Reference library

Python Code Samples

Medium snippets you can copy, study, and run in the browser editor.

240 matches
Big data & Spark medium

Accumulators Global Counter Mock in Python

Shows an accumulator-style global counter with a mock patch to control its value in tests.

accumulator global state mock
Python
import unittest
from unittest.mock import patch

# Module-level global counter accumulator
counter = 0

def increment(by=1):
    """Increment the global counter in place (accumulator pattern)."""
    global counter
    counter += by
    return counter

def reset():
    """Reset the counter to zero."""
    global count…
17 0 Open
Big data & Spark medium

Approximate Distinct Count in Python with HyperLogLog

Mock a large data stream and estimate the number of distinct items with a HyperLogLog-style probabilistic counter to save memory.

hyperloglog distinct-count probabilistic
Python
import random
import string
from collections import Counter
import math

class ApproxCountDistinct:
    def __init__(self, num_buckets=16):
        self.num_buckets = num_buckets
        self.max_zeros = [0] * num_buckets
        
    def _hash(self, item):
        # Simple string hash to a 32-bit integer
        h = …
16 0 Open
Big data & Spark medium

Bloom Filter Join Mock in Python

A mock hash join that uses a Bloom filter to pre-filter one table before performing an exact match, reducing the number of comparisons in large dataset joins.

bloom filter join hashing
Python
import hashlib
import random
import string


class BloomFilter:
    def __init__(self, size: int = 200, num_hashes: int = 3):
        self.bits = [False] * size
        self.size = size
        self.num_hashes = num_hashes

    def _hashes(self, item: str):
        result = []
        for seed in range(self.num_hashes…
14 0 Open
Big data & Spark medium

Delta Lake ACID Transaction Log Mock in Python

Simulates Delta Lake's transactional log with JSON files for atomic commits, versioned operations, and crash recovery

delta-lake transaction-log acid
Python
import json
import time
from pathlib import Path

class DeltaLog:
    def __init__(self, path):
        self.log_dir = Path(path)
        self.log_dir.mkdir(parents=True, exist_ok=True)
        self.version = 0

    def _write_txn(self, action, payload):
        txn = {
            "version": self.version,
           …
17 0 Open
Big data & Spark medium

How to Create a Mock Iceberg Snapshot Manifest in Python

Build a mock Iceberg snapshot manifest structure with metadata and data entries using Python dictionaries and JSON.

iceberg manifest snapshot
Python
import json
from datetime import datetime, timezone


def create_mock_manifest(snapshot_id: int, file_paths: list[str]) -> dict:
    """Create a mock Iceberg snapshot manifest structure."""
    manifest_file = {
        "manifest_path": f"/warehouse/table/metadata/snap-{snapshot_id}-m0.avro",
        "manifest_length"…
15 0 Open
Big data & Spark medium

How to Implement a Mock MapReduce for Word Count in Python

Simulates a MapReduce word count pipeline with mapper, shuffle, and reducer phases using Python dicts and standard library modules.

mapreduce word-count big-data
Python
from collections import defaultdict
import re

def mapper(text):
    """Split text into words and emit (word, 1) pairs."""
    words = re.findall(r'\b\w+\b', text.lower())
    return [(word, 1) for word in words]

def reducer(pairs):
    """Group word-count pairs and sum counts."""
    counts = defaultdict(int)
    fo…
15 0 Open
Big data & Spark medium

How to Implement a Streaming Watermark in Python

Mock structured streaming watermarks in Python to track late event times and compute a watermark for windowed processing.

streaming watermark spark
Python
from datetime import datetime, timedelta
import time

class StreamingWatermark:
    """Mock watermark tracker for structured streaming."""

    def __init__(self, watermark_delay_seconds):
        self.watermark_delay = timedelta(seconds=watermark_delay_seconds)
        self.max_event_time = None

    def observe_even…
14 0 Open
Big data & Spark medium

How to Mock Spark Streaming Micro-Batches in Python

Simulate Spark's micro-batch streaming with a simple deque-based class that collects events over time and processes them in timed batches.

spark streaming micro-batch
Python
import time
from collections import deque
from datetime import datetime


class MicroBatchStream:
    def __init__(self, batch_interval_sec=2):
        self.batch_interval = batch_interval_sec
        self.source = deque()
        self.processed = []

    def add_events(self, events):
        self.source.extend(events…
13 0 Open
Big data & Spark medium

How to Mock a Catalyst Logical Plan in Python

Build a small Python class that mimics Spark Catalyst's logical plan tree for teaching or testing query optimizations.

apache-spark logical-plan catalyst
Python
from typing import Any, Dict, List, Optional


class CatalystLogicalPlan:
    """A minimal mock of Catalyst's logical plan for teaching purposes."""
    
    def __init__(self, node_type: str, **kwargs: Any) -> None:
        self.node_type = node_type
        self.attributes: Dict[str, Any] = kwargs
        self.child…
13 0 Open
Big data & Spark medium

How to Mock a Compute-Collect Action Trigger in Python

Mock a compute-collect action trigger using Python's unittest.mock to simulate Spark-style job execution and assert trigger behavior.

testing mock spark
Python
Here's a Python code sample for the problem title "Action trigger compute collect mock":
14 0 Open
Big data & Spark medium

How to Mock a Parquet partitionBy Sink in Python

Manually write a DataFrame to partitioned Parquet files, mimicking Spark's partitionBy sink behavior without Spark.

parquet pyarrow partition
Python
import pyarrow as pa
import pyarrow.parquet as pq
from pathlib import Path
import tempfile
import shutil


def mock_partition_by_sink(data, output_dir, partition_cols):
    table = pa.Table.from_pandas(data)
    schema = table.schema
    unique_combos = table.select(partition_cols).to_pylist()
    seen = set()
    for…
13 0 Open
Big data & Spark medium

How to Mock a UDAF Aggregate Function in Python

This code provides a minimal mock of a User-Defined Aggregate Function (UDAF), simulating the initialize-update-merge-finalize lifecycle with a defaultdict counter.

udaf aggregate mock
Python
from collections import defaultdict

class MockUDAF:
    """A minimal mock of a User-Defined Aggregate Function.

    Simulates aggregate lifecycle: initialize, update per row,
    and finalize the result.
    """

    def __init__(self):
        self._buffer = defaultdict(int)

    def initialize(self):
        """Re…
13 0 Open
Big data & Spark medium

How to Mock and Test a Rate-Limited Source Stream in Python

Build a class that rate-limits emitted items using a sliding window and test it with a simulated stream in Python.

rate-limiting mock-testing streaming
Python
import time
from collections import deque


class RateLimitedSource:
    def __init__(self, max_rate, window=1.0):
        self.max_rate = max_rate
        self.window = window
        self._timestamps = deque()

    def emit(self, item):
        now = time.monotonic()
        while self._timestamps and self._timestam…
16 0 Open
Big data & Spark medium

How to Simulate a MapReduce Mock with Combine Phase in Python

Simulates a MapReduce pipeline with a combiner that aggregates local counts per reducer to reduce network and compute overhead.

mapreduce combiner hadoop
Python
from collections import defaultdict

def map_phase(lines):
    intermediate = defaultdict(list)
    for line in lines:
        for word in line.strip().lower().split():
            intermediate[word].append(1)
    return dict(intermediate)

def combine_phase(intermediate, num_reducers=3):
    combined = defaultdict(li…
14 0 Open
Big data & Spark medium

How to implement a tumbling window aggregation in Python

Build a mock tumbling window aggregator in Python that groups streaming events into fixed time intervals and computes count, sum, and average per window.

tumbling-window streaming aggregation
Python
import time
from collections import deque

class TumblingWindow:
    def __init__(self, duration_seconds):
        self.duration = duration_seconds
        self.buffer = deque()
        self.window_start = None

    def add(self, item):
        current_time = time.time()
        if self.window_start is None:
         …
13 0 Open
Big data & Spark medium

How to use foreachBatch with a mock sink in PySpark

Demonstrates using Spark Structured Streaming's foreachBatch sink to capture and verify streaming batches by writing them into a custom mock sink object.

pyspark structured-streaming foreachbatch
Python
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, lit

class MockSink:
    def __init__(self):
        self.batches = []
    
    def write_batch(self, batch_df, batch_id):
        # Collect batch data as list of dicts for verification
        records = batch_df.collect()
        self.batches…
14 0 Open
Big data & Spark medium

Lazy Evaluation Transform Lineage Mock in Python

Build a mock lineage tracker for data transforms using lazy evaluation and function wrappers in Python.

lazy-evaluation lineage decorator
Python
import functools


def lazy_transform(pipeline):
    """Build a mock lineage tracker using lazy evaluation."""
    lineage = []

    def wrap(func):
        @functools.wraps(func)
        def wrapper(*args, **kwargs):
            result = func(*args, **kwargs)
            lineage.append({"transform": func.__name__, "a…
16 0 Open
Big data & Spark medium

Mock Predicate Pushdown in Python for Big Data Queries

Simulate predicate pushdown by applying filters at the storage layer before materializing rows, showing how big data engines optimize queries.

big-data query-optimization predicate-pushdown
Python
class Query:
    def __init__(self, table, rows):
        self.table = table
        self.rows = rows

    def filter(self, predicate):
        return Query(
            self.table,
            [row for row in self.rows if all(predicate(row) for predicate in predicate)]
        )

    def filter_pushdown(self, predica…
15 0 Open
Big data & Spark medium

Mock RDD in Python: Simulate Spark RDD Lazy Transformations

Simulate Apache Spark RDD behavior in Python with lazy maps, filters, partitions, and a collect action.

spark rdd big-data
Python
import random

def mock_rdd(data, num_slices=2):
    """
    A simple simulation of Spark RDD behavior with lazy evaluation,
    transformations, and an action.
    """
    class SimpleRDD:
        def __init__(self, data, num_slices=2):
            self.data = data
            self.num_slices = num_slices
           …
13 0 Open
ML engineering pipelines medium

Bayesian Optimization in Python: A Simplified Mock Implementation

A toy Bayesian optimization loop with a Gaussian process prior, expected improvement acquisition, and noisy sampling to find a function's minimum.

bayesian-optimization gaussian-process hyperparameter-tuning
Python
import random
import math

class BayesianOptimizer:
    def __init__(self, noise=0.1):
        self.noise = noise
        self.observations = []
    
    def objective(self, x):
        return (math.sin(3*x) + 0.5*x) / (1 + x**2)
    
    def gaussian_process_prior(self, x1, x2, length_scale=0.5):
        return math.…
13 0 Open
ML engineering pipelines medium

How to Build a Mock ML Pipeline with Prefect in Python

Create a lightweight Prefect flow with mock preprocessing, training, and evaluation tasks to prototype an ML pipeline end-to-end.

prefect machine-learning pipeline
Python
from prefect import task, flow
from datetime import datetime


@task
def preprocess_data(raw_value: float) -> float:
    """Mock preprocessing: normalize the input value."""
    return raw_value / 100.0


@task
def train_model(features: float) -> dict:
    """Mock training: return a fake model artifact."""
    return …
12 0 Open
ML engineering pipelines medium

How to Build an sklearn Pipeline with ColumnTransformer in Python

A mock example showing how to chain preprocessing and a regression model into a single sklearn Pipeline, scaling numeric features and one-hot encoding categorical features with ColumnTransformer.

sklearn pipeline columntransformer
Python
import numpy as np
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.pipeline import Pipeline
from sklearn.linear_model import LinearRegression

# Mock dataset
X = np.array([[1, 'red'], [2, 'blue'], [3, 'red'], [4, 'green'], [5, 'blue']], dtype=o…
13 0 Open
ML engineering pipelines medium

How to Create a Mock ONNX Model in Python

Build and export a minimal mock ONNX model with a Reshape and Gemm layer using the onnx helper API.

onnx model-export mlops
Python
import onnx
import numpy as np
from onnx import helper, TensorProto

def create_mock_model():
    # Define input and output tensors
    input_tensor = helper.make_tensor_value_info('input', TensorProto.FLOAT, [1, 3, 224, 224])
    output_tensor = helper.make_tensor_value_info('output', TensorProto.FLOAT, [1, 10])

   …
16 0 Open
ML engineering pipelines medium

How to Mock Cron Schedule in Python

Compute the next scheduled run time for a cron expression using a pure-Python mock parser.

cron scheduling mock
Python
import re
from datetime import datetime, timedelta

class CronMock:
    def __init__(self, expression):
        self.expression = expression
        self.minutes = self._parse_field(expression.split()[0], 0, 59)
        self.hours = self._parse_field(expression.split()[1], 0, 23)
        self.days = self._parse_field(…
17 0 Open

Browse by section

Each section groups closely related Python snippets.

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.