drugsideeffect 0.1.4

Visualize side effects from textual data

Python package sideeffect

sideeffect is a Python library for processing textual health-related data to extract, analyze, and visualize potential side effects of pharmaceutical drugs from text sources such as social media posts, surveys, or reports.

It provides pipeline for:

• Cleaning and preprocessing text
• Extracting known and uncommon symptoms
• Detecting alert-related keywords
• Generating multiple analytical visualizations

---

Disclaimer

This library is only for informational, educational, and research purposes only.
It is not intended to provide medical advice, diagnosis, or treatment.
Do not use it as a substitute for professional healthcare guidance.
Always consult a qualified healthcare professional regarding any medical concerns.

Note: The code is provided for demonstration purposes; it is not fully optimized and generates only basic visualizations.

---

Installation from PyPI:

%pip install sideeffect

Quick Start Example

import warnings warnings.filterwarnings("ignore") # Hide warnings

import os import pandas as pd

Set working directory

os.chdir(r"C:")

Add the project folder to path

import sys sys.path.append(r"C:")

Import the custom functions

from sideeffect.processing import main from sideeffect.visualization import ( plot_day_of_week_distribution, plot_data_count_per_month, plot_known_symptoms, plot_uncommon_side_effects_pie_chart, plot_proportion_of_english_words, plot_create_side_effects_correlation, plot_create_side_effects_visualizations, plot_sentiment_distribution, plot_visualize, plot_symptom_extraction, )

-----------------------------

Step 1: Load a csv file having at least four columns for example:

Headers: Date, month, RT_Like, text

Row values: 3/15/2023 8:23, 4, 1, Day 2 and I feel better

-----------------------------

df = pd.read_csv("data.csv")

-----------------------------

Step 2: Extract symptoms & alert keywords

-----------------------------

This must be done first so that derived columns like 'extracted_symptoms' exist

plot_symptom_extraction(df)

-----------------------------

Step 3: Basic distributions & sentiment analysis

-----------------------------

plot_day_of_week_distribution(df) plot_data_count_per_month(df) plot_sentiment_distribution(df)

-----------------------------

Step 4: Side effects correlation & detailed visualizations

-----------------------------

plot_create_side_effects_correlation(df) plot_create_side_effects_visualizations(df) plot_visualize(df)

-----------------------------

Step 5: Plots that depend on extracted columns

-----------------------------

plot_known_symptoms(df) plot_uncommon_side_effects_pie_chart(df) plot_proportion_of_english_words(df)

print("All plots generated successfully!")

License

MIT License

Copyright (c) 2026 Briti Deb

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.