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Merged
oldoc63 merged 1 commit into from
Oct 22, 2021
Merged

Proportions are more informative than value.counts #199 #200

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24 changes: 4 additions & 20 deletions categorical/script.py
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Original file line number Diff line number Diff line change
@@ -1,24 +1,8 @@
import pandas as pd
import numpy as np

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

size_labels_ordered = ['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, size_labels_ordered, ordered=True)

# Calculate 25th Percentile Category
p25_tree_diam_index = np.percentile(nyc_trees.tree_diam_category.cat.codes, 25)
print(p25_tree_diam_index)

p25_tree_diam_category = size_labels_ordered[int(p25_tree_diam_index)]
print(p25_tree_diam_category)

# Calculate 75th Percentile Category
p75_tree_diam_index = np.percentile(nyc_trees.tree_diam_category.cat.codes, 75)
print(p75_tree_diam_index)

p75_tree_diam_category = size_labels_ordered[int(p75_tree_diam_index)]
print(p75_tree_diam_category)
# Table of proportions for status
tree_status_proportions = nyc_trees.status.value_counts()/len(nyc_trees['status'])