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Data Cleaning in Data Science: Definition, Process, and Tools

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Eager to dive into what data cleaning is? We have covered everything about data cleaning, from its exact purpose to its implementation steps. Let’s see how data cleaning plays a crucial role in making dataset analysis meaningful.

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 What is Data Cleaning?

Data cleansing, a fundamental aspect of data preparation, involves identifying and rectifying unwanted data points. Errors can be present in various forms, such as incorrect formats, duplicate entries, data corruption, data faults, and insufficient, or non-meaningful data. The correction of these anomalies requires adjusting the values within the dataset. Data cleaning in data science is a crucial procedure that is executed through a structured data pipeline.

Data pipelines are essential for streamlining data processing by automating a series of discrete stages, each with a specific input and output. This modular approach offers several benefits, including easy verification, self-containment, and the ability to restart processing from an intermediate point, which can be a significant economic advantage.

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 Data Cleaning Process

The following steps can help data scientists analyze meaningful and productive datasets for effective decision-making:

Step 1: Eliminating Duplicate Entries

While dealing with large datasets or data from various sources, duplicates are a common issue. These unwanted duplicates not only clutter your data but can also impact your calculations. For example, if you have repeated product serial numbers, you will receive misleading product counts.

Moreover, duplicate email addresses or phone numbers can put your messages in spam. To solve this, it’s essential to establish a solid process for eliminating these duplicates, ensuring that only one instance of each unique data point remains in our dataset. This initial step of handling duplicates is the foundation for clean, accurate data and makes dataset analysis more reliable and effective.

Step 2: Deleting Irrelevant Data

Imagine analyzing customer feedback for a product’s improvement. The dataset contains various fields: feedback date, unique feedback ID, product model, customer age, purchase date, and more. Initially, these fields seem relevant, but the data, such as the employee who recorded the feedback, the location of submission, or detailed customer contact information, is there that doesn’t align to enhance the product.

In data cleaning, the first step is a column-level check. Irrelevant columns are identified and removed from the dataset. For example, we might exclude data related to employees or locations, focusing solely on customer feedback attributes.

Later, we will initiate row-level checks. Suppose a customer submitted feedback, and the dataset includes two records for the same feedback ID. One record indicates ‘product issue,’ while the other suggests ‘feedback received.’ Since both records contain the same feedback ID, it’s necessary to retain only the first occurrence, or the one indicating ‘product issue,’ aligning with our analysis goal.

In this way, effective data cleaning depends on a deep understanding of the data and the problem statement. By eliminating irrelevant data, data scientists ensure that analytical objectives are being achieved.

Step 3: Normalizing Capitalization

In data management, inconsistent capitalization can lead to confusion and inaccuracies. This issue comes from many programming languages being case-sensitive, treating variations as different entities. For example, with the column names “Final Sum” and “Final Sum,” most programming languages would treat them as separate columns.

To overcome this problem, it becomes important to normalize capitalization within your dataset. One option for the convention is the adoption of “snake case”, where all words are rendered in lowercase and separated by underscores. Let’s say the column name “Final Sum” transforms into “final_sum” in snake case.

Otherwise, a “title case” convention is often applied to names and email addresses. Here, every word is capitalized, maintaining clarity and consistency. For example, the name “John Doe” remains “John Doe” in the title case.

Regardless of the convention chosen, consistency is necessary. This consistency not only improves data readability but also provides more accurate analysis. To put this into practice, consider the following guidelines:

  • For column names, opt for the snake case.
  • Ensure uniform capitalization within each column’s data points. If you choose a snake case for column names, maintain the same for data points within those columns.
  • When handling names and email addresses, follow the title case.

Step 4: Converting Data Types

Data typically falls into three primary categories: text, numeric, and date types. Text data includes a wide array of values, from words and numbers to special characters. It also includes names, product types, locations, and email IDs.

When handling CSV data in Python with the help of Pandas, the library often does a perfect job of guessing data types. However, there are instances when we must step in and guide the process. In cases where data types are wrongly identified, by default, they change to a string or text type. To resolve it, we need to explicitly mention data types and date formats. Dates, for example, can appear in various formats, including “May 3, 2024,” “03-5-2024,” “2024/5/03,” or “3-May-2024.”

Numeric data, on the other hand, includes integers and decimal numbers (floats). Numeric columns enable mathematical operations like finding minimums, maximums, averages, and medians, as well as statistical analyses through tools such as histograms and box plots.

However, it’s hard to distinguish between numeric and text columns. While a numeric ID or phone number may seem numeric, they are better treated as text columns. Performing mathematical operations on them typically has little meaningful outcome.

Understanding and managing data types is necessary for accurate data analysis and interpretation.

Step 5: Addressing Outliers

Outliers significantly affect the dataset and are very important in statistical analysis. They can represent either an inherent deviation in measurements or occasional data collection errors, which may create a need for their exclusion from the dataset.

Consider a study examining coffee prices across a sample of 500 cafes in a city. Most coffee prices fall within the range of $2.50 to $6.00. However, upon closer inspection, one data point stands out with an exceptionally high price of $25.00 for a cup of coffee. This outlier is significantly far from the rest, which draws special attention. The detection of such outliers often relies on visual tools like box plots or scatter plots, as they can produce differentiation in the data.

In situations where data is expected to follow a specific distribution, outliers can affect model performance, especially if they lead to tangled data. Two commonly employed strategies for managing outliers are:

  • Outlier Exclusion: The removal of data points featuring outlier values ensures the dataset remains more representative.
  • Transformation Methods: The application of data transformations such as logarithmic, square root, or Box-Cox transformations. These adjustments aim to bring the data closer to a normal or near-normal distribution, thereby reducing the influence of outliers.

Step 6: Rectifying Errors

Data errors can hinder the accuracy of your insights, making error correction an essential aspect of data cleaning. Especially while dealing with data entered manually without validation checks, errors are almost unavoidable.

To fix these issues, start by understanding the dataset thoroughly. Then, build logic to identify and rectify errors. Here are some examples:

  • Mobile Numbers: Ensure uniformity by removing country codes, leaving exactly 10 digits.
  • Unit Removal: Convert units (e.g., weight, height) to numeric values for ease of analysis.
  • Data Format: correct formats, like email addresses.
  • Validation Checks: Implement rules to ensure data integrity, such as validating purchase dates against manufacturing dates or verifying the total amount matches the sum of related values.

Addressing these errors systematically ensures your data remains accurate and ready for meaningful analysis.

Step 7: Syntax Translation

The purpose of datasets for machine translation often originates from various sources, resulting in synonyms and language syntaxes. However, most data evaluation tools rely on monolingual Natural Language Processing (NLP) models, which can’t handle multiple languages. To address this, we use AI-powered language translation models to convert the dataset into a single, unified language.

For example, using a language translational AI model defines consistency in a dataset with product descriptions in different languages for a global e-commerce platform. It translates all descriptions into a common language, enabling seamless analysis and insights.

Step 8: Fixing Missing Values

In data cleaning in data science, addressing missing values is among the most common tasks. Real-world data often has gaps that require attention before analysis can proceed effectively. Handling missing values can be achieved through two primary methods:

  • Data Deletion: the removal of records containing missing values.
  • Data Imputation: filling in missing values using statistical techniques or a deep understanding of the data.

A general guideline is to consider dropping records with missing values if they account for less than five percent of the total records. However, this decision depends on various factors, including the analysis’s nature, the missing values’ significance, the dataset size, and the specific use case.

Imagine a dataset for deciding loan eligibility. This dataset includes attributes such as income, credit score, employment history, loan amount, and loan approval status. Based on these criteria, the goal is to build a model that predicts whether an applicant is eligible for a loan. If there are missing values in the income and credit score columns for some applicants, and these attributes significantly influence loan approval decisions, it is advisable to exclude those incomplete records.

By effectively handling missing data in scenarios like this, data analysts ensure the accuracy and reliability of their analyses, which is particularly crucial in financial decision-making processes.

Data Cleaning Tools

  • Programming languages (Python, R, SQL, Ruby): These languages offer powerful libraries and functions for data cleaning.
  • Python boasts libraries like Pandas, NumPy, and SciPy, which are extensively used for data cleaning.
  • R offers libraries such as dplyr and tidyr for efficient data cleaning. These libraries enable users to filter, reshape, and clean data effectively. 
  • SQL queries allow for data deduplication, data type conversion, and filtering operations directly in the database. It’s particularly valuable when dealing with large datasets.
  • While Ruby itself doesn’t have dedicated data-cleaning libraries, it can leverage gems like Nokogiri for web scraping and data extraction.
  • Tableau: It provides an interactive interface with built-in data cleaning capabilities. It allows users to visually identify and resolve data issues, such as duplicates and outliers, simplifying the cleaning process for non-technical users without any need for code.
  • Microsoft Excel: It’s user-friendly and widely accessible. Excel offers essential data-cleaning functions like duplicate removal, filtering, and sorting. Users can easily format and standardize data, making it more manageable for analysis. 
  • OpenRefine, Trifacta:  These dedicated data cleaning tools offer advanced features for data cleaning, such as automated data profiling, transformation, and error detection, streamlining the data cleaning process. 

 Example of Data Cleaning

  • Standardizing Product Codes: Imagine working with a product inventory dataset that includes unique product codes. Due to variations in data entry, these product codes may appear in different formats, like ‘A1234,’ ‘a-1234,’ ‘A-12-34,’ or ‘a1234.’ During the data cleaning phase, these discrepancies are recognized and standardized to a consistent format, ensuring accurate tracking and analysis of products.
  • Verifying employee IDs: Suppose you are managing HR data for an organization, including employee IDs, names, contact information, and department details. To ensure effective HR operations and analysis, it’s crucial to validate employee IDs. This includes confirming that each ID adheres to a predefined structure and format. Additionally, some entries might lack complete contact details, making them unusable for communication or analysis. During data cleansing, these issues are identified and resolved to maintain data accuracy and functionality.


In this blog, we have discussed the process of maintaining data accuracy and cleanliness. We’ve explained its significance for data analysis and how to do it step by step. We have covered important parts of data cleaning, like removing duplicates, eliminating irrelevant information, correcting errors, and handling missing data. We have also covered some useful tools that can simplify the data cleaning process. By following these steps and utilizing the right tools, you can ensure your data is accurate and well-prepared for meaningful analysis, a crucial aspect in today’s data-driven world.

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About the Author

Principal Data Scientist

Meet Akash, a Principal Data Scientist who worked as a Supply Chain professional with expertise in demand planning, inventory management, and network optimization. With a master’s degree from IIT Kanpur, his areas of interest include machine learning and operations research.