SoFunction
Updated on 2025-03-08

Python implementation exports entity class list data to Excel file

Exporting list data from entity classes (i.e. custom objects) to Excel files is a common task in data processing and report generation. Python provides a variety of libraries to achieve this, the most popular of which are pandas and openpyxl. This article will use a practical case to show how to use these two libraries to export entity class list data into Excel files, while keeping the article easy to understand and logically clear.

1. Environmental preparation

Before you start, make sure you have installed the following Python libraries:

  • pandas: for data processing and export to Excel.
  • openpyxl: As the engine when exporting Excel to pandas (although pandas comes with Excel export function, openpyxl provides more advanced operation options).

You can install these libraries through the following command:

pip install pandas openpyxl

2. Define entity classes

First, we need to define an entity class (also called a data model or object). This class will contain the data fields we want to export to Excel.

class Person:
    def __init__(self, name, age, email):
         = name
         = age
         = email
 
    def __repr__(self):
        return f"Person(name={}, age={}, email={})"

In this example, we define a Person class that has three properties: name, age, and email.

3. Create an entity class list

Next, we create some Person objects and store them in a list.

people = [
    Person("Alice", 30, "alice@"),
    Person("Bob", 25, "bob@"),
    Person("Charlie", 35, "charlie@")
]

4. Convert entity class list to DataFrame

The DataFrame in the pandas library is a two-dimensional label data structure that is ideal for representing tabular data. We can convert the list of entity classes to DataFrame to export to Excel more easily.

import pandas as pd
 
# Extract the attributes of the entity class as a dictionary listdata = [{'name': , 'age': , 'email': } for person in people]
 
# Convert dictionary list to DataFramedf = (data)

In this step, we use list comprehension to convert each Person object into a dictionary and then store these dictionaries in a list. Finally, we use() to convert this dictionary list to a DataFrame.

5. Export DataFrame to Excel file

Now we can export DataFrame to Excel file using pandas' to_excel() method.

# Specify the path to the Excel fileexcel_path = ""
 
# Export DataFrame to Excel filedf.to_excel(excel_path, index=False, engine='openpyxl')

In this step, the index=False parameter of the to_excel() method indicates that the index column of the DataFrame is not exported. The engine='openpyxl' parameter specifies the use of the openpyxl library as the export engine (although this is the default option, explicitly specifying can increase the readability of the code).

6. Complete code example

Here is a complete code example that brings the above steps together:

import pandas as pd
 
# Define entity classesclass Person:
    def __init__(self, name, age, email):
         = name
         = age
         = email
 
    def __repr__(self):
        return f"Person(name={}, age={}, email={})"
 
# Create an entity class listpeople = [
    Person("Alice", 30, "alice@"),
    Person("Bob", 25, "bob@"),
    Person("Charlie", 35, "charlie@")
]
 
# Convert entity class list to DataFramedata = [{'name': , 'age': , 'email': } for person in people]
df = (data)
 
# Specify the path to the Excel fileexcel_path = ""
 
# Export DataFrame to Excel filedf.to_excel(excel_path, index=False, engine='openpyxl')
 
print(f"Data has been exported to {excel_path}")

After running this code, you should see an Excel file named in the current directory containing the data of the Person object.

7. Extended functions

In practice, you may need to perform some additional operations, such as:

  • Format Excel file: Use openpyxl or xlsxwriter library to set cell styles, merge cells, add formulas, etc.
  • Handling complex data structures: If an entity class contains nested objects or lists, you may need to write custom logic to flatten this data.
  • Add title and description: Add title lines, descriptive text, or comments in Excel files.
  • Working with large data sets: For large data sets, you may need to optimize the export process to improve performance.

8. Summary

Through the practical examples of this article, we show how to export entity class list data to Excel files using Python. We define the entity class, create a list of entity classes, convert the list to a DataFrame of pandas, and export the DataFrame to an Excel file using the to_excel() method. This process is simple and clear, and is very suitable for the export task of handling tabular data. Hopefully this case can help you better understand how to implement this function in Python and apply it in real projects.

This is the article about Python implementing exporting entity class list data to Excel files. For more relevant content on exporting Python list data to Excel, please search for my previous articles or continue browsing the related articles below. I hope everyone will support me in the future!