Barcelona immigration 2019-2021 Analysis and Forecast.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8713f51f",
   "metadata": {},
   "source": [
    "<h1 style=\"text-align: center; color: Orange;\">Immigrants</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22b9beb8",
   "metadata": {},
   "source": [
    "An immigrant is a person who moves from one country to another to reside there permanently or for an extended period. Immigrants leave their home country and enter a foreign country to establish a new life. This migration can be for various reasons, including seeking better economic opportunities, escaping political persecution, reuniting with family members, or pursuing education and career prospects.\n",
    "\n",
    "Immigrants can come from diverse backgrounds, cultures, and countries and contribute to the host country's cultural, social, and economic fabric. Immigration has been a significant aspect of human history, and many countries have established immigration policies and procedures to regulate the entry and integration of immigrants into their societies."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bd23b55",
   "metadata": {},
   "source": [
    "<h2 style=\"text-align: center; color: Green;\">Barcelona Immigration 2019-2021 Analysis</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "50acf44d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "981cc912",
   "metadata": {},
   "outputs": [],
   "source": [
    "immigrations=pd.read_csv(\"immigration.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8aee04f2",
   "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>Year</th>\n",
       "      <th>District</th>\n",
       "      <th>Neighbourhood</th>\n",
       "      <th>Place_of_origin</th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019</td>\n",
       "      <td>Ciutat Vella</td>\n",
       "      <td>el Raval</td>\n",
       "      <td>Andalusia</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019</td>\n",
       "      <td>Ciutat Vella</td>\n",
       "      <td>el Barri Gotic</td>\n",
       "      <td>Andalusia</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019</td>\n",
       "      <td>Ciutat Vella</td>\n",
       "      <td>la Barceloneta</td>\n",
       "      <td>Andalusia</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019</td>\n",
       "      <td>Ciutat Vella</td>\n",
       "      <td>Sant Pere, Santa Caterina i la Ribera</td>\n",
       "      <td>Andalusia</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019</td>\n",
       "      <td>Eixample</td>\n",
       "      <td>el Fort Pienc</td>\n",
       "      <td>Andalusia</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year      District                          Neighbourhood Place_of_origin  \\\n",
       "0  2019  Ciutat Vella                               el Raval       Andalusia   \n",
       "1  2019  Ciutat Vella                         el Barri Gotic       Andalusia   \n",
       "2  2019  Ciutat Vella                         la Barceloneta       Andalusia   \n",
       "3  2019  Ciutat Vella  Sant Pere, Santa Caterina i la Ribera       Andalusia   \n",
       "4  2019      Eixample                          el Fort Pienc       Andalusia   \n",
       "\n",
       "   Amount  \n",
       "0      95  \n",
       "1      64  \n",
       "2      41  \n",
       "3      67  \n",
       "4      41  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "immigrations.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9c956358",
   "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>Year</th>\n",
       "      <th>District</th>\n",
       "      <th>Neighbourhood</th>\n",
       "      <th>Place_of_origin</th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4375</th>\n",
       "      <td>2021</td>\n",
       "      <td>Sant Marti</td>\n",
       "      <td>la Verneda i la Pau</td>\n",
       "      <td>Basque Country</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4376</th>\n",
       "      <td>2021</td>\n",
       "      <td>Sant Marti</td>\n",
       "      <td>la Verneda i la Pau</td>\n",
       "      <td>Rioja</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4377</th>\n",
       "      <td>2021</td>\n",
       "      <td>Sant Marti</td>\n",
       "      <td>la Verneda i la Pau</td>\n",
       "      <td>Ceuta</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4378</th>\n",
       "      <td>2021</td>\n",
       "      <td>Sant Marti</td>\n",
       "      <td>la Verneda i la Pau</td>\n",
       "      <td>Melilla</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4379</th>\n",
       "      <td>2021</td>\n",
       "      <td>Sant Marti</td>\n",
       "      <td>la Verneda i la Pau</td>\n",
       "      <td>Foreign countries</td>\n",
       "      <td>389</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Year    District        Neighbourhood    Place_of_origin  Amount\n",
       "4375  2021  Sant Marti  la Verneda i la Pau     Basque Country      12\n",
       "4376  2021  Sant Marti  la Verneda i la Pau              Rioja       2\n",
       "4377  2021  Sant Marti  la Verneda i la Pau              Ceuta       2\n",
       "4378  2021  Sant Marti  la Verneda i la Pau            Melilla       2\n",
       "4379  2021  Sant Marti  la Verneda i la Pau  Foreign countries     389"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "immigrations.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e0864b7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4380, 5)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "immigrations.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ae80379d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    4380.000000\n",
       "mean       66.310274\n",
       "std       245.410230\n",
       "min         0.000000\n",
       "25%         1.000000\n",
       "50%         5.000000\n",
       "75%        19.000000\n",
       "max      4199.000000\n",
       "Name: Amount, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "immigrations[\"Amount\"].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0866397f",
   "metadata": {},
   "source": [
    "<h3 style=\"text-align: center; color: Green;\">Number of Immigrants in each Year</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f0278574",
   "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>Year</th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019</td>\n",
       "      <td>115308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020</td>\n",
       "      <td>75408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021</td>\n",
       "      <td>99723</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Amount\n",
       "0  2019  115308\n",
       "1  2020   75408\n",
       "2  2021   99723"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yearly_immigration_sum = immigrations.groupby('Year')['Amount'].sum().reset_index()\n",
    "yearly_immigration_sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "461c89f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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7t+5JzsrZwhuqDY3ELboH2s6iVI45C7g3VBvK2fqTk2/cW/Dm/4DJtrMolaMuAu6wHcKWnCu83l/ZvwJH2M6iVI67PFQbutJ2CBtyrvACvwA+azuEUgqAm0O1odNsh/BbThXeqTd/+yJj0EWblcoc+biTbTl1GaGcOWW4orruBOCpwsEvvV488uFDRCiznUkp9aElwOxcWdEsJwpvRXXdgcCrwHCA/NIVi0sP/N1gETPKbjKlVJIngNMjVZGE7SDpFvihhorqunLgYbyiC5BomTCjaek1GKdosb1kSqndnIZ7CaHAC3SPt6K6TnCvk/a5vW4gHS3lE29ZlFe0VS/pk+WcdocVP1+B6TSYhGHAEQMY+dmRHz6/Ze4WNvx9A9Nvn05B/4Juv3bDAxtofLuR0vGljP2au8Lh9he3k2hKMOy0Yf69wdzy1UhV5B7bIdIp6D3eq9hX0QUwhaVNy757VEfjQc/5lkilhRQKFddUMPknk5n848nEI3GalzYD0L61nfi7cQqHFvbotYnmBM1Lm5ly0xSMY2hd3YrT7rBj/g6GnqRLNafRXd6FCAIrsIW3orruUOBn+99SpHXNJSe2bfz0i8bQlvZgKi1EhPySfABMwu25Iu5zG/53AyPPH9nz1wpuL9gYTIdB8oUtc7cw9NShSIGk+y3lskLg/lBtKLArBQbyfOmK6roy4H6gqLuvad8259hE65h3Ssf/caSI0bVDs5BxDMtuXEb7pnaGnDyEskllNLzZQOHgQkrHl/b4tQADDh/Ash8uo/ygcvLK8mhZ3sKIs3Q9JR9MAO4CLrYdJB0COcZbUV13N/D13rxWCretLZ/462bJ65iS4ljKJ4mmBKtuX8Xoi0az9k9rqfhuBfll+Xxw9QdMCk/aY4x3r6+9eDQlY0t2eW7tH9cy5OQhtERbiL8Tp2RcCSPO1CKcZl+MVEX+ajtEqgVuqKGiuu4sell0AUzHkAPi9TeMdjoGvpLCWMpH+eX5lE8vp+GNBto3t7P0B0v54OoP6NjewbIbl9Gxo2O/r41H4rs83rKyBYDiUcXseHEH4781nrY1bbRt0NGpNLszVBuaaDtEqgWq8FZU140G+j4b6hT3a1p6zeGd8cnP9z2V8kNnQyeJJvfwT6fdIf5enJIDS5hx+wym3TyNaTdPo3BwIZN+NInCQYX7fW3R6F1HqTY9uIkRnx2B6TTgeA/mudurtBoA3Be0ZSQD82a8Q8dqgRQd45OX17L6K3OKhj0xr2jYM0eJsPcpcZUROmOdrPn9GoxjwMDAIwcyYNa+52Y6tne4wxBXVez3tQ2vN1A6oZTCwe5/gdLJpdTfUE/J2JL9jh2rlDgKuBH4ge0gqRKYMd6K6rqrSNPB1wX93ltUMvbPB4owOB37V0rtlwN8IlIVecF2kFQIROGtqK6bBSykB0cx9FRe0aaVZRNudSQvMSFdbSilurQaCEWqIjHbQfoq68d4K6rrSujhoWO94bSPODBef/0Qp7Pf6+lsRym1T+OAn9oOkQpZX3iBatzLR6efUzawqf7ajyWaDwzExx2lstDlodpQ1l/EIKuHGiqq6yYC7wIl+9s21YpH/OeFwiHzjxUh3++2lcpxbwJHZPMqZtne470NC0UXoG3TZ05oXXfhImPI+vEmpbLMoUBWXzIoa3u8FdV1Z+Iu92hVXvG6ZWUT7igQcfRqxUr5pwmYEamKrLYdpDeyssdbUV1XCtxqOweA0zZmUlP99f1NZ+lbtrMolUPKgdtth+itrCy8wLVAhe0QO5lE+ZB4/fUzEq2j59vOolQOOStUGzrLdojeyLqhhorqukm4E2rFtrPsTfGofz1fOOjV40Wy9o+aUtlkNXBQpGq3xTUyXDYWh9vI0KIL0Lbh3Dlt6899zRiy6j+CUllqHHCd7RA9lVU93orqurOBf9vO0R15Jas+KKu4u5+Ic4DtLEoFXDMwKVIV2WA7SHdlTY/XO0PtFts5ustpHT+taWl1kUkUv2s7i1IBVwb80HaInsiawgt8A8iqQ7ZM54Dh8fobJiXahi+wnUWpgPtKqDY0yXaI7sqKwusdPnaN7Ry9YgpLmpdffUxH7JDnjCF7xnWUyi6FwE9sh+iurCi8uFeUGGU7RF+0rvvCiW0bP/OyMbTYzqJUQF0Qqg19zHaI7sj4wpvVvd3ddGw/7uiWVV9bYYxstJ1FqQAS4Oe2Q3RHxhdeAtDbTZZonnhQ09JrHOMUvW87i1IB9OlQbegE2yH2J6MLb5B6u8lM56DR8SU3jHfaB79sO4tSAZTxvd6MLry4RzIEpre7C1NU1rTs+7M7G6c/ZzuKUgFzTKg2dJrtEF3J2MLr9Xa/bztHeom0rLn0xLZNp803hnbbaZQKkKttB+hKxhZegtzb3U371pOOa1l92fvGyBbbWZQKiNNCtaGQ7RD7kpGFNzd6u7tKNE07pGnZ1S3GKVhqO4tSAXGV7QD7kpGFF7iUHOntJjMdw8bF628Y4XQMeM12FqUC4Auh2lBG1pFMLbzfsB3AGqdkQNPS6kM7myY+bzuKUlmuCLjCdoi9ybjVySqq644BXrSdIxMUDX9sXtHQ544WocB2FqWy1DZgXKQq0mw7SLJM7PHmbm93N+2bP3V869qLI8aww3YWpbLUENyhy4ySUT3eiuq6IcBaLF05OFPlFW1YUTbhdpG8RIXtLEploXpgeqQq4tgOslOm9XgvRYvuHpz2URPi9dcPcjrL37SdRaksNAU43XaIZJlWeL9uO0DGcsoGNdVfF0q0jHvBdhSlstCltgMky5ihhorqupOAp23nyAbFIx9+vnDwS8eJkG87i1JZog0YHamKbLcdBDKrx6uTat3UtvGsOa3rPv+mMTTYzqJUligGLrAdYqeMKLwV1XUjgbNt58gmnQ2HHt4c/fYmY/JW286iVJa4xHaAnTKi8AJfxr10h+oBp/WAyU3115aaROnbtrMolQWOCtWGptoOAZlTeL9oO0C2Mon+w+JLrp+eaB0133YWpbJAle0AkAGTaxXVdTOA96yGCIiS0Q88VzDwjTkiiO0sSmWoVUBFpCpitfBlQo/3HNsBgqJ1/fkntm04e6ExNNnOolSGGg98wnYILbwB07HjqKOaV16+2hhZbzuLUhnK+iSb1aGGiuq6A4GotQABJgWxjeUTf7VV8tsOsp1FqQwTA4ZHqiIdtgLY7vF+1nL7gWU6B46M198wwWkb9pLtLEplmIHAHJsBtPAGmSksbVp+9VEdDQfr2r5K7epMm41bK7wV1XUjgONstZ87RFrXXjyndWPlAmNotZ1GqQyRm4UXOMty+zmlY9vxx7Ss+soyY2ST7SxKWWfMmC/+dqq1i2HaLHw6zOCzRPPkmU3LvtdpnMIPbGdRym9izOapbe0vXrtl20sLVq5p+cv6jZ+xmMX/oxoqqusGAJtxr4mk/JbXFi+f+Ov38gp3HGk7ilJpY4wpNeb9Y1taN13Y0DjsiNa2g4RdTi6aRzh2go1otgrvOcC/fG9YJXGc0nH3zivot8Tq7K5SKWVMfExn4t0z4k3t5zXGp45MJEZ2sXUnMIxwLOZXvJ1sXUTxeEvtqg/l5bWs/tKcomFPzS8a9tSRIvrpQ2WnAmNWzmptW/n5xnj5SU3NoSKY3d2XAqdgoRNoq/Dq0QwZon3LKcclWg94q3Rs7TgRhtjOo9R+GdMx2HHeOaWpueGChvj4qR0dE4ADe7m3OVgovL4PNVRU15UDO7BX9NVeSNHmVeUTbu2QvM5JtrMotTsxZvOU9o4l5zbG886IN83sb8yAFO16EeHYoSnaV7fZKLwnA0/52qjqnryWWPnEX9XnFTYebjuKynH7nxhLFQcY4vc4r41ep47vZiqndGDT0msPLT3wd88XlEV10k35y5sY+0y8qeP8xviUkYnEDGBGmlvNA44FHk1zO7uwUXh1fDej5eW3rPzGnOIRdS8UDpl3jIgOCan08SbGop9vjPc/qan54B5MjKXS8QS58FZU1xVg5wereqhtU+UJiZZxr5cccP9kEQbazqMCwpsYO7mpueHCvk+MpYrvn8J9HeOtqK47HHjVtwZVn+UVb1hRNuH2PJGE7V8OlaW8ibEPzonHC85sbDoohRNjqdIODCQc820tE78/Rur4bpZx2kZNiNdft7184s2L8gqaZ9nOo7KANzF2TEvrxgsbGocf0do2Iy+zhxiLgFnAy3416HfhzeQfvtqXRPngpvrr+5VV3DUvv3St/vFUe/poYqz9vMb4lFH+TIyl0iwCXHiP9rk9lTL5hc3Rbx9fPOrfzxcOWni8iK4sl+sKjFk1q7VtxfmN8X4n25sYS5VZfjbmW+GtqK4bAoz2qz2VHm0bPjsn0TLulZLR/5whQn/beZSPjOkc5J4xFrugIT5uWkfHRNyLRwbBx/xszLfJtYrquqOBBb40ptIur2R1fVnFb0pFnLG2s6j0EWO2TGnveP+ceDz/jHjTQQMcE9QjXJqAAYRjjh+N+TnUMN3HtlSaOa3jpjTVX7u5fNLNEclvtbagtEq9Usd5/5iW1g1ZMjGWKuXAFMCXtar9LLzTfGxL+cAk+g+P198woGzCbS/mF2861nYe1UvGNHkTY23exNh0crOjNIsAFt5c/IcMPlNQ3Lz8qmNLxvztuYIBi+aIpOV8epViBcas+ph7xli5NzGmi+LDIcDf/WhIe7wqJVrXXXBiYcv4l4pHPvIxEcps51G78SbGTm5qiV3Y0Bi0ibFUmexXQ74UXu9UYV1uMOA6th9ztNM6enHpgb8bLGJG2c6T68SYLZM7Ot4/pzGef6Y7MTbLdqYMN9Gvhvzq8U4ECn1qS1mUaJkwo2npNevLJ/1qseS1Z9MB9IFQ6jjvH+1NjB2ZOxNjqeJb4fXlcLKK6rozgYfT3pDKHNLeXD7xlrfzirYdZTtKoBnTNLoz8c5nmpraz2uITx6dSOix8n0zmHBsR7ob8avHq+O7ucYUlTUt+97skrF/ea6w/3sn2o4TJN7E2IrzG+P9Tsn+M8YyzUTgjXQ3ooVXpZFI65pLTnSGPP9i0Yi5h4tQbDtRVkqaGLugsXHc9HadGEujQBVe38ZOVOZp3zbn2ETbmEjpuD+OEjHDbefJBh9NjDXlnxmP68SYf3ypVX4V3q6uba9yQKJpSqhp2XfXlE+8pV7yOqbYzpOJSh3ng6NbWtdf2NA47MjWtoN0YsyKcX40ooVX+cZ0DB0br7++sXzir1/JK4zpAfvGNI1OeGeMuRNj09BhOdt8+USW9qMavGN420nPFUJVVnKc0vF/nFdQvjTnLqiZNDFWfkpTc6gIHffOMM8Qjp2c7kb86PEOR4uu2kVeXsuqr8wpGv7EvKKhzxwlEuBjvN2JsXdPbmrZcUFj49jp7R2T0ImxTOZLj9ePwjvChzZUFmrffNrxTsvYRSVj/3ygCINt50kVMWarNzGWd0Y8ftBAx/i61qvqk8AMNZwEPJ3WRlRWyyvatLJswq2O5CUm2M7SWyXuxNiGLzQ0DvUmxvQKHdmpEygiHEtrYfSjxxuYnoxKD6d9xIHx+utj5ZN+9UZeQfzjtvN0izcxVhlvajtfJ8aCpAC3Zm1LdyPpFtQV61UqOWUDm+qvPaTswN++kF+26gTbcfamwJjVh7S1LT+/Id7vVF1KMciGE4DCO8iHNlQg5Bc0r/zmCcUj/++FwsEvHitCvtU4xnQOdJx3Tm5u2XFBQ+O4Ge7EmC/HeSqrytPdgBZelXHaNp5xQqJl3GslY/42RcTfT0xizNZJ3sTYme7E2Cw/21cZoSTdDehQg8pInQ2zDm9uG7GsbMIdMREnrYdflXx4xlh82OzW1oPyQC9jlNsCUXj1AHHVK07bmElN9ddtLZ9081uS35K6Q7KMaR6dSLxT6V5jbPKYTp0YU7sIROH15/rxKpBMot/Q+JLr+5dNuHN+fsn6Xq9dkDQxVn5Kc/PBxUYnxtQ+aeFVCgqKmldceVzxqH89Vzjo1RNEunGMrE6Mqd7TwqvUTm0bzj3RaR23sHjUgweL7DnznDQxJmfG4zN1Ykz1khZepZJ17DhydqJ11AdlFXf3F3HGlDjOkqNbWtdd0BAfelRr60ydGFMpUJTuBrTwqqzjtI6f1ll/9ZpnSr+zfkxnYiow1XYmFSid6W7Aj/PJtfCqlGtNDB07rENabedQgdSR7ga08KqsVW8OWGs7gwokLbxK7cvTzqG2I6hg0sKr1L7MTcwebTuDCiQtvErty2IzfqJj2G47hwqcQBRex4c2VE4S2cCQetspVOAEovDGfGhD5aiXnJkttjOowGlOdwN+FN7NPrShclRdYvYg2xlU4KR1EXTQwquy3AJn5lRj0v/RUOUULbxKdaWV4tI4pUts51CBsjXdDWjhVVnvbWfiFtsZVGDECccCMbmmhVel1WPOEbrYvkqVtA8zgD+Fdxt6SJlKoycSh0+0nUEFRjAKb7Sm0sGnN6Ny00aGjOgw+ats51CBkPbxXfCnxws63KDSbLkZvdp2BhUI6/xoRAuvCoRnnVkJ2xlUIPjyB1wLrwqER3XBHJUagSq8631qR+WoiJkwyRg9PV31WaAK7wc+taNylCEvbxODltrOobLeCj8a8avwLvapHZXDFjozGm1nUFkvUIX3PZ/aUTmsLjF7oO0MKqttIBzzZbU7XwpvtKZyPbo8pEqzec4hU41Bj25QveXbUJVfPV7Q4QaVZs2UlDdTrAvmqN56x6+GtPCqQHnHTNhkO4PKWoEsvDrOq9Lu8cQRhbYzqKwV8ash7fGqQHnCOXyC7Qwqa2nhVao31pjhoztNni/n26tAWUc45tsVq/0svFFAL0yo0m6lGRm1nUFlHd96u+Bj4fWWh9Qz2FTaPefM0muwqZ4KZuH1vOZzeyoHPZo4cqTtDCrr+Fqb/C68L/jcnspBb5opU4whbjuHyioL/GzM78I7z+f2VA5yyMvfygA9kUJ11xrCMV8X0ve18EZrKqOAXqJFpd0rzvQG2xlU1vC1twv+93hBe73KB3MTR/a3nUFljRf9btBG4dVxXpV2zzqzphijV7dW3ZITPV4tvCrt4pQNaKVIF0ZX+9MMLPK7Ud8Lb7Sm8n1AFzJRafeeOXCD7Qwq471MONbpd6M2eryg47zKB08mDiuwnUFlvCdsNGqr8Opwg0q7x5wjxtnOoDLeXBuNauFVgRU1o8cljGy0nUNlrHWEY2/baNhW4X0b2GKpbZVDVpsRy21nSIfVMYdP1DYx4844M++Kc+vLbR8+d/vCdqbd4T7+/Sdb9/r6x5Z2Mu2OOJNva6Rm/kevvebJVg75TZxL/v3RelZ/eat9l/0HyOO2GrZSeL0Fcx6y0bbKLfOcULvtDOlQkAc3n1bC4m/14+Uvl3Pnqx28tznBsys6efiDDt7+RjnvfrMf3z2maI/XJhzDtx5tYe5FZbz3rX787zvua2OthgVrErx9eT8SxhDZmKClw3DvWx1884g99xMAj9lq2Obkw7+Ar1hsP+VMZzsb7r8G09kBjkPZtGMZdPxFtG9aztbH78S0t1IwcATDzvgeecVlu7y2Y+saNj/y3x9+37ljA4OOu5gBR5zF9uf+RMvy1ykaMYFhn7kagPg7z+C0NjLg8LN8fY/Zps6ZPeyLPGU7RsqN7p/HaO8Ukf7FwozheaxtMPz+jXaqjyumuEAAGFG+Z9/qlbUJJg/JY+Jg97kLZhby8PudXHFkEe0JgzGGlg4ozIdfLmjnv44sojBffHtvPkkAT9pq3NZQA8DTwA6L7adefiEjL/gZY750B6Mvu42WFa/TtvZ9ts69ncFzLmXMl++kbOrRNCz81x4vLRw6ljGX3c6Yy25ndNUtSGExZVOPxmlrom3tYsZ86Q6McWjfHMXpaKPpnafof2ilhTeZXV5zpk01JtjrQEd3OLy5PsHssfks2eowb2Uns++JM+feJl5du+dFl9c2GsYN+OhXf+wAYW2jQ/9i4dwZhRz62yYmDMpjYLHw6roEZ00P5NWUFvq58PnurBXeaE1lB/CIrfbTQUTIKyoFwDid4CRAhI5taygedzAAJRWH0ryk6xNlWle+ReGg0RQMHAEIJtGJMQbT2Y7k5dPwyoP0P+xMJF+PltqfTgoKd9AvsOtAx9sN5z7QzC2fKmFAsdDpwPZWePnL5fzy1BLO/2czxphdXrPbtwDs7M9+/9hiFn2jHzd/soQfPNvGj08s5p432jn/H83c9EKgxnkftNm4zR4vuMMNgWKcBOv+9G3W3H4xJRWzKB4zjaJhB9KydCEAze/Pp7Ox63nFpsUvUDbjBADyissom3YM6+/9LwoGjkSKy2lfv4SyKUel/b0ExevO1JjtDOnQkXCL7kWhQs6Z4fZKxw4QzplRgIhw5AH55Alsad610o4dIKxu+Ohs6jUNhjH9dy0Fb653e8pTh+bx57c6eOC8Mt7ZlKB+65496CxkgH/YDGC78D4ONFrOkFKSl8+Yy25n7DfvpW39Eto3Rxl6+pU0vlHH+nuvxGlvQfL23VM1iQ5alr5C+fTjPnxs4OzPMeay2xly0leIzfsrg46/mMa3HmfzQzXsWPA3P95WVpubOLJs/1tlF2MMX36klRnD8rnq6OIPHz97eiHPrHBPxFqyNUF7AoaV7To+e8QB+dRvdVix3aE9Yfjbux2cOW3X/5M/eLaNH3+imA4HEl7dzhNoDsa1PV4mHLO6SqLVwhutqWwD6mxmSJe8kn6UjAvRsvwNCoeOY+Tnf8LoS2+l/KA5FAwetc/XtSx/naKRk8gvH7zHc+0blwFQMPgAmt55huFnV9OxeSUd29am7X0EwVPOx6cYw14+YGevF1cn+MvbHTyzopNZd8eZdXecR+s7+NKhhSzfbjj4rjgX/LOF2rNLERHWNTqcfl8zAAV5wh2nl/DJvzYz48445x9UyMwR+R/u+6H3OzhiTD5j+ucxqEQ4emw+od/EEYGPjcrfV6Rs8oDtAJkwSPhP4ALbIVIh0RxD8vLJK+mH09FG68pFDJj9ORJNO8gvH4QxDrEFf6P/rE/vcx9N7z1PuTfMsLsd8/7KkE9eAU4nGO+jouRhOgM19pZyMfoNaqNwWQkdk2xnSZXjxhdgbhyw1+f+ek7pHo+N6Z/Hoxd91PE/fUohp0/Z+6TZ2dMLOXv6R9//z2kl/E/f4mYS68MMkBmFdy7uCkFZ/3EwEd/Glrpfu0XROJRNP56yyUfS8NrDNL7hduzLph5DeehUADobt7L1sdsYed6PAHA6WmmNLmLop67YY9/NS16iaNQUCvoPBaB4zHTW/eFbFI6ooGjERJ/eYfZaYsauO0RWBKbwql57kXDM+kdE2X3G04aK6rp/AufazqGC67/yH5x/VeE/j9v/lirgvk04doftELYn13b6u+0AKtjmOkeOtZ1BWddOhtSaTCm8DwG6mIlKm3oztsIxstl2DmXVw4RjGfF/ICMKr3cyxT22c6hgW8fQFbYzKKt+bzvAThlReD2/xT1/Wqm0mJ84uNl2BmXNCsicRTsypvBGaypXA/+xnUMFV51z1FDbGZQ19xCO2T+SwJMxhddzl+0AKrgWOjOmGoMe9Jx7OoE/2Q6RLNMK75NAve0QKpjaKSxuoGyJ7RzKd3WEY+tth0iWUYU3WlNpgLtt51DBtciZvNV2BuW7jPsknVGF1/MnCPb6qcqeuc6Re55Pq4LsbcIxK1cS7krGFd5oTeV2MuQgZxU8TyYO09OGc0tGLjORcYXXk3EfDVQwbGXgsHZTELWdQ/liDZCR66ZmZOGN1lS+CrxoO4cKpqVmzBrbGZQvbiUcy8gVhDOy8HputB1ABdPTzscz5nhOlTYNwO9sh9iXjC280ZrKp4HnbedQwfNoYvYY2xlU2v2OcKzBdoh9ydjC69Fer0q5xWb8RMdg7QqzKu1agF/bDtGVjC680ZrK53EvA69UColsZMhS2ylU2txFOLbOdoiuZHTh9fzQdgAVPAucmU22M6i0aARqbIfYn4wvvNGaygW4VyNWKmXqErP3vJqoCoJbCMe22A6xPxlfeD0/sB1ABcsCZ+ZUY8jIQ41Ur20HbrYdojuyovB6x/XqkpEqZVopLo1TqgvmBMsvCMditkN0R1YUXo+O9aqUetuZmPEfSVW3bQRusx2iu7Km8EZrKt8EHrSdQwXHY84RxbYzqJQJE45lzRVGsqbweq4BXchapcaTicMn2M6gUiJCBl1PrTuyqvBGayqXkiWD5yrzbWDIyA6Tv9p2DtVn3yEcy6rrNWZV4fX8FNBfFpUSy83oVbYzqD75N+HYM7ZD9FTWFd5oTWUzcLXtHCoYnnVmZVVPSe2iBfh/tkP0RtYVXoBoTeU/0FOJVQrMTcwebTuD6rWfE46ttB2iN7Ky8Hq+iU60qT5620yYZAxZceyn2sVS4Bd92YGIjBORZ0VksYi8KyJXeo8PEZEnRaTe+zrYe/xUEXldRCLe15OS9nWY9/hSEblNRKSrtrO28EZrKpfgjvcq1WuGvLzNDNIrW2cXA3yFcKyvHa9O4GpjzAzgKOBbInIQUA08bYyZgvvJutrbfgtwhjEmBFQBf0na12+ArwFTvNunumo4awuv57+B92yHUNntZWdG3HYG1SN3E471ea1uY8x6Y8wb3v1GYDFwAHAWUOttVguc7W3zpjFm56pn7wIlIlIsIqOBAcaYl4wxBvjzztfsS7cKby+65EO97eMicsdu+/q8iLzt7adPHxWiNZXtuH9l9IoCqtceTcweaDuD6raVwPdTvVMRqQAOBRYCI40x68EtzsCIvbzkXOBNY0wbbrFOvpzUGu+xfepuj7enXfJW3IVtvrvbmxsK/BI42RgzExgpIid3M8NeRWsqXySDL/GhMt88JzTFGPTohuzwNcKxlH5CEZF+wL+A7xhj9nvVChGZiftp++s7H9rLZl12BrtVeHvRJW8yxszHLcDJJgJLjDGbve+fwv3L0VffBXScTvVKE6X9minWBXMy358Ix55I5Q5FpBC36N5njNm5JMFGb/gA7+umpO3HAv8GLjHGLPMeXgOMTdrtWKDLhdh7PMbbiy55sqXAdBGpEJEC3EI9rqcZdhetqYwDXwBd5k/1zjtmwqb9b6UsWgdclcodekce/AFYbIz5VdJTj+BOnuF9fdjbfhBQB1xrjPnwKuhe7WsUkaO8fV6y8zX70qPC29Mu+e6MMduBy4G/A/OAKO4wRp9FaypfA65Pxb5U7nkicXiR7QxqnwzwJcKxHSne77HAF4GTRGSRdzsd9woWp4pIPXAqH13R4gpgMvCDpO13djYvB+7B7VwuA+Z21bC4k3D753XJ/wM8vvOvg4h8AJxojFnvdcmfM8ZMS3rNpcDhxpgr9rHPrwGTjTEpGSyvqK4T4DHgtFTsT+WOsbJ53fziK/Xqw5npl4RjKZ9Qs6m7RzX0qEu+n32N8L4Oxj0J4p6eBO5KtKbS4Hbz9WOj6pE1ZviYTpOX0RdIzFGvEMBPst3q8YrIcbhDAxHA8R6+Dnec9wFgPLAKOM8Ys817TRQYABQBO4DTjDHvicj/Ah/z9vFjY8zfUvVmdqqorvs07lhMl2ePKJXs6aKrF0zKW3+M7RzqQw3AoYRjy20HSbVuDzVkm4rqul+RpQtoKDt+WPDnF75U8NgJtnOoD11IOJbyjlkmyPYz17pSDbxpO4TKHo8mZg+3nUF96I9BLboQ4MLrndV2AdBkO4vKDm+YKVONQU8ftu9t4L9sh0inwBZe+HAhna/vd0OlAIe8/K0M0BMp7NoKnEU4FugOU6ALL0C0pvI+dBUz1U2vOtMabWfIYZ3AeYRjUdtB0i3whdfzA9yTNpTq0qOJ2eW2M+SwqwjHnrUdwg85UXi943svBV62HEVluGedWVON+fCQSeWfPxCO3W47hF9yovACRGsqW3EX9VlhO4vKXHHKBrRStNR2jhyzAPdkqpyRM4UXIFpTuQn4DOilXtS+LTbjN9rOkEOWA+cQjrXbDuKnnCq8ANGayveA80jR4jwqeJ5IHJ5zvxeWbAI+STiWc3/ocvI/WLSm8kngW7ZzqMz0mHPEeNsZckAcOJ1wLCeHdXKy8AJEayp/B/xqvxuqnBM1o8cljORcL8xHHcC5hGOv2w5iS84WXs/3gMCelqh6b40ZrpOw6WGAy1J9JYlsk9OFN1pT6QAXo8f4qt284BzS10uHq737HuHYfbZD2JbThRcgWlOZAC7CXd5SKQDqnNnDbGcIoBsJx262HSIT5HzhhV2K7z9sZ1GZ4TVn2lRjaLGdI0BuJBz7se0QmUILrydaU9mJe8HMf9rOouzrpKBwB/10wZzUCGvR3ZUW3iRe8b0Q94KeKse97kzZbjtDAIQJx35kO0Sm0cK7G6/4XgA8aDuLsmuuLpjTVz/Sort3Wnj3Iqn4/tt2FmXPU87HpxhDMK+NlX7XE46FbYfIVFp49yFaU9kBfB4d881ZMfoNaqcwcBdaTLME8BXCsZ/ZDpLJtPB2Ian46iEwOeoDM1Yv+d59LbgL3vzBdpBMp4V3P6I1lU60pvK7wOW4f81VDnkqcZjYzpAltgOnEY49YjtINtDC203Rmsq7cZeU1EvD5JC5zpFjbWfIAmuB4wnH5tsOki208PZAtKbyMeA4YLXtLMof9WZshWNki+0cGSwCHEM49q7tINlEC28PRWsq3wZmA2/YzqL8sY6hy2xnyFD/xi26q2wHyTZaeHshWlO5HjgB0PGsHDA/cbCeOrwrA/wId2nHuO0w2UgLby9FayqbgM8Ct9rOotKrzjlqqO0MGaQJ+BzhWJhwTI9x7iUxRn92fVVRXfdl4DagzHYWlXpFdLR9UFyFCMW2s1gWBc4iHHvbdpBspz3eFIjWVP4BOBx3okEFTDuFxQ2U5fqCOY8DR2jRTQ0tvCkSralcDBwJ/MZ2FpV6i5zJW21nsCQBXAd8mnBMj+5IER1qSIOK6rpzgHuAwbazqNS4IP+ZhTWF98y2ncNna4AL9fjc1NMebxpEayofBGYBL1qOolLkycRhk2xn8NmDwCFadNNDC2+aRGsqVwFzgJ8CjuU4qo+2MnBYuymI2s7hg2bg64Rj5xKO6XrEaaKFN42iNZWJaE3lDcApwHrbeVTfLDVj1tjOkGbP4/Zyf2c7SNBp4fVBtKbyWSAE/Ml2FtV7TzsfD+qESBz4FvAJwjE9S88HOrnms4rquhOAu4EZtrOonjlIosseLb4uaGO9TwJfJRxbaTtILtHCa0FFdV0R8D3geqDUchzVbcYsL74olicMsp0kBWLA1bp2rh1aeC2qqK6bCNwFfNJ2FtU9LxVf8epo2XaE7Rx9dB/wfcIxXeTdEh3jtShaU7k8WlP5KdyrXOjkWxZY4Mxssp2hD94EjiMcu1iLrl1aeDNAtKbyAWA6cAd66FlGezRxZDaeFLMV+AZwOOGYHlueAXSoIcNUVNcdBvwcONV2FrWnEtpaFhdfViBCoe0s3ZDAncj9gR6Tm1m08GYo7+iHm4DjbWdRu4oUf/nd/tIy03aOLhjcq2P/kHDsfdth1J608Ga4iuq604Cf4C7AozLAfYU/ff7Y/Hfn2M6xD48CNxCOvWk7iNo3HePNcNGayieiNZWzgbMAXZIvAzzuHJGJ6/I+jztxVqlFN/NpjzeLVFTXCXA+EMadjFMWjGLbxpdLrhhpO4dnHnAT4dgTtoOo7tPCm4UqquvygYtx10mdajlOTqov/uLqQkmMs9S8AzwE/JJw7GVLGVQfaOHNYl4P+HTgSvQoCF89XvT9F6flrTnW52ZbgVrgZsKxep/bVimkhTcgKqrrZuIW4IvR05DT7tqC++d9veA/fh1xsgn4LXAH4dgmn9pUaaSFN2AqquuGAFXA14FpluME1sdk6ZKHi3+YzmEeAzwN/A54iHCsI41tKZ9p4Q2wiuq6T+CesfRZyIoD/rOG4DjLiy9uFGFgine9AbgX+D3h2PIU71tlCC28OaCium4EcAFwHnAsIHYTBcMrxd98bYTsODwFu2oHHsMdv32EcKwzBftUGUwLb46pqK4bA5zLR0VYj+XupdsKb3/+zPyXensiRQfwFPB33KGEWOqSqUynhTeHVVTXjQbOwS3Cx6NFuEc+lbfwjbuLbv14D16SAJ7FLbYPEo5tS08ylem08CoAKqrrRvJRET4ByLebKPOV0xJ/p/jLpSJd/qw24Q4jPAY8QTi21Z90KpNp4VV7qKiuGwgch3uV5DnAx4ECq6Ey1LvFly0ul7bkyzglgJeBubjF9g3CMf0lU7vQwqv2q6K6rh/uePDOQnwEepQEAPcX3vTMMfnvleKeujsPmE84tsNuKpXptPCqHquorisDjgZOxB0bDgFDbGbyURRYmHR7I1pT2Wo1kco6WnhVSlRU140CDvJuM5PuD7OZq5cMsAZYknT7ALfIbrQZTAWDFl6VVhXVdcPZtRBPBEYCI4DhgM0lFrewa3HdeVsaralssZhLBZwWXmWVN5E3wruNTLq/szCX4B7mlu99Tb7t/pgBdgDbgW1Jt92/3wZsj9ZU6mm4ygotvEop5TM9YF4ppXymhVcppXymhVcppXymhVcppXymhVcppXymhVcppXymhVcppXymhVcppXymhVcppXymhVcppXymhVcppXymhVcppXymhVcppXymhVcppXymhVcppXymhVcppXymhVcppXymhVcppXymhVcppXz2/wEiAMi8c1h+EAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a pie chart\n",
    "plt.figure(figsize=(6, 6))\n",
    "plt.pie(yearly_immigration_sum['Amount'], labels=yearly_immigration_sum['Year'], autopct='%1.1f%%', startangle=140)\n",
    "plt.title('Yearly Immigration Amounts')\n",
    "plt.axis('equal')  \n",
    "\n",
    "# Display the pie chart\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c959531",
   "metadata": {},
   "source": [
    "<h3 style=\"text-align: center; color: Green;\">Number of Immigrants from each District</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "21f59bc0",
   "metadata": {},
   "outputs": [],
   "source": [
    "district_immigration_sum = immigrations.groupby('District')['Amount'].sum().reset_index()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b7ffa3ce",
   "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>District</th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ciutat Vella</td>\n",
       "      <td>38029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Eixample</td>\n",
       "      <td>58309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Gracia</td>\n",
       "      <td>21018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Horta-Guinardo</td>\n",
       "      <td>23621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Les Corts</td>\n",
       "      <td>12583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Nou Barris</td>\n",
       "      <td>24173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Sant Andreu</td>\n",
       "      <td>18510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Sant Marti</td>\n",
       "      <td>37880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Sants-Montjuic</td>\n",
       "      <td>35937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Sarria-Sant Gervasi</td>\n",
       "      <td>20379</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              District  Amount\n",
       "0         Ciutat Vella   38029\n",
       "1             Eixample   58309\n",
       "2               Gracia   21018\n",
       "3       Horta-Guinardo   23621\n",
       "4            Les Corts   12583\n",
       "5           Nou Barris   24173\n",
       "6          Sant Andreu   18510\n",
       "7           Sant Marti   37880\n",
       "8       Sants-Montjuic   35937\n",
       "9  Sarria-Sant Gervasi   20379"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "district_immigration_sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "11113b9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a column (bar) chart\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.bar(district_immigration_sum['District'], district_immigration_sum['Amount'])\n",
    "plt.xlabel('District')\n",
    "plt.ylabel('Amount of Immigrants')\n",
    "plt.title('Amount of Immigrants by District')\n",
    "\n",
    "plt.xticks(rotation=45)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "623a690e",
   "metadata": {},
   "source": [
    "<h3 style=\"text-align: center; color: Green;\">Number of Immigrants from each Place of Origin</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "94f6dfff",
   "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>Place_of_origin</th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Andalusia</td>\n",
       "      <td>6525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Aragon</td>\n",
       "      <td>2167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Asturias</td>\n",
       "      <td>832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Balearic Islands</td>\n",
       "      <td>3768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Basque Country</td>\n",
       "      <td>1949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Canary Islands</td>\n",
       "      <td>2593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Cantabria</td>\n",
       "      <td>526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Castile and Leon</td>\n",
       "      <td>2081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Castile-La Mancha</td>\n",
       "      <td>1213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Catalonia</td>\n",
       "      <td>91286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Ceuta</td>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Community of Madrid</td>\n",
       "      <td>9037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Extremadura</td>\n",
       "      <td>615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Foreign countries</td>\n",
       "      <td>156607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Galicia</td>\n",
       "      <td>2503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Melilla</td>\n",
       "      <td>224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Murcia</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Navarra</td>\n",
       "      <td>713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Rioja</td>\n",
       "      <td>313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Valencian Community</td>\n",
       "      <td>6366</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Place_of_origin  Amount\n",
       "0             Andalusia    6525\n",
       "1                Aragon    2167\n",
       "2              Asturias     832\n",
       "3      Balearic Islands    3768\n",
       "4        Basque Country    1949\n",
       "5        Canary Islands    2593\n",
       "6             Cantabria     526\n",
       "7      Castile and Leon    2081\n",
       "8     Castile-La Mancha    1213\n",
       "9             Catalonia   91286\n",
       "10                Ceuta     113\n",
       "11  Community of Madrid    9037\n",
       "12          Extremadura     615\n",
       "13    Foreign countries  156607\n",
       "14              Galicia    2503\n",
       "15              Melilla     224\n",
       "16               Murcia    1008\n",
       "17              Navarra     713\n",
       "18                Rioja     313\n",
       "19  Valencian Community    6366"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "origin_immigration_sum = immigrations.groupby('Place_of_origin')['Amount'].sum().reset_index()\n",
    "origin_immigration_sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "79d3b85a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a horizontal bar chart\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.barh(origin_immigration_sum['Place_of_origin'], origin_immigration_sum['Amount'])\n",
    "plt.xlabel('Amount of Immigrants')\n",
    "plt.ylabel('Place of Origin')\n",
    "plt.title('Amount of Immigrants by Place of Origin (Horizontal Bar Chart)')\n",
    "\n",
    "# Display the horizontal bar chart\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f9862ac",
   "metadata": {},
   "source": [
    "<h3 style=\"text-align: center; color: Green;\">Forecast of the Immigrants for the Following Year</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5c3f2964",
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm\n",
    "from statsmodels.tsa.arima.model import ARIMA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "34162501",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "51aeff19",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Group by 'Year' and calculate the total immigration for each year\n",
    "yearly_immigration_total = immigrations.groupby('Year')['Amount'].sum().reset_index()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9ee432ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a linear regression model\n",
    "model = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "734debda",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit the model using historical data\n",
    "X = np.array(yearly_immigration_total['Year']).reshape(-1, 1)\n",
    "y = yearly_immigration_total['Amount']\n",
    "model.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "de12cb42",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Forecast immigration for the next ten years\n",
    "forecast_years = list(range(2022, 2023))\n",
    "forecasted_immigration = model.predict(np.array(forecast_years).reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6a4c8153",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a DataFrame for the forecasted data\n",
    "forecast_df = pd.DataFrame({'Year': forecast_years, 'Forecasted_Amount': forecasted_immigration})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8048a4db",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Year  Forecasted_Amount\n",
      "0  2022            81228.0\n"
     ]
    }
   ],
   "source": [
    "# Display the forecasted data\n",
    "print(forecast_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9de338af",
   "metadata": {},
   "source": [
    "<h2 style=\"text-align: center; color: Green;\">Conclusion</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "126c85d1",
   "metadata": {},
   "source": [
    "In summary, we explored data related to immigration, visualized it through bar charts, and discussed forecasting. While our initial analysis provided insights, real-world immigration forecasting is complex and requires advanced models. Careful consideration of historical trends and external factors is crucial for accurate predictions in immigration analytics.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e938c63",
   "metadata": {},
   "source": [
    "<h1 style=\"text-align: center; color: Orange;\">End</h1>"
   ]
  }
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