LLMs + Pandas: How I Use Generative AI to Generate Pandas DataFrame Summaries

datasets and are in search of fast insights with out an excessive amount of handbook grind, you’ve come to the suitable place.

In 2025, datasets typically comprise hundreds of thousands of rows and lots of of columns, which makes handbook evaluation subsequent to not possible. Native Giant Language Fashions can remodel your uncooked DataFrame statistics into polished, readable experiences in seconds — minutes at worst. This method eliminates the tedious strategy of analyzing knowledge by hand and writing government experiences, particularly if the info construction doesn’t change.

Pandas handles the heavy lifting of knowledge extraction whereas LLMs convert your technical outputs into presentable experiences. You’ll nonetheless want to write down features that pull key statistics out of your datasets, nevertheless it’s a one-time effort.

This information assumes you will have Ollama put in domestically. In the event you don’t, you may nonetheless use third-party LLM distributors, however I gained’t clarify how to hook up with their APIs.

Desk of contents:

• Dataset Introduction and Exploration
• The Boring Half: Extracting Abstract Statistics
• The Cool Half: Working with LLMs
• What You May Enhance

Dataset Introduction and Exploration

For this information, I’m utilizing the MBA admissions dataset from Kaggle. Obtain it if you wish to comply with alongside.

The dataset is licensed below the Apache 2.0 license, which implies you should use it freely for each private and business initiatives.

To get began, you’ll want a number of Python libraries put in in your system.

After you have all the pieces put in, import the required libraries in a brand new script or a pocket book:

import pandas as pd
from langchain_ollama import ChatOllama
from typing import Literal

Dataset loading and preprocessing

Begin by loading the dataset with Pandas. This snippet masses the CSV file, prints primary details about the dataset form, and exhibits what number of lacking values exist in every column:

df = pd.read_csv("knowledge/MBA.csv")

# Fundamental dataset information
print(f"Dataset form: {df.form}n")
print("Lacking worth stats:")
print(df.isnull().sum())
print("-" * 25)
df.pattern(5)

Since knowledge cleansing isn’t the principle focus of this text, I’ll preserve the preprocessing minimal. The dataset solely has a few lacking values that want consideration:

df["race"] = df["race"].fillna("Unknown")
df["admission"] = df["admission"].fillna("Deny")

That’s it! Let’s see methods to go from this to a significant report subsequent.

The Boring Half: Extracting Abstract Statistics

Even with all of the advances in AI functionality and availability, you in all probability don’t wish to ship your complete dataset to an LLM supplier. There are a few good the explanation why.

It may devour approach too many tokens, which interprets on to larger prices. Processing massive datasets can take a very long time, particularly if you’re operating fashions domestically by yourself {hardware}. You may additionally be coping with delicate knowledge that shouldn’t go away your group.

Some handbook work remains to be the way in which to go.

This method requires you to write down a operate that extracts key components and statistics out of your Pandas DataFrame. You’ll have to write down this operate from scratch for various datasets, however the core concept transfers simply between initiatives.

The get_summary_context_message() operate takes in a DataFrame and returns a formatted multi-line string with an in depth abstract. Right here’s what it consists of:

• Complete software depend and gender distribution
• Worldwide vs home applicant breakdown
• GPA and GMAT rating quartile statistics
• Admission charges by tutorial main (sorted by price)
• Admission charges by work business (prime 8 industries)
• Work expertise evaluation with categorical breakdowns
• Key insights highlighting top-performing classes

Right here’s the whole supply code for the operate:

def get_summary_context_message(df: pd.DataFrame) -> str:
    """
    Generate a complete abstract report of MBA admissions dataset statistics.
    
    This operate analyzes MBA software knowledge to offer detailed statistics on
    applicant demographics, tutorial efficiency, skilled backgrounds, and
    admission charges throughout numerous classes. The abstract consists of gender and
    worldwide standing distributions, GPA and GMAT rating statistics, admission
    charges by tutorial main and work business, and work expertise influence evaluation.
    
    Parameters
    ----------
    df : pd.DataFrame
        DataFrame containing MBA admissions knowledge with the next anticipated columns:
        - 'gender', 'worldwide', 'gpa', 'gmat', 'main', 'work_industry', 'work_exp', 'admission'
    
    Returns
    -------
    str
        A formatted multi-line string containing complete MBA admissions
        statistics.
    """
    # Fundamental software statistics
    total_applications = len(df)

    # Gender distribution
    gender_counts = df["gender"].value_counts()
    male_count = gender_counts.get("Male", 0)
    female_count = gender_counts.get("Feminine", 0)

    # Worldwide standing
    international_count = (
        df["international"].sum()
        if df["international"].dtype == bool
        else (df["international"] == True).sum()
    )

    # GPA statistics
    gpa_data = df["gpa"].dropna()
    gpa_avg = gpa_data.imply()
    gpa_25th = gpa_data.quantile(0.25)
    gpa_50th = gpa_data.quantile(0.50)
    gpa_75th = gpa_data.quantile(0.75)

    # GMAT statistics
    gmat_data = df["gmat"].dropna()
    gmat_avg = gmat_data.imply()
    gmat_25th = gmat_data.quantile(0.25)
    gmat_50th = gmat_data.quantile(0.50)
    gmat_75th = gmat_data.quantile(0.75)

    # Main evaluation - admission charges by main
    major_stats = []
    for main in df["major"].distinctive():
        major_data = df[df["major"] == main]
        admitted = len(major_data[major_data["admission"] == "Admit"])
        whole = len(major_data)
        price = (admitted / whole) * 100
        major_stats.append((main, admitted, whole, price))

    # Type by admission price (descending)
    major_stats.kind(key=lambda x: x[3], reverse=True)

    # Work business evaluation - admission charges by business
    industry_stats = []
    for business in df["work_industry"].distinctive():
        if pd.isna(business):
            proceed
        industry_data = df[df["work_industry"] == business]
        admitted = len(industry_data[industry_data["admission"] == "Admit"])
        whole = len(industry_data)
        price = (admitted / whole) * 100
        industry_stats.append((business, admitted, whole, price))

    # Type by admission price (descending)
    industry_stats.kind(key=lambda x: x[3], reverse=True)

    # Work expertise evaluation
    work_exp_data = df["work_exp"].dropna()
    avg_work_exp_all = work_exp_data.imply()

    # Work expertise for admitted college students
    admitted_students = df[df["admission"] == "Admit"]
    admitted_work_exp = admitted_students["work_exp"].dropna()
    avg_work_exp_admitted = admitted_work_exp.imply()

    # Work expertise ranges evaluation
    def categorize_work_exp(exp):
        if pd.isna(exp):
            return "Unknown"
        elif exp < 2:
            return "0-1 years"
        elif exp < 4:
            return "2-3 years"
        elif exp < 6:
            return "4-5 years"
        elif exp < 8:
            return "6-7 years"
        else:
            return "8+ years"

    df["work_exp_category"] = df["work_exp"].apply(categorize_work_exp)
    work_exp_category_stats = []

    for class in ["0-1 years", "2-3 years", "4-5 years", "6-7 years", "8+ years"]:
        category_data = df[df["work_exp_category"] == class]
        if len(category_data) > 0:
            admitted = len(category_data[category_data["admission"] == "Admit"])
            whole = len(category_data)
            price = (admitted / whole) * 100
            work_exp_category_stats.append((class, admitted, whole, price))

    # Construct the abstract message
    abstract = f"""MBA Admissions Dataset Abstract (2025)
    
Complete Functions: {total_applications:,} individuals utilized to the MBA program.

Gender Distribution:
- Male candidates: {male_count:,} ({male_count/total_applications*100:.1f}%)
- Feminine candidates: {female_count:,} ({female_count/total_applications*100:.1f}%)

Worldwide Standing:
- Worldwide candidates: {international_count:,} ({international_count/total_applications*100:.1f}%)
- Home candidates: {total_applications-international_count:,} ({(total_applications-international_count)/total_applications*100:.1f}%)

Educational Efficiency Statistics:

GPA Statistics:
- Common GPA: {gpa_avg:.2f}
- twenty fifth percentile: {gpa_25th:.2f}
- fiftieth percentile (median): {gpa_50th:.2f}
- seventy fifth percentile: {gpa_75th:.2f}

GMAT Statistics:
- Common GMAT: {gmat_avg:.0f}
- twenty fifth percentile: {gmat_25th:.0f}
- fiftieth percentile (median): {gmat_50th:.0f}
- seventy fifth percentile: {gmat_75th:.0f}

Main Evaluation - Admission Charges by Educational Background:"""

    for main, admitted, whole, price in major_stats:
        abstract += (
            f"n- {main}: {admitted}/{whole} admitted ({price:.1f}% admission price)"
        )

    abstract += (
        "nnWork Trade Evaluation - Admission Charges by Skilled Background:"
    )

    # Present prime 8 industries by admission price
    for business, admitted, whole, price in industry_stats[:8]:
        abstract += (
            f"n- {business}: {admitted}/{whole} admitted ({price:.1f}% admission price)"
        )

    abstract += "nnWork Expertise Affect on Admissions:nnOverall Work Expertise Comparability:"
    abstract += (
        f"n- Common work expertise (all candidates): {avg_work_exp_all:.1f} years"
    )
    abstract += f"n- Common work expertise (admitted college students): {avg_work_exp_admitted:.1f} years"

    abstract += "nnAdmission Charges by Work Expertise Vary:"
    for class, admitted, whole, price in work_exp_category_stats:
        abstract += (
            f"n- {class}: {admitted}/{whole} admitted ({price:.1f}% admission price)"
        )

    # Key insights
    best_major = major_stats[0]
    best_industry = industry_stats[0]

    abstract += "nnKey Insights:"
    abstract += (
        f"n- Highest admission price by main: {best_major[0]} at {best_major[3]:.1f}%"
    )
    abstract += f"n- Highest admission price by business: {best_industry[0]} at {best_industry[3]:.1f}%"

    if avg_work_exp_admitted > avg_work_exp_all:
        abstract += f"n- Admitted college students have barely extra work expertise on common ({avg_work_exp_admitted:.1f} vs {avg_work_exp_all:.1f} years)"
    else:
        abstract += "n- Work expertise exhibits minimal distinction between admitted and all candidates"

    return abstract

When you’ve outlined the operate, merely name it and print the outcomes:

print(get_summary_context_message(df))

Now let’s transfer on to the enjoyable half.

The Cool Half: Working with LLMs

That is the place issues get fascinating and your handbook knowledge extraction work pays off.

Python helper operate for working with LLMs

You probably have first rate {hardware}, I strongly suggest utilizing native LLMs for easy duties like this. I exploit Ollama and the newest model of the Mistral mannequin for the precise LLM processing.

If you wish to use one thing like ChatGPT by means of OpenAI API, you may nonetheless try this. You’ll simply want to switch the operate beneath to arrange your API key and return the suitable occasion from Langchain.

Whatever the choice you select, a name to get_llm() with a check message shouldn’t return an error:

def get_llm(model_name: str = "mistral:newest") -> ChatOllama:
    """
    Create and configure a ChatOllama occasion for native LLM inference.
    
    This operate initializes a ChatOllama shopper configured to hook up with a
    native Ollama server. The shopper is ready up with deterministic output
    (temperature=0) for constant responses throughout a number of calls with the
    identical enter.
    
    Parameters
    ----------
    model_name : str, non-obligatory
        The title of the Ollama mannequin to make use of for chat completions.
        Have to be a sound mannequin title that's accessible on the native Ollama
        set up. Default is "mistral:newest".
    
    Returns
    -------
    ChatOllama
        A configured ChatOllama occasion prepared for chat completions.
    """
    return ChatOllama(
        mannequin=model_name, base_url="http://localhost:11434", temperature=0
    )


print(get_llm().invoke("check").content material)

Summarization immediate

That is the place you will get inventive and write ultra-specific directions in your LLM. I’ve determined to maintain issues mild for demonstration functions, however be at liberty to experiment right here.

There isn’t a single proper or incorrect immediate.

No matter you do, make sure that to incorporate the format arguments utilizing curly brackets – these values will likely be crammed dynamically later:

SUMMARIZE_DATAFRAME_PROMPT = """
You're an skilled knowledge analyst and knowledge summarizer. Your job is to absorb complicated datasets
and return user-friendly descriptions and findings.

You got this dataset:
- Identify: {dataset_name}
- Supply: {dataset_source}

This dataset was analyzed in a pipeline earlier than it was given to you.
These are the findings returned by the evaluation pipeline:


{context}


Based mostly on these findings, write an in depth report in {report_format} format.
Give the report a significant title and separate findings into sections with headings and subheadings.
Output solely the report in {report_format} and nothing else.

Report:
"""

Summarization Python operate

With the immediate and the get_llm() features declared, the one factor left is to attach the dots. The get_report_summary() operate takes in arguments that can fill the format placeholders within the immediate, then invokes the LLM with that immediate to generate a report.

You may select between Markdown or HTML codecs:

def get_report_summary(
    dataset: pd.DataFrame,
    dataset_name: str,
    dataset_source: str,
    report_format: Literal["markdown", "html"] = "markdown",
) -> str:
    """
    Generate an AI-powered abstract report from a pandas DataFrame.
    
    This operate analyzes a dataset and generates a complete abstract report
    utilizing a big language mannequin (LLM). It first extracts statistical context
    from the dataset, then makes use of an LLM to create a human-readable report within the
    specified format.
    
    Parameters
    ----------
    dataset : pd.DataFrame
        The pandas DataFrame to research and summarize.
    dataset_name : str
        A descriptive title for the dataset that will likely be included within the
        generated report for context and identification.
    dataset_source : str
        Details about the supply or origin of the dataset.
    report_format : {"markdown", "html"}, non-obligatory
        The specified output format for the generated report. Choices are:
        - "markdown" : Generate report in Markdown format (default)
        - "html" : Generate report in HTML format
    
    Returns
    -------
    str
        A formatted abstract report.
    
    """
    context_message = get_summary_context_message(df=dataset)
    immediate = SUMMARIZE_DATAFRAME_PROMPT.format(
        dataset_name=dataset_name,
        dataset_source=dataset_source,
        context=context_message,
        report_format=report_format,
    )
    return get_llm().invoke(enter=immediate).content material

Utilizing the operate is easy – simply move within the dataset, its title, and supply. The report format defaults to Markdown:

md_report = get_report_summary(
    dataset=df, 
    dataset_name="MBA Admissions (2025)",
    dataset_source="https://www.kaggle.com/datasets/taweilo/mba-admission-dataset"
)
print(md_report)

The HTML report is simply as detailed, however may use some styling. Perhaps you could possibly ask the LLM to deal with that as effectively!

What You May Enhance

I may have simply turned this right into a 30-minute learn by optimizing each element of the pipeline, however I stored it easy for demonstration functions. You don’t should (and shouldn’t) cease right here although.

Listed here are the issues you may enhance to make this pipeline much more highly effective:

• Write a operate that saves the report (Markdown or HTML) on to disk. This fashion you may automate the complete course of and generate experiences on a schedule with out handbook intervention.
• Within the immediate, ask the LLM so as to add CSS styling to the HTML report to make it look extra presentable. You would even present your organization’s model colours and fonts to keep up consistency throughout all of your knowledge experiences.
• Increase the immediate to comply with extra particular directions. You may want experiences that target particular enterprise metrics, comply with a specific template, or embrace suggestions based mostly on the findings.
• Increase the get_llm() operate so it may well join each to Ollama and different distributors like OpenAI, Anthropic, or Google. This offers you flexibility to modify between native and cloud-based fashions relying in your wants.
• Do actually something within the get_summary_context_message() operate because it serves as the inspiration for all context knowledge offered to the LLM. That is the place you will get inventive with function engineering, statistical evaluation, and knowledge insights that matter to your particular use case.

I hope this minimal instance has set you heading in the right direction to automate your personal knowledge reporting workflows.