Python Code
Samples
Copy-ready Python snippets by topic and difficulty — short, focused, and runnable in the browser editor.
Count Characters, Words, and Lines in Python Text
Counts characters, words, lines, and the most common words in a given string using Python's standard library.
from collections import Counter
def count_data(text):
"""Count characters, words, lines, and most common words in text."""
char_count = len(text)
word_count = len(text.split())
line_count = text.count("\n") + 1
word_freq = Counter(text.lower().split())
most_common = word_freq.most_common(3)
…How to Inspect String Statistics in Python
A beginner-friendly function that returns detailed statistics about a string, including length, word count, character types, and easy text transformations.
def inspect_text(text: str) -> dict:
"""Return useful stats about a string for beginners."""
words = text.split()
return {
"length": len(text),
"word_count": len(words),
"uppercase": sum(1 for ch in text if ch.isupper()),
"lowercase": sum(1 for ch in text if ch.islower()),
…Add Type Hints to Function Parameters and Return in Python
Add type hints to function parameters and return values in Python for clearer, more maintainable code using the typing module.
from typing import List, Optional, Dict
def average(numbers: List[float]) -> float:
return sum(numbers) / len(numbers)
def full_name(first: str, last: Optional[str] = "") -> str:
return f"{first} {last}".strip()
def build_user(name: str, age: int, email: Optional[str] = None) -> Dict[str, object]:
us…How to Extract IP Address Counts from Access Logs in Python
Read a web server access log, count occurrences of each IP address using regex and Counter, and print the ranked results.
import re
from collections import Counter
from pathlib import Path
def extract_ip_counts(log_file_path):
ip_pattern = r'^(\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3})'
ip_counter = Counter()
with open(log_file_path, 'r') as file:
for line in file:
match = re.match(ip_pattern, line)
…Count Word Frequency in Python with dict
Count how often each word appears in a text using Python's collections.Counter and regular expressions.
from collections import Counter
import re
def count_word_frequency(text):
"""Count frequency of each word in text (case-insensitive)."""
words = re.findall(r"\b\w+\b", text.lower())
return dict(Counter(words))
if __name__ == "__main__":
sample_text = "The quick brown fox jumps over the lazy dog. The …Count Words in Python with Dictionaries and Sets
Text analysis example that counts total words, finds unique words with a set, and tallies character frequencies with a dictionary.
def analyze_text(text: str) -> dict:
"""Count words, find unique words, and show common characters."""
words = text.lower().split()
word_count = len(words)
unique_words = set(words)
char_counts = {}
for word in words:
for char in word:
if char.isalpha():
…Bucket Numbers into Histogram Bin Counts in Python
Partition a list of numbers into equal-width histogram bins and count how many fall into each bin using only the Python standard library.
from collections import Counter
def histogram_bins(numbers, num_bins):
"""Bucket numbers into histogram bin counts."""
if not numbers:
return []
min_val = min(numbers)
max_val = max(numbers)
bin_width = (max_val - min_val) / num_bins
# Handle edge case where all values are id…Parse ReAct Logs into Thought Action Observation Steps in Python
Parse a ReAct agent's textual log into structured steps with thought, action, and observation using regex and named tuples.
import re
from collections import namedtuple
ReActStep = namedtuple("ReActStep", ["thought", "action", "observation"])
def parse_react_log(log: str) -> list[ReActStep]:
"""Parse a ReAct log into structured thought/action/observation steps."""
pattern = re.compile(
r"Thought:\s*(?P<thought>.+?)\s*"
…Detect Circular Imports Across Python Projects Automatically
This script walks through all .py files in a directory, builds an import graph, and uses depth-first search to find cycles—printing each circular dependency chain.
import ast
import sys
from pathlib import Path
from collections import defaultdict, deque
def find_imports(filepath):
"""Return set of module names imported by a Python file."""
imports = set()
try:
with open(filepath) as f:
tree = ast.parse(f.read())
except (SyntaxError, UnicodeDe…Find Dead Code in a Python Project Using AST
Walk a project tree, parse every Python file with ast, and list defined functions that are never called anywhere.
import ast
import os
import sys
def find_dead_code(project_path):
defined_functions = {}
called_functions = set()
for root, dirs, files in os.walk(project_path):
for file in files:
if file.endswith('.py'):
filepath = os.path.join(root, file)
with open(f…Find Sensitive Information in Log Files with Python
Scan log files for emails, IP addresses, API keys, and passwords using regular expressions in Python.
import re
import os
from pathlib import Path
def find_sensitive_info(log_path):
"""Scans log files for patterns like emails, IPs, API keys, and passwords."""
patterns = {
'Email': r'[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}',
'IP Address': r'\b(?:\d{1,3}\.){3}\d{1,3}\b',
'API Key'…Find Unused Python Packages Automatically
Scan a Python project's source files for imports and list installed packages not imported anywhere.
import pkg_resources
import ast
import os
import sys
from pathlib import Path
def find_imports_in_project(project_dir="."):
imports = set()
for py_file in Path(project_dir).rglob("*.py"):
try:
with open(py_file, "r") as f:
tree = ast.parse(f.read())
for node in …How to Generate a Dependency Graph for Python Projects
This script walks through a Python project directory, parses each .py file's imports, and prints a dependency graph showing which modules depend on which other modules.
import os
import ast
from pathlib import Path
from collections import defaultdict
def get_imports(filepath):
with open(filepath) as f:
try:
tree = ast.parse(f.read())
except SyntaxError:
return []
imports = []
for node in ast.walk(tree):
if isinstance(node, …How to Track GitHub Stars, Forks, and Watchers in Python
Automatically fetch and track stars, forks, and watchers for multiple GitHub repositories, saving snapshots locally as JSON files for historical analysis.
import os
import time
import json
import requests
from pathlib import Path
from datetime import datetime
REPOS = [
"psf/requests",
"python/cpython",
"pallets/flask",
]
DATA_DIR = Path("github_metrics")
def fetch_repo_stats(repo):
url = f"https://api.github.com/repos/{repo}"
resp = requests.get(ur…How to check Python files for common coding mistakes
Walks a directory tree parsing each .py file with ast, reporting empty functions, bare try blocks, too many parameters, and empty classes.
import ast
import os
import sys
def check_file(filepath):
try:
with open(filepath) as f:
code = f.read()
tree = ast.parse(code, filename=filepath)
except SyntaxError as e:
print(f"{filepath}: SyntaxError: {e.msg}")
return
issues = []
for node in ast.wal…How to Take Periodic Snapshots of Aggregate State in Python
Build a Python class that accumulates values and periodically captures immutable snapshots of total, count, and average for later analysis.
import time
import random
from collections import defaultdict
class SnapshotAggregator:
def __init__(self):
self.total = 0
self.count = 0
self.history = []
def add(self, value):
self.total += value
self.count += 1
def snapshot(self):
avg = self.total / se…How to Implement Tail Sampling in Python
Sample the slowest subset of calls (tail) for latency analysis using a deque with a random ratio gate.
import random
import time
from collections import deque
class TailSampler:
def __init__(self, tail_ratio=0.1, max_samples=100):
self.tail_ratio = tail_ratio
self.max_samples = max_samples
self.samples = deque(maxlen=max_samples)
self.total_calls = 0
def record(self, latency_ms…How to Calculate Secondary Metrics in Python
Computes distribution, variability, and spread of a numeric dataset using Python's statistics and collections modules.
import random
import statistics
from collections import Counter
def explore_secondary_metrics(data):
"""Calculate secondary metrics: distribution, variability, and spread."""
if not data:
return "No data provided"
total = sum(data)
mean = statistics.mean(data)
median = statistics.medi…How to Perform Intent-to-Treat Analysis in Python
Runs an intent-to-treat analysis on mock A/B test data, comparing outcomes by initial group assignment with a t-test for significance.
import pandas as pd
import numpy as np
def intent_to_treat_analysis(data):
"""Perform intent-to-treat (ITT) analysis.
ITT compares outcomes based on initial treatment assignment,
regardless of whether participants actually received the treatment.
"""
# Create a copy to avoid mutating the origina…Browse by section
Each section groups closely related Python snippets.
Guide: free Python code samples library
Copy-ready Python snippets for learners and developers
PythonSkillset code samples are short, focused examples organised by topic and difficulty. Every snippet is server-rendered HTML — readable by search engines and easy to copy. Open any sample, read the notes, copy the code, then press Try in editor to run it in the browser with Pyodide.
How to use this library
- Pick a topic section — strings, lists, files, functions, and more
- Open a sample, read How it works, and copy the code block
- Run it in the IDE, tweak values, then take a related quiz or tutorial lesson
Samples vs tutorials and challenges
Samples are quick reference — one concept per page. For step-by-step teaching, use our Python tutorials. To test yourself, try quizzes or coding challenges. Clean up style with the Python formatter.