Data Insights CLI Analyzer

A modular Python command-line application that analyzes CSV datasets using pandas. This project demonstrates real-world data processing, functional programming techniques, and clean software structure.

Project Overview

The Data Insights CLI Analyzer is a terminal-based tool that allows users to load, analyze, and extract insights from structured CSV data.

It is designed to simulate real-world data workflows such as:

• Data loading and validation
• Data exploration
• Data transformation
• Aggregation and filtering
• Error handling

The project follows a modular architecture, separating responsibilities across multiple components for better readability and scalability.

Project Structure

data-insight-cli-analyzer/
│
├── data/
│   └── sales_data.csv
├── images/
│   ├── aggregate-results.png
│   ├── data-insights-cli-analyzer.png
│   ├── data-type-checking.png
│   ├── dataset-preview.png
│   ├── discount.png
│   ├── exit-application.png
│   ├── filteting.png
│   ├── main-menu.png
│   ├── statistics.png
│   └── total-sales.png
├── utils/
│   ├── analyzer.py
│   ├── filter.py
│   └── loader.py
│
├── .gitignore
├── main.py
└── README.md

Features

Data Loading

• Reads CSV files using pandas
• Handles missing or invalid file paths using error handling (try/except)

Data Exploration

• Displays dataset preview (head)
• Shows dataset structure (info)

Data Analysis

• Computes basic statistics using built-in functions:
  • min(), max(), sum(), sorted()

Data Transformation

• Calculates total sales per record
• Applies category-based discounts using lambda functions + DataFrame[column].apply()

Flexible Aggregation

• Aggregates selected columns dynamically using *args

Dynamic Filtering

• Filters dataset based on user input using **kwargs

Error Handling

• Handles:
  • Missing files
  • Invalid column names
  • Invalid data inputs
• Uses try, except, and raise

Type Checking

• Displays data types using type()

Application Flow

1. Program starts and loads dataset
2. User is presented with a CLI menu
3. User selects an action
4. Corresponding action processes the request
5. Results are displayed in the terminal
6. User returns to menu or exits

Sample App Navigation

Main Menu

Dataset Preview

Basic Statistics

Total Sales Computation

Discount Application

Aggregate Results

Filtering Data

Data Type Checking

Exit Application

How to Run

1. Clone the Repository

git clone https://github.com/your-username/data-insights-cli.git
cd data-insight-cli-analyzer

2. Install Dependencies

pip install pandas

3. Run the Application

python main.py

Dataset

The project uses a sample dataset:

data/sales_data.csv

Columns:

• order_id
• customer_name
• category
• price
• quantity
• date

Key Learnings

This project helped reinforce the following Python concepts:

• Working with modules and packages
• Data analysis using pandas
• Writing custom functions
• Using *args and **kwargs for flexible logic
• Functional programming with lambda
• Error handling using try, except, and raise
• Writing modular and maintainable code
• Building CLI-based applications