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How to List File Information in a Directory with Python
A helper that walks a directory and returns each file's name, size, and extension as a list of dictionaries.
from pathlib import Path
def get_files_data(directory: str) -> list[dict]:
"""Return basic info about all files in a directory."""
files = []
for path in Path(directory).iterdir():
if path.is_file():
files.append({
"name": path.name,
"size": path.stat()…How to List File Metadata in Python
This code walks a directory and returns a list of JSON-ready dicts with each file's name, size, and modification time.
from pathlib import Path
import json
def format_files_data(directory_path):
"""Return a list of JSON-serializable dicts with file metadata."""
base = Path(directory_path)
if not base.is_dir():
raise ValueError(f"Not a directory: {directory_path}")
files_data = []
for file_path in base.ite…How to List Files Matching a Glob Pattern in Python
Uses pathlib.Path.glob to find and sort all files matching a glob pattern like *.py in a directory.
from pathlib import Path
def list_files_matching(pattern: str, directory: str = ".") -> list[str]:
"""Return sorted list of file paths matching the glob pattern in a directory."""
return sorted(Path(directory).glob(pattern))
if __name__ == "__main__":
# Example: list all .py files in current directory
…How to List Tar Archive Contents in Python
Open a tar archive with the stdlib tarfile module and print each entry's type, size, and name.
import tarfile
from pathlib import Path
def list_tar_contents(archive_path):
"""List all entries in a tar archive."""
entries = []
with tarfile.open(archive_path, "r") as tar:
for member in tar.getmembers():
entry_type = "dir" if member.isdir() else "file"
entries.append(f"…How to Parse NDJSON Lines into a List in Python
Reads a JSON-lines (NDJSON) file line by line and converts each non-empty line into a Python object, returning a list.
import json
from pathlib import Path
def parse_ndjson(file_path: str) -> list:
data = []
with Path(file_path).open("r", encoding="utf-8") as f:
for line in f:
line = line.strip()
if line:
data.append(json.loads(line))
return data
if __name__ == "__main__"…How to Sort Files by Name and Size in Python
Sort a list of file dictionaries by name then size using Python's sorted() with a lambda key.
from pathlib import Path
def sort_files_data(files):
"""Sort a list of file dictionaries by name, then by size."""
return sorted(files, key=lambda f: (f["name"], f["size"]))
if __name__ == "__main__":
files_data = [
{"name": "report.pdf", "size": 2048},
{"name": "data.csv", "size": 1024},…How to Watch a Directory for New Files in Python
Poll a directory at regular intervals and detect newly added files, printing each one as it appears.
import time
import os
from pathlib import Path
WATCH_DIR = Path("watched_files")
def watch_for_new_files(directory: Path, sleep_time: float = 1.0, max_iterations: int = 10):
"""Poll a directory for new files and print when one appears."""
directory.mkdir(exist_ok=True)
existing = set(os.listdir(directory…How to Write a List of Lines to a Text File Safely in Python
This code atomically writes a list of strings as lines to a text file using a temporary file and os.replace to prevent corruption.
from pathlib import Path
import tempfile
import os
def write_lines_safely(lines: list[str], filepath: str | Path) -> None:
"""Write lines to a text file atomically to avoid corruption."""
path = Path(filepath)
path.parent.mkdir(parents=True, exist_ok=True)
fd, temp_path = tempfile.mkstemp(dir=str…Parse CSV with Custom Delimiter and Quote Character in Python
Reads a CSV string with a custom delimiter and quote character using the csv module, returning a list of rows.
import csv
from io import StringIO
def parse_csv(data, delimiter='|', quotechar='"'):
reader = csv.reader(StringIO(data), delimiter=delimiter, quotechar=quotechar)
rows = [row for row in reader]
return rows
if __name__ == "__main__":
sample = 'Alice|"Smith, Jr."|25\nBob|"Johnson, Sr."|30'
result …Read Parquet-Like Columnar CSV Chunks in Python
A Python generator that reads a CSV file column-by-column, yielding dictionary chunks where each key points to a list of values—mirroring how Parquet stores data columnar.
```python
import csv
from pathlib import Path
from typing import Iterator, List
def read_parquet_like_columnar(csv_path: str, column_names: List[str], chunk_size: int = 2) -> Iterator[dict]:
"""Read CSV data in columnar chunks, similar to how parquet stores columns."""
csv_file = Path(csv_path)
with csv_f…Read a CSV File with csv.DictReader in Python
Read a CSV file as a list of dictionaries, using csv.DictReader to map each row to column names.
import csv
from pathlib import Path
def read_csv_with_dictreader(file_path):
data = []
with open(file_path, mode='r', newline='', encoding='utf-8') as csvfile:
reader = csv.DictReader(csvfile)
for row in reader:
data.append(row)
return data
if __name__ == "__main__":
# Cre…Write CSV file with csv DictWriter in Python
Write a list of dictionaries to a CSV file using Python's csv.DictWriter, including a header row.
import csv
from pathlib import Path
fieldnames = ["name", "city", "age"]
rows = [
{"name": "Alice", "city": "New York", "age": 30},
{"name": "Bob", "city": "Los Angeles", "age": 25},
{"name": "Charlie", "city": "Chicago", "age": 35},
]
path = Path("people.csv")
with path.open("w", newline="") as csvfile:…Build a defaultdict histogram of categories in Python
Count occurrences of each category in a list using collections.defaultdict(int) for automatic initialization.
from collections import defaultdict
def build_category_histogram(items):
"""Count occurrences of each category in a list of items."""
histogram = defaultdict(int)
for item in items:
histogram[item] += 1
return dict(histogram)
if __name__ == "__main__":
categories = ["fruit", "vegetable", …Build adjacency dict graph from edges in Python
Convert a list of edges into an undirected adjacency dictionary, mapping each node to its neighbors, with sorted output.
def build_adjacency_dict(edges):
graph = {}
for u, v in edges:
if u not in graph:
graph[u] = []
if v not in graph:
graph[v] = []
graph[u].append(v)
graph[v].append(u)
return graph
if __name__ == "__main__":
edges = [(1, 2), (2, 3), (3, 4), (4, 1)…Convert Lists and Dictionaries to Sets in Python
Convert lists of pairs into dictionaries and lists or dictionaries into sets using simple helper functions.
def convert_to_dict(data):
"""Convert list of tuples or lists into a dictionary."""
return dict(data)
def convert_to_set(data):
"""Convert list or dictionary into a set of its keys/values."""
if isinstance(data, dict):
return set(data.keys())
return set(data)
def convert_collection(data…Find All Leaf Paths in a Nested Dict in Python
Recursively traverse a nested dictionary and yield every leaf path as a list of keys, including paths to empty dictionaries.
def find_leaf_paths(data, path=None):
if path is None:
path = []
if not isinstance(data, dict) or not data:
yield path
return
for key, value in data.items():
yield from find_leaf_paths(value, path + [key])
if __name__ == "__main__":
nested = {
"a": 1,
…How to Aggregate Order Data with Sets and Dictionaries in Python
Combine sets and dictionaries to find unique products and total quantities from a list of orders in Python.
def find_unique_products(orders):
"""Return set of all products ordered across multiple orders."""
all_products = set()
for order in orders:
all_products.update(order.get("items", []))
return all_products
def product_summary(orders):
"""Build a dictionary mapping each product to its total…How to Compute Set Union of Tags from Multiple Items in Python
Collect all unique tags from a list of dictionaries using set union with update() in Python.
items = [
{"id": 1, "tags": {"python", "web"}},
{"id": 2, "tags": {"web", "api", "sql"}},
{"id": 3, "tags": {"python", "data"}},
]
def get_union_of_tags(item_list):
all_tags = set()
for item in item_list:
all_tags.update(item["tags"])
return all_tags
if __name__ == "__main__":
u…How to Count Elements and Find Duplicates in a Python List
Count occurrences of each element in a list, extract unique values, and identify duplicates using Python dictionaries and sets.
def analyze_counts(data):
"""Count elements, return unique values, and find duplicates."""
# Count occurrences using a dictionary
counts = {}
for item in data:
counts[item] = counts.get(item, 0) + 1
# Alternative compact approach with set
unique_items = set(data)
# Fi…How to Count Tags with Sets and Dictionaries in Python
Count tag frequencies and collect unique tags from a list of dictionaries using Counter and sets in Python.
from collections import Counter
import json
def count_tags(entries):
"""Count tag frequencies across a list of entry dicts, using sets/dicts."""
tag_counter = Counter()
all_tags = set()
for entry in entries:
tags = set(entry["tags"])
all_tags.update(tags)
tag_counter.update(ta…How to Count Word Frequencies in Python
Count how often each word appears in a string and list the unique words using Python dictionaries and sets.
def text_processor(text):
words = text.lower().split()
word_count = {}
for word in words:
word_count[word] = word_count.get(word, 0) + 1
unique_words = set(words)
return word_count, unique_words
if __name__ == "__main__":
sample_text = "The quick brown fox jumps over the lazy dog and t…How to Count Word Frequencies in Python with Counter and Sets
This code processes a text string by lowercasing, splitting into words, counting frequencies with Counter, and extracting unique and sorted word lists using sets.
from collections import Counter
def process_text(text):
words = text.lower().split()
word_counts = Counter(words)
unique_words = set(words)
sorted_words = sorted(unique_words)
return {
"total_words": len(words),
"unique_words": len(unique_words),
"word_frequencies": di…How to Create a Dict from Two Parallel Lists in Python (zip)
Build a dictionary by pairing elements from two parallel lists using Python's built-in zip function and dict constructor.
keys = ["name", "age", "city"]
values = ["Alice", 30, "New York"]
result = dict(zip(keys, values))
print(result)How to Extract Data by Category in Python with Dictionaries and Sets
Use set comprehensions and a defaultdict to extract product names by category and compute total prices per category from a list of dictionaries.
from collections import defaultdict
# Sample data: products with categories and prices
product_data = [
{"name": "Apple", "category": "fruit", "price": 0.50},
{"name": "Banana", "category": "fruit", "price": 0.30},
{"name": "Carrot", "category": "vegetable", "price": 0.80},
{"name": "Bread", "category…Browse by section
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