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Find Data From a String in Python: Stats, Clean, Keywords
Three helper functions for beginners: compute character/word/sentence stats, normalize whitespace and case, and extract unique sorted keywords from a string.
def get_text_stats(text):
"""Return basic statistics about a string."""
words = text.split()
sentences = text.replace('!', '.').replace('?', '.').split('.')
sentences = [s for s in sentences if s.strip()]
return {
'characters': len(text),
'words': len(words),
'sentences': le…How to Extract Digits Only from a String in Python
This code uses a regular expression to remove all non-digit characters from a mixed string, returning only the digits.
import re
def extract_digits(text):
"""Return only the digits from the given text as a string."""
return re.sub(r'\D', '', text)
if __name__ == "__main__":
mixed = "abc123def456!@#789"
result = extract_digits(mixed)
print(result)How to Filter Text to Only Letters, Numbers, and Spaces in Python
A beginner-friendly function that filters a string to keep only alphabetic characters, digits, and spaces, removing punctuation and symbols.
def filter_text(text, keep_alpha=True, keep_digits=True, keep_spaces=True):
allowed = set()
if keep_alpha:
allowed.update("abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ")
if keep_digits:
allowed.update("0123456789")
if keep_spaces:
allowed.add(" ")
return "".join(ch f…How to Parse and Clean Text in Python
This code defines three helper functions to parse text into lowercase words, count unique word frequencies, and clean text by removing punctuation and extra whitespace.
def extract_words(text: str) -> list[str]:
"""Return a list of lowercase words from the given text."""
return [word.lower() for word in text.split() if word.isalpha()]
def count_unique_words(text: str) -> dict[str, int]:
"""Return a dictionary with unique words and their frequencies."""
words = extra…How to Remove Duplicate Adjacent Spaces in Python
This Python function collapses any sequence of two or more adjacent spaces into a single space, preserving all other characters.
def remove_duplicate_adjacent_spaces(text):
"""Replace sequences of 2+ spaces with a single space."""
result = []
prev_was_space = False
for char in text:
if char == " ":
if not prev_was_space:
result.append(char)
prev_was_space = True
else:
…How to Remove HTML Tags in Python with Regex
Strips all HTML tags from a string using a regular expression and cleans extra whitespace.
import re
def remove_html_tags(text: str) -> str:
"""Remove all HTML tags from the given text using regex."""
# Remove opening and closing tags
clean = re.sub(r'<[^>]+>', '', text)
# Remove any extra whitespace left behind
clean = re.sub(r'\s+', ' ', clean).strip()
return clean
if __name__ ==…How to Replace Multiple Spaces with a Single Space in Python
This snippet uses the `re` module to collapse runs of consecutive spaces in a string into a single space, cleaning up whitespace.
import re
def collapse_spaces(text):
"""Replace multiple consecutive spaces with a single space."""
return re.sub(r' +', ' ', text)
if __name__ == "__main__":
sample = "This has multiple spaces between words."
result = collapse_spaces(sample)
print(f"Original: '{sample}'")
print(f"Co…How to Strip Whitespace in Python
This code demonstrates how to remove leading and trailing whitespace from a string using the built-in strip() method.
def strip_whitespace(text: str) -> str:
return text.strip()
if __name__ == "__main__":
sample = " Hello, world! "
result = strip_whitespace(sample)
print(f"Original: '{sample}'")
print(f"Stripped: '{result}'")How to build a text helper in Python for beginners
This code provides easy-to-use functions for cleaning text, removing punctuation, counting word frequencies, and summarizing strings — perfect for beginners.
def clean_text(text: str) -> str:
"""Clean and normalize a text string."""
text = text.strip()
text = text.replace(" ", " ")
text = text.capitalize()
text = text.replace(".", ".")
return text
def remove_punctuation(text: str) -> str:
"""Remove common punctuation marks from a string."""
…How to Filter None Values from a Mixed List in Python
Filter None values from a mixed Python list using a list comprehension with the `is not None` condition.
mixed_list = [1, None, "hello", None, 3.14, None, [1, 2], None]
filtered_list = [item for item in mixed_list if item is not None]
print(f"Original list: {mixed_list}")
print(f"Filtered list: {filtered_list}")
print(f"Original length: {len(mixed_list)}, Filtered length: {len(filtered_list)}")How to Parse Delimited Data into a Python List
Splits a pipe-delimited string, strips whitespace, filters empty items, and returns a clean list with a loop.
def parse_data(raw_data):
"""Parse a pipe-delimited string into a list of cleaned items."""
items = raw_data.split("|")
parsed = []
for item in items:
cleaned = item.strip()
if cleaned:
parsed.append(cleaned)
return parsed
if __name__ == "__main__":
data = " apple…Replace Negative Values in a List with Python
This code defines a function that replaces every negative number in a list with a replacement value, defaulting to zero, using a list comprehension.
def replace_if_negative(values, replacement=0):
return [replacement if value < 0 else value for value in values]
if __name__ == "__main__":
numbers = [5, -3, 8, -1, 0, -7, 2]
result = replace_if_negative(numbers)
print(f"Original: {numbers}")
print(f"Replaced: {result}")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.
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…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.
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…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 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 Normalize Data in Python with Dictionaries and Sets
Normalize a list of dicts by keeping selected keys, stripping/lowercasing strings, and extracting unique sorted values using set comprehension.
def normalize_data(data, keys):
"""
Normalize a list of dictionaries by keeping only specified keys
and converting values to proper types.
"""
normalized = []
for item in data:
clean_item = {}
for key in keys:
value = item.get(key)
if isinstance(value, st…How to Normalize Data with Dictionaries and Sets in Python
Normalize dictionary entries to a fixed set of keys and extract unique values using sets in Python.
def normalize_entry(entry: dict, valid_keys: set) -> dict:
result = {}
for key in valid_keys:
result[key] = entry.get(key, "")
return result
def unique_values(entries: list[dict], key: str) -> set:
return {entry.get(key) for entry in entries if entry.get(key) is not None}
if __name__ == "__…How to Recursively Remove None Values from Nested Dictionaries in Python
Recursively removes all None values from nested dictionaries and lists while preserving non-None data.
def prune_none(obj):
if isinstance(obj, dict):
return {
k: prune_none(v)
for k, v in obj.items()
if v is not None and prune_none(v) is not None
}
elif isinstance(obj, list):
pruned = [prune_none(item) for item in obj]
pruned = [item for item i…How to Transform a List of Dictionaries with Sets in Python
Normalize a list of dict records — cleaning names, extracting unique tags with sets, and building a standardized result.
def transform_data(raw_records):
"""Transform a list of dict records into normalized data with sets for unique values."""
normalized = []
unique_names = set()
all_tags = set()
for record in raw_records:
# Normalize name to lowercase and strip whitespace
name = record.get("name"…How to Replace Outliers Beyond Threshold with Cap in Python
Replace values that fall below a lower threshold or above an upper threshold by capping them to the threshold values using a simple Python function.
def replace_outliers_with_cap(data, lower_threshold=None, upper_threshold=None):
"""Replace values beyond given thresholds with the threshold values (capping)."""
if lower_threshold is None and upper_threshold is None:
raise ValueError("At least one threshold must be provided.")
capped_data = …Normalize Data in Python with Comprehensions and Generators
Clean a list by dropping None values with a comprehension, then min-max normalize it using a lazy generator expression — a beginner-friendly data preparation pattern.
import statistics
# Sample raw data including missing and outlier-ish values
raw = [22, 18, None, 25, 30, 19, 22, 17, None, 28, 24]
# Clean the data: drop None values using a list comprehension
clean = [x for x in raw if x is not None]
# Normalize using min-max scaling with a generator expression
min_val = min(clea…How to Validate LLM Output in Python
A beginner-friendly DataValidator class that checks required fields and type constraints on LLM-generated or user JSON data.
import json
from typing import Any, Dict, List, Optional
class DataValidator:
"""Simple helper for validating LLM-generated or user data."""
def __init__(self, required_fields: List[str], schema: Optional[Dict[str, str]] = None):
self.required_fields = required_fields
self.schema = schema or…How to Hash Duplicate Photos and Delete Copies in Python
This script hashes image files in a directory using SHA-256 and deletes duplicate copies while keeping the first occurrence, ideal for cleaning up photo libraries.
from pathlib import Path
import hashlib
def file_hash(path, chunk_size=8192):
hasher = hashlib.sha256()
with open(path, "rb") as f:
for chunk in iter(lambda: f.read(chunk_size), b""):
hasher.update(chunk)
return hasher.hexdigest()
def delete_duplicate_photos(directory):
directory …Browse by section
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