Problem Description

The problem presents a DataFrame named customers with columns customer_id, name, and email. We are informed that there are duplicate records based on the email column. The task is to remove these duplicate rows, but crucially, we must keep only the first occurrence of each email. The DataFrame may contain unique customer_id values and name values, but some emails are associated with more than one customer. The goal is to return a DataFrame where all email addresses appear only once, preserving the record of the first customer who had that email.

Intuition

The drop_duplicates function from the pandas library immediately comes to mind for this task. This function is tailor-made for situations like this, where we need to remove duplicate rows based on one or multiple column values. The function comes with the subset parameter, allowing us to specify on which columns to check for duplicates. In this case, we'll set the subset to ['email'], so the function will only consider the email column for finding duplicates.

By default, drop_duplicates keeps the first occurrence of a duplicated row, which aligns perfectly with our requirements. We do not need to set the keep argument as its default value is 'first'. We can also safely ignore the inplace parameter, or set it to False, as we want to return a new DataFrame rather than modify the original customers DataFrame in place. This provides a simple and efficient solution to the problem that can be implemented and understood with minimal code.

The solution code leverages the described functionality provided by pandas, and thus with a single line of code, we achieve the desired output, effectively removing all duplicates based on the email while keeping the first occurrence of each.

Solution Approach

The solution is straightforward due to the robust capabilities of the Pandas library in Python, which is designed to handle and manipulate data in DataFrame structures.

The key steps of the solution are:

1. Import the Pandas library to use its functionalities.

2. Define a function dropDuplicateEmails, which takes a customers DataFrame as its argument.

3. Use the drop_duplicates method available in the Pandas library to remove duplicate rows based on specific column values. The drop_duplicates method identifies and removes duplicate rows with the following considerations:

   • The subset parameter specifies the columns to consider for identifying duplicate rows. In our case, this is set to ['email'] so that the method looks for duplicates only in the email column.
   • By default, the keep parameter is set to 'first', which means that if duplicates are found, the first occurrence is kept while the subsequent duplicates are removed. This behavior is exactly what we need to solve this problem, so we do not need to specify this parameter explicitly.
   • The method returns a new DataFrame with the duplicates removed, which we immediately return from our function.

Therefore, the core algorithm involves no complex loops or conditionals due to the high-level abstraction provided by Pandas. The data structure used is the DataFrame itself, and the pattern applied is the direct use of a library function designed for this exact purpose.

The implementation is as follows:

1import pandas as pd
2
3def dropDuplicateEmails(customers: pd.DataFrame) -> pd.DataFrame:
4    return customers.drop_duplicates(subset=['email'])

This elegant and concise implementation leverages Pandas' capabilities to solve the problem with minimal code and time complexity.

Example Walkthrough

Let's walk through a small example to illustrate how the solution approach effectively removes duplicate email addresses and retains only the first occurrence of each.

Suppose we have a small customers DataFrame that looks like this:

Here, we can see that Alice appears twice with the same email ([email protected]), and so does Bob with his email ([email protected]). We would like to have only the first occurrence of each unique email address.

Following the solution, this is what we would do:

1. Import the pandas library so that we can work with DataFrames.
2. Create the dropDuplicateEmails function.
3. Use the drop_duplicates method on the customers DataFrame with subset=['email'].

When we apply the function dropDuplicateEmails to our DataFrame, the drop_duplicates method processes the DataFrame as follows:

• It checks the email column for duplicate values since we've set subset=['email'].
• When it finds the duplicate values [email protected] and [email protected], it keeps the first occurrence of each (the rows with customer_id 1 and 2) and discards the other occurrences (the rows with customer_id 3 and 5).

The DataFrame returned by the dropDuplicateEmails function will be:

So the final DataFrame correctly contains only one record for each email address, and the first customers with those emails have been kept. The duplicate rows have been removed, achieving our desired result. The elegance of this solution lies in its simplicity and the use of Pandas' high-level functionality, which makes the code clean, readable, and efficient.

Solution Implementation

Time and Space Complexity

The time complexity of the dropDuplicateEmails function primarily depends on the drop_duplicates method of the pandas DataFrame, which in turn depends on the size of the input DataFrame.

Time Complexity

For pandas drop_duplicates method, the time complexity is generally O(n), where n is the number of rows in the DataFrame because it needs to process each row to check for duplicates.

So, the time complexity for the dropDuplicateEmails function is also O(n).

Space Complexity

The space complexity for drop_duplicates includes the space required to hold the DataFrame and the temporary data structures used to identify duplicates. Typically, it is also O(n) since a new DataFrame is constructed to store the result without duplicates.

Thus, the space complexity of dropDuplicateEmails would also be O(n).