5453 lines
240 KiB
Plaintext
5453 lines
240 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "8c6d5a05-21ea-4f26-adeb-35148f7d8dba",
|
||
"metadata": {},
|
||
"outputs": [],
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||
"source": [
|
||
"import pandas as pd\n",
|
||
"import plotly.graph_objects as go\n",
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||
"import plotly.express as px\n",
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"\n",
|
||
"from pathlib import Path\n",
|
||
"import sys\n",
|
||
"from docx import Document\n",
|
||
"from docx.shared import Inches, Pt, RGBColor\n",
|
||
"from docx.enum.text import WD_ALIGN_PARAGRAPH\n",
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||
"\n",
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||
"notebook_dir = Path().resolve() # Current working directory\n",
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"project_root = notebook_dir.parent # Goes up to root/\n",
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"sys.path.insert(0, str(project_root / \"libs\"))\n",
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"\n",
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"from word_library import WordDocumentBuilder, PageConfig\n",
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"from pasbdc_data_cleaning import clean_center_name "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "54121b33-2152-44ed-8438-cf099a6fa2c2",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>Date</th>\n",
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" <th>Client</th>\n",
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" <th>Contact</th>\n",
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" <th>Survey Definition</th>\n",
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" <th>Center</th>\n",
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" <th>Answers</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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||
" <tr>\n",
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||
" <th>0</th>\n",
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||
" <td>9/29/2025 12:00 AM</td>\n",
|
||
" <td>CurryZone (KUP270729)</td>\n",
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||
" <td>Niru Shrestha</td>\n",
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||
" <td>Quarterly Client Satisfaction Survey</td>\n",
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||
" <td>Kutztown University SBDC</td>\n",
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" <td>1. Using a 1-10 scale, with a 10 being ver...</td>\n",
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" </tr>\n",
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" <tr>\n",
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||
" <th>1</th>\n",
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" <td>9/29/2025 12:00 AM</td>\n",
|
||
" <td>Genie McKinney (PS018642)</td>\n",
|
||
" <td>Genie McKinney</td>\n",
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" <td>Quarterly Client Satisfaction Survey</td>\n",
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" <td>Penn State SBDC</td>\n",
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||
" <td>1. Using a 1-10 scale, with a 10 being ver...</td>\n",
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||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>9/28/2025 12:00 AM</td>\n",
|
||
" <td>Dani's Hair Loft (PI700652)</td>\n",
|
||
" <td>Danielle Kosanovich</td>\n",
|
||
" <td>Quarterly Client Satisfaction Survey</td>\n",
|
||
" <td>University of Pittsburgh SBDC</td>\n",
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||
" <td>1. Using a 1-10 scale, with a 10 being ver...</td>\n",
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||
" </tr>\n",
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" <tr>\n",
|
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" <th>3</th>\n",
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" <td>9/25/2025 12:00 AM</td>\n",
|
||
" <td>First Impressions Early Childhood Development ...</td>\n",
|
||
" <td>Gina Kiesewetter</td>\n",
|
||
" <td>Quarterly Client Satisfaction Survey</td>\n",
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||
" <td>SF - ST. FRANCIS UNIVERSITY SBDC</td>\n",
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||
" <td>1. Using a 1-10 scale, with a 10 being ver...</td>\n",
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||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
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" <td>9/23/2025 12:00 AM</td>\n",
|
||
" <td>Sweet Mom Home Day Care (PI704438)</td>\n",
|
||
" <td>Aissatou Bah</td>\n",
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||
" <td>Quarterly Client Satisfaction Survey</td>\n",
|
||
" <td>University of Pittsburgh SBDC</td>\n",
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||
" <td>1. Using a 1-10 scale, with a 10 being ver...</td>\n",
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||
" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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||
"</div>"
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],
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"text/plain": [
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||
" Date Client \\\n",
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||
"0 9/29/2025 12:00 AM CurryZone (KUP270729) \n",
|
||
"1 9/29/2025 12:00 AM Genie McKinney (PS018642) \n",
|
||
"2 9/28/2025 12:00 AM Dani's Hair Loft (PI700652) \n",
|
||
"3 9/25/2025 12:00 AM First Impressions Early Childhood Development ... \n",
|
||
"4 9/23/2025 12:00 AM Sweet Mom Home Day Care (PI704438) \n",
|
||
"\n",
|
||
" Contact Survey Definition \\\n",
|
||
"0 Niru Shrestha Quarterly Client Satisfaction Survey \n",
|
||
"1 Genie McKinney Quarterly Client Satisfaction Survey \n",
|
||
"2 Danielle Kosanovich Quarterly Client Satisfaction Survey \n",
|
||
"3 Gina Kiesewetter Quarterly Client Satisfaction Survey \n",
|
||
"4 Aissatou Bah Quarterly Client Satisfaction Survey \n",
|
||
"\n",
|
||
" Center \\\n",
|
||
"0 Kutztown University SBDC \n",
|
||
"1 Penn State SBDC \n",
|
||
"2 University of Pittsburgh SBDC \n",
|
||
"3 SF - ST. FRANCIS UNIVERSITY SBDC \n",
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||
"4 University of Pittsburgh SBDC \n",
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"\n",
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||
" Answers \n",
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||
"0 1. Using a 1-10 scale, with a 10 being ver... \n",
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"1 1. Using a 1-10 scale, with a 10 being ver... \n",
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||
"2 1. Using a 1-10 scale, with a 10 being ver... \n",
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||
"3 1. Using a 1-10 scale, with a 10 being ver... \n",
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||
"4 1. Using a 1-10 scale, with a 10 being ver... "
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]
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||
},
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||
"execution_count": 2,
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||
"metadata": {},
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||
"output_type": "execute_result"
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||
}
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],
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"source": [
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||
"survey_df = pd.read_csv('survey_data.csv')\n",
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||
"survey_df.head()"
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||
]
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||
},
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||
{
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||
"cell_type": "code",
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||
"execution_count": 3,
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||
"id": "b8c325c6-0071-426c-88ac-a807122138d8",
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||
"metadata": {},
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||
"outputs": [],
|
||
"source": [
|
||
"import re\n",
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||
"\n",
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||
"question_count = 4\n",
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||
"\n",
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||
"for row_index, row in survey_df.iterrows():\n",
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||
" lines = [x.strip() for x in row['Answers'].split('\\n') if x.strip()] # Remove empty lines\n",
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||
" \n",
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||
" # Find question indices (lines that start with a number followed by a period)\n",
|
||
" question_indices = []\n",
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||
" for i, line in enumerate(lines):\n",
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||
" if re.match(r'^\\d+\\.', line): # Matches \"1.\", \"2.\", etc.\n",
|
||
" question_indices.append(i)\n",
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||
"\n",
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||
" question_number = 1\n",
|
||
" # Extract questions and answers\n",
|
||
" for i, q_idx in enumerate(question_indices):\n",
|
||
" question = lines[q_idx][3:].strip() # Remove \"1. \" prefix\n",
|
||
" \n",
|
||
" # Find where the answer ends (either at next question or end of list)\n",
|
||
" if i + 1 < len(question_indices):\n",
|
||
" answer_end = question_indices[i + 1]\n",
|
||
" else:\n",
|
||
" answer_end = len(lines)\n",
|
||
" \n",
|
||
" # Join all answer lines between this question and the next\n",
|
||
" answer_lines = lines[q_idx + 1:answer_end]\n",
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||
" answer = ' '.join(answer_lines)\n",
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" \n",
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||
" # Assign to dataframe\n",
|
||
" survey_df.at[row_index, f\"Question {question_number} text\"] = question \n",
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||
" survey_df.at[row_index, f\"Question {question_number}\"] = answer\n",
|
||
" question_number += 1"
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||
]
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||
},
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||
{
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||
"cell_type": "code",
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||
"execution_count": 4,
|
||
"id": "d2cb0bd6-832d-469a-8796-35c45442ed16",
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"metadata": {},
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"outputs": [
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{
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"data": {
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" .dataframe thead th {\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
|
||
" <th>Date</th>\n",
|
||
" <th>Client</th>\n",
|
||
" <th>Contact</th>\n",
|
||
" <th>Survey Definition</th>\n",
|
||
" <th>Center</th>\n",
|
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" <th>Answers</th>\n",
|
||
" <th>Question 1 text</th>\n",
|
||
" <th>Question 1</th>\n",
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" <th>Question 2 text</th>\n",
|
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" <th>Question 2</th>\n",
|
||
" <th>Question 3 text</th>\n",
|
||
" <th>Question 3</th>\n",
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" <th>Question 4 text</th>\n",
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||
" <th>Question 4</th>\n",
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||
" </tr>\n",
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" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>9/29/2025 12:00 AM</td>\n",
|
||
" <td>CurryZone (KUP270729)</td>\n",
|
||
" <td>Niru Shrestha</td>\n",
|
||
" <td>Quarterly Client Satisfaction Survey</td>\n",
|
||
" <td>Kutztown University SBDC</td>\n",
|
||
" <td>1. Using a 1-10 scale, with a 10 being ver...</td>\n",
|
||
" <td>Using a 1-10 scale, with a 10 being very likel...</td>\n",
|
||
" <td>10 - Very likely</td>\n",
|
||
" <td>Working with the SBDC is helping me progress t...</td>\n",
|
||
" <td>Strongly agree</td>\n",
|
||
" <td>I am likely to seek assistance from the SBDC a...</td>\n",
|
||
" <td>Strongly agree</td>\n",
|
||
" <td>Please leave any comments regarding your exper...</td>\n",
|
||
" <td>I love how Lorena and Rachel team supported us...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>9/29/2025 12:00 AM</td>\n",
|
||
" <td>Genie McKinney (PS018642)</td>\n",
|
||
" <td>Genie McKinney</td>\n",
|
||
" <td>Quarterly Client Satisfaction Survey</td>\n",
|
||
" <td>Penn State SBDC</td>\n",
|
||
" <td>1. Using a 1-10 scale, with a 10 being ver...</td>\n",
|
||
" <td>Using a 1-10 scale, with a 10 being very likel...</td>\n",
|
||
" <td>10 - Very likely</td>\n",
|
||
" <td>Working with the SBDC is helping me progress t...</td>\n",
|
||
" <td>Strongly agree</td>\n",
|
||
" <td>I am likely to seek assistance from the SBDC a...</td>\n",
|
||
" <td>Strongly agree</td>\n",
|
||
" <td>Please leave any comments regarding your exper...</td>\n",
|
||
" <td>Tom Keiffer is an amazing asset. We could not ...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>9/28/2025 12:00 AM</td>\n",
|
||
" <td>Dani's Hair Loft (PI700652)</td>\n",
|
||
" <td>Danielle Kosanovich</td>\n",
|
||
" <td>Quarterly Client Satisfaction Survey</td>\n",
|
||
" <td>University of Pittsburgh SBDC</td>\n",
|
||
" <td>1. Using a 1-10 scale, with a 10 being ver...</td>\n",
|
||
" <td>Using a 1-10 scale, with a 10 being very likel...</td>\n",
|
||
" <td>10 - Very likely</td>\n",
|
||
" <td>Working with the SBDC is helping me progress t...</td>\n",
|
||
" <td>Agree</td>\n",
|
||
" <td>I am likely to seek assistance from the SBDC a...</td>\n",
|
||
" <td>Agree</td>\n",
|
||
" <td>Please leave any comments regarding your exper...</td>\n",
|
||
" <td>Everyone that has helped me has been great!??</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>9/25/2025 12:00 AM</td>\n",
|
||
" <td>First Impressions Early Childhood Development ...</td>\n",
|
||
" <td>Gina Kiesewetter</td>\n",
|
||
" <td>Quarterly Client Satisfaction Survey</td>\n",
|
||
" <td>SF - ST. FRANCIS UNIVERSITY SBDC</td>\n",
|
||
" <td>1. Using a 1-10 scale, with a 10 being ver...</td>\n",
|
||
" <td>Using a 1-10 scale, with a 10 being very likel...</td>\n",
|
||
" <td>10 - Very likely</td>\n",
|
||
" <td>Working with the SBDC is helping me progress t...</td>\n",
|
||
" <td>Strongly agree</td>\n",
|
||
" <td>I am likely to seek assistance from the SBDC a...</td>\n",
|
||
" <td>Strongly agree</td>\n",
|
||
" <td>Please leave any comments regarding your exper...</td>\n",
|
||
" <td>(No response)</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>9/23/2025 12:00 AM</td>\n",
|
||
" <td>Sweet Mom Home Day Care (PI704438)</td>\n",
|
||
" <td>Aissatou Bah</td>\n",
|
||
" <td>Quarterly Client Satisfaction Survey</td>\n",
|
||
" <td>University of Pittsburgh SBDC</td>\n",
|
||
" <td>1. Using a 1-10 scale, with a 10 being ver...</td>\n",
|
||
" <td>Using a 1-10 scale, with a 10 being very likel...</td>\n",
|
||
" <td>10 - Very likely</td>\n",
|
||
" <td>Working with the SBDC is helping me progress t...</td>\n",
|
||
" <td>Strongly agree</td>\n",
|
||
" <td>I am likely to seek assistance from the SBDC a...</td>\n",
|
||
" <td>Strongly agree</td>\n",
|
||
" <td>Please leave any comments regarding your exper...</td>\n",
|
||
" <td>Brent Rondon gives best assistant</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" Date Client \\\n",
|
||
"0 9/29/2025 12:00 AM CurryZone (KUP270729) \n",
|
||
"1 9/29/2025 12:00 AM Genie McKinney (PS018642) \n",
|
||
"2 9/28/2025 12:00 AM Dani's Hair Loft (PI700652) \n",
|
||
"3 9/25/2025 12:00 AM First Impressions Early Childhood Development ... \n",
|
||
"4 9/23/2025 12:00 AM Sweet Mom Home Day Care (PI704438) \n",
|
||
"\n",
|
||
" Contact Survey Definition \\\n",
|
||
"0 Niru Shrestha Quarterly Client Satisfaction Survey \n",
|
||
"1 Genie McKinney Quarterly Client Satisfaction Survey \n",
|
||
"2 Danielle Kosanovich Quarterly Client Satisfaction Survey \n",
|
||
"3 Gina Kiesewetter Quarterly Client Satisfaction Survey \n",
|
||
"4 Aissatou Bah Quarterly Client Satisfaction Survey \n",
|
||
"\n",
|
||
" Center \\\n",
|
||
"0 Kutztown University SBDC \n",
|
||
"1 Penn State SBDC \n",
|
||
"2 University of Pittsburgh SBDC \n",
|
||
"3 SF - ST. FRANCIS UNIVERSITY SBDC \n",
|
||
"4 University of Pittsburgh SBDC \n",
|
||
"\n",
|
||
" Answers \\\n",
|
||
"0 1. Using a 1-10 scale, with a 10 being ver... \n",
|
||
"1 1. Using a 1-10 scale, with a 10 being ver... \n",
|
||
"2 1. Using a 1-10 scale, with a 10 being ver... \n",
|
||
"3 1. Using a 1-10 scale, with a 10 being ver... \n",
|
||
"4 1. Using a 1-10 scale, with a 10 being ver... \n",
|
||
"\n",
|
||
" Question 1 text Question 1 \\\n",
|
||
"0 Using a 1-10 scale, with a 10 being very likel... 10 - Very likely \n",
|
||
"1 Using a 1-10 scale, with a 10 being very likel... 10 - Very likely \n",
|
||
"2 Using a 1-10 scale, with a 10 being very likel... 10 - Very likely \n",
|
||
"3 Using a 1-10 scale, with a 10 being very likel... 10 - Very likely \n",
|
||
"4 Using a 1-10 scale, with a 10 being very likel... 10 - Very likely \n",
|
||
"\n",
|
||
" Question 2 text Question 2 \\\n",
|
||
"0 Working with the SBDC is helping me progress t... Strongly agree \n",
|
||
"1 Working with the SBDC is helping me progress t... Strongly agree \n",
|
||
"2 Working with the SBDC is helping me progress t... Agree \n",
|
||
"3 Working with the SBDC is helping me progress t... Strongly agree \n",
|
||
"4 Working with the SBDC is helping me progress t... Strongly agree \n",
|
||
"\n",
|
||
" Question 3 text Question 3 \\\n",
|
||
"0 I am likely to seek assistance from the SBDC a... Strongly agree \n",
|
||
"1 I am likely to seek assistance from the SBDC a... Strongly agree \n",
|
||
"2 I am likely to seek assistance from the SBDC a... Agree \n",
|
||
"3 I am likely to seek assistance from the SBDC a... Strongly agree \n",
|
||
"4 I am likely to seek assistance from the SBDC a... Strongly agree \n",
|
||
"\n",
|
||
" Question 4 text \\\n",
|
||
"0 Please leave any comments regarding your exper... \n",
|
||
"1 Please leave any comments regarding your exper... \n",
|
||
"2 Please leave any comments regarding your exper... \n",
|
||
"3 Please leave any comments regarding your exper... \n",
|
||
"4 Please leave any comments regarding your exper... \n",
|
||
"\n",
|
||
" Question 4 \n",
|
||
"0 I love how Lorena and Rachel team supported us... \n",
|
||
"1 Tom Keiffer is an amazing asset. We could not ... \n",
|
||
"2 Everyone that has helped me has been great!?? \n",
|
||
"3 (No response) \n",
|
||
"4 Brent Rondon gives best assistant "
|
||
]
|
||
},
|
||
"execution_count": 4,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"survey_df.head()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 5,
|
||
"id": "b377e032-96c2-412b-868c-a408d3ca528b",
|
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"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Q1\n",
|
||
"======================\n",
|
||
"Question 1\n",
|
||
"10 - Very likely 759\n",
|
||
"8 56\n",
|
||
"9 50\n",
|
||
"7 16\n",
|
||
"1 - Not at all likely 14\n",
|
||
"5 11\n",
|
||
"3 10\n",
|
||
"6 8\n",
|
||
"4 6\n",
|
||
"2 3\n",
|
||
"Name: count, dtype: int64 \n",
|
||
"\n",
|
||
"Q2\n",
|
||
"======================\n",
|
||
"Question 2\n",
|
||
"Strongly agree 624\n",
|
||
"Agree 224\n",
|
||
"Neutral 49\n",
|
||
"Disagree 23\n",
|
||
"Strongly disagree 13\n",
|
||
"Name: count, dtype: int64 \n",
|
||
"\n",
|
||
"Q3\n",
|
||
"======================\n",
|
||
"Question 3\n",
|
||
"Strongly agree 686\n",
|
||
"Agree 182\n",
|
||
"Neutral 40\n",
|
||
"Disagree 14\n",
|
||
"Strongly disagree 11\n",
|
||
"Name: count, dtype: int64 \n",
|
||
"\n",
|
||
"Q4\n",
|
||
"======================\n",
|
||
"Question 4\n",
|
||
"(No response) 373\n",
|
||
"None 4\n",
|
||
"Excellent 2\n",
|
||
"I appreciate that there has been no pressure, only support. Our small business development has been going slowly due to circumstances out of our control but that does not seem to be a problem at all with our consultants. 1\n",
|
||
"I would be nowhere without them! 1\n",
|
||
" ... \n",
|
||
"Thos is a great organization and I would not be where I am today with my small business without the help and information provided by SBDC. 1\n",
|
||
"The kids are great but I have had very limited interactions with them. I guess I thought it would be more than it is. 1\n",
|
||
"I had an excellent experience working with the SBDC. The advisors were professional, knowledgeable, and very supportive throughout the process. Their guidance gave me valuable insights into business planning and helped me feel more confident moving forward with my goals. I truly appreciate the time and attention given to my needs. 1\n",
|
||
"The help that I’m receiving is for my loans or grants, also learning more about the legal process of owning a business. I’m grateful for the support that I hope to get with everything. 1\n",
|
||
"The agents are knowledgeable and friendly, the webinars available are on current topics and speak in layman's language, making it relatable. 1\n",
|
||
"Name: count, Length: 557, dtype: int64 \n",
|
||
"\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"print(\"Q1\")\n",
|
||
"print(\"======================\")\n",
|
||
"print(survey_df['Question 1'].value_counts(), \"\\n\")\n",
|
||
"\n",
|
||
"print(\"Q2\")\n",
|
||
"print(\"======================\")\n",
|
||
"print(survey_df['Question 2'].value_counts(), \"\\n\")\n",
|
||
"\n",
|
||
"print(\"Q3\")\n",
|
||
"print(\"======================\")\n",
|
||
"print(survey_df['Question 3'].value_counts(), \"\\n\")\n",
|
||
"\n",
|
||
"print(\"Q4\")\n",
|
||
"print(\"======================\")\n",
|
||
"print(survey_df['Question 4'].value_counts(), \"\\n\")\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 6,
|
||
"id": "7a9713d6-dd8a-44dd-b0d3-2f8348fa9b69",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# Clean up the answers\n",
|
||
"survey_df['Question 1'] = [int(x[:2]) if len(x) > 2 else int(x) for x in survey_df['Question 1']]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"id": "60ead430-7fe5-4a49-b825-c3aaeee378a8",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"Question 1\n",
|
||
"10 759\n",
|
||
"8 56\n",
|
||
"9 50\n",
|
||
"7 16\n",
|
||
"1 14\n",
|
||
"5 11\n",
|
||
"3 10\n",
|
||
"6 8\n",
|
||
"4 6\n",
|
||
"2 3\n",
|
||
"Name: count, dtype: int64"
|
||
]
|
||
},
|
||
"execution_count": 7,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"survey_df['Question 1'].value_counts()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"id": "411d2475-e5c3-4517-8d74-665a39931ec6",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"Center\n",
|
||
"Pittsburgh 118\n",
|
||
"Clarion 104\n",
|
||
"Temple 97\n",
|
||
"Shippensburg 84\n",
|
||
"Bucknell 74\n",
|
||
"Penn State 73\n",
|
||
"Scranton 70\n",
|
||
"Duquesne 61\n",
|
||
"Kutztown 55\n",
|
||
"Gannon 50\n",
|
||
"Lehigh 44\n",
|
||
"Wilkes 37\n",
|
||
"Widener 37\n",
|
||
"St. Francis 19\n",
|
||
"St. Vincent 10\n",
|
||
"Name: count, dtype: int64"
|
||
]
|
||
},
|
||
"execution_count": 8,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"center_mapping = {\n",
|
||
" \"University of Pittsburgh SBDC\":\"Pittsburgh\",\n",
|
||
" \"TE - TEMPLE SBDC\":\"Temple\",\n",
|
||
" \"Kutztown University SBDC\": \"Kutztown\",\n",
|
||
" \"Kutztown University SBDC \": \"Kutztown\",\n",
|
||
" \"K - Kutztown SBDC\":\"Kutztown\",\n",
|
||
" \"WD - WIDENER SBDC\": \"Widener\",\n",
|
||
" \"The University of Scranton SBDC\": \"Scranton\",\n",
|
||
" \"PennWest University Clarion SBDC\":\"Clarion\",\n",
|
||
" \"WI - WILKES SBDC\":\"Wilkes\",\n",
|
||
" \"LE - LEHIGH UNIVERSITY SBDC\":\"Lehigh\",\n",
|
||
" \"G - GANNON SBDC\":\"Gannon\",\n",
|
||
" \"Penn State SBDC\":\"Penn State\",\n",
|
||
" \"SH - SHIPPENSBURG SBDC\":\"Shippensburg\",\n",
|
||
" \"Duquesne University SBDC\":\"Duquesne\",\n",
|
||
" \"Bucknell SBDC\":\"Bucknell\",\n",
|
||
" \"SF - ST. FRANCIS UNIVERSITY SBDC\": \"St. Francis\",\n",
|
||
" \"SV - ST. VINCENT COLLEGE SBDC\":\"St. Vincent\",\n",
|
||
" \"LE - Bucks County/Lehigh SBDC\":\"Lehigh\",\n",
|
||
" \"G - Mercer\":\"Gannon\",\n",
|
||
" \"G - Meadville\":\"Gannon\",\n",
|
||
" \"SV - Fayette Outreach\":\"St. Vincent\"\n",
|
||
"}\n",
|
||
"\n",
|
||
"survey_df['Center'] = survey_df['Center'].replace(center_mapping)\n",
|
||
"survey_df['Center'].value_counts()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"id": "28cc6d9a-f1be-417d-b4cd-4a17512bf498",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"average_q1_score = survey_df.groupby('Center')['Question 1'].mean().reset_index()\n",
|
||
"network_wide_q1_score = survey_df['Question 1'].mean()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"id": "d0d33f56-050c-4da2-bf74-d92b052255f9",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>Center</th>\n",
|
||
" <th>Question 1</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>Bucknell</td>\n",
|
||
" <td>9.621622</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>Clarion</td>\n",
|
||
" <td>9.451923</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>Duquesne</td>\n",
|
||
" <td>9.852459</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>Gannon</td>\n",
|
||
" <td>9.240000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>Kutztown</td>\n",
|
||
" <td>8.690909</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>5</th>\n",
|
||
" <td>Lehigh</td>\n",
|
||
" <td>8.818182</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>6</th>\n",
|
||
" <td>Penn State</td>\n",
|
||
" <td>9.342466</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>7</th>\n",
|
||
" <td>Pittsburgh</td>\n",
|
||
" <td>9.669492</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>8</th>\n",
|
||
" <td>Scranton</td>\n",
|
||
" <td>9.785714</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>9</th>\n",
|
||
" <td>Shippensburg</td>\n",
|
||
" <td>9.690476</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>10</th>\n",
|
||
" <td>St. Francis</td>\n",
|
||
" <td>9.842105</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>11</th>\n",
|
||
" <td>St. Vincent</td>\n",
|
||
" <td>9.200000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>12</th>\n",
|
||
" <td>Temple</td>\n",
|
||
" <td>9.000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>13</th>\n",
|
||
" <td>Widener</td>\n",
|
||
" <td>8.810811</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>14</th>\n",
|
||
" <td>Wilkes</td>\n",
|
||
" <td>9.540541</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" Center Question 1\n",
|
||
"0 Bucknell 9.621622\n",
|
||
"1 Clarion 9.451923\n",
|
||
"2 Duquesne 9.852459\n",
|
||
"3 Gannon 9.240000\n",
|
||
"4 Kutztown 8.690909\n",
|
||
"5 Lehigh 8.818182\n",
|
||
"6 Penn State 9.342466\n",
|
||
"7 Pittsburgh 9.669492\n",
|
||
"8 Scranton 9.785714\n",
|
||
"9 Shippensburg 9.690476\n",
|
||
"10 St. Francis 9.842105\n",
|
||
"11 St. Vincent 9.200000\n",
|
||
"12 Temple 9.000000\n",
|
||
"13 Widener 8.810811\n",
|
||
"14 Wilkes 9.540541"
|
||
]
|
||
},
|
||
"execution_count": 10,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"average_q1_score"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"id": "8c10631c-c23f-431b-bda5-02670c188a19",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"total_responses = survey_df.groupby('Center').size()\n",
|
||
"total_responses = total_responses.reset_index(name='Responses')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"id": "0e30aa6b-cb82-4e16-a986-5a8aad812c43",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>Center</th>\n",
|
||
" <th>Responses</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>Bucknell</td>\n",
|
||
" <td>74</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>Clarion</td>\n",
|
||
" <td>104</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>Duquesne</td>\n",
|
||
" <td>61</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>Gannon</td>\n",
|
||
" <td>50</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>Kutztown</td>\n",
|
||
" <td>55</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>5</th>\n",
|
||
" <td>Lehigh</td>\n",
|
||
" <td>44</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>6</th>\n",
|
||
" <td>Penn State</td>\n",
|
||
" <td>73</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>7</th>\n",
|
||
" <td>Pittsburgh</td>\n",
|
||
" <td>118</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>8</th>\n",
|
||
" <td>Scranton</td>\n",
|
||
" <td>70</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>9</th>\n",
|
||
" <td>Shippensburg</td>\n",
|
||
" <td>84</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>10</th>\n",
|
||
" <td>St. Francis</td>\n",
|
||
" <td>19</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>11</th>\n",
|
||
" <td>St. Vincent</td>\n",
|
||
" <td>10</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>12</th>\n",
|
||
" <td>Temple</td>\n",
|
||
" <td>97</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>13</th>\n",
|
||
" <td>Widener</td>\n",
|
||
" <td>37</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>14</th>\n",
|
||
" <td>Wilkes</td>\n",
|
||
" <td>37</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" Center Responses\n",
|
||
"0 Bucknell 74\n",
|
||
"1 Clarion 104\n",
|
||
"2 Duquesne 61\n",
|
||
"3 Gannon 50\n",
|
||
"4 Kutztown 55\n",
|
||
"5 Lehigh 44\n",
|
||
"6 Penn State 73\n",
|
||
"7 Pittsburgh 118\n",
|
||
"8 Scranton 70\n",
|
||
"9 Shippensburg 84\n",
|
||
"10 St. Francis 19\n",
|
||
"11 St. Vincent 10\n",
|
||
"12 Temple 97\n",
|
||
"13 Widener 37\n",
|
||
"14 Wilkes 37"
|
||
]
|
||
},
|
||
"execution_count": 12,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"total_responses"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 13,
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|
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|
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|
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|
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|
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|
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|
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|
||
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|
||
" <th>Unnamed: 0</th>\n",
|
||
" <th>Client ID</th>\n",
|
||
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|
||
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|
||
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|
||
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|
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|
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|
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|
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|
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|
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|
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|
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|
||
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|
||
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|
||
" <td>WD04170</td>\n",
|
||
" <td>\\tProinnov@ LLC (WD04170)</td>\n",
|
||
" <td>9/9/2025 12:00 AM</td>\n",
|
||
" <td>WD - WIDENER SBDC</td>\n",
|
||
" <td>Philadelphia</td>\n",
|
||
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|
||
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|
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" <tr>\n",
|
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" <th>1</th>\n",
|
||
" <td>1</td>\n",
|
||
" <td>WD02759</td>\n",
|
||
" <td>\"C.J.A.\"/ Crawley Jones and Allen real estate...</td>\n",
|
||
" <td>10/20/2025 12:00 AM</td>\n",
|
||
" <td>WD - WIDENER SBDC</td>\n",
|
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" <td>Delaware</td>\n",
|
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" <td>531390-OtherActivitiesRelatedtoRealEstate\\r\\r\\...</td>\n",
|
||
" <td>531390 - Other Activities Related to Real Esta...</td>\n",
|
||
" <td>53.0</td>\n",
|
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" <td>2.510127</td>\n",
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||
" <td>2.723982</td>\n",
|
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" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>2</td>\n",
|
||
" <td>PS018402</td>\n",
|
||
" <td>Anjie's Cleaning Bees (PS018402)</td>\n",
|
||
" <td>10/14/2024 12:00 AM</td>\n",
|
||
" <td>Penn State SBDC</td>\n",
|
||
" <td>Lycoming</td>\n",
|
||
" <td>561720-JanitorialServices\\r\\r\\n\\r\\r\\n</td>\n",
|
||
" <td>561720 - Janitorial Services \\r\\r\\n</td>\n",
|
||
" <td>56.0</td>\n",
|
||
" <td>3.605647</td>\n",
|
||
" <td>4.398190</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>3</td>\n",
|
||
" <td>C8538</td>\n",
|
||
" <td>BRENIMAN PROPERTIES, LLC (C8538)</td>\n",
|
||
" <td>10/17/2025 12:00 AM</td>\n",
|
||
" <td>PennWest University Clarion SBDC</td>\n",
|
||
" <td>Clarion</td>\n",
|
||
" <td>531120-LessorsofNonresidentialBuildings(except...</td>\n",
|
||
" <td>531120 - Lessors of Nonresidential Buildings (...</td>\n",
|
||
" <td>53.0</td>\n",
|
||
" <td>2.510127</td>\n",
|
||
" <td>2.723982</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>4</td>\n",
|
||
" <td>BU016079</td>\n",
|
||
" <td>Civil War Cider Co., Inc. (BU016079)</td>\n",
|
||
" <td>10/21/2024 12:00 AM</td>\n",
|
||
" <td>Bucknell SBDC</td>\n",
|
||
" <td>Union</td>\n",
|
||
" <td>312130-Wineries\\r\\r\\n\\r\\r\\n</td>\n",
|
||
" <td>312130 - Wineries \\r\\r\\n</td>\n",
|
||
" <td>31.0</td>\n",
|
||
" <td>2.876304</td>\n",
|
||
" <td>4.995475</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" Unnamed: 0 Client ID Client \\\n",
|
||
"0 0 WD04170 \\tProinnov@ LLC (WD04170) \n",
|
||
"1 1 WD02759 \"C.J.A.\"/ Crawley Jones and Allen real estate... \n",
|
||
"2 2 PS018402 Anjie's Cleaning Bees (PS018402) \n",
|
||
"3 3 C8538 BRENIMAN PROPERTIES, LLC (C8538) \n",
|
||
"4 4 BU016079 Civil War Cider Co., Inc. (BU016079) \n",
|
||
"\n",
|
||
" Last Counseling Center \\\n",
|
||
"0 9/9/2025 12:00 AM WD - WIDENER SBDC \n",
|
||
"1 10/20/2025 12:00 AM WD - WIDENER SBDC \n",
|
||
"2 10/14/2024 12:00 AM Penn State SBDC \n",
|
||
"3 10/17/2025 12:00 AM PennWest University Clarion SBDC \n",
|
||
"4 10/21/2024 12:00 AM Bucknell SBDC \n",
|
||
"\n",
|
||
" Physical Address County NAICs \\\n",
|
||
"0 Philadelphia NaN \n",
|
||
"1 Delaware 531390-OtherActivitiesRelatedtoRealEstate\\r\\r\\... \n",
|
||
"2 Lycoming 561720-JanitorialServices\\r\\r\\n\\r\\r\\n \n",
|
||
"3 Clarion 531120-LessorsofNonresidentialBuildings(except... \n",
|
||
"4 Union 312130-Wineries\\r\\r\\n\\r\\r\\n \n",
|
||
"\n",
|
||
" Primary NAICS NAICS_2 \\\n",
|
||
"0 NaN 0.0 \n",
|
||
"1 531390 - Other Activities Related to Real Esta... 53.0 \n",
|
||
"2 561720 - Janitorial Services \\r\\r\\n 56.0 \n",
|
||
"3 531120 - Lessors of Nonresidential Buildings (... 53.0 \n",
|
||
"4 312130 - Wineries \\r\\r\\n 31.0 \n",
|
||
"\n",
|
||
" PA NAICs Code Percentage PASBDC NAICs Code Percentage \n",
|
||
"0 0.000000 13.809955 \n",
|
||
"1 2.510127 2.723982 \n",
|
||
"2 3.605647 4.398190 \n",
|
||
"3 2.510127 2.723982 \n",
|
||
"4 2.876304 4.995475 "
|
||
]
|
||
},
|
||
"execution_count": 15,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"client_list = pd.read_csv('naics_client_list_tagged.csv')\n",
|
||
"client_list.head()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 16,
|
||
"id": "25c86814-dd7f-4646-8038-1988e8044688",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>Center</th>\n",
|
||
" <th>Client Count</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>Pennsylvania SBDC Lead Office</td>\n",
|
||
" <td>6</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>Bucknell</td>\n",
|
||
" <td>487</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>Clarion</td>\n",
|
||
" <td>847</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>Duquesne</td>\n",
|
||
" <td>747</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>EMAP</td>\n",
|
||
" <td>8</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>5</th>\n",
|
||
" <td>Gannon</td>\n",
|
||
" <td>596</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>6</th>\n",
|
||
" <td>Indiana County</td>\n",
|
||
" <td>4</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>7</th>\n",
|
||
" <td>Kutztown</td>\n",
|
||
" <td>1330</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>8</th>\n",
|
||
" <td>Lehigh</td>\n",
|
||
" <td>565</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>9</th>\n",
|
||
" <td>PI - Washington County</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>10</th>\n",
|
||
" <td>Penn State</td>\n",
|
||
" <td>731</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>11</th>\n",
|
||
" <td>Pittsburgh</td>\n",
|
||
" <td>1154</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>12</th>\n",
|
||
" <td>Scranton</td>\n",
|
||
" <td>713</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>13</th>\n",
|
||
" <td>Shippensburg</td>\n",
|
||
" <td>728</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>14</th>\n",
|
||
" <td>St. Francis</td>\n",
|
||
" <td>285</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>15</th>\n",
|
||
" <td>St. Vincent</td>\n",
|
||
" <td>280</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>16</th>\n",
|
||
" <td>Temple</td>\n",
|
||
" <td>1203</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>17</th>\n",
|
||
" <td>Wharton SBDC</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>18</th>\n",
|
||
" <td>Widener</td>\n",
|
||
" <td>866</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>19</th>\n",
|
||
" <td>Wilkes</td>\n",
|
||
" <td>498</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" Center Client Count\n",
|
||
"0 Pennsylvania SBDC Lead Office 6\n",
|
||
"1 Bucknell 487\n",
|
||
"2 Clarion 847\n",
|
||
"3 Duquesne 747\n",
|
||
"4 EMAP 8\n",
|
||
"5 Gannon 596\n",
|
||
"6 Indiana County 4\n",
|
||
"7 Kutztown 1330\n",
|
||
"8 Lehigh 565\n",
|
||
"9 PI - Washington County 1\n",
|
||
"10 Penn State 731\n",
|
||
"11 Pittsburgh 1154\n",
|
||
"12 Scranton 713\n",
|
||
"13 Shippensburg 728\n",
|
||
"14 St. Francis 285\n",
|
||
"15 St. Vincent 280\n",
|
||
"16 Temple 1203\n",
|
||
"17 Wharton SBDC 1\n",
|
||
"18 Widener 866\n",
|
||
"19 Wilkes 498"
|
||
]
|
||
},
|
||
"execution_count": 16,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"clean_center_name(client_list)\n",
|
||
"client_list = client_list.groupby('Center').size().reset_index(name='Client Count')\n",
|
||
"client_list.head(100)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 17,
|
||
"id": "21751415-6071-4386-a252-ff582acbc632",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>Center</th>\n",
|
||
" <th>Responses</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>Bucknell</td>\n",
|
||
" <td>74</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>Clarion</td>\n",
|
||
" <td>104</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>Duquesne</td>\n",
|
||
" <td>61</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>Gannon</td>\n",
|
||
" <td>50</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>Kutztown</td>\n",
|
||
" <td>55</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" Center Responses\n",
|
||
"0 Bucknell 74\n",
|
||
"1 Clarion 104\n",
|
||
"2 Duquesne 61\n",
|
||
"3 Gannon 50\n",
|
||
"4 Kutztown 55"
|
||
]
|
||
},
|
||
"execution_count": 17,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"total_responses = total_responses\n",
|
||
"total_responses.head()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 18,
|
||
"id": "3403190f-0c62-4ed8-aa81-bb9922efdfb3",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"total_responses = total_responses.merge(client_list, on='Center', how='left')\n",
|
||
"total_responses['Per Client Served'] = total_responses['Responses'] / total_responses['Client Count']"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 19,
|
||
"id": "01fc8cc8-317b-4bb8-a4e4-3ae6fbdd8fdc",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
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|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>Center</th>\n",
|
||
" <th>Responses</th>\n",
|
||
" <th>Client Count</th>\n",
|
||
" <th>Per Client Served</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>Bucknell</td>\n",
|
||
" <td>74</td>\n",
|
||
" <td>487</td>\n",
|
||
" <td>0.151951</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>Clarion</td>\n",
|
||
" <td>104</td>\n",
|
||
" <td>847</td>\n",
|
||
" <td>0.122786</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>Duquesne</td>\n",
|
||
" <td>61</td>\n",
|
||
" <td>747</td>\n",
|
||
" <td>0.081660</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>Gannon</td>\n",
|
||
" <td>50</td>\n",
|
||
" <td>596</td>\n",
|
||
" <td>0.083893</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>Kutztown</td>\n",
|
||
" <td>55</td>\n",
|
||
" <td>1330</td>\n",
|
||
" <td>0.041353</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>5</th>\n",
|
||
" <td>Lehigh</td>\n",
|
||
" <td>44</td>\n",
|
||
" <td>565</td>\n",
|
||
" <td>0.077876</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>6</th>\n",
|
||
" <td>Penn State</td>\n",
|
||
" <td>73</td>\n",
|
||
" <td>731</td>\n",
|
||
" <td>0.099863</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>7</th>\n",
|
||
" <td>Pittsburgh</td>\n",
|
||
" <td>118</td>\n",
|
||
" <td>1154</td>\n",
|
||
" <td>0.102253</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>8</th>\n",
|
||
" <td>Scranton</td>\n",
|
||
" <td>70</td>\n",
|
||
" <td>713</td>\n",
|
||
" <td>0.098177</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>9</th>\n",
|
||
" <td>Shippensburg</td>\n",
|
||
" <td>84</td>\n",
|
||
" <td>728</td>\n",
|
||
" <td>0.115385</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>10</th>\n",
|
||
" <td>St. Francis</td>\n",
|
||
" <td>19</td>\n",
|
||
" <td>285</td>\n",
|
||
" <td>0.066667</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>11</th>\n",
|
||
" <td>St. Vincent</td>\n",
|
||
" <td>10</td>\n",
|
||
" <td>280</td>\n",
|
||
" <td>0.035714</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>12</th>\n",
|
||
" <td>Temple</td>\n",
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" <tr>\n",
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" Center Responses Client Count Per Client Served\n",
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"0 Bucknell 74 487 0.151951\n",
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"1 Clarion 104 847 0.122786\n",
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"---\n",
|
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"\"NPS is calculated by subtracting the percentage of customers who answer the NPS question with a 6 or lower (known as ‘detractors’) from the percentage of customers who answer with a 9 or 10 (known as ‘promoters’).\"\n",
|
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|
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"\"Net Promoter Score® is always expressed as a number from -100 to 100; the score is negative when a company has more detractors than promoters, and positive in the opposite situation.\"\n",
|
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"52 detractors and 809 promoters\n",
|
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|
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"# Calculating the network wide NPS\n",
|
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"total_detractors_count = survey_df[survey_df['Question 1'] <= 6].shape[0]\n",
|
||
"total_promoters_count = survey_df[survey_df['Question 1'] >= 9].shape[0]\n",
|
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|
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"\n",
|
||
"network_nps = ((total_promoters_count / total_responses) - (total_detractors_count / total_responses)) * 100\n",
|
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"print(total_detractors_count, \"detractors and\", total_promoters_count, \"promoters\")\n",
|
||
"print(\"Network wide NPS:\", network_nps)\n",
|
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"\n",
|
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"center_group_df = survey_df[['Center', 'Question 1']].groupby('Center')\n",
|
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|
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"nps_df = pd.DataFrame({\"Center\":[], \"Detractors\":[], \"Promoters\":[], \"NPS\":[]})\n",
|
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"for name, group in center_group_df:\n",
|
||
" detractors_count = group[group['Question 1'] <= 6].shape[0]\n",
|
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" promoters_count = group[group['Question 1'] >= 9].shape[0]\n",
|
||
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|
||
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|
||
"\n",
|
||
" row = pd.DataFrame({\"Center\":[name], \"Detractors\": [detractors_count], \"Promoters\": [promoters_count], \"NPS\": [nps]})\n",
|
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|
||
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|
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|
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|
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"
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"fig = px.bar(nps_df, x='Center', y='NPS', text='NPS', title=\"Net Promoter Score (NPS) By Center FY 25\", height=600, width=1250)\n",
|
||
"\n",
|
||
"fig.update_traces(showlegend=False, marker_color=\"#73e0c6\", texttemplate=\"%{text:.2f}\") \n",
|
||
"\n",
|
||
"fig.add_hline(\n",
|
||
" y=network_nps, \n",
|
||
" line_dash=\"dash\", \n",
|
||
" line_color=\"#004649\", \n",
|
||
" annotation_text=f\"Network NPS: {network_nps:.2f}\", \n",
|
||
" annotation_position=\"bottom right\",\n",
|
||
" )\n",
|
||
"\n",
|
||
"fig.add_annotation(xref='paper', yref='paper', \n",
|
||
" x=0.0, y=1.08,\n",
|
||
" showarrow=False,\n",
|
||
" text=f'NOTE: NPS is calculated as the difference between promoter responses (9 or 10) and the % of detractor responses (1-6).<br> Participents are responding to the question \"How likely is it that you would recommend the SBDC to a friend or colleague? (1-10 scale)\"',\n",
|
||
" align='left')\n",
|
||
"\n",
|
||
"\n",
|
||
"fig.write_image(\"nps_center.png\")\n",
|
||
"fig.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "e055c03a-846b-4f44-a39b-16b699f18869",
|
||
"metadata": {},
|
||
"source": [
|
||
"# Making the word document\n",
|
||
"---"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 73,
|
||
"id": "f1965c2b-98d2-437b-9700-d72a0e34572e",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"from pathlib import Path\n",
|
||
"import sys\n",
|
||
"from docx import Document\n",
|
||
"from docx.shared import Inches, Pt, RGBColor\n",
|
||
"from docx.enum.text import WD_ALIGN_PARAGRAPH\n",
|
||
"\n",
|
||
"notebook_dir = Path().resolve() # Current working directory\n",
|
||
"project_root = notebook_dir.parent # Goes up to root/\n",
|
||
"sys.path.insert(0, str(project_root / \"libs\"))\n",
|
||
"\n",
|
||
"from word_library import WordDocumentBuilder, PageConfig"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 74,
|
||
"id": "5272d714-3799-4621-bf81-f34e057f3af8",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"def client_survey_analysis_page_one(builder: WordDocumentBuilder, responses_count_chart:str=\"survey_response_count.png\", reccomendation_chart:str=\"average_survey_score.png\", per_client_chart:str=\"survey_response_perclient.png\", nps_chart:str=\"nps_center.png\"):\n",
|
||
" for section in builder.doc.sections:\n",
|
||
" section.top_margin = Inches(0.5)\n",
|
||
" section.bottom_margin = Inches(0.5)\n",
|
||
" section.left_margin = Inches(0.5)\n",
|
||
" section.right_margin = Inches(0.5)\n",
|
||
" \n",
|
||
" heading_paragraph = builder.doc.add_paragraph()\n",
|
||
"\n",
|
||
" # TODO: replace this part with using the section numbners of the document builder\n",
|
||
" heading_run = heading_paragraph.add_run(f\"1.10 Client Satisfaction Analysis\") \n",
|
||
" heading_run.font.name = 'Futera'\n",
|
||
" heading_run.font.size = Pt(12)\n",
|
||
" heading_run.font.color.rgb = RGBColor(113, 191, 68)\n",
|
||
" heading_run.bold = True\n",
|
||
"\n",
|
||
"\n",
|
||
" overview_table = builder.doc.add_table(rows=2, cols=2)\n",
|
||
"\n",
|
||
" row1_cells = overview_table.rows[0].cells\n",
|
||
" row2_cells = overview_table.rows[1].cells\n",
|
||
"\n",
|
||
" # Response chart section\n",
|
||
" response_chart_paragrah = row1_cells[0].paragraphs[0]\n",
|
||
" response_chart_run = response_chart_paragrah.add_run()\n",
|
||
" response_chart_run.add_picture(responses_count_chart, width=Inches(4), height=Inches(2))\n",
|
||
"\n",
|
||
" responses_count_note_paragraph = row2_cells[0].paragraphs[0]\n",
|
||
" note_run = responses_count_note_paragraph.add_run(f\"Figure {\"1.10\"}.{builder.figure_number + 1} shows the count of servey responses for clients per center.\") \n",
|
||
" note_run.font.name = 'Futera'\n",
|
||
" note_run.font.size = Pt(7)\n",
|
||
" note_run.font.color.rgb = RGBColor(15, 27, 38)\n",
|
||
" note_run.bold = True\n",
|
||
" \n",
|
||
" builder.figure_number += 1\n",
|
||
"\n",
|
||
" # Response per client section\n",
|
||
" perclient_chart_paragrah = row1_cells[1].paragraphs[0]\n",
|
||
" perclient_chart_run = perclient_chart_paragrah.add_run()\n",
|
||
" perclient_chart_run.add_picture(per_client_chart, width=Inches(4), height=Inches(2))\n",
|
||
"\n",
|
||
" perclient_note_paragraph = row2_cells[1].paragraphs[0]\n",
|
||
" note_run = perclient_note_paragraph.add_run(f\"Figure {\"1.10\"}.{builder.figure_number + 1} shows the count of servey responses per clinet served per center.\") \n",
|
||
" note_run.font.name = 'Futera'\n",
|
||
" note_run.font.size = Pt(7)\n",
|
||
" note_run.font.color.rgb = RGBColor(15, 27, 38)\n",
|
||
" note_run.bold = True\n",
|
||
" \n",
|
||
" builder.figure_number += 1\n",
|
||
"\n",
|
||
" # Reccomendation chart section with NPS\n",
|
||
"\n",
|
||
" \n",
|
||
" q1_table = builder.doc.add_table(rows=2, cols=2)\n",
|
||
"\n",
|
||
" row1_cells = q1_table.rows[0].cells\n",
|
||
" row2_cells = q1_table.rows[1].cells\n",
|
||
"\n",
|
||
" # Response chart section\n",
|
||
" rec_chart_paragrah = row1_cells[0].paragraphs[0]\n",
|
||
" rec_chart_run = rec_chart_paragrah.add_run()\n",
|
||
" rec_chart_run.add_picture(reccomendation_chart, width=Inches(4), height=Inches(2))\n",
|
||
"\n",
|
||
" rec_count_note_paragraph = row2_cells[0].paragraphs[0]\n",
|
||
" rec_run = rec_count_note_paragraph.add_run(f\"Figure {\"1.10\"}.{builder.figure_number + 1} shows how clients responded to the listed question.\") \n",
|
||
" rec_run.font.name = 'Futera'\n",
|
||
" rec_run.font.size = Pt(7)\n",
|
||
" rec_run.font.color.rgb = RGBColor(15, 27, 38)\n",
|
||
" rec_run.bold = True\n",
|
||
" \n",
|
||
" builder.figure_number += 1\n",
|
||
"\n",
|
||
" # Response per client section\n",
|
||
" nps_chart_paragrah = row1_cells[1].paragraphs[0]\n",
|
||
" nps_chart_run = nps_chart_paragrah.add_run()\n",
|
||
" nps_chart_run.add_picture(nps_chart, width=Inches(4), height=Inches(2))\n",
|
||
"\n",
|
||
" nps_note_paragraph = row2_cells[1].paragraphs[0]\n",
|
||
" nps_run = nps_note_paragraph.add_run(f\"Figure {\"1.10\"}.{builder.figure_number + 1} shows the NPS calculated for each center. See https://contentsquare.com/guides/net-promoter-score/ for a more in-depth explaination.\") \n",
|
||
" nps_run.font.name = 'Futera'\n",
|
||
" nps_run.font.size = Pt(7)\n",
|
||
" nps_run.font.color.rgb = RGBColor(15, 27, 38)\n",
|
||
" nps_run.bold = True\n",
|
||
" \n",
|
||
" builder.figure_number += 1"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 75,
|
||
"id": "0a554cf9-a733-4ec1-92b0-e58af2c6f963",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"pages = [\n",
|
||
" PageConfig(client_survey_analysis_page_one, add_page_break=False),\n",
|
||
"]\n",
|
||
"\n",
|
||
"builder = WordDocumentBuilder()\n",
|
||
"\n",
|
||
"doc = builder.create_document(\n",
|
||
" pages,\n",
|
||
" \"section1_10.docx\"\n",
|
||
")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "39b59f15-ffa7-4e13-9c17-844d6c8d62d0",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python 3 (ipykernel)",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.13.7"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|