Data Duplication Removal from Dataset Using Python

As a data scientist, one of the first tasks you will encounter when working with real-world datasets is data cleaning. Among the common issues that can arise during data cleaning, duplicates are one of the most significant. In this section, we’ll explore what duplicates are, how they can affect our analysis, and why it’s crucial to address them.

What Are Duplicates in Data?

In simple terms, duplicates refer to rows or entries in a dataset that are exactly identical to one another. This could happen for various reasons:

• Data entry errors where the same information is recorded multiple times by someone.
• Merging datasets from different sources that may have overlapping data.

These duplicate entries often share the same values across all or selected columns in a dataset, and while they may seem harmless at first glance, they can lead to problems if not addressed.

Why Are Duplicates a Problem?

You might wonder, with so many data storage options available today, why should we be concerned about duplicates? After all, we have large databases and cloud storage systems capable of handling massive amounts of data. But the issue with duplicates is not just about storage capacity; it's about data integrity, accuracy, and the efficiency of our analysis.

1. Skewed Analysis Even if we have plenty of storage, duplicates can significantly distort the results of our analysis. For example, imagine calculating the average salary of employees in a company, but some salary records are repeated. This would inflate the average, giving us a misleading result that doesn’t reflect the true distribution of salaries. In essence, duplicates can lead to faulty conclusions, which is a huge issue in decision-making processes.
2. Inaccurate Models Duplicates can especially hurt machine learning models. If the same data point appears multiple times, it can cause the model to "learn" from these repeated points, leading to overfitting. This means the model may become overly specialized to the duplicate instances and fail to generalize well to new, unseen data. So, while storage space might not be a concern, ensuring that our models are trained on clean, representative data is crucial for good performance.
3. Increased Computational Costs Storing and processing duplicates requires unnecessary computational resources. With large datasets, processing duplicate data takes more time and energy, slowing down your analysis or data pipelines. This inefficiency can affect everything from your system’s performance to the time it takes to deliver results.
4. Data Redundancy and Complexity Duplicates can also introduce complexity. For instance, when data is redundant, it becomes harder to track and maintain accurate records, especially when updates are made. This creates unnecessary complexity for both the system and the people working with the data. It’s often better to clean up duplicates early on, so the data remains simple and easy to manage

Identifying and Handling Duplicates

To deal with duplicates, we first need to identify them in our dataset. This is where tools like pandas come in hand, as it provides functions like duplicated() and drop_duplicates() to efficiently spot and remove duplicate rows.

In the following sections, we will dive deeper into how to identify and remove duplicates using Python and pandas. We will also explore how to handle duplicates based on specific conditions, such as keeping the first or last occurrence of the duplicates

For the upcoming operations,we will use a sample dataset below

import pandas as pd

data = {
    'Name': ['Alice', 'Bob', 'Alice', 'Charlie', 'Bob', 'David'],
    'Age': [25, 30, 25, 35, 30, 40],
    'City': ['New York', 'Los Angeles', 'New York', 'Chicago', 'Los Angeles', 'San Francisco']
}

df = pd.DataFrame(data)
df

Using duplicated() Method

The duplicated() method helps identify duplicate rows in a dataset. It returns a boolean Series indicating whether a row is a duplicate of a previous row.

duplicates = df.duplicated()

duplicates

Output:

Using drop_duplicates() method

The drop_duplicates() method is one of the easiest ways to remove duplicates from a DataFrame in Python. This method removes duplicate rows based on all columns by default or specific columns if required.

df_no_duplicates = df.drop_duplicates()

(df_no_duplicates)

Output:

Removing Duplicates Based on Specific Columns

Sometimes, duplicates might occur in one or two columns rather than the entire dataset. In such cases, you can specify which columns to consider for duplicate detection.

df_no_duplicates_columns = df.drop_duplicates(subset=['Name', 'City'])
(df_no_duplicates_columns)

Output:

Keeping the First or Last Occurrence

By default, drop_duplicates() keeps the first occurrence of each duplicate row. However, you can modify it to retain the last occurrence instead

df_keep_last = df.drop_duplicates(keep='last')
(df_keep_last)

Output:

Conclusion

Data duplication removal is a crucial step in cleaning datasets. Python’s simple yet powerful functions like drop_duplicates() and duplicated() make the process efficient and easy. By removing unnecessary duplicates, we ensure the accuracy of our analysis and optimize the performance of data processing. Whether you are dealing with small or large datasets, understanding and using these methods can significantly improve data quality