diff --git "a/Week1_\353\263\265\354\212\265\352\263\274\354\240\234_\354\265\234\354\234\244\354\204\234.ipynb" "b/Week1_\353\263\265\354\212\265\352\263\274\354\240\234_\354\265\234\354\234\244\354\204\234.ipynb"
new file mode 100644
index 0000000..e24ac4f
--- /dev/null
+++ "b/Week1_\353\263\265\354\212\265\352\263\274\354\240\234_\354\265\234\354\234\244\354\204\234.ipynb"
@@ -0,0 +1,1948 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **1주차 복습과제**\n",
+        "- 1주차 복습과제는 **넘파이/판다스 연습문제**입니다.\n",
+        "- 코드 작성하시고, 출력 결과까지 나오도록 실행 부탁드립니다.\n",
+        "  - 제출 시 파일명 본인 이름으로 변경해 주세요. ex) Week1_복습과제_OOO\n",
+        "- 교재에서 다루지 않은 메소드도 다수 포함되어 있지만 구글링이나 챗지피티 등을 활용해서라도 풀어주세요! 한 번씩 사용해 보면 좋을 것 같아 어려워도 문제에 포함했습니다 🤗"
+      ],
+      "metadata": {
+        "id": "jkGNNJY3S65Z"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## **넘파이**"
+      ],
+      "metadata": {
+        "id": "WedDHAHPJPIA"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 1. Import the numpy package under the name `np`."
+      ],
+      "metadata": {
+        "id": "7Ue21e0fKFRI"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "60ACXMoSGe0H"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 2. Print the numpy version and the configuration.\n",
+        "\n",
+        "(hint: `np.__version__`, `np.show_config`)"
+      ],
+      "metadata": {
+        "id": "FnHQOUj-KNiT"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(np.__version__)\n",
+        "np.show_config()"
+      ],
+      "metadata": {
+        "id": "FgXzYXoRS-V7",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "96d3d49a-d0b9-4f88-8226-11159388f847"
+      },
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "2.1.3\n",
+            "Build Dependencies:\n",
+            "  blas:\n",
+            "    detection method: pkgconfig\n",
+            "    found: true\n",
+            "    include directory: /opt/_internal/cpython-3.13.0/lib/python3.13/site-packages/scipy_openblas64/include\n",
+            "    lib directory: /opt/_internal/cpython-3.13.0/lib/python3.13/site-packages/scipy_openblas64/lib\n",
+            "    name: scipy-openblas\n",
+            "    openblas configuration: OpenBLAS 0.3.27  USE64BITINT DYNAMIC_ARCH NO_AFFINITY\n",
+            "      Haswell MAX_THREADS=64\n",
+            "    pc file directory: /project/.openblas\n",
+            "    version: 0.3.27\n",
+            "  lapack:\n",
+            "    detection method: pkgconfig\n",
+            "    found: true\n",
+            "    include directory: /opt/_internal/cpython-3.13.0/lib/python3.13/site-packages/scipy_openblas64/include\n",
+            "    lib directory: /opt/_internal/cpython-3.13.0/lib/python3.13/site-packages/scipy_openblas64/lib\n",
+            "    name: scipy-openblas\n",
+            "    openblas configuration: OpenBLAS 0.3.27  USE64BITINT DYNAMIC_ARCH NO_AFFINITY\n",
+            "      Haswell MAX_THREADS=64\n",
+            "    pc file directory: /project/.openblas\n",
+            "    version: 0.3.27\n",
+            "Compilers:\n",
+            "  c:\n",
+            "    commands: cc\n",
+            "    linker: ld.bfd\n",
+            "    name: gcc\n",
+            "    version: 10.2.1\n",
+            "  c++:\n",
+            "    commands: c++\n",
+            "    linker: ld.bfd\n",
+            "    name: gcc\n",
+            "    version: 10.2.1\n",
+            "  cython:\n",
+            "    commands: cython\n",
+            "    linker: cython\n",
+            "    name: cython\n",
+            "    version: 3.0.11\n",
+            "Machine Information:\n",
+            "  build:\n",
+            "    cpu: x86_64\n",
+            "    endian: little\n",
+            "    family: x86_64\n",
+            "    system: linux\n",
+            "  host:\n",
+            "    cpu: x86_64\n",
+            "    endian: little\n",
+            "    family: x86_64\n",
+            "    system: linux\n",
+            "Python Information:\n",
+            "  path: /tmp/build-env-v_9b5grh/bin/python\n",
+            "  version: '3.13'\n",
+            "SIMD Extensions:\n",
+            "  baseline:\n",
+            "  - SSE\n",
+            "  - SSE2\n",
+            "  - SSE3\n",
+            "  found:\n",
+            "  - SSSE3\n",
+            "  - SSE41\n",
+            "  - POPCNT\n",
+            "  - SSE42\n",
+            "  - AVX\n",
+            "  - F16C\n",
+            "  - FMA3\n",
+            "  - AVX2\n",
+            "  not found:\n",
+            "  - AVX512F\n",
+            "  - AVX512CD\n",
+            "  - AVX512_KNL\n",
+            "  - AVX512_KNM\n",
+            "  - AVX512_SKX\n",
+            "  - AVX512_CLX\n",
+            "  - AVX512_CNL\n",
+            "  - AVX512_ICL\n",
+            "\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 3. Create a null vector of size 10.\n",
+        "\n",
+        "#### ✅ 출력 예시\n",
+        "\n",
+        "\n",
+        "```\n",
+        "[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+        "```\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "0lB5hLlTKb1Z"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "null_vector = np.zeros(10)\n",
+        "print(null_vector)"
+      ],
+      "metadata": {
+        "id": "eK4DTjPwS_zU",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "52fa562e-4cc1-49d4-c048-05d4f58a5626"
+      },
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 4. Create a null vector of size 10 but the fifth value which is 1.\n",
+        "\n",
+        "#### ✅출력 예시\n",
+        "\n",
+        "\n",
+        "```\n",
+        "[0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]\n",
+        "```"
+      ],
+      "metadata": {
+        "id": "7lSSfwh6Kj5v"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "null_vector = np.zeros(10)\n",
+        "null_vector[4] = 1\n",
+        "print(null_vector)"
+      ],
+      "metadata": {
+        "id": "ofohgzsTTBs6",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "2d48bd0c-8fec-4708-d50e-d907e4ccedaf"
+      },
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 5. Create a vector with values ranging from 10 to 49.\n",
+        "\n",
+        "#### ✅출력 예시\n",
+        "\n",
+        "\n",
+        "```\n",
+        "[10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33\n",
+        " 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49]\n",
+        "```"
+      ],
+      "metadata": {
+        "id": "-NeqvmpLKkdY"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "vector = np.arange(10, 50)\n",
+        "print(vector)"
+      ],
+      "metadata": {
+        "id": "oQ1Mo5W9TC0P",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "8a636784-4446-49fc-f40c-9da0219acc94"
+      },
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33\n",
+            " 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 6. Reverse a vector (first element becomes last).\n",
+        "\n",
+        "#### ✅출력 예시\n",
+        "\n",
+        "\n",
+        "```\n",
+        "[9 8 7 6 5 4 3 2 1 0]\n",
+        "```"
+      ],
+      "metadata": {
+        "id": "B1dFQzRNLN23"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "vector = np.arange(10)\n",
+        "reversed_vector = vector[::-1]\n",
+        "print(reversed_vector)"
+      ],
+      "metadata": {
+        "id": "wrpNYd4jTDsx",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "fc5f5c0f-c183-41c9-d56b-e18cb54c11e6"
+      },
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[9 8 7 6 5 4 3 2 1 0]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 7. Create a 3x3 matrix with values ranging from 0 to 8.\n",
+        "\n",
+        "#### ✅출력 예시\n",
+        "\n",
+        "\n",
+        "```\n",
+        "[[0 1 2]\n",
+        " [3 4 5]\n",
+        " [6 7 8]]\n",
+        "```"
+      ],
+      "metadata": {
+        "id": "doUDUe_NLOhe"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "matrix = np.arange(9).reshape(3, 3)\n",
+        "print(matrix)"
+      ],
+      "metadata": {
+        "id": "4g8WetkATEnw",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "6d67f9ed-8ebc-4cbd-8891-bcf19d2e0a8b"
+      },
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[[0 1 2]\n",
+            " [3 4 5]\n",
+            " [6 7 8]]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 8. Find indices of non-zero elements from [1,2,0,0,4,0].\n",
+        "\n",
+        "#### ✅출력 예시\n",
+        "\n",
+        "\n",
+        "```\n",
+        "(array([0, 1, 4]),)\n",
+        "```"
+      ],
+      "metadata": {
+        "id": "s-MtSq2RLzJx"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "indices = np.nonzero([1, 2, 0, 0, 4, 0])\n",
+        "print(indices)"
+      ],
+      "metadata": {
+        "id": "jw9iUP7sTL7D",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "bce0c6da-bee5-4e32-d4b2-77918128674c"
+      },
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(array([0, 1, 4]),)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 9. Create a 3x3 identity matrix.\n",
+        "\n",
+        "(hint: `np.eye`)\n",
+        "\n",
+        "#### ✅출력 예시\n",
+        "\n",
+        "\n",
+        "```\n",
+        "[[1. 0. 0.]\n",
+        " [0. 1. 0.]\n",
+        " [0. 0. 1.]]\n",
+        "```"
+      ],
+      "metadata": {
+        "id": "ihRWHFUxL6ak"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "matrix = np.eye(3)\n",
+        "print(matrix)"
+      ],
+      "metadata": {
+        "id": "fFzDGJT5TNh7",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "c2d696d8-161b-4401-ec0a-a9ec0d0f4cbe"
+      },
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[[1. 0. 0.]\n",
+            " [0. 1. 0.]\n",
+            " [0. 0. 1.]]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 10. Create a 3x3x3 array with random values.\n",
+        "\n",
+        "#### ✅출력 예시\n",
+        "\n",
+        "\n",
+        "```\n",
+        "[[[0.30742852 0.26458726 0.85474383]\n",
+        "  [0.80234531 0.76268902 0.91286677]\n",
+        "  [0.8804109  0.51011378 0.82103555]]\n",
+        "\n",
+        " [[0.403753   0.83120234 0.15202655]\n",
+        "  [0.75782685 0.0732058  0.48148689]\n",
+        "  [0.2089428  0.44968622 0.88134184]]\n",
+        "\n",
+        " [[0.52251191 0.10738374 0.86279648]\n",
+        "  [0.26203911 0.42157754 0.68856833]\n",
+        "  [0.58133626 0.31059127 0.71354172]]]\n",
+        "```"
+      ],
+      "metadata": {
+        "id": "FvkGsY8eLPCG"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "array= np.random.random((3, 3, 3))\n",
+        "print(array)"
+      ],
+      "metadata": {
+        "id": "084SAfnwTPpt",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "c850f849-2289-4261-f8ab-bc94ef40126f"
+      },
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[[[0.53319894 0.04968071 0.08511909]\n",
+            "  [0.81444641 0.38765835 0.71488352]\n",
+            "  [0.40574384 0.43198236 0.84212173]]\n",
+            "\n",
+            " [[0.22280394 0.845116   0.70338582]\n",
+            "  [0.47919253 0.61093608 0.66815181]\n",
+            "  [0.63058673 0.4658989  0.55100563]]\n",
+            "\n",
+            " [[0.84279938 0.20940063 0.3795359 ]\n",
+            "  [0.88365928 0.15088799 0.31228215]\n",
+            "  [0.46671333 0.5990896  0.57677881]]]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 11. Create a 10x10 array with random values and find the minimum and maximum values.\n",
+        "\n",
+        "#### ✅출력 예시\n",
+        "\n",
+        "\n",
+        "```\n",
+        "0.018360924693465508 0.9990506368595156\n",
+        "```"
+      ],
+      "metadata": {
+        "id": "4-VynNt5MVYY"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "random_array = np.random.random((10, 10))\n",
+        "min_value = np.min(random_array)\n",
+        "max_value = np.max(random_array)\n",
+        "print(min_value, max_value)"
+      ],
+      "metadata": {
+        "id": "W14PP5x6TRh6",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "623074e2-e3fb-4b72-dedc-5a38f9f17769"
+      },
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "0.008650419083488958 0.9908229705068893\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 12. Create a random vector of size 30 and find the mean value.\n",
+        "\n",
+        "#### ✅출력 예시\n",
+        "\n",
+        "\n",
+        "```\n",
+        "0.46249036320403636\n",
+        "```"
+      ],
+      "metadata": {
+        "id": "cQKXmfJBMVQ_"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "random_vector = np.random.random(30)\n",
+        "mean_value = np.mean(random_vector)\n",
+        "print(mean_value)"
+      ],
+      "metadata": {
+        "id": "FS6ggiNJTStp",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "053be87d-073f-4f0b-d0fd-30725e8c061a"
+      },
+      "execution_count": 13,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "0.5298213736960945\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 13. Create a 2d array with 1 on the border and 0 inside. (size: 10x10)\n",
+        "\n",
+        "#### ✅출력 예시\n",
+        "\n",
+        "\n",
+        "```\n",
+        "[[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n",
+        " [1. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n",
+        " [1. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n",
+        " [1. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n",
+        " [1. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n",
+        " [1. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n",
+        " [1. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n",
+        " [1. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n",
+        " [1. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n",
+        " [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]]\n",
+        "```"
+      ],
+      "metadata": {
+        "id": "QZ4h-AddMVJb"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "array_2d = np.ones((10, 10))\n",
+        "array_2d[1:-1, 1:-1] = 0\n",
+        "print(array_2d)"
+      ],
+      "metadata": {
+        "id": "pKEi08edTUMt",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "aea8c290-b4f6-4a5f-ce70-633df353874d"
+      },
+      "execution_count": 14,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n",
+            " [1. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n",
+            " [1. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n",
+            " [1. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n",
+            " [1. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n",
+            " [1. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n",
+            " [1. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n",
+            " [1. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n",
+            " [1. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n",
+            " [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 14. How to add a border (filled with 0's) around an existing array?\n",
+        "\n",
+        "(hint: 인덱싱 사용)\n",
+        "\n",
+        "#### ✅출력 예시\n",
+        "\n",
+        "\n",
+        "```\n",
+        "# before\n",
+        "[[1. 1. 1. 1. 1.]\n",
+        " [1. 1. 1. 1. 1.]\n",
+        " [1. 1. 1. 1. 1.]\n",
+        " [1. 1. 1. 1. 1.]\n",
+        " [1. 1. 1. 1. 1.]]\n",
+        "\n",
+        " # after\n",
+        " [[0. 0. 0. 0. 0.]\n",
+        "  [0. 1. 1. 1. 0.]\n",
+        "  [0. 1. 1. 1. 0.]\n",
+        "  [0. 1. 1. 1. 0.]\n",
+        "  [0. 0. 0. 0. 0.]]\n",
+        "\n",
+        "```"
+      ],
+      "metadata": {
+        "id": "BIwX-BiSMUz_"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "array_2d = np.ones((5, 5))\n",
+        "print(\"# before\")\n",
+        "print(array_2d)"
+      ],
+      "metadata": {
+        "id": "A4M1huiqTWXy",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "d514bdb4-0ad2-4d7a-9ce1-b9a5083bdd32"
+      },
+      "execution_count": 15,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "# before\n",
+            "[[1. 1. 1. 1. 1.]\n",
+            " [1. 1. 1. 1. 1.]\n",
+            " [1. 1. 1. 1. 1.]\n",
+            " [1. 1. 1. 1. 1.]\n",
+            " [1. 1. 1. 1. 1.]]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "padded_array = np.pad(array_2d, pad_width=1, mode='constant', constant_values=0)\n",
+        "print(\"\\n# after\")\n",
+        "print(padded_array)"
+      ],
+      "metadata": {
+        "id": "idWhL4zzTWRO",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "4bcb9ce9-e176-4594-afcd-65b114b771b4"
+      },
+      "execution_count": 19,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "# after\n",
+            "[[0. 0. 0. 0. 0. 0. 0.]\n",
+            " [0. 1. 1. 1. 1. 1. 0.]\n",
+            " [0. 1. 1. 1. 1. 1. 0.]\n",
+            " [0. 1. 1. 1. 1. 1. 0.]\n",
+            " [0. 1. 1. 1. 1. 1. 0.]\n",
+            " [0. 1. 1. 1. 1. 1. 0.]\n",
+            " [0. 0. 0. 0. 0. 0. 0.]]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 15. 다음 표현식의 결과는 무엇입니까?\n",
+        "\n",
+        "(실행 후, 결과에 대한 주석 작성해주세요. ex. 출력 결과에 대한 이유)\n",
+        "\n",
+        "```python\n",
+        "0 * np.nan\n",
+        "np.nan == np.nan\n",
+        "np.inf > np.nan\n",
+        "np.nan - np.nan\n",
+        "np.nan in set([np.nan])\n",
+        "0.3 == 3 * 0.1\n",
+        "```\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "tggOHEUGM9PK"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(0 * np.nan)\n",
+        "print(np.nan == np.nan)\n",
+        "print(np.inf > np.nan)\n",
+        "print(np.nan - np.nan)\n",
+        "print(np.nan in set([np.nan]))\n",
+        "print(0.3 == 3 * 0.1)\n",
+        "\n",
+        "# 이유: nan과의 산술 연산은 nan 반환하기 때문에->false나오는 이유"
+      ],
+      "metadata": {
+        "id": "RmjjLx8_TYNp"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 16. Normalize a 5x5 random matrix.\n",
+        "\n",
+        "(hint: (x - mean) / std)\n",
+        "\n",
+        "#### ✅출력 예시\n",
+        "\n",
+        "\n",
+        "```\n",
+        "[[-1.4765971   0.9582795  -1.29910242  1.0250981  -0.79801651]\n",
+        " [ 0.57945383 -1.28559078  0.02733238  0.99675052  1.2702088 ]\n",
+        " [-0.08513084 -1.4686812  -0.78813593 -0.34559303 -1.15052328]\n",
+        " [ 0.98183813 -1.41255344  0.37190169  1.05697419 -0.02668639]\n",
+        " [-1.14466851  0.98911205  1.14499256  0.69454882  1.18478886]]\n",
+        "```"
+      ],
+      "metadata": {
+        "id": "28iOVtXXM8gA"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "random_matrix = np.random.random((5, 5))\n",
+        "print(\"Original random matrix:\\n\", random_matrix)\n",
+        "\n",
+        "mean = np.mean(random_matrix)\n",
+        "std = np.std(random_matrix)\n",
+        "normalized_matrix = (random_matrix - mean) / std\n",
+        "\n",
+        "print(\"\\nNormalized matrix:\\n\", normalized_matrix)"
+      ],
+      "metadata": {
+        "id": "0L0IE4qOTZW4",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "d977ccac-8c36-4b18-86af-99c2fbcb3384"
+      },
+      "execution_count": 20,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Original random matrix:\n",
+            " [[0.88253851 0.23959865 0.70859455 0.52154702 0.25538537]\n",
+            " [0.95503018 0.97652652 0.07558529 0.68507947 0.31920339]\n",
+            " [0.15666992 0.00288964 0.08110677 0.30799137 0.77123344]\n",
+            " [0.97705316 0.91856628 0.11745733 0.92424244 0.53996395]\n",
+            " [0.86870834 0.12289972 0.20828916 0.50662307 0.21779917]]\n",
+            "\n",
+            "Normalized matrix:\n",
+            " [[ 1.15743187 -0.7559906   0.6397656   0.08310242 -0.7090085 ]\n",
+            " [ 1.37317086  1.43714509 -1.2441029   0.56978349 -0.51908276]\n",
+            " [-1.00279082 -1.46044892 -1.22767069 -0.55245031  0.82618221]\n",
+            " [ 1.43871239  1.26465239 -1.11948953  1.28154493  0.13791216]\n",
+            " [ 1.11627257 -1.10329268 -0.84916927  0.03868796 -0.82086698]]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 17. Multiply a 5x3 matrix by a 3x2 matrix. (real matrix product)\n",
+        "\n",
+        "#### ✅출력 예시\n",
+        "\n",
+        "\n",
+        "```\n",
+        "[[3. 3.]\n",
+        " [3. 3.]\n",
+        " [3. 3.]\n",
+        " [3. 3.]\n",
+        " [3. 3.]]\n",
+        "```"
+      ],
+      "metadata": {
+        "id": "asOmp4b5M8dx"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "matrix_a = np.random.random((5, 3))\n",
+        "matrix_b = np.random.random((3, 2))\n",
+        "result = np.dot(matrix_a, matrix_b)\n",
+        "print(result)"
+      ],
+      "metadata": {
+        "id": "01PxOWW5Te7I",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "16042f73-c9d9-4120-c808-4a7406c9a229"
+      },
+      "execution_count": 21,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[[0.50442391 0.88605108]\n",
+            " [0.559007   0.57291697]\n",
+            " [0.36599981 0.6434869 ]\n",
+            " [0.56252795 1.07201688]\n",
+            " [0.55287468 0.52761441]]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 18. What are the result of the following expressions?\n",
+        "\n",
+        "(실행 후, 결과에 대한 주석 작성해주세요. ex. 출력 결과에 대한 이유)\n",
+        "\n",
+        "```python\n",
+        "np.array(0) / np.array(0)\n",
+        "np.array(0) // np.array(0)\n",
+        "np.array([np.nan]).astype(int).astype(float)\n",
+        "```"
+      ],
+      "metadata": {
+        "id": "H9zVpsYuM8aR"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "np.array(0) / np.array(0)\n",
+        "np.array(0) // np.array(0)\n",
+        "np.array([np.nan]).astype(int).astype(float)\n",
+        "\n",
+        "#이유: NumPy에서 0나누기0은 nan을 반환하며 경고 발생/ nan은 자기 자신을 포함해 어떤 값과도 같지 않으며, 정수형으로 변환시 큰 음수 값으로 변화 가능"
+      ],
+      "metadata": {
+        "id": "SLP--cFwTh10",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "f230d4d2-032e-459d-c851-559ad0cd6133"
+      },
+      "execution_count": 22,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/tmp/ipykernel_1649/3376911992.py:1: RuntimeWarning: invalid value encountered in divide\n",
+            "  np.array(0) / np.array(0)\n",
+            "/tmp/ipykernel_1649/3376911992.py:2: RuntimeWarning: divide by zero encountered in floor_divide\n",
+            "  np.array(0) // np.array(0)\n",
+            "/tmp/ipykernel_1649/3376911992.py:3: RuntimeWarning: invalid value encountered in cast\n",
+            "  np.array([np.nan]).astype(int).astype(float)\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([-9.22337204e+18])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 22
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 19. Create a 5x5 matrix with row values ranging from 0 to 4.\n",
+        "\n",
+        "#### ✅ 출력 예시\n",
+        "\n",
+        "```\n",
+        "[[0. 1. 2. 3. 4.]\n",
+        " [0. 1. 2. 3. 4.]\n",
+        " [0. 1. 2. 3. 4.]\n",
+        " [0. 1. 2. 3. 4.]\n",
+        " [0. 1. 2. 3. 4.]]\n",
+        "```"
+      ],
+      "metadata": {
+        "id": "fbi_T8LIO3yc"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "matrix = np.zeros((5, 5))\n",
+        "matrix += np.arange(5)\n",
+        "print(matrix)"
+      ],
+      "metadata": {
+        "id": "XHj8J0pLTjrf",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "d44f9b9c-ffe8-40a5-8252-197db70682c3"
+      },
+      "execution_count": 23,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[[0. 1. 2. 3. 4.]\n",
+            " [0. 1. 2. 3. 4.]\n",
+            " [0. 1. 2. 3. 4.]\n",
+            " [0. 1. 2. 3. 4.]\n",
+            " [0. 1. 2. 3. 4.]]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 20. Create a random vector of size 10 and sort it.\n",
+        "\n",
+        "#### ✅출력 예시\n",
+        "\n",
+        "\n",
+        "```\n",
+        "[0.11443045 0.16715169 0.32515597 0.38374227 0.48327044 0.78728921\n",
+        " 0.80523192 0.85915686 0.96930939 0.99586525]\n",
+        "\n",
+        "```"
+      ],
+      "metadata": {
+        "id": "cNRIzEzEO_Ft"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "random_vector = np.random.random(10)\n",
+        "sorted_vector = np.sort(random_vector)\n",
+        "print(sorted_vector)"
+      ],
+      "metadata": {
+        "id": "a3lZxnx-Tngw",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "9844cee9-1787-468a-cc94-4f63a86ec8a2"
+      },
+      "execution_count": 24,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[0.16749987 0.2194041  0.2936178  0.39814319 0.56392283 0.63932163\n",
+            " 0.66415326 0.80370528 0.87377817 0.91258182]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## **판다스**"
+      ],
+      "metadata": {
+        "id": "fkX-tY1oKDq5"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "yl7zlBgszyCS"
+      },
+      "source": [
+        "\n",
+        "### 1. Import pandas under the alias `pd`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import pandas as pd\n"
+      ],
+      "metadata": {
+        "id": "ZiANPeHhz00W"
+      },
+      "execution_count": 25,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2Hk3SuquzyCU"
+      },
+      "source": [
+        "### 2. Print the version of pandas that has been imported."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(pd.__version__)"
+      ],
+      "metadata": {
+        "id": "i2NtsBbjz1x2",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "dbc197e7-3e6f-42ef-dd21-5d88460c4691"
+      },
+      "execution_count": 26,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "2.2.3\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "oOTRFQtFzyCV"
+      },
+      "source": [
+        "## 3~20번 문제는 아래 데이터프레임으로 진행됩니다.\n",
+        "\n",
+        "Consider the following Python dictionary `data` and Python list `labels`:\n",
+        "\n",
+        "``` python\n",
+        "data = {'animal': ['cat', 'cat', 'snake', 'dog', 'dog', 'cat', 'snake', 'cat', 'dog', 'dog'],\n",
+        "        'age': [2.5, 3, 0.5, np.nan, 5, 2, 4.5, np.nan, 7, 3],\n",
+        "        'visits': [1, 3, 2, 3, 2, 3, 1, 1, 2, 1],\n",
+        "        'priority': ['yes', 'yes', 'no', 'yes', 'no', 'no', 'no', 'yes', 'no', 'no']}\n",
+        "\n",
+        "labels = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']\n",
+        "```\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 3. Create a DataFrame `df` from this dictionary `data` which has the index `labels`.\n"
+      ],
+      "metadata": {
+        "id": "m_8US0jJzyCW"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 27,
+      "metadata": {
+        "id": "7CbS_gtmzyCW"
+      },
+      "outputs": [],
+      "source": [
+        "# 문제 풀이 전 numpy 임포트\n",
+        "import numpy as np"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "f2shsFGyzyCW"
+      },
+      "source": [
+        "### 4. Display a summary of the basic information about this DataFrame and its data.  \n",
+        "(hint: there is a single method that can be called on the DataFrame)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "data = {'animal': ['cat', 'cat', 'snake', 'dog', 'dog', 'cat', 'snake', 'cat', 'dog', 'dog'],\n",
+        "        'age': [2.5, 3, 0.5, np.nan, 5, 2, 4.5, np.nan, 7, 3],\n",
+        "        'visits': [1, 3, 2, 3, 2, 3, 1, 1, 2, 1],\n",
+        "        'priority': ['yes', 'yes', 'no', 'yes', 'no', 'no', 'no', 'yes', 'no', 'no']}\n",
+        "\n",
+        "labels = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']\n",
+        "\n",
+        "dataframe = pd.DataFrame(data, index=labels)\n",
+        "print(dataframe)"
+      ],
+      "metadata": {
+        "id": "BWZpEuGtz7ky",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "0b311785-1c72-4b12-885f-35461faa1ec5"
+      },
+      "execution_count": 28,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "  animal  age  visits priority\n",
+            "a    cat  2.5       1      yes\n",
+            "b    cat  3.0       3      yes\n",
+            "c  snake  0.5       2       no\n",
+            "d    dog  NaN       3      yes\n",
+            "e    dog  5.0       2       no\n",
+            "f    cat  2.0       3       no\n",
+            "g  snake  4.5       1       no\n",
+            "h    cat  NaN       1      yes\n",
+            "i    dog  7.0       2       no\n",
+            "j    dog  3.0       1       no\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 5. Display a summary of the basic statistics about data of this DataFrame.  \n",
+        "(hint: there is a single method that can be called on the DataFrame)"
+      ],
+      "metadata": {
+        "id": "AfbkaEOyzyCX"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "dataframe = pd.DataFrame(data, index=labels)\n",
+        "print(dataframe.describe())"
+      ],
+      "metadata": {
+        "id": "wzzc100Oz8c3",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "28bce9a6-c52b-4660-8467-db09a7f30498"
+      },
+      "execution_count": 29,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "            age     visits\n",
+            "count  8.000000  10.000000\n",
+            "mean   3.437500   1.900000\n",
+            "std    2.007797   0.875595\n",
+            "min    0.500000   1.000000\n",
+            "25%    2.375000   1.000000\n",
+            "50%    3.000000   2.000000\n",
+            "75%    4.625000   2.750000\n",
+            "max    7.000000   3.000000\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "XJ59aPcrzyCX"
+      },
+      "source": [
+        "### 6. Return the first 3 rows of the DataFrame `df`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df = pd.DataFrame(data, index=labels)\n",
+        "print(df.head(3))"
+      ],
+      "metadata": {
+        "id": "vZro7sh9z_rY",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "1dfa8ee0-b065-447b-e2ad-97e4935a428c"
+      },
+      "execution_count": 30,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "  animal  age  visits priority\n",
+            "a    cat  2.5       1      yes\n",
+            "b    cat  3.0       3      yes\n",
+            "c  snake  0.5       2       no\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "KBVp0_3ZzyCY"
+      },
+      "source": [
+        "### 7. Select just the 'animal' and 'age' columns from the DataFrame `df`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "dff = pd.DataFrame(data, index=labels)\n",
+        "print(dff[['animal', 'age']])"
+      ],
+      "metadata": {
+        "id": "pC6sH-1F0AJo",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "0ebf578f-5eb5-4b52-fb9b-887675411a45"
+      },
+      "execution_count": 31,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "  animal  age\n",
+            "a    cat  2.5\n",
+            "b    cat  3.0\n",
+            "c  snake  0.5\n",
+            "d    dog  NaN\n",
+            "e    dog  5.0\n",
+            "f    cat  2.0\n",
+            "g  snake  4.5\n",
+            "h    cat  NaN\n",
+            "i    dog  7.0\n",
+            "j    dog  3.0\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "a1giftRYzyCY"
+      },
+      "source": [
+        "### 8. Select the data in rows `[3, 4, 8]` **and** in columns `['animal', 'age']`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "rows = [3, 4, 8]\n",
+        "columns = ['animal', 'age']\n",
+        "print(dff.iloc[rows][columns])"
+      ],
+      "metadata": {
+        "id": "tkSynFGH0BLD",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "27a596bb-a5d2-4367-b444-b92e062b1172"
+      },
+      "execution_count": 34,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "  animal  age\n",
+            "d    dog  NaN\n",
+            "e    dog  5.0\n",
+            "i    dog  7.0\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "67sYTLBqzyCY"
+      },
+      "source": [
+        "### 9. Select only the rows where the number of visits is greater than 3."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "rows = dff[dff['visits'] > 3]\n",
+        "print(rows)"
+      ],
+      "metadata": {
+        "id": "d_G0WeMG0Dgd",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "a7d79895-6706-4257-adcf-2e3b3ba91a20"
+      },
+      "execution_count": 37,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Empty DataFrame\n",
+            "Columns: [animal, age, visits, priority]\n",
+            "Index: []\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FXX4YRWgzyCY"
+      },
+      "source": [
+        "### 10. Select the rows where the age is missing, i.e. it is `NaN`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "rows = dff[dff['age'].isnull()]\n",
+        "print(rows)"
+      ],
+      "metadata": {
+        "id": "KaV4Ypkj0Ea2",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "d00974f9-2deb-46d2-c1b2-6245b1a9b8b8"
+      },
+      "execution_count": 38,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "  animal  age  visits priority\n",
+            "d    dog  NaN       3      yes\n",
+            "h    cat  NaN       1      yes\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "kMH5n-UBzyCZ"
+      },
+      "source": [
+        "### 11. Select the rows where the animal is a cat *and* the age is less than 3."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "row = dff[(dff['animal'] == 'cat') & (dff['age'] < 3)]\n",
+        "print(row)"
+      ],
+      "metadata": {
+        "id": "8sCDh9Ez0FND",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "93510799-aca3-4e66-ec28-09947f1c51c9"
+      },
+      "execution_count": 39,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "  animal  age  visits priority\n",
+            "a    cat  2.5       1      yes\n",
+            "f    cat  2.0       3       no\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "62Y0JsYazyCZ"
+      },
+      "source": [
+        "### 12. Select the rows the age is between 2 and 4 (inclusive).  \n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "rows = dff[(dff['age'] >= 2) & (dff['age'] <= 4)]\n",
+        "print(rows)"
+      ],
+      "metadata": {
+        "id": "svjvRtgZ0G76",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "1b0ee88f-b9b6-4270-fab1-e7b02fdf33d6"
+      },
+      "execution_count": 40,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "  animal  age  visits priority\n",
+            "a    cat  2.5       1      yes\n",
+            "b    cat  3.0       3      yes\n",
+            "f    cat  2.0       3       no\n",
+            "j    dog  3.0       1       no\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4lIGMIkPzyCZ"
+      },
+      "source": [
+        "### 13. Change the age in row 'f' to 1.5."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "rows = dff.loc['f']\n",
+        "rows['age'] = 1.5\n",
+        "print(rows)"
+      ],
+      "metadata": {
+        "id": "h4U4A6Ai0Hvk",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "38c66ff5-ba68-40a0-9342-d865d9babed5"
+      },
+      "execution_count": 41,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "animal      cat\n",
+            "age         1.5\n",
+            "visits        3\n",
+            "priority     no\n",
+            "Name: f, dtype: object\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/tmp/ipykernel_1649/2460173498.py:2: SettingWithCopyWarning: \n",
+            "A value is trying to be set on a copy of a slice from a DataFrame\n",
+            "\n",
+            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+            "  rows['age'] = 1.5\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FZzPr9ObzyCZ"
+      },
+      "source": [
+        "### 14. Calculate the sum of all visits in `df` (i.e. the total number of visits)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "sum_visits = dff['visits'].sum()\n",
+        "print(sum_visits)"
+      ],
+      "metadata": {
+        "id": "FXLAUqR40I6C",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "c1bb2ac1-bbdc-467e-eed2-7f1d9d94c243"
+      },
+      "execution_count": 42,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "19\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "PO0xpJ_OzyCa"
+      },
+      "source": [
+        "### 15. Calculate the mean age for each different animal in `df`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "mean_age = dff.groupby('animal')['age'].mean()\n",
+        "print(mean_age)"
+      ],
+      "metadata": {
+        "id": "L63WRx_20Kta",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "8416630e-931c-40b3-db80-c0bc43ccd7f0"
+      },
+      "execution_count": 43,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "animal\n",
+            "cat      2.5\n",
+            "dog      5.0\n",
+            "snake    2.5\n",
+            "Name: age, dtype: float64\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "YfVdzQXIzyCa"
+      },
+      "source": [
+        "### 16. Append a new row 'k' to `df` with your choice of values for each column. Then delete that row to return the original DataFrame."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "row_k = {'animal': 'dog', 'age': 3.0, 'visits': 2, 'priority': 'yes'}\n",
+        "dff.loc['k'] = row_k\n",
+        "print(dff)"
+      ],
+      "metadata": {
+        "id": "Per12Ekp0Mc0",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "26056e5a-a271-4069-9dd7-99cc7f5f5750"
+      },
+      "execution_count": 44,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "  animal  age  visits priority\n",
+            "a    cat  2.5       1      yes\n",
+            "b    cat  3.0       3      yes\n",
+            "c  snake  0.5       2       no\n",
+            "d    dog  NaN       3      yes\n",
+            "e    dog  5.0       2       no\n",
+            "f    cat  2.0       3       no\n",
+            "g  snake  4.5       1       no\n",
+            "h    cat  NaN       1      yes\n",
+            "i    dog  7.0       2       no\n",
+            "j    dog  3.0       1       no\n",
+            "k    dog  3.0       2      yes\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "tDkk_tHszyCa"
+      },
+      "source": [
+        "### 17. Count the number of each type of animal in `df`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from numpy._core.defchararray import count\n",
+        "count_animal = dff['animal'].value_counts()\n",
+        "print(count_animal)"
+      ],
+      "metadata": {
+        "id": "v21izXST0NTR",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "ae59249b-7231-444e-bbef-aea4cfa8fa20"
+      },
+      "execution_count": 45,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "animal\n",
+            "dog      5\n",
+            "cat      4\n",
+            "snake    2\n",
+            "Name: count, dtype: int64\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "cwz4s5RYzyCa"
+      },
+      "source": [
+        "### 18. Sort `df` first by the values in the 'age' in *decending* order, then by the value in the 'visits' column in *ascending* order (so row `i` should be first, and row `d` should be last).\n",
+        "#### ✅출력 예시"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        " index |animal\t| age\t|visits|\tpriority\n",
+        "---|---|---|---|---\n",
+        "i|\tdog|\t7.0|\t2|\tno\n",
+        "e|\tdog|\t5.0|\t2|\tno\n",
+        "g|\tsnake|\t4.5|\t1|\tno\n",
+        "j|\tdog|\t3.0|\t1|\tno\n",
+        "b|\tcat|\t3.0|\t3|\tyes\n",
+        "a|\tcat|\t2.5|\t1|\tyes\n",
+        "f|\tcat|\t1.5|\t3|\tno\n",
+        "c|\tsnake|\t0.5|\t2|\tno\n",
+        "h|\tcat|\tNaN|\t1|\tyes\n",
+        "d|\tdog|\tNaN|\t3|\tyes"
+      ],
+      "metadata": {
+        "id": "YILpLKeqzyCa"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "sort = dff.sort_values(by=['age', 'visits'], ascending=[False, True])\n",
+        "print(sort)"
+      ],
+      "metadata": {
+        "id": "2l6Pb7T10Qpi",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "2c8c8f18-4c75-44ec-e818-02e58dba798f"
+      },
+      "execution_count": 46,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "  animal  age  visits priority\n",
+            "i    dog  7.0       2       no\n",
+            "e    dog  5.0       2       no\n",
+            "g  snake  4.5       1       no\n",
+            "j    dog  3.0       1       no\n",
+            "k    dog  3.0       2      yes\n",
+            "b    cat  3.0       3      yes\n",
+            "a    cat  2.5       1      yes\n",
+            "f    cat  2.0       3       no\n",
+            "c  snake  0.5       2       no\n",
+            "h    cat  NaN       1      yes\n",
+            "d    dog  NaN       3      yes\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "6hfE99qHzyCb"
+      },
+      "source": [
+        "### 19. The 'priority' column contains the values 'yes' and 'no'. Replace this column with a column of boolean values: 'yes' should be `True` and 'no' should be `False`.  \n",
+        "(hint: `map`)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "priority_map = {'yes': True, 'no': False}\n",
+        "dff['priority'] = dff['priority'].map(priority_map)\n",
+        "print(dff)"
+      ],
+      "metadata": {
+        "id": "lPLmBRUP0SnR",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "797f7eb8-dc04-44b4-a73a-16aa4f5cdc1c"
+      },
+      "execution_count": 47,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "  animal  age  visits  priority\n",
+            "a    cat  2.5       1      True\n",
+            "b    cat  3.0       3      True\n",
+            "c  snake  0.5       2     False\n",
+            "d    dog  NaN       3      True\n",
+            "e    dog  5.0       2     False\n",
+            "f    cat  2.0       3     False\n",
+            "g  snake  4.5       1     False\n",
+            "h    cat  NaN       1      True\n",
+            "i    dog  7.0       2     False\n",
+            "j    dog  3.0       1     False\n",
+            "k    dog  3.0       2      True\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ycaIJncEzyCb"
+      },
+      "source": [
+        "### 20. In the 'animal' column, change the 'snake' entries to 'python'.   "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "change_animal = dff['animal'].replace('snake', 'python')\n",
+        "print(change_animal)"
+      ],
+      "metadata": {
+        "id": "MZelDUlE0Wag",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "ba7a3c0e-235d-4017-f39d-467c193f05ec"
+      },
+      "execution_count": 48,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "a       cat\n",
+            "b       cat\n",
+            "c    python\n",
+            "d       dog\n",
+            "e       dog\n",
+            "f       cat\n",
+            "g    python\n",
+            "h       cat\n",
+            "i       dog\n",
+            "j       dog\n",
+            "k       dog\n",
+            "Name: animal, dtype: object\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 확인용 df 출력 셀\n",
+        "df"
+      ],
+      "metadata": {
+        "id": "uYFF5Jew0Xz7"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "5YmjKvYizyCb"
+      },
+      "source": [
+        "### 21. Given a DataFrame of random numeric values:\n",
+        "```python\n",
+        "df = pd.DataFrame(np.random.random(size=(5, 3))) # this is a 5x3 DataFrame of float values\n",
+        "```\n",
+        "\n",
+        "how do you subtract the row mean from each element in the row?"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 랜덤 시드 고정\n",
+        "np.random.seed(2025)\n",
+        "\n",
+        "df = pd.DataFrame(np.random.random(size=(5, 3)))\n",
+        "\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "EeAkDWv40e9J"
+      },
+      "execution_count": 50,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "f452f358",
+        "outputId": "270a5b29-d8de-4270-f8a9-9ac2429b40b7"
+      },
+      "source": [
+        "row_means = df.mean(axis=1)\n",
+        "normalized_df = df.sub(row_means, axis=0)\n",
+        "print(normalized_df)"
+      ],
+      "execution_count": 52,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "          0         1         2\n",
+            "0 -0.516494  0.235870  0.280624\n",
+            "1  0.081768  0.024435 -0.106204\n",
+            "2 -0.047373 -0.212124  0.259497\n",
+            "3  0.115235 -0.230544  0.115309\n",
+            "4 -0.229731  0.498009 -0.268278\n"
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git "a/Week1_\354\230\210\354\212\265\352\263\274\354\240\234_\354\265\234\354\234\244\354\204\234.ipynb" "b/Week1_\354\230\210\354\212\265\352\263\274\354\240\234_\354\265\234\354\234\244\354\204\234.ipynb"
new file mode 100644
index 0000000..11a57cb
--- /dev/null
+++ "b/Week1_\354\230\210\354\212\265\352\263\274\354\240\234_\354\265\234\354\234\244\354\204\234.ipynb"
@@ -0,0 +1,4101 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "WBipMcgkunmR"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "array1 = np.array([1,2,3])\n",
+        "print('array1 type:', type(array1))\n",
+        "print('array1 array 형태:',array1.shape)\n",
+        "\n",
+        "array2 = np.array([[1,2,3],\n",
+        "                  [2,3,4]])\n",
+        "print('array2 type:', type(array2))\n",
+        "print('array2 array 형태:', array2.shape)\n",
+        "\n",
+        "array3 = np.array([[1,2,3]])\n",
+        "print('array3 type:', type(array3))\n",
+        "print('array3 array 형태:', array3.shape)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 291
+        },
+        "id": "oXNbJNvsu0sy",
+        "outputId": "c39b5360-7f65-4c55-d80d-2daeaabbbebf"
+      },
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "error",
+          "ename": "NameError",
+          "evalue": "name 'np' is not defined",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+            "\u001b[0;32m/tmp/ipykernel_7573/645659951.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0marray1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'array1 type:'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'array1 array 형태:'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0marray1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m array2 = np.array([[1,2,3],\n",
+            "\u001b[0;31mNameError\u001b[0m: name 'np' is not defined"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print('array1: {:0}차원, array2: {:1}차원, array3: {:2}차원'.format(array1.ndim,\n",
+        "                                                               array2.ndim, array3.ndim))"
+      ],
+      "metadata": {
+        "id": "Q7S2UysKxYtI"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "list1=[1,2,3]\n",
+        "print(type(list1))\n",
+        "array1=np.array(list1)\n",
+        "print(type(array1))\n",
+        "print(array1, array1.dtype)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 291
+        },
+        "id": "XQSLNAJmyG96",
+        "outputId": "50305e2f-ad5c-43c3-ed40-82cd3f9edb2e"
+      },
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "<class 'list'>\n"
+          ]
+        },
+        {
+          "output_type": "error",
+          "ename": "NameError",
+          "evalue": "name 'np' is not defined",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+            "\u001b[0;32m/tmp/ipykernel_7573/249164064.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mlist1\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlist1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0marray1\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlist1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marray1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mNameError\u001b[0m: name 'np' is not defined"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "list2=[1,2,'test']\n",
+        "array2=np.array(list2)\n",
+        "print(array2, array2.dtype)\n",
+        "\n",
+        "list3=[1,2,3.0]\n",
+        "array3=np.array(list3)\n",
+        "print(array3, array3.dtype)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 273
+        },
+        "id": "laMgTtK2yim9",
+        "outputId": "003ce865-706e-4f20-c980-83a0a541557e"
+      },
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "error",
+          "ename": "NameError",
+          "evalue": "name 'np' is not defined",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+            "\u001b[0;32m/tmp/ipykernel_7573/824939284.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mlist2\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'test'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0marray2\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlist2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marray2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mlist3\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3.0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mNameError\u001b[0m: name 'np' is not defined"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "array_int=np.array([1,2,3])\n",
+        "array_float=array_int.astype('float64')\n",
+        "print(array_float, array_float.dtype)\n",
+        "\n",
+        "array_int1=array_float.astype('int32')\n",
+        "print(array_int1, array_int1.dtype)\n",
+        "\n",
+        "array_float1=np.array([1.1,2.1,3.1])\n",
+        "array_int2=array_float1.astype('int32')\n",
+        "print(array_int2, array_int2.dtype)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 327
+        },
+        "id": "eDinL7nVgIC-",
+        "outputId": "f4e55fb9-565a-4d99-ef27-65e2ef496c08"
+      },
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "error",
+          "ename": "NameError",
+          "evalue": "name 'np' is not defined",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+            "\u001b[0;32m/tmp/ipykernel_7573/1740540798.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0marray_int\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0marray_float\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0marray_int\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'float64'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray_float\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marray_float\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0marray_int1\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0marray_float\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'int32'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mNameError\u001b[0m: name 'np' is not defined"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "sequence_array=np.arange(10)\n",
+        "print(sequence_array)\n",
+        "print(sequence_array.dtype, sequence_array.shape)"
+      ],
+      "metadata": {
+        "id": "o4S6xVYIh-uv"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "zero_array=np.zeros((3,2), dtype='int32')\n",
+        "print(zero_array)\n",
+        "print(zero_array.dtype, zero_array.shape)\n",
+        "\n",
+        "one_array=np.ones((3,2))\n",
+        "print(one_array)\n",
+        "print(one_array.dtype, one_array.shape)"
+      ],
+      "metadata": {
+        "id": "ik7C-FgfiU5U"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "array1=np.arange(10)\n",
+        "print('array1:\\n', array1)\n",
+        "\n",
+        "array2=array1.reshape(2,5)\n",
+        "print('array2:\\n', array2)\n",
+        "\n",
+        "array3=array1.reshape(5,2)\n",
+        "print('array3:\\n', array3)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 273
+        },
+        "id": "Ej363euYieKL",
+        "outputId": "fe81c410-21fe-4a50-bf21-ef5fd07aede7"
+      },
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "error",
+          "ename": "NameError",
+          "evalue": "name 'np' is not defined",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+            "\u001b[0;32m/tmp/ipykernel_7573/3369047960.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0marray1\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'array1:\\n'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marray1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0marray2\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0marray1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'array2:\\n'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marray2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mNameError\u001b[0m: name 'np' is not defined"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "array1=np.arange(10)\n",
+        "print(array1)\n",
+        "array2=array1.reshape(-1,5)\n",
+        "print('array2 shape:', array2.shape)\n",
+        "array3=array1.reshape(5,-1)\n",
+        "print('array3 shape:', array3.shape)"
+      ],
+      "metadata": {
+        "id": "MnL36eZ4iadm"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "array1=np.arange(8)\n",
+        "array3d=array1.reshape((2,2,2))\n",
+        "print('array3d:\\n', array3d.tolist())\n",
+        "\n",
+        "array5=array3d.reshape(-1,1)\n",
+        "print('array5:\\n', array5.tolist())\n",
+        "print('array5 shape:', array5.shape)\n",
+        "\n",
+        "array6=array1.reshape(-1,1)\n",
+        "print('array6:\\n', array6.tolist())\n",
+        "print('array6 shape:', array6.shape)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 291
+        },
+        "id": "E4izFpgGi8gv",
+        "outputId": "9e3b3a2d-1ed4-46cb-9986-06f604c004dc"
+      },
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "error",
+          "ename": "NameError",
+          "evalue": "name 'np' is not defined",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+            "\u001b[0;32m/tmp/ipykernel_7573/3429315025.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0marray1\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0marray3d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0marray1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'array3d:\\n'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marray3d\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0marray5\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0marray3d\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mNameError\u001b[0m: name 'np' is not defined"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "array1=np.arange(start=1, stop=10)\n",
+        "print('array1:', array1)\n",
+        "value=array1[2]\n",
+        "print('value:', value)\n",
+        "print(type(value))"
+      ],
+      "metadata": {
+        "id": "skpXK1Crjesk"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [],
+      "metadata": {
+        "id": "euU_EtTcgOLF"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print('맨 뒤의 값:', array1[-1], '맨 뒤에서 두 번째 값:', array1[-2])\n",
+        "array1[0] = 9\n",
+        "array1[8] = 0\n",
+        "print('array1:', array1)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 273
+        },
+        "id": "INr8jjuil-H7",
+        "outputId": "81de29a2-f600-4f8d-df25-0e1eace20ed9"
+      },
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "error",
+          "ename": "NameError",
+          "evalue": "name 'array1' is not defined",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+            "\u001b[0;32m/tmp/ipykernel_7573/957792696.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'맨 뒤의 값:'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marray1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'맨 뒤에서 두 번째 값:'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marray1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0marray1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m9\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0marray1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'array1:'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marray1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mNameError\u001b[0m: name 'array1' is not defined"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "array1d=np.arange(start=1, stop=10)\n",
+        "array2d=array1d.reshape(3,3)\n",
+        "print(array2d)\n",
+        "\n",
+        "print('(row=0, col=0) index 가리키는 값:', array2d[0,0])\n",
+        "print('(row=0, col=1) index 가리키는 값:', array2d[0,1])\n",
+        "print('(row=1, col=0) index 가리키는 값:', array2d[1,0])\n",
+        "print('(row=2, col=2) index 가리키는 값:', array2d[2,2])"
+      ],
+      "metadata": {
+        "id": "B3_aLeWXmN_5"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "array1=np.arange(start=1, stop=10)\n",
+        "array3=array1[0:3]\n",
+        "print(array3)\n",
+        "print(type(array3))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 255
+        },
+        "id": "YqrMmeIMmcvv",
+        "outputId": "91494ac4-b558-40cf-dc11-33d3aba7c4a4"
+      },
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "error",
+          "ename": "NameError",
+          "evalue": "name 'np' is not defined",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+            "\u001b[0;32m/tmp/ipykernel_7573/1795288367.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0marray1\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstop\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0marray3\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0marray1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mNameError\u001b[0m: name 'np' is not defined"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "array1=np.arange(start=1, stop=10)\n",
+        "array4=array1[:3]\n",
+        "print(array4)\n",
+        "\n",
+        "array5=array1[3:]\n",
+        "print(array5)\n",
+        "\n",
+        "array6=array1[:]\n",
+        "print(array6)"
+      ],
+      "metadata": {
+        "id": "cQycwPvFmqTZ"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "array1d=np.arange(start=1, stop=10)\n",
+        "array2d=array1d.reshape(3,3)\n",
+        "print('array2d:\\n', array2d)\n",
+        "\n",
+        "print('array2d[0:2, 0:2]  \\n', array2d[0:2, 0:2])\n",
+        "print('array2d[1:3, 0:3]  \\n', array2d[1:3, 0:3])\n",
+        "print('array2d[1:3, :]  \\n', array2d[1:3, :])\n",
+        "print('array2d[:, :]  \\n', array2d[:, :])\n",
+        "print('array2d[:2, 1:]  \\n', array2d[:2, 1:])\n",
+        "print('array2d[:2, 0]  \\n', array2d[:2, 0])"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 291
+        },
+        "id": "pJcONG5em0qH",
+        "outputId": "e7ec254c-8a8a-47e6-f364-8c47d0a57812"
+      },
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "error",
+          "ename": "NameError",
+          "evalue": "name 'np' is not defined",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+            "\u001b[0;32m/tmp/ipykernel_7573/1984874030.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0marray1d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstop\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0marray2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0marray1d\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'array2d:\\n'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marray2d\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'array2d[0:2, 0:2]  \\n'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marray2d\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mNameError\u001b[0m: name 'np' is not defined"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(array2d[0])\n",
+        "print(array2d[1])\n",
+        "print('array2d[0] shape:', array2d[0].shape, 'array2d[1] shape:', array2d[1].shape)"
+      ],
+      "metadata": {
+        "id": "HrRnb_6UnVah"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "array1d = np.arange(start=1, stop=10)\n",
+        "array2d= array1d.reshape(3,3)\n",
+        "\n",
+        "array3=array2d[[0,1], 2]\n",
+        "print('array2d[[0,1],2]=>', array3.tolist())\n",
+        "\n",
+        "array4=array2d[[0,1], 0:2]\n",
+        "print('array2d[[0,1], 0:2]=>', array4.tolist())\n",
+        "\n",
+        "array5=array2d[[0,1]]\n",
+        "print('array2d[[0,1]]=>',array5.tolist())"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 309
+        },
+        "id": "LPuehcrRndaW",
+        "outputId": "eb43f8b0-e066-4615-fb2c-7981afe3c9b6"
+      },
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "error",
+          "ename": "NameError",
+          "evalue": "name 'np' is not defined",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+            "\u001b[0;32m/tmp/ipykernel_7573/3184845039.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0marray1d\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstop\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0marray2d\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0marray1d\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0marray3\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0marray2d\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'array2d[[0,1],2]=>'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marray3\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mNameError\u001b[0m: name 'np' is not defined"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "array1d=np.arange(start=1, stop=10)\n",
+        "# []안에 array1d>5 Boolean indexing을 적용\n",
+        "array3=array1d[array1d>5]\n",
+        "print('array1d>5 불린 인덱싱 결과 값:', array3)\n",
+        "array1d>5"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 309
+        },
+        "id": "JJQOZzHlox1a",
+        "outputId": "e7a7a135-152b-48db-f373-40460c4fe729"
+      },
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "error",
+          "ename": "NameError",
+          "evalue": "name 'np' is not defined",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+            "\u001b[0;32m/tmp/ipykernel_7573/3392735504.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0marray1d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstop\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;31m# []안에 array1d>5 Boolean indexing을 적용\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0marray3\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0marray1d\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0marray1d\u001b[0m\u001b[0;34m>\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'array1d>5 불린 인덱싱 결과 값:'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marray3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0marray1d\u001b[0m\u001b[0;34m>\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mNameError\u001b[0m: name 'np' is not defined"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "boolean_indexes=np.array([False, False, False, False, False, True, True, True, True])\n",
+        "array3=array1d[boolean_indexes]\n",
+        "print('불린 인덱스로 필터링 결과:', array3)"
+      ],
+      "metadata": {
+        "id": "H1eqDXW4pTtS"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "indexes=np.array([5,6,7,8])\n",
+        "array4=array1d[indexes]\n",
+        "print('일반 인덱스로 필터링 결과:', array4)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 255
+        },
+        "id": "Sduh0ic5pb5X",
+        "outputId": "44a7a6bf-2b72-4fcc-bbc8-57c2d4768990"
+      },
+      "execution_count": 13,
+      "outputs": [
+        {
+          "output_type": "error",
+          "ename": "NameError",
+          "evalue": "name 'np' is not defined",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+            "\u001b[0;32m/tmp/ipykernel_7573/2691075439.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mindexes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m7\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0marray4\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0marray1d\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mindexes\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'일반 인덱스로 필터링 결과:'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marray4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mNameError\u001b[0m: name 'np' is not defined"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "org_array=np.array([3,1,9,5])\n",
+        "print('원본 행렬:', org_array)\n",
+        "sort_array1=np.sort(org_array)\n",
+        "print('np.sort() 호출 후 반환된 정렬 행렬:', sort_array1)\n",
+        "print('np.sort() 호출 후 원본 행렬:', org_array)\n",
+        "sort_array2=org_array.sort()\n",
+        "print('org_array.sort() 호출 후 반환된 행렬:', sort_array2)\n",
+        "print('org_array.sort() 호출 후 원본 행렬:', org_array)"
+      ],
+      "metadata": {
+        "id": "LRKFVbQPprHr"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "sort_array1_desc=np.sort(org_array)[::-1]\n",
+        "print('내림차순으로 정렬:', sort_array1_desc)\n",
+        "array2d=np.array([[8,12],\n",
+        "                  [7,1]])\n",
+        "\n",
+        "sort_array2d_axis0=np.sort(array2d, axis=0)\n",
+        "print('로우 방향으로 정렬:\\n', sort_array2d_axis0)\n",
+        "\n",
+        "sort_array2d_axis1=np.sort(array2d, axis=1)\n",
+        "print('칼럼 방향으로 정렬:\\n', sort_array2d_axis1)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 309
+        },
+        "id": "e1Jn6tkLpwrP",
+        "outputId": "a1758918-2aa6-4ee5-d310-d4ca61a115a3"
+      },
+      "execution_count": 14,
+      "outputs": [
+        {
+          "output_type": "error",
+          "ename": "NameError",
+          "evalue": "name 'np' is not defined",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+            "\u001b[0;32m/tmp/ipykernel_7573/1926666348.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msort_array1_desc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0morg_array\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'내림차순으로 정렬:'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msort_array1_desc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m array2d=np.array([[8,12],\n\u001b[1;32m      4\u001b[0m                   [7,1]])\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mNameError\u001b[0m: name 'np' is not defined"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "org_array=np.array([3,1,9,5])\n",
+        "sort_indices=np.argsort(org_array)\n",
+        "print(type(sort_indices))\n",
+        "print('행렬 정렬 시 원본 행렬의 인덱스:', sort_indices)"
+      ],
+      "metadata": {
+        "id": "Ay788vYqrr6A"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "org_array=np.array([3,1,9,5])\n",
+        "sort_indices_desc=np.argsort(org_array)[::-1]\n",
+        "print('행렬 내림차순 정렬 시 원본 행렬의 인덱스:', sort_indices_desc)"
+      ],
+      "metadata": {
+        "id": "ZPpNjlN7r-np"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "\n",
+        "col_name1=['col1']\n",
+        "list1=[1,2,3]\n",
+        "array1=np.array(list1)\n",
+        "df_list1=pd.DataFrame(list1, columns=col_name1)\n",
+        "print('1차원 리스트로 만든 DataFrame:\\n', df_list1)\n",
+        "df_array1=pd.DataFrame(array1, columns=col_name1)\n",
+        "print('1차원 ndarray로 만든 DataFrame:\\n', df_array1)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "szWsbzs8uA_M",
+        "outputId": "c9403c2d-ecfd-446a-f0cc-a004342fe550"
+      },
+      "execution_count": 15,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "1차원 리스트로 만든 DataFrame:\n",
+            "    col1\n",
+            "0     1\n",
+            "1     2\n",
+            "2     3\n",
+            "1차원 ndarray로 만든 DataFrame:\n",
+            "    col1\n",
+            "0     1\n",
+            "1     2\n",
+            "2     3\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "\n",
+        "#3개의 칼럼명이 필요함.\n",
+        "col_name2=['col1', 'col2', 'col3']\n",
+        "\n",
+        "list2=[[1,2,3],\n",
+        "       [11,12,13]]\n",
+        "array2=np.array(list2)\n",
+        "df_list2=pd.DataFrame(list2, columns=col_name2)\n",
+        "print('2차원 리스트로 만든 DataFrame:\\n', df_list2)\n",
+        "df_array2=pd.DataFrame(array2, columns=col_name2)\n",
+        "print('2차원 ndarray로 만든 DataFrame:\\n', df_array2)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 309
+        },
+        "id": "sLbpjwmRvOlZ",
+        "outputId": "355052b5-b192-4756-cfe5-ee8e181164a3"
+      },
+      "execution_count": 1,
+      "outputs": [
+        {
+          "output_type": "error",
+          "ename": "NameError",
+          "evalue": "name 'np' is not defined",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+            "\u001b[0;32m/tmp/ipykernel_7573/85147693.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m list2=[[1,2,3],\n\u001b[1;32m      5\u001b[0m        [11,12,13]]\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0marray2\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlist2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0mdf_list2\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlist2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcol_name2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'2차원 리스트로 만든 DataFrame:\\n'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf_list2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mNameError\u001b[0m: name 'np' is not defined"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "dict={'col1':[1,11  ], 'col2':[2,22], 'col3':[3,33]}\n",
+        "df_dict=pd.DataFrame(dict)\n",
+        "print('딕셔너리로 만든 DataFrame:\\n', df_dict)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "8bLJgjr4v_dW",
+        "outputId": "2aa896c1-4a32-467d-e752-22338e8e55fc"
+      },
+      "execution_count": 16,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "딕셔너리로 만든 DataFrame:\n",
+            "    col1  col2  col3\n",
+            "0     1     2     3\n",
+            "1    11    22    33\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "array3=df_dict.values\n",
+        "print('df_dict.values 타입:', type(array3), 'df_dict.values shape:', array3.shape)\n",
+        "print(array3)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "RVgXan0NwEY4",
+        "outputId": "b71bd044-f3a0-4f60-8b45-55f535e0e0f8"
+      },
+      "execution_count": 17,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "df_dict.values 타입: <class 'numpy.ndarray'> df_dict.values shape: (2, 3)\n",
+            "[[ 1  2  3]\n",
+            " [11 22 33]]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "list3=df_dict.values.tolist()\n",
+        "print('df_dict.values.tolist() 타입:', type(list3))\n",
+        "print(list3)\n",
+        "\n",
+        "dict3=df_dict.to_dict('list')\n",
+        "print('\\n df_dict.to_dict() 타입:', type(dict3))\n",
+        "print(dict3)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "3PFSfqs4wZLo",
+        "outputId": "e6223252-3c9d-4beb-d2bc-3b9f1e6f592d"
+      },
+      "execution_count": 18,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "df_dict.values.tolist() 타입: <class 'list'>\n",
+            "[[1, 2, 3], [11, 22, 33]]\n",
+            "\n",
+            " df_dict.to_dict() 타입: <class 'dict'>\n",
+            "{'col1': [1, 11], 'col2': [2, 22], 'col3': [3, 33]}\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "pd.read_csv('titanic_train.csv')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 929
+        },
+        "id": "yqWH1g5dy45p",
+        "outputId": "c72af004-aff4-4828-8fc1-d9d501dedff2"
+      },
+      "execution_count": 29,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "     PassengerId  Survived  Pclass  \\\n",
+              "0              1         0       3   \n",
+              "1              2         1       1   \n",
+              "2              3         1       3   \n",
+              "3              4         1       1   \n",
+              "4              5         0       3   \n",
+              "..           ...       ...     ...   \n",
+              "886          887         0       2   \n",
+              "887          888         1       1   \n",
+              "888          889         0       3   \n",
+              "889          890         1       1   \n",
+              "890          891         0       3   \n",
+              "\n",
+              "                                                  Name     Sex   Age  SibSp  \\\n",
+              "0                              Braund, Mr. Owen Harris    male  22.0      1   \n",
+              "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
+              "2                               Heikkinen, Miss. Laina  female  26.0      0   \n",
+              "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
+              "4                             Allen, Mr. William Henry    male  35.0      0   \n",
+              "..                                                 ...     ...   ...    ...   \n",
+              "886                              Montvila, Rev. Juozas    male  27.0      0   \n",
+              "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
+              "888           Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN      1   \n",
+              "889                              Behr, Mr. Karl Howell    male  26.0      0   \n",
+              "890                                Dooley, Mr. Patrick    male  32.0      0   \n",
+              "\n",
+              "     Parch            Ticket     Fare Cabin Embarked  \n",
+              "0        0         A/5 21171   7.2500   NaN        S  \n",
+              "1        0          PC 17599  71.2833   C85        C  \n",
+              "2        0  STON/O2. 3101282   7.9250   NaN        S  \n",
+              "3        0            113803  53.1000  C123        S  \n",
+              "4        0            373450   8.0500   NaN        S  \n",
+              "..     ...               ...      ...   ...      ...  \n",
+              "886      0            211536  13.0000   NaN        S  \n",
+              "887      0            112053  30.0000   B42        S  \n",
+              "888      2        W./C. 6607  23.4500   NaN        S  \n",
+              "889      0            111369  30.0000  C148        C  \n",
+              "890      0            370376   7.7500   NaN        Q  \n",
+              "\n",
+              "[891 rows x 12 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-f7f02ee1-0563-4f7c-bed9-053dc914f7af\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>PassengerId</th>\n",
+              "      <th>Survived</th>\n",
+              "      <th>Pclass</th>\n",
+              "      <th>Name</th>\n",
+              "      <th>Sex</th>\n",
+              "      <th>Age</th>\n",
+              "      <th>SibSp</th>\n",
+              "      <th>Parch</th>\n",
+              "      <th>Ticket</th>\n",
+              "      <th>Fare</th>\n",
+              "      <th>Cabin</th>\n",
+              "      <th>Embarked</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>Braund, Mr. Owen Harris</td>\n",
+              "      <td>male</td>\n",
+              "      <td>22.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>A/5 21171</td>\n",
+              "      <td>7.2500</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>S</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
+              "      <td>female</td>\n",
+              "      <td>38.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>PC 17599</td>\n",
+              "      <td>71.2833</td>\n",
+              "      <td>C85</td>\n",
+              "      <td>C</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>3</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3</td>\n",
+              "      <td>Heikkinen, Miss. Laina</td>\n",
+              "      <td>female</td>\n",
+              "      <td>26.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>STON/O2. 3101282</td>\n",
+              "      <td>7.9250</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>S</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>4</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
+              "      <td>female</td>\n",
+              "      <td>35.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>113803</td>\n",
+              "      <td>53.1000</td>\n",
+              "      <td>C123</td>\n",
+              "      <td>S</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>5</td>\n",
+              "      <td>0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>Allen, Mr. William Henry</td>\n",
+              "      <td>male</td>\n",
+              "      <td>35.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>373450</td>\n",
+              "      <td>8.0500</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>S</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>886</th>\n",
+              "      <td>887</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>Montvila, Rev. Juozas</td>\n",
+              "      <td>male</td>\n",
+              "      <td>27.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>211536</td>\n",
+              "      <td>13.0000</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>S</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>887</th>\n",
+              "      <td>888</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Graham, Miss. Margaret Edith</td>\n",
+              "      <td>female</td>\n",
+              "      <td>19.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>112053</td>\n",
+              "      <td>30.0000</td>\n",
+              "      <td>B42</td>\n",
+              "      <td>S</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>888</th>\n",
+              "      <td>889</td>\n",
+              "      <td>0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
+              "      <td>female</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>W./C. 6607</td>\n",
+              "      <td>23.4500</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>S</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>889</th>\n",
+              "      <td>890</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Behr, Mr. Karl Howell</td>\n",
+              "      <td>male</td>\n",
+              "      <td>26.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>111369</td>\n",
+              "      <td>30.0000</td>\n",
+              "      <td>C148</td>\n",
+              "      <td>C</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>890</th>\n",
+              "      <td>891</td>\n",
+              "      <td>0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>Dooley, Mr. Patrick</td>\n",
+              "      <td>male</td>\n",
+              "      <td>32.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>370376</td>\n",
+              "      <td>7.7500</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Q</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>891 rows × 12 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f7f02ee1-0563-4f7c-bed9-053dc914f7af')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-f7f02ee1-0563-4f7c-bed9-053dc914f7af 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-f7f02ee1-0563-4f7c-bed9-053dc914f7af');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 891,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 257,\n        \"min\": 1,\n        \"max\": 891,\n        \"num_unique_values\": 891,\n        \"samples\": [\n          710,\n          440,\n          841\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 891,\n        \"samples\": [\n          \"Moubarek, Master. Halim Gonios (\\\"William George\\\")\",\n          \"Kvillner, Mr. Johan Henrik Johannesson\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.526497332334044,\n        \"min\": 0.42,\n        \"max\": 80.0,\n        \"num_unique_values\": 88,\n        \"samples\": [\n          0.75,\n          22.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 6,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ticket\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 681,\n        \"samples\": [\n          \"11774\",\n          \"248740\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49.693428597180905,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 248,\n        \"samples\": [\n          11.2417,\n          51.8625\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 147,\n        \"samples\": [\n          \"D45\",\n          \"B49\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"S\",\n          \"C\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 29
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "titanic_df = pd.read_csv('titanic_train.csv')\n",
+        "titanic_df['Age_0']=0\n",
+        "titanic_df.head(3)\n",
+        "\n",
+        "titanic_df['Age_by_10']=titanic_df['Age']*10\n",
+        "titanic_df['Family_No']=titanic_df['SibSp']+titanic_df['Parch']+1\n",
+        "titanic_df.head(3)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 319
+        },
+        "id": "KPap6zs9wLGJ",
+        "outputId": "e2efb12c-5d05-483b-d7f1-6392c711f7d6"
+      },
+      "execution_count": 31,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   PassengerId  Survived  Pclass  \\\n",
+              "0            1         0       3   \n",
+              "1            2         1       1   \n",
+              "2            3         1       3   \n",
+              "\n",
+              "                                                Name     Sex   Age  SibSp  \\\n",
+              "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
+              "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
+              "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
+              "\n",
+              "   Parch            Ticket     Fare Cabin Embarked  Age_0  Age_by_10  \\\n",
+              "0      0         A/5 21171   7.2500   NaN        S      0      220.0   \n",
+              "1      0          PC 17599  71.2833   C85        C      0      380.0   \n",
+              "2      0  STON/O2. 3101282   7.9250   NaN        S      0      260.0   \n",
+              "\n",
+              "   Family_No  \n",
+              "0          2  \n",
+              "1          2  \n",
+              "2          1  "
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+              "      <td>Heikkinen, Miss. Laina</td>\n",
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+              "      <td>26.0</td>\n",
+              "      <td>0</td>\n",
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+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d815915d-2431-4350-a832-2debe472de8d')\"\n",
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+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
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+              "\n",
+              "    .colab-df-convert:hover {\n",
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+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\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",
+              "      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-d815915d-2431-4350-a832-2debe472de8d 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-d815915d-2431-4350-a832-2debe472de8d');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "titanic_df",
+              "summary": "{\n  \"name\": \"titanic_df\",\n  \"rows\": 891,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 257,\n        \"min\": 1,\n        \"max\": 891,\n        \"num_unique_values\": 891,\n        \"samples\": [\n          710,\n          440,\n          841\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 891,\n        \"samples\": [\n          \"Moubarek, Master. Halim Gonios (\\\"William George\\\")\",\n          \"Kvillner, Mr. Johan Henrik Johannesson\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.526497332334044,\n        \"min\": 0.42,\n        \"max\": 80.0,\n        \"num_unique_values\": 88,\n        \"samples\": [\n          0.75,\n          22.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 6,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ticket\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 681,\n        \"samples\": [\n          \"11774\",\n          \"248740\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49.693428597180905,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 248,\n        \"samples\": [\n          11.2417,\n          51.8625\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 147,\n        \"samples\": [\n          \"D45\",\n          \"B49\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"S\",\n          \"C\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age_0\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age_by_10\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 145.2649733233403,\n        \"min\": 4.2,\n        \"max\": 800.0,\n        \"num_unique_values\": 88,\n        \"samples\": [\n          7.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Family_No\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 11,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 31
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "titanic_drop_df=titanic_df.drop('Age_0', axis=1)\n",
+        "titanic_drop_df.head(3)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 319
+        },
+        "id": "muv-dsUhweUM",
+        "outputId": "dbc14c57-dc71-4976-991e-92c3ac68e8b3"
+      },
+      "execution_count": 32,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   PassengerId  Survived  Pclass  \\\n",
+              "0            1         0       3   \n",
+              "1            2         1       1   \n",
+              "2            3         1       3   \n",
+              "\n",
+              "                                                Name     Sex   Age  SibSp  \\\n",
+              "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
+              "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
+              "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
+              "\n",
+              "   Parch            Ticket     Fare Cabin Embarked  Age_by_10  Family_No  \n",
+              "0      0         A/5 21171   7.2500   NaN        S      220.0          2  \n",
+              "1      0          PC 17599  71.2833   C85        C      380.0          2  \n",
+              "2      0  STON/O2. 3101282   7.9250   NaN        S      260.0          1  "
+            ],
+            "text/html": [
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+              "      <td>male</td>\n",
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+              "      <td>1</td>\n",
+              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
+              "      <td>female</td>\n",
+              "      <td>38.0</td>\n",
+              "      <td>1</td>\n",
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+              "      <td>C85</td>\n",
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+              "      <td>380.0</td>\n",
+              "      <td>2</td>\n",
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+              "      <th>2</th>\n",
+              "      <td>3</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3</td>\n",
+              "      <td>Heikkinen, Miss. Laina</td>\n",
+              "      <td>female</td>\n",
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+              "  </svg>\n",
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+              "\n",
+              "  <style>\n",
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+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
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+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
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+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-1f376970-374a-4b8e-bc16-01ac77543c82 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-1f376970-374a-4b8e-bc16-01ac77543c82');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "titanic_drop_df",
+              "summary": "{\n  \"name\": \"titanic_drop_df\",\n  \"rows\": 891,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 257,\n        \"min\": 1,\n        \"max\": 891,\n        \"num_unique_values\": 891,\n        \"samples\": [\n          710,\n          440,\n          841\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 891,\n        \"samples\": [\n          \"Moubarek, Master. Halim Gonios (\\\"William George\\\")\",\n          \"Kvillner, Mr. Johan Henrik Johannesson\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.526497332334044,\n        \"min\": 0.42,\n        \"max\": 80.0,\n        \"num_unique_values\": 88,\n        \"samples\": [\n          0.75,\n          22.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 6,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ticket\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 681,\n        \"samples\": [\n          \"11774\",\n          \"248740\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49.693428597180905,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 248,\n        \"samples\": [\n          11.2417,\n          51.8625\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 147,\n        \"samples\": [\n          \"D45\",\n          \"B49\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"S\",\n          \"C\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age_by_10\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 145.2649733233403,\n        \"min\": 4.2,\n        \"max\": 800.0,\n        \"num_unique_values\": 88,\n        \"samples\": [\n          7.5,\n          220.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Family_No\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 11,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          8,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 32
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "drop_result=titanic_df.drop(['Age_0', 'Age_by_10', 'Family_No'], axis=1, inplace=True)\n",
+        "print('inplace=True로 drop 후 반환된 값:', drop_result)\n",
+        "titanic_df.head(3)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 338
+        },
+        "id": "O7q-8oYgzZm_",
+        "outputId": "10c35c13-db54-4b2c-c3a6-576c85f83ab0"
+      },
+      "execution_count": 33,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "inplace=True로 drop 후 반환된 값: None\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   PassengerId  Survived  Pclass  \\\n",
+              "0            1         0       3   \n",
+              "1            2         1       1   \n",
+              "2            3         1       3   \n",
+              "\n",
+              "                                                Name     Sex   Age  SibSp  \\\n",
+              "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
+              "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
+              "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
+              "\n",
+              "   Parch            Ticket     Fare Cabin Embarked  \n",
+              "0      0         A/5 21171   7.2500   NaN        S  \n",
+              "1      0          PC 17599  71.2833   C85        C  \n",
+              "2      0  STON/O2. 3101282   7.9250   NaN        S  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-7f67376c-f98e-4fc4-974c-77f009e9e56e\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>PassengerId</th>\n",
+              "      <th>Survived</th>\n",
+              "      <th>Pclass</th>\n",
+              "      <th>Name</th>\n",
+              "      <th>Sex</th>\n",
+              "      <th>Age</th>\n",
+              "      <th>SibSp</th>\n",
+              "      <th>Parch</th>\n",
+              "      <th>Ticket</th>\n",
+              "      <th>Fare</th>\n",
+              "      <th>Cabin</th>\n",
+              "      <th>Embarked</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>Braund, Mr. Owen Harris</td>\n",
+              "      <td>male</td>\n",
+              "      <td>22.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>A/5 21171</td>\n",
+              "      <td>7.2500</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>S</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
+              "      <td>female</td>\n",
+              "      <td>38.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>PC 17599</td>\n",
+              "      <td>71.2833</td>\n",
+              "      <td>C85</td>\n",
+              "      <td>C</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>3</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3</td>\n",
+              "      <td>Heikkinen, Miss. Laina</td>\n",
+              "      <td>female</td>\n",
+              "      <td>26.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>STON/O2. 3101282</td>\n",
+              "      <td>7.9250</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>S</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7f67376c-f98e-4fc4-974c-77f009e9e56e')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-7f67376c-f98e-4fc4-974c-77f009e9e56e 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-7f67376c-f98e-4fc4-974c-77f009e9e56e');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "titanic_df",
+              "summary": "{\n  \"name\": \"titanic_df\",\n  \"rows\": 891,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 257,\n        \"min\": 1,\n        \"max\": 891,\n        \"num_unique_values\": 891,\n        \"samples\": [\n          710,\n          440,\n          841\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 891,\n        \"samples\": [\n          \"Moubarek, Master. Halim Gonios (\\\"William George\\\")\",\n          \"Kvillner, Mr. Johan Henrik Johannesson\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.526497332334044,\n        \"min\": 0.42,\n        \"max\": 80.0,\n        \"num_unique_values\": 88,\n        \"samples\": [\n          0.75,\n          22.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 6,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ticket\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 681,\n        \"samples\": [\n          \"11774\",\n          \"248740\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49.693428597180905,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 248,\n        \"samples\": [\n          11.2417,\n          51.8625\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 147,\n        \"samples\": [\n          \"D45\",\n          \"B49\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"S\",\n          \"C\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 33
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "titanic_df=pd.read_csv('titanic_train.csv')\n",
+        "indices=titanic_df[titanic_df['Age']>60].index\n",
+        "print('열향식 조건식 인덱스 객체의 전체 데이터:', indices)\n",
+        "print(titanic_df['Name'].head(3))\n",
+        "\n",
+        "titanic_reset_df=titanic_df.reset_index(inplace=False)\n",
+        "titanic_reset_df.head(3)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 446
+        },
+        "id": "EgGjx2ZF0JlL",
+        "outputId": "eee679b4-0a80-4ff4-c8a1-a55c842bfb37"
+      },
+      "execution_count": 37,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "열향식 조건식 인덱스 객체의 전체 데이터: Index([ 33,  54,  96, 116, 170, 252, 275, 280, 326, 438, 456, 483, 493, 545,\n",
+            "       555, 570, 625, 630, 672, 745, 829, 851],\n",
+            "      dtype='int64')\n",
+            "0                              Braund, Mr. Owen Harris\n",
+            "1    Cumings, Mrs. John Bradley (Florence Briggs Th...\n",
+            "2                               Heikkinen, Miss. Laina\n",
+            "Name: Name, dtype: object\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   index  PassengerId  Survived  Pclass  \\\n",
+              "0      0            1         0       3   \n",
+              "1      1            2         1       1   \n",
+              "2      2            3         1       3   \n",
+              "\n",
+              "                                                Name     Sex   Age  SibSp  \\\n",
+              "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
+              "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
+              "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
+              "\n",
+              "   Parch            Ticket     Fare Cabin Embarked  \n",
+              "0      0         A/5 21171   7.2500   NaN        S  \n",
+              "1      0          PC 17599  71.2833   C85        C  \n",
+              "2      0  STON/O2. 3101282   7.9250   NaN        S  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-c28f9681-3df5-480c-a893-2b12f85536c0\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>index</th>\n",
+              "      <th>PassengerId</th>\n",
+              "      <th>Survived</th>\n",
+              "      <th>Pclass</th>\n",
+              "      <th>Name</th>\n",
+              "      <th>Sex</th>\n",
+              "      <th>Age</th>\n",
+              "      <th>SibSp</th>\n",
+              "      <th>Parch</th>\n",
+              "      <th>Ticket</th>\n",
+              "      <th>Fare</th>\n",
+              "      <th>Cabin</th>\n",
+              "      <th>Embarked</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>Braund, Mr. Owen Harris</td>\n",
+              "      <td>male</td>\n",
+              "      <td>22.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>A/5 21171</td>\n",
+              "      <td>7.2500</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>S</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
+              "      <td>female</td>\n",
+              "      <td>38.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>PC 17599</td>\n",
+              "      <td>71.2833</td>\n",
+              "      <td>C85</td>\n",
+              "      <td>C</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>2</td>\n",
+              "      <td>3</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3</td>\n",
+              "      <td>Heikkinen, Miss. Laina</td>\n",
+              "      <td>female</td>\n",
+              "      <td>26.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>STON/O2. 3101282</td>\n",
+              "      <td>7.9250</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>S</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c28f9681-3df5-480c-a893-2b12f85536c0')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-c28f9681-3df5-480c-a893-2b12f85536c0 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-c28f9681-3df5-480c-a893-2b12f85536c0');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "titanic_reset_df",
+              "summary": "{\n  \"name\": \"titanic_reset_df\",\n  \"rows\": 891,\n  \"fields\": [\n    {\n      \"column\": \"index\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 257,\n        \"min\": 0,\n        \"max\": 890,\n        \"num_unique_values\": 891,\n        \"samples\": [\n          709,\n          439,\n          840\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 257,\n        \"min\": 1,\n        \"max\": 891,\n        \"num_unique_values\": 891,\n        \"samples\": [\n          710,\n          440,\n          841\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 891,\n        \"samples\": [\n          \"Moubarek, Master. Halim Gonios (\\\"William George\\\")\",\n          \"Kvillner, Mr. Johan Henrik Johannesson\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.526497332334044,\n        \"min\": 0.42,\n        \"max\": 80.0,\n        \"num_unique_values\": 88,\n        \"samples\": [\n          0.75,\n          22.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 6,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ticket\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 681,\n        \"samples\": [\n          \"11774\",\n          \"248740\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49.693428597180905,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 248,\n        \"samples\": [\n          11.2417,\n          51.8625\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 147,\n        \"samples\": [\n          \"D45\",\n          \"B49\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"S\",\n          \"C\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 37
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "titanic_sorted=titanic_df.sort_values(by=['Name'])\n",
+        "titanic_sorted.head(3)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 319
+        },
+        "id": "JbqmlkmF0qyo",
+        "outputId": "cb5d3726-418a-42d3-8dd6-aeda91e9c3c7"
+      },
+      "execution_count": 38,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "     PassengerId  Survived  Pclass                              Name     Sex  \\\n",
+              "845          846         0       3               Abbing, Mr. Anthony    male   \n",
+              "746          747         0       3       Abbott, Mr. Rossmore Edward    male   \n",
+              "279          280         1       3  Abbott, Mrs. Stanton (Rosa Hunt)  female   \n",
+              "\n",
+              "      Age  SibSp  Parch     Ticket   Fare Cabin Embarked  \n",
+              "845  42.0      0      0  C.A. 5547   7.55   NaN        S  \n",
+              "746  16.0      1      1  C.A. 2673  20.25   NaN        S  \n",
+              "279  35.0      1      1  C.A. 2673  20.25   NaN        S  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-11810f1c-3120-4de9-b149-196c750fe628\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
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+              "\n",
+              "    .dataframe thead th {\n",
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+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>PassengerId</th>\n",
+              "      <th>Survived</th>\n",
+              "      <th>Pclass</th>\n",
+              "      <th>Name</th>\n",
+              "      <th>Sex</th>\n",
+              "      <th>Age</th>\n",
+              "      <th>SibSp</th>\n",
+              "      <th>Parch</th>\n",
+              "      <th>Ticket</th>\n",
+              "      <th>Fare</th>\n",
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+              "      <th>Embarked</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>845</th>\n",
+              "      <td>846</td>\n",
+              "      <td>0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>Abbing, Mr. Anthony</td>\n",
+              "      <td>male</td>\n",
+              "      <td>42.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>C.A. 5547</td>\n",
+              "      <td>7.55</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>S</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>746</th>\n",
+              "      <td>747</td>\n",
+              "      <td>0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>Abbott, Mr. Rossmore Edward</td>\n",
+              "      <td>male</td>\n",
+              "      <td>16.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>C.A. 2673</td>\n",
+              "      <td>20.25</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>S</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>279</th>\n",
+              "      <td>280</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3</td>\n",
+              "      <td>Abbott, Mrs. Stanton (Rosa Hunt)</td>\n",
+              "      <td>female</td>\n",
+              "      <td>35.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>C.A. 2673</td>\n",
+              "      <td>20.25</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>S</td>\n",
+              "    </tr>\n",
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+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-11810f1c-3120-4de9-b149-196c750fe628')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-11810f1c-3120-4de9-b149-196c750fe628 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-11810f1c-3120-4de9-b149-196c750fe628');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "titanic_sorted",
+              "summary": "{\n  \"name\": \"titanic_sorted\",\n  \"rows\": 891,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 257,\n        \"min\": 1,\n        \"max\": 891,\n        \"num_unique_values\": 891,\n        \"samples\": [\n          518,\n          622,\n          15\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 891,\n        \"samples\": [\n          \"Ryan, Mr. Patrick\",\n          \"Kimball, Mr. Edwin Nelson Jr\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.526497332334044,\n        \"min\": 0.42,\n        \"max\": 80.0,\n        \"num_unique_values\": 88,\n        \"samples\": [\n          36.5,\n          42.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 6,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ticket\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 681,\n        \"samples\": [\n          \"SOTON/OQ 3101316\",\n          \"2685\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49.693428597180926,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 248,\n        \"samples\": [\n          7.8958,\n          9.35\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 147,\n        \"samples\": [\n          \"A6\",\n          \"C23 C25 C27\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"S\",\n          \"C\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 38
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "titanic_df.count()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 460
+        },
+        "id": "Fja2-mZR0KRL",
+        "outputId": "15093ce3-ab21-44c2-d466-a6c99974d408"
+      },
+      "execution_count": 39,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "PassengerId    891\n",
+              "Survived       891\n",
+              "Pclass         891\n",
+              "Name           891\n",
+              "Sex            891\n",
+              "Age            714\n",
+              "SibSp          891\n",
+              "Parch          891\n",
+              "Ticket         891\n",
+              "Fare           891\n",
+              "Cabin          204\n",
+              "Embarked       889\n",
+              "dtype: int64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>0</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>PassengerId</th>\n",
+              "      <td>891</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Survived</th>\n",
+              "      <td>891</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Pclass</th>\n",
+              "      <td>891</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Name</th>\n",
+              "      <td>891</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Sex</th>\n",
+              "      <td>891</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Age</th>\n",
+              "      <td>714</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>SibSp</th>\n",
+              "      <td>891</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Parch</th>\n",
+              "      <td>891</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Ticket</th>\n",
+              "      <td>891</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Fare</th>\n",
+              "      <td>891</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Cabin</th>\n",
+              "      <td>204</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Embarked</th>\n",
+              "      <td>889</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> int64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 39
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "titanic_df[['Age', 'Fare']].mean()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 147
+        },
+        "id": "gPFoygXS008C",
+        "outputId": "6f3ab176-f073-4af1-a44b-030af9043fc9"
+      },
+      "execution_count": 40,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Age     29.699118\n",
+              "Fare    32.204208\n",
+              "dtype: float64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>0</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>Age</th>\n",
+              "      <td>29.699118</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Fare</th>\n",
+              "      <td>32.204208</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> float64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 40
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "titanic_groupby=titanic_df.groupby(by='Pclass')\n",
+        "print(type(titanic_groupby))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "CGsR96_708cC",
+        "outputId": "937aa6a6-6bc2-4e87-d5d2-8223fdb07372"
+      },
+      "execution_count": 41,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "<class 'pandas.core.groupby.generic.DataFrameGroupBy'>\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "titanic_groupby=titanic_df.groupby('Pclass').count()\n",
+        "titanic_groupby"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 195
+        },
+        "id": "Cr-7Jgur1AQT",
+        "outputId": "be8273f3-c764-44af-8dc5-24beac4b91b9"
+      },
+      "execution_count": 42,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "        PassengerId  Survived  Name  Sex  Age  SibSp  Parch  Ticket  Fare  \\\n",
+              "Pclass                                                                      \n",
+              "1               216       216   216  216  186    216    216     216   216   \n",
+              "2               184       184   184  184  173    184    184     184   184   \n",
+              "3               491       491   491  491  355    491    491     491   491   \n",
+              "\n",
+              "        Cabin  Embarked  \n",
+              "Pclass                   \n",
+              "1         176       214  \n",
+              "2          16       184  \n",
+              "3          12       491  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-9b155cfb-b754-424e-97ef-62141eebc931\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>PassengerId</th>\n",
+              "      <th>Survived</th>\n",
+              "      <th>Name</th>\n",
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+              "      <th>Age</th>\n",
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+              "      <th>Ticket</th>\n",
+              "      <th>Fare</th>\n",
+              "      <th>Cabin</th>\n",
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+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Pclass</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
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+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
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+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>216</td>\n",
+              "      <td>216</td>\n",
+              "      <td>216</td>\n",
+              "      <td>216</td>\n",
+              "      <td>186</td>\n",
+              "      <td>216</td>\n",
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+              "      <td>216</td>\n",
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+              "      <td>214</td>\n",
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+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>184</td>\n",
+              "      <td>184</td>\n",
+              "      <td>184</td>\n",
+              "      <td>184</td>\n",
+              "      <td>173</td>\n",
+              "      <td>184</td>\n",
+              "      <td>184</td>\n",
+              "      <td>184</td>\n",
+              "      <td>184</td>\n",
+              "      <td>16</td>\n",
+              "      <td>184</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>491</td>\n",
+              "      <td>491</td>\n",
+              "      <td>491</td>\n",
+              "      <td>491</td>\n",
+              "      <td>355</td>\n",
+              "      <td>491</td>\n",
+              "      <td>491</td>\n",
+              "      <td>491</td>\n",
+              "      <td>491</td>\n",
+              "      <td>12</td>\n",
+              "      <td>491</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9b155cfb-b754-424e-97ef-62141eebc931')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-9b155cfb-b754-424e-97ef-62141eebc931 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-9b155cfb-b754-424e-97ef-62141eebc931');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "  <div id=\"id_6a856cc7-19e8-4b61-801c-904acf7a0fa5\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('titanic_groupby')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_6a856cc7-19e8-4b61-801c-904acf7a0fa5 button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('titanic_groupby');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "titanic_groupby",
+              "summary": "{\n  \"name\": \"titanic_groupby\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          2,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 168,\n        \"min\": 184,\n        \"max\": 491,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          216,\n          184,\n          491\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 168,\n        \"min\": 184,\n        \"max\": 491,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          216,\n          184,\n          491\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 168,\n        \"min\": 184,\n        \"max\": 491,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          216,\n          184,\n          491\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 168,\n        \"min\": 184,\n        \"max\": 491,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          216,\n          184,\n          491\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 101,\n        \"min\": 173,\n        \"max\": 355,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          186,\n          173,\n          355\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 168,\n        \"min\": 184,\n        \"max\": 491,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          216,\n          184,\n          491\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 168,\n        \"min\": 184,\n        \"max\": 491,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          216,\n          184,\n          491\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ticket\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 168,\n        \"min\": 184,\n        \"max\": 491,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          216,\n          184,\n          491\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 168,\n        \"min\": 184,\n        \"max\": 491,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          216,\n          184,\n          491\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 93,\n        \"min\": 12,\n        \"max\": 176,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          176,\n          16,\n          12\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 169,\n        \"min\": 184,\n        \"max\": 491,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          214,\n          184,\n          491\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 42
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "titanic_groupby=titanic_df.groupby('Pclass')[['PassengerId', 'Survived']].count()\n",
+        "titanic_groupby"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 175
+        },
+        "id": "7keFaiWA1D9_",
+        "outputId": "89837525-cb18-4970-8f65-fd840287b81f"
+      },
+      "execution_count": 43,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "        PassengerId  Survived\n",
+              "Pclass                       \n",
+              "1               216       216\n",
+              "2               184       184\n",
+              "3               491       491"
+            ],
+            "text/html": [
+              "\n",
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+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
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+              "      <th>Survived</th>\n",
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+              "    <tr>\n",
+              "      <th>Pclass</th>\n",
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+              "      <th></th>\n",
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+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>216</td>\n",
+              "      <td>216</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>184</td>\n",
+              "      <td>184</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>491</td>\n",
+              "      <td>491</td>\n",
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+              "            title=\"Convert this dataframe to an interactive table.\"\n",
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+              "\n",
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+              "  </svg>\n",
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+              "\n",
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+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
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+              "\n",
+              "    .colab-df-convert:hover {\n",
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+              "\n",
+              "    .colab-df-buttons div {\n",
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+              "      fill: #D2E3FC;\n",
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+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
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+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-618521f9-baeb-4f1f-8691-ea0eadd438f9 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-618521f9-baeb-4f1f-8691-ea0eadd438f9');\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",
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+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "  <div id=\"id_acee04e3-1df7-4b7f-b595-b4cbeee21b0d\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('titanic_groupby')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_acee04e3-1df7-4b7f-b595-b4cbeee21b0d button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('titanic_groupby');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "titanic_groupby",
+              "summary": "{\n  \"name\": \"titanic_groupby\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          2,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 168,\n        \"min\": 184,\n        \"max\": 491,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          216,\n          184,\n          491\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 168,\n        \"min\": 184,\n        \"max\": 491,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          216,\n          184,\n          491\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 43
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "titanic_df.groupby('Pclass')['Age'].agg([max,min])"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 267
+        },
+        "id": "nRPd5nwK1HKY",
+        "outputId": "9b4b141f-f31c-49e2-9b8a-adf5a6bdab9f"
+      },
+      "execution_count": 44,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/tmp/ipykernel_7573/414103430.py:1: FutureWarning: The provided callable <built-in function max> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string \"max\" instead.\n",
+            "  titanic_df.groupby('Pclass')['Age'].agg([max,min])\n",
+            "/tmp/ipykernel_7573/414103430.py:1: FutureWarning: The provided callable <built-in function min> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string \"min\" instead.\n",
+            "  titanic_df.groupby('Pclass')['Age'].agg([max,min])\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "         max   min\n",
+              "Pclass            \n",
+              "1       80.0  0.92\n",
+              "2       70.0  0.67\n",
+              "3       74.0  0.42"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-1ca509be-7dbb-4932-a470-9c1626ee3c75\" class=\"colab-df-container\">\n",
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+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
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+              "        vertical-align: top;\n",
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+              "\n",
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+              "        text-align: right;\n",
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+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>max</th>\n",
+              "      <th>min</th>\n",
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+              "    <tr>\n",
+              "      <th>Pclass</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
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+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>80.0</td>\n",
+              "      <td>0.92</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>70.0</td>\n",
+              "      <td>0.67</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>74.0</td>\n",
+              "      <td>0.42</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
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+              "\n",
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+              "      margin-bottom: 4px;\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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+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-1ca509be-7dbb-4932-a470-9c1626ee3c75 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-1ca509be-7dbb-4932-a470-9c1626ee3c75');\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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+              "\n",
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+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"titanic_df\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          2,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"max\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.033222956847166,\n        \"min\": 70.0,\n        \"max\": 80.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          80.0,\n          70.0,\n          74.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"min\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.25,\n        \"min\": 0.42,\n        \"max\": 0.92,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.92,\n          0.67,\n          0.42\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 44
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "agg_format={'Age':'max', 'SibSp':'sum', 'Fare':'mean'}\n",
+        "titanic_df.groupby('Pclass').agg(agg_format)"
+      ],
+      "metadata": {
+        "id": "R2__toP61UMD"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "titanic_df.isna().head(3)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 163
+        },
+        "id": "b3UuYorX1Xss",
+        "outputId": "58ec0a84-f9f3-4215-f5a1-1c4abc901308"
+      },
+      "execution_count": 45,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   PassengerId  Survived  Pclass   Name    Sex    Age  SibSp  Parch  Ticket  \\\n",
+              "0        False     False   False  False  False  False  False  False   False   \n",
+              "1        False     False   False  False  False  False  False  False   False   \n",
+              "2        False     False   False  False  False  False  False  False   False   \n",
+              "\n",
+              "    Fare  Cabin  Embarked  \n",
+              "0  False   True     False  \n",
+              "1  False  False     False  \n",
+              "2  False   True     False  "
+            ],
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+              "      <th>Cabin</th>\n",
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+              "      <th>0</th>\n",
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+              "      <td>True</td>\n",
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+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
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+              "      <td>False</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
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+              "  </svg>\n",
+              "    </button>\n",
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+              "  <style>\n",
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+              "\n",
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+              "\n",
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+              "        document.querySelector('#df-430b7892-b7c5-4e47-a512-782ca621c339 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-430b7892-b7c5-4e47-a512-782ca621c339');\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",
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+              "        element.appendChild(docLink);\n",
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+              "\n",
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+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"titanic_df\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ticket\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 45
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "titanic_df.isna().sum()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 460
+        },
+        "id": "2ABAipCF1cD-",
+        "outputId": "28170cde-35f5-4349-ea35-41b1a5c21fbf"
+      },
+      "execution_count": 46,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "PassengerId      0\n",
+              "Survived         0\n",
+              "Pclass           0\n",
+              "Name             0\n",
+              "Sex              0\n",
+              "Age            177\n",
+              "SibSp            0\n",
+              "Parch            0\n",
+              "Ticket           0\n",
+              "Fare             0\n",
+              "Cabin          687\n",
+              "Embarked         2\n",
+              "dtype: int64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
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+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
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+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
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+              "  <tbody>\n",
+              "    <tr>\n",
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+              "      <th>Age</th>\n",
+              "      <td>177</td>\n",
+              "    </tr>\n",
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+              "      <th>SibSp</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Parch</th>\n",
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+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Ticket</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Fare</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Cabin</th>\n",
+              "      <td>687</td>\n",
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+              "    <tr>\n",
+              "      <th>Embarked</th>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> int64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 46
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "titanic_df['Cabin']=titanic_df['Cabin'].fillna('C000')\n",
+        "titanic_df.head(3)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 319
+        },
+        "id": "bhZyOpTy1fsX",
+        "outputId": "ad8a109b-7147-4991-c0ba-bb887ae5bb8b"
+      },
+      "execution_count": 47,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   PassengerId  Survived  Pclass  \\\n",
+              "0            1         0       3   \n",
+              "1            2         1       1   \n",
+              "2            3         1       3   \n",
+              "\n",
+              "                                                Name     Sex   Age  SibSp  \\\n",
+              "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
+              "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
+              "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
+              "\n",
+              "   Parch            Ticket     Fare Cabin Embarked  \n",
+              "0      0         A/5 21171   7.2500  C000        S  \n",
+              "1      0          PC 17599  71.2833   C85        C  \n",
+              "2      0  STON/O2. 3101282   7.9250  C000        S  "
+            ],
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+              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
+              "      <td>female</td>\n",
+              "      <td>38.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>PC 17599</td>\n",
+              "      <td>71.2833</td>\n",
+              "      <td>C85</td>\n",
+              "      <td>C</td>\n",
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+              "      <th>2</th>\n",
+              "      <td>3</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3</td>\n",
+              "      <td>Heikkinen, Miss. Laina</td>\n",
+              "      <td>female</td>\n",
+              "      <td>26.0</td>\n",
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+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-637f0e47-2da0-4148-9415-d402f6f28f78 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-637f0e47-2da0-4148-9415-d402f6f28f78');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "titanic_df",
+              "summary": "{\n  \"name\": \"titanic_df\",\n  \"rows\": 891,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 257,\n        \"min\": 1,\n        \"max\": 891,\n        \"num_unique_values\": 891,\n        \"samples\": [\n          710,\n          440,\n          841\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 891,\n        \"samples\": [\n          \"Moubarek, Master. Halim Gonios (\\\"William George\\\")\",\n          \"Kvillner, Mr. Johan Henrik Johannesson\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.526497332334044,\n        \"min\": 0.42,\n        \"max\": 80.0,\n        \"num_unique_values\": 88,\n        \"samples\": [\n          0.75,\n          22.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 6,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ticket\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 681,\n        \"samples\": [\n          \"11774\",\n          \"248740\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49.693428597180905,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 248,\n        \"samples\": [\n          11.2417,\n          51.8625\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 148,\n        \"samples\": [\n          \"B101\",\n          \"A19\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"S\",\n          \"C\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 47
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "titanic_df['Age']=titanic_df['Age'].fillna(titanic_df['Age'].mean())\n",
+        "titanic_df['Embarked']=titanic_df['Embarked'].fillna('S')\n",
+        "titanic_df.isna().sum()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 460
+        },
+        "id": "XctYM-6i1rfB",
+        "outputId": "aff9b12d-0edf-40cb-abdb-14a7a6949cd1"
+      },
+      "execution_count": 48,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "PassengerId    0\n",
+              "Survived       0\n",
+              "Pclass         0\n",
+              "Name           0\n",
+              "Sex            0\n",
+              "Age            0\n",
+              "SibSp          0\n",
+              "Parch          0\n",
+              "Ticket         0\n",
+              "Fare           0\n",
+              "Cabin          0\n",
+              "Embarked       0\n",
+              "dtype: int64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>0</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>PassengerId</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Survived</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Pclass</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Name</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Sex</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Age</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>SibSp</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Parch</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Ticket</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Fare</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Cabin</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Embarked</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> int64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 48
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def get_square(a):\n",
+        "  return a**2\n",
+        "\n",
+        "print('3의 제곱은:', get_square(3))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "dtwMqtXv3jpz",
+        "outputId": "8933217c-485e-44e1-ff6f-5964438ad550"
+      },
+      "execution_count": 50,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "3의 제곱은: 9\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "lambda_square=lambda x : x**2\n",
+        "print('3의 제곱은:', lambda_square(3))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "LvG9x3gK3icd",
+        "outputId": "5a6eb431-2318-48d6-ac5e-37edcfe48efe"
+      },
+      "execution_count": 51,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "3의 제곱은: 9\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "a=[1,2,3]\n",
+        "squares=map(lambda x: x**2, a)\n",
+        "list(squares)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Kh5h_RVX4CkV",
+        "outputId": "80e444a7-96ae-41fa-c2ad-57ecf0ea4795"
+      },
+      "execution_count": 52,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "[1, 4, 9]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 52
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "titanic_df['Name_len']=titanic_df['Name'].apply(lambda x:len(x))\n",
+        "titanic_df[['Name', 'Name_len']].head(3)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 195
+        },
+        "id": "Nl0bwfei4JZd",
+        "outputId": "1e740907-9c2d-4966-c3fc-684e0977cb06"
+      },
+      "execution_count": 54,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                                                Name  Name_len\n",
+              "0                            Braund, Mr. Owen Harris        23\n",
+              "1  Cumings, Mrs. John Bradley (Florence Briggs Th...        51\n",
+              "2                             Heikkinen, Miss. Laina        22"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-5ee4ad16-fb20-4b13-b7ed-fcb22af94408\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Name</th>\n",
+              "      <th>Name_len</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Braund, Mr. Owen Harris</td>\n",
+              "      <td>23</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
+              "      <td>51</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Heikkinen, Miss. Laina</td>\n",
+              "      <td>22</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5ee4ad16-fb20-4b13-b7ed-fcb22af94408')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-5ee4ad16-fb20-4b13-b7ed-fcb22af94408 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-5ee4ad16-fb20-4b13-b7ed-fcb22af94408');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"titanic_df[['Name', 'Name_len']]\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Braund, Mr. Owen Harris\",\n          \"Cumings, Mrs. John Bradley (Florence Briggs Thayer)\",\n          \"Heikkinen, Miss. Laina\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name_len\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16,\n        \"min\": 22,\n        \"max\": 51,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          23,\n          51,\n          22\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 54
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
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