chapter_preliminaries/1_data_manipulation_and_processing.ipynb

{
  "cells": [
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "<a target=\"_blank\" href=\"https://colab.research.google.com/github/ashkan-mokarian/d2l-pytorch/blob/main/chapter_preliminaries/1_data_manipulation_and_processing.ipynb\">\n",
        "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
        "</a>"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "UkmAb445HNac"
      },
      "source": [
        "# Data\n",
        "\n",
        "PyTorch/Tensorflow _Tensor_ resemble _ndarray_ class in numpy, but they also support **automatic differentiation** and support **GPU** computations."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "bA4UJ6TEMgU3"
      },
      "outputs": [],
      "source": [
        "import torch"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "mdvAjCmyI5dy"
      },
      "source": [
        "## Data manipulation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jXvQAW9pH_k-",
        "outputId": "fba0026c-61c1-44d1-be76-edfa92f29f18"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "torch.Size([3, 4])"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "x = torch.arange(12, dtype=torch.float32)  # pytorch by default allocates on cpu.\n",
        "x.numel()\n",
        "X = x.reshape((3, 4))  # or implicitely x.reshape((3, -1))\n",
        "X.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "qcyuXHgIOzkK",
        "outputId": "9e4e255c-7bb2-476f-910d-20a64054efed"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "tensor([[1., 1., 1., 1.],\n",
            "        [1., 1., 1., 1.],\n",
            "        [1., 1., 1., 1.]])\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "tensor([[-1.1544,  1.1118, -0.9316, -0.6459],\n",
              "        [ 2.2682,  0.9379,  0.6351, -0.1691],\n",
              "        [-2.2795,  1.4738,  1.1451, -0.3036]])"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "torch.Size([2, 3])"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X_zeros = torch.zeros((3, 4))\n",
        "X_ones = torch.ones((3, 4))\n",
        "X_randn = torch.randn((3, 4))\n",
        "X_manual = torch.tensor([[1, 2, 3], [4, 5, 6]])\n",
        "print(X_ones)\n",
        "display(X_randn)\n",
        "X_manual.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jje31qUiPCJW",
        "outputId": "16d58333-570e-48b5-afc5-d067dded9091"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(tensor([[ 0.,  1.,  2.,  3.],\n",
              "         [ 4.,  5.,  6.,  7.],\n",
              "         [ 8.,  9., 10., 11.]]),\n",
              " tensor([ 8.,  9., 10., 11.]),\n",
              " tensor([[ 4.,  5.,  6.,  7.],\n",
              "         [ 8.,  9., 10., 11.]]))"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# slicing and indexing\n",
        "X, X[-1], X[1:3]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8TL0ke82PkyV",
        "outputId": "8bffa6b6-d2b4-4e98-8026-2eff6e9fe03d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "tensor([[ 0.,  1.,  2.,  3.],\n",
              "        [ 4.,  5., 12.,  7.],\n",
              "        [ 8.,  9., 10., 11.]])"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X[1, 2] = 12\n",
        "X"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "22RDkQIpPwgh",
        "outputId": "b5a1515c-678b-4bd4-aa52-5d6fdd5ae15c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "tensor([1.0000e+00, 2.7183e+00, 7.3891e+00, 2.0086e+01, 5.4598e+01, 1.4841e+02,\n",
              "        1.6275e+05, 1.0966e+03, 2.9810e+03, 8.1031e+03, 2.2026e+04, 5.9874e+04])"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# unary function\n",
        "torch.exp(x)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3_tdp9ZWQQ2L",
        "outputId": "8fa439eb-64b6-46b6-c9a9-517afb2491f5"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(tensor([ 3.,  4.,  6., 10.]),\n",
              " tensor([-1.,  0.,  2.,  6.]),\n",
              " tensor([ 2.,  4.,  8., 16.]),\n",
              " tensor([0.5000, 1.0000, 2.0000, 4.0000]),\n",
              " tensor([ 1.,  4., 16., 64.]))"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# binary scalar operators\n",
        "x = torch.tensor([1.0, 2, 4, 8])\n",
        "y = torch.tensor([2, 2, 2, 2])\n",
        "x + y, x - y, x * y, x / y, x ** y"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3ymLBRs8Qf2B",
        "outputId": "374f9943-0293-4b1e-9d75-5a96956db0bc"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(tensor([[ 0.,  1.,  2.,  3.],\n",
              "         [ 4.,  5.,  6.,  7.],\n",
              "         [ 8.,  9., 10., 11.],\n",
              "         [ 2.,  1.,  4.,  3.],\n",
              "         [ 1.,  2.,  3.,  4.],\n",
              "         [ 4.,  3.,  2.,  1.]]),\n",
              " tensor([[ 0.,  1.,  2.,  3.,  2.,  1.,  4.,  3.],\n",
              "         [ 4.,  5.,  6.,  7.,  1.,  2.,  3.,  4.],\n",
              "         [ 8.,  9., 10., 11.,  4.,  3.,  2.,  1.]]))"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X = torch.arange(12, dtype=torch.float32).reshape(3, 4)\n",
        "Y = torch.tensor([[2.0, 1, 4, 3], [1 ,2, 3, 4], [4, 3, 2, 1]])\n",
        "torch.cat([X, Y], dim=0), torch.cat([X, Y], dim=1)  # concatenation along axis=0(rows) and axis=1(cols)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CrqUrjcfRc0i",
        "outputId": "c4039221-6711-4617-9e9f-f2c25500bafb"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "tensor([[False,  True, False,  True],\n",
              "        [False, False, False, False],\n",
              "        [False, False, False, False]])"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# element-wise logical operator\n",
        "X == Y"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AxS5Ly0_RoL0",
        "outputId": "0fb244cf-7d80-491c-e04f-a0d9ba8305ec"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "tensor(66.)"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X.sum()"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "TEGxcGmSRpvA"
      },
      "source": [
        "### Broadcasting\n",
        "We saw how to do elementwise operations on two tensors. If the shapes don't match, we can still do broadcasting. Concatenate the same tensors along one of the dimensions for both, so we get a matching shape, and then do the elementwise operation."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O29idBEFSY2v",
        "outputId": "a64551f3-eda6-467c-d968-8efbb6d5fd64"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(tensor([[0],\n",
              "         [1],\n",
              "         [2]]),\n",
              " tensor([[0, 1]]))"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "a = torch.arange(3).reshape(3, 1)\n",
        "b = torch.arange(2).reshape(1, 2)\n",
        "a, b"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_6RXHMncSgZ3",
        "outputId": "349d391d-16e5-40a3-c08e-579554e1e8ab"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(tensor([[0, 1],\n",
              "         [1, 2],\n",
              "         [2, 3]]),\n",
              " tensor([[0, 1],\n",
              "         [1, 2],\n",
              "         [2, 3]]))"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "a + b, b + a"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "B6-G4xQTSjGf"
      },
      "source": [
        "### Saving memory\n",
        "in the expression Y = Y + X, here the rhs Y memory is allocated and used to evaluate rhs, then Y is dereferenced, a new memory is allocated for lhs Y."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l1llqEhkTGtO",
        "outputId": "34e31a70-1d2d-4f8c-ad62-5082a1e4d9f9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "140536925117376\n",
            "140536926214384\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "print(id(Y))\n",
        "before = id(Y)\n",
        "Y = Y + X\n",
        "print(id(Y))\n",
        "id(Y) == before"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "rs_XsFnXTUj5"
      },
      "source": [
        "In ML, this is not desirable and has memory executation overhead. Instead we can do assignment **in-place** using the notation ```[:] = rhs``` or ```+=```."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ODtT8zJVUCLa",
        "outputId": "91e71ee0-5ac5-43ff-8dc0-0bb9eff42b09"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "id(Z): 140536776162368\n",
            "id(X) 140536776162368\n"
          ]
        }
      ],
      "source": [
        "Z = torch.zeros_like(Y)\n",
        "print('id(Z):', id(Z))\n",
        "Z[:] = X + Y\n",
        "print('id(Z):', id(Z))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iHQKbWuGUVhg",
        "outputId": "7a1ac2b3-9363-400d-d64c-5b723aa116b8"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "before = id(X)\n",
        "X += Y\n",
        "id(X) == before"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "m9McbmqHVLyA"
      },
      "source": [
        "### Converting tensors to other python objects\n",
        "They share the underlying memory. when one is changed in-place (otherwise memory is derefered and we might even get memory leak), the other changes too."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0WkKg7ZEUcWg",
        "outputId": "b7ca8786-cacf-46e4-c6cb-06575d65a3de"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "False\n",
            "False\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "(numpy.ndarray, torch.Tensor)"
            ]
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "A = X.numpy()\n",
        "B = torch.from_numpy(A)\n",
        "print(id(A) == id(B))  # ??? memory is not the same\n",
        "print(id(A) == id(X))  # ??? here also not the same\n",
        "(type(A), type(B))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y55JY_emVIwX",
        "outputId": "3c347327-bbf1-4031-e775-ecbd9d4b5f59"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(tensor(3.5000), 3.5, 3.5, 3)"
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# We can convert a single tensor in many ways to a python primitive type\n",
        "a = torch.tensor(3.5)\n",
        "(a, a.item(), float(a), int(a))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9cSK_OtrXtXS",
        "outputId": "448179bd-7fb4-4fd6-8ddf-5030904508c6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(True, torch.Tensor)"
            ]
          },
          "execution_count": 49,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import numpy as np\n",
        "x = torch.tensor(2)\n",
        "y = 3\n",
        "before = id(x)\n",
        "x += y\n",
        "(id(x) == before, type(x))"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "BQxVvzB1bKr7"
      },
      "source": [
        "## Data Preprocessing"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "tN1GXiD1bOTT"
      },
      "source": [
        "CSV = comma seperated value\n",
        "\n",
        "and pandas good in working with them.\n",
        "\n",
        "iloc : integer-location based indexing\n",
        "\n",
        "pandas replaces missing values, e.g. `NA` or `1,2,,,4` with `NaN` "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "id": "tKQ5mK40bppr"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "\n",
        "os.makedirs(os.path.join('.', 'data'), exist_ok=True)\n",
        "data_file = os.path.join('.', 'data', 'house_tiny.csv')\n",
        "with open(data_file, 'w') as f:\n",
        "    f.write('''NumRooms,RoofType,Price\n",
        "NA,NA,127500\n",
        "2,NA,106000\n",
        "4,Slate,178100\n",
        "NA,NA,140000''')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SCK_aLPxccbV",
        "outputId": "0e0b4f06-1dc3-4af5-c2a5-9bf202eca81b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "   NumRooms RoofType   Price\n",
            "0       NaN      NaN  127500\n",
            "1       2.0      NaN  106000\n",
            "2       4.0    Slate  178100\n",
            "3       NaN      NaN  140000\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "data = pd.read_csv(data_file)\n",
        "print(data)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "0pvuwgSZc5tz"
      },
      "source": [
        "### Data Cleaning\n",
        "Missing values are a problem. Two workarounds:\n",
        "- deletion: which is just deleting either entry or the attribute\n",
        "- imputation: for categorical attributes, NaN could itself be considered as a category. For numerical, we can replace by mean for that attribute."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ewkeXjEHeuxe",
        "outputId": "ade9f777-f2e6-410b-d9db-3721252edf5f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "   NumRooms  RoofType_Slate  RoofType_nan\n",
            "0       NaN               0             1\n",
            "1       2.0               0             1\n",
            "2       4.0               1             0\n",
            "3       NaN               0             1\n"
          ]
        }
      ],
      "source": [
        "inputs, targets = data.iloc[:, 0:2], data.iloc[:, 2]\n",
        "inputs = pd.get_dummies(inputs, dummy_na=True)\n",
        "print(inputs)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1hj30ef_fAGy",
        "outputId": "0d7b7239-4a71-49b7-bd4a-dfb0a8cfe610"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "   NumRooms  RoofType_Slate  RoofType_nan\n",
            "0       3.0               0             1\n",
            "1       2.0               0             1\n",
            "2       4.0               1             0\n",
            "3       3.0               0             1\n"
          ]
        }
      ],
      "source": [
        "inputs = inputs.fillna(inputs.mean())\n",
        "print(inputs)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5nlnsFwifKeb",
        "outputId": "f838d965-e07a-4146-9f63-a562ce8c55d1"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(tensor([[3., 0., 1.],\n",
              "         [2., 0., 1.],\n",
              "         [4., 1., 0.],\n",
              "         [3., 0., 1.]], dtype=torch.float64),\n",
              " tensor([127500, 106000, 178100, 140000]))"
            ]
          },
          "execution_count": 62,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# transforming to tensors from pandas\n",
        "X, y = torch.tensor(inputs.values), torch.tensor(targets.values)  # Don't forget the .values\n",
        "X, y"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "y7cScnorgiWv"
      },
      "source": [
        "# Exercise\n",
        "Trying Abalone data from here: https://archive.ics.uci.edu/ml/datasets.php"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 71,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B6XLzIaogtQg",
        "outputId": "8259728d-afcd-47c3-a952-b17f1c9fb014"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "('./data/abalone.names', <http.client.HTTPMessage at 0x7fd144829cc0>)"
            ]
          },
          "execution_count": 71,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import urllib.request\n",
        "\n",
        "DATASET_URL = \"https://archive.ics.uci.edu/ml/machine-learning-databases/abalone/abalone.data\"\n",
        "DATASET_NAMES_URL = \"https://archive.ics.uci.edu/ml/machine-learning-databases/abalone/abalone.names\"\n",
        "abalone_dataset = os.path.join('.', 'data', 'abalone.data')\n",
        "abalone_names = os.path.join('.', 'data', 'abalone.names')\n",
        "\n",
        "urllib.request.urlretrieve(DATASET_URL, abalone_dataset)\n",
        "urllib.request.urlretrieve(DATASET_NAMES_URL, abalone_names)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 72,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "T0eTONIxhs-l",
        "outputId": "a7851c89-f649-470b-ddb4-72d3e30aa338"
      },
      "outputs": [
        {
          "data": {
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Sex</th>\n",
              "      <th>Length</th>\n",
              "      <th>Diameter</th>\n",
              "      <th>Height</th>\n",
              "      <th>Whole weight</th>\n",
              "      <th>Shucked weight</th>\n",
              "      <th>Viscera weight</th>\n",
              "      <th>Shell weight</th>\n",
              "      <th>Rings</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>M</td>\n",
              "      <td>0.455</td>\n",
              "      <td>0.365</td>\n",
              "      <td>0.095</td>\n",
              "      <td>0.5140</td>\n",
              "      <td>0.2245</td>\n",
              "      <td>0.1010</td>\n",
              "      <td>0.150</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>M</td>\n",
              "      <td>0.350</td>\n",
              "      <td>0.265</td>\n",
              "      <td>0.090</td>\n",
              "      <td>0.2255</td>\n",
              "      <td>0.0995</td>\n",
              "      <td>0.0485</td>\n",
              "      <td>0.070</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>F</td>\n",
              "      <td>0.530</td>\n",
              "      <td>0.420</td>\n",
              "      <td>0.135</td>\n",
              "      <td>0.6770</td>\n",
              "      <td>0.2565</td>\n",
              "      <td>0.1415</td>\n",
              "      <td>0.210</td>\n",
              "      <td>9</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>M</td>\n",
              "      <td>0.440</td>\n",
              "      <td>0.365</td>\n",
              "      <td>0.125</td>\n",
              "      <td>0.5160</td>\n",
              "      <td>0.2155</td>\n",
              "      <td>0.1140</td>\n",
              "      <td>0.155</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>I</td>\n",
              "      <td>0.330</td>\n",
              "      <td>0.255</td>\n",
              "      <td>0.080</td>\n",
              "      <td>0.2050</td>\n",
              "      <td>0.0895</td>\n",
              "      <td>0.0395</td>\n",
              "      <td>0.055</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>I</td>\n",
              "      <td>0.425</td>\n",
              "      <td>0.300</td>\n",
              "      <td>0.095</td>\n",
              "      <td>0.3515</td>\n",
              "      <td>0.1410</td>\n",
              "      <td>0.0775</td>\n",
              "      <td>0.120</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>F</td>\n",
              "      <td>0.530</td>\n",
              "      <td>0.415</td>\n",
              "      <td>0.150</td>\n",
              "      <td>0.7775</td>\n",
              "      <td>0.2370</td>\n",
              "      <td>0.1415</td>\n",
              "      <td>0.330</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>F</td>\n",
              "      <td>0.545</td>\n",
              "      <td>0.425</td>\n",
              "      <td>0.125</td>\n",
              "      <td>0.7680</td>\n",
              "      <td>0.2940</td>\n",
              "      <td>0.1495</td>\n",
              "      <td>0.260</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>M</td>\n",
              "      <td>0.475</td>\n",
              "      <td>0.370</td>\n",
              "      <td>0.125</td>\n",
              "      <td>0.5095</td>\n",
              "      <td>0.2165</td>\n",
              "      <td>0.1125</td>\n",
              "      <td>0.165</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>F</td>\n",
              "      <td>0.550</td>\n",
              "      <td>0.440</td>\n",
              "      <td>0.150</td>\n",
              "      <td>0.8945</td>\n",
              "      <td>0.3145</td>\n",
              "      <td>0.1510</td>\n",
              "      <td>0.320</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c0039948-3699-478f-b021-0de4ea1d2248')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "          document.querySelector('#df-c0039948-3699-478f-b021-0de4ea1d2248 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-c0039948-3699-478f-b021-0de4ea1d2248');\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",
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              "  </div>\n",
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            ],
            "text/plain": [
              "  Sex  Length  Diameter  Height  Whole weight  Shucked weight  Viscera weight  \\\n",
              "0   M   0.455     0.365   0.095        0.5140          0.2245          0.1010   \n",
              "1   M   0.350     0.265   0.090        0.2255          0.0995          0.0485   \n",
              "2   F   0.530     0.420   0.135        0.6770          0.2565          0.1415   \n",
              "3   M   0.440     0.365   0.125        0.5160          0.2155          0.1140   \n",
              "4   I   0.330     0.255   0.080        0.2050          0.0895          0.0395   \n",
              "5   I   0.425     0.300   0.095        0.3515          0.1410          0.0775   \n",
              "6   F   0.530     0.415   0.150        0.7775          0.2370          0.1415   \n",
              "7   F   0.545     0.425   0.125        0.7680          0.2940          0.1495   \n",
              "8   M   0.475     0.370   0.125        0.5095          0.2165          0.1125   \n",
              "9   F   0.550     0.440   0.150        0.8945          0.3145          0.1510   \n",
              "\n",
              "   Shell weight  Rings  \n",
              "0         0.150     15  \n",
              "1         0.070      7  \n",
              "2         0.210      9  \n",
              "3         0.155     10  \n",
              "4         0.055      7  \n",
              "5         0.120      8  \n",
              "6         0.330     20  \n",
              "7         0.260     16  \n",
              "8         0.165      9  \n",
              "9         0.320     19  "
            ]
          },
          "execution_count": 72,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "abalone_column_names = ['Sex', 'Length', 'Diameter', 'Height', 'Whole weight',\n",
        "                        'Shucked weight', 'Viscera weight', 'Shell weight', 'Rings']\n",
        "data = pd.read_csv(abalone_dataset, names=abalone_column_names)\n",
        "data.head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 73,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YZyj7yARib4x",
        "outputId": "3d7a6431-9606-44ee-9668-b9ca6da490d2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 4177 entries, 0 to 4176\n",
            "Data columns (total 9 columns):\n",
            " #   Column          Non-Null Count  Dtype  \n",
            "---  ------          --------------  -----  \n",
            " 0   Sex             4177 non-null   object \n",
            " 1   Length          4177 non-null   float64\n",
            " 2   Diameter        4177 non-null   float64\n",
            " 3   Height          4177 non-null   float64\n",
            " 4   Whole weight    4177 non-null   float64\n",
            " 5   Shucked weight  4177 non-null   float64\n",
            " 6   Viscera weight  4177 non-null   float64\n",
            " 7   Shell weight    4177 non-null   float64\n",
            " 8   Rings           4177 non-null   int64  \n",
            "dtypes: float64(7), int64(1), object(1)\n",
            "memory usage: 293.8+ KB\n"
          ]
        }
      ],
      "source": [
        "data.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 74,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G0KUnTIMin2c",
        "outputId": "d78d4493-3385-4eac-96d4-329fc08db543"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "M    1528\n",
              "I    1342\n",
              "F    1307\n",
              "Name: Sex, dtype: int64"
            ]
          },
          "execution_count": 74,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data['Sex'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 75,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "DyUYclzHk0To",
        "outputId": "cd9fa403-d5fe-4ffd-808e-89f7d0bed633"
      },
      "outputs": [
        {
          "data": {
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              "\n",
              "  <div id=\"df-7f2bc0d2-9ad1-44e7-9e0d-6fb2905a2a57\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .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",
              "      <th>Length</th>\n",
              "      <th>Diameter</th>\n",
              "      <th>Height</th>\n",
              "      <th>Whole weight</th>\n",
              "      <th>Shucked weight</th>\n",
              "      <th>Viscera weight</th>\n",
              "      <th>Shell weight</th>\n",
              "      <th>Rings</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>4177.000000</td>\n",
              "      <td>4177.000000</td>\n",
              "      <td>4177.000000</td>\n",
              "      <td>4177.000000</td>\n",
              "      <td>4177.000000</td>\n",
              "      <td>4177.000000</td>\n",
              "      <td>4177.000000</td>\n",
              "      <td>4177.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.523992</td>\n",
              "      <td>0.407881</td>\n",
              "      <td>0.139516</td>\n",
              "      <td>0.828742</td>\n",
              "      <td>0.359367</td>\n",
              "      <td>0.180594</td>\n",
              "      <td>0.238831</td>\n",
              "      <td>9.933684</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.120093</td>\n",
              "      <td>0.099240</td>\n",
              "      <td>0.041827</td>\n",
              "      <td>0.490389</td>\n",
              "      <td>0.221963</td>\n",
              "      <td>0.109614</td>\n",
              "      <td>0.139203</td>\n",
              "      <td>3.224169</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.075000</td>\n",
              "      <td>0.055000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.002000</td>\n",
              "      <td>0.001000</td>\n",
              "      <td>0.000500</td>\n",
              "      <td>0.001500</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.450000</td>\n",
              "      <td>0.350000</td>\n",
              "      <td>0.115000</td>\n",
              "      <td>0.441500</td>\n",
              "      <td>0.186000</td>\n",
              "      <td>0.093500</td>\n",
              "      <td>0.130000</td>\n",
              "      <td>8.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.545000</td>\n",
              "      <td>0.425000</td>\n",
              "      <td>0.140000</td>\n",
              "      <td>0.799500</td>\n",
              "      <td>0.336000</td>\n",
              "      <td>0.171000</td>\n",
              "      <td>0.234000</td>\n",
              "      <td>9.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.615000</td>\n",
              "      <td>0.480000</td>\n",
              "      <td>0.165000</td>\n",
              "      <td>1.153000</td>\n",
              "      <td>0.502000</td>\n",
              "      <td>0.253000</td>\n",
              "      <td>0.329000</td>\n",
              "      <td>11.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>0.815000</td>\n",
              "      <td>0.650000</td>\n",
              "      <td>1.130000</td>\n",
              "      <td>2.825500</td>\n",
              "      <td>1.488000</td>\n",
              "      <td>0.760000</td>\n",
              "      <td>1.005000</td>\n",
              "      <td>29.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7f2bc0d2-9ad1-44e7-9e0d-6fb2905a2a57')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-7f2bc0d2-9ad1-44e7-9e0d-6fb2905a2a57 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-7f2bc0d2-9ad1-44e7-9e0d-6fb2905a2a57');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "            Length     Diameter       Height  Whole weight  Shucked weight  \\\n",
              "count  4177.000000  4177.000000  4177.000000   4177.000000     4177.000000   \n",
              "mean      0.523992     0.407881     0.139516      0.828742        0.359367   \n",
              "std       0.120093     0.099240     0.041827      0.490389        0.221963   \n",
              "min       0.075000     0.055000     0.000000      0.002000        0.001000   \n",
              "25%       0.450000     0.350000     0.115000      0.441500        0.186000   \n",
              "50%       0.545000     0.425000     0.140000      0.799500        0.336000   \n",
              "75%       0.615000     0.480000     0.165000      1.153000        0.502000   \n",
              "max       0.815000     0.650000     1.130000      2.825500        1.488000   \n",
              "\n",
              "       Viscera weight  Shell weight        Rings  \n",
              "count     4177.000000   4177.000000  4177.000000  \n",
              "mean         0.180594      0.238831     9.933684  \n",
              "std          0.109614      0.139203     3.224169  \n",
              "min          0.000500      0.001500     1.000000  \n",
              "25%          0.093500      0.130000     8.000000  \n",
              "50%          0.171000      0.234000     9.000000  \n",
              "75%          0.253000      0.329000    11.000000  \n",
              "max          0.760000      1.005000    29.000000  "
            ]
          },
          "execution_count": 75,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 79,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 611
        },
        "id": "_b1W3pQwk7yO",
        "outputId": "d4a4b0b3-ff5c-4a1f-c434-9d06e61c8ba0"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[<Axes: title={'center': 'Length'}>,\n",
              "        <Axes: title={'center': 'Diameter'}>,\n",
              "        <Axes: title={'center': 'Height'}>],\n",
              "       [<Axes: title={'center': 'Whole weight'}>,\n",
              "        <Axes: title={'center': 'Shucked weight'}>,\n",
              "        <Axes: title={'center': 'Viscera weight'}>],\n",
              "       [<Axes: title={'center': 'Shell weight'}>,\n",
              "        <Axes: title={'center': 'Rings'}>, <Axes: >]], dtype=object)"
            ]
          },
          "execution_count": 79,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
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            "text/plain": [
              "<Figure size 1500x500 with 9 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "data.hist(bins=30, figsize=(15, 5))"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
    "orig_nbformat": 4
  },
  "nbformat": 4,
  "nbformat_minor": 0
}