{ "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": [ "
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P&L LabelAmount
0COR - Direct33260.50
1Customer Success (COR) Payroll127663.90
2Sales Payroll291654.09
3Marketing Payroll84550.83
4G&A Payroll160174.56
5Product Payroll137638.41
6Engineering Payroll229355.49
7Travel expenses14306.11
8Legal and Accounting52466.96
9Outside Services77173.46
10Sales & Marketing Expenses94161.34
11Facility Cost75241.53
12Admin & Office Expense50056.37
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" ], "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": [ "
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P&L LabelAmountPerc of TotalExpPerc of TotalRevChart Label
0Sales Payroll291654.0920.432.0Sales Payroll (32.0%)
1Engineering Payroll229355.4916.125.1Engineering Payroll (25.1%)
2G&A Payroll160174.5611.217.6G&A Payroll (17.6%)
3Product Payroll137638.419.615.1Product Payroll (15.1%)
4Customer Success (COR) Payroll127663.908.914.0Customer Success (COR) Payroll (14.0%)
5Sales & Marketing Expenses94161.346.610.3Sales & Marketing Expenses (10.3%)
6Marketing Payroll84550.835.99.3Marketing Payroll (9.3%)
7Outside Services77173.465.48.5Outside Services (8.5%)
8Facility Cost75241.535.38.2Facility Cost (8.2%)
9Legal and Accounting52466.963.75.8Legal and Accounting (5.8%)
10Admin & Office Expense50056.373.55.5Admin & Office Expense (5.5%)
11COR - Direct33260.502.33.6COR - Direct (3.6%)
12Travel expenses14306.111.01.6Travel expenses (1.6%)
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" ], "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": [ "
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P&L LabelAmountPerc of TotalExpPerc of TotalRev% total PPayroll Label
0Sales Payroll291654.0920.432.028.3Sales (32.0%)
1Engineering Payroll229355.4916.125.122.2Engineering (25.1%)
2G&A Payroll160174.5611.217.615.5G&A (17.6%)
3Product Payroll137638.419.615.113.3Product (15.1%)
4Customer Success (COR) Payroll127663.908.914.012.4Customer Success (COR) (14.0%)
5Marketing Payroll84550.835.99.38.2Marketing (9.3%)
\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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"text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Chart of All expenses\n", "fig, ax = plt.subplots()\n", "# colormap\n", "cmap = matplotlib.cm.summer\n", "# max and min values\n", "max_val = max(df['Amount'])\n", "min_val = min(df['Amount'])\n", "# creating colors for each tile\n", "norm = matplotlib.colors.Normalize(vmax = max_val, vmin = min_val)\n", "colors = [cmap(norm(value)) for value in df['Amount']]\n", "# create the chart with .plot\n", "squarify.plot(sizes=df['Amount'],label=df['Chart Label'],alpha = 0.75, color = colors)\n", "# Remove axis line\n", "plt.axis('off')\n", "# invert the y-axis so largest percentages are at the upper left\n", "plt.gca().invert_yaxis()\n", "# Title and positioning\n", "plt.title('Expenses (% of GAAP Rev) for SaaS Company in Growth Stage',fontsize = 32)\n", "title_pos = ax.title\n", "title_pos.set_position([.5, 1.01])\n", "#set figure size and save\n", "fig.set_size_inches(18.5, 10.5)\n", "fig.savefig('tree1.jpg', dpi=100)\n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Create Chart for Payroll categories only\n", "# Chart of All expenses\n", "fig2, ax2 = plt.subplots()\n", "# colormap\n", "cmap = matplotlib.cm.summer\n", "# max and min values\n", "max_val = max(df_payroll['Amount'])\n", "min_val = min(df_payroll['Amount'])\n", "# creating colors for each tile\n", "norm = matplotlib.colors.Normalize(vmax = max_val, vmin = min_val)\n", "colors = [cmap(norm(value)) for value in df_payroll['Amount']]\n", "# create the chart with .plot\n", "squarify.plot(sizes=df_payroll['Amount'],label=df_payroll['Payroll Label'],alpha = 0.75, color = colors)\n", "# Remove axis line\n", "plt.axis('off')\n", "# invert the y-axis so largest percentages are at the upper left\n", "plt.gca().invert_yaxis()\n", "# Title and positioning\n", "plt.title('Dept. Payroll (% of Gaap Rev) for SaaS Company in Growth Stage',fontsize = 32)\n", "title_pos = ax2.title\n", "title_pos.set_position([.5, 1.01])\n", "#set figure size and save\n", "fig2.set_size_inches(18.5, 10.5)\n", "fig2.savefig('tree2.jpg', dpi=100)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.7.2" } }, "nbformat": 4, "nbformat_minor": 2 }