thunder_strike_EDA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1beaec86-ebdf-462b-bd0f-3865ebd66ea1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import datetime as dt\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1ce3d9f2-8aeb-44b7-9218-0176d03b8813",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read in the 2018 lightning strike dataset.\n",
    "df = pd.read_csv('eda_using_basic_data_functions_in_python_dataset1.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d2e72bef-d416-416b-9089-9e47f408b28c",
   "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>date</th>\n",
       "      <th>number_of_strikes</th>\n",
       "      <th>center_point_geom</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-01-03</td>\n",
       "      <td>194</td>\n",
       "      <td>POINT(-75 27)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-01-03</td>\n",
       "      <td>41</td>\n",
       "      <td>POINT(-78.4 29)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-01-03</td>\n",
       "      <td>33</td>\n",
       "      <td>POINT(-73.9 27)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-01-03</td>\n",
       "      <td>38</td>\n",
       "      <td>POINT(-73.8 27)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-01-03</td>\n",
       "      <td>92</td>\n",
       "      <td>POINT(-79 28)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2018-01-03</td>\n",
       "      <td>119</td>\n",
       "      <td>POINT(-78 28)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2018-01-03</td>\n",
       "      <td>35</td>\n",
       "      <td>POINT(-79.3 28)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2018-01-03</td>\n",
       "      <td>60</td>\n",
       "      <td>POINT(-79.1 28)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2018-01-03</td>\n",
       "      <td>41</td>\n",
       "      <td>POINT(-78.7 28)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2018-01-03</td>\n",
       "      <td>119</td>\n",
       "      <td>POINT(-78.6 28)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  number_of_strikes center_point_geom\n",
       "0  2018-01-03                194     POINT(-75 27)\n",
       "1  2018-01-03                 41   POINT(-78.4 29)\n",
       "2  2018-01-03                 33   POINT(-73.9 27)\n",
       "3  2018-01-03                 38   POINT(-73.8 27)\n",
       "4  2018-01-03                 92     POINT(-79 28)\n",
       "5  2018-01-03                119     POINT(-78 28)\n",
       "6  2018-01-03                 35   POINT(-79.3 28)\n",
       "7  2018-01-03                 60   POINT(-79.1 28)\n",
       "8  2018-01-03                 41   POINT(-78.7 28)\n",
       "9  2018-01-03                119   POINT(-78.6 28)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0a871c51-a16b-45e1-8e8c-4e77b95becf2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3401012, 3)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "15073a42-4b81-4b52-9c12-624a28e2163c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['date'] = pd.to_datetime(df['date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "508fa67f-ed0c-498b-85c3-6053068c9236",
   "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>number_of_strikes</th>\n",
       "      <th>center_point_geom</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-08-29</th>\n",
       "      <td>1070457</td>\n",
       "      <td>POINT(-85.7 29.1)POINT(-85.6 29.1)POINT(-84.5 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-17</th>\n",
       "      <td>969774</td>\n",
       "      <td>POINT(-80.6 20.3)POINT(-80.5 20.3)POINT(-80.4 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-28</th>\n",
       "      <td>917199</td>\n",
       "      <td>POINT(-108.2 28)POINT(-108.1 28)POINT(-108 28)...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-27</th>\n",
       "      <td>824589</td>\n",
       "      <td>POINT(-109 24.8)POINT(-108 24.8)POINT(-88.1 24...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-30</th>\n",
       "      <td>802170</td>\n",
       "      <td>POINT(-75.7 19)POINT(-75.6 19)POINT(-78 20)POI...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            number_of_strikes  \\\n",
       "date                            \n",
       "2018-08-29            1070457   \n",
       "2018-08-17             969774   \n",
       "2018-08-28             917199   \n",
       "2018-08-27             824589   \n",
       "2018-08-30             802170   \n",
       "\n",
       "                                            center_point_geom  \n",
       "date                                                           \n",
       "2018-08-29  POINT(-85.7 29.1)POINT(-85.6 29.1)POINT(-84.5 ...  \n",
       "2018-08-17  POINT(-80.6 20.3)POINT(-80.5 20.3)POINT(-80.4 ...  \n",
       "2018-08-28  POINT(-108.2 28)POINT(-108.1 28)POINT(-108 28)...  \n",
       "2018-08-27  POINT(-109 24.8)POINT(-108 24.8)POINT(-88.1 24...  \n",
       "2018-08-30  POINT(-75.7 19)POINT(-75.6 19)POINT(-78 20)POI...  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['date']).sum().sort_values('number_of_strikes', ascending= False).head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "86610fde-1b82-4038-9c21-4489a51bef5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['month'] = df['date'].dt.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8c486963-ed9f-4387-a813-c291cc0ea4ec",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "datetime64 type does not support sum operations",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[18], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m df\u001b[38;5;241m.\u001b[39mgroupby([\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmonth\u001b[39m\u001b[38;5;124m'\u001b[39m])\u001b[38;5;241m.\u001b[39msum()\u001b[38;5;241m.\u001b[39msort_values(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnumber_of_strikes\u001b[39m\u001b[38;5;124m'\u001b[39m, ascending\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\u001b[38;5;241m.\u001b[39mhead(\u001b[38;5;241m12\u001b[39m)\n",
      "File \u001b[1;32mD:\\App_installations\\Anaconda\\Lib\\site-packages\\pandas\\core\\groupby\\groupby.py:3146\u001b[0m, in \u001b[0;36mGroupBy.sum\u001b[1;34m(self, numeric_only, min_count, engine, engine_kwargs)\u001b[0m\n\u001b[0;32m   3141\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m   3142\u001b[0m     \u001b[38;5;66;03m# If we are grouping on categoricals we want unobserved categories to\u001b[39;00m\n\u001b[0;32m   3143\u001b[0m     \u001b[38;5;66;03m# return zero, rather than the default of NaN which the reindexing in\u001b[39;00m\n\u001b[0;32m   3144\u001b[0m     \u001b[38;5;66;03m# _agg_general() returns. GH #31422\u001b[39;00m\n\u001b[0;32m   3145\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m com\u001b[38;5;241m.\u001b[39mtemp_setattr(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mobserved\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[1;32m-> 3146\u001b[0m         result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_agg_general(\n\u001b[0;32m   3147\u001b[0m             numeric_only\u001b[38;5;241m=\u001b[39mnumeric_only,\n\u001b[0;32m   3148\u001b[0m             min_count\u001b[38;5;241m=\u001b[39mmin_count,\n\u001b[0;32m   3149\u001b[0m             alias\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msum\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m   3150\u001b[0m             npfunc\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39msum,\n\u001b[0;32m   3151\u001b[0m         )\n\u001b[0;32m   3153\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reindex_output(result, fill_value\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\n",
      "File \u001b[1;32mD:\\App_installations\\Anaconda\\Lib\\site-packages\\pandas\\core\\groupby\\groupby.py:1906\u001b[0m, in \u001b[0;36mGroupBy._agg_general\u001b[1;34m(self, numeric_only, min_count, alias, npfunc, **kwargs)\u001b[0m\n\u001b[0;32m   1896\u001b[0m \u001b[38;5;129m@final\u001b[39m\n\u001b[0;32m   1897\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_agg_general\u001b[39m(\n\u001b[0;32m   1898\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1904\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[0;32m   1905\u001b[0m ):\n\u001b[1;32m-> 1906\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cython_agg_general(\n\u001b[0;32m   1907\u001b[0m         how\u001b[38;5;241m=\u001b[39malias,\n\u001b[0;32m   1908\u001b[0m         alt\u001b[38;5;241m=\u001b[39mnpfunc,\n\u001b[0;32m   1909\u001b[0m         numeric_only\u001b[38;5;241m=\u001b[39mnumeric_only,\n\u001b[0;32m   1910\u001b[0m         min_count\u001b[38;5;241m=\u001b[39mmin_count,\n\u001b[0;32m   1911\u001b[0m         \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[0;32m   1912\u001b[0m     )\n\u001b[0;32m   1913\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m result\u001b[38;5;241m.\u001b[39m__finalize__(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgroupby\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[1;32mD:\\App_installations\\Anaconda\\Lib\\site-packages\\pandas\\core\\groupby\\groupby.py:1998\u001b[0m, in \u001b[0;36mGroupBy._cython_agg_general\u001b[1;34m(self, how, alt, numeric_only, min_count, **kwargs)\u001b[0m\n\u001b[0;32m   1995\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_agg_py_fallback(how, values, ndim\u001b[38;5;241m=\u001b[39mdata\u001b[38;5;241m.\u001b[39mndim, alt\u001b[38;5;241m=\u001b[39malt)\n\u001b[0;32m   1996\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m result\n\u001b[1;32m-> 1998\u001b[0m new_mgr \u001b[38;5;241m=\u001b[39m data\u001b[38;5;241m.\u001b[39mgrouped_reduce(array_func)\n\u001b[0;32m   1999\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_wrap_agged_manager(new_mgr)\n\u001b[0;32m   2000\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m how \u001b[38;5;129;01min\u001b[39;00m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124midxmin\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124midxmax\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n",
      "File \u001b[1;32mD:\\App_installations\\Anaconda\\Lib\\site-packages\\pandas\\core\\internals\\managers.py:1472\u001b[0m, in \u001b[0;36mBlockManager.grouped_reduce\u001b[1;34m(self, func)\u001b[0m\n\u001b[0;32m   1470\u001b[0m             result_blocks \u001b[38;5;241m=\u001b[39m extend_blocks(applied, result_blocks)\n\u001b[0;32m   1471\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1472\u001b[0m         applied \u001b[38;5;241m=\u001b[39m blk\u001b[38;5;241m.\u001b[39mapply(func)\n\u001b[0;32m   1473\u001b[0m         result_blocks \u001b[38;5;241m=\u001b[39m extend_blocks(applied, result_blocks)\n\u001b[0;32m   1475\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(result_blocks) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n",
      "File \u001b[1;32mD:\\App_installations\\Anaconda\\Lib\\site-packages\\pandas\\core\\internals\\blocks.py:393\u001b[0m, in \u001b[0;36mBlock.apply\u001b[1;34m(self, func, **kwargs)\u001b[0m\n\u001b[0;32m    387\u001b[0m \u001b[38;5;129m@final\u001b[39m\n\u001b[0;32m    388\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mapply\u001b[39m(\u001b[38;5;28mself\u001b[39m, func, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mlist\u001b[39m[Block]:\n\u001b[0;32m    389\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    390\u001b[0m \u001b[38;5;124;03m    apply the function to my values; return a block if we are not\u001b[39;00m\n\u001b[0;32m    391\u001b[0m \u001b[38;5;124;03m    one\u001b[39;00m\n\u001b[0;32m    392\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 393\u001b[0m     result \u001b[38;5;241m=\u001b[39m func(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalues, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    395\u001b[0m     result \u001b[38;5;241m=\u001b[39m maybe_coerce_values(result)\n\u001b[0;32m    396\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_split_op_result(result)\n",
      "File \u001b[1;32mD:\\App_installations\\Anaconda\\Lib\\site-packages\\pandas\\core\\groupby\\groupby.py:1973\u001b[0m, in \u001b[0;36mGroupBy._cython_agg_general.<locals>.array_func\u001b[1;34m(values)\u001b[0m\n\u001b[0;32m   1971\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21marray_func\u001b[39m(values: ArrayLike) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m ArrayLike:\n\u001b[0;32m   1972\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 1973\u001b[0m         result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_grouper\u001b[38;5;241m.\u001b[39m_cython_operation(\n\u001b[0;32m   1974\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124maggregate\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m   1975\u001b[0m             values,\n\u001b[0;32m   1976\u001b[0m             how,\n\u001b[0;32m   1977\u001b[0m             axis\u001b[38;5;241m=\u001b[39mdata\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39m,\n\u001b[0;32m   1978\u001b[0m             min_count\u001b[38;5;241m=\u001b[39mmin_count,\n\u001b[0;32m   1979\u001b[0m             \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[0;32m   1980\u001b[0m         )\n\u001b[0;32m   1981\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m:\n\u001b[0;32m   1982\u001b[0m         \u001b[38;5;66;03m# generally if we have numeric_only=False\u001b[39;00m\n\u001b[0;32m   1983\u001b[0m         \u001b[38;5;66;03m# and non-applicable functions\u001b[39;00m\n\u001b[0;32m   1984\u001b[0m         \u001b[38;5;66;03m# try to python agg\u001b[39;00m\n\u001b[0;32m   1985\u001b[0m         \u001b[38;5;66;03m# TODO: shouldn't min_count matter?\u001b[39;00m\n\u001b[0;32m   1986\u001b[0m         \u001b[38;5;66;03m# TODO: avoid special casing SparseArray here\u001b[39;00m\n\u001b[0;32m   1987\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m how \u001b[38;5;129;01min\u001b[39;00m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124many\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mall\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(values, SparseArray):\n",
      "File \u001b[1;32mD:\\App_installations\\Anaconda\\Lib\\site-packages\\pandas\\core\\groupby\\ops.py:831\u001b[0m, in \u001b[0;36mBaseGrouper._cython_operation\u001b[1;34m(self, kind, values, how, axis, min_count, **kwargs)\u001b[0m\n\u001b[0;32m    829\u001b[0m ids, _, _ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgroup_info\n\u001b[0;32m    830\u001b[0m ngroups \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mngroups\n\u001b[1;32m--> 831\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m cy_op\u001b[38;5;241m.\u001b[39mcython_operation(\n\u001b[0;32m    832\u001b[0m     values\u001b[38;5;241m=\u001b[39mvalues,\n\u001b[0;32m    833\u001b[0m     axis\u001b[38;5;241m=\u001b[39maxis,\n\u001b[0;32m    834\u001b[0m     min_count\u001b[38;5;241m=\u001b[39mmin_count,\n\u001b[0;32m    835\u001b[0m     comp_ids\u001b[38;5;241m=\u001b[39mids,\n\u001b[0;32m    836\u001b[0m     ngroups\u001b[38;5;241m=\u001b[39mngroups,\n\u001b[0;32m    837\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[0;32m    838\u001b[0m )\n",
      "File \u001b[1;32mD:\\App_installations\\Anaconda\\Lib\\site-packages\\pandas\\core\\groupby\\ops.py:541\u001b[0m, in \u001b[0;36mWrappedCythonOp.cython_operation\u001b[1;34m(self, values, axis, min_count, comp_ids, ngroups, **kwargs)\u001b[0m\n\u001b[0;32m    537\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_axis(axis, values)\n\u001b[0;32m    539\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(values, np\u001b[38;5;241m.\u001b[39mndarray):\n\u001b[0;32m    540\u001b[0m     \u001b[38;5;66;03m# i.e. ExtensionArray\u001b[39;00m\n\u001b[1;32m--> 541\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m values\u001b[38;5;241m.\u001b[39m_groupby_op(\n\u001b[0;32m    542\u001b[0m         how\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhow,\n\u001b[0;32m    543\u001b[0m         has_dropped_na\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhas_dropped_na,\n\u001b[0;32m    544\u001b[0m         min_count\u001b[38;5;241m=\u001b[39mmin_count,\n\u001b[0;32m    545\u001b[0m         ngroups\u001b[38;5;241m=\u001b[39mngroups,\n\u001b[0;32m    546\u001b[0m         ids\u001b[38;5;241m=\u001b[39mcomp_ids,\n\u001b[0;32m    547\u001b[0m         \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[0;32m    548\u001b[0m     )\n\u001b[0;32m    550\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cython_op_ndim_compat(\n\u001b[0;32m    551\u001b[0m     values,\n\u001b[0;32m    552\u001b[0m     min_count\u001b[38;5;241m=\u001b[39mmin_count,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    556\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[0;32m    557\u001b[0m )\n",
      "File \u001b[1;32mD:\\App_installations\\Anaconda\\Lib\\site-packages\\pandas\\core\\arrays\\datetimelike.py:1671\u001b[0m, in \u001b[0;36mDatetimeLikeArrayMixin._groupby_op\u001b[1;34m(self, how, has_dropped_na, min_count, ngroups, ids, **kwargs)\u001b[0m\n\u001b[0;32m   1668\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m dtype\u001b[38;5;241m.\u001b[39mkind \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mM\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m   1669\u001b[0m     \u001b[38;5;66;03m# Adding/multiplying datetimes is not valid\u001b[39;00m\n\u001b[0;32m   1670\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m how \u001b[38;5;129;01min\u001b[39;00m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msum\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mprod\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcumsum\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcumprod\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvar\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mskew\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n\u001b[1;32m-> 1671\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdatetime64 type does not support \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mhow\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m operations\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m   1672\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m how \u001b[38;5;129;01min\u001b[39;00m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124many\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mall\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n\u001b[0;32m   1673\u001b[0m         \u001b[38;5;66;03m# GH#34479\u001b[39;00m\n\u001b[0;32m   1674\u001b[0m         warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[0;32m   1675\u001b[0m             \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mhow\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m with datetime64 dtypes is deprecated and will raise in a \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1676\u001b[0m             \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfuture version. Use (obj != pd.Timestamp(0)).\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mhow\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m() instead.\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m   1677\u001b[0m             \u001b[38;5;167;01mFutureWarning\u001b[39;00m,\n\u001b[0;32m   1678\u001b[0m             stacklevel\u001b[38;5;241m=\u001b[39mfind_stack_level(),\n\u001b[0;32m   1679\u001b[0m         )\n",
      "\u001b[1;31mTypeError\u001b[0m: datetime64 type does not support sum operations"
     ]
    }
   ],
   "source": [
    "df.groupby(['month']).sum().sort_values('number_of_strikes', ascending=False).head(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5e9025ef-d49d-4b7a-bf35-6130267067d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3401012 entries, 0 to 3401011\n",
      "Data columns (total 4 columns):\n",
      " #   Column             Dtype         \n",
      "---  ------             -----         \n",
      " 0   date               datetime64[ns]\n",
      " 1   number_of_strikes  int64         \n",
      " 2   center_point_geom  object        \n",
      " 3   month              int32         \n",
      "dtypes: datetime64[ns](1), int32(1), int64(1), object(1)\n",
      "memory usage: 90.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4e83cbb6-396b-4582-a96a-d647e7795f15",
   "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
}