Adventures in Machine Learning

Mastering Complex Cohort Analysis Graphs with Plotly API and HTML Forms

Building Effective Graphs with Plotly API and HTML Forms

Data analysis has become a crucial aspect of various industries, from business to healthcare, to education. With the abundance of data that we have access to, it is essential that we can visualize the data to derive insights and make informed decisions.

In this article, we will discuss how to use Plotly API and HTML forms to build a basic graph and consume data from Plotly API. We will then focus on cohort analysis and discuss how to create a bar graph for effective data visualization.

Lastly, we will discuss setting up the project structure by downloading and running a gist and installing necessary dependencies.

GET and POST Requests with HTML Forms

The ability to obtain and display data in a user-friendly and engaging manner is a vital tool for data analysts. HTML forms allow for user input, which can be used to query data from an API.

In our case, we will use the Plotly API. The request methods used are GET and POST.

The GET method retrieves data from the server based on a URL query string. In contrast, the POST method sends data to the server within the messages of an HTTP request.

The request URL does not contain the data being posted.

Consuming Data from Plotly API

Once we have understood the basics of GET and POST requests in HTML forms, it is time to move on to data consumption using the Plotly API. Plotly is a popular charting library that makes it easy to build charts and graphs in Python and R.

By utilizing a wide range of visualization types, including heat maps, scatterplots, line graphs, and bar charts, Plotly has become a go-to tool for data analysts.

Creating a Bar Graph for Cohort Analysis

The bar graph is a popular graph type for cohort analysis because it is a great tool for visualizing the data to compare trends over periods. Cohort analysis is a process of analyzing the behavior of a specific group of people over a specified period.

In this section, we will focus on building a bar graph for cohort analysis using Plotly. To create a bar graph in Plotly, we need to start with importing the necessary libraries and installing Plotly with pip3.

Then, we need to create the CSV file of the data we want to analyze. After creating the CSV file, we can then create the graph using Plotly.

A proper understanding of HTML forms, Plotly API, and cohort analysis will serve as the foundation to create an efficient data visualization tool.

Setting up the Project Structure

The project structure is critical for successful development. It helps create an organized workflow for developers.

Before any development begins, it is important to define the structure of the project. In this section, we will download and run a gist and install required dependencies.

We can download and run the gist by obtaining the GitHub URL and running the following Bash command. Once we have obtained the URL, we will use Git to clone the repository using the git clone command.

Installing the required dependencies is also important to ensure smooth development. We will use Virtualenv to create a virtual environment and install all the packages listed in the requirements.txt file.


In conclusion, this article has discussed how to use HTML forms and the Plotly API to create a basic graph and consume data. We have focused on cohort analysis and how to use a bar graph to visualize the data and derive insights.

Lastly, we have discussed setting up the project structure to ensure smooth development and efficiency. As a data analyst, it is important to possess a thorough understanding of all the concepts discussed in this article.

This knowledge will serve as the foundation for building efficient data visualization tools in the future.

Creating Effective Graphs with HTML Forms and Plotly API Part 2

In the previous article, we discussed how to create a basic graph using Plotly API and HTML forms, along with cohort analysis and setting up the project structure. In this continuation article, we will dive deeper into building complex graphs with cohort analysis.

We will discuss upgrading to a Simple Template Engine, creating a Data.JSON file, accessing it for Plotly API calls, form creation and templating, defining the GET and POST functions to retrieve and manipulate form data, and displaying the resulting graph.

Upgrading to Simple Template Engine

A Simple Template Engine (STE) is a web application where the templates are stored as simple HTML or text files. STEs are highly customizable and can be designed to interact with APIs. Moreover, STEs simplify the development process and can be used to create reusable templates.

When using STEs to build a complex graph, it is essential to make sure the templates are styled correctly. Therefore, we must add CSS and Javascript files that contain styles for our templates.

Creating the Data.JSON File

When working with APIs, we often need to interact with sensitive and confidential data. Hence we must store and secure such essential information.

One way to store and access data securely is by utilizing a Data.JSON file. A Data.JSON file stores data in a structured JSON format, which makes it easy to read and manipulative.

Accessing Data.JSON for Plotly API Call

Once we have created the Data.JSON file, we need to access it for use in making API calls. To achieve this, we will need to define a variable that holds the path to the Data.JSON file.

Then, we read the file’s contents and parse it into Python objects. We can then interact with Plotly API and retrieve data as we need it.

Creating the Form and Template

When building a complex graph, it is essential to create a form that collects user data for subsequent display. The form should include all the relevant data points, including labels, dropdown menus, text areas, checkboxes, and radio buttons.

After creating the form, we need to define the templates responsible for displaying the form data. The templates should have all the relevant styles and Javascript data files included.

Defining the GET and POST Functions

Once we have the form and the corresponding templates, we will need to create the GET and POST functions. The GET function is responsible for fetching form data from the server, while the POST function is responsible for posting the retrieved data to the server.

The GET function should have a URL route that corresponds to the path the form data is submitted to. On the other hand, the POST function should store the form data submission on the server-side and allow us to fetch the data when necessary.

Displaying Cohort Analysis Graph

After defining the GET and POST functions, we can now display the resulting graph. When displaying a cohort analysis graph, it is essential to have all the relevant data points, including the number of users, the period, and the number of sign-ups.

We can use Plotly APIs to display this information as a bar graph or a line graph. Once we have the graph, we can continuously update it when new data is submitted to the server.


In conclusion, this article has explored how to upgrade to Simple Template Engine when building a complex graph, create a Data.JSON file for secure data storage, access it for Plotly API calls, create essential forms, and define the GET and POST functions responsible for manipulating form data. Finally, we have displayed a cohort analysis graph and continuously updated it with new data.

With these tools, data analysts can seamlessly create effective Graphs with HTML Forms and Plotly APIS. In this article, we delved deeper into creating effective graphs using HTML Forms and the Plotly API by discussing how to build complex graphs with cohort analysis.

We explored the importance of upgrading to a Simple Template Engine, securing data using Data.JSON files, and integrating forms with templates to create effective displays. Additionally, we learned how to define GET and POST functions to manipulate the form data, making API calls to retrieve new data for graph displays.

The use of these tools demonstrates how data analysts can create effective workflows for graph building with minimal effort. The takeaway is that properly structured tools can be key to ease the process of data manipulation and graph building.