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How to Merge Sorted Chunk Files in Python
Merge multiple sorted text files into one sorted output file using a heap for efficient k-way merging.
import heapq
def merge_sorted_chunks(chunks, output_path):
"""Merge multiple sorted iterables into single sorted output file."""
with open(output_path, "w") as out_f:
# Open all chunk files
handles = [open(chunk, "r") for chunk in chunks]
try:
# Heap of (value, index) tupl…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 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:
…Normalize CSV Column Names to snake_case in Python
Convert CSV header names to snake_case using a regular expression and write the updated file in place.
import csv
import re
import sys
def to_snake_case(header):
header = re.sub(r"(?<=[a-z0-9])(?=[A-Z])", "_", header)
header = re.sub(r"[^a-zA-Z0-9]+", "_", header).strip("_").lower()
return header
def normalize_csv_headers(input_path, output_path=None):
with open(input_path, newline="", encoding="utf…Parse Fixed Width Data File by Column Slices in Python
Extract fields from fixed-width text by slicing each line at defined column offsets, with a dictionary describing the boundaries.
from pathlib import Path
def parse_fixed_width(data: str, slices: dict[str, tuple[int, int]]) -> list[dict[str, str]]:
lines = data.strip().splitlines()
records = []
for line in lines:
record = {}
for name, (start, end) in slices.items():
record[name] = line[start:end].strip()…Split CSV Files into Smaller Chunks in Python
Splits a large CSV file into multiple smaller chunk files, preserving the header row in each chunk.
import csv
import os
def split_csv(input_file, chunk_size=1000, output_prefix="chunk"):
"""Split a large CSV file into smaller chunks."""
with open(input_file, 'r', newline='') as infile:
reader = csv.reader(infile)
header = next(reader)
file_count = 1
row_count = 0
…Count Words in Python with Dictionaries and Sets
Text analysis example that counts total words, finds unique words with a set, and tallies character frequencies with a dictionary.
def analyze_text(text: str) -> dict:
"""Count words, find unique words, and show common characters."""
words = text.lower().split()
word_count = len(words)
unique_words = set(words)
char_counts = {}
for word in words:
for char in word:
if char.isalpha():
…Count word frequency in Python with dict and Counter
Count how often each word appears in a string using Counter, converted to a plain dict, and print results alphabetically.
from collections import Counter
import re
def count_word_frequency(text):
words = re.findall(r'\b\w+\b', text.lower())
return dict(Counter(words))
if __name__ == "__main__":
sample_text = "The quick brown fox jumps over the lazy dog. The dog barks, and the fox runs."
frequency = count_word_frequency(…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 Count Words and Find Common Words in Python with Dictionaries and Sets
Build a simple text processor that counts unique words with dictionaries and finds common words across text halves using sets.
def process_text(text):
"""Process text: count unique words with counts, find common words."""
words = text.lower().replace(",", "").replace(".", "").split()
word_counts = {}
for word in words:
word_counts[word] = word_counts.get(word, 0) + 1
total_words = len(words)
unique_wo…How to Filter a List of Dictionaries by Category in Python
Filter a list of dictionaries to include only records whose category is in an allowed set.
def filter_data(records, categories):
"""Return only records whose category is in the allowed set."""
allowed = set(categories)
filtered = []
for record in records:
if record["category"] in allowed:
filtered.append(record)
return filtered
if __name__ == "__main__":
data = …How to Group Data by Category in Python with a Split Data Helper
This code groups a list of (category, item) pairs into a dictionary where each key is a category and each value is a list of items belonging to that category.
def split_data(categories):
"""
Group data items into buckets based on a key function.
Returns a dict where keys are bucket names and values are lists of items.
"""
buckets = {}
for category, item in categories:
if category not in buckets:
buckets[category] = []
buck…How to Validate Text and Count Words in Python
Count word frequencies, find unique and repeated words in a text using Python dictionaries and sets for beginner text validation.
def validate_text(text):
words = text.lower().split()
word_counts = {}
for word in words:
cleaned = word.strip('.,!?;:"\'')
if cleaned:
word_counts[cleaned] = word_counts.get(cleaned, 0) + 1
unique_words = set(word_counts.keys())
repeated_words = {word for word…How to count words and find unique words in Python
Build a beginner-friendly text processor that counts word frequencies, finds unique words, and identifies words with vowels using dictionaries and sets.
def text_processor(text):
words = text.lower().replace(",", "").replace(".", "").split()
word_count = {}
for word in words:
word_count[word] = word_count.get(word, 0) + 1
unique_words = set(words)
vowels = set("aeiou")
words_with_vowels = {word for word in unique_words if vowe…Text Processor with Dictionaries and Sets in Python
Build a simple text processor that counts word frequencies with a dictionary and tracks unique words with a set.
def analyze_text(text):
words = text.lower().split()
word_freq = {}
unique_words = set()
for word in words:
clean_word = word.strip('.,!?;:')
if clean_word:
word_freq[clean_word] = word_freq.get(clean_word, 0) + 1
unique_words.add(clean_word)
return…How to merge dictionaries by a key in Python with a class
This code defines a DataMerger class that collects dictionary records and merges them by a specified key, combining fields from multiple records with the same key.
class DataMerger:
def __init__(self):
self.records = []
def add_record(self, record):
if isinstance(record, dict):
self.records.append(record)
else:
raise TypeError("Record must be a dictionary")
def merge_by_key(self, key):
merged = {}
for …How to Combine filter and map with a List Comprehension in Python
This Python code demonstrates how to combine filtering and mapping in a single list comprehension and shows the equivalent filter() and map() approach.
def square(x):
return x * x
def is_even(x):
return x % 2 == 0
numbers = [1, 2, 3, 4, 5, 6, 7, 8]
result = [square(x) for x in numbers if is_even(x)]
print(f"Original numbers: {numbers}")
print(f"Squares of even numbers: {result}")
# Combined filter + map equivalent
filtered = filter(is_even, numbers)
mapp…Build a lazy generator to read file lines in Python
Create a generator function that yields file lines one at a time, avoiding loading the entire file into memory, and demonstrate its lazy processing.
def lazy_lines(filepath):
"""Yield lines from a file one at a time without loading the whole file into memory."""
with open(filepath, 'r', encoding='utf-8') as file:
for line in file:
yield line.rstrip('\n')
if __name__ == "__main__":
# Create a sample file to demonstrate
sample_c…How to Lazily Transform Items in Python with a Generator
Map a transform function over an iterable lazily with a generator so items are processed on demand, not up front.
def lazy_map(items, transform):
for item in items:
yield transform(item)
def double(x):
return x * 2
def upper(s):
return s.upper()
if __name__ == "__main__":
numbers = [1, 2, 3, 4, 5]
doubled = lazy_map(numbers, double)
print("Doubled numbers:", end=" ")
for value in doubled:
…How to Parse CSV Rows as Generator Dicts in Python
Reads a CSV file and yields each row as a dictionary one at a time using a generator, so the file is processed lazily.
import csv
from pathlib import Path
def csv_to_dicts(filepath):
with open(filepath, mode="r", newline="", encoding="utf-8") as file:
reader = csv.DictReader(file)
for row in reader:
yield row
if __name__ == "__main__":
sample_csv = Path("sample_data.csv")
sample_csv.write_text…Memory efficient map over large file in Python
A generator-based streaming map that processes a large file line by line without loading the whole file into memory.
import sys
def process_lines(file_path):
"""Memory-efficient map over a large file: yields processed lines."""
with open(file_path, 'r') as f:
for line in f:
# Example mapping: strip whitespace and uppercase
yield line.strip().upper()
if __name__ == "__main__":
# Use a sma…How to Batch Embed a List of Strings in Python
Batch embed a list of strings into deterministic pseudo-random vectors using a mock encoder class.
class MockEncoder:
def __init__(self, dim=8, seed=42):
self.dim = dim
self.seed = seed
def embed(self, text):
# Deterministic pseudo-random embedding based on text content
hash_val = hash(text)
import random
rng = random.Random(hash_val + self.seed)
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