data_visualization.ipynb

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              "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
              "0         0       3    male  22.0      1      0   7.2500        S  Third   \n",
              "1         1       1  female  38.0      1      0  71.2833        C  First   \n",
              "2         1       3  female  26.0      0      0   7.9250        S  Third   \n",
              "3         1       1  female  35.0      1      0  53.1000        S  First   \n",
              "4         0       3    male  35.0      0      0   8.0500        S  Third   \n",
              "\n",
              "     who  adult_male deck  embark_town alive  alone  \n",
              "0    man        True  NaN  Southampton    no  False  \n",
              "1  woman       False    C    Cherbourg   yes  False  \n",
              "2  woman       False  NaN  Southampton   yes   True  \n",
              "3  woman       False    C  Southampton   yes  False  \n",
              "4    man        True  NaN  Southampton    no   True  "
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            ]
          },
          "metadata": {},
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        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "df=sns.load_dataset(\"titanic\")\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#matplot düşük bir seviye bir görselleştirme aracıdır.\n",
        "#iki kütüphane karşılaştırıldığında matplot çok daha düşük seviye kalır.\n",
        "#elimizde kategorik bir değişken varsa sutün grafik veya pasta grafik kullanılır (seaborn:countplot,matplot içerisinde bar plot)\n",
        "#değişken sayısalsa 2 istatiksel grafik vardır birisi histogram diğeri kutu grafiktir.\n",
        "#Python veri görselleştirme için en iyi yer değildir.PowerBI veya tableu gibi araçlar veri tabanıyla direk iletişimde olduğu için veri görselleştirmeye daha uygundur.\n",
        "#Python küçük çaplı işler için daha uygundur.\n"
      ],
      "metadata": {
        "id": "AlJd71BfuHPS"
      },
      "execution_count": 118,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['class'].value_counts().columns()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        }
      },
      "execution_count": 122,
      "outputs": [
        {
          "ename": "AttributeError",
          "evalue": "ignored",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-122-ce100f513ec2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'class'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue_counts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   5485\u001b[0m         ):\n\u001b[1;32m   5486\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5487\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5488\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5489\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'Series' object has no attribute 'columns'"
          ]
        }
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    {
      "cell_type": "code",
      "source": [
        "df.describe().T"
      ],
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              "          count       mean        std   min      25%      50%   75%       max\n",
              "survived  891.0   0.383838   0.486592  0.00   0.0000   0.0000   1.0    1.0000\n",
              "pclass    891.0   2.308642   0.836071  1.00   2.0000   3.0000   3.0    3.0000\n",
              "age       714.0  29.699118  14.526497  0.42  20.1250  28.0000  38.0   80.0000\n",
              "sibsp     891.0   0.523008   1.102743  0.00   0.0000   0.0000   1.0    8.0000\n",
              "parch     891.0   0.381594   0.806057  0.00   0.0000   0.0000   0.0    6.0000\n",
              "fare      891.0  32.204208  49.693429  0.00   7.9104  14.4542  31.0  512.3292"
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              "    <tr>\n",
              "      <th>sibsp</th>\n",
              "      <td>891.0</td>\n",
              "      <td>0.523008</td>\n",
              "      <td>1.102743</td>\n",
              "      <td>0.00</td>\n",
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              "      <td>0.0000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>8.0000</td>\n",
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              "    <tr>\n",
              "      <th>parch</th>\n",
              "      <td>891.0</td>\n",
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              "      <td>7.9104</td>\n",
              "      <td>14.4542</td>\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-58361738-fb87-4e34-bdb6-49e4efbfe59b 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-58361738-fb87-4e34-bdb6-49e4efbfe59b');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 120
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "zero_fare_count = df[df['fare'] == 0].shape[0]\n",
        "zero_fare_count"
      ],
      "metadata": {
        "id": "N5UAWkNG0TUD",
        "outputId": "dea7de0f-09fd-46f9-f585-f4c6f8738bf4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "15"
            ]
          },
          "metadata": {},
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['fare']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YC_7ahKMHL7d",
        "outputId": "9b49f833-49af-4253-d4b7-633f9298fb00"
      },
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       7.2500\n",
              "1      71.2833\n",
              "2       7.9250\n",
              "3      53.1000\n",
              "4       8.0500\n",
              "        ...   \n",
              "886    13.0000\n",
              "887    30.0000\n",
              "888    23.4500\n",
              "889    30.0000\n",
              "890     7.7500\n",
              "Name: fare, Length: 891, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#value_count kategorik değişkenler için en önemli fonksiyondur.Hızlıca veriyi betimler\n",
        "df[\"sex\"].value_counts().plot(kind='bar')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        },
        "id": "gx6k0mQGHbbt",
        "outputId": "aaa4c663-1c83-45b5-9d6e-0af2702e2a97"
      },
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"class\"].value_counts().plot(kind='bar')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        },
        "id": "2oGYb33DH-C9",
        "outputId": "2ffbb579-a3b6-498d-961b-391df514024a"
      },
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(df[\"fare\"])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "cWOGYTpeIUNU",
        "outputId": "e2c0755b-8422-48ee-cd01-0e6baa559bab"
      },
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class_counts = df['class'].value_counts()\n",
        "labels = class_counts.index\n",
        "sizes = class_counts.values\n",
        "colors=[\"red\",\"green\",\"blue\"]\n",
        "plt.pie(sizes, labels=labels, colors=colors,)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "x6uXO6DaxNKU",
        "outputId": "2ef9a8ad-d6da-456a-e628-59086beb8713",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        }
      },
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.boxplot(df[\"fare\"])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "Z5aCVftsIiLj",
        "outputId": "015f0559-15c8-413d-d735-94b772be8342"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x=np.array([1,8])\n",
        "y=np.array([3,50])\n",
        "plt.plot(x,y)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "ykqPFK3NI2nD",
        "outputId": "31d2e87a-9f09-498b-c1df-d53053c156ca"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(x,y,'o')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "D9H2Wg1vLDUD",
        "outputId": "5b63a9df-0387-4c64-bfa0-22cb9cadb100"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x=np.array([1,3,5,7,4])\n",
        "y=np.array([2,4,5,6,8])\n",
        "plt.plot(x,y,'o')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "NK0e6PqHLP1j",
        "outputId": "fcf7e765-330d-43f3-f6c1-e0a1b3d0ef3c"
      },
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y=np.array([1,3,5,7,50,100])\n",
        "plt.plot(y,marker='*')\n",
        "plt.show() # çok daha fazla marker var"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "lAZs-R2v0p6A",
        "outputId": "586b0173-136f-464a-a30f-a1f1adb3a880"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#line garph\n",
        "y=np.array([1,3,5,7,50,100])\n",
        "plt.plot(y,linestyle='dotted')\n",
        "plt.show()#marker koymayınca düz line olmuş oluyor"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "RLubb0Cm1C4d",
        "outputId": "71bb076c-9558-4015-bf63-57ea9c5bb41a"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#line garph\n",
        "y=np.array([1,3,5,7,50,100])\n",
        "plt.plot(y,linestyle='dashed',color='r')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "SrlIGEpX1ruc",
        "outputId": "0a2c4467-823f-437e-9055-aed48c5d3de3"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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zh3/6J6is1HZ5OUhtGRFp2OOPw7x5cOed0L9/7GrkCGnkLiIHW7MGrr0WBg0K0x8l5yjcReRg69dD165hy7yiotjVSDOoLSMiBzvzTHjvPQV7DtPIXURqvfoq3H132DpPwZ7TFO4iEnzxBVx2WVg/ZufO2NVIktSWEZFg3Dj45BP47/+G9u1jVyNJ0shdRODpp+GXv4Rbb4Vvfzt2NZICCneRQrdjB4wdCwMHwi23xK5GUkRtGZFC164dPPtsmPrYQpGQL/RPUqSQbdgAZWVw+umxK5EUU1tGpFAtXQonnghPPRW7EkkDhbtIIdq5E0aOhL/4Cxg6NHY1kgZqy4gUohtugA8/hEWLoKQkdjWSBhq5ixSa556DmTPhX/4lLDMgeUnhLlJotm4Nc9mnTIldiaSRwl2k0FxxRfgWasuWsSuRNFK4ixSKmTNhzpxw/yj9p5/v9E9YpBCsXAnjx4dpj+6xq5EMaHa4m1l3M3vZzFaZ2btmdl3i+U5m9qKZfZj4qUvxIjF99RVceil07AiPPgpmsSuSDEhm5F4NTHT3bwADgbFm9g3gJ8Aid+8FLEo8FpFYJk2Cd96Bxx4L89qlIDQ73N19k7u/mbi/HVgNdAMuBGYnXjYbuCjZIkWkmVauhAceCMv56stKBSUlX2Iys3LgFOB1oIu7b0oc2gx0aeR3xgBjAI4//vhUlCEiB+rXDxYsgLPPjl2JZFjSF1TNrB0wD5jg7l/UPebuDjR49cbdZ7p7hbtXlJaWJluGiNTlDh99FO6ffz60bh23Hsm4pMLdzIoJwf6ku89PPP2ZmXVNHO8KbEmuRBE5YrNmQZ8+8OabsSuRSJKZLWPAo8Bqd7+vzqEFwOjE/dHAc80vT0SO2AcfwIQJcMYZcPLJsauRSJLpuZ8G/COw0szeSjx3M3AHMNfMrgQ+BoYnV6KINNmePWHaY6tWMHu2vqxUwJod7u7+CtDYhNmzmvu+IpKE226DZctg/nw47rjY1UhE+t+6SL65+mq4+OLYVUhkWs9dJJ9MnarlBQTQyF0kP9x4I/znf4b7Wl5AULiL5L4nn4R77w3L+IokKNxFctm6dXDNNXD66WENGZEEhbtIrqquhssuC/d/+UsoKopbj2QVXVAVyVVz5sCrr8ITT0B5eexqJMso3EVy1ciR0KkTnHtu7EokC6ktI5JrduyADRvCt08V7NIIhbtIrpkwAU45BaqqYlciWUzhLpJL5s8PW+WNGRO2zRNphMJdJFds3BiWFqioCGvIiByCwl0kF+zfD6NHh82un3wSiotjVyRZTuEukgt274Zjj4Vp0+Cv/ip2NZIDNBVSJBe0bh3ms2tRMGkijdxFstmuXTBiBKxaFR5rUTBpIoW7SDb78Y/h6afDvHaRI6BwF8lWCxfCQw+Fee2DB8euRnKMwl0kG23ZAj/8IfTrBz/9aexqJAfpgqpIJuzbB59/Dl/7GrRtC9u2wR/+EH7W3Kqq4IorYMCAcOzzz2HRorDZtcgRUriLNNXevWGZ3datYc+eELx1g3nbttA+GTw49MjPP7/2+BdfhPeYPj2sv/7JJzB8eO17FxdDSQmcfXYI9/794amnoG/fOJ9Vcp7CXQqHexhBt0j8a//aa7B1a/3Rc9++MGxY+NLQoEH1g3vnTpg4Ee65J8w7P+ec+u/fujUcc0wI9zZtoHv30FYpKam9nX56eO3Xvw4rV4bnOnYMr687E6Z373ATaSaFu+Su996DzZvrh3NpKYwaFY5ffjl88EH94+edB/PmheMXXAB/+lP997ziihDuRx0VArdz5/rh/O1vh9e1axfWUq977Gtfq32fTp1gwYLGa2/VSqNySSuFu8SzaRN8+mn91oYZXHVVOH777SFA646ee/SAN94Ix0eNqr1f49RTa8N99+7Q3y4rC6PjkhI4+eTa1/7mNyGQa8K5Q4faUT3A8883XrtZbdCLZCGFuzTfF1+EgK4J3poQ/ud/DuH32GPw29/WD+f9++Hjj8PvT5gAc+fWf88uXWrDfcuWcI7OnaFnzxDAf/mXta+9777Q+65pbZSUwNFH1x6fM+fQ9de0SETykMK9kO3Zc3DPeds2uOgiaN8eXnghXNQ78PiKFaHtcMcdDU/TGz06tDQ++SS0TkpKoFu30Ibo1Cn0vs1CuI8cWRvMNbca06cfun6Fs0ijFO65zD3synPgdLqBA8MiU2+/DbNmHXx8zhw46SSYPTusC36glStDEK9bBy+9VBu6J54Yftasb/IP/wB//df1g7ljx9qpe5Mnh1tjTj015X8SEQkU7rHt3x/mM9dtXfTsCSecENbvfuih+se2bYN//3cYMiRMxfve9w5+zwULwjS8jRvD8rB12xbdukHLluF13/kOzJhRP5hLSsK5IQR/Q+Ff45RTwk1Eso7CPRX27q0fvjU94l274P77Dw7nUaNC62LdutBDPnClv2nTYPz48Dv33FM/eDt1ql3Lu08fuPvu+sdLSsK5IUzV+/OfG69b0+1E8pbCvcZXX9WGb3Ex9OoVnn/44XBhr25r44wzwnzn/ftDqG7fXv+9xo8PAW0G//qvoU1Rt3VRE+alpXDLLfWPlZTUrtf9jW+EvnhjKwF26wY33piev4eI5LT8Cfea/nNVVQjEE08Mzz/7LKxdW7/nXF4OU6aE4wMHht70V1/Vvtf559fOUb799jCXun372hFyTSviqKNg3Ljw5ZW6rY2acG7VKozeG/v6eNu2ocXSGC3vKiLNlJZwN7MhwDSgCJjl7nek4zxAaG8sXBhCu7o6PNe3b7goCHDXXeGbiGZhHnPd2RgA554bRuJ1Wxt1p9utXh2+sNKikT/V1KmHrk/rgohIBCkPdzMrAqYD3wM2AG+Y2QJ3X5XqcwFhFN22bf2ec7dutcdrvqhy9NFhpH2gW2899Ptrh3kRyUHpGLkPANa4+0cAZvYr4EIgPeE+YcKhj3fpkpbTiohks3Ss594NWF/n8YbEc/WY2RgzW2pmSysrK9NQhohI4Yq2WYe7z3T3CnevKC0tjVWGiEheSke4bwS613lclnhOREQyJB3h/gbQy8x6mFlLYARwiLVPRUQk1VJ+QdXdq81sHPB7wlTIn7v7u6k+j4iINC4t89zdfSGwMB3vLSIihxftgqqIiKSPwl1EJA+ZH7giYYwizCqBj5v568cAfzrsq/KLPnNh0GcuDMl85hPcvcG55FkR7skws6XuXhG7jkzSZy4M+syFIV2fWW0ZEZE8pHAXEclD+RDuM2MXEIE+c2HQZy4MafnMOd9zFxGRg+XDyF1ERA6gcBcRyUM5He5mNsTM3jezNWb2k9j1pJuZ/dzMtpjZO7FryRQz625mL5vZKjN718yui11TuplZKzP7o5m9nfjMt8euKRPMrMjMlpvZb2PXkglmts7MVprZW2a2NOXvn6s998R2fh9QZzs/4Adp284vC5jZGcAO4Bfu3jd2PZlgZl2Bru7+ppm1B5YBF+X5P2cD2rr7DjMrBl4BrnP3JZFLSyszuwGoAI529/Ni15NuZrYOqHD3tHxpK5dH7v+3nZ+77wFqtvPLW+7+X8CfY9eRSe6+yd3fTNzfDqymgZ298okHOxIPixO33ByFNZGZlQHnArNi15Ivcjncm7Sdn+QPMysHTgFej1tJ+iVaFG8BW4AX3T3fP/MDwI+B/bELySAHXjCzZWY2JtVvnsvhLgXEzNoB84AJ7v5F7HrSzd33ufvJhJ3MBphZ3rbhzOw8YIu7L4tdS4ad7u79gaHA2ETbNWVyOdy1nV+BSPSd5wFPuvv82PVkkrtXAS8DQ2LXkkanARcketC/Ar5rZk/ELSn93H1j4ucW4DeEVnPK5HK4azu/ApC4uPgosNrd74tdTyaYWamZdUzcb02YNPBe3KrSx90nuXuZu5cT/jt+yd0vi1xWWplZ28QEAcysLTAYSOksuJwNd3evBmq281sNzM337fzMbA7wGvB1M9tgZlfGrikDTgP+kTCaeytxOyd2UWnWFXjZzFYQBjEvuntBTA8sIF2AV8zsbeCPwP9z9+dTeYKcnQopIiKNy9mRu4iINE7hLiKShxTuIiJ5SOEuIpKHFO4iInlI4S4ikocU7iIieej/A+x7Iaz12oM6AAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# multiple lines\n",
        "x=np.array([2,5,7,45,89,78])\n",
        "y=np.array([1,3,5,7,50,100])\n",
        "plt.plot(x,linestyle='dashed')\n",
        "plt.plot(y,color='r')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "neClbdiz2VBN",
        "outputId": "6e7938e8-4b0a-4ce0-bef9-c95493a13ef6"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#labeling\n",
        "x=np.array([2,5,7,45,89,78])\n",
        "y=np.array([1,3,5,7,50,100])\n",
        "plt.plot(x,y)\n",
        "plt.title(\"Ana başlık\")\n",
        "plt.xlabel(\"x ekseni\")\n",
        "plt.ylabel(\"y ekseni\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "ZW4XPbIk4dek",
        "outputId": "f7be6480-cf75-4bdb-cfbd-024bde017976"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# grid ile ızgara ekleme\n",
        "x=np.array([2,5,7,45,89,78])\n",
        "y=np.array([1,3,5,7,50,100])\n",
        "plt.plot(x,y)\n",
        "plt.grid(linestyle='--', color='r')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "mkpxeh-542Nb",
        "outputId": "c8f1b2ae-d892-4ec1-c7b2-643fe88a1f7b"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#subplotlar\n",
        "# İlk grafiği oluşturun\n",
        "plt.subplot(2, 2, 1)\n",
        "plt.plot([1, 2, 3, 4])\n",
        "plt.title(\"İlk Grafik\")\n",
        "\n",
        "# İkinci grafiği oluşturun\n",
        "plt.subplot(2, 2, 2)\n",
        "plt.plot([1, 4, 9, 16])\n",
        "plt.title(\"İkinci Grafik\")\n",
        "\n",
        "# Üçüncü grafiği oluşturun\n",
        "plt.subplot(2, 2, 3)\n",
        "plt.plot([1, 8, 27, 64])\n",
        "plt.title(\"Üçüncü Grafik\")\n",
        "\n",
        "# Dördüncü grafiği oluşturun\n",
        "plt.subplot(2, 2, 4)\n",
        "plt.plot([1, 16, 81, 256])\n",
        "plt.title(\"Dördüncü Grafik\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "ASQcy1AX7Rxx",
        "outputId": "bc204912-8986-4c3e-9b44-bec0c081caad"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Dördüncü Grafik')"
            ]
          },
          "metadata": {},
          "execution_count": 55
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#bunları seaborn da daha hızlı bir şekilde yapabiliyoruz."
      ],
      "metadata": {
        "id": "4NId-toBJLQO"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "from matplotlib import pyplot as plt"
      ],
      "metadata": {
        "id": "4pTowmKENgtA"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df=sns.load_dataset(\"tips\")\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "m4aU6AG9UG-9",
        "outputId": "ab5fea43-b169-41e8-8816-622742884a29"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   total_bill   tip     sex smoker  day    time  size\n",
              "0       16.99  1.01  Female     No  Sun  Dinner     2\n",
              "1       10.34  1.66    Male     No  Sun  Dinner     3\n",
              "2       21.01  3.50    Male     No  Sun  Dinner     3\n",
              "3       23.68  3.31    Male     No  Sun  Dinner     2\n",
              "4       24.59  3.61  Female     No  Sun  Dinner     4"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2402fe54-ac18-4d14-bdb0-9eac8d171b32\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>total_bill</th>\n",
              "      <th>tip</th>\n",
              "      <th>sex</th>\n",
              "      <th>smoker</th>\n",
              "      <th>day</th>\n",
              "      <th>time</th>\n",
              "      <th>size</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>16.99</td>\n",
              "      <td>1.01</td>\n",
              "      <td>Female</td>\n",
              "      <td>No</td>\n",
              "      <td>Sun</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10.34</td>\n",
              "      <td>1.66</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>Sun</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>21.01</td>\n",
              "      <td>3.50</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>Sun</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>23.68</td>\n",
              "      <td>3.31</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>Sun</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>24.59</td>\n",
              "      <td>3.61</td>\n",
              "      <td>Female</td>\n",
              "      <td>No</td>\n",
              "      <td>Sun</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2402fe54-ac18-4d14-bdb0-9eac8d171b32')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-2402fe54-ac18-4d14-bdb0-9eac8d171b32 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-2402fe54-ac18-4d14-bdb0-9eac8d171b32');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#1 kategorik 2 sayısal değişkenler\n",
        "# burada neler var bakalım"
      ],
      "metadata": {
        "id": "DLj9ePloXF90"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RwIOGd-DXcWN",
        "outputId": "0de5b381-7d02-4b12-adf8-71aae6f144d2"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 244 entries, 0 to 243\n",
            "Data columns (total 7 columns):\n",
            " #   Column      Non-Null Count  Dtype   \n",
            "---  ------      --------------  -----   \n",
            " 0   total_bill  244 non-null    float64 \n",
            " 1   tip         244 non-null    float64 \n",
            " 2   sex         244 non-null    category\n",
            " 3   smoker      244 non-null    category\n",
            " 4   day         244 non-null    category\n",
            " 5   time        244 non-null    category\n",
            " 6   size        244 non-null    int64   \n",
            "dtypes: category(4), float64(2), int64(1)\n",
            "memory usage: 7.4 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"sex\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8p7lAZj_XdEN",
        "outputId": "d261591f-43e9-4138-d84f-79d2b247b6e1"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Male      157\n",
              "Female     87\n",
              "Name: sex, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x=df[\"sex\"],data=df)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "Ll3HLoQyXiQu",
        "outputId": "e6164b76-3727-4396-eb84-ebf5bbc93dc1"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"sex\"].value_counts().plot(kind=\"barh\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "JqnKFLMgXrdv",
        "outputId": "7b518053-f7c1-4836-c63a-4717a13019ff"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#sayısalları nasıl görselleştireceğiz \n",
        "sns.boxplot(x=df[\"total_bill\"],data=df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "En4dAqeOYrFg",
        "outputId": "4e4f9b81-1ebe-490f-f3ba-634508f27f3f"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3c7894f3d0>"
            ]
          },
          "metadata": {},
          "execution_count": 64
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.hist()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 372
        },
        "id": "kmAFLC6uZSmY",
        "outputId": "e5313f86-9da9-49a4-cb8f-37c19f0f6067"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f3c78361640>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f3c7893e3d0>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f3c787c27f0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f3c7838cc10>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 65
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#EDA=hızlı bir şekilde genel fonksyonlar ile elimize gelen verileri analiz etmek olarak isimlendirilebilir.\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from  matplotlib import pyplot as plt\n",
        "import seaborn as sns \n",
        "df=sns.load_dataset(\"titanic\")\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "-qq0zasfZZQI",
        "outputId": "73e4be5a-55ee-4bd5-c67b-2cb72399548b"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
              "0         0       3    male  22.0      1      0   7.2500        S  Third   \n",
              "1         1       1  female  38.0      1      0  71.2833        C  First   \n",
              "2         1       3  female  26.0      0      0   7.9250        S  Third   \n",
              "3         1       1  female  35.0      1      0  53.1000        S  First   \n",
              "4         0       3    male  35.0      0      0   8.0500        S  Third   \n",
              "\n",
              "     who  adult_male deck  embark_town alive  alone  \n",
              "0    man        True  NaN  Southampton    no  False  \n",
              "1  woman       False    C    Cherbourg   yes  False  \n",
              "2  woman       False  NaN  Southampton   yes   True  \n",
              "3  woman       False    C  Southampton   yes  False  \n",
              "4    man        True  NaN  Southampton    no   True  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-198dedd9-8ac2-4fa3-8283-9c695e96365e\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>survived</th>\n",
              "      <th>pclass</th>\n",
              "      <th>sex</th>\n",
              "      <th>age</th>\n",
              "      <th>sibsp</th>\n",
              "      <th>parch</th>\n",
              "      <th>fare</th>\n",
              "      <th>embarked</th>\n",
              "      <th>class</th>\n",
              "      <th>who</th>\n",
              "      <th>adult_male</th>\n",
              "      <th>deck</th>\n",
              "      <th>embark_town</th>\n",
              "      <th>alive</th>\n",
              "      <th>alone</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>S</td>\n",
              "      <td>Third</td>\n",
              "      <td>man</td>\n",
              "      <td>True</td>\n",
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              "      <td>1</td>\n",
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              "      <td>38.0</td>\n",
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              "                                                     [key], {});\n",
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              "          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",
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          "metadata": {},
          "execution_count": 66
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    {
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        "outputId": "84619eea-5c4d-460f-ccc0-8da9ce3e82bf"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 891 entries, 0 to 890\n",
            "Data columns (total 15 columns):\n",
            " #   Column       Non-Null Count  Dtype   \n",
            "---  ------       --------------  -----   \n",
            " 0   survived     891 non-null    int64   \n",
            " 1   pclass       891 non-null    int64   \n",
            " 2   sex          891 non-null    object  \n",
            " 3   age          714 non-null    float64 \n",
            " 4   sibsp        891 non-null    int64   \n",
            " 5   parch        891 non-null    int64   \n",
            " 6   fare         891 non-null    float64 \n",
            " 7   embarked     889 non-null    object  \n",
            " 8   class        891 non-null    category\n",
            " 9   who          891 non-null    object  \n",
            " 10  adult_male   891 non-null    bool    \n",
            " 11  deck         203 non-null    category\n",
            " 12  embark_town  889 non-null    object  \n",
            " 13  alive        891 non-null    object  \n",
            " 14  alone        891 non-null    bool    \n",
            "dtypes: bool(2), category(2), float64(2), int64(4), object(5)\n",
            "memory usage: 80.7+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.tail()"
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        "id": "ydgMIkbPbo3v",
        "outputId": "072a78f6-c558-4e9f-c9d5-ea4eedb993e6"
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      "execution_count": 68,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "     survived  pclass     sex   age  sibsp  parch   fare embarked   class  \\\n",
              "886         0       2    male  27.0      0      0  13.00        S  Second   \n",
              "887         1       1  female  19.0      0      0  30.00        S   First   \n",
              "888         0       3  female   NaN      1      2  23.45        S   Third   \n",
              "889         1       1    male  26.0      0      0  30.00        C   First   \n",
              "890         0       3    male  32.0      0      0   7.75        Q   Third   \n",
              "\n",
              "       who  adult_male deck  embark_town alive  alone  \n",
              "886    man        True  NaN  Southampton    no   True  \n",
              "887  woman       False    B  Southampton   yes   True  \n",
              "888  woman       False  NaN  Southampton    no  False  \n",
              "889    man        True    C    Cherbourg   yes   True  \n",
              "890    man        True  NaN   Queenstown    no   True  "
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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      "execution_count": 69,
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        {
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          "data": {
            "text/plain": [
              "(891, 15)"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
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      "source": [
        "df.columns"
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      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['survived', 'pclass', 'sex', 'age', 'sibsp', 'parch', 'fare',\n",
              "       'embarked', 'class', 'who', 'adult_male', 'deck', 'embark_town',\n",
              "       'alive', 'alone'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
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        "df.index"
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        "id": "7B__SSjscWX3",
        "outputId": "f80acdf9-c6b7-4414-e9b0-325858a4de50"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RangeIndex(start=0, stop=891, step=1)"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe().T"
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      "metadata": {
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        },
        "id": "WVaqYwTPcZ7R",
        "outputId": "6db3a53d-1bb3-4a2e-8656-1fbd93ad1325"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          count       mean        std   min      25%      50%   75%       max\n",
              "survived  891.0   0.383838   0.486592  0.00   0.0000   0.0000   1.0    1.0000\n",
              "pclass    891.0   2.308642   0.836071  1.00   2.0000   3.0000   3.0    3.0000\n",
              "age       714.0  29.699118  14.526497  0.42  20.1250  28.0000  38.0   80.0000\n",
              "sibsp     891.0   0.523008   1.102743  0.00   0.0000   0.0000   1.0    8.0000\n",
              "parch     891.0   0.381594   0.806057  0.00   0.0000   0.0000   0.0    6.0000\n",
              "fare      891.0  32.204208  49.693429  0.00   7.9104  14.4542  31.0  512.3292"
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            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().values.any()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4TRuWQugcfTn",
        "outputId": "748a3144-8163-4d6c-c15d-85cf147db293"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WyyBiDe4czDS",
        "outputId": "a5521b8d-2aaa-4b73-d6de-f010dc67d155"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "survived         0\n",
              "pclass           0\n",
              "sex              0\n",
              "age            177\n",
              "sibsp            0\n",
              "parch            0\n",
              "fare             0\n",
              "embarked         2\n",
              "class            0\n",
              "who              0\n",
              "adult_male       0\n",
              "deck           688\n",
              "embark_town      2\n",
              "alive            0\n",
              "alone            0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[df[\"embark_town\"].isnull()].index"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nQ2kFxMrc3eZ",
        "outputId": "9a5a4444-82c4-4c71-d3cb-d9d0e89da1ca"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Int64Index([61, 829], dtype='int64')"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "1r7lQbylSRf5"
      },
      "execution_count": 76,
      "outputs": []
    }
  ]
}