{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f75fc25e-b87f-4a3f-b3da-0a5ede79d5da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='year', ylabel='Count'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "[encoded data, 27024 characters]",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "\n",
    "df = pd.DataFrame(pd.read_csv(\"ged171.csv\"))\n",
    "df.head()\n",
    "df.describe()\n",
    "\n",
    "sns.histplot(df[\"year\"] )\n",
    "# sns.scatterplot(df[\"region\"])\n",
    "# scatterplot\n",
    "# boxplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5217b30d-e7f4-4bf6-b728-0bec5e467a93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='years'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "[encoded data, 23888 characters]",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "\n",
    "df = pd.DataFrame(pd.read_csv(\"ged171.csv\"))\n",
    "\n",
    "data = { \"years\": df[\"year\"], \"deaths\": df['best']}\n",
    "new_df = pd.DataFrame(data)\n",
    "\n",
    "a = new_df.groupby(\"years\").sum()\n",
    "sns.scatterplot(a)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "0bb4c3f8-e9b8-47ba-9fe6-63a1bb13b098",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='region', ylabel='best'>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "[encoded data, 21576 characters]",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "\n",
    "df = pd.DataFrame(pd.read_csv(\"ged171.csv\"))\n",
    "\n",
    "sns.boxplot(\n",
    "    x=\"region\", \n",
    "    y=\"best\", \n",
    "    data=df,\n",
    "    \n",
    ")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6c7d18c9-b454-419c-94cb-e015ca4c4f35",
   "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": "3.13.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
