# Handling Randomness with Python’s Standard Library: `random` (Selection, Sampling, Shuffling, Reproducibility)

> **Series 04 – Generating Random Numbers and Mixing Data**

"Pick something at random", "shuffle", "choose a few without duplicates", "assign different probabilities" – these tasks pop up in games, test data creation, simple simulations, and data splitting.

![Wizard’s Casino Trick](/media/editor_temp/6/d231fe37-ba66-4222-bca7-4d3dd8e33399.png)

Python’s `random` module solves these needs with concise code. Just remember it’s a **pseudo‑random number generator (PRNG)**, not suitable for cryptographic purposes.

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## 1. The Nature of “Randomness” Produced by `random` {#sec-455748bb14be}

`random` generates **pseudo‑random numbers**. Instead of truly unpredictable natural randomness, it creates a sequence that *looks* random based on an internal state.

* Starting from the same seed yields identical sequences.
* This predictability aids debugging and reproducible experiments.
* It’s unsuitable for security scenarios where an attacker could infer the state (see `secrets` later).

---

## 2. Basic Random Numbers: Drawing Numbers {#sec-d5b02eb13283}

### 2.1 Uniform floats in [0, 1): `random()` {#sec-0219f1479073}

```python
import random

x = random.random()
print(x)  # 0.0 <= x < 1.0
```

### 2.2 Integer ranges: `randint(a, b)` / `randrange()` {#sec-db1bdddd505e}

```python
import random

print(random.randint(1, 6))   # 1–6 inclusive
print(random.randrange(0, 10, 2))  # 0,2,4,6,8
```

* `randint(a, b)` includes both ends.
* `randrange(start, stop, step)` follows the usual range rules (stop is exclusive).

### 2.3 Uniform floats in [a, b]: `uniform(a, b)` {#sec-b2ac63ac3cef}

```python
import random

print(random.uniform(1.5, 3.5))
```

---

## 3. Choosing from Collections: Selection, Sampling, Shuffling {#sec-8b4cde32f256}

### 3.1 Pick one: `choice()` {#sec-deb7c9f088ca}

```python
import random

items = ["rock", "paper", "scissors"]
print(random.choice(items))
```

### 3.2 Pick many without replacement: `sample()` {#sec-3f22607831e5}

```python
import random

nums = list(range(1, 46))
picked = random.sample(nums, k=6)  # 6 unique numbers
print(picked)
```

* `sample()` does **not** modify the original list.
* It guarantees no duplicates.

### 3.3 Shuffle a list in place: `shuffle()` {#sec-ccdf58c09400}

```python
import random

deck = list(range(10))
random.shuffle(deck)
print(deck)
```

* `shuffle()` mutates the list directly.
* To keep the original, create a shuffled copy with `sample()`:

```python
import random

deck = list(range(10))
shuffled = random.sample(deck, k=len(deck))
print(deck)
print(shuffled)
```

---

## 4. Weighted Choices: `choices()` {#sec-aa8997456e04}

When you need “common, rare, legendary” probabilities, `choices()` is handy.

```python
import random

items = ["common", "rare", "legendary"]
weights = [85, 14, 1]  # can be raw weights or probabilities

result = random.choices(items, weights=weights, k=10)
print(result)
```

* By default, `choices()` allows duplicates.
* `weights` can be raw counts or normalized probabilities.

---

## 5. Reproducible Results: `seed()` {#sec-bc5ab726676b}

Using the same seed guarantees the same sequence, which is invaluable for tests and demos.

```python
import random

random.seed(42)
print(random.randint(1, 100))
print(random.randint(1, 100))
```

Even with a mix of functions, the order of calls determines the outcome.

> Note: `seed()` affects the global state. In large projects, consider using a dedicated `random.Random()` instance.

---

## 6. Local RNGs with `random.Random()` {#sec-1834e1c42abe}

Avoid touching the global state by creating a separate RNG.

```python
import random

rng = random.Random(42)  # local RNG
print(rng.randint(1, 100))
print(rng.randint(1, 100))
```

This pattern is especially useful in libraries or modules that need isolated randomness.

---

## 7. Cryptographic‑Safe Randomness: Use `secrets` Instead of `random` {#sec-362a8ba26346}

`random` is predictable; for tokens, auth codes, or password reset links, use the `secrets` module.

```python
import secrets

token = secrets.token_urlsafe(16)
print(token)
```

`secrets` taps into the operating system’s secure random source.

---

## 8. Common Patterns in Practice {#sec-5f6bd9bdf408}

### 8.1 Randomly pick one or more from data {#sec-366d7b6c1b6f}

```python
import random

users = ["u1", "u2", "u3", "u4", "u5"]
print(random.choice(users))        # one user
print(random.sample(users, k=2))   # two unique users
```

### 8.2 Shuffle data and use the first part {#sec-8d04aefaf202}

```python
import random

data = list(range(100))
random.shuffle(data)
train = data[:80]
test = data[80:]
```

### 8.3 Simple weighted roulette {#sec-d3ba1df51400}

```python
import random

prizes = ["A", "B", "C", "D"]
weights = [1, 3, 10, 86]  # D appears most often
print(random.choices(prizes, weights=weights, k=1)[0])
```

---

## 9. Wrap‑up {#sec-ecbe78d7ffd8}

`random` consolidates the broad concept of “random selection” into a small set of APIs:

* Pick one: `choice`
* Pick without replacement: `sample`
* Shuffle: `shuffle`
* Weighted pick: `choices`
* Reproducibility: `seed` or `random.Random()`
* Secure random: `secrets`

Next time, we’ll explore numeric‑oriented standard libraries like `statistics` and `math`.

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**Related posts:**

- [[Python Standard Library – 0] What is the Python Standard Library? A Beginner’s Guide](/ko/whitedec/2026/1/29/python-standard-library/)
- [[Python Standard Library – 1] Mastering the File System & OS Environment: pathlib vs os](/ko/whitedec/2026/1/29/file-system-os-environment-master-pathlib-vs-os/)
- [[Python Standard Library – 2] Data Storage & Serialization: json, pickle, csv](/ko/whitedec/2026/1/30/python-json-pickle-csv/)
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