{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# for data tables and analysis\n",
"import pandas as pd\n",
"# for charting\n",
"import matplotlib\n",
"from matplotlib import style\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"#for Treemap\n",
"import squarify\n",
"\n",
"sns.set()\n",
"%matplotlib inline\n",
"# plot a size that is easy to read\n",
"matplotlib.rcParams['figure.figsize'] = (16.0,9.0)\n",
"style.use('seaborn')"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" P&L Label | \n",
" Amount | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" COR - Direct | \n",
" 33260.50 | \n",
"
\n",
" \n",
" 1 | \n",
" Customer Success (COR) Payroll | \n",
" 127663.90 | \n",
"
\n",
" \n",
" 2 | \n",
" Sales Payroll | \n",
" 291654.09 | \n",
"
\n",
" \n",
" 3 | \n",
" Marketing Payroll | \n",
" 84550.83 | \n",
"
\n",
" \n",
" 4 | \n",
" G&A Payroll | \n",
" 160174.56 | \n",
"
\n",
" \n",
" 5 | \n",
" Product Payroll | \n",
" 137638.41 | \n",
"
\n",
" \n",
" 6 | \n",
" Engineering Payroll | \n",
" 229355.49 | \n",
"
\n",
" \n",
" 7 | \n",
" Travel expenses | \n",
" 14306.11 | \n",
"
\n",
" \n",
" 8 | \n",
" Legal and Accounting | \n",
" 52466.96 | \n",
"
\n",
" \n",
" 9 | \n",
" Outside Services | \n",
" 77173.46 | \n",
"
\n",
" \n",
" 10 | \n",
" Sales & Marketing Expenses | \n",
" 94161.34 | \n",
"
\n",
" \n",
" 11 | \n",
" Facility Cost | \n",
" 75241.53 | \n",
"
\n",
" \n",
" 12 | \n",
" Admin & Office Expense | \n",
" 50056.37 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" P&L Label Amount\n",
"0 COR - Direct 33260.50\n",
"1 Customer Success (COR) Payroll 127663.90\n",
"2 Sales Payroll 291654.09\n",
"3 Marketing Payroll 84550.83\n",
"4 G&A Payroll 160174.56\n",
"5 Product Payroll 137638.41\n",
"6 Engineering Payroll 229355.49\n",
"7 Travel expenses 14306.11\n",
"8 Legal and Accounting 52466.96\n",
"9 Outside Services 77173.46\n",
"10 Sales & Marketing Expenses 94161.34\n",
"11 Facility Cost 75241.53\n",
"12 Admin & Office Expense 50056.37"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# read the excel file to get data\n",
"mysheetname = 'data'\n",
"df = pd.read_excel('data.xlsx', sheet_name= mysheetname)\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" P&L Label | \n",
" Amount | \n",
" Perc of TotalExp | \n",
" Perc of TotalRev | \n",
" Chart Label | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" Sales Payroll | \n",
" 291654.09 | \n",
" 20.4 | \n",
" 32.0 | \n",
" Sales Payroll (32.0%) | \n",
"
\n",
" \n",
" 1 | \n",
" Engineering Payroll | \n",
" 229355.49 | \n",
" 16.1 | \n",
" 25.1 | \n",
" Engineering Payroll (25.1%) | \n",
"
\n",
" \n",
" 2 | \n",
" G&A Payroll | \n",
" 160174.56 | \n",
" 11.2 | \n",
" 17.6 | \n",
" G&A Payroll (17.6%) | \n",
"
\n",
" \n",
" 3 | \n",
" Product Payroll | \n",
" 137638.41 | \n",
" 9.6 | \n",
" 15.1 | \n",
" Product Payroll (15.1%) | \n",
"
\n",
" \n",
" 4 | \n",
" Customer Success (COR) Payroll | \n",
" 127663.90 | \n",
" 8.9 | \n",
" 14.0 | \n",
" Customer Success (COR) Payroll (14.0%) | \n",
"
\n",
" \n",
" 5 | \n",
" Sales & Marketing Expenses | \n",
" 94161.34 | \n",
" 6.6 | \n",
" 10.3 | \n",
" Sales & Marketing Expenses (10.3%) | \n",
"
\n",
" \n",
" 6 | \n",
" Marketing Payroll | \n",
" 84550.83 | \n",
" 5.9 | \n",
" 9.3 | \n",
" Marketing Payroll (9.3%) | \n",
"
\n",
" \n",
" 7 | \n",
" Outside Services | \n",
" 77173.46 | \n",
" 5.4 | \n",
" 8.5 | \n",
" Outside Services (8.5%) | \n",
"
\n",
" \n",
" 8 | \n",
" Facility Cost | \n",
" 75241.53 | \n",
" 5.3 | \n",
" 8.2 | \n",
" Facility Cost (8.2%) | \n",
"
\n",
" \n",
" 9 | \n",
" Legal and Accounting | \n",
" 52466.96 | \n",
" 3.7 | \n",
" 5.8 | \n",
" Legal and Accounting (5.8%) | \n",
"
\n",
" \n",
" 10 | \n",
" Admin & Office Expense | \n",
" 50056.37 | \n",
" 3.5 | \n",
" 5.5 | \n",
" Admin & Office Expense (5.5%) | \n",
"
\n",
" \n",
" 11 | \n",
" COR - Direct | \n",
" 33260.50 | \n",
" 2.3 | \n",
" 3.6 | \n",
" COR - Direct (3.6%) | \n",
"
\n",
" \n",
" 12 | \n",
" Travel expenses | \n",
" 14306.11 | \n",
" 1.0 | \n",
" 1.6 | \n",
" Travel expenses (1.6%) | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" P&L Label Amount Perc of TotalExp \\\n",
"0 Sales Payroll 291654.09 20.4 \n",
"1 Engineering Payroll 229355.49 16.1 \n",
"2 G&A Payroll 160174.56 11.2 \n",
"3 Product Payroll 137638.41 9.6 \n",
"4 Customer Success (COR) Payroll 127663.90 8.9 \n",
"5 Sales & Marketing Expenses 94161.34 6.6 \n",
"6 Marketing Payroll 84550.83 5.9 \n",
"7 Outside Services 77173.46 5.4 \n",
"8 Facility Cost 75241.53 5.3 \n",
"9 Legal and Accounting 52466.96 3.7 \n",
"10 Admin & Office Expense 50056.37 3.5 \n",
"11 COR - Direct 33260.50 2.3 \n",
"12 Travel expenses 14306.11 1.0 \n",
"\n",
" Perc of TotalRev Chart Label \n",
"0 32.0 Sales Payroll (32.0%) \n",
"1 25.1 Engineering Payroll (25.1%) \n",
"2 17.6 G&A Payroll (17.6%) \n",
"3 15.1 Product Payroll (15.1%) \n",
"4 14.0 Customer Success (COR) Payroll (14.0%) \n",
"5 10.3 Sales & Marketing Expenses (10.3%) \n",
"6 9.3 Marketing Payroll (9.3%) \n",
"7 8.5 Outside Services (8.5%) \n",
"8 8.2 Facility Cost (8.2%) \n",
"9 5.8 Legal and Accounting (5.8%) \n",
"10 5.5 Admin & Office Expense (5.5%) \n",
"11 3.6 COR - Direct (3.6%) \n",
"12 1.6 Travel expenses (1.6%) "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Create a new column in data frame for our treemap label. Sort by largest expense item\n",
"df = df.sort_values(by='Amount',ascending = False)\n",
"# Find percentage of total expenses, create column\n",
"df['Perc of TotalExp'] = round((df['Amount'] / sum(df['Amount']))*100,1)\n",
"# Find percentage of total revenue, create column\n",
"# GAAP Revenue (not ARR or subscription)\n",
"tot_rev = 912160\n",
"df['Perc of TotalRev'] = round((df['Amount'] / tot_rev)*100,1)\n",
"# Create new column for labels for treemap which are python strings\n",
"df['Chart Label'] = df['P&L Label'] + \" (\" + df['Perc of TotalRev'].astype('str') + \"%)\"\n",
"df.reset_index(inplace=True, drop=True)\n",
"df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Payroll is the largest category. What is the total payroll as a % of total rev?"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" P&L Label | \n",
" Amount | \n",
" Perc of TotalExp | \n",
" Perc of TotalRev | \n",
" % total P | \n",
" Payroll Label | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" Sales Payroll | \n",
" 291654.09 | \n",
" 20.4 | \n",
" 32.0 | \n",
" 28.3 | \n",
" Sales (32.0%) | \n",
"
\n",
" \n",
" 1 | \n",
" Engineering Payroll | \n",
" 229355.49 | \n",
" 16.1 | \n",
" 25.1 | \n",
" 22.2 | \n",
" Engineering (25.1%) | \n",
"
\n",
" \n",
" 2 | \n",
" G&A Payroll | \n",
" 160174.56 | \n",
" 11.2 | \n",
" 17.6 | \n",
" 15.5 | \n",
" G&A (17.6%) | \n",
"
\n",
" \n",
" 3 | \n",
" Product Payroll | \n",
" 137638.41 | \n",
" 9.6 | \n",
" 15.1 | \n",
" 13.3 | \n",
" Product (15.1%) | \n",
"
\n",
" \n",
" 4 | \n",
" Customer Success (COR) Payroll | \n",
" 127663.90 | \n",
" 8.9 | \n",
" 14.0 | \n",
" 12.4 | \n",
" Customer Success (COR) (14.0%) | \n",
"
\n",
" \n",
" 5 | \n",
" Marketing Payroll | \n",
" 84550.83 | \n",
" 5.9 | \n",
" 9.3 | \n",
" 8.2 | \n",
" Marketing (9.3%) | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" P&L Label Amount Perc of TotalExp \\\n",
"0 Sales Payroll 291654.09 20.4 \n",
"1 Engineering Payroll 229355.49 16.1 \n",
"2 G&A Payroll 160174.56 11.2 \n",
"3 Product Payroll 137638.41 9.6 \n",
"4 Customer Success (COR) Payroll 127663.90 8.9 \n",
"5 Marketing Payroll 84550.83 5.9 \n",
"\n",
" Perc of TotalRev % total P Payroll Label \n",
"0 32.0 28.3 Sales (32.0%) \n",
"1 25.1 22.2 Engineering (25.1%) \n",
"2 17.6 15.5 G&A (17.6%) \n",
"3 15.1 13.3 Product (15.1%) \n",
"4 14.0 12.4 Customer Success (COR) (14.0%) \n",
"5 9.3 8.2 Marketing (9.3%) "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# create another data table with just the payroll info\n",
"df_payroll = df[df['P&L Label'].str.contains('Payroll')].copy()\n",
"df_payroll.reset_index(inplace=True,drop=True)\n",
"# create a column for % of total payroll\n",
"# Find percentage of total expense, create column\n",
"df_payroll['% total P'] = round((df_payroll['Amount'] / sum(df_payroll['Amount']))*100,1)\n",
"\n",
"# Create new column for labels for treemap which are python strings\n",
"df_payroll['Payroll Label'] = df_payroll['P&L Label'].apply(lambda x: x[:-8]) + \\\n",
" \" (\" + df_payroll['Perc of TotalRev'].astype('str') + \"%)\"\n",
"df_payroll.drop('Chart Label',axis = 1, inplace=True)\n",
"df_payroll"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"72.2"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# The answer to total payroll as % of total expense:\n",
"total_payroll_as_perc_of_tot = round((sum(df_payroll['Amount'])/sum(df['Amount']))*100,1)\n",
"total_payroll_as_perc_of_tot"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"113.1"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# total payroll as % of Gaap Rev\n",
"sum(df_payroll['Perc of TotalRev'])"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"156"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# total expenses as % of Gaap Revenue\n",
"int(sum(df['Perc of TotalRev']))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The level of spending is actually not that unusual for a growing SaaS company which is investing in it sales team. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"CREATE THE CHARTS FROM THE 2 SETS OF DATA"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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