 280+ categories. Our CSV dataset files contain the following columns:
Gigasheet is an online no-code big data analytics tool that can process expansive datasets with 1 billion+ rows without hassle. It supports all popular file types including CSV, JSON, LOG, XLSX, and more. All we have to do is log in to Gigasheet and upload our datasets.
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Our datasets are in a zipped file, which is not an issue. We will simply upload the zipped file and Gigasheet will process all three CSV datasets. Next, we will select all three Product Hunt datasets and combine them to create a single dataset using Gigasheet’s Combine file function.
Datasets with duplicate values and empty rows can give inaccurate insights that harm decision-making. So, we will remove incomplete and repetitive data to have complete and accurate data for exploration.
We can find rows with duplicate entries in cells by heading to the bottom of the column we want to check for duplicates, bringing up the drop menu, and selecting the Percent Unique or Unique option. Percent Unique returns the percentage of unique entries, while Unique shows the exact number of unique cells.
Then, we will head to the Data Cleanup tab, click Delete Duplicates, and select the columns we want to delete duplicate entries from, as shown below:
Similarly, we will look if our Product Hunt dataset has empty rows by selecting the Empty or Percent Empty option for a column we want to check. But to delete empty cells we need to create a filter to exclude those cells by right-clicking an empty cell and selecting Filter to exclude this option.
Again, we will head back to the Data Cleanup tab, but this time we will select Delete Excluded Rows.
Now that our files are combined and cleaned up, we are ready to begin our data analysis. We will answer 3 questions about the data, but this is only the start of what is possible using Gigasheet!
For our first analysis, we want to explore the top five product categories with the most products listed. We will use Gigasheet’s Group feature, which allows users to categorize data into groups and sub-groups. We will click on Group in the menu bar and group data by Topic column.
Next, we will sort the data in descending order to bring categories with the most products at the top. First, ensure the row count is visible for a column, then sort the column in descending order.
Here is what our analysis tells us about the Product Hunt dataset:
We can visualize this trend using a chart. First, select the column with row count for the top five categories > right-click > Chart Range > Pie.
When it comes to pricing, our dataset has three types of products – free, free options, and paid. Dataset also provides information like upvotes and comment counts that signify the popularity of a product. So, let us find the five most popular across these three pricing categories.
First, we will group data by Pricing Type column. To make the dataset easier to work with, we can also create a filter to only show products with more than 850 upvotes. We will head to the Filter tab in the menu bar and create a filter as shown below:
Here are the top five products in Free category:
Here are the top five products in the Free Options category:
Top five products in Payment Required category
Our previous analysis shows that productivity-related apps are the most popular category of products category on the Product Hunt website. The dataset also has a category of apps related to remote working. Besides, we also have data from three years – 2020, 2021, and 2022.
2020 was the year when the coronavirus pandemic peaked, while 2021 and 2022 were years of the post-pandemic period. It was when remote work became popular, and people sought apps to help them improve their workflows.
So, is there a correlation between the popularity of productivity and remote work-centric apps on ProductHunt.com? Let us analyze our dataset to find out.
We have a Date column, but we do not need the full date but only the year, for our analysis. Therefore, we will use Gigasheet’s Split Column functionality under the Data Cleanup section to extract the year from the full date, as shown below:
We will get three new columns: Date - Date_split_1, Date - Date_split_2, and Date - Date_split_3. The Date - Date_split_1 column is the year column. We can also rename it by clicking the header and selecting Rename from the context menu. Besides, we will delete the other two columns as we do not need them.
Then we will group data by Year and Title columns. And we will also create the following filter to show data for Productivity and Remote Work categories.
We can see that most productivity and remote work-related apps were listed in 2021 and 2022. However, only 24 productivity-centric apps and zero remote work-related apps were listed on Product Hunt in 2020. But that changed as people embraced the new normal and businesses turned towards tech to facilitate remote working.
In 2021 there were 1966 and 13 apps listed under productivity and remote work categories, respectively. Similarly, there were more than 2,000 productivity apps and 9 remote work apps in 2022.
That was our analysis of the Product Hunt dataset using Gigasheet. You can work with expansive datasets, clean your data, apply filters, group data, and do more without writing a single line of code. Besides, you get to work with complex data in a familiar spreadsheet interface that is intuitive and accessible.
But the best part about Gigasheet is that it is absolutely free to use data analytics applications. Sign up today!