594 lines
20 KiB
Plaintext
594 lines
20 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": "036980a4-7309-4ea4-8dac-e8901c4525cf",
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ba89b925-5fc4-41bc-8b5e-81e53cfe1e4e",
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"metadata": {},
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"source": [
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"# Getting the client counseling sessions data\n",
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"---\n",
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"The data can be obtained from this filter. You will need to break it up into smaller chunks and export them into the folder client_counseling\n",
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"https://pasbdc.neoserra.com/activity/list/10?__formid=10&remove=&savename=&sort=DATE&sortdir=DESC&expr=&field_1=DATE&opt_auto_1=pfy&field_2=CLI_TYPE2&opt_2=&opt_2=AC&opt_2=IC&field_3=F_CENTER_ID&opt_3=2805&opt_3=2790&opt_3=2782&opt_3=2784&opt_3=2806&opt_3=2789&opt_3=4491&opt_3=2783&opt_3=2807&opt_3=2809&opt_3=2788&opt_3=2780&opt_3=2808&opt_3=2786&opt_3=2785&opt_3=2787&opt_3=2791&opt_3=2781&field_4=&sortdir=DESC\n",
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"\n",
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"Then combine the csvs into one big file"
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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": "7c3bf1e4-57bc-419b-909d-fca0043c1df7",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"<class 'pandas.core.frame.DataFrame'>\n",
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"RangeIndex: 828614 entries, 0 to 828613\n",
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"Data columns (total 9 columns):\n",
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" # Column Non-Null Count Dtype \n",
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"--- ------ -------------- ----- \n",
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" 0 Session Date 828614 non-null object \n",
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" 1 Client 828614 non-null object \n",
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" 2 Client ID 828614 non-null object \n",
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" 3 Counselor 823892 non-null object \n",
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" 4 Session Type 828611 non-null object \n",
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" 5 Contact Type 828614 non-null object \n",
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" 6 Center 828614 non-null object \n",
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" 7 Prep+Contact 828614 non-null float64\n",
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" 8 Total Hours 828614 non-null float64\n",
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"dtypes: float64(2), object(7)\n",
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"memory usage: 56.9+ MB\n"
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]
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}
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],
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"source": [
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"sessions_df = pd.read_csv('counselling_sessions_fy2225.csv')\n",
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"sessions_df.info()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "acb44859-ef4f-4dcb-82a9-80e5c30ce778",
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"metadata": {},
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"source": [
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"# Get the unique clients list\n",
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"---\n",
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"Download and load the unique clients list with these columns\n",
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"https://pasbdc.neoserra.com/clients?__formid=3&remove=&savename=&sort=CLIENT_ID&sortdir=ASC&expr=&field_1=REVIEWID&opt_1=13213656&field_2=&sortdir=ASC"
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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,
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"id": "4563ea40-4d94-41c5-86f4-6d620a45c1de",
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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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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\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>Unnamed: 0</th>\n",
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" <th>Client ID</th>\n",
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" <th>Client</th>\n",
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" <th>Last Counseling</th>\n",
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" <th>Center</th>\n",
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" <th>Physical Address County</th>\n",
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" <th>NAICs</th>\n",
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" <th>Primary NAICS</th>\n",
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" <th>NAICS_2</th>\n",
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" <th>PA NAICs Code Percentage</th>\n",
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" <th>PASBDC NAICs Code Percentage</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>0</td>\n",
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" <td>WD04170</td>\n",
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" <td>\\tProinnov@ LLC (WD04170)</td>\n",
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" <td>9/9/2025 12:00 AM</td>\n",
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" <td>WD - WIDENER SBDC</td>\n",
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" <td>Philadelphia</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>0.0</td>\n",
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" <td>0.000000</td>\n",
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" <td>13.809955</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>1</td>\n",
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" <td>WD02759</td>\n",
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" <td>\"C.J.A.\"/ Crawley Jones and Allen real estate...</td>\n",
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" <td>10/20/2025 12:00 AM</td>\n",
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" <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",
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" <td>531390 - Other Activities Related to Real Esta...</td>\n",
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" <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",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>2</td>\n",
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" <td>PS018402</td>\n",
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" <td>Anjie's Cleaning Bees (PS018402)</td>\n",
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" <td>10/14/2024 12:00 AM</td>\n",
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" <td>Penn State SBDC</td>\n",
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" <td>Lycoming</td>\n",
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" <td>561720-JanitorialServices\\r\\r\\n\\r\\r\\n</td>\n",
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" <td>561720 - Janitorial Services \\r\\r\\n</td>\n",
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" <td>56.0</td>\n",
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" <td>3.605647</td>\n",
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" <td>4.398190</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>3</td>\n",
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" <td>C8538</td>\n",
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" <td>BRENIMAN PROPERTIES, LLC (C8538)</td>\n",
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" <td>10/17/2025 12:00 AM</td>\n",
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" <td>PennWest University Clarion SBDC</td>\n",
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" <td>Clarion</td>\n",
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" <td>531120-LessorsofNonresidentialBuildings(except...</td>\n",
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" <td>531120 - Lessors of Nonresidential Buildings (...</td>\n",
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" <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",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>4</td>\n",
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" <td>BU016079</td>\n",
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" <td>Civil War Cider Co., Inc. (BU016079)</td>\n",
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" <td>10/21/2024 12:00 AM</td>\n",
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" <td>Bucknell SBDC</td>\n",
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" <td>Union</td>\n",
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" <td>312130-Wineries\\r\\r\\n\\r\\r\\n</td>\n",
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" <td>312130 - Wineries \\r\\r\\n</td>\n",
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" <td>31.0</td>\n",
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" <td>2.876304</td>\n",
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" <td>4.995475</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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" Unnamed: 0 Client ID Client \\\n",
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"0 0 WD04170 \\tProinnov@ LLC (WD04170) \n",
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"1 1 WD02759 \"C.J.A.\"/ Crawley Jones and Allen real estate... \n",
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"2 2 PS018402 Anjie's Cleaning Bees (PS018402) \n",
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"3 3 C8538 BRENIMAN PROPERTIES, LLC (C8538) \n",
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"4 4 BU016079 Civil War Cider Co., Inc. (BU016079) \n",
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"\n",
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" Last Counseling Center \\\n",
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"0 9/9/2025 12:00 AM WD - WIDENER SBDC \n",
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"1 10/20/2025 12:00 AM WD - WIDENER SBDC \n",
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"2 10/14/2024 12:00 AM Penn State SBDC \n",
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"3 10/17/2025 12:00 AM PennWest University Clarion SBDC \n",
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"4 10/21/2024 12:00 AM Bucknell SBDC \n",
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"\n",
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" Physical Address County NAICs \\\n",
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"0 Philadelphia NaN \n",
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"1 Delaware 531390-OtherActivitiesRelatedtoRealEstate\\r\\r\\... \n",
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"2 Lycoming 561720-JanitorialServices\\r\\r\\n\\r\\r\\n \n",
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"3 Clarion 531120-LessorsofNonresidentialBuildings(except... \n",
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"4 Union 312130-Wineries\\r\\r\\n\\r\\r\\n \n",
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"\n",
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" Primary NAICS NAICS_2 \\\n",
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"0 NaN 0.0 \n",
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"1 531390 - Other Activities Related to Real Esta... 53.0 \n",
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"2 561720 - Janitorial Services \\r\\r\\n 56.0 \n",
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"3 531120 - Lessors of Nonresidential Buildings (... 53.0 \n",
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"4 312130 - Wineries \\r\\r\\n 31.0 \n",
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"\n",
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" PA NAICs Code Percentage PASBDC NAICs Code Percentage \n",
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"0 0.000000 13.809955 \n",
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"1 2.510127 2.723982 \n",
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"2 3.605647 4.398190 \n",
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"3 2.510127 2.723982 \n",
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"4 2.876304 4.995475 "
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]
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},
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"execution_count": 4,
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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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"client_df = pd.read_csv('naics_client_list_tagged.csv')\n",
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"client_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": 5,
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"id": "9307bc7a-8ba1-4c7f-b7eb-e2b5f6d4c8d7",
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"metadata": {},
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"data": {
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"text/html": [
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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>Session Type</th>\n",
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" <th>count</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>Administrative</td>\n",
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" <td>476099</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>Follow-up</td>\n",
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" <td>316915</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>Initial/New</td>\n",
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" <td>17024</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>Close-out</td>\n",
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" <td>14902</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>Impact</td>\n",
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" <td>3671</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"text/plain": [
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" Session Type count\n",
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"0 Administrative 476099\n",
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"1 Follow-up 316915\n",
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"2 Initial/New 17024\n",
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"3 Close-out 14902\n",
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"4 Impact 3671"
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]
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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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"session_type_value_counts = sessions_df['Session Type'].value_counts().reset_index()\n",
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"session_type_value_counts"
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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": 6,
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"id": "97465a49-4e13-4e23-88ba-6bd7d72c6520",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th>Session Type</th>\n",
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" <th>Client ID</th>\n",
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" <th>Center</th>\n",
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" <th>Administrative</th>\n",
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" <th>Close-out</th>\n",
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" <th>Follow-up</th>\n",
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" <th>Impact</th>\n",
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" <th>Initial/New</th>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>D 14632</td>\n",
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" <td>Duquesne University SBDC</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>2</td>\n",
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" <td>0</td>\n",
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" <td>1</td>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>S015040</td>\n",
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" <td>The University of Scranton SBDC</td>\n",
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" <td>0</td>\n",
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" <td>1</td>\n",
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" <td>7</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>00000043</td>\n",
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" <td>TE - TEMPLE SBDC</td>\n",
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" <td>1</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>0</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>00000052</td>\n",
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" <td>WD - WIDENER SBDC</td>\n",
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" <td>3</td>\n",
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" <td>0</td>\n",
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" <td>8</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>000002</td>\n",
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" <td>Kutztown University SBDC</td>\n",
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" <td>2</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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"text/plain": [
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"Session Type Client ID Center Administrative \\\n",
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"0 D 14632 Duquesne University SBDC 0 \n",
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"1 S015040 The University of Scranton SBDC 0 \n",
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"2 00000043 TE - TEMPLE SBDC 1 \n",
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"3 00000052 WD - WIDENER SBDC 3 \n",
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"4 000002 Kutztown University SBDC 2 \n",
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"\n",
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"Session Type Close-out Follow-up Impact Initial/New \n",
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"0 0 2 0 1 \n",
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"1 1 7 0 0 \n",
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"2 0 0 0 0 \n",
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"3 0 8 0 0 \n",
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"4 0 0 0 0 "
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]
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},
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"execution_count": 6,
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"metadata": {},
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}
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],
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"source": [
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"session_counts = sessions_df.groupby('Client ID')['Session Type'].value_counts()\n",
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"client_centers = sessions_df.groupby('Client ID')['Center'].first()\n",
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"\n",
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"\n",
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"unified_counts_df = session_counts.unstack(fill_value=0)\n",
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"unified_counts_df['Center'] = client_centers\n",
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"\n",
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"\n",
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"\n",
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"all_cols = list(unified_counts_df.columns)\n",
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"all_cols.remove('Center')\n",
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"all_cols.insert(0, 'Center')\n",
|
|
"\n",
|
|
"unified_counts_df = unified_counts_df[all_cols].reset_index()\n",
|
|
"unified_counts_df.head()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 7,
|
|
"id": "0eb344e3-a1e2-4ae8-b2ad-a40cc775eec9",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"Center\n",
|
|
"University of Pittsburgh SBDC 11263\n",
|
|
"TE - TEMPLE SBDC 8851\n",
|
|
"Kutztown University SBDC 8454\n",
|
|
"WD - WIDENER SBDC 4452\n",
|
|
"The University of Scranton SBDC 3798\n",
|
|
"K - Kutztown SBDC 2608\n",
|
|
"PennWest University Clarion SBDC 2574\n",
|
|
"WI - WILKES SBDC 2387\n",
|
|
"LE - LEHIGH UNIVERSITY SBDC 2211\n",
|
|
"G - GANNON SBDC 1794\n",
|
|
"Penn State SBDC 1782\n",
|
|
"SH - SHIPPENSBURG SBDC 1751\n",
|
|
"Duquesne University SBDC 1604\n",
|
|
"Bucknell SBDC 1149\n",
|
|
"SF - ST. FRANCIS UNIVERSITY SBDC 1033\n",
|
|
"SV - ST. VINCENT COLLEGE SBDC 738\n",
|
|
"G - Meadville 186\n",
|
|
"SV - Fayette Outreach 176\n",
|
|
"G - Mercer 131\n",
|
|
"Indiana County 125\n",
|
|
"Clarion CARES Act 20\n",
|
|
"G - Warren 1\n",
|
|
"SC - Monroe Outreach 1\n",
|
|
"LE - Bucks County/Lehigh SBDC 1\n",
|
|
"Name: count, dtype: int64"
|
|
]
|
|
},
|
|
"execution_count": 7,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"unified_counts_df['Center'].value_counts()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 8,
|
|
"id": "e38f57b8-f362-4bba-ae20-b858f2be4504",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"Center\n",
|
|
"Pittsburgh 11263\n",
|
|
"Temple 8851\n",
|
|
"Kutztown University SBDC 8454\n",
|
|
"Widner 4452\n",
|
|
"Scranton 3798\n",
|
|
"Kutztown 2608\n",
|
|
"Clarion 2574\n",
|
|
"Wilkes 2387\n",
|
|
"Lehigh 2212\n",
|
|
"Gannon 1794\n",
|
|
"Penn State 1782\n",
|
|
"Shippensburg 1751\n",
|
|
"Duquesne 1604\n",
|
|
"Bucknell 1149\n",
|
|
"St. Francis 1033\n",
|
|
"St. Vincent 738\n",
|
|
"G - Meadville 186\n",
|
|
"SV - Fayette Outreach 176\n",
|
|
"G - Mercer 131\n",
|
|
"Indiana County 125\n",
|
|
"Clarion CARES Act 20\n",
|
|
"G - Warren 1\n",
|
|
"SC - Monroe Outreach 1\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",
|
|
" \"K - Kutztown SBDC\":\"Kutztown\",\n",
|
|
" \"WD - WIDENER SBDC\": \"Widner\",\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",
|
|
"}\n",
|
|
"\n",
|
|
"unified_counts_df['Center'] = unified_counts_df['Center'].replace(center_mapping)\n",
|
|
"unified_counts_df['Center'].value_counts()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "39772f4d-0c9b-40cb-ab7a-0d3e152bd7a3",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "369dddf9-4b1a-47ea-aa08-cc0404c0f60a",
|
|
"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
|
|
}
|