pandas-text-comparer 0.1.1

Compare texts within a pandas DataFrame, highlighting changes and computing similarity ratios

A library designed to compare texts within a pandas DataFrame, highlighting changes and computing similarity ratios. It utilizes difflib.SequenceMatcher to perform detailed comparisons and generate results that can be easily interpreted and displayed in HTML format.

Features

Medium.com - How to compare texts in Pandas

• HTML Output: Generate an HTML representation of the comparison results, which highlights additions, deletions, and modifications in the text.
• Similarity Assessment: Compute a similarity ratio for each pair of compared texts, allowing quick assessment of text changes.
• Flexible Integration: Designed to work directly with pandas DataFrames, making it easy to integrate into existing data processing pipelines.

Usage

Installation:

pip install pandas-text-comparer

Necessary imports:

from IPython import display
import pandas as pd
from pandas_text_comparer import TextComparer

Nice-to-haves (not required). Read more here and here

from pandarallel import pandarallel  # multi-core processing
from tqdm.auto import tqdm  # progress bar

tqdm.pandas()
pandarallel.initialize(progress_bar=True)

1. Run the comparison

Specify the names of your columns and run the computation.

# A toy GPT-4 generated dataset. Replace with your data
df = pd.read_csv("https://github.com/n-splv/pandas-text-comparer/raw/main/data/demo/review-responses.csv.gz")

comparer = TextComparer(df, column_a="llm_response", column_b="human_response")
comparer.run()

2. Explore the difference

Generate an HTML table. It can be viewed with IPython.display in Jupyter. Alternatively, you can write it to a file and open in any web browser.

html = comparer.get_html()
display.HTML(html)

3. Sort by the severity of edits

Sort the result by ratio - difflib.SequenceMatcher's metric of similarity between two texts, on the scale from 0 to 1. Higher values mean that the texts are more similar.

html = comparer.get_html(sort_by_ratio="desc")  # or "asc"

4. Add columns to the view

Add any columns from the original data to the HTML by simply passing a slice of the DataFrame to get_html method.

columns = ["review_id", "company_name"]
html = comparer.get_html(df[columns])

5. Filter rows to display

When you provide any pandas object with an index (i.e. pd.DataFrame, pd.Series or pd.Indes) as an argument to get_html, it is also used to filter the rows.

filt = df.company_name == "FitFusion"

# Filter rows & add columns
html = comparer.get_html(df[filt])

# Just filter rows
html = comparer.get_html(df[filt].index)

6. Save and load the results

A comparer stores its results in a DataFrame - comparer.result. This data can be persisted and used later on to create a new comparer. This way, you avoid the re-computation:

result_filepath = "data/comparer_result.csv"
comparer.result.to_csv(result_filepath)

# Don't forget to specify the index column
loaded_result = pd.read_csv(result_filepath, index_col=0)
new_comparer = TextComparer.from_result(loaded_result)

Also, if you need to further process your data based on the computed similarities of texts, just grab this column from the result:

df["similarity_ratio"] = comparer.result.ratio