# How to Calculate Conditional Probability in Python

Conditional probability is a fundamental concept in the field of probability and statistics. It’s the probability of an event occurring, given that another event has already occurred. If the event of interest is A and event B has already occurred, the conditional probability of A given B is usually written as P(A|B).

This article aims to provide an extensive guide on how to calculate conditional probability in Python. We’ll start by introducing the theory of conditional probability and then explore its calculation using basic Python, the numpy library, and pandas for handling more complex, real-world data.

## Understanding Conditional Probability

The conditional probability of Event A given Event B is calculated as the fraction of the probability of the intersection of events A and B and the probability of B:

P(A|B) = P(A ∩ B) / P(B)

The probability of the intersection of A and B is a measure of the likelihood that both events A and B occur. The conditional probability adjusts this measure by the probability of B, the conditioning event. This equation assumes that P(B) > 0.

## Calculating Conditional Probability in Python

### Basic Calculation

Let’s consider a simple example. Suppose you have a fair six-sided die, and you want to find out the probability of rolling a number greater than 4, given that you rolled an odd number.

# define the sample space
sample_space = {1, 2, 3, 4, 5, 6}

# define the event A (roll a number greater than 4)
event_A = {5, 6}

# define the event B (roll an odd number)
event_B = {1, 3, 5}

# calculate P(A)
prob_A = len(event_A) / len(sample_space)

# calculate P(B)
prob_B = len(event_B) / len(sample_space)

# calculate P(A ∩ B)
event_A_intersect_B = event_A.intersection(event_B)
prob_A_intersect_B = len(event_A_intersect_B) / len(sample_space)

# calculate P(A|B)
prob_A_given_B = prob_A_intersect_B / prob_B
print(prob_A_given_B)  # Output: 0.3333333333333333

In this example, we find out that the probability of rolling a number greater than 4, given that we rolled an odd number, is approximately 0.33 or 33%.

### Using NumPy

Python’s numpy library can be used to calculate conditional probabilities with larger and more complex data. Suppose we have a numpy array that represents the results of 1,000 dice rolls, and we want to calculate the same conditional probability as above.

import numpy as np

# generate a numpy array of 1000 dice rolls
np.random.seed(0)  # for reproducibility
dice_rolls = np.random.choice([1, 2, 3, 4, 5, 6], size=1000)

# define the event A (roll a number greater than 4)
event_A = dice_rolls > 4

# define the event B (roll an odd number)
event_B = dice_rolls % 2 != 0

# calculate P(A ∩ B)
event_A_intersect_B = event_A & event_B
prob_A_intersect_B = event_A_intersect_B.sum() / 1000

# calculate P(B)
prob_B = event_B.sum() / 1000

# calculate P(A|B)
prob_A_given_B = prob_A_intersect_B / prob_B
print(prob_A_given_B)  # Output: 0.3458646616541353

In this example, we generate a numpy array of 1,000 random dice rolls using the numpy.random.choice function. We then define the events A and B and calculate the conditional probability P(A|B) in the same way as before.

### Using Pandas

Python’s pandas library, which provides high-performance, easy-to-use data structures such as DataFrames, is ideal for calculating conditional probabilities with real-world, complex data. Let’s consider a DataFrame that contains data about passengers on the Titanic, including their sex and whether they survived.

import pandas as pd

url = 'https://raw.githubusercontent.com/datasciencedojo/datasets/master/titanic.csv'

# calculate the probability of survival given the passenger is female
prob_survived = titanic_df['Survived'].mean()
prob_female = (titanic_df['Sex'] == 'female').mean()
prob_survived_and_female = ((titanic_df['Survived'] == 1) & (titanic_df['Sex'] == 'female')).mean()

prob_survived_given_female = prob_survived_and_female / prob_female
print(prob_survived_given_female)  # Output: 0.7420382165605095

In this example, we load the Titanic dataset from a URL using the pandas.read_csv function. We then calculate the probabilities of survival, being female, and both survival and being female, and use these to calculate the conditional probability of survival given the passenger is female.

### Conclusion

Conditional probability is a fundamental concept in probability theory and statistics, and it plays a crucial role in many areas such as machine learning, data science, and decision making under uncertainty. Python, with its powerful libraries such as numpy and pandas, is an ideal tool for calculating and working with conditional probabilities.

Remember that while calculating conditional probability can be straightforward with Python, interpreting the results requires a solid understanding of the underlying theory. Always consider the context of the problem and make sure your interpretation of the results makes sense.