DogBreeds_Dataset_EDA.ipynb

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      "source": [
        "# **Köpek Irkları Verisetinin Tanımı**\n",
        "\n",
        "Veriseti Kaynağı Linki: https://github.com/rfordatascience/tidytuesday/tree/master/data/2022/2022-02-01\n",
        "\n",
        "Bir araya getirilen verisetlerinin kaynağı, [American Kennel Club (AKC) ](https://www.akc.org/) köpek kayıt sistemidir."
      ],
      "metadata": {
        "id": "MPFr3HaP-Qw2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Gereksinimlerin İndirilmesi ve Import Edilmesi**"
      ],
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        "id": "efpilIKS_Zjy"
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      "source": [
        "%pip install pandas matplotlib seaborn plotly_express lfs --upgrade --quiet"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import plotly_express as px"
      ],
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        "id": "akDbfeMk8xP8"
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      "source": [
        "**Verinin Çekilmesi**\n",
        "\n",
        "Keşifsel veri analizi sırasında kullanılacak olan kaynaklar, 2 farklı veri setinden oluşmaktadır:\n",
        "\n",
        "**1. breed_traits.csv:** Köpek cinslerinin karakteristik özelliklerini ve bu özelliklere ait skor bilgisini içerir.\n",
        "\n",
        "**2. breed_rank_all.csv:** 2013 ve 2020 yılları arasındaki, köpek türlerinin popülerliğini inceleyen AKC kayıt sistemi istatiklerini içerir.\n",
        "\n",
        "**trait_description.csv:** Skor değerlerine ait detaylı açıklama ve yorumları içerir. Dataset, analiz aşamasında kullanılmamış olup sonuçları yorumlama noktasında kullanılmıştır."
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        "breed_rank_url = 'https://raw.githubusercontent.com/rfordatascience/tidytuesday/refs/heads/master/data/2022/2022-02-01/breed_rank.csv'\n",
        "breed_rank_df  = pd.read_csv(breed_rank_url)"
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        "breed_traits_url = 'https://raw.githubusercontent.com/rfordatascience/tidytuesday/refs/heads/master/data/2022/2022-02-01/breed_traits.csv'\n",
        "breed_traits_df  = pd.read_csv(breed_traits_url)"
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        "trait_description_url = 'https://raw.githubusercontent.com/rfordatascience/tidytuesday/refs/heads/master/data/2022/2022-02-01/trait_description.csv'\n",
        "trait_description_df  = pd.read_csv(trait_description_url)\n",
        "trait_description_df"
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              "                         Trait                     Trait_1  \\\n",
              "0     Affectionate With Family                 Independent   \n",
              "1     Good With Young Children             Not Recommended   \n",
              "2         Good With Other Dogs             Not Recommended   \n",
              "3               Shedding Level                 No Shedding   \n",
              "4      Coat Grooming Frequency                     Monthly   \n",
              "5               Drooling Level        Less Likely to Drool   \n",
              "6                    Coat Type                           -   \n",
              "7                  Coat Length                           -   \n",
              "8        Openness To Strangers                    Reserved   \n",
              "9            Playfulness Level  Only When You Want To Play   \n",
              "10  Watchdog/Protective Nature        What's Mine Is Yours   \n",
              "11          Adaptability Level           Lives For Routine   \n",
              "12          Trainability Level                 Self-Willed   \n",
              "13                Energy Level                Couch Potato   \n",
              "14               Barking Level               Only To Alert   \n",
              "15    Mental Stimulation Needs             Happy to Lounge   \n",
              "\n",
              "                       Trait_5  \\\n",
              "0                  Lovey-Dovey   \n",
              "1           Good With Children   \n",
              "2         Good With Other Dogs   \n",
              "3              Hair Everywhere   \n",
              "4                        Daily   \n",
              "5          Always Have a Towel   \n",
              "6                            -   \n",
              "7                            -   \n",
              "8   Everyone Is My Best Friend   \n",
              "9                     Non-Stop   \n",
              "10                    Vigilant   \n",
              "11            Highly Adaptable   \n",
              "12             Eager to Please   \n",
              "13                 High Energy   \n",
              "14                  Very Vocal   \n",
              "15     Needs a Job or Activity   \n",
              "\n",
              "                                          Description  \n",
              "0   How affectionate a breed is likely to be with ...  \n",
              "1   A breed's level of tolerance and patience with...  \n",
              "2   How generally friendly a breed is towards othe...  \n",
              "3   How much fur and hair you can expect the breed...  \n",
              "4   How frequently a breed requires bathing, brush...  \n",
              "5   How drool-prone a breed tends to be. If you're...  \n",
              "6   Canine coats come in many different types, dep...  \n",
              "7   How long the breed's coat is expected to be. S...  \n",
              "8   How welcoming a breed is likely to be towards ...  \n",
              "9   How enthusiastic about play a breed is likely ...  \n",
              "10  A breed's tendency to alert you that strangers...  \n",
              "11  How easily a breed handles change. This can in...  \n",
              "12  How easy it will be to train your dog, and how...  \n",
              "13  The amount of exercise and mental stimulation ...  \n",
              "14  How often this breed vocalizes, whether it's w...  \n",
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              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('trait_description_df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
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              "\n",
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              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "trait_description_df",
              "summary": "{\n  \"name\": \"trait_description_df\",\n  \"rows\": 16,\n  \"fields\": [\n    {\n      \"column\": \"Trait\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 16,\n        \"samples\": [\n          \"Affectionate With Family\",\n          \"Good With Young Children\",\n          \"Drooling Level\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Trait_1\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 14,\n        \"samples\": [\n          \"Lives For Routine\",\n          \"Couch Potato\",\n          \"Independent\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Trait_5\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 15,\n        \"samples\": [\n          \"Vigilant\",\n          \"Eager to Please\",\n          \"Lovey-Dovey\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Description\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 16,\n        \"samples\": [\n          \"How affectionate a breed is likely to be with family members, or other people he knows well. Some breeds can be aloof with everyone but their owner, while other breeds treat everyone they know like their best friend.\",\n          \"A breed's level of tolerance and patience with childrens' behavior, and overall family-friendly nature. Dogs should always be supervised around young children, or children of any age who have little exposure to dogs.\",\n          \"How drool-prone a breed tends to be. If you're a neat freak, dogs that can leave ropes of slobber on your arm or big wet spots on your clothes may not be the right choice for you.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Verisetinde Ön İşleme Adımları**"
      ],
      "metadata": {
        "id": "T_BLOrX3GqgN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# BREED RANK DATASETİ İNCELEMESİ\n",
        "\n",
        "breed_rank_df.head() # Analiz esnasında, Links ve Image kolonları kullanılmayacaktır, bu sebeple drop edilmişlerdir.\n",
        "breed_rank_df = breed_rank_df.drop(\"links\", axis='columns')\n",
        "breed_rank_df = breed_rank_df.drop(\"Image\", axis='columns')\n",
        "breed_rank_df # 195 satır, 9 sütun"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "Z9M57CWkGyUH",
        "outputId": "820570e9-0088-40a2-c3f7-862b68b22c51"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     Breed  2013 Rank  2014 Rank  2015 Rank  2016 Rank  \\\n",
              "0    Retrievers (Labrador)        1.0        1.0        1.0        1.0   \n",
              "1          French Bulldogs       11.0        9.0        6.0        6.0   \n",
              "2     German Shepherd Dogs        2.0        2.0        2.0        2.0   \n",
              "3      Retrievers (Golden)        3.0        3.0        3.0        3.0   \n",
              "4                 Bulldogs        5.0        4.0        4.0        4.0   \n",
              "..                     ...        ...        ...        ...        ...   \n",
              "190         Cesky Terriers      174.0      182.0      179.0      182.0   \n",
              "191     American Foxhounds      176.0      180.0      181.0      189.0   \n",
              "192               Azawakhs        NaN        NaN        NaN        NaN   \n",
              "193      English Foxhounds      177.0      183.0      184.0      187.0   \n",
              "194   Norwegian Lundehunds      175.0      184.0      182.0      188.0   \n",
              "\n",
              "     2017 Rank  2018 Rank  2019 Rank  2020 Rank  \n",
              "0          1.0        1.0        1.0          1  \n",
              "1          4.0        4.0        4.0          2  \n",
              "2          2.0        2.0        2.0          3  \n",
              "3          3.0        3.0        3.0          4  \n",
              "4          5.0        5.0        5.0          5  \n",
              "..         ...        ...        ...        ...  \n",
              "190      185.0      185.0      190.0        191  \n",
              "191      187.0      186.0      191.0        192  \n",
              "192        NaN        NaN        NaN        193  \n",
              "193      189.0      188.0      195.0        194  \n",
              "194      190.0      191.0      194.0        195  \n",
              "\n",
              "[195 rows x 9 columns]"
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              "      <th></th>\n",
              "      <th>Breed</th>\n",
              "      <th>2013 Rank</th>\n",
              "      <th>2014 Rank</th>\n",
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              "      <th>0</th>\n",
              "      <td>Retrievers (Labrador)</td>\n",
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              "      <td>1.0</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>French Bulldogs</td>\n",
              "      <td>11.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>German Shepherd Dogs</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Retrievers (Golden)</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Bulldogs</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>190</th>\n",
              "      <td>Cesky Terriers</td>\n",
              "      <td>174.0</td>\n",
              "      <td>182.0</td>\n",
              "      <td>179.0</td>\n",
              "      <td>182.0</td>\n",
              "      <td>185.0</td>\n",
              "      <td>185.0</td>\n",
              "      <td>190.0</td>\n",
              "      <td>191</td>\n",
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              "    <tr>\n",
              "      <th>191</th>\n",
              "      <td>American Foxhounds</td>\n",
              "      <td>176.0</td>\n",
              "      <td>180.0</td>\n",
              "      <td>181.0</td>\n",
              "      <td>189.0</td>\n",
              "      <td>187.0</td>\n",
              "      <td>186.0</td>\n",
              "      <td>191.0</td>\n",
              "      <td>192</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>192</th>\n",
              "      <td>Azawakhs</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>193</td>\n",
              "    </tr>\n",
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              "      <th>193</th>\n",
              "      <td>English Foxhounds</td>\n",
              "      <td>177.0</td>\n",
              "      <td>183.0</td>\n",
              "      <td>184.0</td>\n",
              "      <td>187.0</td>\n",
              "      <td>189.0</td>\n",
              "      <td>188.0</td>\n",
              "      <td>195.0</td>\n",
              "      <td>194</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>194</th>\n",
              "      <td>Norwegian Lundehunds</td>\n",
              "      <td>175.0</td>\n",
              "      <td>184.0</td>\n",
              "      <td>182.0</td>\n",
              "      <td>188.0</td>\n",
              "      <td>190.0</td>\n",
              "      <td>191.0</td>\n",
              "      <td>194.0</td>\n",
              "      <td>195</td>\n",
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              "  </tbody>\n",
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              "<p>195 rows × 9 columns</p>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "breed_rank_df",
              "summary": "{\n  \"name\": \"breed_rank_df\",\n  \"rows\": 195,\n  \"fields\": [\n    {\n      \"column\": \"Breed\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 195,\n        \"samples\": [\n          \"German Pinschers\",\n          \"Cavalier King Charles Spaniels\",\n          \"Pumik\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"2013 Rank\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 51.18412395904169,\n        \"min\": 1.0,\n        \"max\": 177.0,\n        \"num_unique_values\": 176,\n        \"samples\": [\n          15.0,\n          62.0,\n          158.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"2014 Rank\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 53.938167762340996,\n        \"min\": 1.0,\n        \"max\": 184.0,\n        \"num_unique_values\": 176,\n        \"samples\": [\n          17.0,\n          62.0,\n          169.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"2015 Rank\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 53.06554143991832,\n        \"min\": 1.0,\n        \"max\": 184.0,\n        \"num_unique_values\": 176,\n        \"samples\": [\n          19.0,\n          60.0,\n          158.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"2016 Rank\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 54.38029508476846,\n        \"min\": 1.0,\n        \"max\": 189.0,\n        \"num_unique_values\": 176,\n        \"samples\": [\n          20.0,\n          56.0,\n          155.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"2017 Rank\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 54.59913627355458,\n        \"min\": 1.0,\n        \"max\": 190.0,\n        \"num_unique_values\": 176,\n        \"samples\": [\n          20.0,\n          52.0,\n          150.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"2018 Rank\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 54.908783864282995,\n        \"min\": 1.0,\n        \"max\": 191.0,\n        \"num_unique_values\": 176,\n        \"samples\": [\n          20.0,\n          52.0,\n          164.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"2019 Rank\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 55.76585639005516,\n        \"min\": 1.0,\n        \"max\": 195.0,\n        \"num_unique_values\": 175,\n        \"samples\": [\n          178.0,\n          165.0,\n          115.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"2020 Rank\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 56,\n        \"min\": 1,\n        \"max\": 195,\n        \"num_unique_values\": 195,\n        \"samples\": [\n          139,\n          17,\n          156\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Null Değer İncelemesi\n",
        "\n",
        "breed_rank_df.isna().sum() # 2020 hariç tüm yıllarda, null değer gelen sıralama değerleri olduğu gözlemlenmektedir."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "collapsed": true,
        "id": "lpmUel-_KOMv",
        "outputId": "a64174c1-3789-4141-fc59-ddd333996c6b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Breed         0\n",
              "2013 Rank    19\n",
              "2014 Rank    19\n",
              "2015 Rank    19\n",
              "2016 Rank    19\n",
              "2017 Rank    19\n",
              "2018 Rank    19\n",
              "2019 Rank    20\n",
              "2020 Rank     0\n",
              "dtype: int64"
            ],
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              "      <th>2019 Rank</th>\n",
              "      <td>20</td>\n",
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              "</div><br><label><b>dtype:</b> int64</label>"
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Null gelen satırların (köpek cinsleri) tespit edilip verisetlerinden çıkarılması gerekmektedir.\n",
        "# Eksik veri bulunduran toplam 20 köpek cinsi -1 ile doldurulup cins isimleri tespit edilmiştir.\n",
        "\n",
        "breed_rank_df = breed_rank_df.fillna(-1)\n",
        "df_2019_Rank_Null = breed_rank_df[breed_rank_df[\"2019 Rank\"] == -1 ]\n",
        "print(df_2019_Rank_Null['Breed'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "-ocFTvU0NHW_",
        "outputId": "b0ceb6b2-c71d-4791-9fd6-5a3c39f63c87"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "30      Miniature American Shepherds\n",
            "79                   Coton de Tulear\n",
            "82                      Rat Terriers\n",
            "87                 Lagotti Romagnoli\n",
            "92                   Dogo Argentinos\n",
            "122                        Boerboels\n",
            "128       American Hairless Terriers\n",
            "142                            Pulik\n",
            "144                          Barbets\n",
            "148               Spanish Water Dogs\n",
            "150                   Berger Picards\n",
            "154               Wirehaired Vizslas\n",
            "155                            Pumik\n",
            "161       Nederlandse Kooikerhondjes\n",
            "166                     Plott Hounds\n",
            "172             Bergamasco Sheepdogs\n",
            "178               Cirnechi dell Etna\n",
            "187    Grand Basset Griffon Vendeens\n",
            "188                         Sloughis\n",
            "192                         Azawakhs\n",
            "Name: Breed, dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# -1 ile işaretlenen NA değerli köpek cinsleri, verisetinden drop edilmiştir.\n",
        "breed_to_remove = [\"Miniature American Shepherds\",\"Coton de Tulear\",\"Rat Terriers\",\"Lagotti Romagnoli\",\"Dogo Argentinos\",\"Boerboels\",\"American Hairless Terriers\",\"Pulik\",\t\"Barbets\",\"Spanish Water Dogs\",\"Berger Picards\",\t\"Wirehaired Vizslas\",\"Pumik\",\"Nederlandse Kooikerhondjes\",\t\"Plott Hounds\", \"Bergamasco Sheepdogs\",\"Cirnechi dell Etna\",\"Grand Basset Griffon Vendeens\",\"Sloughis\",\"Azawakhs\"]\n",
        "breed_rank_df = breed_rank_df[~breed_rank_df['Breed'].isin(breed_to_remove)]\n",
        "\n",
        "# Veri tipleri analiz edilmiştir.\n",
        "display(breed_rank_df.dtypes)\n",
        "\n",
        "# 2013-2019 yılları arasındaki Rank değerleri float tipinde iken 2020 yılındaki Rank değeri int tipindedir.,\n",
        "# Rank küsürat içeren bir değer olamayacağı için veri tipleri int' e çevrilmiştir.\n",
        "breed_rank_df['2013 Rank'] = breed_rank_df['2013 Rank'].astype(int)\n",
        "breed_rank_df['2014 Rank'] = breed_rank_df['2014 Rank'].astype(int)\n",
        "breed_rank_df['2015 Rank'] = breed_rank_df['2015 Rank'].astype(int)\n",
        "breed_rank_df['2016 Rank'] = breed_rank_df['2016 Rank'].astype(int)\n",
        "breed_rank_df['2017 Rank'] = breed_rank_df['2017 Rank'].astype(int)\n",
        "breed_rank_df['2018 Rank'] = breed_rank_df['2018 Rank'].astype(int)\n",
        "breed_rank_df['2019 Rank'] = breed_rank_df['2019 Rank'].astype(int)\n",
        "\n",
        "# Veri Boyutu\n",
        "\n",
        "rows = len(breed_rank_df.axes[0])\n",
        "cols = len(breed_rank_df.axes[1])\n",
        "\n",
        "print(\"Final Number of Rows: \", rows) # 175\n",
        "print(\"Final Number of Columns: \", cols)\n",
        "\n",
        "# Kolonlar arasındaki boşluk yerine '_' karakteri eklenmiştir.\n",
        "breed_rank_df.columns.values[1] = '2013_Rank'\n",
        "breed_rank_df.columns.values[2] = '2014_Rank'\n",
        "breed_rank_df.columns.values[3] = '2015_Rank'\n",
        "breed_rank_df.columns.values[4] = '2016_Rank'\n",
        "breed_rank_df.columns.values[5] = '2017_Rank'\n",
        "breed_rank_df.columns.values[6] = '2018_Rank'\n",
        "breed_rank_df.columns.values[7] = '2019_Rank'\n",
        "breed_rank_df.columns.values[8] = '2020_Rank'\n",
        "\n",
        "breed_rank_df\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "Wiu5zPJqJCWL",
        "outputId": "da22b5a1-2646-4d9d-8a66-2232e4efc4a0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Breed         object\n",
              "2013 Rank    float64\n",
              "2014 Rank    float64\n",
              "2015 Rank    float64\n",
              "2016 Rank    float64\n",
              "2017 Rank    float64\n",
              "2018 Rank    float64\n",
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          "text": [
            "Final Number of Rows:  175\n",
            "Final Number of Columns:  9\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     Breed  2013_Rank  2014_Rank  2015_Rank  2016_Rank  \\\n",
              "0    Retrievers (Labrador)          1          1          1          1   \n",
              "1          French Bulldogs         11          9          6          6   \n",
              "2     German Shepherd Dogs          2          2          2          2   \n",
              "3      Retrievers (Golden)          3          3          3          3   \n",
              "4                 Bulldogs          5          4          4          4   \n",
              "..                     ...        ...        ...        ...        ...   \n",
              "189               Harriers        173        181        183        186   \n",
              "190         Cesky Terriers        174        182        179        182   \n",
              "191     American Foxhounds        176        180        181        189   \n",
              "193      English Foxhounds        177        183        184        187   \n",
              "194   Norwegian Lundehunds        175        184        182        188   \n",
              "\n",
              "     2017_Rank  2018_Rank  2019_Rank  2020_Rank  \n",
              "0            1          1          1          1  \n",
              "1            4          4          4          2  \n",
              "2            2          2          2          3  \n",
              "3            3          3          3          4  \n",
              "4            5          5          5          5  \n",
              "..         ...        ...        ...        ...  \n",
              "189        183        189        188        190  \n",
              "190        185        185        190        191  \n",
              "191        187        186        191        192  \n",
              "193        189        188        195        194  \n",
              "194        190        191        194        195  \n",
              "\n",
              "[175 rows x 9 columns]"
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              "      <td>11</td>\n",
              "      <td>9</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>German Shepherd Dogs</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Retrievers (Golden)</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Bulldogs</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>189</th>\n",
              "      <td>Harriers</td>\n",
              "      <td>173</td>\n",
              "      <td>181</td>\n",
              "      <td>183</td>\n",
              "      <td>186</td>\n",
              "      <td>183</td>\n",
              "      <td>189</td>\n",
              "      <td>188</td>\n",
              "      <td>190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>190</th>\n",
              "      <td>Cesky Terriers</td>\n",
              "      <td>174</td>\n",
              "      <td>182</td>\n",
              "      <td>179</td>\n",
              "      <td>182</td>\n",
              "      <td>185</td>\n",
              "      <td>185</td>\n",
              "      <td>190</td>\n",
              "      <td>191</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>191</th>\n",
              "      <td>American Foxhounds</td>\n",
              "      <td>176</td>\n",
              "      <td>180</td>\n",
              "      <td>181</td>\n",
              "      <td>189</td>\n",
              "      <td>187</td>\n",
              "      <td>186</td>\n",
              "      <td>191</td>\n",
              "      <td>192</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>193</th>\n",
              "      <td>English Foxhounds</td>\n",
              "      <td>177</td>\n",
              "      <td>183</td>\n",
              "      <td>184</td>\n",
              "      <td>187</td>\n",
              "      <td>189</td>\n",
              "      <td>188</td>\n",
              "      <td>195</td>\n",
              "      <td>194</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>194</th>\n",
              "      <td>Norwegian Lundehunds</td>\n",
              "      <td>175</td>\n",
              "      <td>184</td>\n",
              "      <td>182</td>\n",
              "      <td>188</td>\n",
              "      <td>190</td>\n",
              "      <td>191</td>\n",
              "      <td>194</td>\n",
              "      <td>195</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>175 rows × 9 columns</p>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "breed_rank_df",
              "repr_error": "'2013_Rank'"
            }
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# BREED TRAITS DATASETİ İNCELEMESİ\n",
        "\n",
        "breed_traits_df # 195 satır, 17 sütun"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "aYV1K7fIM0oU",
        "outputId": "59f7232a-a1f5-47ca-e393-44a7b7081d7c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     Breed  Affectionate With Family  \\\n",
              "0    Retrievers (Labrador)                         5   \n",
              "1          French Bulldogs                         5   \n",
              "2     German Shepherd Dogs                         5   \n",
              "3      Retrievers (Golden)                         5   \n",
              "4                 Bulldogs                         4   \n",
              "..                     ...                       ...   \n",
              "190         Cesky Terriers                         4   \n",
              "191     American Foxhounds                         3   \n",
              "192               Azawakhs                         3   \n",
              "193      English Foxhounds                         5   \n",
              "194   Norwegian Lundehunds                         3   \n",
              "\n",
              "     Good With Young Children  Good With Other Dogs  Shedding Level  \\\n",
              "0                           5                     5               4   \n",
              "1                           5                     4               3   \n",
              "2                           5                     3               4   \n",
              "3                           5                     5               4   \n",
              "4                           3                     3               3   \n",
              "..                        ...                   ...             ...   \n",
              "190                         5                     3               2   \n",
              "191                         5                     5               3   \n",
              "192                         3                     3               2   \n",
              "193                         5                     5               3   \n",
              "194                         3                     3               3   \n",
              "\n",
              "     Coat Grooming Frequency  Drooling Level Coat Type Coat Length  \\\n",
              "0                          2               2    Double       Short   \n",
              "1                          1               3    Smooth       Short   \n",
              "2                          2               2    Double      Medium   \n",
              "3                          2               2    Double      Medium   \n",
              "4                          3               3    Smooth       Short   \n",
              "..                       ...             ...       ...         ...   \n",
              "190                        2               1      Wavy      Medium   \n",
              "191                        1               1    Smooth       Short   \n",
              "192                        2               1    Smooth       Short   \n",
              "193                        1               2    Double       Short   \n",
              "194                        2               1    Double       Short   \n",
              "\n",
              "     Openness To Strangers  Playfulness Level  Watchdog/Protective Nature  \\\n",
              "0                        5                  5                           3   \n",
              "1                        5                  5                           3   \n",
              "2                        3                  4                           5   \n",
              "3                        5                  4                           3   \n",
              "4                        4                  4                           3   \n",
              "..                     ...                ...                         ...   \n",
              "190                      4                  3                           3   \n",
              "191                      3                  3                           3   \n",
              "192                      1                  3                           3   \n",
              "193                      4                  4                           3   \n",
              "194                      3                  3                           3   \n",
              "\n",
              "     Adaptability Level  Trainability Level  Energy Level  Barking Level  \\\n",
              "0                     5                   5             5              3   \n",
              "1                     5                   4             3              1   \n",
              "2                     5                   5             5              3   \n",
              "3                     5                   5             3              1   \n",
              "4                     3                   4             3              2   \n",
              "..                  ...                 ...           ...            ...   \n",
              "190                   4                   3             3              3   \n",
              "191                   3                   3             4              5   \n",
              "192                   3                   2             3              1   \n",
              "193                   4                   4             4              5   \n",
              "194                   3                   3             3              3   \n",
              "\n",
              "     Mental Stimulation Needs  \n",
              "0                           4  \n",
              "1                           3  \n",
              "2                           5  \n",
              "3                           4  \n",
              "4                           3  \n",
              "..                        ...  \n",
              "190                         3  \n",
              "191                         3  \n",
              "192                         3  \n",
              "193                         4  \n",
              "194                         3  \n",
              "\n",
              "[195 rows x 17 columns]"
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              "      <th>Coat Type</th>\n",
              "      <th>Coat Length</th>\n",
              "      <th>Openness To Strangers</th>\n",
              "      <th>Playfulness Level</th>\n",
              "      <th>Watchdog/Protective Nature</th>\n",
              "      <th>Adaptability Level</th>\n",
              "      <th>Trainability Level</th>\n",
              "      <th>Energy Level</th>\n",
              "      <th>Barking Level</th>\n",
              "      <th>Mental Stimulation Needs</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Retrievers (Labrador)</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>Double</td>\n",
              "      <td>Short</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>French Bulldogs</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Smooth</td>\n",
              "      <td>Short</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>German Shepherd Dogs</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>Double</td>\n",
              "      <td>Medium</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Retrievers (Golden)</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>Double</td>\n",
              "      <td>Medium</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Bulldogs</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>Smooth</td>\n",
              "      <td>Short</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>190</th>\n",
              "      <td>Cesky Terriers</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>Wavy</td>\n",
              "      <td>Medium</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>191</th>\n",
              "      <td>American Foxhounds</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Smooth</td>\n",
              "      <td>Short</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>192</th>\n",
              "      <td>Azawakhs</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>Smooth</td>\n",
              "      <td>Short</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>193</th>\n",
              "      <td>English Foxhounds</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>Double</td>\n",
              "      <td>Short</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>194</th>\n",
              "      <td>Norwegian Lundehunds</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>Double</td>\n",
              "      <td>Short</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>195 rows × 17 columns</p>\n",
              "</div>\n",
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              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "breed_traits_df",
              "summary": "{\n  \"name\": \"breed_traits_df\",\n  \"rows\": 195,\n  \"fields\": [\n    {\n      \"column\": \"Breed\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 195,\n        \"samples\": [\n          \"German\\u00a0Pinschers\",\n          \"Cavalier\\u00a0King\\u00a0Charles\\u00a0Spaniels\",\n          \"Pumik\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Affectionate With Family\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          4,\n          0,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Good With Young Children\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          3,\n          0,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Good With Other Dogs\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 5,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          5,\n          4,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Shedding Level\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 5,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          4,\n          3,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Coat Grooming Frequency\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 5,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          2,\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Drooling Level\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 5,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          2,\n          3,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Coat Type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"Corded\",\n          \"Smooth\",\n          \"Wiry\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Coat Length\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Medium\",\n          \"Plott\\u00a0Hounds\",\n          \"Short\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Openness To Strangers\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 5,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          5,\n          3,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Playfulness Level\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          4,\n          0,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Watchdog/Protective Nature\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 5,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          3,\n          5,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Adaptability Level\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 5,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3,\n          0,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Trainability Level\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 5,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          5,\n          4,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Energy Level\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          3,\n          0,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Barking Level\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 5,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          3,\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Mental Stimulation Needs\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 5,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3,\n          0,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "breed_traits_df.isna().sum() # Null bir değer dönmemektedir."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "ATmsxzJNJp7z",
        "outputId": "ac302708-e37d-49c1-9d39-bf5980d31892"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Breed                         0\n",
              "Affectionate With Family      0\n",
              "Good With Young Children      0\n",
              "Good With Other Dogs          0\n",
              "Shedding Level                0\n",
              "Coat Grooming Frequency       0\n",
              "Drooling Level                0\n",
              "Coat Type                     0\n",
              "Coat Length                   0\n",
              "Openness To Strangers         0\n",
              "Playfulness Level             0\n",
              "Watchdog/Protective Nature    0\n",
              "Adaptability Level            0\n",
              "Trainability Level            0\n",
              "Energy Level                  0\n",
              "Barking Level                 0\n",
              "Mental Stimulation Needs      0\n",
              "dtype: int64"
            ],
            "text/html": [
              "<div>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Breed Rank verisetinden, Null olduğu için çıkartılan 20 köpek cinsi, verisetlerinin birleştirilmesi öncesinde\n",
        "# Breed Traits verisetinden de çıkartılmalıdır. Breed Traits verisetinde, 6 köpek cinsi ismi verilerek çıkarılabiliyorken,\n",
        "# 14 köpek cinsi isminde özel karakter içerdiği için index kullanılarak verisetinden çıkarılmıştır.\n",
        "\n",
        "print(df_2019_Rank_Null['Breed'])\n",
        "breed_traits_df = breed_traits_df.loc[breed_traits_df.index.drop([30, 79, 82, 87, 92, 128, 148, 150, 154, 161, 166, 172, 178, 187])]\n",
        "\n",
        "breed_to_remove = [\"Miniature American Shepherds\",\"Coton de Tulear\",\"Rat Terriers\",\"Lagotti Romagnoli\",\"Dogo Argentinos\",\"Boerboels\",\"American Hairless Terriers\",\"Pulik\",\t\"Barbets\",\"Spanish Water Dogs\",\"Berger Picards\",\t\"Wirehaired Vizslas\",\"Pumik\",\"Nederlandse Kooikerhondjes\",\t\"Plott Hounds\", \"Bergamasco Sheepdogs\",\"Cirnechi dell Etna\",\"Grand Basset Griffon Vendeens\",\"Sloughis\",\"Azawakhs\"]\n",
        "breed_traits_df = breed_traits_df[~breed_traits_df['Breed'].isin(breed_to_remove)]\n",
        "\n",
        "# Unique değer kontrolü yapılmıştır.\n",
        "\n",
        "breed_traits_df.nunique()\n",
        "\n",
        "# Veri Boyutu\n",
        "\n",
        "rows = len(breed_traits_df.axes[0])\n",
        "cols = len(breed_traits_df.axes[1])\n",
        "\n",
        "print(\"Final Number of Rows: \", rows) # 175\n",
        "print(\"Final Number of Columns: \", cols)\n",
        "\n",
        "breed_traits_df\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "CCRDBe6bfQCz",
        "outputId": "096367e7-b8fb-4216-bd79-a606d25bfe33"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "30      Miniature American Shepherds\n",
            "79                   Coton de Tulear\n",
            "82                      Rat Terriers\n",
            "87                 Lagotti Romagnoli\n",
            "92                   Dogo Argentinos\n",
            "122                        Boerboels\n",
            "128       American Hairless Terriers\n",
            "142                            Pulik\n",
            "144                          Barbets\n",
            "148               Spanish Water Dogs\n",
            "150                   Berger Picards\n",
            "154               Wirehaired Vizslas\n",
            "155                            Pumik\n",
            "161       Nederlandse Kooikerhondjes\n",
            "166                     Plott Hounds\n",
            "172             Bergamasco Sheepdogs\n",
            "178               Cirnechi dell Etna\n",
            "187    Grand Basset Griffon Vendeens\n",
            "188                         Sloughis\n",
            "192                         Azawakhs\n",
            "Name: Breed, dtype: object\n",
            "Final Number of Rows:  175\n",
            "Final Number of Columns:  17\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     Breed  Affectionate With Family  \\\n",
              "0    Retrievers (Labrador)                         5   \n",
              "1          French Bulldogs                         5   \n",
              "2     German Shepherd Dogs                         5   \n",
              "3      Retrievers (Golden)                         5   \n",
              "4                 Bulldogs                         4   \n",
              "..                     ...                       ...   \n",
              "189               Harriers                         5   \n",
              "190         Cesky Terriers                         4   \n",
              "191     American Foxhounds                         3   \n",
              "193      English Foxhounds                         5   \n",
              "194   Norwegian Lundehunds                         3   \n",
              "\n",
              "     Good With Young Children  Good With Other Dogs  Shedding Level  \\\n",
              "0                           5                     5               4   \n",
              "1                           5                     4               3   \n",
              "2                           5                     3               4   \n",
              "3                           5                     5               4   \n",
              "4                           3                     3               3   \n",
              "..                        ...                   ...             ...   \n",
              "189                         5                     5               3   \n",
              "190                         5                     3               2   \n",
              "191                         5                     5               3   \n",
              "193                         5                     5               3   \n",
              "194                         3                     3               3   \n",
              "\n",
              "     Coat Grooming Frequency  Drooling Level Coat Type Coat Length  \\\n",
              "0                          2               2    Double       Short   \n",
              "1                          1               3    Smooth       Short   \n",
              "2                          2               2    Double      Medium   \n",
              "3                          2               2    Double      Medium   \n",
              "4                          3               3    Smooth       Short   \n",
              "..                       ...             ...       ...         ...   \n",
              "189                        1               2    Double       Short   \n",
              "190                        2               1      Wavy      Medium   \n",
              "191                        1               1    Smooth       Short   \n",
              "193                        1               2    Double       Short   \n",
              "194                        2               1    Double       Short   \n",
              "\n",
              "     Openness To Strangers  Playfulness Level  Watchdog/Protective Nature  \\\n",
              "0                        5                  5                           3   \n",
              "1                        5                  5                           3   \n",
              "2                        3                  4                           5   \n",
              "3                        5                  4                           3   \n",
              "4                        4                  4                           3   \n",
              "..                     ...                ...                         ...   \n",
              "189                      4                  4                           3   \n",
              "190                      4                  3                           3   \n",
              "191                      3                  3                           3   \n",
              "193                      4                  4                           3   \n",
              "194                      3                  3                           3   \n",
              "\n",
              "     Adaptability Level  Trainability Level  Energy Level  Barking Level  \\\n",
              "0                     5                   5             5              3   \n",
              "1                     5                   4             3              1   \n",
              "2                     5                   5             5              3   \n",
              "3                     5                   5             3              1   \n",
              "4                     3                   4             3              2   \n",
              "..                  ...                 ...           ...            ...   \n",
              "189                   4                   4             4              5   \n",
              "190                   4                   3             3              3   \n",
              "191                   3                   3             4              5   \n",
              "193                   4                   4             4              5   \n",
              "194                   3                   3             3              3   \n",
              "\n",
              "     Mental Stimulation Needs  \n",
              "0                           4  \n",
              "1                           3  \n",
              "2                           5  \n",
              "3                           4  \n",
              "4                           3  \n",
              "..                        ...  \n",
              "189                         4  \n",
              "190                         3  \n",
              "191                         3  \n",
              "193                         4  \n",
              "194                         3  \n",
              "\n",
              "[175 rows x 17 columns]"
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              "      <th></th>\n",
              "      <th>Breed</th>\n",
              "      <th>Affectionate With Family</th>\n",
              "      <th>Good With Young Children</th>\n",
              "      <th>Good With Other Dogs</th>\n",
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              "      <td>Retrievers (Labrador)</td>\n",
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              "      <td>3</td>\n",
              "    </tr>\n",
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              "      <th>2</th>\n",
              "      <td>German Shepherd Dogs</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
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              "      <th>3</th>\n",
              "      <td>Retrievers (Golden)</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>Double</td>\n",
              "      <td>Medium</td>\n",
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              "      <th>4</th>\n",
              "      <td>Bulldogs</td>\n",
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              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>189</th>\n",
              "      <td>Harriers</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>Double</td>\n",
              "      <td>Short</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>190</th>\n",
              "      <td>Cesky Terriers</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>Wavy</td>\n",
              "      <td>Medium</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>191</th>\n",
              "      <td>American Foxhounds</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Smooth</td>\n",
              "      <td>Short</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>193</th>\n",
              "      <td>English Foxhounds</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>Double</td>\n",
              "      <td>Short</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>194</th>\n",
              "      <td>Norwegian Lundehunds</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>Double</td>\n",
              "      <td>Short</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>175 rows × 17 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f1551060-ddb7-400f-b624-5ceb9297cacf')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-f1551060-ddb7-400f-b624-5ceb9297cacf button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-f1551060-ddb7-400f-b624-5ceb9297cacf');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-2e60d9ad-9f2f-4847-acb2-9b0997283027\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2e60d9ad-9f2f-4847-acb2-9b0997283027')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-2e60d9ad-9f2f-4847-acb2-9b0997283027 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_cf800c0b-e380-4e4e-bbe2-5dbf5217bb97\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('breed_traits_df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_cf800c0b-e380-4e4e-bbe2-5dbf5217bb97 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('breed_traits_df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "breed_traits_df",
              "summary": "{\n  \"name\": \"breed_traits_df\",\n  \"rows\": 175,\n  \"fields\": [\n    {\n      \"column\": \"Breed\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 175,\n        \"samples\": [\n          \"Spaniels\\u00a0(Sussex)\",\n          \"Retrievers\\u00a0(Curly-Coated)\",\n          \"Setters\\u00a0(Gordon)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Affectionate With Family\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 5,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          4,\n          1,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Good With Young Children\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 5,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3,\n          1,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Good With Other Dogs\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          4,\n          2,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Shedding Level\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          3,\n          5,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Coat Grooming Frequency\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1,\n          5,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Drooling Level\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          3,\n          5,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Coat Type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          \"Rough\",\n          \"Smooth\",\n          \"Wiry\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Coat Length\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Short\",\n          \"Medium\",\n          \"Long\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Openness To Strangers\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          3,\n          1,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Playfulness Level\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 2,\n        \"max\": 5,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          4,\n          2,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Watchdog/Protective Nature\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          5,\n          1,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Adaptability Level\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 3,\n        \"max\": 5,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          5,\n          3,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Trainability Level\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          4,\n          1,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Energy Level\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 2,\n        \"max\": 5,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3,\n          2,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Barking Level\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1,\n          5,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Mental Stimulation Needs\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 3,\n        \"max\": 5,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          4,\n          3,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# VERİSETLERİNİN BİRLEŞTİRİLMESİ\n",
        "\n",
        "# Breed ortak kolonunu taşıyan \"Breed Rank\" ve \"Breed Traits\" verisetleri birleştirilmiştir.\n",
        "final_dataset = pd.concat([breed_traits_df, breed_rank_df], axis=1)\n",
        "\n",
        "# Birleşim sonrası duplike olan Breed kolonlarından bir tanesi yeniden adlandırılarak verisetinden çıkartılmıştır.\n",
        "final_dataset.columns.values[17] = \"Breed_Drop\"\n",
        "final_dataset = final_dataset.drop(['Breed_Drop'], axis=1)\n",
        "final_dataset\n",
        "\n",
        "# Birleşim sonrası Null kontrolü yapılmıştır.\n",
        "\n",
        "print(final_dataset.isna().sum())\n",
        "\n",
        "# Birleşim sonrası Unique değer kontrolü yapılmıştır.\n",
        "\n",
        "final_dataset.nunique()\n",
        "\n",
        "# Veri Boyutu\n",
        "\n",
        "rows = len(final_dataset.axes[0])\n",
        "cols = len(final_dataset.axes[1])\n",
        "\n",
        "print(\"Final Number of Rows: \", rows) # 175\n",
        "print(\"Final Number of Columns: \", cols)\n",
        "\n",
        "# Veriye ait İstatistik\n",
        "\n",
        "print(final_dataset.describe())\n",
        "\n",
        "final_dataset.head(10)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "XBUf_hG_aw9E",
        "outputId": "229d6501-2e42-4473-9bd9-0935005cf838"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Breed                         0\n",
            "Affectionate With Family      0\n",
            "Good With Young Children      0\n",
            "Good With Other Dogs          0\n",
            "Shedding Level                0\n",
            "Coat Grooming Frequency       0\n",
            "Drooling Level                0\n",
            "Coat Type                     0\n",
            "Coat Length                   0\n",
            "Openness To Strangers         0\n",
            "Playfulness Level             0\n",
            "Watchdog/Protective Nature    0\n",
            "Adaptability Level            0\n",
            "Trainability Level            0\n",
            "Energy Level                  0\n",
            "Barking Level                 0\n",
            "Mental Stimulation Needs      0\n",
            "2013_Rank                     0\n",
            "2014_Rank                     0\n",
            "2015_Rank                     0\n",
            "2016_Rank                     0\n",
            "2017_Rank                     0\n",
            "2018_Rank                     0\n",
            "2019_Rank                     0\n",
            "2020_Rank                     0\n",
            "dtype: int64\n",
            "Final Number of Rows:  175\n",
            "Final Number of Columns:  25\n",
            "       Affectionate With Family  Good With Young Children  \\\n",
            "count                175.000000                175.000000   \n",
            "mean                   4.497143                  3.902857   \n",
            "std                    0.808534                  1.003868   \n",
            "min                    1.000000                  1.000000   \n",
            "25%                    4.000000                  3.000000   \n",
            "50%                    5.000000                  3.000000   \n",
            "75%                    5.000000                  5.000000   \n",
            "max                    5.000000                  5.000000   \n",
            "\n",
            "       Good With Other Dogs  Shedding Level  Coat Grooming Frequency  \\\n",
            "count            175.000000      175.000000               175.000000   \n",
            "mean               3.537143        2.657143                 2.308571   \n",
            "std                0.939273        0.807396                 0.913932   \n",
            "min                1.000000        1.000000                 1.000000   \n",
            "25%                3.000000        2.000000                 2.000000   \n",
            "50%                3.000000        3.000000                 2.000000   \n",
            "75%                4.000000        3.000000                 3.000000   \n",
            "max                5.000000        5.000000                 5.000000   \n",
            "\n",
            "       Drooling Level  Openness To Strangers  Playfulness Level  \\\n",
            "count      175.000000             175.000000         175.000000   \n",
            "mean         1.811429               3.491429           3.651429   \n",
            "std          0.984877               0.870121           0.701956   \n",
            "min          1.000000               1.000000           2.000000   \n",
            "25%          1.000000               3.000000           3.000000   \n",
            "50%          2.000000               3.000000           4.000000   \n",
            "75%          2.000000               4.000000           4.000000   \n",
            "max          5.000000               5.000000           5.000000   \n",
            "\n",
            "       Watchdog/Protective Nature  Adaptability Level  ...  Barking Level  \\\n",
            "count                  175.000000          175.000000  ...     175.000000   \n",
            "mean                     3.765714            3.805714  ...       3.200000   \n",
            "std                      0.926600            0.622353  ...       1.103807   \n",
            "min                      1.000000            3.000000  ...       1.000000   \n",
            "25%                      3.000000            3.000000  ...       3.000000   \n",
            "50%                      4.000000            4.000000  ...       3.000000   \n",
            "75%                      5.000000            4.000000  ...       4.000000   \n",
            "max                      5.000000            5.000000  ...       5.000000   \n",
            "\n",
            "       Mental Stimulation Needs   2013_Rank   2014_Rank   2015_Rank  \\\n",
            "count                175.000000  175.000000  175.000000  175.000000   \n",
            "mean                   3.651429   88.388571   91.862857   89.977143   \n",
            "std                    0.668405   51.204659   53.907899   52.997284   \n",
            "min                    3.000000    1.000000    1.000000    1.000000   \n",
            "25%                    3.000000   44.500000   45.500000   44.500000   \n",
            "50%                    4.000000   88.000000   90.000000   89.000000   \n",
            "75%                    4.000000  131.500000  138.500000  134.500000   \n",
            "max                    5.000000  177.000000  184.000000  184.000000   \n",
            "\n",
            "        2016_Rank   2017_Rank   2018_Rank   2019_Rank   2020_Rank  \n",
            "count  175.000000  175.000000  175.000000  175.000000  175.000000  \n",
            "mean    91.788571   91.960000   92.205714   93.582857   93.331429  \n",
            "std     54.299149   54.624987   54.827346   55.765856   55.946443  \n",
            "min      1.000000    1.000000    1.000000    1.000000    1.000000  \n",
            "25%     45.500000   45.500000   45.500000   45.500000   45.500000  \n",
            "50%     90.000000   90.000000   91.000000   93.000000   92.000000  \n",
            "75%    137.500000  137.500000  137.500000  139.500000  138.500000  \n",
            "max    189.000000  190.000000  191.000000  195.000000  195.000000  \n",
            "\n",
            "[8 rows x 22 columns]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                           Breed  Affectionate With Family  \\\n",
              "0          Retrievers (Labrador)                         5   \n",
              "1                French Bulldogs                         5   \n",
              "2           German Shepherd Dogs                         5   \n",
              "3            Retrievers (Golden)                         5   \n",
              "4                       Bulldogs                         4   \n",
              "5                        Poodles                         5   \n",
              "6                        Beagles                         3   \n",
              "7                    Rottweilers                         5   \n",
              "8  Pointers (German Shorthaired)                         5   \n",
              "9                     Dachshunds                         5   \n",
              "\n",
              "   Good With Young Children  Good With Other Dogs  Shedding Level  \\\n",
              "0                         5                     5               4   \n",
              "1                         5                     4               3   \n",
              "2                         5                     3               4   \n",
              "3                         5                     5               4   \n",
              "4                         3                     3               3   \n",
              "5                         5                     3               1   \n",
              "6                         5                     5               3   \n",
              "7                         3                     3               3   \n",
              "8                         5                     4               3   \n",
              "9                         3                     4               2   \n",
              "\n",
              "   Coat Grooming Frequency  Drooling Level Coat Type Coat Length  \\\n",
              "0                        2               2    Double       Short   \n",
              "1                        1               3    Smooth       Short   \n",
              "2                        2               2    Double      Medium   \n",
              "3                        2               2    Double      Medium   \n",
              "4                        3               3    Smooth       Short   \n",
              "5                        4               1     Curly        Long   \n",
              "6                        2               1    Smooth       Short   \n",
              "7                        1               3    Smooth       Short   \n",
              "8                        2               2    Smooth       Short   \n",
              "9                        2               2    Smooth       Short   \n",
              "\n",
              "   Openness To Strangers  ...  Barking Level  Mental Stimulation Needs  \\\n",
              "0                      5  ...              3                         4   \n",
              "1                      5  ...              1                         3   \n",
              "2                      3  ...              3                         5   \n",
              "3                      5  ...              1                         4   \n",
              "4                      4  ...              2                         3   \n",
              "5                      5  ...              4                         5   \n",
              "6                      3  ...              4                         4   \n",
              "7                      3  ...              1                         5   \n",
              "8                      4  ...              3                         5   \n",
              "9                      4  ...              5                         3   \n",
              "\n",
              "   2013_Rank  2014_Rank  2015_Rank  2016_Rank  2017_Rank  2018_Rank  \\\n",
              "0          1          1          1          1          1          1   \n",
              "1         11          9          6          6          4          4   \n",
              "2          2          2          2          2          2          2   \n",
              "3          3          3          3          3          3          3   \n",
              "4          5          4          4          4          5          5   \n",
              "5          8          7          8          7          7          7   \n",
              "6          4          5          5          5          6          6   \n",
              "7          9         10          9          8          8          8   \n",
              "8         13         12         11         11         10          9   \n",
              "9         10         11         13         13         13         12   \n",
              "\n",
              "   2019_Rank  2020_Rank  \n",
              "0          1          1  \n",
              "1          4          2  \n",
              "2          2          3  \n",
              "3          3          4  \n",
              "4          5          5  \n",
              "5          6          6  \n",
              "6          7          7  \n",
              "7          8          8  \n",
              "8          9          9  \n",
              "9         11         10  \n",
              "\n",
              "[10 rows x 25 columns]"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Breed</th>\n",
              "      <th>Affectionate With Family</th>\n",
              "      <th>Good With Young Children</th>\n",
              "      <th>Good With Other Dogs</th>\n",
              "      <th>Shedding Level</th>\n",
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              "      <th>Coat Type</th>\n",
              "      <th>Coat Length</th>\n",
              "      <th>Openness To Strangers</th>\n",
              "      <th>...</th>\n",
              "      <th>Barking Level</th>\n",
              "      <th>Mental Stimulation Needs</th>\n",
              "      <th>2013_Rank</th>\n",
              "      <th>2014_Rank</th>\n",
              "      <th>2015_Rank</th>\n",
              "      <th>2016_Rank</th>\n",
              "      <th>2017_Rank</th>\n",
              "      <th>2018_Rank</th>\n",
              "      <th>2019_Rank</th>\n",
              "      <th>2020_Rank</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Retrievers (Labrador)</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
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              "      <td>French Bulldogs</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
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              "      <td>3</td>\n",
              "      <td>Smooth</td>\n",
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              "      <td>5</td>\n",
              "      <td>...</td>\n",
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              "      <td>3</td>\n",
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              "      <td>6</td>\n",
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              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "      <td>German Shepherd Dogs</td>\n",
              "      <td>5</td>\n",
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              "      <td>3</td>\n",
              "      <td>4</td>\n",
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              "      <td>2</td>\n",
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              "      <td>3</td>\n",
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              "      <td>5</td>\n",
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              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
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              "      <td>Bulldogs</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>Smooth</td>\n",
              "      <td>Short</td>\n",
              "      <td>4</td>\n",
              "      <td>...</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
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              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
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              "      <td>5</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Poodles</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
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              "      <td>7</td>\n",
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              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
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              "      <th>6</th>\n",
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              "      <td>5</td>\n",
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              "      <td>Smooth</td>\n",
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              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Rottweilers</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Smooth</td>\n",
              "      <td>Short</td>\n",
              "      <td>3</td>\n",
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              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>9</td>\n",
              "      <td>10</td>\n",
              "      <td>9</td>\n",
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              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Pointers (German Shorthaired)</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>Smooth</td>\n",
              "      <td>Short</td>\n",
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              "      <td>4</td>\n",
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              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>Smooth</td>\n",
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              "      <td>4</td>\n",
              "      <td>...</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>10</td>\n",
              "      <td>11</td>\n",
              "      <td>13</td>\n",
              "      <td>13</td>\n",
              "      <td>13</td>\n",
              "      <td>12</td>\n",
              "      <td>11</td>\n",
              "      <td>10</td>\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-2334bb6e-5a55-4f23-b0d4-da9a9c17cced button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-2334bb6e-5a55-4f23-b0d4-da9a9c17cced');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </button>\n",
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              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
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              "\n",
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              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "    60% {\n",
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              "      border-right-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
              "      let quickchartButtonEl =\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "final_dataset"
            }
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Keşifler ve Hipotez**"
      ],
      "metadata": {
        "id": "DhCmL9bo9N6m"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**HİPOTEZ:** Halk arasında popüler/trend olarak görülen köpek türlerinin seçiminde: Aile, çocuklarla, yabancılarla anlaşabilme ve oyuncu olma özelliklerinin gelişmiş olması özellikleri sıralamayı pozitif yönde etkilerken; havlama seviyesi, salyalanma miktarı ve tüy dökme miktarının fazla olması sıralamayı negatif yönde etkiler.\n",
        "\n",
        "**Olumlu Karakteristikler:**\n",
        "* Aile ile Sevecen Olma\n",
        "* Küçük Çocuklarla İyi Anlaşabilme\n",
        "* Yabancılara karşı açıklık\n",
        "* Oyuncu Olma\n",
        "\n",
        "**Olumsuz Karakteristikler:**\n",
        "* Salyalanma Miktarı\n",
        "* Havlama Seviyesi\n",
        "* Tüy Dökme Miktarı"
      ],
      "metadata": {
        "id": "sEWfSm-Mkii1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**1. KEŞİF:** Sıralama (Breed Rank) verisi işlenmeden önce, hipotezde verilen karakteristikler yakın çağrışımlar yaptığı için, aralarında herhangi bir ilişki olup olmadığı Correlation Heatmap kullanılarak analiz edilmiştir."
      ],
      "metadata": {
        "id": "A6XH_kZkPMjo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 1. GRAFİK - CORRELATION HEATMAP\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sb\n",
        "import numpy as np\n",
        "\n",
        "# Köpek cinslerine ait özellikler kolonlarında, birbiri ile pozitif ya da negatif korelasyonu olabileceği düşünülen kolonlar bulunmaktadır.\n",
        "\n",
        "# print(breed_traits_df.dtypes)\n",
        "\n",
        "# Breed, Coat Type ve Coat Length kolonları, veri tipleri uygun olmadığı için dataframe' den çıkartılmıştır.\n",
        "\n",
        "breed_traits_df_corr = breed_traits_df.drop('Breed', axis=1)\n",
        "breed_traits_df_corr = breed_traits_df.drop('Coat Type', axis=1)\n",
        "breed_traits_df_corr = breed_traits_df.drop('Coat Length', axis=1)\n",
        "\n",
        "# Kolon Adlarının Kısaltılması\n",
        "new_labels = {\n",
        "       'Affectionate With Family': 'Aff. w/ Fam.',\n",
        "       'Good With Young Children': 'Good w/ Child.',\n",
        "       'Good With Other Dogs': 'Good w/ Dog',\n",
        "       'Shedding Level': 'Shedding',\n",
        "       'Coat Grooming Frequency': 'Grooming',\n",
        "       'Drooling Level': 'Drooling',\n",
        "       'Energy Level': 'Energy',\n",
        "       'Playfulness Level': 'Playful',\n",
        "       'Barking Level': 'Barking',\n",
        "       'Mental Stimulation Needs': 'Mental Sti.',\n",
        "       'Adaptability Level': 'Adaptability',\n",
        "       'Openness To Strangers': 'Open. to Human',\n",
        "       'Watchdog/Protective Nature': 'Protective',\n",
        "       'Trainability Level': 'Trainability'\n",
        "   }\n",
        "\n",
        "# Korelasyon Matrisinin Çıkartılması\n",
        "breed_traits_for_corr = breed_traits_df_corr.select_dtypes(include=['int64'])\n",
        "correlation_matrix = breed_traits_for_corr.corr()\n",
        "correlation_matrix = correlation_matrix.rename(columns=new_labels, index=new_labels)\n",
        "mask = np.triu(np.ones_like(correlation_matrix, dtype=bool))\n",
        "\n",
        "# Yüksek Çözünürlük - Rapor\n",
        "#plt.figure(figsize = (15, 12), dpi = 150) # kolon adlarının x eksenine sığması için matris büyütüldü ve çözünürlük arttırıldı\n",
        "#hx.set_xticklabels(hx.get_xticklabels(), rotation=0, horizontalalignment='center', fontsize=8) # isimler yatay hale getirildi\n",
        "\n",
        "# Normal Görünüm\n",
        "plt.figure(figsize = (10, 10))\n",
        "plt.rcParams.update({'font.size': 9})\n",
        "hx = sns.heatmap(correlation_matrix, cmap = 'viridis', vmin = -1, vmax = 1, center = 0, annot=True, fmt=\".2f\", square=True, linewidths=.5, mask=mask,\n",
        "cbar_kws={\"shrink\": 0.5, \"aspect\": 10, \"pad\": 0.02}) # legend büyüklüğü güncellendi\n",
        "hx.set_xticklabels(hx.get_xticklabels(), horizontalalignment='center', fontsize=9)\n",
        "hx.set_yticklabels(hx.get_yticklabels(), fontsize=8)\n",
        "hx.xaxis.set_tick_params(pad=2) # x ekseni ile arasındaki boşluk azaltıldı\n",
        "hx.yaxis.set_tick_params(pad=2) # y ekseni ile arasındaki boşluk azaltıldı\n",
        "plt.tight_layout(pad=3.0)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X7wsiT10x71w",
        "outputId": "42b3a77c-3399-4bcc-a33c-9a9cb5aff9c6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Korelasyon Matrisinin Analizi**\n",
        "\n",
        "**Bazı Pozitif Korelasyonlar**\n",
        "\n",
        "*  Zihinsel olarak uyarım ihtiyacı (Mental Stimulation Needs) özelliği ve enerji seviyesi (Energy Level) özelliği arasında 0.58 oranında pozitif korelasyon gözlemlemekteyiz.\n",
        "*  Aile ile sevecen olma (Affectionate With Family) özelliği ile uyum sağlama özelliği (Adaptability Level) arasında 0.55 oranında pozitif korelasyon gözlemlemekteyiz.\n",
        "*   Oyuncu olma seviyesi (Playfulness Level) özelliği ve uyum sağlama özelliği (Adaptability Level) arasında 0.50 oranında pozitif korelasyon gözlemlemekteyiz.\n",
        "*   Zihinsel olarak uyarım ihtiyacı (Mental Stimulation Needs) özelliği ve oyuncu olma seviyesi (Playfulness Level) arasında 0.44 oranında pozitif korelasyon gözlemlemekteyiz.\n",
        "*    Yabancılara karşı açıklık (Openness To Strangers) özelliği ve oyuncu olma seviyesi (Playfulness Level) arasında 0.44 oranında pozitif korelasyon gözlemlemekteyiz.\n",
        "*   Yabancılara karşı açıklık (Openness To Strangers) özelliği ve küçük çocuklarla iyi anlaşabilme (Good With Young Children) özelliği arasında 0.39 oranında pozitif korelasyon gözlemlemekteyiz.\n",
        "\n",
        "**Bazı Negatif Korelasyonlar**\n",
        "\n",
        "*    Koruma içgüdüsü özelliği (Watchdog/Protective Nature) ile diğer köpeklerle iyi geçinme (Good With Other Dogs) özelliği arasında 0.45 oranında negatif korelasyon gözlemlenmiştir.\n",
        "*   Salyalanma miktarı (Drooling Level) ile havlama düzeyi (Barking Level) arasında 0.25 oranında negatif korelasyon gözlemlenmiştir.\n",
        "* Tüy Dökme (Shedding Level) özelliği ve taranma ihtiyacı sıklığı (Coat Grooming Frequency) arasında 0.24 oranında negatif korelasyon gözlemlenmiştir."
      ],
      "metadata": {
        "id": "lhwTX0rGC1g9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**2. KEŞİF:** Korelasyon matrisinde birbirleri ile olan korelasyon değerleri incelenen ve hipotezde olumlu ya da olumsuz olarak betimlenen bazı köpek karakteristiklerine ait analizler dağılım grafiği kullanılarak detaylandırılmıştır."
      ],
      "metadata": {
        "id": "xiDaOSuf7J6i"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 2. GRAFİK - SCATTER\n",
        "\n",
        "# 1. SUBPLOT -> Yabancılara karşı açıklık (Openness To Strangers) özelliği ve küçük çocuklarla iyi anlaşabilme (Good With Young Children) özelliği arasındaki ilişki incelenmiştir.\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "font1 = {'family': 'sans-serif', 'size': 10, 'color': 'grey'}\n",
        "\n",
        "plt.rcParams['figure.figsize'] = (5, 5)\n",
        "\n",
        "sns.regplot(\n",
        "    x='Openness To Strangers',\n",
        "    y='Good With Young Children',\n",
        "    data=breed_traits_df_corr,\n",
        "    ci=None,\n",
        "    line_kws={\"color\": \"black\", \"lw\": 1},\n",
        "    scatter_kws={\"s\": 40, \"alpha\": 0.7, \"color\": \"#4898c5\"}\n",
        ")\n",
        "\n",
        "px = plt.gca()\n",
        "px.spines['top'].set_visible(False)\n",
        "px.spines['right'].set_visible(False)\n",
        "\n",
        "plt.grid(color='grey', linestyle='-', linewidth=0.5, alpha=0.5)\n",
        "\n",
        "plt.title('Openness To Strangers vs. Good With Young Children',  fontsize=10, pad=20)\n",
        "plt.xlabel('Openness To Strangers', fontdict=font1, labelpad=15)\n",
        "plt.ylabel('Good With Young Children', fontdict=font1, labelpad=15)\n",
        "\n",
        "px.set_facecolor('white')\n",
        "\n",
        "plt.xlim(0.5, 5.5)\n",
        "plt.ylim(0.5, 5.5)\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8mo6WPZ69wuE",
        "outputId": "db8cff0a-10ba-42e4-f5d6-e77c0fe9c308"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 2. SUBPLOT -> Koruma içgüdüsü özelliği  (Watchdog/Protective Nature) ve diğer köpeklerle iyi geçinme (Good With Other Dogs) özelliği arasındaki ilişki incelenmiştir.\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "font1 = {'family': 'sans-serif', 'size': 10, 'color': 'grey'}\n",
        "\n",
        "plt.rcParams['figure.figsize'] = (5, 5)\n",
        "\n",
        "sns.regplot(\n",
        "    x='Watchdog/Protective Nature',\n",
        "    y='Good With Other Dogs',\n",
        "    data=breed_traits_df_corr,\n",
        "    ci=None,\n",
        "    line_kws={\"color\": \"black\", \"lw\": 1},\n",
        "    scatter_kws={\"s\": 40, \"alpha\": 0.7, \"color\": \"#4898c5\"}\n",
        ")\n",
        "\n",
        "px = plt.gca()\n",
        "px.spines['top'].set_visible(False)\n",
        "px.spines['right'].set_visible(False)\n",
        "\n",
        "plt.grid(color='grey', linestyle='-', linewidth=0.5, alpha=0.5)\n",
        "\n",
        "plt.title('Good With Other Dogs vs. Watchdog/Protective Nature',  fontsize=10, pad=20)\n",
        "plt.xlabel('Watchdog/Protective Nature', fontdict=font1, labelpad=15)\n",
        "plt.ylabel('Good With Other Dogs', fontdict=font1, labelpad=15)\n",
        "\n",
        "px.set_facecolor('white')\n",
        "\n",
        "plt.xlim(0.5, 5.5)\n",
        "plt.ylim(0.5, 5.5)\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zMQDI0HpKjim",
        "outputId": "ea1cbf66-3d5b-4d2b-fae5-42457be59f24"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**1. ve 2. Yardımcı Dağılım Grafiklerinin Analizi**\n",
        "\n",
        "**1. Subplot**: Bu grafik ile yabancılara karşı açıklık skoru yüksek olan köpek cinslerinin, küçük çocuklara iyi anlaşma eğiliminde olduğu gözlemlenmiştir. İki değişken arasında pozitif korelasyondan söz edilebilir. Yine de verisetinde, yabancılara karşı açık olmayıp çocuklarla iyi anlaşabilen köpek cinslerinin de mevcut olduğu outlier örnekler görülmüştür.\n",
        "\n",
        "**2. Subplot:** Koruma içgüdüsü skoru yüksek olan köpeklerin, diğer köpeklerle iyi anlaşamama eğiliminde olduğu gözlemlenmiştir. İki değişken arasında negatif korelasyondan söz edilebilir. Veride sıradışı bir outlier gözlemlenmemiştir."
      ],
      "metadata": {
        "id": "Fts1cHUIRow_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**3. KEŞİF:** Verisetindeki tüm yıllarda (ilk ve son 4 sıralamasına girmiş) köpek cinslerine ait bazı **olumlu** ve birbirleri ile korelasyonu olan karakteristiklerin popülerlik sıralamasıyla olan ilişkisi sütun grafiği kullanılarak araştırılmıştır."
      ],
      "metadata": {
        "id": "XcORGPRb-lgK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Veriseti içerisindeki tüm zamanlarda ilk 3 ve son 1 sıralamasına girme sıklığı en fazla olan 4 köpek cins seçilmiştir.\n",
        "\n",
        "%pip install pandasql\n",
        "from pandasql import sqldf\n",
        "import pandas as pd\n",
        "\n",
        "# Tüm zamanlarda (2013-2020) Popüler Olan (Sıralamada İlk 3' e Giren) 4 Köpek Cinsi -> 1.Retrievers (Labrador), 2.French Bulldogs, 3.German Shepherd Dogs, 4. Retrievers (Golden)\n",
        "\n",
        "print('En Popüler 1.Sıra:' + sqldf('''SELECT [Breed] FROM final_dataset WHERE [2013_Rank] = 1 OR [2014_Rank] = 1 OR [2015_Rank] = 1 OR [2016_Rank] = 1 OR [2017_Rank] = 1 OR [2018_Rank] = 1 OR [2019_Rank] = 1 OR [2020_Rank] = 1'''))\n",
        "\n",
        "print('En Popüler 2.Sıra:' + sqldf('''SELECT [Breed] FROM final_dataset WHERE [2013_Rank] = 2 OR [2014_Rank] = 2 OR [2015_Rank] = 2 OR [2016_Rank] = 2 OR [2017_Rank] = 2 OR [2018_Rank] = 2 OR [2019_Rank] = 2 OR [2020_Rank] = 2'''))\n",
        "\n",
        "print('En Popüler 3.Sıra:' + sqldf('''SELECT [Breed] FROM final_dataset WHERE [2013_Rank] = 3 OR [2014_Rank] = 3 OR [2015_Rank] = 3 OR [2016_Rank] = 3 OR [2017_Rank] = 3 OR [2018_Rank] = 3 OR [2019_Rank] = 3 OR [2020_Rank] = 3'''))\n",
        "\n",
        "# Tüm zamanlarda (2013-2020) Popüler Olmayan (Sıralamada Sonuncu) 4 Köpek Cinsi      -> 1.Canaan Dogs, 2.American English Coonhounds, 3.Chinooks, 4.Norwegian Lundehunds\n",
        "\n",
        "print('En Az Popüler:' + sqldf('''SELECT [Breed] FROM final_dataset WHERE [2013_Rank] = 175 OR [2014_Rank] = 175 OR [2015_Rank] = 175 OR [2016_Rank] = 175 OR [2017_Rank] = 175 OR [2018_Rank] = 175 OR [2019_Rank] = 175 OR [2020_Rank] = 175'''))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UUZYvypiV_9j",
        "outputId": "7ed8d074-6a8f-4062-b987-3ca055e23284",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: pandasql in /usr/local/lib/python3.10/dist-packages (0.7.3)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from pandasql) (1.26.4)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from pandasql) (2.2.3)\n",
            "Requirement already satisfied: sqlalchemy in /usr/local/lib/python3.10/dist-packages (from pandasql) (2.0.36)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas->pandasql) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->pandasql) (2024.2)\n",
            "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.10/dist-packages (from pandas->pandasql) (2024.2)\n",
            "Requirement already satisfied: typing-extensions>=4.6.0 in /usr/local/lib/python3.10/dist-packages (from sqlalchemy->pandasql) (4.12.2)\n",
            "Requirement already satisfied: greenlet!=0.4.17 in /usr/local/lib/python3.10/dist-packages (from sqlalchemy->pandasql) (3.1.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas->pandasql) (1.16.0)\n",
            "                                     Breed\n",
            "0  En Popüler 1.Sıra:Retrievers (Labrador)\n",
            "                                    Breed\n",
            "0       En Popüler 2.Sıra:French Bulldogs\n",
            "1  En Popüler 2.Sıra:German Shepherd Dogs\n",
            "                                    Breed\n",
            "0  En Popüler 3.Sıra:German Shepherd Dogs\n",
            "1   En Popüler 3.Sıra:Retrievers (Golden)\n",
            "                                       Breed\n",
            "0            En Az Popüler:Spaniels (Sussex)\n",
            "1     En Az Popüler:Polish Lowland Sheepdogs\n",
            "2                En Az Popüler:Skye Terriers\n",
            "3                  En Az Popüler:Canaan Dogs\n",
            "4  En Az Popüler:American English Coonhounds\n",
            "5                     En Az Popüler:Chinooks\n",
            "6         En Az Popüler:Norwegian Lundehunds\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Tüm zamanlarda, popülerlik sıralamasındaki en popüler ve en az popüler olan 4 köpek cinsi, hipotezde sıralamaya olumlu etkisi olduğu öne sürülen bazı karakteristik\n",
        "# özellikler noktasında karşılaştırılmıştır.\n",
        "\n",
        "# Bazı Olumlu Özellikler ile Subset OLuşturulması\n",
        "best_features_dataframe = final_dataset[['Breed','Good With Young Children', 'Affectionate With Family', 'Adaptability Level', 'Playfulness Level', 'Openness To Strangers']]\n",
        "\n",
        "# Sıralamada ilk 4'e giren köpek cinslerinin hipotezdeki olumlu özelliklerle olan bağlantısını incelemek için subset oluşturulmuştur.\n",
        "best_features_top_4_df = best_features_dataframe[:4]\n",
        "print(best_features_top_4_df)\n",
        "\n",
        "# Sıralamada son 4'e giren köpek cinslerinin hipotezdeki olumlu özelliklerle olan bağlantısını incelemek için subset oluşturulmuştur.\n",
        "best_features_last_4_df = best_features_dataframe[-9:]\n",
        "best_features_last_4_df = best_features_last_4_df.drop([best_features_last_4_df.index[3], best_features_last_4_df.index[4],  best_features_last_4_df.index[5],  best_features_last_4_df.index[6],  best_features_last_4_df.index[7]])\n",
        "print(best_features_last_4_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UK7JbJRbo8XJ",
        "outputId": "596ca473-03ab-4f93-8118-92dcba7ec1fc",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   Breed  Good With Young Children  Affectionate With Family  \\\n",
            "0  Retrievers (Labrador)                         5                         5   \n",
            "1        French Bulldogs                         5                         5   \n",
            "2   German Shepherd Dogs                         5                         5   \n",
            "3    Retrievers (Golden)                         5                         5   \n",
            "\n",
            "   Adaptability Level  Playfulness Level  Openness To Strangers  \n",
            "0                   5                  5                      5  \n",
            "1                   5                  5                      5  \n",
            "2                   5                  4                      3  \n",
            "3                   5                  4                      5  \n",
            "                           Breed  Good With Young Children  \\\n",
            "183                  Canaan Dogs                         3   \n",
            "184  American English Coonhounds                         3   \n",
            "185                     Chinooks                         5   \n",
            "194         Norwegian Lundehunds                         3   \n",
            "\n",
            "     Affectionate With Family  Adaptability Level  Playfulness Level  \\\n",
            "183                         3                   3                  3   \n",
            "184                         3                   3                  3   \n",
            "185                         4                   4                  3   \n",
            "194                         3                   3                  3   \n",
            "\n",
            "     Openness To Strangers  \n",
            "183                      3  \n",
            "184                      3  \n",
            "185                      3  \n",
            "194                      3  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 3. GRAFİK - BAR CHART\n",
        "\n",
        "# Popülerlik sıralamasında ilk ve son 4' e giren köpek cinslerinin, sıralamaya pozitif etkisi kanıtlanmak istenen,\n",
        "# Aileye Karşı Sevecen Olma (Affectionate With Family) ve Oyuncu Olma (Playfulness Level) özellikleri ile ilişkisi bar chart kullanılarak araştırılmıştır.\n",
        "# 1. SUBPLOT -> Sıralamadaki İlk 4 Köpek Cinsi ve Aileye Karşı Sevecen Olma Skoru Grafiği\n",
        "# 2. SUBPLOT -> Sıralamadaki Son 4 Köpek Cinsi ve Aileye Karşı Sevecen Olma Skoru Grafiği\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "cutoff = 3 # eşik değer\n",
        "color_cutoff = 3\n",
        "highlight_colour = '#FFA500' # turuncu, eşik değere eşit ya da altında kalırsa\n",
        "non_highlight_colour = '#1f78b4' # mavi, eşik değerin üzerine çıkarsa\n",
        "\n",
        "best_features_top_4_df = best_features_top_4_df.sort_values(by='Affectionate With Family')\n",
        "best_features_last_4_df = best_features_last_4_df.sort_values(by='Affectionate With Family')\n",
        "\n",
        "best_features_last_4_df['colors'] = best_features_last_4_df['Affectionate With Family'].apply(lambda x:highlight_colour if x <= color_cutoff else non_highlight_colour)\n",
        "\n",
        "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 4))\n",
        "\n",
        "font1 = {'family': 'sans-serif', 'size': 10, 'color': 'grey'}\n",
        "\n",
        "for i in range(1, 6):\n",
        "  ax1.axvline(x=i, zorder=0, color='grey', ls='-', lw=0.3)\n",
        "  ax2.axvline(x=i, zorder=0, color='grey', ls='-', lw=0.3)\n",
        "\n",
        "ax1.axvline(x=0, linestyle='-', color='grey', alpha=0.5)\n",
        "ax2.axvline(x=0, linestyle='-', color='grey', alpha=0.5)\n",
        "\n",
        "ax1.spines['top'].set_visible(False)\n",
        "ax1.spines['right'].set_visible(False)\n",
        "ax1.spines['left'].set_visible(False)\n",
        "\n",
        "ax2.spines['top'].set_visible(False)\n",
        "ax2.spines['right'].set_visible(False)\n",
        "ax2.spines['left'].set_visible(False)\n",
        "\n",
        "# 1. SUBPLOT - İlk 4 Köpek Cinsi ve Aileye Karşı Sevecen Olma Skoru\n",
        "\n",
        "bars1 = ax1.barh(best_features_top_4_df['Breed'], best_features_top_4_df['Affectionate With Family'], height=0.5)\n",
        "ax1.set_xticks(np.arange(0, 6))\n",
        "ax1.set_xlim([0, 5])\n",
        "ax1.xaxis.set_visible(True)\n",
        "ax1.axvline(x=cutoff, zorder=0, color='grey', ls='--', lw=1.5)\n",
        "ax1.set_title('İlk 4 Köpek Cinsi ve Aileye Karşı Sevecen Olma Skoru', fontsize=10, pad=15)\n",
        "ax1.set_xlabel(\"Aileye Karşı Sevecen Olma Skoru\", fontdict=font1, labelpad=15)\n",
        "ax1.xaxis.set_tick_params(pad=2)\n",
        "ax1.set_ylabel(\"İlk 4 Köpek Cinsi\", fontdict=font1, labelpad=10)\n",
        "ax1.set_yticklabels(['Fr. Bulldog','Retrievers(Lab.)','Retrievers(Gol.)', 'Ger. Shepherd'], fontsize=9, ha='right')\n",
        "\n",
        "# 2. SUBPLOT - Son 4 Köpek Cinsi ve Aileye Karşı Sevecen Olma Skoru\n",
        "\n",
        "bars2 = ax2.barh(best_features_last_4_df['Breed'], best_features_last_4_df['Affectionate With Family'], height=0.5, color = best_features_last_4_df['colors'])\n",
        "ax2.set_xticks(np.arange(0, 6))\n",
        "ax2.set_xlim([0, 5])\n",
        "ax2.xaxis.set_visible(True)\n",
        "ax2.axvline(x=cutoff, zorder=0, color='grey', ls='--', lw=1.5)\n",
        "ax2.text(x=cutoff, y=1, s=f'cutoff: {cutoff}', ha='left', fontsize=10, bbox=dict(facecolor='white', edgecolor='grey')) # cutoff etiketi\n",
        "ax2.set_title('Son 4 Köpek Cinsi ve Aileye Karşı Sevecen Olma Skoru', fontsize=10, pad=15)\n",
        "ax2.set_xlabel(\"Aileye Karşı Sevecen Olma Skoru\", fontdict=font1, labelpad=15)\n",
        "ax2.xaxis.set_tick_params(pad=2)\n",
        "ax2.set_ylabel(\"Son 4 Köpek Cinsi\", fontdict=font1)\n",
        "ax2.set_yticklabels(['Nor.Lundehunds','AE. Coonhound','Canaan','Chinooks'], fontsize=9, ha='right')\n",
        "\n",
        "# iki grafik arası boşluğun konumlandırılması\n",
        "plt.tight_layout(pad=2.0, w_pad=7.0, h_pad=3.0)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hrjrI90t6bEe",
        "outputId": "6cb356e7-d661-4f2f-ef3c-77840db85eae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-63-c0fbaeed8970>:51: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
            "  ax1.set_yticklabels(['Fr. Bulldog','Retrievers(Lab.)','Retrievers(Gol.)', 'Ger. Shepherd'], fontsize=9, ha='right')\n",
            "<ipython-input-63-c0fbaeed8970>:65: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
            "  ax2.set_yticklabels(['Nor.Lundehunds','AE. Coonhound','Canaan','Chinooks'], fontsize=9, ha='right')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**1. ve 2. Yardımcı Sütun Grafiklerinin Analizi**\n",
        "\n",
        "Bu grafikler ile birlikte, sıralamadaki ilk ve son 4 köpek cinsinin, hipotezde olumlu olarak sunulan **Aileye Karşı Sevecen Olma** özelliğini nasıl yansıttığı incelenmiştir.\n",
        "\n",
        "**Aileye Karşı Sevecen Olma Seviyesi**' ne baktığımızda ilk 4 köpeğin tamamı eşik değerin üzerinde maksimum değere sahipken; son 3 köpek eşik değerine eşit ve 1 köpek ise eşik değerinin üzerinde değere sahiptir. Bu durum da, hipotezde sunulan, aileye karşı sevecen olma seviyesinin köpek cinsinin popülerlik sıralamasını olumlu etkilediğini göstermiştir."
      ],
      "metadata": {
        "id": "mooMG_1c5p-3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 3. SUBPLOT -> Sıralamadaki İlk 4 Köpek Cinsi ve Oyuncu Olma Skoru Grafiği\n",
        "# 4. SUBPLOT -> Sıralamadaki Son 4 Köpek Cinsi ve Oyuncu Olma Skoru Grafiği\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "cutoff = 3 # eşik değer\n",
        "color_cutoff = 3\n",
        "highlight_colour = '#FFA500' # turuncu, eşik değere eşit ya da altında kalırsa\n",
        "non_highlight_colour = '#1f78b4' # mavi, eşik değerin üzerine çıkarsa\n",
        "\n",
        "best_features_top_4_df = best_features_top_4_df.sort_values(by='Playfulness Level')\n",
        "best_features_last_4_df = best_features_last_4_df.sort_values(by='Playfulness Level')\n",
        "\n",
        "best_features_last_4_df['colors'] = best_features_last_4_df['Playfulness Level'].apply(lambda x:highlight_colour if x <= color_cutoff else non_highlight_colour)\n",
        "\n",
        "fig, (ax3, ax4) = plt.subplots(1, 2, figsize=(10, 4))\n",
        "\n",
        "font1 = {'family': 'sans-serif', 'size': 10, 'color': 'grey'}\n",
        "\n",
        "for i in range(1, 6):\n",
        "  ax3.axvline(x=i, zorder=0, color='grey', ls='-', lw=0.3)\n",
        "  ax4.axvline(x=i, zorder=0, color='grey', ls='-', lw=0.3)\n",
        "\n",
        "ax3.axvline(x=0, linestyle='-', color='grey', alpha=0.5)\n",
        "ax4.axvline(x=0, linestyle='-', color='grey', alpha=0.5)\n",
        "\n",
        "ax3.spines['top'].set_visible(False)\n",
        "ax3.spines['right'].set_visible(False)\n",
        "ax3.spines['left'].set_visible(False)\n",
        "\n",
        "ax4.spines['top'].set_visible(False)\n",
        "ax4.spines['right'].set_visible(False)\n",
        "ax4.spines['left'].set_visible(False)\n",
        "\n",
        "# 3. SUBPLOT - İlk 4 Köpek Cinsi ve Oyuncu Olma\n",
        "\n",
        "bars1 = ax3.barh(best_features_top_4_df['Breed'], best_features_top_4_df['Playfulness Level'], height=0.5)\n",
        "ax3.set_xticks(np.arange(0, 6))\n",
        "ax3.set_xlim([0, 5])\n",
        "ax3.xaxis.set_visible(True)\n",
        "ax3.axvline(x=cutoff, zorder=0, color='grey', ls='--', lw=1.5)\n",
        "#ax3.text(x=cutoff, y=1, s=f'cutoff: {cutoff}', ha='left', fontsize=10, bbox=dict(facecolor='white', edgecolor='grey'))\n",
        "ax3.set_title('İlk 4 Köpek Cinsi ve Oyuncu Olma Skoru', fontsize=10, pad=15)\n",
        "ax3.set_xlabel(\"Oyuncu Olma Skoru\", fontdict=font1, labelpad=10)\n",
        "ax3.xaxis.set_tick_params(pad=2)\n",
        "ax3.set_ylabel(\"İlk 4 Köpek Cinsi\", fontdict=font1, labelpad=10)\n",
        "ax3.set_yticklabels(['Fr. Bulldog','Retrievers(Lab.)','Retrievers(Gol.)', 'Ger. Shepherd'], fontsize=9, ha='right')\n",
        "\n",
        "# 2. SUBPLOT - Son 4 Köpek Cinsi ve Oyuncu Olma\n",
        "\n",
        "bars2 = ax4.barh(best_features_last_4_df['Breed'], best_features_last_4_df['Playfulness Level'], height=0.5, color = best_features_last_4_df['colors'])\n",
        "ax4.set_xticks(np.arange(0, 6))\n",
        "ax4.set_xlim([0, 5])\n",
        "ax4.xaxis.set_visible(True)\n",
        "ax4.axvline(x=cutoff, zorder=0, color='grey', ls='--', lw=1.5)\n",
        "ax4.text(x=cutoff, y=1, s=f'cutoff: {cutoff}', ha='left', fontsize=10, bbox=dict(facecolor='white', edgecolor='grey')) # cutoff etiketi\n",
        "ax4.set_title('Son 4 Köpek Cinsi ve ve Oyuncu Olma Skoru', fontsize=10, pad=15)\n",
        "ax4.set_xlabel(\"Oyuncu Olma Skoru\", fontdict=font1, labelpad=10)\n",
        "ax4.xaxis.set_tick_params(pad=2)\n",
        "ax4.set_ylabel(\"Son 4 Köpek Cinsi\", fontdict=font1, labelpad=10)\n",
        "ax4.set_yticklabels(['Chinooks','Nor.Lundehunds','AE. Coonhound','Canaan'], fontsize=9, ha='right')\n",
        "\n",
        "# iki grafik arası boşluğun konumlandırılması\n",
        "plt.tight_layout(pad=2.0, w_pad=7.0, h_pad=3.0)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fZEMuVFvK8W3",
        "outputId": "fd05703f-e65a-4d56-af7b-fe26a51946e7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-64-accb49516a53>:48: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
            "  ax3.set_yticklabels(['Fr. Bulldog','Retrievers(Lab.)','Retrievers(Gol.)', 'Ger. Shepherd'], fontsize=9, ha='right')\n",
            "<ipython-input-64-accb49516a53>:62: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
            "  ax4.set_yticklabels(['Chinooks','Nor.Lundehunds','AE. Coonhound','Canaan'], fontsize=9, ha='right')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**3. ve 4. Yardımcı Sütun Grafiklerinin Analizi**\n",
        "\n",
        "Bu grafikler ile birlikte, sıralamadaki ilk ve son 4 köpek cinsinin, hipotezde olumlu olarak sunulan **Oyuncu Olma** özelliğini nasıl yansıttığı incelenmiştir.\n",
        "\n",
        "**Oyuncu Olma Seviyesi**' ne baktığımızda ilk 4 köpeğin tamamı eşik değerin üzerinde değerlere sahipken; son 4 köpeğin tamamı eşik değerine eşit değerlere sahiptir. Bu durum da, hipotezde sunulan, oyuncu olma seviyesinin köpek cinsinin popülerlik sıralamasını olumlu etkilediğini göstermiştir."
      ],
      "metadata": {
        "id": "pPst6GlL4bI4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Tüm zamanlarda, popülerlik sıralamasındaki en popüler ve en az popüler olan 4 köpek cinsi, hipotezde sıralamaya\n",
        "# olumsuz etkisi olduğu öne sürülen bazı karakteristik özellikler noktasında karşılaştırılmıştır.\n",
        "\n",
        "# Bazı Olumsuz Özellikler ile Subset Oluşturulması\n",
        "worst_features_dataframe = final_dataset[['Breed','Shedding Level', 'Barking Level', 'Drooling Level']]\n",
        "\n",
        "# Sıralamada ilk 4'e giren köpek cinslerinin hipotezdeki olumsuz özelliklerle olan bağlantısını incelemek için subset oluşturulmuştur.\n",
        "worst_features_top_4_df = worst_features_dataframe[:4]\n",
        "print(worst_features_top_4_df)\n",
        "\n",
        "# Sıralamada son 4'e giren köpek cinslerinin hipotezdeki olumsuz özelliklerle olan bağlantısını incelemek için subset oluşturulmuştur.\n",
        "worst_features_last_4_df = worst_features_dataframe[-9:]\n",
        "worst_features_last_4_df = worst_features_last_4_df.drop([worst_features_last_4_df.index[3], worst_features_last_4_df.index[4],  worst_features_last_4_df.index[5],  worst_features_last_4_df.index[6],  worst_features_last_4_df.index[7]])\n",
        "print(worst_features_last_4_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "bAEnqR5jkP1b",
        "outputId": "34b0ee39-f84a-429a-92b7-5f9d0f02f802"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   Breed  Shedding Level  Barking Level  Drooling Level\n",
            "0  Retrievers (Labrador)               4              3               2\n",
            "1        French Bulldogs               3              1               3\n",
            "2   German Shepherd Dogs               4              3               2\n",
            "3    Retrievers (Golden)               4              1               2\n",
            "                           Breed  Shedding Level  Barking Level  \\\n",
            "183                  Canaan Dogs               4              5   \n",
            "184  American English Coonhounds               2              4   \n",
            "185                     Chinooks               3              5   \n",
            "194         Norwegian Lundehunds               3              3   \n",
            "\n",
            "     Drooling Level  \n",
            "183               1  \n",
            "184               1  \n",
            "185               1  \n",
            "194               1  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**4. KEŞİF:** Verisetindeki tüm yıllarda (ilk ve son 4 sıralamasına girmiş) köpek cinslerine ait bazı **olumsuz** ve birbirleri ile korelasyonu olan karakteristiklerin popülerlik sıralamasıyla olan ilişkisi gruplandırılmış sütun grafiği kullanılarak araştırılmıştır."
      ],
      "metadata": {
        "id": "VbUBgtFb0nce"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 4. GRAFİK - GROUPED BAR CHART\n",
        "\n",
        "# Popülerlik sıralamasında ilk ve son 4' e giren köpek cinslerinin, sıralamaya negatif etkisi kanıtlanmak istenen, Tüy Dökme Miktarı (Shedding Level),\n",
        "# Havlama Miktarı (Barking Level) ve Salyalanma Miktarı (Drooling Level) özellikleri ile ilişkisi grouped bar chart kullanılarak araştırılmıştır.\n",
        "\n",
        "import matplotlib.pyplot  as plt\n",
        "import matplotlib.patches as mpatches\n",
        "import numpy as np\n",
        "\n",
        "bar_width = 0.27\n",
        "top_4_breeds = worst_features_top_4_df['Breed'].unique()\n",
        "bottom_4_breeds = worst_features_last_4_df['Breed'].unique()\n",
        "features = ['Shedding Level', 'Barking Level', 'Drooling Level']\n",
        "\n",
        "colors = ['#2d6d99', '#84b2d4', '#b0c4df']\n",
        "font2 = {'family': 'sans-serif', 'size': 10, 'color': 'grey'}\n",
        "fig, (bx1, bx2) = plt.subplots(1, 2, figsize=(13, 5))\n",
        "\n",
        "bx1.axhline(y=cutoff, zorder=0, color='orange', ls='-', lw=1.7)\n",
        "bx2.axhline(y=cutoff, zorder=0, color='orange', ls='-', lw=1.7)\n",
        "\n",
        "bx1.axhline(y=5, linestyle='-', color='grey', alpha=0.5)\n",
        "bx1.axhline(y=0, linestyle='-', color='grey', alpha=0.5)\n",
        "bx2.axhline(y=0, linestyle='-', color='grey', alpha=0.5)\n",
        "\n",
        "bx1.spines['top'].set_visible(False)\n",
        "bx1.spines['right'].set_visible(False)\n",
        "bx1.spines['bottom'].set_visible(False)\n",
        "bx1.spines['left'].set_visible(False)\n",
        "\n",
        "bx2.spines['top'].set_visible(False)\n",
        "bx2.spines['right'].set_visible(False)\n",
        "bx2.spines['bottom'].set_visible(False)\n",
        "bx2.spines['left'].set_visible(False)\n",
        "\n",
        "# 1. SUBPLOT - İlk 4 Köpek Cinsi ve Tüy Dökme Miktarı, Havlama Miktarı ve Salyalanma Miktarı Skorları\n",
        "\n",
        "x_pos_top_4 = np.arange(len(top_4_breeds))\n",
        "for i, feature in enumerate(features):\n",
        "    bars = bx1.bar(x_pos_top_4 + (i * bar_width), worst_features_top_4_df[feature], bar_width, label=feature, color=colors[i % len(colors)])\n",
        "    for bar in bars:\n",
        "        bx1.axhline(y=bar.get_height(), linestyle='-', color='grey', alpha=0.1)\n",
        "\n",
        "bx1.set_yticks(np.arange(0, 6))\n",
        "bx1.set_ylim([0, 5])\n",
        "bx1.set_xticks(x_pos_top_4 + (bar_width * (len(features) / 2)))\n",
        "bx1.set_xticklabels(['Retrievers(Lab.)','Fr. Bulldog','Ger. Shepherd','Retrievers(Gol.)'], fontsize=9, ha='center')\n",
        "bx1.set_ylabel(\"Tüy Dökme/Havlama/Salyalanma Skorları\", fontdict=font2, labelpad=15)\n",
        "bx1.set_xlabel(\"İlk 4 Köpek Cinsi\", fontdict=font2, labelpad=10)\n",
        "bx1.xaxis.set_tick_params(pad=2)\n",
        "bx1.set_title('İlk 4 Köpek Cinsi ve Tüy Dökme/Havlama/Salyalanma Skorları', fontsize=10, pad=15)\n",
        "\n",
        "# 2. SUBPLOT - Son 4 Köpek Cinsi ve Tüy Dökme Miktarı, Havlama Miktarı ve Salyalanma Miktarı Skorları\n",
        "\n",
        "x_pos_bottom_4 = np.arange(len(bottom_4_breeds))\n",
        "for i, feature in enumerate(features):\n",
        "    bars = bx2.bar(x_pos_bottom_4 + (i * bar_width), worst_features_last_4_df[feature], bar_width, label=feature, color=colors[i % len(colors)])\n",
        "    for bar in bars:\n",
        "        bx2.axhline(y=bar.get_height(), linestyle='-', color='grey', alpha=0.1)\n",
        "\n",
        "bx2.set_yticks(np.arange(0, 6))\n",
        "bx2.set_ylim([0, 5])\n",
        "bx2.set_xticks(x_pos_bottom_4 + (bar_width * (len(features) / 2)))\n",
        "bx2.set_xticklabels(['Canaan','AE. Coonhound','Chinooks','Nor.Lundehunds'],fontsize=9, ha='center')\n",
        "bx2.set_ylabel(\"Tüy Dökme/Havlama/Salyalanma Skorları\", fontdict=font2, labelpad=15)\n",
        "bx2.set_xlabel(\"Son 4 Köpek Cinsi\", fontdict=font2, labelpad=10)\n",
        "bx2.xaxis.set_tick_params(pad=2)\n",
        "bx2.set_title('Son 4 Köpek Cinsi ve Tüy Dökme/Havlama/Salyalanma Skorları', fontsize=10, pad=15)\n",
        "\n",
        "# legend' ın konumlandırılması\n",
        "\n",
        "bx2.legend()\n",
        "legend = bx2.get_legend()\n",
        "legend = plt.legend(loc=\"right\", edgecolor=\"white\", borderaxespad=-15)\n",
        "legend.get_frame().set_alpha(None)\n",
        "legend.get_frame().set_facecolor((0, 0, 0, 0))\n",
        "\n",
        "# iki grafik arası boşluğun konumlandırılması\n",
        "plt.tight_layout(pad=1.0, w_pad=8.0, h_pad=3.0)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qaykq0GzoMjY",
        "outputId": "ecb17021-44d5-453f-c94f-5897be15e53c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1300x500 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Gruplandırılmış Sütun Grafiğinin Analizi**\n",
        "\n",
        "Bu grafik ile birlikte sıralamadaki ilk ve son 4 köpek cinsinin tüy dökme, havlama ve salyalanma miktarı gibi hipotezde olumsuz olarak sunulan özellikleri nasıl yansıttıkları incelenmiştir.\n",
        "\n",
        "**Tüy Dökme Seviyesi** oranına baktığımızda ilk 4 köpekten 3' ü eşik değerinin üzerinde 4 skoruna sahipken; son 4 köpekten sadece 1' i eşik değerinin üzerinde bir değere sahiptir. Bu durum da, hipotezde sunulanın aksine, tüy dökme miktarının artmasının köpek cinsinin popülerlik sıralamasını olumsuz etkilemediğini göstermiştir.\n",
        "\n",
        "**Havlama Seviyesi** oranına baktığımızda ilk 4 köpekten 2' si eşik değerin oldukça altında ya da eşit kalmıştır. Fakat son 4 köpeğe baktığımızda, 4 köpekten 3'ü eşik değerin üzerinde kalıyorken yalnızca 1' ü eşik değerine eşit kalmıştır. Bu durum da, hipotezde sunulduğu gibi, havlama seviyesinin artmasının köpek cinsinin popülerlik sıralamasını olumsuz etkilediğini göstermiştir.\n",
        "\n",
        "**Salyalanma Miktarı** oranına baktığımızda, her iki grafikte de değerler eşik değerine eşit ya da altındadır. Fakat dikkat çeken nokta, son 4 köpek cinsinin salyalanma düzeyinin minimum düzeyde olmasıdır. Bu durum da, hipotezde sunulanın aksine, salyalanma miktarının artmasının köpek cinsinin popülerlik sıralamasını olumsuz etkilemediğini göstermiştir.\n"
      ],
      "metadata": {
        "id": "cMNAJKY51Pde"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Sonuçlar**"
      ],
      "metadata": {
        "id": "mgzPMmtb-9xX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Keşifsel veri analizinin ardından hipotez, ortaya çıkan sonuçların önderliğinde yeniden\n",
        "incelendiğinde, aile/çocuklarla/yabancılarla anlaşabilme ve oyuncu olma karakteristik\n",
        "özelliklerinin popülerlik sıralamasına orta pozitif ya da güçlü pozitif şekilde olumlu etkisinin\n",
        "olduğu gözlemlenmiştir.\n",
        "\n",
        "Hipotezde popülerlik sıralamasına negatif etkidiği öne sürülen olumsuz özellikler (havlama\n",
        "seviyesi, salyalanma miktarı, tüy dökme miktarı) incelendiğinde ise havlama miktarının\n",
        "popülerlik skoruna orta ya da güçlü negatif şekilde olumsuz etkisinin olduğu gözlemlenmiştir.\n",
        "Fakat salyalanma ve tüy dökme miktarının popülerlik sıralamasına olumsuz bir etkisi\n",
        "bulunamamıştır.\n",
        "\n",
        "Ortaya çıkan sonuçların yanı sıra, outlier olarak ele alınan köpek cinsleri de veri seti içerisinde\n",
        "mevcuttur. Bu durum, her bir köpek cinsinin doğal ya da yapay yollar aracılığıyla, kendine has\n",
        "ve ayırt edici özelliklerle evrimleşmesi ya da çevresel faktörlerle açıklanabilir.\n",
        "\n",
        "Benzer şekilde köpek cinslerine ait popülerlik sıralamalarını, sahiplendirilme noktası haricinde,\n",
        "etkileyebilecek olgular günlük yaşam içerisinde de mevcuttur. Sunulan hipotez içerisinde ele\n",
        "alınmayan fakat değerlendirilen noktalar arasında: Coğrafi konum, kültür, sahiplendirmeye\n",
        "teşvik, cins hayvan alış/satışı ve sosyal medya trendleri (film, video, kitaplar vb.) sayılabilir."
      ],
      "metadata": {
        "id": "FOefjnRf_Ddz"
      }
    }
  ]
}