From c1d8771d4214d1ba7435cec7881d2b7d8d08eef7 Mon Sep 17 00:00:00 2001
From: mcoth <mcoth@dtu.dk>
Date: Sun, 17 Nov 2024 16:49:32 +0100
Subject: [PATCH] Upload New File
---
Descriptives.py | 119 ++++++++++++++++++++++++++++++++++++++++++++++++
1 file changed, 119 insertions(+)
create mode 100644 Descriptives.py
diff --git a/Descriptives.py b/Descriptives.py
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+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Sun Sep 1 17:39:05 2024
+
+@author: Maya Coulson Theodorsen (mcoth@dtu.dk)
+
+This script calculates descriptive statistics for variables across the entire dataset
+and by cluster. Outputs are formatted for LaTeX tables, including median and
+interquartile range (IQR) for continuous variables and frequencies/percentages for
+categorical variables.
+
+"""
+import pandas as pd
+
+def total_descriptives(data_complete, questionnaireClusters, categorical_variables, continuous_variables, binary_variables, sorter):
+ # Median and IQR
+ # Note: Output intended for LaTeX
+ descriptives = {}
+
+ # Descriptives for categorical variables
+ for var, label, df in categorical_variables:
+ # Get N and %
+ if var in binary_variables:
+ count = df[var].value_counts()
+ percent = df[var].value_counts(normalize=True) * 100
+ if 1 in count:
+ cnt = count[1]
+ pct = percent[1]
+ descriptives[label] = f"{cnt} ({pct:.1f}\\%)"
+ else:
+ # Calculate median and IQR
+ median = df[var].median()
+ q25 = df[var].quantile(0.25)
+ q75 = df[var].quantile(0.75)
+ descriptives[label] = f"{median:.1f} ({q25:.1f}–{q75:.1f})"
+
+ # Descriptives for continuous variables
+ for var, label, df in continuous_variables:
+ median = df[var].median()
+ q25 = df[var].quantile(0.25)
+ q75 = df[var].quantile(0.75)
+ descriptives[label] = f"{median:.1f} ({q25:.1f}–{q75:.1f})"
+
+ # Convert to a DataFrame for easier display
+ descriptives_total = pd.DataFrame(descriptives.items(), columns=['Variable', 'Median(IQR)/N(%)'])
+ descriptives_total = descriptives_total.set_index('Variable')
+ descriptives_total = descriptives_total.reindex(sorter)
+ descriptives_total = descriptives_total.reset_index()
+
+ return descriptives_total
+
+
+def cluster_descriptives(data_complete, questionnaireClusters, categorical_variables, continuous_variables, cluster_column, binary_variables, sorter):
+
+ # Empty dictionary for results
+ descriptives = {}
+
+ # Group data by cluster
+ grouped_data_complete = data_complete.groupby(cluster_column)
+ grouped_data_questionnaire = questionnaireClusters.groupby(cluster_column)
+
+ # Combine both grouped datasets to handle variables from either df
+ all_grouped_data = {'data_complete': grouped_data_complete, 'questionnaireClusters': grouped_data_questionnaire}
+
+ # Loop through categorical variables
+ for var, label, df in categorical_variables:
+ descriptives[label] = {}
+ grouped_data = all_grouped_data['data_complete'] if df is data_complete else all_grouped_data['questionnaireClusters']
+
+ for cluster, cluster_data in grouped_data:
+ if var in cluster_data.columns:
+ if var in binary_variables:
+ count = cluster_data[var].value_counts()
+ percent = cluster_data[var].value_counts(normalize=True) * 100
+ if 1 in count:
+ cnt = count[1]
+ pct = percent[1]
+ descriptives[label][f'Cluster {cluster}'] = f"{cnt} ({pct:.1f}\\%)"
+ else:
+ mean = cluster_data[var].mean()
+ std = cluster_data[var].std()
+ descriptives[label][f'Cluster {cluster}'] = f"{mean:.1f} ({std:.1f})"
+
+
+ # Loop through continuous variables
+ for var, label, df in continuous_variables:
+ descriptives[label] = {}
+ grouped_data = all_grouped_data['data_complete'] if df is data_complete else all_grouped_data['questionnaireClusters']
+
+ for cluster, cluster_data in grouped_data:
+ if var in cluster_data.columns:
+ # Calculate median, 25th & 75th percentile
+ median = cluster_data[var].median()
+ q25 = cluster_data[var].quantile(0.25)
+ q75 = cluster_data[var].quantile(0.75)
+ descriptives[label][f'Cluster {cluster}'] = f"{median:.1f} ({q25:.1f}–{q75:.1f})"
+
+
+ # Convert to a DataFrame
+ descriptives_cluster = pd.DataFrame(descriptives).T # Transpose for correct format
+ descriptives_cluster = descriptives_cluster.reindex(sorter) # Reorder variables according to sorter
+
+ return descriptives_cluster
+
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