Reading and Writing files in Python

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Knowing how to read and write files is one of the most essential parts of programming, as it allows us to move information around in whatever system we build. Thankfully, Python makes it really easy to write I/O logic.

Basic Python File I/O

Before reaching for library-specific I/O, it’s important to see what Python can do on its own. The built-in open() function is the foundation:

with open("data.txt", "r") as f:
    content = f.read()

Breaking this down:

with is the context manager that automatically closes the file when you complete the indented code below the statement, even if an error arises. Always use this.
open is a built-in function that handles the I/O for you. It has 2 arguments:
- The name of the file - in this case, data.txt
- The mode - in this case, we are reading from the file, denoted by "r".

Below is a listing of the most common modes:

Mode	What it does
`"r"`	Reads (default)
`"w"`	Overwrites existing content
`"a"`	Appends to existing content
`"rb" / "wb"`	Read/Write binary

Reading from a file

Once you’ve opened a file, you have two main ways to get the data into memory - and that’s either through read() or readlines():

# Using `read()`
with open("sales.csv", "r") as f:
    content = f.read()

# content is one big string
print(type(content))  # <class 'str'>

# OR using `readlines()`
with open("sales.csv", "r") as f:
    lines = f.readlines()

# lines is a list, each item is a row
print(type(lines))   # <class 'list'>
print(lines[0])      # 'name,department,sales,quarter\n'

In my experience, I’ve found to use readlines() significantly more than read(), as it’s easier to work with.

A third option that is more memory-efficient is to iterate over the file object (in our example, f):

with open("sales.csv", "r") as f:
    for line in f:
        print(line.strip())

Writing to a file

To write to a file, we’d flip the "r" to a "w":

with open("data.txt", "w") as f:
    f.write("Alice,Engineering,95000,Q1\n")

There’s a few things to note when writing to files:

"w" mode overwrites the file if it already exists (creates it if it doesn’t).
"a" mode appends without destroying existing content.
write() does not add a new line for you - you must include \n to the end of your string, as shown above.
To write multiple lines at once, writelines() takes a list:

rows = [
    "Alice,Engineering,95000,Q1\n",
    "Bob,Finance,82000,Q1\n",
    "Carol,Marketing,67000,Q1\n",
]

with open("output.csv", "w") as f:
    f.writelines(rows)

File I/O with different file types and packages

Pandas and Polars

Plain Python (like above) is fine for raw text, but structured data formats have libraries that handle edge cases for you. For instance, when reading CSV file, you may want to use pandas or polars:

import pandas as pd
df = pd.read_csv("sales.csv")

import polars as pl
df = pl.read_csv("sales.csv")

» For a rundown of what you can do once the data's loaded, I've covered 8 essential Pandas operations.

Writing with Pandas and polars is also straight forward:

# pandas
df.to_csv("output.csv", index = False)

# polars
df.write_csv("output.csv")

JSON

The json module is built into the Python standard library. Use json.load() to read from a file:

import json

with open("config.json", "r") as f:
    config = json.load(f)

db_host = config["database"]["host"]
debug_mode = config.get("debug", False)

If you’re working with a string instead of a file (very common when consuming API responses), use json.loads() instead:

response_text = '{"status": "ok", "records": 42}'
data = json.loads(response_text)

» Pandas can also read JSON directly when the data is tabular: df = pd.read_json()

To write using the json library, use json.dump():

import json

report = {
    "department": "Engineering",
    "total_sales": 95000,
    "quarter": "Q1"
}

with open("report.json", "w") as f:
    json.dump(report, f, indent=2)

Excel files

One of the great things about Panda is that it takes multiple file types, including excel:

# Reading using Pandas
df = pd.read_excel("sales.xlsx", sheet_name="Q1 Data")

# Writing using Pandas
df.to_excel("output.xlsx", sheet_name="Q1 Data", index=False)

YAML files

Yet Another Markup Language (YAML) is heavily used in projects in 2026 - to read/write them natively, you’ll need to install pyyaml:

pip install pyyaml

import yaml

with open("config.yaml", "r") as f:
    config = yaml.safe_load(f)

» Always use safe_load(), never load(). The load() function can execute arbitrary Python code embedded in a YAML file, which is a real security risk if you’re loading files from untrusted sources.

Writing is also straight-forward:

import yaml

with open("config.yaml", "w") as f:
    yaml.dump(config, f, default_flow_style=False)

What file formats do you work with most in your day-to-day? Are there any you'd want to see covered in more depth? Reply and let me know - I read every response.

Happy coding!

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