Skip to content
My Glossary Project

Data Wrangling

TL;DR

Section titled “TL;DR”
  • Turn messy raw data into a consistent, analysis-ready form.
  • Typical tasks include correcting errors, filling missing values, and unifying formats.
  • Often time-consuming but essential for accurate, reliable analysis and visualization.

Definition

Section titled “Definition”

Data wrangling, also known as data munging, is the process of cleaning and transforming raw data into a format that is more suitable for analysis and visualization. This involves tasks such as identifying and correcting errors in the data, filling in missing values, and converting data into a consistent format.

Explanation

Section titled “Explanation”

Data wrangling is a set of preparatory steps applied to raw datasets so they can be effectively analyzed or visualized. The work commonly includes finding and fixing errors, handling missing entries, and standardizing disparate formats so multiple sources can be combined. These transformations make downstream analysis methods and visualizations more reliable.

Examples

Section titled “Examples”

Handling missing values

Section titled “Handling missing values”

For example, imagine you have a dataset containing information about customers, including their name, address, and age. Some entries in the age column are missing, which can make it difficult to perform analyses that involve age. To fix this, you could impute the missing values by replacing them with the average age of the other customers, or use a machine learning algorithm to predict the missing values based on the other information in the dataset.

Converting data from different sources

Section titled “Converting data from different sources”

Another example is converting data from different sources into a consistent format. You might have customer information from two sources stored in different formats: one in a CSV file and the other in a SQL database. To use the data together, you would extract the relevant data from each source, clean and format it, and combine the two datasets into a common structure such as a Pandas DataFrame.

Notes or pitfalls

Section titled “Notes or pitfalls”
  • Data wrangling can be time-consuming and tedious.
  • It is crucial for obtaining accurate and reliable insights from data.
Section titled “Related terms”
  • data munging
  • imputation (filling in missing values)
  • Pandas DataFrame
  • CSV
  • SQL
  • machine learning algorithm