{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e1b04b1d",
   "metadata": {},
   "source": [
    "# Boxplot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17bd885e",
   "metadata": {},
   "source": [
    "- Used to find outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "dab91803",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "data = {'Month':['Jan','Jan','March','April','Jan','Jan','March','April','Jan','Jan','March','April'],\n",
    "        'Sales': [99, 102, 905, 120,12,12,12,22,12,12,12,430]\n",
    "       }\n",
    "df=pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "39ba15f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(\n",
    "x='Sales',\n",
    "data=df,\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "364f514a",
   "metadata": {},
   "source": [
    "## Basic Stats"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea152830",
   "metadata": {},
   "source": [
    "![image](./Images/Box.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55e64cc7",
   "metadata": {},
   "source": [
    "- Median - 50 Percentile\n",
    "- 1st and 3rd Quartiles (25th & 75th percentiles) ( Most important !!)\n",
    "    - 50% \n",
    "        - 50 percent of data will be inside this box (inside Q1 and Q3)\n",
    "        - IQR = difference between this Q1 & Q3"
   ]
  }
 ],
 "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}