Working with files

pip install pandas

import pandas as pd
import json
import os

# Read the CSV file
df = pd.read_csv('data/sales.csv')
print("CSV Data:")
print(df)
print(f"\nShape: {df.shape[0]} rows, {df.shape[1]} columns")

# Quick operation: calculate total for each row
df['total'] = df['quantity'] * df['price']
print("\nWith totals:")
print(df)

# Create output directory
os.makedirs('output', exist_ok=True)

# Save as different formats
# 1. JSON format (good for web APIs)
df.to_json('output/sales_data.json', orient='records', indent=2)

# 2. Excel format (good for sharing)
df.to_excel('output/sales_data.xlsx', index=False)

# 3. Updated CSV (with our new total column)
df.to_csv('output/sales_with_totals.csv', index=False)

print("\nFiles saved:")
print("- output/sales_data.json")
print("- output/sales_data.xlsx") 
print("- output/sales_with_totals.csv")

# JSON - Great for APIs and web applications
{
  "date": "2024-01-01",
  "product": "Laptop",
  "quantity": 2,
  "price": 999.99
}

# CSV - Simple, universal, good for data analysis
date,product,quantity,price
2024-01-01,Laptop,2,999.99

# Excel - Feature-rich, good for business users
# (Binary format with formatting, formulas, etc.)

# CSV
df = pd.read_csv('data/file.csv')

# JSON
df = pd.read_json('data/file.json')
# or for simple JSON:
with open('data/config.json', 'r') as f:
    data = json.load(f)

# Excel
df = pd.read_excel('data/file.xlsx')

# Text files
with open('data/file.txt', 'r') as f:
    text = f.read()