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ord_info_function.py

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+# -*- coding: utf-8 -*-
+"""ord_info_function.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1wXpxeji9dvkf_LuXuxA54BElELaBYg6m
+"""
+
+import pandas as pd
+import numpy as np
+
+indicators = {'not satisfied', 'ok', 'excellent'}
+
+ds = ['not satisfied', 'not satisfied', 'not satisfied', 'ok', 'ok', 'ok','ok','ok','ok','ok','ok','ok','ok', 'excellent',
+              'excellent','excellent','excellent','excellent','excellent','excellent','excellent','excellent','excellent','excellent','excellent','excellent','excellent',
+              'excellent','excellent','excellent','excellent','excellent','excellent','excellent','excellent','excellent','excellent','excellent','excellent','excellent',
+              'excellent','excellent','excellent','excellent','excellent','excellent','excellent','excellent']
+
+def ord_info(ds, indicators):
+
+    # Calculate frequency using pandas value_counts()
+    freq_counts = pd.value_counts(ds, ascending=True)
+    freq_counts_df = pd.DataFrame(freq_counts, columns = ['frequency'])
+
+    # Calculate percent
+    total_responses = len(ds)
+    freq_counts_df["percent"] = (freq_counts_df["frequency"] / total_responses) * 100
+
+    # Create DataFrame with all possible indicators
+    indicators_df = pd.DataFrame(indicators, columns=["indicator"])
+    indicators_df.set_index("indicator", inplace=True)
+
+    # Merge the frequency and percent DataFrames to include 0 counts for missing indicators
+    summary_df = indicators_df.merge(freq_counts_df, how="left", left_index=True, right_index=True)
+    summary_df.fillna(0, inplace=True)
+
+    # Calculate cumulative percent
+    summary_df["cumulative"] = summary_df["percent"].cumsum()
+
+    # Set the last cumulative value to 100
+    summary_df.loc[summary_df["cumulative"] > 100, "cumulative"] = 100
+
+    # Create the final DataFrame
+    summary_df.index.name = "indicators"
+    return summary_df
+
+ord_info(ds,indicators)
+