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Reference library

Python Code Samples

Copy-ready Python snippets by topic and difficulty — short, focused, and runnable in the browser editor.

3 matches
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Files & data easy

Automatically Highlight Data Validation Errors Inside Excel Files in Python

Load an Excel file with openpyxl, iterate over cells, and highlight invalid data (empty, negative) with a red fill and error message.

excel validation openpyxl
Python
import openpyxl
from openpyxl.styles import PatternFill
from pathlib import Path

def highlight_validation_errors(filepath: str, output_path: str = None):
    wb = openpyxl.load_workbook(filepath)
    red_fill = PatternFill(start_color="FF0000", end_color="FF0000", fill_type="solid")
    
    for sheet in wb.worksheet…
4 0 Open
Files & data easy

Detect Outliers in CSV Data Using Z-Score in Python

Read a CSV file and detect outliers in a numeric column by computing z-scores, flagging those exceeding a given threshold — no machine learning required.

outlier-detection z-score csv
Python
import csv
import statistics
from math import sqrt

def detect_outliers(csv_path, column_name, threshold=2.0):
    """Detect outliers in a numeric column using z-score method."""
    values = []
    with open(csv_path, 'r', newline='') as f:
        reader = csv.DictReader(f)
        if column_name not in reader.field…
2 0 Open
Data pipelines & processing medium

How to Find Missing Values in Large Datasets in Python

Analyze missing values across multiple large pandas DataFrames with counts and percentages.

pandas missing-data data-cleaning
Python
import pandas as pd
import numpy as np

def find_missing_values_summary(datasets):
    """Analyze missing values across multiple datasets (dict of name: DataFrame)."""
    summary = {}
    for name, df in datasets.items():
        missing_count = df.isnull().sum()
        total_rows = len(df)
        missing_pct = (mi…
3 0 Open

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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

  1. Pick a topic section — strings, lists, files, functions, and more
  2. Open a sample, read How it works, and copy the code block
  3. 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.