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Copy-ready Python snippets by topic and difficulty — short, focused, and runnable in the browser editor.
How to compute exact match metric in Python
Computes the exact match (EM) metric for LLM outputs by normalizing text and comparing predictions against references.
def compute_exact_match(predictions, references):
def normalize(text):
import re
text = text.lower().strip()
text = re.sub(r'\b(a|an|the)\b', ' ', text)
text = re.sub(r'[^a-z0-9\s]', '', text)
text = ' '.join(text.split())
return text
matches = sum(1 for pred, r…How to Define Dagster ML Assets in Python
Define a chain of Dagster software-defined assets that compute raw features, normalized features, and predictions for an ML pipeline.
from dagster import asset
@asset
def raw_features():
return {"sepal_length": [5.1, 4.9, 6.2], "sepal_width": [3.5, 3.0, 3.4]}
@asset
def normalized_features(raw_features):
values = raw_features["sepal_length"]
mean = sum(values) / len(values)
std = (sum((x - mean) ** 2 for x in values) / len(values…How to Run Batch Predictions with a Mock Model in Python
Build a lightweight mock model class and run predictions across a batch of samples, returning results as a plain Python list.
import numpy as np
class MockModel:
def __init__(self, weights):
self.weights = np.array(weights)
def predict(self, X):
return X @ self.weights
def predict_batch(model, batch):
"""Run predictions for a batch of samples and return results as a list."""
return model.predict(np.array(ba…How to Save and Load a Mock Model with Pickle and joblib in Python
Serialize a custom machine learning model to a .joblib file with joblib.dump, reload it, and run a prediction with joblib.load.
import joblib
from pathlib import Path
class MockModel:
def __init__(self, weights):
self.weights = weights
def predict(self, features):
return sum(w * f for w, f in zip(self.weights, features))
def save_model_pickle(model, filepath):
with open(filepath, "wb") as f:
joblib.dump(…Browse by section
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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
- Pick a topic section — strings, lists, files, functions, and more
- Open a sample, read How it works, and copy the code block
- 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.