In dataset analysis, duplicate data frequently appear, particularly when working with large datasets.
Managing duplicates is a crucial step in data cleaning. Their presence can lead to errors in data analysis and processes that use the data, such as machine learning models.
In addition to managing duplications already present in the dataset, it is crucial to explore the underlying causes that led to their occurrence. This deeper understanding is essential for implementing effective organizational corrections and process improvements. By identifying the root causes of data duplication, whether they originate from human error, system limitations, or procedural inefficiencies, organizations can develop targeted strategies to mitigate these issues. This proactive approach not only addresses current data quality concerns but also establishes a foundation for preventing similar errors in the future. Ultimately, this comprehensive strategy enhances data integrity, improves analytical accuracy, and cultivates a culture of data quality awareness throughout the organization.
Common Causes of Data Duplication
Unintentional Entries
When using manual, non-automated systems, users may inadvertently enter the same data multiple times. This often occurs through actions like repeatedly clicking the “insert” command.
Collecting data from multiple sources
Data is often extracted from multiple sources, particularly in companies using several unsynchronized databases. When these databases are merged without proper data quality control, duplicate entries can easily arise.
Join and Merge Errors
Improper use of commands operating on different tables can result in duplicates if join keys are not correctly specified.
Slightly different data entries
During data entry, names can be mistyped, leading to related records being treated as separate entities. This often happens when a name is entered incorrectly or inconsistently: “John Doe,” “Jon Doe,” “Jhon Doe,” and “J. Doe,” though all referring to the same person, might create distinct records due to typing errors.
Incomplete or Partial Records
Incomplete information is sometimes entered with the intention of completing it later. For instance, if a patient’s contact details are unknown, one might input only the basic personal information. Without proper control systems in place, the subsequent addition of missing data might be mistakenly treated as a new registration, leading to duplication.
Database Design Flaws
The absence of primary keys or uniqueness constraints in database design can permit the insertion of duplicate data.
Lack of Data Standardization and Normalization
The lack of data entry rules can lead to duplications. For example, not standardizing the encoding method for a region can result in entries such as “New York”, “NYC”, or “New York City”, which are interpreted as different.
In healthcare, data duplication can have serious consequences. It may lead to clinical errors, misdiagnoses, unnecessary treatments, and inflated costs.
Data duplication in healthcare has various causes and occurs daily.
Patients with similar names may be mistakenly registered as one person or vice versa. Minor differences like spelling variations, typos, or abbreviated names can create duplicate or separate records for the same individual.
When patients receive care at multiple facilities (e.g., different hospitals or specialist clinics), each may use its own Electronic Health Record (EHR) system. Merging data without proper quality controls often leads to duplicates.
Transcribing healthcare data from paper to digital systems can introduce errors. Inconsistent or non-standardized data entry practices frequently result in duplicates.
Updating patient information (e.g., address, phone number, or medical history) sometimes creates a new record instead of modifying the existing one.
Multiple doctors treating the same patient might independently enter diagnoses and prescriptions, creating duplicate or partially duplicate records.
The primary cause of errors, however, is the lack of interoperability between computer systems. A regional health system may receive data from various hospitals. If a patient visits multiple hospitals that aren’t properly synchronized, it can result in multiple entries for the same patient.
To identify and manage duplicates during exploratory data analysis, we can utilize a variety of powerful Python libraries. These libraries offer robust functionalities specifically designed to handle duplicate data efficiently, even when working with large-scale datasets.
Some of these libraries are especially proficient at processing and manipulating extensive datasets, employing sophisticated algorithms and optimized data structures to ensure rapid performance. This capability is crucial when dealing with big data scenarios, where traditional methods might prove too time-consuming or resource-intensive. These efficient libraries enable analysts to quickly detect, evaluate, and address duplicate entries, thus enhancing the overall quality and reliability of their data.
Identifying Duplicates
Duplicate identification can be performed efficiently using the Pandas library:
import pandas as pd
# Creating a sample DataFrame with a duplicate ('Alice')
data = {'Name': ['Alice', 'Bob', 'Alice', 'Eve', 'Bob'],
'Age': [25, 30, 25, 22, 30]}
df = pd.DataFrame(data)
# IIdentifying and printing duplicates as boolean
duplicates = df.duplicated()
print(duplicates)
print(" \\n")
# Displaying duplicates into the DataFrame
df['Is_Duplicate']= duplicates
print("Dataframe with duplicates")
print(df)
Removing Duplicates
After identifying duplicates, the next step is to remove them if necessary. Pandas offers methods to eliminate either the first or second occurrence of a duplicate entry.
import pandas as pd
# Creating a sample DataFrame with duplicates
data = {'Name': ['Alice', 'Bob', 'Alice', 'Eve', 'Bob'],
'Age': [25, 30, 25, 22, 30]}
df = pd.DataFrame(data)
# Removing duplicates while preserving the first occurrence
df_without_duplicates = df.drop_duplicates(keep='first')
# Removing duplicates while preserving the last occurrence
df_without_duplicates_last = df.drop_duplicates(keep='last')
print("Original DataFrame:")
print(df)
print("\\nDataFrame without duplicates (keeping first occurrence):")
print(df_without_duplicates)
print("\\nDataFrame without duplicates (keeping last occurrence):")
print(df_without_duplicates_last)
Sometimes records are considered duplicates even when only some columns are similar, while others differ. For instance, if there’s an erroneous double entry to update a patient’s residence, the two rows won’t be identical because the residence differs, yet it’s still a duplicate. In such cases, we limit the duplicate identification to specific fields like name, surname, and date of birth.
import pandas as pd
# Creating a sample DataFrame with 3 fields and duplicates in the 'Name' field
data = {
'Name': ['Alice', 'Bob', 'Alice', 'Eve', 'Bob', 'Charlie'],
'Age': [25, 30, 28, 22, 31, 35],
'City': ['New York', 'London', 'Paris', 'Tokyo', 'Berlin', 'Rome']
}
df = pd.DataFrame(data)
# Printing the original DataFrame
print("Original DataFrame:")
print(df)
print()
# Removing duplicates based only on the 'Name' field, keeping the first occurrence
df_without_duplicates_first = df.drop_duplicates(subset=['Name'], keep='first')
# Printing the DataFrame without duplicates (first occurrence)
print("DataFrame without duplicates (keeping the first occurrence):")
print(df_without_duplicates_first)
print()
# Removing duplicates based only on the 'Name' field, keeping the last occurrence
df_without_duplicates_last = df.drop_duplicates(subset=['Name'], keep='last')
# Printing the DataFrame without duplicates (last occurrence)
print("DataFrame without duplicates (keeping the last occurrence):")
print(df_without_duplicates_last)
When working with large datasets, searching for duplicates can be resource-intensive, both in terms of memory usage and processing time. In such cases, it’s best to employ specialized libraries that utilize techniques like sampling, chunking, or parallelization to optimize the operation.
Dask and Vaex are Python libraries engineered to manage and analyze massive datasets that won’t fit in memory. These powerful tools enable data manipulation and analysis at an impressive scale, proving invaluable when handling datasets that exceed a computer’s memory capacity.
