{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e1b04b1d",
   "metadata": {},
   "source": [
    "# Pairplot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17bd885e",
   "metadata": {},
   "source": [
    "- Find relationship between numerical variables\n",
    "- Note: Its not useful to scatterplot x variable with xx variable\n",
    "    - So in these cases, histogram is plotted automatically instead of line plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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",
    "        'Profit': [9, 12, 905, 120,120,12,102,22,192,12,12,40],\n",
    "        'Discount': [9, 12, 905, 120,120,12,102,22,192,12,12,40]\n",
    "       }\n",
    "df=pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "39ba15f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 540x540 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca5b11c8",
   "metadata": {},
   "source": [
    "- We can see that Discount and Profit are directly proportional"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8042baf4",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}