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Files in AI projects

When working with Python for AI, you’ll constantly work with data files. Your data might come as:
  • CSV files - Spreadsheet data from Excel or databases
  • JSON files - API responses and configuration data
  • XML files - Structured data from various systems
  • Text files - Raw text for processing
  • Parquet files - Efficient data storage format
The good news? Python has excellent libraries for all of these.

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Common libraries for files

Each file type has specialized libraries: CSV files:
  • pandas - Best for data analysis (recommended)
  • csv module - Built-in, for simple operations
JSON files:
  • json module - Built-in, handles all JSON operations
  • pandas - Can read/write JSON with DataFrames
Other formats:
  • xml.etree - Built-in XML parsing
  • openpyxl - Excel files (.xlsx)
  • PyPDF2 - PDF files

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Working with our sales data

Let’s work with our CSV file and convert it to different formats. First, install pandas: 
pip install pandas
If you get an error, try pip3 install pandas or install it through VS Code’s terminal.
Update your analyzer.py: 
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")

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File format comparison

Different formats have different uses: 
# 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.)

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Loading different file types

Here’s how to load various formats: 
# 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()

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Learn more

To dive deeper into file handling:

Organizing code

Split your code into reusable functions