Introduction

Reading a text file into Python is simple, but the right approach depends on what you want the result to look like. Sometimes you need raw lines, sometimes stripped strings, and sometimes you need parsed numbers or a NumPy array.

The Most Common Case: A List Of Lines

If you want one list element per line, open the file and iterate over it.

1with open("data.txt", "r", encoding="utf-8") as fh:
2    lines = [line.rstrip("\n") for line in fh]
3
4print(lines)

This is a good default because:

• it is easy to read
• it removes trailing newlines cleanly
• it works well for ordinary text files

If you want to keep the newline characters, use fh.readlines() instead.

with open("data.txt", "r", encoding="utf-8") as fh:
    lines = fh.readlines()

Reading The Whole File As One String

Sometimes the first step is not a list at all. If you need the entire text, read it as one string and split later if necessary.

1with open("data.txt", "r", encoding="utf-8") as fh:
2    text = fh.read()
3
4print(text)

This is useful when you want paragraph-level processing or regular-expression parsing across the whole file.

Converting Lines To Numbers

If each line contains a number, convert as you read.

1with open("numbers.txt", "r", encoding="utf-8") as fh:
2    values = [int(line.strip()) for line in fh if line.strip()]
3
4print(values)

This gives you a Python list of integers rather than a list of strings.

If malformed input is possible, add error handling.

1parsed = []
2with open("numbers.txt", "r", encoding="utf-8") as fh:
3    for line_number, raw in enumerate(fh, start=1):
4        text = raw.strip()
5        if not text:
6            continue
7        try:
8            parsed.append(int(text))
9        except ValueError:
10            print(f"Skipping bad line {line_number}: {text}")
11
12print(parsed)

Reading Into A NumPy Array

If you really mean array in the numerical computing sense, use NumPy rather than a plain Python list.

1import numpy as np
2
3arr = np.loadtxt("numbers.txt", dtype=int)
4print(arr)
5print(type(arr))

This is the better choice for scientific code, matrix operations, or machine learning preprocessing.

Structured Delimited Files

If the file is comma-separated or otherwise structured, do not treat it like arbitrary text. Parse it with the right tool.

1import csv
2
3with open("records.csv", "r", encoding="utf-8") as fh:
4    rows = list(csv.reader(fh))
5
6print(rows)

For simple ad hoc data, splitting may be enough:

1with open("records.txt", "r", encoding="utf-8") as fh:
2    rows = [line.strip().split(",") for line in fh if line.strip()]
3
4print(rows)

But once quoting or escaping matters, use the csv module.

Large Files Need Streaming

For large files, do not read everything into memory unless you have a reason. Iterate and process line by line.

1matches = []
2with open("huge.log", "r", encoding="utf-8") as fh:
3    for line in fh:
4        if "ERROR" in line:
5            matches.append(line.strip())
6
7print(len(matches))

This is still collecting matching lines, but it avoids loading the entire file at once.

pathlib Makes File Code Cleaner

If you prefer modern path handling, pathlib works well.

1from pathlib import Path
2
3path = Path("data.txt")
4lines = path.read_text(encoding="utf-8").splitlines()
5print(lines)

This is concise, though it reads the whole file into memory first.

Common Pitfalls

The most common mistake is forgetting the file's encoding. Using encoding="utf-8" explicitly avoids many cross-platform surprises.

Another mistake is accidentally keeping newline characters and then wondering why string comparisons fail.

Developers also sometimes read a giant file into memory when a streaming loop would be simpler and safer.

Finally, if the file is really CSV or another structured format, use a parser instead of treating it as plain text lines.

Summary

• Use a list comprehension over the file handle to read one stripped string per line.
• Use read() when you need the whole file as one string.
• Convert during reading if the lines represent numbers.
• Use NumPy if you need a numeric array rather than a Python list.
• Stream large files and use proper parsers for structured formats.