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Identify and remove duplicate records or entries to improve data quality.

Data deduplication definition:

The presence of duplicates in your data can lead to incorrect analysis results and cause storage and processing inefficiencies, leading to increased costs.

There are several Python functions and techniques for de-duplicating data in regular programming, such as using hash functions, using set data structure, and comparing columns to identify duplicates. While traditional Python object lists and sets can be used for deduplicating data, they may not be the most efficient choice for large-scale data processing. This is because lists and sets require storing all data in memory, which can quickly become a bottleneck and slow down the processing.

For deduplicating large datasets, it's recommended to use specialized libraries and data structures designed for efficient processing, such as the pandas library in Python. Pandas has built-in functions like drop_duplicates() that can handle large datasets with ease. Additionally, distributed processing frameworks like Apache Spark and Dask can be used to process large datasets in a distributed and parallelized manner, which can further improve the performance of data deduplication.

Deduplicating data in Python

Please note that you need to have the necessary Python libraries installed in your Python environment to run the code examples below.

Python provides several libraries and functions for de-duplicating data, such as:

Pandas: Pandas provides a drop_duplicates() function that can be used to remove duplicate rows from a Pandas DataFrame. Given an input file data.csv with 56 rows of which 10 are duplicates, the following code…

import pandas as pd

df = pd.read_csv('data.csv')
print(f"This dataframe has {len(df)} rows.")
df = df.drop_duplicates()
print(f"This dataframe now has {len(df)} rows.")

… might yield this output:

This dataframe has 56 rows.
This dataframe now has 44 rows.

Dask: Dask is a parallel computing library in Python that can be used for big data processing. Dask provides a drop_duplicates() function that can be used to remove duplicates from a Dask DataFrame.

In the following example, the first line imports the dask.dataframe module as dd.

The second line reads in CSV files using the dd.read_csv() function, which returns a Dask DataFrame. The in the filename parameter data.csv is a wildcard character that matches any file with a name starting with "data" and ending with ".csv". If there are multiple files that match this pattern, Dask will concatenate them into a single dataframe.

import dask.dataframe as dd

df = dd.read_csv('data*.csv')
df = df.drop_duplicates()

The resulting dataframe will be distributed across multiple workers, which can perform computations in parallel.

PySpark: PySpark is the Python API for Apache Spark, a big data processing framework. PySpark provides a dropDuplicates() function that can be used to remove duplicates from a PySpark DataFrame.

Given the input data.csv file:


We can use PySpark to deduplicate the data:

from pyspark.sql import SparkSession

spark = SparkSession.builder.appName("De-duplication").getOrCreate()
df ="data.csv", header=True, inferSchema=True)
df = df.dropDuplicates()

The command will provide us with the output:

|  name|   zip|amount|
| James| 12345|    99|
|   Bob| 19876|    23|
|Claire|212565|   124|
|Claire|212565|   123|

Note that the lines have to be identical across all columns to be considered a duplicate, which is why the last two lines remain in the dataframe.

Other data engineering terms related to
Data Management:


Move rarely accessed data to a low-cost, long-term storage solution to reduce costs. store data for long-term retention and compliance.


Add new data or information to an existing dataset to enhance its value. Enhance data with additional information or attributes to enrich analysis and reporting.


Create a copy of data to protect against loss or corruption.


Select, organize and annotate data to make it more useful for analysis and modeling.


Analyzing the number of features or attributes in the data to improve performance.


Enhance data with additional information from external sources.


Extract data from a system for use in another system or application.


Create an optimized data structure for fast search and retrieval.


combine data from different sources to create a unified view for analysis or reporting.


Store the results of expensive function calls and reusing them when the same inputs occur again.


Combine data from multiple datasets into a single dataset.


Extract useful information, patterns or insights from large volumes of data using statistics and machine learning.


Create a conceptual representation of data objects.


Track data processing metrics and system health to ensure high availability and performance.

Named Entity Recognition

Locate and classify named entities in text into pre-defined categories.


Interpret and convert data from one format to another.


Divide data into smaller subsets for improved performance.


Transform your data so it is fit-for-purpose.


Transform raw data before data analysis or machine learning modeling.


Create a copy of data for redundancy or distributed processing.


Increasing the capacity or performance of a system to handle more data or traffic.

Schema Mapping

Translate data from one schema or structure to another to facilitate data integration.


Ensure that data in different systems or databases are in sync and up-to-date.


Check data for completeness, accuracy, and consistency.


Maintain a history of changes to data for auditing and tracking purposes.