In data science, you will need to learn how to generate random numbers, which in Python, we can do with the `random`

module.

In this tutorial, you’ll learn how to solve the following programming challenge:

βοΈ **Challenge**: How to do random sampling from a list in Python?

Without further ado, let’s dive right into it!

## Method 1: Using Function **random.choice()**

Description from the help function:

help(choice)

Code snippet:

from random import choice seq = list(range(0,10)) print(new) for i in range(5): print(choice(seq))

This function uses the **random module** to pick 5 numbers from the sequence in the list. Note that the numbers can repeat. If the list is empty, it will return `IndexError`

.

It can also work for strings:

For example:

Choose a random Beethoven symphony (by opus number) to listen to. This `random.choice()`

command gives me one from the list β`sym_opus`

β

Or from a list:

Here’s another example:

import random #Create the list of the 9 symphonies: sym = [1, 2, 3, 4, 5, 6, 7, 8, 9] print('Select Symphony No. '+str(random.choice(sym)))

Output:

Select Symphony No. 9

π **Recommended Tutorial**: Sample a Random Number from a Probability Distribution in Python

## Method 2: Using Function **random.choices()**

If you need to choose a sample with replacement/duplicates

In the example below, we draw five cards (we set `k=5`

) from a 52-card deck to determine the suits. Each card draw has equal probability of being drawn.

choices(self, population, weights=None, *, cum_weights=None, k=1)

Here’s a code example:

import random #Create list of suits suits = ['Spades','Hearts','Clubs','Diamonds'] draw = random.choices(suits, k=5) print(draw)

Here we see that ** Clubs **are drawn three times.

### Weighted Outcomes

What happens if there are different weights/probabilities to the outcome?

For example, in a roulette wheel, the outcomes are black, red, and green. But there are 18 spots for black and red, but only 2 for green. So, in the code we can weigh it accordingly.Β

import random roulette = ['Black', 'Red', 'Green'] spin = random.choices(roulette, weights=[18,18,2], k=1) print(spin)

### Bonus Example

Another example:

import random import pandas as pd suits = ['Spades','Hearts','Clubs','Diamonds'] fulldeck = (list(range(1,11))+[10]*3)*4 #print(len(fulldeck)) base_name = ['A'] + list(range(2,11)) + ['J', 'Q', 'K'] cards = [] for suit in ['Spades','Hearts','Clubs','Diamonds']: cards.extend(str(num) + " of "+ suit for num in base_name) deck = pd.Series(fulldeck, index=cards) #deck def draw(deck, n=5): return deck.sample(n) draw(deck)

Output:

4 of Clubs 4 J of Clubs 10 10 of Clubs 10 9 of Spades 9 10 of Spades 10 dtype: int64

## Method 3: Using Function **random.sample()**

**Raffle Ticket Draw:Β **

If the list itself has duplicate elements – like when picking winners in a raffle (in the example below, the number 23, then it could be returned.

import random raffle = [23, 88, 41, 23, 7, 95, 101, 33, 67, 16, 23, 41, 23] random.sample(raffle, k=5)

What about picking unique draws – like cards from a deck?

We could name the cards from 1 to 52, and run the ** sample() **function, to pick out 5 cards:

import random deck = list(range(1,53)) random.sample(deck, k=5)