practice.ipynb

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      "metadata": {
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xg9czitWE85Y"
      },
      "source": [
        "# Python Data practice"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "i6HqgvA6FEp4"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
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        "id": "9k_z8bRmFTvQ",
        "outputId": "e6b7bae5-b03e-4851-f024-72822487848c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Country   Age   Salary Purchased\n",
              "0   France  44.0  72000.0        No\n",
              "1    Spain  27.0  48000.0       Yes\n",
              "2  Germany  30.0  54000.0        No\n",
              "3    Spain  38.0  61000.0        No\n",
              "4  Germany  40.0      NaN       Yes"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Country</th>\n",
              "      <th>Age</th>\n",
              "      <th>Salary</th>\n",
              "      <th>Purchased</th>\n",
              "    </tr>\n",
              "  </thead>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>France</td>\n",
              "      <td>44.0</td>\n",
              "      <td>72000.0</td>\n",
              "      <td>No</td>\n",
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              "      <th>1</th>\n",
              "      <td>Spain</td>\n",
              "      <td>27.0</td>\n",
              "      <td>48000.0</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Germany</td>\n",
              "      <td>30.0</td>\n",
              "      <td>54000.0</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Spain</td>\n",
              "      <td>38.0</td>\n",
              "      <td>61000.0</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Germany</td>\n",
              "      <td>40.0</td>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"Country\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"France\",\n          \"Spain\",\n          \"Germany\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.693792591722527,\n        \"min\": 27.0,\n        \"max\": 50.0,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          50.0,\n          27.0,\n          35.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Salary\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12265.579661982732,\n        \"min\": 48000.0,\n        \"max\": 83000.0,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          83000.0,\n          48000.0,\n          52000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Purchased\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Yes\",\n          \"No\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "df = pd.read_csv('Data.csv')\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "r5v_kGw6XXEg"
      },
      "outputs": [],
      "source": [
        "x = df.iloc[:, :-1].values\n",
        "y = df.iloc[:, -1].values\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XhSpBN94Xkur",
        "outputId": "96fdb237-23c7-412f-f198-6752e46673e1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[['France' 44.0 72000.0]\n",
            " ['Spain' 27.0 48000.0]\n",
            " ['Germany' 30.0 54000.0]\n",
            " ['Spain' 38.0 61000.0]\n",
            " ['Germany' 40.0 nan]\n",
            " ['France' 35.0 58000.0]\n",
            " ['Spain' nan 52000.0]\n",
            " ['France' 48.0 79000.0]\n",
            " ['Germany' 50.0 83000.0]\n",
            " ['France' 37.0 67000.0]]\n",
            "['No' 'Yes' 'No' 'No' 'Yes' 'Yes' 'No' 'Yes' 'No' 'Yes']\n"
          ]
        }
      ],
      "source": [
        "print(x)\n",
        "print(y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "pTZ7q-2QXoR7"
      },
      "outputs": [],
      "source": [
        "from sklearn.impute import SimpleImputer\n",
        "\n",
        "imputer = SimpleImputer(missing_values=np.nan, strategy='mean')\n",
        "imputer.fit(x[:, 1:3])\n",
        "x[:, 1:3] = imputer.transform(x[:, 1:3])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FjMZNw7ldVTK",
        "outputId": "5ecea752-e59f-49ad-91a0-ba989e91aa5d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[['France' 44.0 72000.0]\n",
            " ['Spain' 27.0 48000.0]\n",
            " ['Germany' 30.0 54000.0]\n",
            " ['Spain' 38.0 61000.0]\n",
            " ['Germany' 40.0 63777.77777777778]\n",
            " ['France' 35.0 58000.0]\n",
            " ['Spain' 38.77777777777778 52000.0]\n",
            " ['France' 48.0 79000.0]\n",
            " ['Germany' 50.0 83000.0]\n",
            " ['France' 37.0 67000.0]]\n"
          ]
        }
      ],
      "source": [
        "print(x)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "xSjiM9cV9mBt"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "ct = ColumnTransformer(transformers=[('encoder', OneHotEncoder(), [0])], remainder='passthrough')\n",
        "x = np.array(ct.fit_transform(x))\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oU8IcBKBAaV_",
        "outputId": "5d4203a5-392e-4d72-9aa4-b4da85ac70da"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1.0 0.0 0.0 44.0 72000.0]\n",
            " [0.0 0.0 1.0 27.0 48000.0]\n",
            " [0.0 1.0 0.0 30.0 54000.0]\n",
            " [0.0 0.0 1.0 38.0 61000.0]\n",
            " [0.0 1.0 0.0 40.0 63777.77777777778]\n",
            " [1.0 0.0 0.0 35.0 58000.0]\n",
            " [0.0 0.0 1.0 38.77777777777778 52000.0]\n",
            " [1.0 0.0 0.0 48.0 79000.0]\n",
            " [0.0 1.0 0.0 50.0 83000.0]\n",
            " [1.0 0.0 0.0 37.0 67000.0]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "le = LabelEncoder()\n",
        "y = le.fit_transform(y)\n",
        "\n",
        "print(y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0L_2yGdNBHY4",
        "outputId": "42ee4663-2c98-486e-8098-f5b37c72ae7f"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0 1 0 0 1 1 0 1 0 1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=1)\n"
      ],
      "metadata": {
        "id": "dnTN_LTrDYNy"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(x_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lO5jJUL4EzE7",
        "outputId": "41745a01-33f3-414f-baa3-f40af4e68112"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0.0 0.0 1.0 38.77777777777778 52000.0]\n",
            " [0.0 1.0 0.0 40.0 63777.77777777778]\n",
            " [1.0 0.0 0.0 44.0 72000.0]\n",
            " [0.0 0.0 1.0 38.0 61000.0]\n",
            " [0.0 0.0 1.0 27.0 48000.0]\n",
            " [1.0 0.0 0.0 48.0 79000.0]\n",
            " [0.0 1.0 0.0 50.0 83000.0]\n",
            " [1.0 0.0 0.0 35.0 58000.0]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(x_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OndO0tmhEzIh",
        "outputId": "87093176-13f4-4cd6-f18e-3d2a34c64fdc"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0.0 1.0 0.0 30.0 54000.0]\n",
            " [1.0 0.0 0.0 37.0 67000.0]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JiRhwGDkEzMb",
        "outputId": "ca8863d6-ad00-415c-faa7-641c7a40b745"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0 1 0 0 1 1 0 1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "13ot0Ju6EzQB",
        "outputId": "04d3ba80-f6bc-4357-fca7-1f176ecbb671"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0 1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "sc = StandardScaler()\n",
        "x_train[:, 3:] = sc.fit_transform(x_train[:, 3:])\n",
        "x_test[:, 3:] = sc.transform(x_test[:, 3:])"
      ],
      "metadata": {
        "id": "bhNuC8NbrpbR"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(x_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fjvq3PZArpdp",
        "outputId": "0a2d6ed6-7241-4c48-e788-ba2d80a8980a"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0.0 0.0 1.0 -0.1915918438457856 -1.0781259408412427]\n",
            " [0.0 1.0 0.0 -0.014117293757057902 -0.07013167641635401]\n",
            " [1.0 0.0 0.0 0.5667085065333239 0.6335624327104546]\n",
            " [0.0 0.0 1.0 -0.3045301939022488 -0.30786617274297895]\n",
            " [0.0 0.0 1.0 -1.901801144700799 -1.4204636155515822]\n",
            " [1.0 0.0 0.0 1.1475343068237056 1.2326533634535488]\n",
            " [0.0 1.0 0.0 1.4379472069688966 1.5749910381638883]\n",
            " [1.0 0.0 0.0 -0.7401495441200352 -0.5646194287757336]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(x_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HuVLefVPrpf9",
        "outputId": "9cdda02b-4b8c-4c8b-9fec-32176840cf3a"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0.0 1.0 0.0 30.0 54000.0]\n",
            " [1.0 0.0 0.0 37.0 67000.0]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "zY-92wIurpjn"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}