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Basic Usage

Practical examples for common use cases.

dataframeit() writes the results and the status into the pandas DataFrame it receives; see Input modified in place. That is why the examples on this page pass df.copy(): without the copy, each example would find the df already processed by the previous one.

Sentiment Analysis

from pydantic import BaseModel, Field
from typing import Literal
import pandas as pd
from dataframeit import dataframeit

class Sentiment(BaseModel):
    sentiment: Literal['positive', 'negative', 'neutral']
    confidence: Literal['high', 'medium', 'low']

df = pd.DataFrame({
    'text': [
        'Love this product!',
        'Terrible, do not recommend.',
        'Average, nothing special.'
    ]
})

result = dataframeit(df.copy(), Sentiment, "Analyze the sentiment of the text.")

Category Classification

from pydantic import BaseModel, Field
from typing import Literal

class Category(BaseModel):
    category: Literal['technology', 'health', 'finance', 'education', 'other']
    subcategory: str = Field(description="More specific subcategory")

result = dataframeit(
    df.copy(),
    Category,
    "Classify the text into the most appropriate category."
)

Entity Extraction

from pydantic import BaseModel, Field
from typing import List, Optional

class Entities(BaseModel):
    people: List[str] = Field(description="Names of people mentioned")
    organizations: List[str] = Field(description="Company/organization names")
    locations: List[str] = Field(description="Locations mentioned")
    dates: List[str] = Field(description="Dates mentioned")

PROMPT = """
Extract all named entities from the text.
If there are no entities of some type, return an empty list.
"""

result = dataframeit(df.copy(), Entities, PROMPT)

Text Summarization

from pydantic import BaseModel, Field

class Summary(BaseModel):
    summary: str = Field(description="Summary in up to 50 words")
    key_points: list[str] = Field(description="List of 3-5 main points")
    main_topic: str = Field(description="Central topic in one word")

PROMPT = """
Analyze the text and extract a concise summary.
Identify the main points and central topic.
"""

result = dataframeit(df.copy(), Summary, PROMPT)

Using Different Input Types

With List

texts = ['Text 1', 'Text 2', 'Text 3']
result = dataframeit(texts, Sentiment, PROMPT)
# Returns DataFrame with numeric index

With Dictionary

documents = {
    'doc_001': 'Content of document 1',
    'doc_002': 'Content of document 2',
}
result = dataframeit(documents, Sentiment, PROMPT)
# Returns DataFrame with keys as index

With Series

series = pd.Series(['Text A', 'Text B'], index=['id_1', 'id_2'])
result = dataframeit(series, Sentiment, PROMPT)
# Preserves original index

Using Different Providers

# OpenAI with gpt-6-luna (default)
result = dataframeit(df.copy(), Sentiment, PROMPT)

# Google Gemini
result = dataframeit(
    df.copy(), Sentiment, PROMPT,
    provider='google_genai',
    model='gemini-3.8-flash'
)

# Anthropic Claude
result = dataframeit(
    df.copy(), Sentiment, PROMPT,
    provider='anthropic',
    model='claude-sonnet-5'
)

Next Steps