{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Severity index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import geopandas as gpd\n",
    "import seaborn as sns\n",
    "import matplotlib as mpl\n",
    "import pandas as pd\n",
    "import matplotlib.ticker as ticker\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = {\n",
    "    1946: np.nan,\n",
    "    1947: 5,\n",
    "    1948: 2,\n",
    "    1950: 2,\n",
    "    1955: 2,\n",
    "    1956: 2,\n",
    "    1958: 1,\n",
    "    1962: 3,\n",
    "    1963: 3,\n",
    "    1974: 3,\n",
    "    1975: 3,\n",
    "    1976: 5,\n",
    "    1985: 3,\n",
    "    1986: 3,\n",
    "    1987: 1,\n",
    "    1995: 1,\n",
    "    1998: 1,\n",
    "    1999: 1,\n",
    "    2001: 1,\n",
    "    2002: 1,\n",
    "    2007: 1,\n",
    "    2008: 1,\n",
    "    2009: 1,\n",
    "    2012: 5,\n",
    "    2013: 5,\n",
    "    2018: 3,\n",
    "    2020: 1,\n",
    "    2022: 1,\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_ = pd.DataFrame(data, index=[0]).transpose().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_[\"time\"] = pd.to_datetime(data_[\"index\"], format=\"%Y\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_ = data_.set_index(\"time\").resample(\"Y\").mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_[\"year\"] = data_.index.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1152x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 5))\n",
    "ax = sns.barplot(data_, x=\"year\", y=0, color=\"#762a83\")\n",
    "ax.set_xlabel(\"Year\")\n",
    "ax.set_ylabel(\"Severity Index\")\n",
    "ax.grid(which=\"major\", axis=\"y\")\n",
    "ax.tick_params(axis=\"x\", labelrotation=90)\n",
    "ax.xaxis.set_major_locator(ticker.MultipleLocator(2))\n",
    "sns.despine()\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ClimAg",
   "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": "3.10.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}