Contextually, I also add the Group column, which specifies the group each record belongs to: df = pd.DataFrame() for i in range(0,N): group_col = ] * len(df_list) df_list = group_col df = df. There are already quite a few similar solutions for Python, either HTML generators or templating engines (see links at the end of this article). This is mostly convenient to generate reports in HTML or simple web applications in lightweight frameworks such as CherryPy. Firstly I define an empty dataframe, and then I append to it all the dataframes contained in the df_list variable. HTML.py has been developed to easily generate HTML code for tables and lists in Python scripts. Install the HTMLTableParser and urllib.

This module works only in Python 3 ver s ion. This one doesn’t need any other external module. In this method, we will use the HTMLTableParser module to scrap HTML Table exclusively. Now I group all the dataframes through the append() function. Method 3: Using HTMLTableParser to Parse HTML Table. The groups_names variable contains the following values: I define a list containing all the groups (A-F): import string N = len(df_list) groups_names = list(string.ascii_uppercase) In order to maintain difference among the tables, I need to add a new column, which stores the group which each record belongs to. Now I group all the extracted tables into a single table. We created a library that by pass the inherent issues of having a table in the body of the email (CSS limitations due to the email providers for instance). Group all the Extracted Tables into a Single Table If you ever need to generate an HTML table from file data, here is a basic Python 3 script that should do the trick. The backend programming language used is Python and after analyzing the available packages, we realized that we would not find what we were searching for. I note that the first column Unnamed: 0 has not been recognised correctly.

All the dataframes have the same structures.

Python create html tables