Exploratory Data Analysis (EDA) is a crucial step in any data science project. When dealing with categorical variables, analyzing them effectively can reveal patterns, relationships, and insights that shape further modeling and decision-making. Here’s a detailed guide on how to analyze categorical variables in EDA.
Understanding Categorical Variables
Categorical variables represent data that can be divided into distinct groups or categories. These categories may be nominal (no intrinsic order, like gender or color) or ordinal (with a natural order, like rating scales or education levels).
1. Initial Inspection
Start by identifying which variables in your dataset are categorical. This can usually be done by checking data types or the number of unique values:
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Use
.info()or.dtypesin pandas. -
Consider variables with relatively few unique values as categorical.
2. Frequency Distribution
The simplest way to analyze categorical variables is to look at their frequency counts.
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Use
.value_counts()in pandas to see how many observations fall into each category. -
Visualize with bar plots or count plots to understand the distribution.
What to look for:
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Dominant categories with very high counts.
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Rare categories with very low counts.
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Missing values or categories marked as “Unknown”.
3. Proportion and Percentage Analysis
Beyond counts, examine proportions to understand the relative size of each category.
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Normalize the value counts to get proportions.
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This helps compare distributions across different categorical variables or groups.
4. Handling Missing Values
Categorical variables may have missing data represented as NaN or a special category (like “Unknown”).
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Analyze the frequency and proportion of missing data.
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Decide whether to impute, drop, or treat missing values as a separate category.
5. Relationship Between Categorical Variables and Target
When you have a target variable (especially classification problems), explore how categories relate to the target.
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Use cross-tabulations (
pd.crosstab) to see the joint frequency of categorical variables with the target. -
Calculate proportions within categories to detect patterns.
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Visualize using stacked bar charts or grouped bar charts.
6. Statistical Tests for Association
To check if the relationship between categorical variables and the target is statistically significant:
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Use Chi-Square test of independence for nominal variables.
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For ordinal variables, consider tests like Cochran-Armitage trend test or calculate measures like Cramér’s V for association strength.
7. Encoding and Transformation Insights
While encoding is a step before modeling, analyzing the need for encoding helps in EDA:
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Identify high cardinality variables that may cause issues with certain encoding techniques.
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Detect if categories need grouping or merging based on frequency and similarity.
8. Visualizations for Categorical Variables
Visual tools help in better understanding:
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Bar plots / Count plots: Show frequency of each category.
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Pie charts: Sometimes used but less recommended due to perception issues.
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Stacked bar plots: To visualize categorical relationship with another variable.
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Mosaic plots: Show joint distribution of two categorical variables.
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Box plots by category: Useful when comparing a numerical variable across categories.
9. Analyzing Multiple Categorical Variables Together
Explore relationships between two or more categorical variables:
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Use contingency tables to summarize.
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Visualize with heatmaps to show frequency or proportions.
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Look for patterns like mutual exclusivity, dependencies, or clustering.
10. Dealing with Rare Categories
Rare categories can be problematic:
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Group rare categories into “Other” to reduce noise.
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Consider domain knowledge when grouping to avoid losing important information.
By systematically applying these steps, you can uncover meaningful insights from categorical variables, guide feature engineering, and improve the overall quality of your analysis and models.
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