D9_Siralama_Algoritmalari.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "EfxKW7fgggMc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ca4593c6-2faf-456d-d919-1a6aafb8988c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Merhaba, İGÜ - İSTKA / Ders 9\n"
          ]
        }
      ],
      "source": [
        "print(\"Merhaba, İGÜ - İSTKA / Ders 9\") # print ekrana çıktı veren bir koddur."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import sys # import python programlama dilinde yazılıma kütüphane eklemeye yarayan bir kod blogudur.\n",
        "print(sys.version)"
      ],
      "metadata": {
        "id": "KQ5OfwrfguPW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dce6fbb9-bdd7-493d-c6e8-ca339fed6612"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3.10.12 (main, Jul 29 2024, 16:56:48) [GCC 11.4.0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive # google colab - drive bağlantısı\n",
        "\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "ZONtsQb2gzQE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9ac9fab5-9b4c-4974-dde9-730e86cd11b2"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "\n",
        "os.chdir('/content/drive/My Drive/VERIYAPILARI_ISTKA')\n",
        "\n",
        "!pwd # bize kodun çıktısını dosya yolu olarak veren kod blogudur."
      ],
      "metadata": {
        "id": "vTwXKpPJg0mk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7a735a00-eea4-424a-fd13-6c42df9b404b"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/My Drive/VERIYAPILARI_ISTKA\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## VERİ YAPILARI ve ALGORİTMALAR\n",
        "# Sıralama, Arama ve Graf Algoritmaları\n",
        "\n"
      ],
      "metadata": {
        "id": "1A8Z5581HpVM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "W0PmaFNcIKT7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "VeriSetiYolu = '/content/drive/My Drive/VERIYAPILARI_ISTKA/algveriseti'\n",
        "\n",
        "for file in os.listdir(VeriSetiYolu):                         # Klasördeki tüm dosyaları listele\n",
        "\n",
        "    if file.endswith(('.xlsx', '.xls')):                     # Sadece Excel dosyalarını seç\n",
        "\n",
        "        df = pd.read_excel(os.path.join(VeriSetiYolu, file))  # Excel dosyasını pandas ile oku\n",
        "\n",
        "        df.to_csv(os.path.join(VeriSetiYolu, file.rsplit('.', 1)[0] + '.csv'), index=False)\n",
        "                                                               # DataFrame'i CSV formatında kaydet\n",
        "        print(f\"{file} dosyası CSV formatına çevrildi.\")     # Çevrim İşlemi Çıktısı\n",
        "\n",
        "# İşlem tamamlandığında kaç tane excel dosyası var ise o kadar *.csv formatına çevildiğinin\n",
        "# bilgisini almaktayız"
      ],
      "metadata": {
        "id": "kuKubeTWg2Ur"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import pandas as pd\n",
        "\n",
        "VeriSetiYolu = '/content/drive/My Drive/VERIYAPILARI_ISTKA/algveriseti'\n",
        "\n",
        "# VeriSetiYolu içindeki .csv dosyalarını bul ve say\n",
        "csv_dosyalari = [dosya for dosya in os.listdir(VeriSetiYolu) if dosya.endswith('.csv')]\n",
        "\n",
        "# .csv dosya sayısını ekrana yazdır\n",
        "csv_dosya_sayisi = len(csv_dosyalari)\n",
        "print(f\"VeriSetiYolu içerisinde {csv_dosya_sayisi} tane .csv dosyası bulunuyor.\")\n",
        "\n",
        "# Mevcut *.csv uzantılı dosyaların isimlerini getiriyoruz.\n",
        "print(\"Bulunan CSV Dosyalarının İsimleri Aşağıdadır --> \")\n",
        "for dosya in csv_dosyalari:\n",
        "    print(dosya)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4sJA4kaMHI1m",
        "outputId": "5c53a316-a4d9-4d88-fb49-8da46e8de946"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "VeriSetiYolu içerisinde 1 tane .csv dosyası bulunuyor.\n",
            "Bulunan CSV Dosyalarının İsimleri Aşağıdadır --> \n",
            "ilcesuveriseti.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# CSV dosyasını oku\n",
        "dosya_yolu = '/content/drive/My Drive/VERIYAPILARI_ISTKA/algveriseti/ilcesuveriseti.csv'\n",
        "csv_dosya = pd.read_csv(dosya_yolu)\n",
        "\n",
        "print(csv_dosya)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AisPlEQJHSUS",
        "outputId": "77b9559a-87d5-44bd-e726-31cb7afd30b0"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   ID;ILCELER;2015;2016;2017;2018;2019;2020;2021;2022\n",
            "0   1;ADALAR;1.432.494;1.472.276;1.366.581;1.399.1...\n",
            "1   2;ARNAVUTKOY;8.002.123;9.178.953;10.176.132;11...\n",
            "2   3;ATASEHIR;18.597.049;19.974.097;21.205.911;21...\n",
            "3   4;AVCILAR;16.220.476;17.052.253;17.558.403;18....\n",
            "4   5;BUYUKCEKMECE;9.915.435;10.788.924;11.172.787...\n",
            "5   6;BAHCELIEVLER;23.610.609;24.320.533;24.868.59...\n",
            "6   7;BAKIRKOY;11.510.117;11.793.169;12.509.301;15...\n",
            "7   8;BAYRAMPASA;11.545.682;11.914.393;12.516.185;...\n",
            "8   9;BAGCILAR;26.691.720;28.182.511;29.362.186;31...\n",
            "9   10;BASAKSEHIR;16.174.934;18.100.255;19.576.095...\n",
            "10  11;BEYKOZ;10.254.740;10.891.434;11.331.655;12....\n",
            "11  12;BEYLIKDUZU;12.346.365;13.630.387;14.948.006...\n",
            "12  13;BEYOGLU;11.167.382;11.311.755;11.420.559;12...\n",
            "13  14;BESIKTAS;11.976.186;12.385.216;12.609.953;1...\n",
            "14  15;ESENLER;15.854.265;16.584.976;17.162.651;17...\n",
            "15  16;ESENYURT;27.933.397;32.799.007;36.083.030;3...\n",
            "16  17;EYUPSULTAN;17.116.911;17.901.848;18.222.331...\n",
            "17  18;FATIH;22.358.121;21.781.211;21.983.686;23.8...\n",
            "18  19;GOP;17.730.853;18.637.751;18.940.716;19.614...\n",
            "19  20;GUNGOREN;18.994.573;12.600.044;12.769.886;1...\n",
            "20  21;KUCUKCEKMECE;29.188.561;31.086.665;31.994.3...\n",
            "21  22;KADIKOY;23.910.542;24.310.256;24.797.597;26...\n",
            "22  23;KARTAL;19.155.143;20.007.459;20.375.603;21....\n",
            "23  24;KAGITHANE;18.207.418;19.030.632;19.286.724;...\n",
            "24  25;MALTEPE;21.906.064;22.794.286;22.703.875;24...\n",
            "25  26;PENDIK;26.499.977;28.474.258;29.743.117;31....\n",
            "26  27;SANCAKTEPE;12.313.398;14.090.151;15.239.008...\n",
            "27  28;SARIYER;17.555.072;18.711.570;19.427.187;20...\n",
            "28  29;SULTANBEYLI;9.599.250;10.224.559;10.814.861...\n",
            "29  30;SALTANGAZI;17.142.236;18.219.393;19.163.693...\n",
            "30  31;SILIVRI;7.620.350;8.227.604;8.452.805;8.941...\n",
            "31  32;TUZLA;11.546.080;12.690.324;13.625.147;14.9...\n",
            "32  33;ZEYTINBURNU;12.576.945;13.068.037;13.469.51...\n",
            "33  34;CATALCA;2.931.018;3.222.450;3.311.210;3.665...\n",
            "34  35;CEKMEKOY;8.951.936;9.705.483;10.205.702;10....\n",
            "35  36;UMRANIYE;28.085.173;30.065.065;31.061.816;3...\n",
            "36  37;USKUDAR;25.142.059;26.110.542;26.353.986;27...\n",
            "37  38;SILE;1.835.302;2.098.928;2.196.246;2.522.41...\n",
            "38  39;SISLI;15.717.630;15.948.709;16.076.563;17.0...\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Veri setinin yolunun gösterimi ve yüklemesi\n",
        "VeriSetiYolu = '/content/drive/My Drive/VERIYAPILARI_ISTKA/algveriseti/ilcesuveriseti.csv'\n",
        "data = pd.read_csv(VeriSetiYolu, delimiter=';')\n",
        "\n",
        "# ; verilerini temizleyip , verisini dönüştürme işlemidir.\n",
        "for i in data.columns[2:]:\n",
        "    data[i] = data[i].str.replace('.', '').astype(int)\n",
        "\n",
        "print(data.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ufndJ0iCHkji",
        "outputId": "1477d8a9-9b64-44d6-cbcc-71119e1d7859"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   ID       ILCELER      2015      2016      2017      2018      2019  \\\n",
            "0   1        ADALAR   1432494   1472276   1366581   1399182   1371291   \n",
            "1   2    ARNAVUTKOY   8002123   9178953  10176132  11404878  13818204   \n",
            "2   3      ATASEHIR  18597049  19974097  21205911  21496185  22428468   \n",
            "3   4       AVCILAR  16220476  17052253  17558403  18312736  19485453   \n",
            "4   5  BUYUKCEKMECE   9915435  10788924  11172787  12222470  13606571   \n",
            "\n",
            "       2020      2021      2022  \n",
            "0   1513070   1435991   1543403  \n",
            "1  15570466  17102127  18891299  \n",
            "2  23737630  24281319  24377611  \n",
            "3  20824893  20690506  21556002  \n",
            "4  16015731  16167480  17412198  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.ticker as ticker\n",
        "\n",
        "# Veri setini yükleme ve temizleme\n",
        "data = pd.read_csv(VeriSetiYolu, delimiter=';')\n",
        "\n",
        "# Sayısal kolonlardaki verileri temizleyip sayıya dönüştürme\n",
        "for i in data.columns[2:]:\n",
        "    data[i] = data[i].str.replace('.', '').astype(int)\n",
        "\n",
        "# Belirtilen ilçeler için veriyi filtreleme\n",
        "ilceler = [\"AVCILAR\", \"BEYLIKDUZU\", \"BUYUKCEKMECE\", \"KUCUKCEKMECE\", \"SILIVRI\", \"ESENYURT\"]\n",
        "filtre = data[data['ILCELER'].isin(ilceler)]\n",
        "\n",
        "# Seçilen ilçeler için yıllık su tüketim miktarlarını çizme\n",
        "plt.figure(figsize=(15, 8))\n",
        "\n",
        "for ilce in ilceler:\n",
        "    ilce_data = filtre[filtre['ILCELER'] == ilce]\n",
        "    plt.plot(data.columns[2:], ilce_data.iloc[0, 2:], label=ilce)\n",
        "\n",
        "plt.xlabel('Yıllar')\n",
        "plt.ylabel('Su Tüketimi (m3)')\n",
        "plt.title('Belirli İlçelerin Yıllara Göre Su Tüketim Miktarları')\n",
        "\n",
        "# Y eksenine binlik ayırıcı ekleme\n",
        "ax = plt.gca()\n",
        "ax.yaxis.set_major_formatter(ticker.FuncFormatter(lambda x, pos: f'{int(x):,}'))\n",
        "\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 807
        },
        "id": "Q0Tgn9qvOpag",
        "outputId": "4f7d755a-e513-49eb-f2f5-b53249ccb24c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x800 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Su veri seti üzerinin görselleştirilmesinde graph ve plot ekleme ile görselin detaylandırılması - plotly Kütüphane Kurulum İşlemi !pip install plotly\n"
      ],
      "metadata": {
        "id": "l6wvwdhQO2c3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install plotly"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UUm5zfPaPKU3",
        "outputId": "915e629f-c721-487d-e2ef-552990e1d255"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: plotly in /usr/local/lib/python3.10/dist-packages (5.15.0)\n",
            "Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from plotly) (9.0.0)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from plotly) (24.1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import plotly.graph_objects as go\n",
        "\n",
        "# Veri setini yükleme ve temizleme\n",
        "data = pd.read_csv(VeriSetiYolu, delimiter=';')\n",
        "\n",
        "# Sayısal kolonlardaki verileri temizleyip sayıya dönüştürme\n",
        "for i in data.columns[2:]:\n",
        "    data[i] = data[i].str.replace('.', '').astype(int)\n",
        "\n",
        "# Belirtilen ilçeler için veriyi filtreleme\n",
        "ilce_bilgileri = [\"AVCILAR\", \"BEYLIKDUZU\", \"BUYUKCEKMECE\", \"KUCUKCEKMECE\", \"SILIVRI\", \"ESENYURT\"]\n",
        "filtre = data[data['ILCELER'].isin(ilce_bilgileri)]\n",
        "\n",
        "# Plotly kullanarak interaktif grafik oluşturma\n",
        "fig = go.Figure()\n",
        "\n",
        "for ilce in ilce_bilgileri:\n",
        "    ilce_data = filtre[filtre['ILCELER'] == ilce]\n",
        "    fig.add_trace(go.Scatter(\n",
        "         x=data.columns[2:],\n",
        "         y=ilce_data.iloc[0, 2:],\n",
        "         mode='lines+markers',\n",
        "         line=dict(width=3),\n",
        "         name=ilce\n",
        "        ))\n",
        "\n",
        "fig.update_layout(\n",
        "        title='AVCILAR, BEYLIKDUZU, BUYUKCEKMECE, KUCUKCEKMECE, SILIVRI, ESENYURT İlçelerin Yıllara Göre Su Tüketim Miktarları',\n",
        "        xaxis_title='Yıllar',\n",
        "        yaxis_title='Su Tüketimi (m3)',\n",
        "        yaxis=dict(tickformat=','),\n",
        "        template='plotly_white'\n",
        ")\n",
        "\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "mscWOn5VO3nl",
        "outputId": "1581fe40-b43c-4c43-a141-ff9175f05c2d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
              "    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
              "        <script charset=\"utf-8\" src=\"https://cdn.plot.ly/plotly-2.24.1.min.js\"></script>                <div id=\"5dcfc184-3fa8-445c-b184-ff85da6f00a7\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"5dcfc184-3fa8-445c-b184-ff85da6f00a7\")) {                    Plotly.newPlot(                        \"5dcfc184-3fa8-445c-b184-ff85da6f00a7\",                        [{\"line\":{\"width\":3},\"mode\":\"lines+markers\",\"name\":\"AVCILAR\",\"x\":[\"2015\",\"2016\",\"2017\",\"2018\",\"2019\",\"2020\",\"2021\",\"2022\"],\"y\":[16220476,17052253,17558403,18312736,19485453,20824893,20690506,21556002],\"type\":\"scatter\"},{\"line\":{\"width\":3},\"mode\":\"lines+markers\",\"name\":\"BEYLIKDUZU\",\"x\":[\"2015\",\"2016\",\"2017\",\"2018\",\"2019\",\"2020\",\"2021\",\"2022\"],\"y\":[12346365,13630387,14948006,16738110,17387725,20325425,20415406,22961265],\"type\":\"scatter\"},{\"line\":{\"width\":3},\"mode\":\"lines+markers\",\"name\":\"BUYUKCEKMECE\",\"x\":[\"2015\",\"2016\",\"2017\",\"2018\",\"2019\",\"2020\",\"2021\",\"2022\"],\"y\":[9915435,10788924,11172787,12222470,13606571,16015731,16167480,17412198],\"type\":\"scatter\"},{\"line\":{\"width\":3},\"mode\":\"lines+markers\",\"name\":\"KUCUKCEKMECE\",\"x\":[\"2015\",\"2016\",\"2017\",\"2018\",\"2019\",\"2020\",\"2021\",\"2022\"],\"y\":[29188561,31086665,31994320,33889979,35731905,38012294,38030116,38832413],\"type\":\"scatter\"},{\"line\":{\"width\":3},\"mode\":\"lines+markers\",\"name\":\"SILIVRI\",\"x\":[\"2015\",\"2016\",\"2017\",\"2018\",\"2019\",\"2020\",\"2021\",\"2022\"],\"y\":[7620350,8227604,8452805,8941729,10056059,11744060,12232563,13216660],\"type\":\"scatter\"},{\"line\":{\"width\":3},\"mode\":\"lines+markers\",\"name\":\"ESENYURT\",\"x\":[\"2015\",\"2016\",\"2017\",\"2018\",\"2019\",\"2020\",\"2021\",\"2022\"],\"y\":[27933397,32799007,36083030,39205682,43576187,59865610,48988082,52836878],\"type\":\"scatter\"}],                        {\"template\":{\"data\":{\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"white\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"white\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"#C8D4E3\",\"linecolor\":\"#C8D4E3\",\"minorgridcolor\":\"#C8D4E3\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"#C8D4E3\",\"linecolor\":\"#C8D4E3\",\"minorgridcolor\":\"#C8D4E3\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"choropleth\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"choropleth\"}],\"contourcarpet\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"contourcarpet\"}],\"contour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"contour\"}],\"heatmapgl\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmapgl\"}],\"heatmap\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmap\"}],\"histogram2dcontour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2dcontour\"}],\"histogram2d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2d\"}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"mesh3d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"mesh3d\"}],\"parcoords\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"parcoords\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}],\"scatter3d\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter3d\"}],\"scattercarpet\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattercarpet\"}],\"scattergeo\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergeo\"}],\"scattergl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergl\"}],\"scattermapbox\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattermapbox\"}],\"scatterpolargl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolargl\"}],\"scatterpolar\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolar\"}],\"scatter\":[{\"fillpattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2},\"type\":\"scatter\"}],\"scatterternary\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterternary\"}],\"surface\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"surface\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}]},\"layout\":{\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"autotypenumbers\":\"strict\",\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]],\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]},\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"geo\":{\"bgcolor\":\"white\",\"lakecolor\":\"white\",\"landcolor\":\"white\",\"showlakes\":true,\"showland\":true,\"subunitcolor\":\"#C8D4E3\"},\"hoverlabel\":{\"align\":\"left\"},\"hovermode\":\"closest\",\"mapbox\":{\"style\":\"light\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"white\",\"polar\":{\"angularaxis\":{\"gridcolor\":\"#EBF0F8\",\"linecolor\":\"#EBF0F8\",\"ticks\":\"\"},\"bgcolor\":\"white\",\"radialaxis\":{\"gridcolor\":\"#EBF0F8\",\"linecolor\":\"#EBF0F8\",\"ticks\":\"\"}},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"white\",\"gridcolor\":\"#DFE8F3\",\"gridwidth\":2,\"linecolor\":\"#EBF0F8\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"#EBF0F8\"},\"yaxis\":{\"backgroundcolor\":\"white\",\"gridcolor\":\"#DFE8F3\",\"gridwidth\":2,\"linecolor\":\"#EBF0F8\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"#EBF0F8\"},\"zaxis\":{\"backgroundcolor\":\"white\",\"gridcolor\":\"#DFE8F3\",\"gridwidth\":2,\"linecolor\":\"#EBF0F8\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"#EBF0F8\"}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"ternary\":{\"aaxis\":{\"gridcolor\":\"#DFE8F3\",\"linecolor\":\"#A2B1C6\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"#DFE8F3\",\"linecolor\":\"#A2B1C6\",\"ticks\":\"\"},\"bgcolor\":\"white\",\"caxis\":{\"gridcolor\":\"#DFE8F3\",\"linecolor\":\"#A2B1C6\",\"ticks\":\"\"}},\"title\":{\"x\":0.05},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"#EBF0F8\",\"linecolor\":\"#EBF0F8\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"#EBF0F8\",\"zerolinewidth\":2},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"#EBF0F8\",\"linecolor\":\"#EBF0F8\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"#EBF0F8\",\"zerolinewidth\":2}}},\"yaxis\":{\"title\":{\"text\":\"Su T\\u00fcketimi (m3)\"},\"tickformat\":\",\"},\"title\":{\"text\":\"AVCILAR, BEYLIKDUZU, BUYUKCEKMECE, KUCUKCEKMECE, SILIVRI, ESENYURT \\u0130l\\u00e7elerin Y\\u0131llara G\\u00f6re Su T\\u00fcketim Miktarlar\\u0131\"},\"xaxis\":{\"title\":{\"text\":\"Y\\u0131llar\"}}},                        {\"responsive\": true}                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('5dcfc184-3fa8-445c-b184-ff85da6f00a7');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })                };                            </script>        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## SIRALAMA ALGORİTMALARI\n",
        "\n",
        "1. Kabarcık (Bubble) Sıralama Algoritması -> Dizi içerisinde ardışık öğeler karşılaştırılır ve gerektiğinde yer değiştirir.\n",
        "\n",
        "2. Seçim (Selection) Sıralama Algoritması -> En küçük veya en büyük öğeyi seçip, onu listenin sonuna yerleştirme işlemidir.\n",
        "\n",
        "3. Insertion (Eklemeli) Sıralama Algoritması -> Veriyi sırayla ele alır ve her yeni öğeyi sıralanmış bir alt listeye ekleme işlemidir.\n",
        "\n",
        "4. Kova (Bucket) Sıralama Algoritması -> Veriyi birden çok kısmi gruba ayırarak sıralar.\n",
        "\n",
        "5. Birleştirmeli (Merge) Sıralama Algoritması -> Böl ve yönet işlemi gerçekleştirilir.\n",
        "\n",
        "6. Hızlı (Quick) Sıralama Algoritması -> Böl ve yönet işlemi gerçekleştirilir."
      ],
      "metadata": {
        "id": "mrEP1crsPiI1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 1. Kabarcık (Bubble) Sıralama Algoritması\n",
        "\n",
        "dizi = [64, 34, 90, 1]\n",
        "\n",
        "def kabarcikalgoritmasi(dizi):\n",
        "    n = len(dizi)  # 1. Adım: Dizinin uzunluğunu alır ve n değişkenine atar.\n",
        "    for i in range(n):  # 2. Adım: i değişkeni 0'dan n-1'e kadar döner, bu dış döngüdür.\n",
        "        for j in range(0, n-i-1):  # 3. Adım: j değişkeni, 0'dan n-i-1'e kadar döner, bu iç döngüdür.\n",
        "            if dizi[j] > dizi[j+1]:  # 4. Adım: Şu anki eleman ile bir sonraki elemanı karşılaştırır.\n",
        "               dizi[j], dizi[j+1] = dizi[j+1], dizi[j]  # 5. Adım: Eğer şu anki eleman bir sonrakinden büyükse, yer değiştirir.\n",
        "    return dizi  # 6. Adım: Sıralanmış diziyi döner.\n",
        "\n",
        "kabarcik_siralama = kabarcikalgoritmasi(dizi)\n",
        "print(\"Sıralama Sonucu: \", kabarcik_siralama)"
      ],
      "metadata": {
        "id": "Nnkhg9LHPUBr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7955445d-6f6b-4386-87e5-e38f2401f0db"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sıralama Sonucu:  [1, 34, 64, 90]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Başlangıç Dizisi: [64, 34, 90, 1]\n",
        "\n",
        "1) i = 0 durumunda\n",
        "\n",
        "Adım 1: 64 ve 34 karşılaştırılır. 64 büyük olduğu için yer değiştirirler.\n",
        "\n",
        "Dizinin Yeni Hali -> [34, 64, 90, 1]\n",
        "-------------------------------------------------------------------------------\n",
        "Adım 2: 64 ve 90 karşılaştırılır. 64 küçük olduğu için yer değiştirme olmaz.\n",
        "\n",
        "Dizinin Yeni Hali -> [34, 64, 90, 1]\n",
        "-------------------------------------------------------------------------------\n",
        "Adım 3: 90 ve 1 karşılaştırılır. 90 büyük olduğu için yer değiştirirler.\n",
        "\n",
        "Dizinin Yeni Hali -> [34, 64, 1, 90]\n",
        "\n",
        "-------------------------------------------------------------------------------\n",
        "-------------------------------------------------------------------------------\n",
        "\n",
        "2) i = 1 durumunda\n",
        "\n",
        "Adım 1: 34 ve 64 karşılaştırılır. 34 küçük olduğu için yer değiştirme olmaz.\n",
        "\n",
        "Dizinin Yeni Hali -> [34, 64, 1, 90]\n",
        "-------------------------------------------------------------------------------\n",
        "Adım 2: 64 ve 1 karşılaştırılır. 64 büyük olduğu için yer değiştirirler.\n",
        "\n",
        "Dizinin Yeni Hali -> [34, 1, 64, 90]\n",
        "\n",
        "-------------------------------------------------------------------------------\n",
        "-------------------------------------------------------------------------------\n",
        "\n",
        "3) i = 2 durumunda\n",
        "\n",
        "Adım 1: 34 ve 1 karşılaştırılır. 34 büyük olduğu için yer değiştirirler.\n",
        "\n",
        "Dizinin Yeni Hali -> [1, 34, 64, 90]\n",
        "\n",
        "-------------------------------------------------------------------------------\n",
        "-------------------------------------------------------------------------------\n",
        "\n",
        "4) i = 3 durumunda\n",
        "\n",
        "Kalan tek eleman zaten sıralı olduğu için hiçbir işlem yapılmaz.\n",
        "\n",
        "Dizinin Son Hali -> [1, 34, 64, 90]\n"
      ],
      "metadata": {
        "id": "ZQUm1oQlWRSy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 2. Seçim (Selection) Sıralama Algoritması\n",
        "\n",
        "dizi = [64, 34, 90, 1]\n",
        "\n",
        "def secimalgoritmasi(dizi):\n",
        "    n = len(dizi)  # 1. Adım: Dizinin uzunluğunu alır ve 'n' değişkenine atar.\n",
        "    for i in range(n):  # 2. Adım: 'i' değişkeni 0'dan n-1'e kadar döner. Bu dış döngüdür ve dizinin her öğesini sırasıyla ele alır.\n",
        "        degisken = i  # 3. Adım: Şu anki döngüdeki en küçük elemanın indeksini 'degisken' olarak kabul eder. Başlangıçta 'i' indeksini alır.\n",
        "        for j in range(i+1, n):  # 4. Adım: 'j' değişkeni, 'i+1'den n'e kadar döner. Bu iç döngüdür ve 'i'nin sağındaki elemanları tarar.\n",
        "            if dizi[j] < dizi[degisken]:  # 5. Adım: Eğer 'dizi[j]' değeri 'dizi[degisken]' değerinden küçükse,\n",
        "                degisken = j  # 6. Adım: 'degisken' güncellenir ve yeni minimum değerin bulunduğu indeks olarak atanır.\n",
        "        dizi[i], dizi[degisken] = dizi[degisken], dizi[i]  # 7. Adım: 'degisken' ile 'i' indeksi arasındaki elemanlar yer değiştirilir.\n",
        "\n",
        "    return dizi  # 8. Adım: Sıralanmış diziyi döner.\n",
        "\n",
        "# Test\n",
        "secimsiralamasi = secimalgoritmasi(dizi)\n",
        "print(\"Sıralama Sonucu:\", secimsiralamasi)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0DArwseDEFp0",
        "outputId": "d75f1ce9-b7b1-4b05-a98e-4087eca13a1a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sıralama Sonucu: [1, 34, 64, 90]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Başlangıç Dizisi: [64, 34, 90, 1]\n",
        "\n",
        "# 1) i = 0 durumunda\n",
        "\n",
        "Adım 1: degisken değişkeni i = 0 olarak başlar, yani başlangıçta en küçük elemanın indeksi 0 kabul edilir (degisken = 0).\n",
        "\n",
        "Adım 2: j = 1 için, 34 < 64 olduğundan, degisken = 1 olarak güncellenir.\n",
        "\n",
        "Adım 3: j = 2 için, 90 < 34 olmadığından degisken değişmez.\n",
        "\n",
        "Adım 4: j = 3 için, 1 < 34 olduğundan, degisken = 3 olarak güncellenir.\n",
        "\n",
        "Adım 5: degisken = 3 ile i = 0 arasında yer değiştirilir. 1 ile 64 yer değiştirir.\n",
        "\n",
        "Dizinin Yeni Hali -> [1, 34, 90, 64]\n",
        "\n",
        "Not: Bu iterasyonun sonunda en küçük değer (1) dizinin başına yerleşmiş olur.\n",
        "\n",
        "----------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
        "\n",
        "# 2) i = 1 durumunda\n",
        "\n",
        "Adım 1: degisken değişkeni i = 1 olarak başlar, yani en küçük elemanın indeksi 1 kabul edilir (degisken = 1).\n",
        "\n",
        "Adım 2: j = 2 için, 90 < 34 olmadığından degisken değişmez.\n",
        "\n",
        "Adım 3: j = 3 için, 64 < 34 olmadığından degisken değişmez.\n",
        "\n",
        "Adım 4: degisken değişmediği için yer değiştirme yapılmaz.\n",
        "\n",
        "Dizinin Yeni Hali -> [1, 34, 90, 64]\n",
        "\n",
        "Not: Bu iterasyonun sonunda dizinin ikinci en küçük değeri (34) yerinde kalır.\n",
        "\n",
        "----------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
        "\n",
        "# 3) i = 2 durumunda\n",
        "\n",
        "Adım 1: degisken değişkeni i = 2 olarak başlar (degisken = 2).\n",
        "\n",
        "Adım 2: j = 3 için, 64 < 90 olduğundan, degisken = 3 olarak güncellenir.\n",
        "\n",
        "Adım 3: degisken = 3 ile i = 2 arasında yer değiştirilir. 64 ile 90 yer değiştirir.\n",
        "\n",
        "Dizinin Yeni Hali -> [1, 34, 64, 90]\n",
        "\n",
        "Not: Bu iterasyonun sonunda dizinin üçüncü en küçük değeri (64) yerleşmiş olur.\n",
        "\n",
        "----------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
        "\n",
        "# 4) i = 3 durumunda\n",
        "\n",
        "Adım 1: Sıralı süreç sağlandı hiçbir işlem yapılmaz.\n",
        "\n",
        "Dizinin son hali -> [1, 34, 64, 90]"
      ],
      "metadata": {
        "id": "u-toDPlcYPHB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 3. Insertion (Eklemeli) Sıralama Algoritması\n",
        "\n",
        "dizi = [64, 34, 90, 1]\n",
        "\n",
        "def eklemelialgoritma(dizi):\n",
        "    for i in range(1, len(dizi)):  # 1. Adım: 'i' değişkeni 1'den başlayarak dizinin sonuna kadar döner.\n",
        "        degisken = dizi[i]  # 2. Adım: 'degisken' değişkeni, şu anda incelenen elemanı tutar.\n",
        "        j = i-1  # 3. Adım: 'j' değişkeni, 'i' indeksinin bir öncesindeki indeksi gösterir.\n",
        "\n",
        "        while j >= 0 and degisken < dizi[j]:  # 4. Adım: 'degisken', 'dizi[j]'den küçük olduğu sürece ve 'j' 0'dan büyük ya da eşit olduğu sürece döngü devam eder.\n",
        "            dizi[j + 1] = dizi[j]  # 5. Adım: 'dizi[j]' değerini bir sağa kaydırır.\n",
        "            j -= 1  # 6. Adım: 'j' indeksini bir azaltır ve döngü devam eder.\n",
        "\n",
        "        dizi[j + 1] = degisken  # 7. Adım: 'degisken' değişkenini dizi içerisine yerleştirir.\n",
        "\n",
        "    return dizi  # 8. Adım: Sıralanmış diziyi döner.\n",
        "\n",
        "eklemelisiralama = eklemelialgoritma(dizi)\n",
        "print(\"Secim Sıralaması:\", eklemelisiralama)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-lV8-A0-HxX1",
        "outputId": "91f42707-51a2-4dfd-c4b5-845364bd117a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Secim Sıralaması: [1, 34, 64, 90]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Başlangıç Dizisi: [64, 34, 90, 1]\n",
        "\n",
        "# 1) i = 1 durumunda\n",
        "\n",
        "Adım 1: degisken = dizi[i] = 34\n",
        "\n",
        "Adım 2: j = i - 1 = 0\n",
        "\n",
        "Adım 3: dizi[j] = 64\n",
        "\n",
        "Adım 4: 34 < 64, bu yüzden dizi[j + 1] = dizi[j] olur:\n",
        "\n",
        "Dizi: [64, 64, 90, 1]\n",
        "\n",
        "Adım 5: j -= 1 ile j = -1 olur, döngü biter.\n",
        "\n",
        "Adım 6: dizi[j + 1] = degisken olur, 34 yerine yerleştirilir:\n",
        "\n",
        "Dizi: [34, 64, 90, 1]\n",
        "\n",
        "Not: İlk iki eleman (34 ve 64) doğru sıralanmış olur.\n",
        "\n",
        "----------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
        "\n",
        "# 2) i = 2 durumunda\n",
        "\n",
        "Adım 1: degisken = dizi[i] = 90\n",
        "\n",
        "Adım 2: j = i - 1 = 1\n",
        "\n",
        "Adım 3: dizi[j] = 64\n",
        "\n",
        "Adım 4: 90 > 64, bu yüzden döngü devam etmez.\n",
        "\n",
        "Adım 5: dizi[j + 1] = degisken olduğu gibi kalır:\n",
        "\n",
        "Dizi: [34, 64, 90, 1]\n",
        "\n",
        "----------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
        "\n",
        "# 3) i = 3 durumunda\n",
        "\n",
        "Adım 1: degisken = dizi[i] = 1\n",
        "\n",
        "Adım 2: j = i - 1 = 2\n",
        "\n",
        "Adım 3: dizi[j] = 90\n",
        "\n",
        "Adım 4: 1 < 90, bu yüzden dizi[j + 1] = dizi[j] olur:\n",
        "\n",
        "Dizi: [34, 64, 90, 90]\n",
        "\n",
        "Adım 5: j -= 1 ile j = 1 olur.\n",
        "\n",
        "Adım 6: dizi[j] = 64\n",
        "\n",
        "Adım 7: 1 < 64, bu yüzden dizi[j + 1] = dizi[j] olur:\n",
        "\n",
        "Dizi: [34, 64, 64, 90]\n",
        "\n",
        "Adım 8: j -= 1 ile j = 0 olur.\n",
        "\n",
        "Adım 9: dizi[j] = 34\n",
        "\n",
        "Adım 10: 1 < 34, bu yüzden dizi[j + 1] = dizi[j] olur:\n",
        "\n",
        "Dizi: [34, 34, 64, 90]\n",
        "\n",
        "Adım 11: j -= 1 ile j = -1 olur, döngü biter.\n",
        "\n",
        "Adım 12: dizi[j + 1] = degisken olur, 1 doğru yerine yerleştirilir:\n",
        "\n",
        "Dizi: [1, 34, 64, 90]\n",
        "\n",
        "Dizinin son hali -> [1, 34, 64, 90]."
      ],
      "metadata": {
        "id": "UI3EV3sMZhNY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Su veri seti üzerinde sıralama algoritmasının uygulanması örneği:\n",
        "\n",
        "import pandas as pd\n",
        "\n",
        "# Veri setini okuyalım\n",
        "file_path = '/content/drive/My Drive/VERIYAPILARI_ISTKA/algveriseti/ilcesuveriseti.csv'\n",
        "df = pd.read_csv(file_path, sep=';')\n",
        "\n",
        "# Veri setinin ilk birkaç satırına göz atalım\n",
        "print(df.head())\n",
        "\n",
        "# Selection Sort Uygulaması\n",
        "def selection_sort(arr):\n",
        "    n = len(arr)\n",
        "    for i in range(n):\n",
        "        min_idx = i\n",
        "        for j in range(i+1, n):\n",
        "            if arr[j][1] < arr[min_idx][1]:\n",
        "                min_idx = j\n",
        "        # Sadece elemanları sıralamak için değil, aynı zamanda ilçe isimlerini de yer değiştirmeliyiz\n",
        "        arr[i][1], arr[min_idx][1] = arr[min_idx][1], arr[i][1]  # Su tüketimi sıralama\n",
        "        arr[i][0], arr[min_idx][0] = arr[min_idx][0], arr[i][0]  # İlçe isimlerini de sıralama\n",
        "    return arr\n",
        "\n",
        "# 2022 yılı verileri üzerinde Selection Sort uygulayalım\n",
        "df_selection_sort = df[['ILCELER', '2022']].copy()\n",
        "df_selection_sort['2022'] = df_selection_sort['2022'].str.replace('.', '').astype(int)\n",
        "df_selection_sort = df_selection_sort.to_numpy()\n",
        "sorted_selection = selection_sort(df_selection_sort)\n",
        "sorted_selection_df = pd.DataFrame(sorted_selection, columns=['ILCELER', '2022'])\n",
        "\n",
        "# Sıralanmış veri çerçevesini ekrana yazdıralım\n",
        "print(sorted_selection_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MXUc79iNKLuM",
        "outputId": "57091512-c7d6-4938-e9e5-f2be676aa455"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   ID       ILCELER        2015        2016        2017        2018  \\\n",
            "0   1        ADALAR   1.432.494   1.472.276   1.366.581   1.399.182   \n",
            "1   2    ARNAVUTKOY   8.002.123   9.178.953  10.176.132  11.404.878   \n",
            "2   3      ATASEHIR  18.597.049  19.974.097  21.205.911  21.496.185   \n",
            "3   4       AVCILAR  16.220.476  17.052.253  17.558.403  18.312.736   \n",
            "4   5  BUYUKCEKMECE   9.915.435  10.788.924  11.172.787  12.222.470   \n",
            "\n",
            "         2019        2020        2021        2022  \n",
            "0   1.371.291   1.513.070   1.435.991   1.543.403  \n",
            "1  13.818.204  15.570.466  17.102.127  18.891.299  \n",
            "2  22.428.468  23.737.630  24.281.319  24.377.611  \n",
            "3  19.485.453  20.824.893  20.690.506  21.556.002  \n",
            "4  13.606.571  16.015.731  16.167.480  17.412.198  \n",
            "         ILCELER      2022\n",
            "0         ADALAR   1543403\n",
            "1           SILE   4247815\n",
            "2        CATALCA   4961608\n",
            "3        SILIVRI  13216660\n",
            "4     BAYRAMPASA  14188089\n",
            "5         BEYKOZ  14201827\n",
            "6       GUNGOREN  14305999\n",
            "7    SULTANBEYLI  14450593\n",
            "8       CEKMEKOY  14902585\n",
            "9       BAKIRKOY  15658908\n",
            "10      BESIKTAS  15995329\n",
            "11       BEYOGLU  16331938\n",
            "12   ZEYTINBURNU  16887563\n",
            "13  BUYUKCEKMECE  17412198\n",
            "14    ARNAVUTKOY  18891299\n",
            "15       ESENLER  20316717\n",
            "16         SISLI  20376715\n",
            "17       AVCILAR  21556002\n",
            "18           GOP  21711282\n",
            "19    SANCAKTEPE  21957759\n",
            "20    EYUPSULTAN  22896218\n",
            "21    BEYLIKDUZU  22961265\n",
            "22     KAGITHANE  23152747\n",
            "23    SALTANGAZI  24147507\n",
            "24      ATASEHIR  24377611\n",
            "25         TUZLA  24758012\n",
            "26       SARIYER  25503566\n",
            "27        KARTAL  25696738\n",
            "28       MALTEPE  27824535\n",
            "29         FATIH  28078147\n",
            "30  BAHCELIEVLER  29152163\n",
            "31       USKUDAR  29904256\n",
            "32       KADIKOY  30933432\n",
            "33    BASAKSEHIR  31722402\n",
            "34      BAGCILAR  35848593\n",
            "35      UMRANIYE  37618657\n",
            "36        PENDIK  38555916\n",
            "37  KUCUKCEKMECE  38832413\n",
            "38      ESENYURT  52836878\n"
          ]
        }
      ]
    }
  ]
}