# How to Calculate Expected Value in Python

Expected value is a fundamental concept in statistics and probability theory, referring to the long-run average value of a random variable. This article will offer a comprehensive exploration of how to calculate the expected value in Python, an incredibly versatile language widely used in data science.

1. Understanding Expected Value
2. The Math Behind Expected Value
3. Getting Started: Python Libraries
4. Calculating Expected Value in Python
5. Application: Expected Value in Gaming
6. Advanced Concepts: Expected Value in DataFrames
7. Use Case: Expected Value in Finance
8. Conclusion

### 1. Understanding Expected Value

The expected value (EV), also known as the expectation, mathematical expectation, mean, average, or first moment, is a key concept in probability theory and statistics. In simple terms, the expected value of a random variable gives us a sense of what to expect in the long run when we perform an experiment multiple times.

For example, when we roll a fair six-sided die, we know that each outcome (1, 2, 3, 4, 5, 6) is equally likely. The expected value in this case would be the sum of the outcomes multiplied by their probabilities, which is 3.5. Of course, we can’t roll a die and get 3.5. But if we were to roll the die many times and average the results, the number we’d get would be close to 3.5.

### 2. The Math Behind Expected Value

The formula to calculate the expected value (E) of a discrete random variable X is:

E(X) = Σ [x * P(x)]

In this formula, x represents each value in the dataset, P(x) denotes the probability of each value occurring, and Σ symbolizes the sum over all possible values of x.

For a continuous random variable, the expected value is calculated using integration instead of summation:

E(X) = ∫ [x * f(x)] dx

Here, f(x) is the probability density function of the random variable X.

### 3. Getting Started: Python Libraries

To calculate the expected value in Python, we primarily need NumPy, a library for numerical computations. If you don’t have NumPy installed, you can do so using pip:

pip install numpy

After installing NumPy, you can import it into your Python environment:

import numpy as np

### 4. Calculating Expected Value in Python

Now that we have NumPy imported, let’s calculate the expected value. Let’s consider a simple example where we have a list of outcomes and their corresponding probabilities.

# List of outcomes
outcomes = np.array([1, 2, 3, 4, 5, 6])

# Corresponding probabilities
probabilities = np.array([1/6, 1/6, 1/6, 1/6, 1/6, 1/6])

# Calculate expected value
expected_value = np.sum(outcomes * probabilities)

print("The expected value is: ", expected_value)

In this example, outcomes * probabilities computes the product of outcomes and their corresponding probabilities, and np.sum sums these products to get the expected value.

### 5. Application: Expected Value in Gaming

Let’s consider a more practical example. Suppose you’re playing a game where you can roll a fair six-sided die, and if you roll a number greater than 4, you win $10 . If you roll any other number, you lose$3. We can calculate the expected value of this game, which can help us understand whether or not it would be profitable to play in the long run.

First, let’s define the outcomes and their corresponding probabilities:

# Outcomes (gains or losses)
outcomes = np.array([10, -3, -3, -3, -3, -3])

# Probabilities
probabilities = np.array([1/6, 1/6, 1/6, 1/6, 1/6, 1/6])

We then calculate the expected value as before:

# Calculate expected value
expected_value = np.sum(outcomes * probabilities)

print("The expected value is: ", expected_value)

The expected value here will tell us the average outcome we can expect per game if we play many times. If the expected value is positive, it indicates that we can expect to win money on average in the long run. Conversely, a negative expected value suggests that we’ll lose money on average.

### 6. Advanced Concepts: Expected Value in DataFrames

In more complex scenarios, you might be dealing with datasets stored in pandas DataFrames. Thankfully, you can also calculate expected values with pandas.

Let’s say we have a DataFrame with two columns: ‘Outcomes’ and ‘Probabilities’.

import pandas as pd

# Create DataFrame
df = pd.DataFrame({
'Outcomes': [10, -3, -3, -3, -3, -3],
'Probabilities': [1/6, 1/6, 1/6, 1/6, 1/6, 1/6]
})

# Calculate expected value
expected_value = np.sum(df['Outcomes'] * df['Probabilities'])

print("The expected value is: ", expected_value)

This code will produce the same expected value as before. When working with larger, more complex datasets, this approach can be extremely useful.

### 7. Use Case: Expected Value in Finance

The concept of expected value is widely used in finance, particularly in investment analysis and risk management.

For instance, suppose you’re considering an investment that has a 20% chance of a $200 return, a 50% chance of a$100 return, and a 30% chance of a \$50 return. Should you make the investment? The expected value can guide your decision.

# Outcomes
outcomes = np.array([200, 100, 50])

# Probabilities
probabilities = np.array([0.2, 0.5, 0.3])

# Calculate expected value
expected_value = np.sum(outcomes * probabilities)

print("The expected value is: ", expected_value)


If the expected value exceeds the cost of the investment, it might be a good idea to proceed with it, as you can expect a positive return on average over the long term.

### 8. Conclusion

The expected value is a crucial concept in statistics and probability theory, providing a measure of the “center” of a probability distribution. Python, with its powerful numerical computing libraries such as NumPy and pandas, offers straightforward methods for computing expected values.

However, it’s important to note that the expected value only provides an average outcome over many trials. It does not guarantee a specific outcome in a single experiment or trial. Therefore, while the expected value can be a helpful guide in decision-making scenarios, it should always be considered alongside other statistical measures and relevant non-statistical factors.

Moreover, the expected value assumes that the probabilities are known and accurate, which might not always be the case in real-world situations. In such cases, it may be necessary to use techniques like statistical estimation, machine learning, or expert judgment to estimate the probabilities.

When used appropriately, the expected value is a powerful tool that can offer valuable insights in various fields, including data science, finance, business, healthcare, and more. By understanding how to calculate and interpret expected values, you’ll be better equipped to make data-driven decisions and predictions.