⚖️ Pandas Comparing Categories – Perform Logical Operations with Ordered Categorical Data

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🧲 Introduction – Why Compare Categorical Values?

In Pandas, comparing categorical data (like "low" < "high") only works if the column is defined as an ordered categorical type. This ensures accurate rank-based filtering, sorting, and selection when working with values that have semantic order (e.g., severity, size, education level).

🎯 In this guide, you’ll learn:

• How to define and compare ordered categories
• Use logical comparisons with <, >, ==
• Apply comparisons in filters and conditional logic
• Avoid common comparison errors with unordered data

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📥 1. Create a Sample Series

import pandas as pd
from pandas.api.types import CategoricalDtype

grades = pd.Series(['low', 'medium', 'high', 'medium', 'low'])

By default, comparison operators won’t work on plain string/object types.

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🧱 2. Define Ordered Categories

grade_order = CategoricalDtype(categories=['low', 'medium', 'high'], ordered=True)
grades_cat = grades.astype(grade_order)

✔️ Converts the series to ordered categorical, enabling logical comparisons.

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🔍 3. Compare Categories with Relational Operators

grades_cat > 'low'

👉 Output:

0    False
1     True
2     True
3     True
4    False
dtype: bool

✔️ Now you can use:

• > and < → for greater or lesser categories
• >= and <= → for inclusive comparisons
• == and != → for exact matches

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🔗 4. Filter DataFrame Based on Category Comparison

df = pd.DataFrame({
    'Priority': ['low', 'high', 'medium', 'medium', 'low'],
    'Task': ['A', 'B', 'C', 'D', 'E']
})

priority_order = CategoricalDtype(['low', 'medium', 'high'], ordered=True)
df['Priority'] = df['Priority'].astype(priority_order)

# Filter tasks with priority greater than 'low'
df[df['Priority'] > 'low']

👉 Output:

  Priority Task
1     high    B
2   medium    C
3   medium    D

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🚫 5. Attempting Comparisons on Unordered Categories

df['Priority'].astype('category') > 'low'

🛑 Raises:

TypeError: Cannot compare Categorical with string, use 'CategoricalDtype' with 'ordered=True'

✔️ Always define ordered=True for comparisons to work.

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🧠 6. Chain Logical Comparisons

df[(df['Priority'] >= 'medium') & (df['Priority'] < 'high')]

✔️ Filters tasks with priority exactly 'medium'.

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📌 Summary – Key Takeaways

Pandas supports comparing categorical values only when they are explicitly ordered using CategoricalDtype. Once defined, you can use full relational logic to filter, analyze, and rank categorical features.

🔍 Key Takeaways:

• Use CategoricalDtype(ordered=True) to enable comparisons
• Compare using ==, <, >, <=, >=
• Avoid comparing unordered categorical types
• Use logical comparisons in filters and Boolean masks

⚙️ Real-world relevance: Used in task prioritization, grading, severity ranking, sorting pipelines, and category-based filtering.

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❓ FAQs – Comparing Categories in Pandas

❓ Why does 'high' > 'low' fail in my categorical column?
Because the category is not ordered. Define it using CategoricalDtype(ordered=True).

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❓ Can I use .between() on categorical values?
No, .between() is for numeric data. Use chained comparisons:

(df['Priority'] >= 'medium') & (df['Priority'] <= 'high')

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❓ Can I sort by category and then compare?
Sorting is independent of comparisons. You must define an ordered CategoricalDtype for both to work correctly.

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❓ What happens if I compare an ordered category to a value not in its category list?
It will raise a TypeError or result in NaN. Ensure comparisons involve only valid category values.

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