Project_44

📊 Project_44: Data Analysis Mini Tasks (CSV Parsing & Filtering)

Author: Pinaka

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🎯 Project Overview

This project is a CLI-based CSV Analytics Tool that allows users to:

• 📂 Select a CSV file
• 📊 View metadata (rows, columns)
• 🔍 Sort data by a selected column
• 💾 Save filtered results
• 🎨 View everything beautifully using rich

It combines:

• csv → CSV parsing
• tkinter → File selection dialog
• rich → Styled CLI output
• DictReader → Dictionary-based row access

This is a real-world beginner data analysis tool.

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🧠 What This Project Teaches

• Reading CSV files properly
• Working with structured tabular data
• Sorting dictionaries
• Handling user input safely
• CLI UI styling
• Saving processed data
• Mini data pipeline thinking

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🏗️ Project Flow (Step-by-Step Thinking)

1. User selects CSV file
2. Load file into memory
3. Show summary (rows + columns)
4. Ask which column to sort
5. Ask ascending/descending
6. Sort data
7. Display table
8. Ask if user wants to save
9. Save filtered data

Simple. Clean. Practical.

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📦 Required Installation

pip install rich

No need to install tkinter (comes pre-installed with Python in most systems).

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🔍 Code Breakdown

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1️⃣ Loading CSV

reader = csv.DictReader(f)

This converts:

name,age,city
John,25,Delhi

Into:

{
  "name": "John",
  "age": "25",
  "city": "Delhi"
}

Each row becomes a dictionary.

✔ Clean ✔ Flexible ✔ Easy column access

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2️⃣ Getting CSV Info

data_count = len(rows)
column_count = len(headers)

Gives:

• Total rows
• Total columns
• Column names

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3️⃣ Sorting Data

sorted(rows, key=lambda x: x[column])

This sorts rows based on selected column.

⚠ Important:

Right now sorting is string-based.

If numbers exist:

1, 10, 2

It sorts like:

1, 10, 2

Not numerically.

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🚀 Improvement (Optional Upgrade)

For numeric sorting:

key=lambda x: float(x[column]) if x[column].replace('.', '', 1).isdigit() else x[column]

This makes sorting smarter.

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4️⃣ Printing Table with Rich

table = Table(
    title=title,
    show_lines=True,
    box=box.SIMPLE_HEAVY
)

This makes your CLI look professional.

Each column:

table.add_column(header, style="cyan")

Each row:

table.add_row(*(str(row[h]) for h in headers))

Beautiful output. No manual formatting needed.

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5️⃣ Saving Filtered Data

Uses:

csv.DictWriter

Preserves structure and headers.

Automatically creates:

filtered_data.csv

inside selected folder.

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💡 Mini Data Analysis Tasks You Can Practice

Try modifying this tool to:

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🟢 Task 1: Filter by Value

Add feature:

• Show rows where city == Delhi

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🟢 Task 2: Top 5 Rows Only

After sorting:

filtered_data = filtered_data[:5]

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🟢 Task 3: Column Statistics

Add:

• Min value
• Max value
• Average

For numeric columns.

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🟢 Task 4: Multi-Column Sort

Sort by:

• age
• then name

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🟢 Task 5: Search Feature

Ask user:

Enter keyword:

Return rows containing that word.

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🧪 Real-World Applications

This logic applies to:

• Sales reports
• Student marksheets
• Expense tracking
• Log filtering
• Stock price CSV
• Survey results
• HR records

This is how basic data tools are built.

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⚠ Limitations of Current Version

• Loads entire file into memory
• No numeric detection
• No filtering condition (only sorting)
• No large file optimization

But for beginner → intermediate level, this is excellent.

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🏆 Skill Level

Skill	Level
CSV Parsing	Beginner+
Sorting	Intermediate
CLI Styling	Intermediate
Data Handling	Intermediate
Real-World Thinking	Advanced Beginner

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🧠 Problem-Solving Prompts for This Project

Ask yourself:

• What is the structure of CSV internally?
• Why use DictReader over reader?
• Why convert everything to string before printing?
• What happens if column doesn’t exist?
• What if file is empty?
• What if column contains mixed data types?

These are real engineer questions.

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