Files & data
Read and write files safely; parse JSON, CSV, and common text formats.
How to Audit Environment Variable Files for Missing Values in Python
A Python tool that reads an environment variable file and reports any variables with empty or missing values.
import os
import re
from pathlib import Path
def audit_env_file(filepath: str) -> None:
"""
Audit an environment variable file for missing values.
Prints file status and lists variables that have empty values.
"""
path = Path(filepath)
if not path.exists():
print(f"Error: File '{filepa…How to Convert CSV Column Types While Reading in Python
Read a CSV file and automatically convert column values to int, float, str, or bool based on type suffixes in the header names.
import csv
from pathlib import Path
from typing import Any
def read_csv_with_types(filepath: str) -> list[dict[str, Any]]:
"""Read CSV and convert column types based on header suffixes."""
converters = {
"int": int,
"float": float,
"str": str,
"bool": lambda v: v.strip().lower(…How to Filter CSV Rows by Column Value in Python
Filter CSV rows based on a column value condition using the standard csv module and a lambda function.
import csv
def filter_csv(input_file, output_file, column, condition):
with open(input_file, newline='', encoding='utf-8') as infile, \
open(output_file, 'w', newline='', encoding='utf-8') as outfile:
reader = csv.DictReader(infile)
fieldnames = reader.fieldnames
writer = csv.Dict…How to Handle Missing Values in a CSV Numeric Column in Python
Clean missing entries in a CSV numeric column by filling them with the mean, median, a custom value, or dropping rows.
import csv
from pathlib import Path
import statistics
def clean_csv_numeric(input_path: str, output_path: str, column: str, strategy: str = "mean") -> None:
"""
Handles missing values in a numeric column of a CSV file.
Strategies: 'mean', 'median', 'drop', or 'fill' with a specified value.
"""
row…How to Load a YAML Subset in Python Without PyYAML
Parse a flat, key-value YAML file with the Python standard library (re and pathlib), handling comments, quotes, and inline comments while skipping nested structures.
import re
from pathlib import Path
def load_yaml_subset(path):
"""Load a flat YAML file (key: value) without external dependencies."""
data = {}
with open(path, 'r', encoding='utf-8') as f:
for line in f:
# Skip empty lines and comments
line = line.strip()
if no…How to Parse INI Config Files in Python with configparser
Load and read settings from an INI file using Python's built-in configparser module, with type-safe value access.
import configparser
from pathlib import Path
# Create a sample INI file for demonstration
sample_content = """
[Database]
host = localhost
port = 5432
user = admin
password = secret123
[Logging]
level = INFO
file = app.log
max_size = 10MB
"""
config_file = Path("sample_config.ini")
config_file.write_text(sample_con…How to Sum a CSV Column by Group in Python
This code reads a CSV string and sums a specified column for each unique value of a group key using the csv module and defaultdict.
import csv
from collections import defaultdict
from io import StringIO
def aggregate_csv(csv_data, group_key, sum_column):
totals = defaultdict(float)
reader = csv.DictReader(StringIO(csv_data))
for row in reader:
key = row[group_key]
totals[key] += float(row[sum_column])
return dict(t…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…Browse by section
Each section groups closely related Python snippets.
Files & data — Python code examples
What you will find here
This page collects files & data snippets — short, copy-ready Python you can paste into our free online IDE and run without installing anything. Each sample includes a plain-English explanation and the full source code.
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.