Visual Aids for EDA – Explained in Detail
Visual aids are graphical tools used in Exploratory Data Analysis (EDA) to understand data patterns, detect anomalies, observe relationships, and present results clearly. They help data scientists extract knowledge and communicate findings effectively. EDA visual aids are classified based on the type of data.
1. Visual Aids for Univariate Data
(Used when the dataset contains only one variable)
Univariate plots help understand the distribution, frequency, shape, spread, and central tendencies of a single variable.
Important Visual Aids
Histogram
X-axis: bins (ranges of values)
Y-axis: frequency
Shows shape of distribution, skewness, outliers, spread.
Bar Chart
Used for categorical single-variable data.
Helps compare frequencies of categories.
Line Chart
Shows trends for continuous single-variable data over time.
Pie Chart
Represents percentage share of categories.
Best when categories are few and distinct.
Box Plot
Shows five-number summary (min, Q1, median, Q3, max).
Useful for detecting outliers and spread.
Scatter Plot
Rare in univariate use; shows visual distribution.
Stacked Area Plot
Combination of line + bar; shows cumulative changes.
Probability Distribution Plots
Shows how data fits theoretical distributions (e.g., normal curve).
Table Chart
Represents frequency or summary in numeric tabular form.
2. Visual Aids for Bivariate Data
(Used when data contains two variables)
Bivariate plots help study relationships, correlations, and comparisons between two variables.
Important Visual Aids
Bar Chart
Compares two categorical variables.
Scatter Plot
Plots paired numerical values.
Shows direction, strength, and form of relationship.
Box Plot
Compares distribution of a numerical variable across categories of another variable.
Polar Chart
Circular representation; useful for periodic data.
Lollipop Chart
Variant of bar chart; highlights precise value differences.
Pie Chart
Used for part-to-whole comparison between two attributes.
Density Plot
Smoothed histogram for comparing distributions.
Contour Plot
Shows density of two variables; useful for pattern recognition.
3. Visual Aids for Multivariate Data
(Used when the dataset contains more than two variables)
Multivariate visualizations help understand high-dimensional patterns, especially in machine learning and deep learning contexts.
Important Visual Aids
Scatter Plot (3-D or colored)
Uses color, shape, or size to add more variables.
Heatmap
Shows correlations or intensities using color gradients.
Useful for correlation matrices.
PCA (Principal Component Analysis) Plot
Reduces high-dimensional data into 2D/3D.
Helps identify clusters and variance structure.
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