9.0 Prologue

Reading and writing data is a core programming task. This chapter covers Python's built-in file I/O, the with statement for safe resource management, and the standard modules for structured data formats.

9.1 open() and the with Statement

open(path, mode, encoding) returns a file object. Always use a with block so the file is closed automatically even if an exception occurs: # text mode — default, newlines translated, encoding applies with open("notes.txt", "w", encoding="utf-8") as f: f.write("line one\n") f.write("line two\n") with open("notes.txt", encoding="utf-8") as f: for line in f: # iterate line by line print(line.rstrip()) # binary mode — no encoding, no newline translation with open("data.bin", "rb") as f: header = f.read(4) # read exactly 4 bytes Common modes: "r" (read), "w" (write/create), "a" (append), "x" (exclusive create, fails if exists), "b" (binary), "+" (read+write).

9.2 Reading Text Files

Several patterns for reading: from pathlib import Path # read entire file as a string text = Path("notes.txt").read_text(encoding="utf-8") # read all lines into a list (newlines included) with open("notes.txt") as f: lines = f.readlines() # read lines stripping newlines with open("notes.txt") as f: lines = [line.rstrip("\n") for line in f] # read fixed-size chunks with open("large.dat") as f: while chunk := f.read(4096): process(chunk)

9.3 csv Module

The csv module handles comma-separated values, including quoting and different delimiters. DictReader maps each row to a dict keyed by the header row: import csv # write with open("people.csv", "w", newline="", encoding="utf-8") as f: writer = csv.DictWriter(f, fieldnames=["name", "age"]) writer.writeheader() writer.writerow({"name": "Alice", "age": 30}) writer.writerow({"name": "Bob", "age": 25}) # read with open("people.csv", encoding="utf-8") as f: for row in csv.DictReader(f): print(row["name"], int(row["age"])) Use newline="" when opening for writing to prevent double newlines on Windows. Set delimiter="\t" for TSV files.

9.4 json Module

json serializes Python objects (dicts, lists, str, int, float, bool, None) to JSON strings and vice versa: import json from pathlib import Path data = {"name": "Alice", "scores": [95, 87, 91], "active": True} # serialize to string text = json.dumps(data, indent=2) # write to file Path("data.json").write_text(json.dumps(data, indent=2), encoding="utf-8") # read from file loaded = json.loads(Path("data.json").read_text(encoding="utf-8")) # use json.dump / json.load for file objects with open("out.json", "w") as f: json.dump(data, f, indent=2) For types not natively serializable (e.g., datetime, Decimal) provide a custom default function or subclass json.JSONEncoder.

9.5 pickle Module

pickle serializes arbitrary Python objects to binary data. Unlike JSON it can round-trip almost any Python object, but the output is Python-specific and should never be loaded from untrusted sources: import pickle obj = {"key": [1, 2, 3], "fn": lambda x: x * 2} # lambdas are NOT picklable # safe objects data = {"key": [1, 2, 3], "value": 42.0} with open("data.pkl", "wb") as f: pickle.dump(data, f) with open("data.pkl", "rb") as f: loaded = pickle.load(f) # ONLY load trusted pickle files Prefer JSON or other portable formats when interoperability or security matters. Use pickle for caching Python-specific objects (ML models, numpy arrays) in a trusted environment.

9.6 io.StringIO and io.BytesIO

In-memory file-like objects are useful for testing and for functions that expect a file object: import io, csv # build a CSV in memory, then parse it buf = io.StringIO() writer = csv.writer(buf) writer.writerows([["a", 1], ["b", 2]]) buf.seek(0) for row in csv.reader(buf): print(row)

9.7 Epilogue

This chapter covered text and binary file I/O, the with statement, and the csv, json, and pickle modules. The next chapter covers the collections, itertools, and functools modules.

9.8 References

io module — python.org
csv module — python.org
json module — python.org
pickle module — python.org