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Build a Personal Work Hours Tracker in Python
A Python class that logs daily work hours to a CSV file and produces a weekly summary of total hours worked.
import csv
from pathlib import Path
from datetime import datetime, date
class WorkHoursTracker:
def __init__(self, file_path="work_hours.csv"):
self.file_path = Path(file_path)
if not self.file_path.exists():
with open(self.file_path, "w", newline="") as f:
writer = csv…How to Build a CSV Comparison Tool That Highlights Every Changed Cell in Python
Read two CSV files with DictReader, compare cell by cell, and return a list of dictionaries describing each changed cell using only the standard library.
import csv
from pathlib import Path
def csv_cell_diff(file_a: str, file_b: str) -> list[dict]:
rows_a = list(csv.DictReader(Path(file_a).open('r', newline='')))
rows_b = list(csv.DictReader(Path(file_b).open('r', newline='')))
if not rows_a or not rows_b:
return []
columns = list(rows_a[0].key…How to Generate an Inventory Report of All Files in Python
Walk a directory tree, collect metadata for every file, and write a CSV inventory report using Python's os, pathlib, and csv modules.
import os
import csv
from pathlib import Path
from datetime import datetime
def generate_inventory_report(root_dir: str = "/", output_file: str = "inventory_report.csv"):
headers = ["File Path", "Size (bytes)", "Last Modified", "File Type"]
rows = []
start_time = datetime.now()
for dirpath, dirna…How to Stream Large CSV Files in Python
Process a large CSV file in memory-efficient chunks using Python's csv module, yielding batches of rows instead of loading everything at once.
import csv
from pathlib import Path
def process_csv_in_chunks(file_path, chunk_size=1000):
"""Yield rows from a large CSV file in chunks without loading all into memory."""
with open(file_path, 'r', newline='') as f:
reader = csv.DictReader(f)
chunk = []
for row in reader:
…Join two CSV files on shared key column in Python
Merge rows from two CSV files by a common key column, outputting combined records to a new file.
import csv
def join_csv(file1, file2, key, output="joined.csv"):
# Read first CSV into dict keyed by the join column
with open(file1, newline="") as f1:
reader1 = csv.DictReader(f1)
data1 = {row[key]: row for row in reader1}
# Read second CSV and merge matching rows
with open(file2, n…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…Scrape HTML Tables and Convert Them to CSV Using Beautiful Soup in Python
Scrape a Wikipedia table with Beautiful Soup and write the data to a CSV file using the csv module.
import requests
from bs4 import BeautifulSoup
import csv
url = "https://en.wikipedia.org/wiki/List_of_countries_by_GDP_(nominal)"
response = requests.get(url)
soup = BeautifulSoup(response.text, 'html.parser')
tables = soup.find_all('table', {'class': 'wikitable'})
if tables:
target_table = tables[2]
rows =…Automatically Generate Charts from CSV Files with One Command
Read a CSV file with headers, extract the first two numeric columns, and save a matplotlib line chart as a PNG image.
import csv
import sys
from pathlib import Path
import matplotlib.pyplot as plt
def generate_chart(csv_path: str) -> None:
"""Read a CSV file with headers and plot the first two numeric columns."""
data = []
with open(csv_path, 'r', newline='') as f:
reader = csv.reader(f)
headers = next(re…Build a Complete Web Scraper with Requests and BeautifulSoup in Python
Scrape multiple paginated pages from a website using Requests and BeautifulSoup, with retry logic, error handling, and CSV export.
import requests
from bs4 import BeautifulSoup
import csv
import time
from typing import List, Dict, Optional
class WebScraper:
def __init__(self, base_url: str, output_file: str = "scraped_data.csv"):
self.base_url = base_url
self.output_file = output_file
self.session = requests.Session()…How to Validate Fact Table Grain Row Counts in Python
Validate fact table grain by checking dimension key references, unique grain combinations, duplicate rows, and dimension cardinality from a CSV file.
import csv
import hashlib
from pathlib import Path
def validate_fact_grain(fact_file: Path, expected_dim_keys: dict[str, set[str]]) -> dict:
"""
Validate fact table grain by checking each row's dimension keys exist
in expected dimension tables and row count consistency.
"""
dim_references = {}
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