# Choose element(s) from List with different probability in Python

Have you ever wondered how to select random elements from a list with different probability in Python? In this article, we will discuss how to do the same. Let’s first consider the below example.

## Python3

`import` `random` ` ` `sam_Lst ` `=` `[` `10` `, ` `20` `, ` `3` `, ` `4` `, ` `100` `]` `ran ` `=` `random.choice(sam_Lst)` `print` `(ran)` |

In the above example, the probability of getting any element from the list is equal. But we want such methods in which the probability of choosing one element from the list is different. This is known as the weighted random choice in Python. In order to find weighted random choices in Python, there exist two ways:

**Relative weights****Cumulative weights**

The function which will help us in this situation is **random.choices()****. **This function allows making weighted random choices in python with replacement.

**Syntax:**

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

Here, the ‘weight’ parameter plays an important role.

**Case 1: Using Relative weights **

The weight assigned to an element is known as relative weight.

**Example 1:**

## Python3

`import` `random` ` ` `# Creating a number list` `num_lst ` `=` `[` `1` `, ` `22` `, ` `43` `, ` `19` `, ` `13` `, ` `29` `]` ` ` `print` `(random.choices(num_lst, weights` `=` `(` ` ` `14` `, ` `25` `, ` `30` `, ` `45` `, ` `55` `, ` `10` `), k` `=` `6` `))` |

**Output:**

[19, 19, 13, 22, 13, 13]

In the above example, we assign **weights** to every element of the list. The weight of the element ‘**13′** is highest i.e 55, so the probability of its occurrence is maximum. As we can see in the output, element 13 occurs 3 times, 19 occurs 2 times, and so on. So, now the probability of choosing an element from the list is different.

**Example 2: **

## Python3

`import` `random` ` ` `# Creating a name list` `name_lst ` `=` `[` `'October'` `, ` `'November'` `, ` `'December'` `,` ` ` `'January'` `, ` `'March'` `, ` `'June'` `]` ` ` `print` `(random.choices(name_lst, weights` `=` `(` ` ` `40` `, ` `25` `, ` `30` `, ` `5` `, ` `15` `, ` `80` `), k` `=` `3` `))` |

**Output:**

['June', 'October', 'June']

In the above example, the weight of element ‘**June’ **is maximum so its probability of selection will be maximum. And here, k=3 which means we are choosing only the top 3 elements from the list.

**Example 3:**

## Python3

`import` `random` ` ` `# Creating a name list` `name_lst ` `=` `[` `'Fit'` `, ` `'Infected'` `, ` `'Recovered'` `, ` `'Danger'` `]` ` ` `# Using FOR loop` `# to choose the element from list with` `# different probability` `for` `i ` `in` `range` `(` `6` `):` ` ` `print` `(` `"Random choice"` `, i` `+` `1` `)` ` ` ` ` `randomele ` `=` `random.choices(name_lst, weights` `=` `(` ` ` `50` `, ` `80` `, ` `10` `, ` `5` `), k` `=` `1` `)` ` ` ` ` `print` `(randomele[` `0` `])` |

**Output:**

Random choice 1 Recovered Random choice 2 Danger Random choice 3 Infected Random choice 4 Infected Random choice 5 Infected Random choice 6 Fit

In the above example, we use FOR loop to choose an element from a list with a different probability.

**Case 2: Using Cumulative weights**

The cumulative weight of an element is determined by adding the weight of its previous element and its own weight.

**Example 1: **

## Python3

`import` `random` ` ` `# Creating a number list` `num_lst ` `=` `[` `1` `, ` `22` `, ` `93` `, ` `19` `, ` `13` `, ` `25` `]` ` ` `print` `(random.choices(num_lst, cum_weights` `=` `(` ` ` `7` `, ` `13` `, ` `15` `, ` `20` `, ` `25` `, ` `20` `), k` `=` `6` `))` |

**Output:**

[1, 22, 93, 22, 19, 1]

In the above example, the cumulative weight of element ‘**19′ **is maximum, so the probability of its selection will also be maximum.

**Example 2:**

## Python3

`import` `random` ` ` `# Creating a name list` `name_lst ` `=` `[` `'October'` `, ` `'November'` `, ` `'December'` `,` ` ` `'January'` `, ` `'March'` `, ` `'June'` `]` ` ` `print` `(random.choices(name_lst, cum_weights` `=` `(` ` ` `40` `, ` `20` `, ` `3` `, ` `7` `, ` `15` `, ` `15` `), k` `=` `3` `))` |

**Output:**

['January', 'March', 'January']

In the above example, we choose k=3 so we get the top 3 elements that have the maximum probability of selection.

**Example 3:**

## Python3

`import` `random` ` ` `# Creating a name list ` `name_lst ` `=` `[` `'October'` `, ` `'November'` `, ` `'December'` `, ` ` ` `'January'` `, ` `'March'` `, ` `'June'` `]` ` ` `# Using FOR loop` `# to choose the element from list with` `# different probability` `for` `i ` `in` `range` `(` `6` `):` ` ` `print` `(` `"Random choice"` `, i` `+` `1` `)` ` ` ` ` `randomele ` `=` `random.choices(name_lst, cum_weights` `=` `(` ` ` `7` `, ` `13` `, ` `15` `, ` `20` `, ` `25` `, ` `20` `), k` `=` `1` `)` ` ` ` ` `print` `(randomele[` `0` `])` |

**Output:**

Random choice 1 November Random choice 2 January Random choice 3 October Random choice 4 December Random choice 5 November Random choice 6 January

In the above example, we use FOR loop to choose an element from a list with a different probability.

**Note: **The value of **k** depends on the users, and since its relative weight so the **total sum** of weights can **exceed 100.**

Attention geek! Strengthen your foundations with the **Python Programming Foundation** Course and learn the basics.

To begin with, your interview preparations Enhance your Data Structures concepts with the **Python DS** Course. And to begin with your Machine Learning Journey, join the **Machine Learning – Basic Level Course**