Most data science starts with a file. Python's built-in open() reads and writes text files — and a few string methods already give you basic text statistics.

Modes

Mode	Meaning	If the file exists	If it doesn't
`'r'`	Read (default)	Reads from the start	`FileNotFoundError`
`'w'`	Write	Erases it first	Creates it
`'a'`	Append	Writes at the end	Creates it
`'r+'`	Read and write	Starts at the beginning	`FileNotFoundError`
`'x'`	Create only	`FileExistsError`	Creates it

Writing: write vs writelines

Python

lines = ["This is Delhi \n", "This is Paris \n", "This is London \n"]
with open("myfile.txt", "w", encoding="utf-8") as f:
    f.write("Hello \n")          # one string
    f.writelines(lines)          # an iterable of strings (no newlines are added for you)

with open("myfile.txt", encoding="utf-8") as f:
    print(f.read())

Output

Hello
This is Delhi
This is Paris
This is London

Reading: read, readline, readlines, seek

Python

with open("myfile.txt", encoding="utf-8") as f:
    print(repr(f.read(9)))       # 'Hello \nTh'   ← 9 characters, across lines
    f.seek(0)                    # jump back to the start
    print(repr(f.readline()))    # 'Hello \n'     ← up to and including the newline
    f.seek(0)
    print(f.readlines())         # ['Hello \n', 'This is Delhi \n', 'This is Paris \n', 'This is London \n']

Method	Returns
`f.read()` / `f.read(n)`	The whole file / the next `n` characters, as one string
`f.readline()`	The next line, including its `\n`
`f.readlines()`	A list of all remaining lines
`for line in f:`	Lines one at a time — memory-friendly for big files
`f.seek(n)`	Moves the position to byte `n` (0 = start)

Append vs write

Python

with open("notes.txt", "w") as f:
    f.write("Today \n")
with open("notes.txt", "a") as f:        # append keeps what was there
    f.write("Tomorrow \n")
with open("notes.txt") as f:
    print(f.readlines())                  # ['Today \n', 'Tomorrow \n']

with open("notes.txt", "w") as f:        # write erases it first
    f.write("Only this \n")
with open("notes.txt") as f:
    print(f.readlines())                  # ['Only this \n']

Quick text statistics

Python

text = """Data science turns raw data into insight. It mixes statistics and programming.

Python is the most popular language for it."""

paragraphs = [p for p in text.split("\n\n") if p.strip()]
sentences = [s for s in text.replace("\n", " ").split(".") if s.strip()]
words = text.split()                     # split() with no argument handles any whitespace

print(len(paragraphs), len(sentences), len(words))   # 2 3 20

Cleaning unwanted characters

Python

raw = "Hello!! World@ #2026 [7] [12]"
for ref in [f"[{i}]" for i in range(1, 20)]:     # remove citation markers like [7]
    raw = raw.replace(ref, "")
for ch in "!@#":                                 # remove unwanted symbols
    raw = raw.replace(ch, "")
print(" ".join(raw.split()))                     # Hello World 2026