FAQ: Summary Statistics for Categorical Variables - Ordinal Categorical Variables - Central Tendency I

This community-built FAQ covers the “Ordinal Categorical Variables - Central Tendency I” exercise from the lesson “Summary Statistics for Categorical Variables”.

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This exercise can be found in the following Codecademy content:

Master Statistics with Python

FAQs on the exercise Ordinal Categorical Variables - Central Tendency I

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in this block of code (for full exercise see link below):

# Convert to categorical type
nyc_trees['health'] = pd.Categorical(
    nyc_trees['health'], health_categories, ordered=True
)

# Calculate median values
median_index = np.median(nyc_trees['health'].cat.codes)
median_health_status = health_categories[int(median_index)]
print(median_health_status)

In the line where I calculate the median_index, I do not understand how when i call cat .codes, it knows the order of the categories and assigns the right integer to them. Could someone explain that to me?

Thanks in advance :slight_smile:

When you use pd.Categorial and pass a list with ordered=True you are giving Python an order of values.

Print in the console nyc_trees['health] and you will see the following:

Name: health, Length: 50000, dtype: category
Categories (3, object): [Poor < Fair < Good]

That makes sense, thank you bjrompal!