✅ Python Project Load & Clean CSV Data

 

✅ Python Project Load & Clean CSV Data 🧹📂

📌 Problem Statement:
Write a Python script that loads a raw CSV file, cleans it, and makes it ready for analysis. This is one of the most important steps in data science and business reporting.

🧠 What You’ll Learn

  • How to read CSV files with pandas

  • Removing duplicates & handling missing values

  • Fixing column types (e.g., converting text to dates/numbers)

  • Saving cleaned data for future use

✅ Python Code

import pandas as pd  

# 1. Load raw CSV file  
df = pd.read_csv("raw_data.csv")  

# 2. Remove duplicate rows  
df = df.drop_duplicates()  

# 3. Handle missing values (example: fill empty 'Sales' with 0)  
df["Sales"] = df["Sales"].fillna(0)  

# 4. Convert 'Date' column into proper datetime format  
df["Date"] = pd.to_datetime(df["Date"])  

# 5. Drop rows missing essential fields  
df = df.dropna(subset=["Region", "Product"])  

# 6. Save cleaned data to a new CSV  
df.to_csv("cleaned_data.csv", index=False)

📤 What This Script Does

  1. Loads messy CSV data

  2. Fixes common issues (nulls, duplicates, wrong data types)

  3. Exports a clean dataset ready for analysis

🛠️ Requirements

pip install pandas

🔧 Try Improving It

  • Make all column names lowercase and snake_case

  • Add logging to track cleaning steps

  • Save output to Excel with multiple sheets

💡 Real-World Use Cases

✅ Preprocessing data before analysis
✅ Cleaning exported system reports
✅ Fixing manual entries from users

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