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Generating report cards with Python: from theory to practice

Popularity：852 ℃/2025-01-16 17:14:12

Today, as education informatization becomes increasingly popular, automatically generating and processing student report cards has become an important task for schools and educational institutions. Python, being a powerful and easy-to-learn programming language, is ideally suited for such data processing and report generation tasks. This article details how to generate a report card using Python, including a theoretical overview and complete code examples.

1. Theoretical Overview

1. Data storage and processing

The first step in generating a grade report is to store and process student grade data. Common data storage methods include CSV files, Excel files and databases. In this article, we will use CSV files as the data storage method because it is simple and easy to read and write.

2. Data analysis and statistics

Before generating a score report, some basic analysis and statistics need to be performed on the score data, such as calculating the average score, highest score, lowest score, etc. These statistics not only help teachers understand a student's overall performance, but also provide personalized feedback to students.

3. Report generation and formatting

The final step in generating a report card is to output the analysis results in a formatted format. This usually involves combining text and tabular data to produce a clear, easy-to-read report. Python provides a variety of libraries, such aspandas、csvandreportlab, can help us achieve this goal.

2. Code examples

Below is a complete Python code example for reading student grade data from a CSV file, calculating statistics, and generating a formatted grade report card.

1. Environment preparation

Before starting, make sure you have the following libraries installed in your Python environment:

  • pandas: Used for data processing and analysis.
  • csv: used to read and write CSV files (althoughpandasCan also handle CSV files, but here we will show how to use nativecsvlibrary for simple read and write operations).
  • reportlab: Used to generate score reports in PDF format.

You can install these libraries using the following command:

bashcopy code

 pip install pandas reportlab
2. Data preparation

Suppose we have a file calledCSV file, the content is as follows:

Student name, math, English, physics, chemistry
 Zhang San,85,90,78,88
 Li Si,92,85,90,82
 Wang Wu,76,80,72,78
3. Code implementation

Below is the complete Python code for readingfile, calculate performance statistics, and generate a score report in PDF format.

import pandas as pd
 from import letter
 from import canvas
 
 # Read CSV file
 def read_scores(file_path):
     df = pd.read_csv(file_path)
     return df
 
 # Calculate performance statistics
 def calculate_statistics(df):
     statistics = {}
     for subject in [1:]: # Skip the first column (student name)
         stats = df[subject].describe()
         statistics[subject] = {
             'Average score': stats['mean'],
             'Highest score': stats['max'],
             'Minimum score': stats['min'],
             'Standard deviation': stats['std']
         }
     return statistics
 
 # Generate a score report in PDF format
 def generate_report(df, statistics, output_path):
     c = (output_path, pagesize=letter)
     width, height = letter
 
     # Set font and size
     ("Helvetica", 12)
 
     #Add title
     (100, height - 100, "Student Score Report")
     (72, height - 110, width - 72, height - 110)
 
     #Add student information header
     header = ["Student Name"] + list([1:])
     ("Helvetica-Bold", 10)
     col_widths = [70] + [50] * (len(header) - 1)
     y=height-120
     for i, header_text in enumerate(header):
         (sum(col_widths[:i]) - col_widths[i] // 2, y, header_text)
 
     #Add student information
     ("Helvetica", 10)
     y -= 15
     for index, row in ():
         for i, cell in enumerate(row):
             if i == 0:
                 (sum(col_widths[:i]) - col_widths[i] // 2, y, cell)
             else:
                 (sum(col_widths[:i]) - col_widths[i] // 2 - 5, y, f"{cell:.2f}")
         y -= 15
 
     #Add statistics
     (72, y - 10, width - 72, y - 10)
     y -= 20
     (100, y, "score statistics")
     y -= 15
     for subject, stats in ():
         (72, y, f"{subject} average score: {stats['average score']:.2f}")
         y -= 15
         (72, y, f"{subject} highest score: {stats['highest score']:.2f}")
         y -= 15
         (72, y, f"{subject} lowest score: {stats['lowest score']:.2f}")
         y -= 15
         (72, y, f"{subject} standard deviation: {stats['standard deviation']:.2f}")
         y -= 20
 
     # Save PDF file
     ()
 
 # Main function
 def main():
     file_path = ""
     output_path = ""
 
     df = read_scores(file_path)
     statistics = calculate_statistics(df)
     generate_report(df, statistics, output_path)
 
     print(f"The score report has been generated and saved as {output_path}")
 
 if __name__ == "__main__":
     main()
4. Code explanation
  1. Read CSV file:
    • usepandas.read_csvThe function reads the CSV file and stores it in a DataFrame object.
  2. Calculate grade statistics:
    • To loop through each column in the DataFrame (except the first column "Student Name"), usedescribeMethods calculate statistics including mean score, top score, bottom score, and standard deviation.
    • Store statistical information in a dictionary for subsequent use.
  3. Generate score report in PDF format:
    • useClass creates a PDF canvas.
    • Set font and size, add titles and table headers.
    • Iterate through each row in the DataFrame and draw the student information onto the PDF canvas.
    • Added performance statistics section, including average score, highest score, lowest score and standard deviation for each subject.
    • Save the generated PDF file.
  4. Main function:
    • Define the CSV file path and output PDF file path.
    • Call functions that read CSV files, calculate statistics, and generate PDF reports.
    • Print a prompt message to inform the user that the score report has been generated and saved.

3. Summary

This article introduces in detail how to use Python to generate score reports, including key steps such as data storage and processing, data analysis and statistics, and report generation and formatting. Through the complete code examples provided, readers can easily implement this function and apply it to actual educational scenarios. Python's power and rich library resources make it ideal for handling such tasks. I hope this article can provide some valuable reference and inspiration for educators and developers.

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