fast_food_eda.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import plotly.express as px\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RecordID</th>\n",
       "      <th>ZipCode</th>\n",
       "      <th>City</th>\n",
       "      <th>County</th>\n",
       "      <th>State</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>DominantAreaCode</th>\n",
       "      <th>ResidentialDeliveries</th>\n",
       "      <th>ResidentialPOBoxes</th>\n",
       "      <th>...</th>\n",
       "      <th>MedianHomeValue</th>\n",
       "      <th>MedianAge</th>\n",
       "      <th>MedianAgeMale</th>\n",
       "      <th>MedianAgeFemale</th>\n",
       "      <th>EducationNinthGradeOrLess</th>\n",
       "      <th>EducationSomeHighSchool</th>\n",
       "      <th>EducationHighSchoolGraduate</th>\n",
       "      <th>EducationSomeCollegeWithoutDiploma</th>\n",
       "      <th>EducationAssociatesDegree</th>\n",
       "      <th>EducationBachelorsDegree</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>236</td>\n",
       "      <td>90620</td>\n",
       "      <td>BUENA PARK</td>\n",
       "      <td>ORANGE</td>\n",
       "      <td>CA</td>\n",
       "      <td>33.8425</td>\n",
       "      <td>-118.0124</td>\n",
       "      <td>714</td>\n",
       "      <td>13804</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>806000.0</td>\n",
       "      <td>37.2</td>\n",
       "      <td>36.2</td>\n",
       "      <td>38.4</td>\n",
       "      <td>1762</td>\n",
       "      <td>2280</td>\n",
       "      <td>8709</td>\n",
       "      <td>9540</td>\n",
       "      <td>3239</td>\n",
       "      <td>8044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>237</td>\n",
       "      <td>90621</td>\n",
       "      <td>BUENA PARK</td>\n",
       "      <td>ORANGE</td>\n",
       "      <td>CA</td>\n",
       "      <td>33.8706</td>\n",
       "      <td>-117.9963</td>\n",
       "      <td>714</td>\n",
       "      <td>11651</td>\n",
       "      <td>112</td>\n",
       "      <td>...</td>\n",
       "      <td>819000.0</td>\n",
       "      <td>35.9</td>\n",
       "      <td>33.8</td>\n",
       "      <td>38.2</td>\n",
       "      <td>2262</td>\n",
       "      <td>2146</td>\n",
       "      <td>6475</td>\n",
       "      <td>6982</td>\n",
       "      <td>2398</td>\n",
       "      <td>5681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>238</td>\n",
       "      <td>90623</td>\n",
       "      <td>BUENA PARK</td>\n",
       "      <td>ORANGE</td>\n",
       "      <td>CA</td>\n",
       "      <td>33.8502</td>\n",
       "      <td>-118.0399</td>\n",
       "      <td>714</td>\n",
       "      <td>5231</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1018000.0</td>\n",
       "      <td>44.2</td>\n",
       "      <td>43.7</td>\n",
       "      <td>44.6</td>\n",
       "      <td>305</td>\n",
       "      <td>339</td>\n",
       "      <td>2358</td>\n",
       "      <td>2827</td>\n",
       "      <td>1103</td>\n",
       "      <td>3760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>239</td>\n",
       "      <td>90623</td>\n",
       "      <td>LA PALMA</td>\n",
       "      <td>ORANGE</td>\n",
       "      <td>CA</td>\n",
       "      <td>33.8502</td>\n",
       "      <td>-118.0399</td>\n",
       "      <td>714</td>\n",
       "      <td>5231</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1018000.0</td>\n",
       "      <td>44.2</td>\n",
       "      <td>43.7</td>\n",
       "      <td>44.6</td>\n",
       "      <td>305</td>\n",
       "      <td>339</td>\n",
       "      <td>2358</td>\n",
       "      <td>2827</td>\n",
       "      <td>1103</td>\n",
       "      <td>3760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>240</td>\n",
       "      <td>90630</td>\n",
       "      <td>CYPRESS</td>\n",
       "      <td>ORANGE</td>\n",
       "      <td>CA</td>\n",
       "      <td>33.8215</td>\n",
       "      <td>-118.0400</td>\n",
       "      <td>714</td>\n",
       "      <td>16799</td>\n",
       "      <td>904</td>\n",
       "      <td>...</td>\n",
       "      <td>936000.0</td>\n",
       "      <td>41.2</td>\n",
       "      <td>40.9</td>\n",
       "      <td>41.3</td>\n",
       "      <td>1838</td>\n",
       "      <td>1332</td>\n",
       "      <td>6165</td>\n",
       "      <td>8348</td>\n",
       "      <td>3183</td>\n",
       "      <td>11404</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     RecordID  ZipCode        City  County State  Latitude  Longitude  \\\n",
       "235       236    90620  BUENA PARK  ORANGE    CA   33.8425  -118.0124   \n",
       "236       237    90621  BUENA PARK  ORANGE    CA   33.8706  -117.9963   \n",
       "237       238    90623  BUENA PARK  ORANGE    CA   33.8502  -118.0399   \n",
       "238       239    90623    LA PALMA  ORANGE    CA   33.8502  -118.0399   \n",
       "239       240    90630     CYPRESS  ORANGE    CA   33.8215  -118.0400   \n",
       "\n",
       "     DominantAreaCode  ResidentialDeliveries  ResidentialPOBoxes  ...  \\\n",
       "235               714                  13804                   0  ...   \n",
       "236               714                  11651                 112  ...   \n",
       "237               714                   5231                   0  ...   \n",
       "238               714                   5231                   0  ...   \n",
       "239               714                  16799                 904  ...   \n",
       "\n",
       "     MedianHomeValue  MedianAge MedianAgeMale  MedianAgeFemale  \\\n",
       "235         806000.0       37.2          36.2             38.4   \n",
       "236         819000.0       35.9          33.8             38.2   \n",
       "237        1018000.0       44.2          43.7             44.6   \n",
       "238        1018000.0       44.2          43.7             44.6   \n",
       "239         936000.0       41.2          40.9             41.3   \n",
       "\n",
       "     EducationNinthGradeOrLess  EducationSomeHighSchool  \\\n",
       "235                       1762                     2280   \n",
       "236                       2262                     2146   \n",
       "237                        305                      339   \n",
       "238                        305                      339   \n",
       "239                       1838                     1332   \n",
       "\n",
       "     EducationHighSchoolGraduate  EducationSomeCollegeWithoutDiploma  \\\n",
       "235                         8709                                9540   \n",
       "236                         6475                                6982   \n",
       "237                         2358                                2827   \n",
       "238                         2358                                2827   \n",
       "239                         6165                                8348   \n",
       "\n",
       "     EducationAssociatesDegree  EducationBachelorsDegree  \n",
       "235                       3239                      8044  \n",
       "236                       2398                      5681  \n",
       "237                       1103                      3760  \n",
       "238                       1103                      3760  \n",
       "239                       3183                     11404  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load in data\n",
    "\n",
    "fast_dat = pd.read_csv('Fast Food Chain_Labeled - Sheet1.csv')\n",
    "#fast_dat = fast_dat[]\n",
    "\n",
    "\n",
    "zip_dat = pd.read_csv('ZipData.csv')\n",
    "oc_data = zip_dat[zip_dat['County']=='ORANGE']\n",
    "oc_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fast Food Name</th>\n",
       "      <th>ZipCode</th>\n",
       "      <th>State</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Wendy's</td>\n",
       "      <td>92395.0</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Baker's Drive Thru</td>\n",
       "      <td>92399.0</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Del Taco</td>\n",
       "      <td>92404.0</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Burger King</td>\n",
       "      <td>92404.0</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Burger King</td>\n",
       "      <td>92407.0</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "       Fast Food Name  ZipCode State\n",
       "0             Wendy's  92395.0    CA\n",
       "1  Baker's Drive Thru  92399.0    CA\n",
       "2            Del Taco  92404.0    CA\n",
       "3         Burger King  92404.0    CA\n",
       "4         Burger King  92407.0    CA"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fast_dat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "674"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(fast_dat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "421"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(fast_dat['ZipCode'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "90"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(fast_dat['Fast Food Name'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "zip_oc = oc_data['ZipCode']\n",
    "zip_fast = fast_dat['ZipCode']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "235    90620\n",
       "236    90621\n",
       "237    90623\n",
       "238    90623\n",
       "239    90630\n",
       "       ...  \n",
       "657    92868\n",
       "658    92869\n",
       "659    92870\n",
       "660    92886\n",
       "661    92887\n",
       "Name: ZipCode, Length: 144, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zip_oc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      92395.0\n",
       "1      92399.0\n",
       "2      92404.0\n",
       "3      92404.0\n",
       "4      92407.0\n",
       "        ...   \n",
       "669    90006.0\n",
       "670    90007.0\n",
       "671    90007.0\n",
       "672    90008.0\n",
       "673    90011.0\n",
       "Name: ZipCode, Length: 674, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zip_fast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Merge the two datasets on the 'ZipCode' column\n",
    "\n",
    "merged_data = pd.merge(oc_data, fast_dat, on='ZipCode', how='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "119"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(merged_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RecordID</th>\n",
       "      <th>ZipCode</th>\n",
       "      <th>City</th>\n",
       "      <th>County</th>\n",
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       "      <th>Latitude</th>\n",
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       "      <th>MedianAgeFemale</th>\n",
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       "      <th>EducationSomeHighSchool</th>\n",
       "      <th>EducationHighSchoolGraduate</th>\n",
       "      <th>EducationSomeCollegeWithoutDiploma</th>\n",
       "      <th>EducationAssociatesDegree</th>\n",
       "      <th>EducationBachelorsDegree</th>\n",
       "      <th>Fast Food Name</th>\n",
       "      <th>State_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>236</td>\n",
       "      <td>90620</td>\n",
       "      <td>BUENA PARK</td>\n",
       "      <td>ORANGE</td>\n",
       "      <td>CA</td>\n",
       "      <td>33.8425</td>\n",
       "      <td>-118.0124</td>\n",
       "      <td>714</td>\n",
       "      <td>13804</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>36.2</td>\n",
       "      <td>38.4</td>\n",
       "      <td>1762</td>\n",
       "      <td>2280</td>\n",
       "      <td>8709</td>\n",
       "      <td>9540</td>\n",
       "      <td>3239</td>\n",
       "      <td>8044</td>\n",
       "      <td>Carl's Jr.</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>240</td>\n",
       "      <td>90630</td>\n",
       "      <td>CYPRESS</td>\n",
       "      <td>ORANGE</td>\n",
       "      <td>CA</td>\n",
       "      <td>33.8215</td>\n",
       "      <td>-118.0400</td>\n",
       "      <td>714</td>\n",
       "      <td>16799</td>\n",
       "      <td>904</td>\n",
       "      <td>...</td>\n",
       "      <td>40.9</td>\n",
       "      <td>41.3</td>\n",
       "      <td>1838</td>\n",
       "      <td>1332</td>\n",
       "      <td>6165</td>\n",
       "      <td>8348</td>\n",
       "      <td>3183</td>\n",
       "      <td>11404</td>\n",
       "      <td>Burger King</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>240</td>\n",
       "      <td>90630</td>\n",
       "      <td>CYPRESS</td>\n",
       "      <td>ORANGE</td>\n",
       "      <td>CA</td>\n",
       "      <td>33.8215</td>\n",
       "      <td>-118.0400</td>\n",
       "      <td>714</td>\n",
       "      <td>16799</td>\n",
       "      <td>904</td>\n",
       "      <td>...</td>\n",
       "      <td>40.9</td>\n",
       "      <td>41.3</td>\n",
       "      <td>1838</td>\n",
       "      <td>1332</td>\n",
       "      <td>6165</td>\n",
       "      <td>8348</td>\n",
       "      <td>3183</td>\n",
       "      <td>11404</td>\n",
       "      <td>Carl's Jr.</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>241</td>\n",
       "      <td>90631</td>\n",
       "      <td>LA HABRA</td>\n",
       "      <td>ORANGE</td>\n",
       "      <td>CA</td>\n",
       "      <td>33.9314</td>\n",
       "      <td>-117.9524</td>\n",
       "      <td>562</td>\n",
       "      <td>22849</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>35.9</td>\n",
       "      <td>39.5</td>\n",
       "      <td>3564</td>\n",
       "      <td>3285</td>\n",
       "      <td>12328</td>\n",
       "      <td>14086</td>\n",
       "      <td>5136</td>\n",
       "      <td>9771</td>\n",
       "      <td>Domino's Pizza</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>241</td>\n",
       "      <td>90631</td>\n",
       "      <td>LA HABRA</td>\n",
       "      <td>ORANGE</td>\n",
       "      <td>CA</td>\n",
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       "      <td>-117.9524</td>\n",
       "      <td>562</td>\n",
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       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>35.9</td>\n",
       "      <td>39.5</td>\n",
       "      <td>3564</td>\n",
       "      <td>3285</td>\n",
       "      <td>12328</td>\n",
       "      <td>14086</td>\n",
       "      <td>5136</td>\n",
       "      <td>9771</td>\n",
       "      <td>KFC</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   RecordID  ZipCode        City  County State_x  Latitude  Longitude  \\\n",
       "0       236    90620  BUENA PARK  ORANGE      CA   33.8425  -118.0124   \n",
       "1       240    90630     CYPRESS  ORANGE      CA   33.8215  -118.0400   \n",
       "2       240    90630     CYPRESS  ORANGE      CA   33.8215  -118.0400   \n",
       "3       241    90631    LA HABRA  ORANGE      CA   33.9314  -117.9524   \n",
       "4       241    90631    LA HABRA  ORANGE      CA   33.9314  -117.9524   \n",
       "\n",
       "   DominantAreaCode  ResidentialDeliveries  ResidentialPOBoxes  ...  \\\n",
       "0               714                  13804                   0  ...   \n",
       "1               714                  16799                 904  ...   \n",
       "2               714                  16799                 904  ...   \n",
       "3               562                  22849                   0  ...   \n",
       "4               562                  22849                   0  ...   \n",
       "\n",
       "   MedianAgeMale  MedianAgeFemale EducationNinthGradeOrLess  \\\n",
       "0           36.2             38.4                      1762   \n",
       "1           40.9             41.3                      1838   \n",
       "2           40.9             41.3                      1838   \n",
       "3           35.9             39.5                      3564   \n",
       "4           35.9             39.5                      3564   \n",
       "\n",
       "   EducationSomeHighSchool  EducationHighSchoolGraduate  \\\n",
       "0                     2280                         8709   \n",
       "1                     1332                         6165   \n",
       "2                     1332                         6165   \n",
       "3                     3285                        12328   \n",
       "4                     3285                        12328   \n",
       "\n",
       "   EducationSomeCollegeWithoutDiploma  EducationAssociatesDegree  \\\n",
       "0                                9540                       3239   \n",
       "1                                8348                       3183   \n",
       "2                                8348                       3183   \n",
       "3                               14086                       5136   \n",
       "4                               14086                       5136   \n",
       "\n",
       "   EducationBachelorsDegree  Fast Food Name  State_y  \n",
       "0                      8044      Carl's Jr.       CA  \n",
       "1                     11404     Burger King       CA  \n",
       "2                     11404      Carl's Jr.       CA  \n",
       "3                      9771  Domino's Pizza       CA  \n",
       "4                      9771             KFC       CA  \n",
       "\n",
       "[5 rows x 36 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Count the frequency of fast food places in each zip code\n",
    "zipcode_counts = merged_data['ZipCode'].value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the frequency of fast food places by zip code\n",
    "plt.figure(figsize=(10, 6))\n",
    "zipcode_counts.plot(kind='bar')\n",
    "plt.title('Frequency of Fast Food Places by Zip Code')\n",
    "plt.xlabel('Zip Code')\n",
    "plt.ylabel('Frequency')\n",
    "plt.xticks(rotation=45)\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}