5 DataFrame Operations LLMs Handle Better Than Code

Source: Dev.to

1. Filter by Qualitative Criteria

You have 3,616 job postings and want only the ones that are remote‑friendly, senior‑level, AND disclose salary.
df[df['posting'].str.contains('remote')] matches “No remote work available.”

Cost: $4.24 for 3,616 rows (≈ 9.9 min)

from everyrow.ops import screen
from pydantic import BaseModel, Field

class JobScreenResult(BaseModel):
    qualifies: bool = Field(description="True if meets ALL criteria")

result = await screen(
    task="""
    A job posting qualifies if it meets ALL THREE criteria:
    1. Remote‑friendly: Explicitly allows remote work
    2. Senior‑level: Title contains Senior/Staff/Lead/Principal
    3. Salary disclosed: Specific compensation numbers mentioned
    """,
    input=jobs,
    response_model=JobScreenResult,
)

Result: 216 of 3,616 passed (6%).
Note: The pass rate has climbed from 1.7 % in 2020 to 14.5 % in 2025 as more companies offer remote work and disclose salaries.

Full guide with dataset •
Screening job postings by criteria (case study)

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2. Classify Rows Into Categories

You need to label 200 job postings into categories (backend, frontend, data, ML/AI, devops, etc.). Keyword matching misses anything that isn’t an exact match, and training a classifier is overkill for a one‑off task.

Cost: $1.74 for 200 rows (≈ 2.1 min)
At scale: ~ $9 for 1,000 rows, ~ $90 for 10,000 rows.

from everyrow.ops import agent_map
from typing import Literal
from pydantic import BaseModel, Field

class JobClassification(BaseModel):
    category: Literal[
        "backend", "frontend", "fullstack", "data",
        "ml_ai", "devops_sre", "mobile", "security", "other"
    ] = Field(description="Primary role category")
    reasoning: str = Field(description="Why this category was chosen")

result = await agent_map(
    task="Classify this job posting by primary role...",
    input=jobs,
    response_model=JobClassification,
)

The Literal type constrains the LLM to your predefined set, so no post‑processing is needed. Confidence scores and multi‑label support can be added by extending the Pydantic model.

Full guide with dataset

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3. Add a Column Using Web Research

You have a list of 246 SaaS products and need the annual price of each product’s lowest‑paid tier. There’s no API for this because pricing pages vary widely.

Cost: $6.68 for 246 rows (≈ 15.7 min) – 99.6 % success rate.

from everyrow.ops import agent_map
from pydantic import BaseModel, Field

class PricingInfo(BaseModel):
    lowest_paid_tier_annual_price: float = Field(
        description="Annual price in USD for the lowest paid tier"
    )
    tier_name: str = Field(description="Name of the tier")

result = await agent_map(
    task="""
    Find the pricing for this SaaS product's lowest paid tier.
    Visit the product's pricing page.
    Report the annual price in USD and the tier name.
    """,
    input=df,
    response_model=PricingInfo,
)

Each result includes a research column that shows how the answer was found, with citations.
Example: Slack’s entry references slack.com/pricing/pro and shows the math: $7.25/month × 12 = $87/year.

Full guide with dataset •
Matching software vendors to requirements (case study)

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4. Join DataFrames Without a Shared Key

You have two tables of S&P 500 data—one with company names and market caps, the other with stock tickers and fair values. No common column means pd.merge() can’t be used.

Cost: $1.00 for 438 rows (≈ 30 s) – 100 % accuracy.

from everyrow.ops import merge

result = await merge(
    task="Match companies to their stock tickers",
    left_table=companies,    # columns: company, price, mkt_cap
    right_table=valuations,  # columns: ticker, fair_value
)
# Example matches: "3M" → "MMM", "Alphabet Inc." → "GOOGL", etc.

Under the hood the operation runs a cascade: exact match → fuzzy match → LLM reasoning → web search.
Result: 99.8 % of rows matched via LLM alone. Even with 10 % character‑level noise (e.g., “Alphaeet Iqc.”), it achieved 100 % accuracy at a cost of $0.44.

Full guide with dataset •
LLM‑powered merging at scale (case study)

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5. Rank by a Metric That’s Not in Your Data

You have 300 PyPI packages and want to rank them by days since last release and number of GitHub contributors. This information lives on PyPI and GitHub, not in your DataFrame.

Cost:

• Days‑since‑release: $3.90 (≈ 5 min)
• GitHub contributors: $4.13 (≈ 5 min)

from everyrow.ops import rank

# Rank by days since the last PyPI release
result = await rank(
    task="Rank by number of days since the last PyPI release",
    input=packages,
)

The SDK launches a web‑research agent per row to fetch the metric, then returns a ranked DataFrame. The same pattern works for any external metric (e.g., GitHub stars, Stack Overflow tags, etc.).

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These five examples illustrate how LLM‑powered operations can extend pandas‑style workflows to handle qualitative filtering, ad‑hoc classification, web‑based enrichment, key‑less joins, and external ranking—without building custom pipelines or training models.

Overview

The approach works for any metric you can describe in natural language, as long as the metric is findable on the web.

🔗 Full guide with dataset:
Rank by External Metric – everyrow.io

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Cost Summary

All of these steps are performed with a single function call on a pandas DataFrame.

The orchestration—batching, parallelism, retries, rate limiting, and model selection—is handled by everyrow, an open‑source Python SDK.

Free credit: New accounts receive $20 in free credit, which comfortably covers the five examples above with room to spare.

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Next Steps

The full code and datasets for each example are linked in the guide above. Feel free to explore, adapt, and integrate these patterns into your own workflows.