Understanding the Difference Between Data Wrangling and Data Cleaning

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In data analytics, it is important to understand the different data preparation phases. Two crucial phases in this process are data preparation and data cleaning. Let’s take a closer look at the nuances of these two terms.

Data Cleaning

Definition

Data cleaning refers to the process where errors and inconsistencies in data are identified and subsequently corrected or removed, thereby enhancing its quality.

Scope

This stage encompasses tasks such as handling missing values, smoothing noisy data, detecting and removing outliers, and sorting out inconsistencies in the data.

Techniques

Some standard techniques involved in data cleaning are:

Imputation of missing values: Replacing missing data with statistical estimates of the missing values.
Outlier detection and removal: Identifying and removing data points that deviate significantly from most data.
Data transformation: Including normalization or standardization to make the data conform to a standard scale.
Error correction: Rectifying typos and spelling mistakes that might have occurred during data entry.

Goal

The ultimate goal of data cleaning is to curate a clean, consistent, accurate dataset, ready to undergo deeper analysis.

Data Wrangling (or Data Munging)

Definition

Data wrangling, a broader process, involves transforming and mapping data from a “raw” format into another, facilitating more convenient data consumption and analysis.

Scope

Data wrangling is a comprehensive process incorporating a wide range of activities, including but not limited to data cleaning. It may involve data transformation, data enrichment, and even data visualization.

Techniques

The techniques often used in data wrangling include:

Parsing data files: This could involve interpreting files such as XML or JSON.
Data reshaping: Such as pivoting or melting datasets to get the data into a more usable format.
Data merging/joining: This entails combining data from multiple sources to enrich the dataset.
Creating new derived columns: To add new perspectives and insights to the dataset.
Converting data types: Altering the data type of columns for consistent data analysis.
Data aggregation: This includes operations where data is summarized into groups.

Goal

Data wrangling aims to transform data into a structured format, making it significantly easier to analyze and facilitating its use in data modeling or machine learning tasks.

Conclusion

While data cleaning can be seen as a subset of data wrangling, it is essential to note that it involves a broader set of operations on data. Data cleaning focuses mainly on enhancing data quality, while data wrangling is about transforming and mapping data into a more analyzable and usable format.