SeaBorn.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2d10210b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0d0a85f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>mark</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>b</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>c</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  name  mark\n",
       "0    a     1\n",
       "1    b     2\n",
       "2    c     1"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df=pd.DataFrame({\"name\":['a','b','c'],\"mark\":[1,2,1]})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4c6a33cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x=\"name\",y=\"mark\",data=df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6a3289ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import pandas as pd\n",
    "df=pd.DataFrame({\"name\":['a','b','c'],\"mark\":[1,2,1],\"hi\":[4,92,4]})\n",
    "df\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f23b997a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>mark</th>\n",
       "      <th>hi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>b</td>\n",
       "      <td>2</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>c</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  name  mark  hi\n",
       "0    a     1   4\n",
       "1    b     2  92\n",
       "2    c     1   4"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6e19a9ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.lineplot(x=\"name\",y=\"mark\",data=df , hue=\"hi\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b5d7006a",
   "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": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}