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Careful when reporting a mean of an ordinal categorical variable #195 #196

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oldoc63 merged 1 commit into from
Oct 21, 2021
Merged

Careful when reporting a mean of an ordinal categorical variable #195 #196

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29 changes: 12 additions & 17 deletions categorical/script.py
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Original file line number Diff line number Diff line change
@@ -1,24 +1,19 @@
import pandas as pd
import numpy as np

# Read NYC trees data
nyc_trees = pd.read_csv("./nyc_tree_census.csv")
# Read NYC Trees Data
nyc_trees = pd.read_csv("nyc_tree_census2.csv")

# Find the unique values in the column health
tree_health_statuses = nyc_trees.health.unique()
print(tree_health_statuses)

#Create a list named health_categories which lists the categories from worst to best. Exclude NaN.
health_categories = ['Poor', 'Fair', 'Good']

#convert health to 'category'
nyc_trees['health'] = pd.Categorical(nyc_trees['health'], health_categories, ordered=True)

#calculate the median value of health
median_index = np.median(nyc_trees['health'].cat.codes)
print(median_index)
median_health_status = health_categories[int(median_index)]
print(median_health_status)
correct_order = ['Small (0-3in)', 'Medium (3-10in)', 'Medium-Large (10-18in)', 'Large (18-24in)','Very large (>24in)']

nyc_trees.tree_diam_category = pd.Categorical(nyc_trees.tree_diam_category, ['Small (0-3in)', 'Medium (3-10in)', 'Medium-Large (10-18in)', 'Large (18-24in)','Very large (>24in)'], ordered=True)

# Get Mean Diam of diameter variable, `trunk_diam`
mean_diam = nyc_trees.trunk_diam.mean()
print(mean_diam)

# Get Mean Category of `tree_diam_category`
mean_diam_cat = np.mean(nyc_trees['tree_diam_category'].cat.codes)
print(mean_diam_cat)
median_category = correct_order[int(mean_diam_cat)]
print(median_category)