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Data Wrangling vs EDA

 

AspectData Wrangling (Data Preprocessing)Exploratory Data Analysis (EDA)
ObjectivePrepare raw data for modeling by cleaning, transforming, and formatting it appropriately.Explore and understand the data to gain insights, identify patterns, and make decisions on data handling and modeling.
OrderTypically performed as a preliminary step before EDA.Usually conducted after data wrangling to further investigate data characteristics.
Data HandlingFocuses on data cleaning, filling missing values, encoding categorical variables, and scaling features.Involves data visualization, statistical analysis, and summary statistics to uncover patterns, relationships, and anomalies.
TechniquesTechniques include imputation, outlier detection, feature scaling, and one-hot encoding.Techniques include histograms, scatter plots, box plots, correlation matrices, and descriptive statistics.
Data TransformationInvolves structural changes to the dataset, such as feature engineering, data normalization, and dimensionality reduction.Primarily explores existing data structures and relationships without altering the data's fundamental structure.
ToolsCommon tools include libraries like pandas and scikit-learn in Python.Utilizes tools like matplotlib, seaborn, and statistical analysis packages (e.g., R) for visualization and analysis.
OutputsThe output is a clean, preprocessed dataset ready for model training.The output includes visualizations, summary statistics, and insights used for feature selection, model choice, and problem understanding.
PurposeAims to prepare data in a format suitable for machine learning algorithms, ensuring they can effectively learn from the data.Aims to uncover data characteristics, relationships, and potential challenges to inform modeling decisions.
IterationOften an iterative process, as issues discovered during EDA may require revisiting data wrangling steps.Typically a one-time or limited iteration process to understand the data before modeling.
ExamplesRemoving duplicate records, filling missing values, scaling features, encoding categorical variables.Creating histograms to visualize data distributions, generating scatter plots to examine relationships between variables, calculating summary statistics.
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