Building an Automated Financial Report Generation Workflow with Polars and GPT-4: Data Analysis, Insight Extraction, and Visualization

Are you wasting time manually collecting and analyzing financial data? We introduce a method to build a workflow that automatically extracts insights from data and generates beautifully visualized financial reports by combining Polars' overwhelming speed with GPT-4's natural language processing capabilities. Save time and increase analysis accuracy.

1. The Challenge / Context

Financial report generation is a task that requires significant time and effort. It involves collecting data from various sources, organizing and analyzing it using spreadsheets or programming languages, and then visualizing the analysis results effectively. This process becomes even more cumbersome, especially with large and complex datasets. The problem is that this process is repetitive and time-consuming. This hinders analysts from focusing on more critical strategic decision-making and wastes resources needed for report generation. Furthermore, manual data processing increases the likelihood of errors, potentially reducing accuracy. Therefore, automating the financial data analysis and report generation process is crucial for enhancing productivity and improving accuracy.

2. Deep Dive: Polars

Polars is a fast DataFrame library written in Rust. It offers an API similar to pandas but significantly outperforms it in terms of memory efficiency and speed. Polars uses a columnar data format and leverages multithreading to process data in parallel. This means tasks that would take minutes or hours with pandas can be completed in seconds with Polars. It is particularly well-suited for financial analysis dealing with large datasets. Key features include:

  • Speed: Provides significantly faster data processing speeds than pandas.
  • Memory Efficiency: Minimizes memory usage to efficiently process even large datasets.
  • lazy evaluation: Performs computations only when necessary, reducing unnecessary operations.
  • Parallel Processing: Enhances speed further by processing data in parallel through multithreading.
  • Expression-based API: Provides a powerful and flexible expression-based API, making complex data transformations easy to perform.

3. Deep Dive: GPT-4

GPT-4 is a state-of-the-art natural language processing model developed by OpenAI. It offers significantly more powerful performance than previous versions and can perform various tasks such as text generation, translation, summarization, and question answering. In particular, GPT-4 excels at understanding complex data and interpreting it in natural language, making it highly useful for extracting insights from financial data analysis results and generating reports based on them. By leveraging GPT-4, you can automatically generate reports based on data analysis results and refine necessary parts to complete the final report. Key features include:

  • Powerful Natural Language Processing Capabilities: Can perform various NLP tasks such as text generation, translation, summarization, and question answering.
  • Data Understanding and Interpretation: Can understand complex data and interpret it in natural language.
  • Automated Report Generation: Can automatically generate reports based on data analysis results.
  • Diverse API Offerings: Provides APIs to use GPT-4 in various programming languages.

3. Step-by-Step Guide / Implementation

Now, let's explain step-by-step how to build an automated financial report generation workflow using Polars and GPT-4.

Step 1: Data Collection and Preprocessing

First, collect the necessary financial data. Data can exist in various formats such as CSV, Excel, or databases. Use Polars to load the data and perform necessary preprocessing tasks. For example, you can handle missing values, convert data types, or remove outliers.


    import polars as pl

    # Load data from CSV file
    df = pl.read_csv("financial_data.csv")

    # Handle missing values (replace with mean)
    df = df.fill_null(pl.col("수익").mean())

    # Convert data type (convert string to date format)
    df = df.with_columns(pl.col("날짜").str.strptime(pl.Date, "%Y-%m-%d"))

    # Remove outliers (remove data where revenue is in the top 1% or bottom 1%)
    q1 = df["수익"].quantile(0.01)
    q99 = df["수익"].quantile(0.99)
    df = df.filter((pl.col("수익") >= q1) & (pl.col("수익") <= q99))

    print(df.head())

Step 2: Data Analysis

Use the preprocessed data to perform the necessary analysis. Polars' powerful expression-based API allows you to perform various statistical analyses, trend analyses, correlation analyses, and more. For example, you can analyze monthly revenue trends or revenue changes over a specific period.


    # Analyze monthly revenue trends
    monthly_revenue = df.group_by(pl.col("날짜").dt.strftime("%Y-%m")).agg(pl.col("수익").sum()).sort("날짜")

    # Analyze revenue changes over a specific period (e.g., January 2023 to June 2023)
    start_date = "2023-01-01"
    end_date = "2023-06-30"
    period_revenue = df.filter((pl.col("날짜") >= start_date) & (pl.col("날짜") <= end_date))["수익"].sum()

    print(monthly_revenue)
    print(f"Total revenue from January 2023 to June 2023: {period_revenue}")

Step 3: Insight Extraction using GPT-4

Input the data analysis results into GPT-4 to extract insights. GPT-4 can interpret analysis results in natural language and identify meaningful patterns or trends in the data. For example, you can ask GPT-4 questions like, "What characteristics does the revenue change from January 2023 to June 2023 show compared to the previous period?" You can integrate with GPT-4 using the OpenAI API.


    import openai

    # Set OpenAI API key
    openai.api_key = "YOUR_OPENAI_API_KEY"

    # Generate GPT-4 prompt
    prompt = f"""
    2023 Monthly Revenue Data:
    {monthly_revenue}

    Total Revenue from January 2023 to June 2023: {period_revenue}

    Based on the above data, analyze the revenue change trends and characteristics, and derive 3 important insights.
    """

    # Call GPT-4 API
    response = openai.Completion.create(
        engine="text-davinci-003",  # Use GPT-3.5 model (change to gpt-4 if you have GPT-4 access)
        prompt=prompt,
        max_tokens=200,
        n=1,
        stop=None,
        temperature=0.7,
    )

    # Print GPT-4 response
    insights = response.choices[0].text.strip()
    print(insights)

Note: Accessing the GPT-4 API requires an OpenAI API