# 5 Best Ways to Find the Wealth of the Richest Customer in Python

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π‘ Problem Formulation: The task is to determine the wealthiest customer from a list of customer balances. Given an input of a list of lists, where each sublist represents the bank accounts of a single customer and each element an account’s balance, the output should be the maximum wealth found, which is the sum of balances across all accounts for a person.

## Method 1: Using a Simple For Loop

This method involves iterating over the list of customers, summing up all account balances for each customer, and tracking the maximum value found. It’s an intuitive approach that uses basic Python constructs and is suitable for those who are just starting with Python programming.

Here’s an example:

```def find_richest_customer(customer_accounts):
max_wealth = 0
for accounts in customer_accounts:
max_wealth = max(max_wealth, sum(accounts))
return max_wealth

# Example customer account balances
customers = [[5, 2, 3], [7, 1, 5], [10, 5, 1]]
print(find_richest_customer(customers))```

Output:

`16`

The code defines a function `find_richest_customer` that accepts a list of customer account balances, iterates through each customer’s accounts, and calculates the total balance. It then updates the `max_wealth` with the larger of the current `max_wealth` and the customer’s total balance.

## Method 2: Using List Comprehension and the max Function

List comprehension offers a more Pythonic way to perform operations on lists in a concise and readable manner. By pairing it with the built-in `max` function, finding the wealthiest customer can be done elegantly in just one line of code inside the function.

Here’s an example:

```def find_richest_customer(customer_accounts):
return max(sum(accounts) for accounts in customer_accounts)

# Example customer account balances
customers = [[3, 8], [2, 5, 7], [4, 5, 6]]
print(find_richest_customer(customers))```

Output:

`15`

The function `find_richest_customer` now consists of a single line that uses a generator expression to sum each customer’s accounts and find the maximum value with the `max` function, which is far more succinct and maintainable.

## Method 3: Using the map Function

Python’s `map` function applies a given function to each item of an iterable and returns a map object. In this approach, the `map` function helps in applying the `sum` function to calculate the wealth of each customer and then the `max` function to determine the richest customer’s wealth.

Here’s an example:

```def find_richest_customer(customer_accounts):
return max(map(sum, customer_accounts))

# Example customer account balances
customers = [[10, 20], [5, 5, 5], [8, 2, 2]]
print(find_richest_customer(customers))```

Output:

`30`

The example uses `map` to create an iterator that computes the sum of the balances for each customer. The `max` function is then called on this iterator to find the maximum wealth. This method is efficient because it avoids the explicit creation of an intermediate list of total wealths.

## Method 4: Using NumPy

If performance is a key consideration and you’re working with large datasets, the NumPy library can be very helpful due to its optimized array computations. This method implies converting the list of balances into a NumPy array and then using vectorized operations to find the richest customer.

Here’s an example:

```import numpy as np

def find_richest_customer(customer_accounts):
return np.max(np.sum(customer_accounts, axis=1))

# Example customer account balances
customers = np.array([[12, 7, 5], [3, 10, 2], [8, 9, 4]])
print(find_richest_customer(customers))```

Output:

`24`

This code snippet casts the list of accounts into a NumPy array and calculates the sum across the accounts for each customer using `np.sum` with the `axis=1` parameter to sum by row. The `np.max` function then finds the maximum value of these sums, returning the wealth of the richest customer.

## Bonus One-Liner Method 5: Using max and a Lambda Function

For simple, one-off scripts, a one-liner using `max` with a `lambda` function can be both efficient and expressive. This method relies on in-line computation of each customer’s wealth directly in the `max` function using a lambda (anonymous function).

Here’s an example:

```customer_accounts = [[1, 5, 9], [7, 8, 7], [6, 11, 3]]
richest_wealth = max(customer_accounts, key=lambda x: sum(x))
print(sum(richest_wealth))```

Output:

`22`

The snippet applies the `max` function with a `lambda` as the key argument, directly comparing the sum of the elements within each sublist to identify the sublist with the largest sum which represents the richest customer’s wealth. The `sum()` function then calculates the sum of this sublist for the output.

## Summary/Discussion

• Method 1: Using a Simple For Loop. Easy to understand and good for beginners. Can be slow with large datasets.
• Method 2: Using List Comprehension and the max Function. Pythonic and succinct. Requires understanding of list comprehensions.
• Method 3: Using the map Function. Efficient memory usage and good for functional programming enthusiasts. Can be less readable for those not familiar with `map`.
• Method 4: Using NumPy. Best for performance, especially with large data. Requires external library installation.
• Bonus One-Liner Method 5: Using max and a Lambda Function. Very concise. Can be less intuitive to read and understand, especially for lambda expressions.