{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "8c6d5a05-21ea-4f26-adeb-35148f7d8dba", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import plotly.graph_objects as go\n", "import plotly.express as px\n", "\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", "\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\n", "from pasbdc_data_cleaning import clean_center_name " ] }, { "cell_type": "code", "execution_count": 2, "id": "54121b33-2152-44ed-8438-cf099a6fa2c2", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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DateClientContactSurvey DefinitionCenterAnswers
09/29/2025 12:00 AMCurryZone (KUP270729)Niru ShresthaQuarterly Client Satisfaction SurveyKutztown University SBDC1. Using a 1-10 scale, with a 10 being ver...
19/29/2025 12:00 AMGenie McKinney (PS018642)Genie McKinneyQuarterly Client Satisfaction SurveyPenn State SBDC1. Using a 1-10 scale, with a 10 being ver...
29/28/2025 12:00 AMDani's Hair Loft (PI700652)Danielle KosanovichQuarterly Client Satisfaction SurveyUniversity of Pittsburgh SBDC1. Using a 1-10 scale, with a 10 being ver...
39/25/2025 12:00 AMFirst Impressions Early Childhood Development ...Gina KiesewetterQuarterly Client Satisfaction SurveySF - ST. FRANCIS UNIVERSITY SBDC1. Using a 1-10 scale, with a 10 being ver...
49/23/2025 12:00 AMSweet Mom Home Day Care (PI704438)Aissatou BahQuarterly Client Satisfaction SurveyUniversity of Pittsburgh SBDC1. Using a 1-10 scale, with a 10 being ver...
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" ], "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... " ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "survey_df = pd.read_csv('survey_data.csv')\n", "survey_df.head()" ] }, { "cell_type": "code", "execution_count": 3, "id": "b8c325c6-0071-426c-88ac-a807122138d8", "metadata": {}, "outputs": [], "source": [ "import re\n", "\n", "question_count = 4\n", "\n", "for row_index, row in survey_df.iterrows():\n", " lines = [x.strip() for x in row['Answers'].split('\\n') if x.strip()] # Remove empty lines\n", " \n", " # Find question indices (lines that start with a number followed by a period)\n", " question_indices = []\n", " for i, line in enumerate(lines):\n", " if re.match(r'^\\d+\\.', line): # Matches \"1.\", \"2.\", etc.\n", " question_indices.append(i)\n", "\n", " 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", " answer = ' '.join(answer_lines)\n", " \n", " # Assign to dataframe\n", " survey_df.at[row_index, f\"Question {question_number} text\"] = question \n", " survey_df.at[row_index, f\"Question {question_number}\"] = answer\n", " question_number += 1" ] }, { "cell_type": "code", "execution_count": 4, "id": "d2cb0bd6-832d-469a-8796-35c45442ed16", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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DateClientContactSurvey DefinitionCenterAnswersQuestion 1 textQuestion 1Question 2 textQuestion 2Question 3 textQuestion 3Question 4 textQuestion 4
09/29/2025 12:00 AMCurryZone (KUP270729)Niru ShresthaQuarterly Client Satisfaction SurveyKutztown University SBDC1. Using a 1-10 scale, with a 10 being ver...Using a 1-10 scale, with a 10 being very likel...10 - Very likelyWorking with the SBDC is helping me progress t...Strongly agreeI am likely to seek assistance from the SBDC a...Strongly agreePlease leave any comments regarding your exper...I love how Lorena and Rachel team supported us...
19/29/2025 12:00 AMGenie McKinney (PS018642)Genie McKinneyQuarterly Client Satisfaction SurveyPenn State SBDC1. Using a 1-10 scale, with a 10 being ver...Using a 1-10 scale, with a 10 being very likel...10 - Very likelyWorking with the SBDC is helping me progress t...Strongly agreeI am likely to seek assistance from the SBDC a...Strongly agreePlease leave any comments regarding your exper...Tom Keiffer is an amazing asset. We could not ...
29/28/2025 12:00 AMDani's Hair Loft (PI700652)Danielle KosanovichQuarterly Client Satisfaction SurveyUniversity of Pittsburgh SBDC1. Using a 1-10 scale, with a 10 being ver...Using a 1-10 scale, with a 10 being very likel...10 - Very likelyWorking with the SBDC is helping me progress t...AgreeI am likely to seek assistance from the SBDC a...AgreePlease leave any comments regarding your exper...Everyone that has helped me has been great!??
39/25/2025 12:00 AMFirst Impressions Early Childhood Development ...Gina KiesewetterQuarterly Client Satisfaction SurveySF - ST. FRANCIS UNIVERSITY SBDC1. Using a 1-10 scale, with a 10 being ver...Using a 1-10 scale, with a 10 being very likel...10 - Very likelyWorking with the SBDC is helping me progress t...Strongly agreeI am likely to seek assistance from the SBDC a...Strongly agreePlease leave any comments regarding your exper...(No response)
49/23/2025 12:00 AMSweet Mom Home Day Care (PI704438)Aissatou BahQuarterly Client Satisfaction SurveyUniversity of Pittsburgh SBDC1. Using a 1-10 scale, with a 10 being ver...Using a 1-10 scale, with a 10 being very likel...10 - Very likelyWorking with the SBDC is helping me progress t...Strongly agreeI am likely to seek assistance from the SBDC a...Strongly agreePlease leave any comments regarding your exper...Brent Rondon gives best assistant
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" ], "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", "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": [ "
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CenterQuestion 1
0Bucknell9.621622
1Clarion9.451923
2Duquesne9.852459
3Gannon9.240000
4Kutztown8.690909
5Lehigh8.818182
6Penn State9.342466
7Pittsburgh9.669492
8Scranton9.785714
9Shippensburg9.690476
10St. Francis9.842105
11St. Vincent9.200000
12Temple9.000000
13Widener8.810811
14Wilkes9.540541
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CenterResponses
0Bucknell74
1Clarion104
2Duquesne61
3Gannon50
4Kutztown55
5Lehigh44
6Penn State73
7Pittsburgh118
8Scranton70
9Shippensburg84
10St. Francis19
11St. Vincent10
12Temple97
13Widener37
14Wilkes37
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y='Responses',\n", " text='Responses',\n", " height=500\n", ")\n", "\n", "# Add a total sum of responses\n", "grand_total = total_responses['Responses'].sum()\n", "fig.add_annotation(xref='paper', yref='paper', \n", " x=0.0, y=1.03,\n", " showarrow=False,\n", " text=f\"{grand_total} total responses\")\n", "\n", "fig.update_layout(\n", " xaxis_title='Center', \n", " yaxis_title='Survey Responses',\n", " title='Client Satisfaction Survey Responses Per Center FY 25', \n", " height=700,\n", " width=1500,\n", ")\n", "fig.update_traces(showlegend=False, marker_color=\"#71bf44\") \n", "fig.show()\n", "fig.write_image('survey_response_count.png')" ] }, { "cell_type": "code", "execution_count": 14, "id": "4d7aed8a-02d1-4c85-b75c-6271146400d9", "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hovertemplate": "Center=%{x}
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"title": { "text": "Average Score FY 25 - How likely is it that you would recommend the SBDC to a friend or colleague? (1-10 scale)" }, "width": 1500, "xaxis": { "anchor": "y", "domain": [ 0, 1 ], "title": { "text": "Center" } }, "yaxis": { "anchor": "x", "domain": [ 0, 1 ], "title": { "text": "Average" } } } } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = px.bar(average_q1_score, height=500, x='Center', y='Question 1', text='Question 1')\n", "fig.update_layout(\n", " xaxis_title='Center', \n", " yaxis_title='Average', \n", " title='Average Score FY 25 - How likely is it that you would recommend the SBDC to a friend or colleague? (1-10 scale)',\n", " height=700,\n", " width=1500,\n", ")\n", "\n", "# Add a network wide value\n", "fig.add_hline(\n", " y=network_wide_q1_score, \n", " line_dash=\"dash\", \n", " line_color=\"#73e0c6\", \n", " annotation_text=f\"Network Total: {network_wide_q1_score:.2f}\", \n", " annotation_position=\"top right\",\n", " annotation_y=9.5)\n", "\n", "fig.update_traces(\n", " showlegend=False, \n", " marker_color=\"#197f60\", \n", " texttemplate='%{text:.2f}'\n", ")\n", "\n", "fig.show()\n", "fig.write_image('average_survey_score.png')" ] }, { "cell_type": "code", "execution_count": 15, "id": "97c5e1de-c314-4c09-b6fb-d638425d903d", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Unnamed: 0Client IDClientLast CounselingCenterPhysical Address CountyNAICsPrimary NAICSNAICS_2PA NAICs Code PercentagePASBDC NAICs Code Percentage
00WD04170\\tProinnov@ LLC (WD04170)9/9/2025 12:00 AMWD - WIDENER SBDCPhiladelphiaNaNNaN0.00.00000013.809955
11WD02759\"C.J.A.\"/ Crawley Jones and Allen real estate...10/20/2025 12:00 AMWD - WIDENER SBDCDelaware531390-OtherActivitiesRelatedtoRealEstate\\r\\r\\...531390 - Other Activities Related to Real Esta...53.02.5101272.723982
22PS018402Anjie's Cleaning Bees (PS018402)10/14/2024 12:00 AMPenn State SBDCLycoming561720-JanitorialServices\\r\\r\\n\\r\\r\\n561720 - Janitorial Services \\r\\r\\n56.03.6056474.398190
33C8538BRENIMAN PROPERTIES, LLC (C8538)10/17/2025 12:00 AMPennWest University Clarion SBDCClarion531120-LessorsofNonresidentialBuildings(except...531120 - Lessors of Nonresidential Buildings (...53.02.5101272.723982
44BU016079Civil War Cider Co., Inc. (BU016079)10/21/2024 12:00 AMBucknell SBDCUnion312130-Wineries\\r\\r\\n\\r\\r\\n312130 - Wineries \\r\\r\\n31.02.8763044.995475
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" ], "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": [ "
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CenterClient Count
0Pennsylvania SBDC Lead Office6
1Bucknell487
2Clarion847
3Duquesne747
4EMAP8
5Gannon596
6Indiana County4
7Kutztown1330
8Lehigh565
9PI - Washington County1
10Penn State731
11Pittsburgh1154
12Scranton713
13Shippensburg728
14St. Francis285
15St. Vincent280
16Temple1203
17Wharton SBDC1
18Widener866
19Wilkes498
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" ], "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": [ "
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CenterResponses
0Bucknell74
1Clarion104
2Duquesne61
3Gannon50
4Kutztown55
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" ], "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": [ "
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CenterResponsesClient CountPer Client Served
0Bucknell744870.151951
1Clarion1048470.122786
2Duquesne617470.081660
3Gannon505960.083893
4Kutztown5513300.041353
5Lehigh445650.077876
6Penn State737310.099863
7Pittsburgh11811540.102253
8Scranton707130.098177
9Shippensburg847280.115385
10St. Francis192850.066667
11St. Vincent102800.035714
12Temple9712030.080632
13Widener378660.042725
14Wilkes374980.074297
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" ], "text/plain": [ " Center Responses Client Count Per Client Served\n", "0 Bucknell 74 487 0.151951\n", "1 Clarion 104 847 0.122786\n", "2 Duquesne 61 747 0.081660\n", "3 Gannon 50 596 0.083893\n", "4 Kutztown 55 1330 0.041353\n", "5 Lehigh 44 565 0.077876\n", "6 Penn State 73 731 0.099863\n", "7 Pittsburgh 118 1154 0.102253\n", "8 Scranton 70 713 0.098177\n", "9 Shippensburg 84 728 0.115385\n", "10 St. Francis 19 285 0.066667\n", "11 St. Vincent 10 280 0.035714\n", "12 Temple 97 1203 0.080632\n", "13 Widener 37 866 0.042725\n", "14 Wilkes 37 498 0.074297" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "total_responses.head(100)" ] }, { "cell_type": "code", "execution_count": 20, "id": "77811f70-7d95-4278-bd5c-0b04fb2b3c2f", "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hovertemplate": "Center=%{x}
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[ "display_df = total_responses.copy()\n", "display_df['Per Client Served'] = display_df['Per Client Served'] * 100\n", "\n", "fig = px.bar(display_df, x='Center', y='Per Client Served', text='Per Client Served', height=500)\n", "fig.update_layout(\n", " xaxis_title='Center', \n", " yaxis_title='Survey Responses Per 100 Clients Served', \n", " title='Survey Responses Per 100 Clients Served FY 25', \n", " height=700,\n", " width=1500,\n", ")\n", "fig.update_traces(showlegend=False, marker_color=\"#71bf44\", texttemplate=\"%{text:.2f}\") \n", "fig.show()\n", "fig.write_image('survey_response_perclient.png')" ] }, { "cell_type": "markdown", "id": "b6fee2f8-d51d-4401-bfa6-e816805dcfb5", "metadata": {}, "source": [ "# Investigating Net Promoter Score\n", "---\n", "\"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", " \n", "\"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", " \n", "https://contentsquare.com/guides/net-promoter-score/" ] }, { "cell_type": "code", "execution_count": 58, "id": "6e8a6116-1f74-4152-9497-53dd8ee254c0", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "52 detractors and 809 promoters\n", "Network wide NPS: 87.92102206736354\n" ] }, { "data": { "text/html": [ "
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CenterDetractorsPromotersNPS
0Bucknell2.068.094.285714
1Clarion4.088.091.304348
2Duquesne0.059.0100.000000
3Gannon5.042.078.723404
4Kutztown7.042.071.428571
\n", "
" ], "text/plain": [ " Center Detractors Promoters NPS\n", "0 Bucknell 2.0 68.0 94.285714\n", "1 Clarion 4.0 88.0 91.304348\n", "2 Duquesne 0.0 59.0 100.000000\n", "3 Gannon 5.0 42.0 78.723404\n", "4 Kutztown 7.0 42.0 71.428571" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Calculating the network wide NPS\n", "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", "total_responses = total_detractors_count + total_promoters_count\n", "\n", "network_nps = ((total_promoters_count / total_responses) - (total_detractors_count / total_responses)) * 100\n", "print(total_detractors_count, \"detractors and\", total_promoters_count, \"promoters\")\n", "print(\"Network wide NPS:\", network_nps)\n", "\n", "center_group_df = survey_df[['Center', 'Question 1']].groupby('Center')\n", "\n", "nps_df = pd.DataFrame({\"Center\":[], \"Detractors\":[], \"Promoters\":[], \"NPS\":[]})\n", "for name, group in center_group_df:\n", " detractors_count = group[group['Question 1'] <= 6].shape[0]\n", " promoters_count = group[group['Question 1'] >= 9].shape[0]\n", " total = detractors_count + promoters_count\n", " nps = ((promoters_count / total) - (detractors_count / total)) * 100\n", "\n", " row = pd.DataFrame({\"Center\":[name], \"Detractors\": [detractors_count], \"Promoters\": [promoters_count], \"NPS\": [nps]})\n", "\n", " nps_df = pd.concat([nps_df, row], ignore_index=True)\n", "\n", "\n", "nps_df.to_csv(\"NPS_by_center.csv\")\n", "nps_df.head()" ] }, { "cell_type": "code", "execution_count": 72, "id": "ae332ed4-15af-422b-9a9b-3c29a5efb737", "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hovertemplate": "Center=%{x}
NPS=%{text}", "legendgroup": "", "marker": { "color": "#73e0c6", "pattern": { "shape": "" } }, "name": "", "orientation": "v", "showlegend": false, "text": { "bdata": "JEmSJEmSV0C+6U1vetNWQAAAAAAAAFlAgphcQUyuU0C2bdu2bdtRQAAAAAAAgFFA5tqBuXZgVUCv5eBmvxBYQIVfjYn0QFhAEWflJ8RZWEAAAAAAAABZQAAAAAAAAFRA/uhHP/rRUkAtLS0tLS1QQHzwwQcffFdA", "dtype": "f8" }, "textposition": "auto", "texttemplate": "%{text:.2f}", "type": "bar", "x": [ "Bucknell", "Clarion", "Duquesne", "Gannon", "Kutztown", "Lehigh", "Penn State", "Pittsburgh", "Scranton", "Shippensburg", "St. Francis", "St. Vincent", "Temple", "Widener", "Wilkes" ], "xaxis": "x", "y": { "bdata": "JEmSJEmSV0C+6U1vetNWQAAAAAAAAFlAgphcQUyuU0C2bdu2bdtRQAAAAAAAgFFA5tqBuXZgVUCv5eBmvxBYQIVfjYn0QFhAEWflJ8RZWEAAAAAAAABZQAAAAAAAAFRA/uhHP/rRUkAtLS0tLS1QQHzwwQcffFdA", "dtype": "f8" }, "yaxis": "y" } ], "layout": { "annotations": [ { "showarrow": false, "text": "Network NPS: 87.92", "x": 1, "xanchor": "right", "xref": "x domain", "y": 87.92102206736354, "yanchor": "top", "yref": "y" }, { "align": "left", "showarrow": false, "text": "NOTE: NPS is calculated as the difference between promoter responses (9 or 10) and the % of detractor responses (1-6).
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"title": { "text": "Net Promoter Score (NPS) By Center FY 25" }, "width": 1250, "xaxis": { "anchor": "y", "domain": [ 0, 1 ], "title": { "text": "Center" } }, "yaxis": { "anchor": "x", "domain": [ 0, 1 ], "title": { "text": "NPS" } } } }, "image/png": 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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).
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 }