01-commission-200-thb-per-step.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a5dfdff8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as mtick\n",
    "from matplotlib.ticker import PercentFormatter\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "14fcbfe7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Input parameters\n",
    "START_PRICE  = 200    # Starting price\n",
    "STEP_PRICE   = 200     # Price to increase in each row\n",
    "END_PRICE    = 100000  # Price at the end of the table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2799e113",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fastwork_commission(price):\n",
    "    # Tier 1\n",
    "    tier1 = min(price,10000) * 0.17\n",
    "    # Tier 2\n",
    "    tier2 = min(max(0,price-10000) * 0.12,4800)\n",
    "    # Tier 3\n",
    "    tier3 = max(0,price-50000) * 0.07\n",
    "    \n",
    "    return tier1+tier2+tier3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "afea8484",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(range(START_PRICE,END_PRICE,STEP_PRICE),columns=['Price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c8cf3a81",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Fastwork_Commission_Fee'] = df['Price'].apply(fastwork_commission)\n",
    "df['Fee_Proportion'] = df['Fastwork_Commission_Fee'] / df['Price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "209e73b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "STEP_IN_GRAPH = 5000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d71905e5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 1600x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 9))\n",
    "ax = sns.lineplot(data=df,x=\"Price\", y=\"Fee_Proportion\")\n",
    "ax.set_frame_on(False)\n",
    "ax.tick_params(axis='both',which='both')\n",
    "ax.set_ybound(lower=0.05,upper=0.2)\n",
    "ax.set_xbound(lower=0,upper=100000)\n",
    "ax.minorticks_on()\n",
    "ax.grid(visible=True,which='both')\n",
    "plt.savefig(\"Commission_Proportion.png\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
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