miuul_mlsc_numpy_pandas_7_17july_2022.ipynb

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      "name": "miuul.mlsc.numpy.pandas.7-17july.2022.ipynb",
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      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# NUMPY \n",
        "\n",
        "Nümerik ve hesaplamalı işlemler için geliştirilmiş bir kütüphanedir. \n",
        "\n",
        "**Neden Numpy ?**\n",
        "\n",
        "Sabit tipte veri tuttuğu için daha hızlı işlem yapar ve verimli veri saklar. \n",
        "\n",
        "Vektörel işlemler yapma imkanı sağlar. \n",
        "\n",
        "Daha az çabayla daha fazla işlem gerçekleştirir."
      ],
      "metadata": {
        "id": "xXidaWBqdRuV"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "z7MEE17v_E9g"
      },
      "outputs": [],
      "source": [
        "import numpy as np "
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a = [1, 2, 3, 4]\n",
        "b = [2, 3, 4, 5]\n",
        "ab = []\n",
        "\n",
        "for i in range(0, len(a)):\n",
        "  ab.append(a[i] * b[i])\n",
        "ab\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k5sNRfJPZySO",
        "outputId": "94c534a5-ee9c-43f1-ac99-07eb8de4f8f2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[2, 6, 12, 20]"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a = np.array([1, 2, 3, 4])\n",
        "b = np.array([2, 3, 4, 5])\n",
        "a*b"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ISzMdX9HaEat",
        "outputId": "619a5565-96ce-4a79-b456-506f73aac3b5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 2,  6, 12, 20])"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Creating Numpy Arrays"
      ],
      "metadata": {
        "id": "TmeJjtOhd-N5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "np.array([1, 2, 3, 4, 5])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DiaqApc0cvhZ",
        "outputId": "f61e1973-04ab-44ff-d62e-f6aa42f0a9c4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 2, 3, 4, 5])"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(np.array([1, 2, 3, 4, 5]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mrWB1J64eOgr",
        "outputId": "9d04bc67-665d-49d9-97c0-1c2eb8307870"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.zeros(10, dtype = int)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i8xLEpJVeSmq",
        "outputId": "2da47d2e-179e-4fbd-f34d-8e9b84dbe539"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.random.randint(0, 10, size = 12)  # (0 ile 10 arasındaki sayılardan 12 tane yazdır)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-pkH-UGteccM",
        "outputId": "2046d0b7-9afe-43b8-d0d3-8b9778c8cb56"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2, 7, 0, 7, 6, 1, 8, 2, 5, 2, 4, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# np.random.normal(ortalama, standart sapma, (matris satır, matris sütun))\n",
        "\n",
        "np.random.normal(10, 4, (3, 4))   # ortalaması 10 ss 4 olan 3 e 4 lük rastgele değerli normal dağılımlı bir array oluşturdu"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K0x_ytQTeiqO",
        "outputId": "8a4e3773-14a8-49dc-fc32-d117d46f296c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 4.79065828,  6.95974764, 17.21419752, 14.90154656],\n",
              "       [10.34169022, 17.17053007,  7.42836007, 16.96219318],\n",
              "       [11.8891819 , 16.19580818, 10.21889731,  6.17683579]])"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Attributes of NumPy Arrays \n",
        "\n",
        "ndim : boyut sayısı\n",
        "\n",
        "shape : boyut bilgisi\n",
        "\n",
        "size : toplam eleman sayısı\n",
        "\n",
        "dtype : aarray veri tipi"
      ],
      "metadata": {
        "id": "khMV7_jyfeYQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "a = np.random.randint(10, size = 12)"
      ],
      "metadata": {
        "id": "ioBlXvrNe7aC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "a.ndim"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oX8dNe2npHY6",
        "outputId": "4b231fbc-10e8-4cd3-8bf7-be5cd0ab6869"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a.shape "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tqhmz2O9pK2r",
        "outputId": "6f9f319f-af84-4759-caeb-df01bffd39f4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(12,)"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a.size"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dBNJASLqpPK9",
        "outputId": "0f23c284-9180-46d9-a49b-360b3924af11"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "12"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a.dtype"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vIlF2HgOpSp0",
        "outputId": "d4159029-1685-4e43-f864-63cb9f84368a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dtype('int64')"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Reshaping "
      ],
      "metadata": {
        "id": "lQTDw8jwpg6m"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "np.random.randint(1, 10, size = 12)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mRqk7eXWptFn",
        "outputId": "2f1e03ac-21eb-4465-cf52-728d452edaa8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 4, 1, 5, 6, 8, 3, 3, 8, 3, 6, 9])"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.random.randint(1, 10, size = 12).reshape(3, 4)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w_YteLCdpU4D",
        "outputId": "65770679-3fe8-4da9-9750-d69a7840df6f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[6, 1, 3, 8],\n",
              "       [2, 6, 3, 8],\n",
              "       [8, 6, 6, 5]])"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# INDEX İşlemleri ***\n",
        "\n",
        "## Index Selection"
      ],
      "metadata": {
        "id": "jXncuoJoQqR-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "a = np.random.randint(10, size = 12)"
      ],
      "metadata": {
        "id": "arc_D6ImppPl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "a[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OhKdAfUmQ_JM",
        "outputId": "84e9add4-5a6e-4278-9fb0-7ecb5f648167"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a[0:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JZNfZSNHRACb",
        "outputId": "62f29804-7e21-41e3-ad58-a20a366b2c05"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([3, 8, 6, 8, 6])"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a[0] = 999\n",
        "a"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D8kOn7tORDhu",
        "outputId": "393c8cfe-e697-4842-ab5a-1e5c294fdb6a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([999,   8,   6,   8,   6,   6,   8,   2,   4,   3,   3,   4])"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "m = np.random.randint(10, size = (3, 5))\n",
        "m"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uUVr0x7MRJ3d",
        "outputId": "92db79f0-f87f-45f7-b1cd-0406e57361db"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[8, 9, 9, 0, 9],\n",
              "       [4, 2, 4, 4, 2],\n",
              "       [2, 1, 9, 5, 9]])"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "m[0, 2] "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eA2jQw8URSE2",
        "outputId": "516ffbda-99a3-4777-c47e-c4a8cabe706c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "m[1, 1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J5cMUueHRX1y",
        "outputId": "d7cc83f6-a97e-416b-d237-542da0fd2d6a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "m[2, 3] = 99\n",
        "m"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jdn_u09SRgQR",
        "outputId": "6d82f6fe-2e60-4866-d3c5-dd5f121e5ee2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 8,  9,  9,  0,  9],\n",
              "       [ 4,  2,  4,  4,  2],\n",
              "       [ 2,  1,  9, 99,  9]])"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "float bir değer girdin fakat integer bir değere dönüştürdü. NumPy der ki ben verimliyim bütün değerlerimi aynı özellikte tutarım.\n"
      ],
      "metadata": {
        "id": "UWcChnseSXvB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "m[2, 3] = 8.5  \n",
        "m"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EpInoxQlRtQO",
        "outputId": "92abfdb5-fc78-4878-cb56-50c458912eb8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[8, 9, 9, 0, 9],\n",
              "       [4, 2, 4, 4, 2],\n",
              "       [2, 1, 9, 8, 9]])"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "m[0:2, 0:1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WKDPhAhzTc21",
        "outputId": "b382a44f-8ea7-499e-f419-c22fa5881039"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[8],\n",
              "       [4]])"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Fancy Index\n",
        "\n",
        "Birden fazla index bilgisini verir."
      ],
      "metadata": {
        "id": "X-4fcDLkTjbH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "v = np.arange(0, 30, 3) # 0 dan 30 a kadar 3 er 3 er artacak elemanlar\n",
        "v"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mMb94aHMTf2S",
        "outputId": "9dacd0a0-003b-4d07-e036-26be52931f55"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0,  3,  6,  9, 12, 15, 18, 21, 24, 27])"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "catch = [1, 2, 3]\n",
        "v[catch]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A-5rkxvJTpAs",
        "outputId": "d8873900-bc89-4123-90e8-03fb65a2c87a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([3, 6, 9])"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Conditions on Numpy "
      ],
      "metadata": {
        "id": "UKheGhhcUlSl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "v = np.array([1, 2, 3, 4, 5])\n",
        "\n",
        "#klasik yolla liste içinden 3 ten küçük elemanları yakala\n",
        "ab = []\n",
        "\n",
        "for i in v:\n",
        "  if i < 3:\n",
        "    ab.append(i)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fAhPNgZuULzj",
        "outputId": "afd2b615-d528-4be2-f490-c05feaf7845c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1\n",
            "2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "v < 3"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ho33LCUsWXP4",
        "outputId": "2f377517-8698-469b-c917-ce66188d73c1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ True,  True, False, False, False])"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Numpy ile\n",
        "\n",
        "v[v < 3]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mYHcqGPDU49b",
        "outputId": "fb0beddb-a766-4a85-c69c-3c1b642ade2e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 2])"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(v[v > 3])\n",
        "print(v[v >= 3])\n",
        "print(v[v <= 3])\n",
        "print(v[v != 3])\n",
        "print(v[v == 3])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qVQKMYaCWev6",
        "outputId": "cedd9da0-5a30-4ed2-a2d7-340f5d072cc8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[4 5]\n",
            "[3 4 5]\n",
            "[1 2 3]\n",
            "[1 2 4 5]\n",
            "[3]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Mathematical Operations"
      ],
      "metadata": {
        "id": "GSQ-DkAVXLH9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "v = np.array([1, 2, 3, 4, 5])\n",
        "\n",
        "print(v/5)\n",
        "print(v*5/5)\n",
        "print(v**5)\n",
        "print(v-5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bq_wurxvWzhm",
        "outputId": "16546442-bb0f-4e88-966d-22fd208d088b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.2 0.4 0.6 0.8 1. ]\n",
            "[1. 2. 3. 4. 5.]\n",
            "[   1   32  243 1024 3125]\n",
            "[-4 -3 -2 -1  0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(np.subtract(v, 1))  # çıkarma\n",
        "print(np.add(v, 1)) # toplama\n",
        "print(np.mean(v)) # ortalama\n",
        "print(np.sum(v)) # liste içindeki bütün elemanların toplamı\n",
        "print(np.min(v)) # listedeki en küçük eleman\n",
        "print(np.max(v)) # listedeki en büyük eleman\n",
        "print(np.var(v)) # varyasyonu"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aq7vjfm6XT3I",
        "outputId": "f6479a3d-6871-414c-eb7a-87556e4efaf9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0 1 2 3 4]\n",
            "[2 3 4 5 6]\n",
            "3.0\n",
            "15\n",
            "1\n",
            "5\n",
            "2.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        " #iki bilinmeyenli denklemin çözümü\n",
        " # 5*x0 + x1 = 12\n",
        " # x0 + 3*x1 = 10\n",
        "\n",
        "a =np.array([[5, 1], [1, 3]])\n",
        "b= np.array([12, 10])\n",
        "\n",
        "np.linalg.solve(a, b)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aAP1RbdaX6nL",
        "outputId": "e30361b7-0802-429b-92c4-b338dfe509dc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1.85714286, 2.71428571])"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#PANDAS \n",
        "\n",
        "Veri işlemesi ve analizi için Python programlama dilinde yazılmış olan bir yazılım kütüphanesidir\n",
        "\n",
        "###PANDAS SERIES"
      ],
      "metadata": {
        "id": "mAaRSSY-h52s"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
      "metadata": {
        "id": "4k-Uoy7Qgu7y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "s = pd.Series([10, 77, 12, 4, 5])\n",
        "type(s)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A2xvnt3DiTya",
        "outputId": "802af09f-7eba-46a6-e4f7-2523019ac592"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.series.Series"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "s.index"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bYGGDU3wiePd",
        "outputId": "6ad45c79-cddb-4d50-8ee3-5e1be3b61f20"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RangeIndex(start=0, stop=5, step=1)"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "s.dtype"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cmkjNXQ1i1hy",
        "outputId": "03ab5d79-e365-40e5-93fb-dc4f6babfe01"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dtype('int64')"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "s.size"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "phC1syBKi6ZX",
        "outputId": "182c2061-adf5-43e5-deff-d17e87e60aae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "s.ndim  #pandas serileri tek boyutludur"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GHsbf0tki7h0",
        "outputId": "bc719195-9a60-4bd5-be32-39c55e7166e3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "s.values # indexle ilgilenmiyorum dediğin için numpy array olarak döndürür"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g0FFOMuBi8bt",
        "outputId": "fb04c478-0ad3-4bac-ca27-fd4e96dfbb13"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([10, 77, 12,  4,  5])"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(s.values) "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9DSr4YkmjCC8",
        "outputId": "f37de035-cbff-45d2-c1cd-d472de8ba401"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "s.head(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "08tIC5lcjRdR",
        "outputId": "1f4d83ba-5cfd-4c7b-8300-b29d68a76423"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    10\n",
              "1    77\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "s.tail(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rdNB_xnvjhI-",
        "outputId": "8703d766-63bc-45d0-a82c-6e34e941896b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2    12\n",
              "3     4\n",
              "4     5\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "s.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C-2qPchTjkbT",
        "outputId": "deb6eabe-0ccd-40bc-a464-1f70b816ea1c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    10\n",
              "1    77\n",
              "2    12\n",
              "3     4\n",
              "4     5\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# READING DATA"
      ],
      "metadata": {
        "id": "-_yTKRZ5juVb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount(\"/content/gdrive\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XYn-WtvV8cF3",
        "outputId": "034dafa7-8d17-482f-d50c-19e1d6ec0c4e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "adv = pd.read_csv('/content/gdrive/My Drive/advertising.csv')\n",
        "df = adv.copy()\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "cpcezpto8m0j",
        "outputId": "92c69cd3-110e-498e-c755-8497d0592856"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      TV  radio  newspaper  sales\n",
              "1  230.1   37.8       69.2   22.1\n",
              "2   44.5   39.3       45.1   10.4\n",
              "3   17.2   45.9       69.3    9.3\n",
              "4  151.5   41.3       58.5   18.5\n",
              "5  180.8   10.8       58.4   12.9"
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              "      <th>TV</th>\n",
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              "      <th>newspaper</th>\n",
              "      <th>sales</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>230.1</td>\n",
              "      <td>37.8</td>\n",
              "      <td>69.2</td>\n",
              "      <td>22.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>44.5</td>\n",
              "      <td>39.3</td>\n",
              "      <td>45.1</td>\n",
              "      <td>10.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>17.2</td>\n",
              "      <td>45.9</td>\n",
              "      <td>69.3</td>\n",
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          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Quick Look at Data"
      ],
      "metadata": {
        "id": "k_ZNGdM09GBd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = sns.load_dataset(\"titanic\")\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "CoPcAF668mQq",
        "outputId": "05a11090-14b1-416e-e430-d4904d06e18a"
      },
      "execution_count": null,
      "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  "
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              "      <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",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
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              "      <td>0</td>\n",
              "      <td>7.2500</td>\n",
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              "      <td>Third</td>\n",
              "      <td>man</td>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>no</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C</td>\n",
              "      <td>First</td>\n",
              "      <td>woman</td>\n",
              "      <td>False</td>\n",
              "      <td>C</td>\n",
              "      <td>Cherbourg</td>\n",
              "      <td>yes</td>\n",
              "      <td>False</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>S</td>\n",
              "      <td>Third</td>\n",
              "      <td>woman</td>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>yes</td>\n",
              "      <td>True</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>S</td>\n",
              "      <td>First</td>\n",
              "      <td>woman</td>\n",
              "      <td>False</td>\n",
              "      <td>C</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>yes</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0500</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>no</td>\n",
              "      <td>True</td>\n",
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              "                                                     [key], {});\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1_vqCTMe9Qh9",
        "outputId": "16363556-97fc-4915-eb18-453cb2aac4cb"
      },
      "execution_count": null,
      "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.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8cutUQBA_iKc",
        "outputId": "e7691e62-44a1-44dc-c342-0c44e0355f43"
      },
      "execution_count": null,
      "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": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.index"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z0dmHPJg_y28",
        "outputId": "b6337500-09f3-4351-d514-4e9030a0367a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RangeIndex(start=0, stop=891, step=1)"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe().T"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "UHVnugHd_0x9",
        "outputId": "522e496c-ee88-41b2-e4bf-f74d5d2c2d90"
      },
      "execution_count": null,
      "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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              "    <tr>\n",
              "      <th>pclass</th>\n",
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              "      <td>0.42</td>\n",
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              "      <td>38.0</td>\n",
              "      <td>80.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sibsp</th>\n",
              "      <td>891.0</td>\n",
              "      <td>0.523008</td>\n",
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              "      <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",
              "      <td>0.381594</td>\n",
              "      <td>0.806057</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.0000</td>\n",
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              "    <tr>\n",
              "      <th>fare</th>\n",
              "      <td>891.0</td>\n",
              "      <td>32.204208</td>\n",
              "      <td>49.693429</td>\n",
              "      <td>0.00</td>\n",
              "      <td>7.9104</td>\n",
              "      <td>14.4542</td>\n",
              "      <td>31.0</td>\n",
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          "metadata": {},
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    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().values"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SkFg7Z_vAK0f",
        "outputId": "5e072adf-1222-424e-8682-22403a499cfe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[False, False, False, ..., False, False, False],\n",
              "       [False, False, False, ..., False, False, False],\n",
              "       [False, False, False, ..., False, False, False],\n",
              "       ...,\n",
              "       [False, False, False, ..., False, False, False],\n",
              "       [False, False, False, ..., False, False, False],\n",
              "       [False, False, False, ..., False, False, False]])"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().values.any() #en az bir tane eksik değer var mı ?"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LMEBrUwB_4mz",
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            "text/plain": [
              "True"
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          "execution_count": 11
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      "execution_count": null,
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        {
          "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",
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              "embark_town      2\n",
              "alive            0\n",
              "alone            0\n",
              "dtype: int64"
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            "text/plain": [
              "0      male\n",
              "1    female\n",
              "2    female\n",
              "3    female\n",
              "4      male\n",
              "Name: sex, dtype: object"
            ]
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            "text/plain": [
              "male      577\n",
              "female    314\n",
              "Name: sex, dtype: int64"
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              "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
              "1         1       1  female  38.0      1      0  71.2833        C  First   \n",
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              "5         0       3    male   NaN      0      0   8.4583        Q  Third   \n",
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              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>S</td>\n",
              "      <td>First</td>\n",
              "      <td>woman</td>\n",
              "      <td>False</td>\n",
              "      <td>C</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>yes</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>S</td>\n",
              "      <td>Third</td>\n",
              "      <td>man</td>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>no</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.4583</td>\n",
              "      <td>Q</td>\n",
              "      <td>Third</td>\n",
              "      <td>man</td>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Queenstown</td>\n",
              "      <td>no</td>\n",
              "      <td>True</td>\n",
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              "  </tbody>\n",
              "</table>\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-4f217069-a6c3-4e75-b6ed-d83d1b6baf6a');\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": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "delete_indexes = [1, 3, 5, 7]\n",
        "df.drop(delete_indexes, axis = 0).head() # bu silme işlemi kalıcı değildir.\n",
        "\n",
        "#kalıcı olması için df e tekrar atama yapabilirsin. \n",
        "# df = df.drop(delete_indexes, axis = 0).head() şeklinde olur. Ya da\n",
        "# df.drop(delete_indexes, axis = 0, inplace = True).head() # inplace = True eklersin."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "ATJxweFqBHtm",
        "outputId": "046a898f-83e2-4f6f-ed77-4c90039d0d9e"
      },
      "execution_count": null,
      "outputs": [
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            "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",
              "2         1       3  female  26.0      0      0   7.9250        S  Third   \n",
              "4         0       3    male  35.0      0      0   8.0500        S  Third   \n",
              "6         0       1    male  54.0      0      0  51.8625        S  First   \n",
              "8         1       3  female  27.0      0      2  11.1333        S  Third   \n",
              "\n",
              "     who  adult_male deck  embark_town alive  alone  \n",
              "0    man        True  NaN  Southampton    no  False  \n",
              "2  woman       False  NaN  Southampton   yes   True  \n",
              "4    man        True  NaN  Southampton    no   True  \n",
              "6    man        True    E  Southampton    no   True  \n",
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              "  </style>\n",
              "\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ea3ced0c-d2c9-41a7-bd4e-6b54919c21a0');\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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Önemli :** Değişkeni Index'e çevirme"
      ],
      "metadata": {
        "id": "F5GLPGhSB8dT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#df[\"age\"].head()\n",
        "df.age.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Lq2QEvELBZP2",
        "outputId": "ac1ae14a-f7d3-4240-a243-ab27c04dbb9d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    22.0\n",
              "1    38.0\n",
              "2    26.0\n",
              "3    35.0\n",
              "4    35.0\n",
              "Name: age, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.index = df[\"age\"]   #yaş bilgisi indexe eklendi\n",
        "df.index"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-igdskfRCQoU",
        "outputId": "c5506299-2c4c-453b-f6d9-7e8990687412"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Float64Index([22.0, 38.0, 26.0, 35.0, 35.0,  nan, 54.0,  2.0, 27.0, 14.0,\n",
              "              ...\n",
              "              33.0, 22.0, 28.0, 25.0, 39.0, 27.0, 19.0,  nan, 26.0, 32.0],\n",
              "             dtype='float64', name='age', length=891)"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop(\"age\", axis = 1).head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "id": "qfhFhjMiCaSj",
        "outputId": "d40e28f7-bcdc-4046-ee2a-d3822c1879f4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      survived  pclass     sex  sibsp  parch     fare embarked  class    who  \\\n",
              "age                                                                            \n",
              "22.0         0       3    male      1      0   7.2500        S  Third    man   \n",
              "38.0         1       1  female      1      0  71.2833        C  First  woman   \n",
              "26.0         1       3  female      0      0   7.9250        S  Third  woman   \n",
              "35.0         1       1  female      1      0  53.1000        S  First  woman   \n",
              "35.0         0       3    male      0      0   8.0500        S  Third    man   \n",
              "\n",
              "      adult_male deck  embark_town alive  alone  \n",
              "age                                              \n",
              "22.0        True  NaN  Southampton    no  False  \n",
              "38.0       False    C    Cherbourg   yes  False  \n",
              "26.0       False  NaN  Southampton   yes   True  \n",
              "35.0       False    C  Southampton   yes  False  \n",
              "35.0        True  NaN  Southampton    no   True  "
            ],
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              "            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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop(\"age\", axis = 1, inplace = True)\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "id": "dtk-nZCaCkMp",
        "outputId": "acfae4c3-ba36-4d60-e4cb-289281adda76"
      },
      "execution_count": null,
      "outputs": [
        {
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            "text/plain": [
              "      survived  pclass     sex  sibsp  parch     fare embarked  class    who  \\\n",
              "age                                                                            \n",
              "22.0         0       3    male      1      0   7.2500        S  Third    man   \n",
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              "\n",
              "      adult_male deck  embark_town alive  alone  \n",
              "age                                              \n",
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              "26.0       False  NaN  Southampton   yes   True  \n",
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              "            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",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"age\"] = df.index #indexteki değer kolona eklendi"
      ],
      "metadata": {
        "id": "UaVFc9H_CqpP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
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          "height": 302
        },
        "id": "_SxQn36dHwrP",
        "outputId": "7e9f4678-2ac5-4b1c-80b9-81bac4c1a89e"
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      "execution_count": null,
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      survived  pclass     sex  sibsp  parch     fare embarked  class    who  \\\n",
              "age                                                                            \n",
              "22.0         0       3    male      1      0   7.2500        S  Third    man   \n",
              "38.0         1       1  female      1      0  71.2833        C  First  woman   \n",
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              "\n",
              "      adult_male deck  embark_town alive  alone   age  \n",
              "age                                                    \n",
              "22.0        True  NaN  Southampton    no  False  22.0  \n",
              "38.0       False    C    Cherbourg   yes  False  38.0  \n",
              "26.0       False  NaN  Southampton   yes   True  26.0  \n",
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              "      <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",
              "      <th>age</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>age</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>22.0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</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",
              "      <td>NaN</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>no</td>\n",
              "      <td>False</td>\n",
              "      <td>22.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38.0</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C</td>\n",
              "      <td>First</td>\n",
              "      <td>woman</td>\n",
              "      <td>False</td>\n",
              "      <td>C</td>\n",
              "      <td>Cherbourg</td>\n",
              "      <td>yes</td>\n",
              "      <td>False</td>\n",
              "      <td>38.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26.0</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>female</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>S</td>\n",
              "      <td>Third</td>\n",
              "      <td>woman</td>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>yes</td>\n",
              "      <td>True</td>\n",
              "      <td>26.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35.0</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>S</td>\n",
              "      <td>First</td>\n",
              "      <td>woman</td>\n",
              "      <td>False</td>\n",
              "      <td>C</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>yes</td>\n",
              "      <td>False</td>\n",
              "      <td>35.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35.0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>S</td>\n",
              "      <td>Third</td>\n",
              "      <td>man</td>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>no</td>\n",
              "      <td>True</td>\n",
              "      <td>35.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1a337f3d-fb98-4798-9dda-f4afb0504285')\"\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\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",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1a337f3d-fb98-4798-9dda-f4afb0504285 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-1a337f3d-fb98-4798-9dda-f4afb0504285');\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": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ikinci yol \n",
        "df.drop(\"age\", axis = 1, inplace = True)\n",
        "df = df.reset_index().head()"
      ],
      "metadata": {
        "id": "7hxejDHdH3sN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "8GrIAobNHyJl",
        "outputId": "a52a71b0-a167-4e4b-a98a-1bc937d1f0f4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    age  survived  pclass     sex  sibsp  parch     fare embarked  class  \\\n",
              "0  22.0         0       3    male      1      0   7.2500        S  Third   \n",
              "1  38.0         1       1  female      1      0  71.2833        C  First   \n",
              "2  26.0         1       3  female      0      0   7.9250        S  Third   \n",
              "3  35.0         1       1  female      1      0  53.1000        S  First   \n",
              "4  35.0         0       3    male      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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              "<style scoped>\n",
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              "      <td>38.0</td>\n",
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              "      <td>Cherbourg</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>26.0</td>\n",
              "      <td>1</td>\n",
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              "      <td>female</td>\n",
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              "      <td>7.9250</td>\n",
              "      <td>S</td>\n",
              "      <td>Third</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
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              "      <td>yes</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>S</td>\n",
              "      <td>Third</td>\n",
              "      <td>man</td>\n",
              "      <td>True</td>\n",
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              "      <td>Southampton</td>\n",
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              "      <td>True</td>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ace094e4-37f4-4312-95ac-91e7ccb4a6f8')\"\n",
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              "      </button>\n",
              "      \n",
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              "      gap: 12px;\n",
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              "\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
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              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-ace094e4-37f4-4312-95ac-91e7ccb4a6f8 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-ace094e4-37f4-4312-95ac-91e7ccb4a6f8');\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": 51
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Değişkenler Üzerinden İşlemler"
      ],
      "metadata": {
        "id": "KEBFgEdYIhDs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# çok fazla kolon olduğu zaman ara kolonlarıda görmek için eklemen gereken kod:\n",
        "# pd.set_option(\"display.max_columns\", None)"
      ],
      "metadata": {
        "id": "ZAVBUccMIfKr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "type(df[\"age\"].head())  "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YKoch2QhI42g",
        "outputId": "0560169b-05e0-4f7c-fd70-c51a9ae11313"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.series.Series"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        " #değişken seçerken o değişkenin dataframe olarak kalmasını istiyorsan iki köşeli parantez kullanmalısın.\n",
        " df[[\"age\"]].head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "UnEb8tVcJTH9",
        "outputId": "c28a06d6-2e75-4371-90fa-b56273e8de29"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    age\n",
              "0  22.0\n",
              "1  38.0\n",
              "2  26.0\n",
              "3  35.0\n",
              "4  35.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4c2493a8-e5c7-4988-bfde-ad43e45cae0c\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>22.0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>38.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>26.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>35.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>35.0</td>\n",
              "    </tr>\n",
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              "</table>\n",
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              "            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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type( df[[\"age\"]].head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2vtqfRZDJnOJ",
        "outputId": "6c557abd-14c4-4c9a-f357-39dfeebfdae9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.frame.DataFrame"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ikiden fazla değişken seçme\n",
        "\n",
        "df[[\"age\", \"alive\", \"alone\"]]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "mX23luFhJvqr",
        "outputId": "48695d10-7adb-426a-875b-450db5cb87f8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    age alive  alone\n",
              "0  22.0    no  False\n",
              "1  38.0   yes  False\n",
              "2  26.0   yes   True\n",
              "3  35.0   yes  False\n",
              "4  35.0    no   True"
            ],
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              "  </tbody>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\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",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a43e5590-34b9-444b-b948-4785dc486957 button.colab-df-convert');\n",
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              "\n",
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              "          const element = document.querySelector('#df-a43e5590-34b9-444b-b948-4785dc486957');\n",
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              "            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",
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              "          element.appendChild(docLink);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# yeni değişken ekleme\n",
        "\n",
        "df[\"age2\"] = df[\"age\"]**2\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "So1DdoapJ7dm",
        "outputId": "80c25fa6-a889-4f29-95ca-011a62276378"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    age  survived  pclass     sex  sibsp  parch     fare embarked  class  \\\n",
              "0  22.0         0       3    male      1      0   7.2500        S  Third   \n",
              "1  38.0         1       1  female      1      0  71.2833        C  First   \n",
              "2  26.0         1       3  female      0      0   7.9250        S  Third   \n",
              "3  35.0         1       1  female      1      0  53.1000        S  First   \n",
              "4  35.0         0       3    male      0      0   8.0500        S  Third   \n",
              "\n",
              "     who  adult_male deck  embark_town alive  alone    age2  \n",
              "0    man        True  NaN  Southampton    no  False   484.0  \n",
              "1  woman       False    C    Cherbourg   yes  False  1444.0  \n",
              "2  woman       False  NaN  Southampton   yes   True   676.0  \n",
              "3  woman       False    C  Southampton   yes  False  1225.0  \n",
              "4    man        True  NaN  Southampton    no   True  1225.0  "
            ],
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              "\n",
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "df.drop(\"age2\", axis = 1, inplace=True)"
      ],
      "metadata": {
        "id": "Y_lmL4AAKSN2"
      },
      "execution_count": null,
      "outputs": []
    },
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        "df.head()"
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              "    age  survived  pclass     sex  sibsp  parch     fare embarked  class  \\\n",
              "0  22.0         0       3    male      1      0   7.2500        S  Third   \n",
              "1  38.0         1       1  female      1      0  71.2833        C  First   \n",
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              "3  35.0         1       1  female      1      0  53.1000        S  First   \n",
              "4  35.0         0       3    male      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",
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    {
      "cell_type": "code",
      "source": [
        "# birden fazla değişken silmek istiyorsan ayrı bir liste tanımla ve \"age2\" nin olduğu yere yeni tanımladığın listenin ismini yaz.\n",
        "\n",
        "# mesela del_columns = [\"age\", \"alone\", \"fare\"]\n",
        "# df.drop(del_columns, axis = 1, inplace=True)"
      ],
      "metadata": {
        "id": "1Hq6ZK4NKoN6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "LOC & ILOC \n",
        "\n",
        "loc : label based selection\n",
        "\n",
        "iloc: integer based selection\n",
        "\n",
        "DataFrame lerde seçim işlemi yapmak için kullanılır."
      ],
      "metadata": {
        "id": "WvK1Qcf4MBD_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "a = df.loc[:, df.columns.str.contains(\"age\")]\n",
        "a.head()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "K1uRZq5-La8V",
        "outputId": "29275b79-13ed-46bd-c7ea-8819c71e4cd2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    age\n",
              "0  22.0\n",
              "1  38.0\n",
              "2  26.0\n",
              "3  35.0\n",
              "4  35.0"
            ],
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
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    {
      "cell_type": "code",
      "source": [
        "# tilda \"~\" işareti bunun dışındakileri seç demek yani age dışındakileri seçtik\n",
        "b = df.loc[:, ~df.columns.str.contains(\"age\")]\n",
        "b"
      ],
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          "height": 270
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        "id": "gWowN1ICLhMx",
        "outputId": "c8918606-5c55-498c-cad6-97d2740b2583"
      },
      "execution_count": null,
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        {
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          "data": {
            "text/plain": [
              "   survived  pclass     sex  sibsp  parch     fare embarked  class    who  \\\n",
              "0         0       3    male      1      0   7.2500        S  Third    man   \n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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    {
      "cell_type": "code",
      "source": [
        "df.loc[0:3] # 0'dan başlayıp 3 de seçti"
      ],
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          "base_uri": "https://localhost:8080/",
          "height": 239
        },
        "id": "Pr_byhcoMdUp",
        "outputId": "46338702-8c20-4ef3-ec27-59340571a90f"
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      "execution_count": null,
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            "text/plain": [
              "    age  survived  pclass     sex  sibsp  parch     fare embarked  class  \\\n",
              "0  22.0         0       3    male      1      0   7.2500        S  Third   \n",
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              "3  35.0         1       1  female      1      0  53.1000        S  First   \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",
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              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C</td>\n",
              "      <td>First</td>\n",
              "      <td>woman</td>\n",
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              "      <td>C</td>\n",
              "      <td>Cherbourg</td>\n",
              "      <td>yes</td>\n",
              "      <td>False</td>\n",
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              "      <th>2</th>\n",
              "      <td>26.0</td>\n",
              "      <td>1</td>\n",
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              "      <td>female</td>\n",
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              "      <td>0</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>S</td>\n",
              "      <td>Third</td>\n",
              "      <td>woman</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>yes</td>\n",
              "      <td>True</td>\n",
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              "      <td>35.0</td>\n",
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              "      <td>53.1000</td>\n",
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              "      <td>First</td>\n",
              "      <td>woman</td>\n",
              "      <td>False</td>\n",
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              "      <td>Southampton</td>\n",
              "      <td>yes</td>\n",
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              "  "
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          },
          "metadata": {},
          "execution_count": 69
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        "df.iloc[0:3, 0:3]"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    age  survived  pclass\n",
              "0  22.0         0       3\n",
              "1  38.0         1       1\n",
              "2  26.0         1       3"
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              "                                                     [key], {});\n",
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              "\n",
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    {
      "cell_type": "code",
      "source": [
        "df.loc[0:3, \"age\"]"
      ],
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        "id": "P1KQgBHINOrD",
        "outputId": "4338bb11-8943-452f-8498-decb7008191d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    22.0\n",
              "1    38.0\n",
              "2    26.0\n",
              "3    35.0\n",
              "Name: age, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "col_names = [\"age\", \"embarked\", \"alive\"]\n",
        "df.loc[0:3, col_names]"
      ],
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        "id": "MIA9QGzJNX_j",
        "outputId": "b7b3c5d9-2867-42a3-c2a7-4c1208c44d9f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    age embarked alive\n",
              "0  22.0        S    no\n",
              "1  38.0        C   yes\n",
              "2  26.0        S   yes\n",
              "3  35.0        S   yes"
            ],
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              "\n",
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              "\n",
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              "  </style>\n",
              "\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-7fca2b8b-3e45-41eb-910b-6f41a1819a59');\n",
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              "            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",
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              "  "
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          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# CONDITIONAL SELECTION"
      ],
      "metadata": {
        "id": "jlNsnc4lNxfi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "nor-4FwTRom1",
        "outputId": "812e24d2-16c5-4a93-8864-77e310ca5193"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    age  survived  pclass     sex  sibsp  parch     fare embarked  class  \\\n",
              "0  22.0         0       3    male      1      0   7.2500        S  Third   \n",
              "1  38.0         1       1  female      1      0  71.2833        C  First   \n",
              "2  26.0         1       3  female      0      0   7.9250        S  Third   \n",
              "3  35.0         1       1  female      1      0  53.1000        S  First   \n",
              "4  35.0         0       3    male      0      0   8.0500        S  Third   \n",
              "\n",
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          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[df[\"age\"] > 50]\n",
        "df[df[\"age\"] > 20].count()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xfGGyoPhNs9D",
        "outputId": "10cdeec5-a3ee-4888-bb44-bcb884fe5d2e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age            5\n",
              "survived       5\n",
              "pclass         5\n",
              "sex            5\n",
              "sibsp          5\n",
              "parch          5\n",
              "fare           5\n",
              "embarked       5\n",
              "class          5\n",
              "who            5\n",
              "adult_male     5\n",
              "deck           2\n",
              "embark_town    5\n",
              "alive          5\n",
              "alone          5\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[df[\"age\"] > 20][\"age\"].count()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "EUtp0R-9RkQ3",
        "outputId": "3ee25c58-b0f4-49f6-e1cc-02372cb1a8c6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.loc[df[\"age\"] > 20, [\"age\", \"class\"]].head()"
      ],
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          "height": 206
        },
        "id": "cdbZpbedSWd9",
        "outputId": "37f7a665-3cb1-4e80-8f03-ce5f88c56d0d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    age  class\n",
              "0  22.0  Third\n",
              "1  38.0  First\n",
              "2  26.0  Third\n",
              "3  35.0  First\n",
              "4  35.0  Third"
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              "                                                     [key], {});\n",
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              "\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",
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# birden fazla koşul gireceksen parantez içine al \n",
        "\n",
        "df.loc[(df[\"age\"] > 20) & (df[\"sex\"] == \"male\"), [\"age\", \"class\"]].head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "eVKSEOSZSo9A",
        "outputId": "4de5ecbd-461d-4680-d5d4-e60f1f877c49"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    age  class\n",
              "0  22.0  Third\n",
              "4  35.0  Third"
            ],
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              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
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              "      background-color: #3B4455;\n",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-3e7664c8-6ed1-4b89-96f6-05d677120e7d 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-3e7664c8-6ed1-4b89-96f6-05d677120e7d');\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": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"embark_town\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Cm0_nnCAUpmW",
        "outputId": "e86386cf-d083-47e1-8c8c-967d7fcbc2e5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Southampton    644\n",
              "Cherbourg      168\n",
              "Queenstown      77\n",
              "Name: embark_town, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_new = df.loc[(df[\"age\"] > 50) & \n",
        "       (df[\"sex\"] == \"male\")  & \n",
        "       ((df[\"embark_town\"] == \"Cherbourg\") | (df[\"embark_town\"] == \"Southampton\")), \n",
        "        [\"age\", \"class\",\"embark_town\"]]\n",
        "df_new"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "x1PPn12GTGvD",
        "outputId": "49eb0f1d-5d39-4ee3-ecb7-78b3455d5adc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      age   class  embark_town\n",
              "6    54.0   First  Southampton\n",
              "33   66.0  Second  Southampton\n",
              "54   65.0   First    Cherbourg\n",
              "94   59.0   Third  Southampton\n",
              "96   71.0   First    Cherbourg\n",
              "124  54.0   First  Southampton\n",
              "150  51.0  Second  Southampton\n",
              "152  55.5   Third  Southampton\n",
              "155  51.0   First    Cherbourg\n",
              "170  61.0   First  Southampton\n",
              "174  56.0   First    Cherbourg\n",
              "222  51.0   Third  Southampton\n",
              "232  59.0  Second  Southampton\n",
              "249  54.0  Second  Southampton\n",
              "252  62.0   First  Southampton\n",
              "262  52.0   First  Southampton\n",
              "317  54.0  Second  Southampton\n",
              "326  61.0   Third  Southampton\n",
              "406  51.0   Third  Southampton\n",
              "438  64.0   First  Southampton\n",
              "449  52.0   First  Southampton\n",
              "456  65.0   First  Southampton\n",
              "467  56.0   First  Southampton\n",
              "487  58.0   First    Cherbourg\n",
              "492  55.0   First  Southampton\n",
              "493  71.0   First    Cherbourg\n",
              "545  64.0   First  Southampton\n",
              "555  62.0   First  Southampton\n",
              "570  62.0  Second  Southampton\n",
              "582  54.0  Second  Southampton\n",
              "587  60.0   First    Cherbourg\n",
              "625  61.0   First  Southampton\n",
              "630  80.0   First  Southampton\n",
              "631  51.0   Third  Southampton\n",
              "647  56.0   First    Cherbourg\n",
              "659  58.0   First    Cherbourg\n",
              "672  70.0  Second  Southampton\n",
              "684  60.0  Second  Southampton\n",
              "694  60.0   First  Southampton\n",
              "695  52.0  Second  Southampton\n",
              "714  52.0  Second  Southampton\n",
              "745  70.0   First  Southampton\n",
              "851  74.0   Third  Southampton\n",
              "857  51.0   First  Southampton"
            ],
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              "    <tr>\n",
              "      <th>672</th>\n",
              "      <td>70.0</td>\n",
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              "      <td>Southampton</td>\n",
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              "      <th>684</th>\n",
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              "      <td>Southampton</td>\n",
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              "      <td>First</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-33aa762a-682d-4574-a386-20e83263191e')\"\n",
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              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "    .colab-df-convert:hover {\n",
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              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-33aa762a-682d-4574-a386-20e83263191e 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-33aa762a-682d-4574-a386-20e83263191e');\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",
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            ]
          },
          "metadata": {},
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_new[\"embark_town\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tpocAxBUTagN",
        "outputId": "2a7882b3-4daa-416a-e3df-c12c1f49c5f5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Southampton    35\n",
              "Cherbourg       9\n",
              "Name: embark_town, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#  Aggregation & Grouping"
      ],
      "metadata": {
        "id": "llv7ggcLVe3F"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"age\"].mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s6DXzYPQWddK",
        "outputId": "15098876-96af-4cc6-b5cb-9953cb7c05b2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "29.69911764705882"
            ]
          },
          "metadata": {},
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.groupby(\"sex\")[\"age\"].mean()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rl_ZQMBoVGf5",
        "outputId": "9cbeb6ca-765b-4215-a46e-ee79f21de2c2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sex\n",
              "female    27.915709\n",
              "male      30.726645\n",
              "Name: age, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.groupby(\"sex\").agg({\"age\": \"mean\"}) #dictionary formatını daha sık kullanırsın öğren !"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 144
        },
        "id": "tFODeg9rWcK4",
        "outputId": "b503af51-cc26-421d-e4cd-353fb0d021b4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "              age\n",
              "sex              \n",
              "female  27.915709\n",
              "male    30.726645"
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            "text/plain": [
              "              age          \n",
              "             mean       sum\n",
              "sex                        \n",
              "female  27.915709   7286.00\n",
              "male    30.726645  13919.17"
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        "df.groupby(\"sex\").agg({\"age\": [\"mean\", \"sum\"],\n",
        "                       \"embark_town\": \"count\",\n",
        "                       \"survived\": \"mean\"})"
      ],
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        {
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          "data": {
            "text/plain": [
              "              age           embark_town  survived\n",
              "             mean       sum       count      mean\n",
              "sex                                              \n",
              "female  27.915709   7286.00         312  0.742038\n",
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              "                                                     [key], {});\n",
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    {
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        "df.groupby([\"sex\", \"embark_town\"]).agg({\"age\": \"mean\", \"survived\": \"mean\"})"
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              "                          age  survived\n",
              "sex    embark_town                     \n",
              "female Cherbourg    28.344262  0.876712\n",
              "       Queenstown   24.291667  0.750000\n",
              "       Southampton  27.771505  0.689655\n",
              "male   Cherbourg    32.998841  0.305263\n",
              "       Queenstown   30.937500  0.073171\n",
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    {
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      "source": [
        "df.groupby([\"sex\", \"embark_town\", \"class\"]).agg({\"age\": \"mean\",\n",
        "                                                 \"survived\": \"mean\", \n",
        "                                                 \"sex\": \"count\"})"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 645
        },
        "id": "jEyTmYpcX8dA",
        "outputId": "ae95bbb3-4abe-4edf-da36-bfbc15d2d60e"
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      "execution_count": null,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                 age  survived  sex\n",
              "sex    embark_town class                           \n",
              "female Cherbourg   First   36.052632  0.976744   43\n",
              "                   Second  19.142857  1.000000    7\n",
              "                   Third   14.062500  0.652174   23\n",
              "       Queenstown  First   33.000000  1.000000    1\n",
              "                   Second  30.000000  1.000000    2\n",
              "                   Third   22.850000  0.727273   33\n",
              "       Southampton First   32.704545  0.958333   48\n",
              "                   Second  29.719697  0.910448   67\n",
              "                   Third   23.223684  0.375000   88\n",
              "male   Cherbourg   First   40.111111  0.404762   42\n",
              "                   Second  25.937500  0.200000   10\n",
              "                   Third   25.016800  0.232558   43\n",
              "       Queenstown  First   44.000000  0.000000    1\n",
              "                   Second  57.000000  0.000000    1\n",
              "                   Third   28.142857  0.076923   39\n",
              "       Southampton First   41.897188  0.354430   79\n",
              "                   Second  30.875889  0.154639   97\n",
              "                   Third   26.574766  0.128302  265"
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              "      <th rowspan=\"3\" valign=\"top\">Queenstown</th>\n",
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              "                                                     [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",
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          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# PIVOT TABLE\n",
        "\n",
        "df.pivot_table(\"ilk değişkenin ortalaması, \"satır değeri\", \"sütun değeri\")"
      ],
      "metadata": {
        "id": "n6n8TArWer5c"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.pivot_table(\"survived\", \"sex\", \"embarked\")"
      ],
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          "base_uri": "https://localhost:8080/",
          "height": 144
        },
        "id": "Bgl5KX20YVQ4",
        "outputId": "5ca621ed-c2a6-426a-8faf-dd44ee7eabc0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "embarked         C         Q         S\n",
              "sex                                   \n",
              "female    0.876712  0.750000  0.689655\n",
              "male      0.305263  0.073171  0.174603"
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              "      <th>embarked</th>\n",
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        "df.pivot_table(\"survived\", \"sex\", \"embarked\", aggfunc = \"std\")"
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "embarked         C         Q         S\n",
              "sex                                   \n",
              "female    0.331042  0.439155  0.463778\n",
              "male      0.462962  0.263652  0.380058"
            ],
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "df.pivot_table(\"survived\", \"sex\", [\"embarked\",\"class\"])"
      ],
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          "base_uri": "https://localhost:8080/",
          "height": 175
        },
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        "outputId": "16a84978-0025-4ab9-aaf4-f8b6baa92aa7"
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      "execution_count": null,
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        {
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            "text/plain": [
              "embarked         C                      Q                          S  \\\n",
              "class        First Second     Third First Second     Third     First   \n",
              "sex                                                                    \n",
              "female    0.976744    1.0  0.652174   1.0    1.0  0.727273  0.958333   \n",
              "male      0.404762    0.2  0.232558   0.0    0.0  0.076923  0.354430   \n",
              "\n",
              "embarked                      \n",
              "class       Second     Third  \n",
              "sex                           \n",
              "female    0.910448  0.375000  \n",
              "male      0.154639  0.128302  "
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              "                                                     [key], {});\n",
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              "\n",
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "df = sns.load_dataset(\"titanic\")\n",
        "df.head()"
      ],
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "YNcfYKxqg8pH",
        "outputId": "c5500f4a-92de-46cc-86c7-2d78886e4ed0"
      },
      "execution_count": null,
      "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  "
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    {
      "cell_type": "code",
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        "df[\"new_age\"] = pd.cut(df[\"age\"], [0, 10, 18, 25, 40, 90])\n",
        "df.head()"
      ],
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        "id": "mXuU-9A0fPZx",
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      "execution_count": null,
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            "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   new_age  \n",
              "0    man        True  NaN  Southampton    no  False  (18, 25]  \n",
              "1  woman       False    C    Cherbourg   yes  False  (25, 40]  \n",
              "2  woman       False  NaN  Southampton   yes   True  (25, 40]  \n",
              "3  woman       False    C  Southampton   yes  False  (25, 40]  \n",
              "4    man        True  NaN  Southampton    no   True  (25, 40]  "
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              "                                                     [key], {});\n",
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              "\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",
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    {
      "cell_type": "code",
      "source": [
        "df.pivot_table(\"survived\", \"sex\", [\"new_age\", \"class\"])"
      ],
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          "height": 239
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        "id": "9luCvci4gYT2",
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        {
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          "data": {
            "text/plain": [
              "new_age (0, 10]                   (10, 18]                   (18, 25]  \\\n",
              "class     First Second     Third     First Second     Third     First   \n",
              "sex                                                                     \n",
              "female      0.0    1.0  0.500000  1.000000    1.0  0.523810  0.941176   \n",
              "male        1.0    1.0  0.363636  0.666667    0.0  0.103448  0.333333   \n",
              "\n",
              "new_age                      (25, 40]                      (40, 90]            \\\n",
              "class      Second     Third     First    Second     Third     First    Second   \n",
              "sex                                                                             \n",
              "female   0.933333  0.500000  1.000000  0.906250  0.464286  0.961538  0.846154   \n",
              "male     0.047619  0.115385  0.513514  0.071429  0.172043  0.280000  0.095238   \n",
              "\n",
              "new_age            \n",
              "class       Third  \n",
              "sex                \n",
              "female   0.111111  \n",
              "male     0.064516  "
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              "      <th colspan=\"3\" halign=\"left\">(25, 40]</th>\n",
              "      <th colspan=\"3\" halign=\"left\">(40, 90]</th>\n",
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              "    <tr>\n",
              "      <th>class</th>\n",
              "      <th>First</th>\n",
              "      <th>Second</th>\n",
              "      <th>Third</th>\n",
              "      <th>First</th>\n",
              "      <th>Second</th>\n",
              "      <th>Third</th>\n",
              "      <th>First</th>\n",
              "      <th>Second</th>\n",
              "      <th>Third</th>\n",
              "      <th>First</th>\n",
              "      <th>Second</th>\n",
              "      <th>Third</th>\n",
              "      <th>First</th>\n",
              "      <th>Second</th>\n",
              "      <th>Third</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sex</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>female</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.523810</td>\n",
              "      <td>0.941176</td>\n",
              "      <td>0.933333</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.906250</td>\n",
              "      <td>0.464286</td>\n",
              "      <td>0.961538</td>\n",
              "      <td>0.846154</td>\n",
              "      <td>0.111111</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>male</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.363636</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.103448</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.047619</td>\n",
              "      <td>0.115385</td>\n",
              "      <td>0.513514</td>\n",
              "      <td>0.071429</td>\n",
              "      <td>0.172043</td>\n",
              "      <td>0.280000</td>\n",
              "      <td>0.095238</td>\n",
              "      <td>0.064516</td>\n",
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              "\n",
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              "\n",
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              "            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",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# APPLY & LAMBDA"
      ],
      "metadata": {
        "id": "5huRPCG_hxCa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "mIZ9rMRJJB3D"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = sns.load_dataset(\"titanic\")\n",
        "df.head()"
      ],
      "metadata": {
        "id": "cXnFJUq9hHqC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "4fdf52ca-fc9f-4aab-de2b-29efcc9b2e06"
      },
      "execution_count": null,
      "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",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c7b829fb-5e10-4805-a264-78248455fc6e');\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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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"age2\"] = df[\"age\"]*2\n",
        "df[\"age3\"] = df[\"age\"]*5"
      ],
      "metadata": {
        "id": "YdRKqT35JK3n"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "IdBx2cx8Yldq",
        "outputId": "85b39fdf-000c-4ca8-ce22-725034ecca3d"
      },
      "execution_count": null,
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        {
          "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  age2   age3  \n",
              "0    man        True  NaN  Southampton    no  False  44.0  110.0  \n",
              "1  woman       False    C    Cherbourg   yes  False  76.0  190.0  \n",
              "2  woman       False  NaN  Southampton   yes   True  52.0  130.0  \n",
              "3  woman       False    C  Southampton   yes  False  70.0  175.0  \n",
              "4    man        True  NaN  Southampton    no   True  70.0  175.0  "
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              "      <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",
              "      <td>NaN</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>no</td>\n",
              "      <td>False</td>\n",
              "      <td>44.0</td>\n",
              "      <td>110.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C</td>\n",
              "      <td>First</td>\n",
              "      <td>woman</td>\n",
              "      <td>False</td>\n",
              "      <td>C</td>\n",
              "      <td>Cherbourg</td>\n",
              "      <td>yes</td>\n",
              "      <td>False</td>\n",
              "      <td>76.0</td>\n",
              "      <td>190.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>S</td>\n",
              "      <td>Third</td>\n",
              "      <td>woman</td>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>yes</td>\n",
              "      <td>True</td>\n",
              "      <td>52.0</td>\n",
              "      <td>130.0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>S</td>\n",
              "      <td>First</td>\n",
              "      <td>woman</td>\n",
              "      <td>False</td>\n",
              "      <td>C</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>yes</td>\n",
              "      <td>False</td>\n",
              "      <td>70.0</td>\n",
              "      <td>175.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>S</td>\n",
              "      <td>Third</td>\n",
              "      <td>man</td>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>no</td>\n",
              "      <td>True</td>\n",
              "      <td>70.0</td>\n",
              "      <td>175.0</td>\n",
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              "  </tbody>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-823e0595-b856-4dc1-acc4-e4d7e29d02c7')\"\n",
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              "                                                     [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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "(df[\"age\"]/10).head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Uk1QSTbBYqCy",
        "outputId": "23f1aa59-67df-4455-e58f-df19a988fcf8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    2.2\n",
              "1    3.8\n",
              "2    2.6\n",
              "3    3.5\n",
              "4    3.5\n",
              "Name: age, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for col in df.columns:\n",
        "  if \"age\" in col:\n",
        "    print(col)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xxFH7gcEY27h",
        "outputId": "ac393572-65b8-484f-d440-67a24bd234bd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "age\n",
            "age2\n",
            "age3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#içinde age değişkeni olan bütün kolonları 10'a böl bu kod geçici olarak atadı\n",
        "for col in df.columns:\n",
        "  if \"age\" in col:\n",
        "    print((df[col]/10).head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I3nm4igGZCex",
        "outputId": "493aeea0-8f74-485c-b644-dbd71458eaaf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0    2.2\n",
            "1    3.8\n",
            "2    2.6\n",
            "3    3.5\n",
            "4    3.5\n",
            "Name: age, dtype: float64\n",
            "0    4.4\n",
            "1    7.6\n",
            "2    5.2\n",
            "3    7.0\n",
            "4    7.0\n",
            "Name: age2, dtype: float64\n",
            "0    11.0\n",
            "1    19.0\n",
            "2    13.0\n",
            "3    17.5\n",
            "4    17.5\n",
            "Name: age3, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# kalıcı olarak atamak için\n",
        "\n",
        "for col in df.columns:\n",
        "  if \"age\" in col:\n",
        "    df[col] = df[col]/10\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "ECTSYuPSZSgl",
        "outputId": "e889dd2d-48b2-4030-a895-88274f0814e7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   survived  pclass     sex  age  sibsp  parch     fare embarked  class  \\\n",
              "0         0       3    male  2.2      1      0   7.2500        S  Third   \n",
              "1         1       1  female  3.8      1      0  71.2833        C  First   \n",
              "2         1       3  female  2.6      0      0   7.9250        S  Third   \n",
              "3         1       1  female  3.5      1      0  53.1000        S  First   \n",
              "4         0       3    male  3.5      0      0   8.0500        S  Third   \n",
              "\n",
              "     who  adult_male deck  embark_town alive  alone  age2  age3  \n",
              "0    man        True  NaN  Southampton    no  False   4.4  11.0  \n",
              "1  woman       False    C    Cherbourg   yes  False   7.6  19.0  \n",
              "2  woman       False  NaN  Southampton   yes   True   5.2  13.0  \n",
              "3  woman       False    C  Southampton   yes  False   7.0  17.5  \n",
              "4    man        True  NaN  Southampton    no   True   7.0  17.5  "
            ],
            "text/html": [
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "          const element = document.querySelector('#df-3db03970-6477-4f97-a425-020af05614b4');\n",
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              "                                                     [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",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[[\"age\", \"age2\", \"age3\"]].apply(lambda x: x/10).head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "NggNgYDyZrdh",
        "outputId": "a106f45c-9750-422f-8494-49d24f0f1f3a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    age  age2  age3\n",
              "0  0.22  0.44  1.10\n",
              "1  0.38  0.76  1.90\n",
              "2  0.26  0.52  1.30\n",
              "3  0.35  0.70  1.75\n",
              "4  0.35  0.70  1.75"
            ],
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              "\n",
              "  <div id=\"df-5f764f81-4210-4438-8578-a7fc41902b74\">\n",
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              "      <th>age</th>\n",
              "      <th>age2</th>\n",
              "      <th>age3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.22</td>\n",
              "      <td>0.44</td>\n",
              "      <td>1.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.38</td>\n",
              "      <td>0.76</td>\n",
              "      <td>1.90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.26</td>\n",
              "      <td>0.52</td>\n",
              "      <td>1.30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.35</td>\n",
              "      <td>0.70</td>\n",
              "      <td>1.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.35</td>\n",
              "      <td>0.70</td>\n",
              "      <td>1.75</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5f764f81-4210-4438-8578-a7fc41902b74')\"\n",
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              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\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",
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              "  </style>\n",
              "\n",
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              "        buttonEl.style.display =\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-5f764f81-4210-4438-8578-a7fc41902b74');\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": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#2.yol apply lambda ile döngü yazmadan değişkenler üzerinde gezme işlemi sağladı\n",
        "df.loc[:, df.columns.str.contains(\"age\")].apply(lambda x: x/10).head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "9zLr4DQ1Z368",
        "outputId": "09225004-95bb-4716-8e9b-e64e0f9f0d73"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    age  age2  age3\n",
              "0  0.22  0.44  1.10\n",
              "1  0.38  0.76  1.90\n",
              "2  0.26  0.52  1.30\n",
              "3  0.35  0.70  1.75\n",
              "4  0.35  0.70  1.75"
            ],
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              "\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
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              "      <th>0</th>\n",
              "      <td>0.22</td>\n",
              "      <td>0.44</td>\n",
              "      <td>1.10</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.38</td>\n",
              "      <td>0.76</td>\n",
              "      <td>1.90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.26</td>\n",
              "      <td>0.52</td>\n",
              "      <td>1.30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.35</td>\n",
              "      <td>0.70</td>\n",
              "      <td>1.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.35</td>\n",
              "      <td>0.70</td>\n",
              "      <td>1.75</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-47536aef-29dd-47fe-af73-c46443997b2b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "      padding: 0 0 0 0;\n",
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              "\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-47536aef-29dd-47fe-af73-c46443997b2b 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-47536aef-29dd-47fe-af73-c46443997b2b');\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": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.loc[:, df.columns.str.contains(\"age\")].apply(lambda x: (x-x.mean())/ x.std()).head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "O3Y_HwagaM5z",
        "outputId": "f9d56781-30b1-4b31-dbbd-a8bf23682c62"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        age      age2      age3\n",
              "0 -0.530005 -0.530005 -0.530005\n",
              "1  0.571430  0.571430  0.571430\n",
              "2 -0.254646 -0.254646 -0.254646\n",
              "3  0.364911  0.364911  0.364911\n",
              "4  0.364911  0.364911  0.364911"
            ],
            "text/html": [
              "\n",
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              "      <th>0</th>\n",
              "      <td>-0.530005</td>\n",
              "      <td>-0.530005</td>\n",
              "      <td>-0.530005</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.571430</td>\n",
              "      <td>0.571430</td>\n",
              "      <td>0.571430</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.254646</td>\n",
              "      <td>-0.254646</td>\n",
              "      <td>-0.254646</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.364911</td>\n",
              "      <td>0.364911</td>\n",
              "      <td>0.364911</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.364911</td>\n",
              "      <td>0.364911</td>\n",
              "      <td>0.364911</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "              style=\"display:none;\">\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
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              "          const element = document.querySelector('#df-ec90a328-f8b1-4998-aa8f-bccc093442c8');\n",
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              "            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": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def standart_scaler(col_name):\n",
        "  return(col_name - col_name.mean()) / col_name.std()\n",
        "df.loc[:, df.columns.str.contains(\"age\")].apply(standart_scaler).head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
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        "id": "GsqEcPIQcDTV",
        "outputId": "76d59d0e-7290-47ec-a980-000c8c15585e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        age      age2      age3\n",
              "0 -0.530005 -0.530005 -0.530005\n",
              "1  0.571430  0.571430  0.571430\n",
              "2 -0.254646 -0.254646 -0.254646\n",
              "3  0.364911  0.364911  0.364911\n",
              "4  0.364911  0.364911  0.364911"
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              "\n",
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              "      <th>age3</th>\n",
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              "      <th>0</th>\n",
              "      <td>-0.530005</td>\n",
              "      <td>-0.530005</td>\n",
              "      <td>-0.530005</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.571430</td>\n",
              "      <td>0.571430</td>\n",
              "      <td>0.571430</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.254646</td>\n",
              "      <td>-0.254646</td>\n",
              "      <td>-0.254646</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.364911</td>\n",
              "      <td>0.364911</td>\n",
              "      <td>0.364911</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.364911</td>\n",
              "      <td>0.364911</td>\n",
              "      <td>0.364911</td>\n",
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              "          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": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#kaydetmek için 1.yol:\n",
        "df.loc[:, [\"age\", \"age2\", \"age3\"]] = df.loc[:, df.columns.str.contains(\"age\")].apply(standart_scaler).head()"
      ],
      "metadata": {
        "id": "juzl2Dm4clIy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#kaydetmek için 2.yol:\n",
        "df.loc[:, df.columns.str.contains(\"age\")] = df.loc[:, df.columns.str.contains(\"age\")].apply(standart_scaler).head()"
      ],
      "metadata": {
        "id": "issgzXAvd_xS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "HWc5SCgheSmx",
        "outputId": "812b3627-0f8c-4b04-82a6-488aac431577"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   survived  pclass     sex       age  sibsp  parch     fare embarked  class  \\\n",
              "0         0       3    male -1.346261      1      0   7.2500        S  Third   \n",
              "1         1       1  female  0.995063      1      0  71.2833        C  First   \n",
              "2         1       3  female -0.760930      0      0   7.9250        S  Third   \n",
              "3         1       1  female  0.556064      1      0  53.1000        S  First   \n",
              "4         0       3    male  0.556064      0      0   8.0500        S  Third   \n",
              "\n",
              "     who  adult_male deck  embark_town alive  alone      age2      age3  \n",
              "0    man        True  NaN  Southampton    no  False -1.346261 -1.346261  \n",
              "1  woman       False    C    Cherbourg   yes  False  0.995063  0.995063  \n",
              "2  woman       False  NaN  Southampton   yes   True -0.760930 -0.760930  \n",
              "3  woman       False    C  Southampton   yes  False  0.556064  0.556064  \n",
              "4    man        True  NaN  Southampton    no   True  0.556064  0.556064  "
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              "      <td>First</td>\n",
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              "      <td>0.556064</td>\n",
              "      <td>0.556064</td>\n",
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              "      <th>4</th>\n",
              "      <td>0</td>\n",
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              "      <td>male</td>\n",
              "      <td>0.556064</td>\n",
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              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
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              "\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",
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              "\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",
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              "  </style>\n",
              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-270d0f06-e658-42b8-bd75-8e5a02e4c71a 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-270d0f06-e658-42b8-bd75-8e5a02e4c71a');\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": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# BİRLEŞTİRME (JOIN) İŞLEMLERİ"
      ],
      "metadata": {
        "id": "XIS7mS4OkAI6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "m = np.random.randint(1, 30, size = (5, 3))\n",
        "\n",
        "df1 = pd.DataFrame(m, columns=[\"var1\", \"var2\", \"var3\"])\n",
        "\n",
        "df2 = df1 + 99\n",
        "print(m)\n",
        "print(df1)\n",
        "print(df2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JAjcuJQieVc8",
        "outputId": "1d80f3ef-fc86-4cdd-9b10-0ef20477a984"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[15 10  1]\n",
            " [14  5 12]\n",
            " [23  6 25]\n",
            " [ 5  6 24]\n",
            " [27 13 12]]\n",
            "   var1  var2  var3\n",
            "0    15    10     1\n",
            "1    14     5    12\n",
            "2    23     6    25\n",
            "3     5     6    24\n",
            "4    27    13    12\n",
            "   var1  var2  var3\n",
            "0   114   109   100\n",
            "1   113   104   111\n",
            "2   122   105   124\n",
            "3   104   105   123\n",
            "4   126   112   111\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.concat([df1, df2]) "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "i2HZVl4intjZ",
        "outputId": "6b06a968-9ef8-46af-eb8b-b84a06e59c51"
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              "                                                     [key], {});\n",
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              "\n",
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    },
    {
      "cell_type": "markdown",
      "source": [
        "**ignore_index = True** : Birleştirilen 2.dataframe index 'i 0 dan başlatır. Bunu yok etmek için kullanılır"
      ],
      "metadata": {
        "id": "znvjllK9o6JS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pd.concat([df1, df2], ignore_index = True) "
      ],
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        "colab": {
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          "height": 363
        },
        "id": "UM6S_6gAoAAl",
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              "   var1  var2  var3\n",
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              "                                                     [key], {});\n",
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              "\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",
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# example\n",
        "\n",
        "df1 = pd.DataFrame({\"employees\": [\"John\", \"Dennis\", \"Mark\", \"Maria\"],\n",
        "                    \"group\": [\"accounting\", \"engineering\", \"engineering\", \"hr\"]})\n",
        "\n",
        "\n",
        "df2 = pd.DataFrame({\"employees\": [\"John\", \"Dennis\", \"Mark\", \"Maria\"],\n",
        "                    \"start_date\": [2010, 2009, 2014, 2019]})\n",
        "print(df1)\n",
        "print(df2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eLTarCFopBVi",
        "outputId": "9da79de2-21c6-47e3-dc27-e52b62d2bfd6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  employees        group\n",
            "0      John   accounting\n",
            "1    Dennis  engineering\n",
            "2      Mark  engineering\n",
            "3     Maria           hr\n",
            "  employees  start_date\n",
            "0      John        2010\n",
            "1    Dennis        2009\n",
            "2      Mark        2014\n",
            "3     Maria        2019\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.merge(df1, df2) #belirtmeden employees e göre birleştirdi. "
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "kDHDtlEfp3x1",
        "outputId": "f9003e7c-c77d-4c1b-aad5-655e17687cde"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  employees        group  start_date\n",
              "0      John   accounting        2010\n",
              "1    Dennis  engineering        2009\n",
              "2      Mark  engineering        2014\n",
              "3     Maria           hr        2019"
            ],
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-2f2ac16c-0fc3-4d29-b4a8-0c0031f8b44c 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-2f2ac16c-0fc3-4d29-b4a8-0c0031f8b44c');\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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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#belirtmek istersende aşağıdaki gibi yapabilirsin.\n",
        "\n",
        "pd.merge(df1, df2, on = \"employees\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "AjAAtLroqQL-",
        "outputId": "24c853f1-61fe-4dc9-f246-f3bfcdbb6dfb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  employees        group  start_date\n",
              "0      John   accounting        2010\n",
              "1    Dennis  engineering        2009\n",
              "2      Mark  engineering        2014\n",
              "3     Maria           hr        2019"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e68694a5-137f-4dd5-9113-851e0c001fb4\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "<style scoped>\n",
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              "\n",
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              "    }\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>employees</th>\n",
              "      <th>group</th>\n",
              "      <th>start_date</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>John</td>\n",
              "      <td>accounting</td>\n",
              "      <td>2010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Dennis</td>\n",
              "      <td>engineering</td>\n",
              "      <td>2009</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Mark</td>\n",
              "      <td>engineering</td>\n",
              "      <td>2014</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Maria</td>\n",
              "      <td>hr</td>\n",
              "      <td>2019</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e68694a5-137f-4dd5-9113-851e0c001fb4')\"\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",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
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              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "\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-e68694a5-137f-4dd5-9113-851e0c001fb4 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-e68694a5-137f-4dd5-9113-851e0c001fb4');\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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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# AMAÇ: Her çalışanın müdürünün bilgisine erişmek istiyoruz.\n",
        "\n",
        "df3 = pd.merge(df1, df2)\n",
        "\n",
        "df4 = pd.DataFrame({\"group\": [\"accounting\", \"engineering\", \"hr\"],\n",
        "        \"manager\": [\"Caner\", \"Mustafa\", \"Berkcan\"]})\n",
        "print(df4)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "syUVI6e9qhVU",
        "outputId": "544fa8e2-d100-4889-913f-059404fc8550"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "         group  manager\n",
            "0   accounting    Caner\n",
            "1  engineering  Mustafa\n",
            "2           hr  Berkcan\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.merge(df3, df4, on = \"group\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "O7OXLBdPrmzE",
        "outputId": "6a28569c-5d1c-4e1e-a255-0cbfb09b5e01"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  employees        group  start_date  manager\n",
              "0      John   accounting        2010    Caner\n",
              "1    Dennis  engineering        2009  Mustafa\n",
              "2      Mark  engineering        2014  Mustafa\n",
              "3     Maria           hr        2019  Berkcan"
            ],
            "text/html": [
              "\n",
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "</style>\n",
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              "      <td>John</td>\n",
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              "      <td>Caner</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Dennis</td>\n",
              "      <td>engineering</td>\n",
              "      <td>2009</td>\n",
              "      <td>Mustafa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Mark</td>\n",
              "      <td>engineering</td>\n",
              "      <td>2014</td>\n",
              "      <td>Mustafa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Maria</td>\n",
              "      <td>hr</td>\n",
              "      <td>2019</td>\n",
              "      <td>Berkcan</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-099c72ac-8e3e-4f58-bd6d-2a12ba728f1b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "    }\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-099c72ac-8e3e-4f58-bd6d-2a12ba728f1b button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-099c72ac-8e3e-4f58-bd6d-2a12ba728f1b');\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": 34
        }
      ]
    }
  ]
}