Display Pandas Dataframe in Flask HTML Table: Quick Guide

Are you struggling to display Pandas Dataframe on Flask HTML table? Don’t worry because we have a quick guide that will help you do exactly that! In this article, we will be discussing the step-by-step process of displaying Pandas Dataframe in Flask HTML table.As we all know, Pandas is a powerful data manipulation library for Python. It is widely used by data scientists, developers, and other professionals for data analysis, data visualization, and data cleaning. On the other hand, Flask is a lightweight Python web application framework that is designed for creating small to medium-sized web applications. By combining these two powerful tools, we can easily create a web application that presents data in an organized manner.In this article, we will guide you on how to display Pandas Dataframe in Flask HTML table using Bootstrap table styles. Throughout this guide, you will learn how to install and configure the required packages, data preparation, and data presentation in the form of HTML tables. So sit back, relax, and let’s dive into the wonderful world of data representation on web pages.

“How To Show A Pandas Dataframe Into A Existing Flask Html Table?” ~ bbaz

Introduction

If you are working with a Flask web application and need to display a pandas DataFrame in an HTML table, this quick guide will show you how to do it. We will discuss two approaches: directly embedding HTML in your Flask application code or using the Flask-Table extension.

Approach 1 – Directly Embedding HTML

If you are comfortable with HTML and are looking for a fast and simple solution, you can directly embed HTML in your Flask application to display a pandas DataFrame. This approach involves creating a template file with HTML code that defines the structure of the table, and then inserting dynamic data from a pandas DataFrame based on the user’s request using Flask’s templating engine.

Creating the Template File

The first step is to create an HTML template file in the templates directory of your Flask application. In the example below, we create a simple HTML table with columns for Name, Age, and Gender, and an unnamed row:

Name	Age	Gender
{{ name }}	{{ age }}	{{ gender }}

Inserting Dynamic Data

To insert data from a pandas DataFrame dynamically, you need to pass the DataFrame to the Flask application, extract the relevant data based on the user’s input, and use Flask’s templating engine to render the table. In the example below, we pass a pandas DataFrame to the Flask application and filter the data based on the user’s search query:

from flask import Flask, render_templateimport pandas as pdapp = Flask(__name__)@app.route(/)def index():    data = pd.read_csv(data.csv)    query = request.args.get(q)    if query:        data = data[data[Name].str.contains(query)]    return render_template(table.html, rows=data.to_dict(records))if __name__ == __main__:    app.run(debug=True)

We first read in a CSV file with pandas and store it in a variable called data. Then, we retrieve the user’s search query from the URL using Flask’s request module. If a query is present, we filter the DataFrame to only include rows where the Name column contains the search query. Finally, we pass the filtered DataFrame to the template and use the to_dict() method to convert the DataFrame into a list of dictionaries that can be easily displayed in an HTML table.

Approach 2 – Using Flask-Table Extension

If you prefer a more modular approach, you can use the Flask-Table extension to create a dynamic HTML table that is based on your pandas DataFrame. This approach involves creating a table class that inherits from Flask-Table’s Table class, defining the columns and rendering options, and then passing the pandas DataFrame to the table class to generate the HTML code automatically.

Installing Flask-Table

The first step is to install the Flask-Table extension using pip:

pip install flask_table

Defining the Table Class

The next step is to define a table class that extends Flask-Table’s Table class and defines the columns and rendering options. In the example below, we create a simple table class with columns for Name, Age, and Gender:

from flask_table import Table, Colclass UserTable(Table):    name = Col('Name')    age = Col('Age')    gender = Col('Gender')

Passing the DataFrame to the Table

To display the DataFrame in an HTML table, we need to pass the data to the table class and render it. In the example below, we create a Flask route that reads in a CSV file with pandas, passes the DataFrame to the UserTable class, and then renders the table using Flask’s render_template() function:

@app.route(/)def index():    data = pd.read_csv(data.csv)    table = UserTable(data.to_dict(records))    return render_template(table.html, table=table)

We first read in the CSV file with pandas and store it in a variable called data. Then, we convert the DataFrame into a list of dictionaries using the to_dict() method and pass it to the UserTable constructor. Finally, we pass the table object to the template and use Flask-Table’s render() method to automatically generate the HTML code.

Comparison

Both approaches have their strengths and weaknesses. The first approach is fast and simple, but can become cumbersome if you have many columns or need to perform complex data manipulations. The second approach is more modular and flexible, but requires more upfront setup and can result in additional overhead.

Pros and Cons of Approach 1

• Pros: Fast and simple. Suitable for simple datasets and small applications.
• Cons: Hard to maintain and update. Can become unweildy for complex data manipulations.

Pros and Cons of Approach 2

• Pros: Modular and flexible. Suitable for large datasets and complex data manipulations.
• Cons: Requires more upfront setup. Can result in additional overhead.

Conclusion

In conclusion, displaying a pandas DataFrame in an HTML table in a Flask application is a common task that can be accomplished in multiple ways. The choice of approach depends on the complexity of the data and the needs of the application. By using the methods discussed in this quick guide, you can easily display your data in an elegant and readable format.

Thank you for taking the time to read through our guide on how to display a Pandas Dataframe in Flask HTML tables. We hope that you found the information valuable and informative.

As you can see, displaying data in tables is an essential aspect of web development, and Flask makes it incredibly easy to integrate Pandas Dataframes into your projects. By following the steps outlined in this article, you should now have a solid foundation to build upon and start displaying your data in HTML tables.

If you have any questions or comments about the process, please don’t hesitate to reach out to us. We always appreciate feedback from our readers and are more than happy to provide additional help if needed.

When it comes to displaying Pandas Dataframe in Flask HTML Table, there are some common questions that people often ask. Here are some of the most frequently asked questions and their answers:

1. What is a Pandas Dataframe?

   A Pandas Dataframe is a two-dimensional table-like data structure that can store data of different types (numeric, string, boolean, etc.). It is a powerful tool for data manipulation, analysis, and visualization.

2. How do I display a Pandas Dataframe in Flask HTML Table?

   You can display a Pandas Dataframe in Flask HTML Table by using the Flask-Table library. This library provides an easy way to convert a Pandas Dataframe into an HTML table that can be rendered in Flask.

3. What are the benefits of displaying a Pandas Dataframe in Flask HTML Table?

   Displaying a Pandas Dataframe in Flask HTML Table makes it easier to visualize and analyze the data. It also provides a more user-friendly interface for presenting the data to others.

4. Can I customize the appearance of the Flask HTML Table?

   Yes, you can customize the appearance of the Flask HTML Table by using CSS (Cascading Style Sheets). You can change the font, color, background, and other visual aspects of the table to match your website’s design.

5. Is it possible to add pagination to the Flask HTML Table?

   Yes, it is possible to add pagination to the Flask HTML Table by using the Flask-Pageinate library. This library provides an easy way to paginate large tables and improve the performance of your website.