📊 Advanced CSV Processing — Performance and Pandas

Work with large CSV files efficiently and leverage pandas for analysis.

🎯 Chunked CSV Reading

import csv

def read_csv_chunks(filename, chunk_size=1000):
    """Read CSV in chunks for memory efficiency"""
    with open(filename, "r") as f:
        reader = csv.DictReader(f)
        chunk = []
        for row in reader:
            chunk.append(row)
            if len(chunk) == chunk_size:
                yield chunk
                chunk = []
        if chunk:
            yield chunk

# Process multi-GB CSV files
for chunk in read_csv_chunks("large.csv", chunk_size=10000):
    process_batch(chunk)

💡 Pandas for Analysis

import pandas as pd

# Fast reading (optimized C parser)
df = pd.read_csv("data.csv", dtype={"id": "int32"})

# Chunked reading
chunks = pd.read_csv("large.csv", chunksize=50000)
for chunk in chunks:
    analyze(chunk)

# Performance: pandas 10-100x faster than csv module

🔑 Key Takeaways

• ✅ Chunked reading prevents memory issues

• ✅ Custom dialects for different CSV formats

• ✅ Pandas for statistical analysis

• ✅ dtype specification for memory efficiency

• ✅ Vectorized operations faster than row iteration

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