data_preprocessing_practice_4.ipynb

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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.cluster import KMeans\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from statsmodels.tsa.arima.model import ARIMA"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cov_ds = pd.read_csv('/content/drive/MyDrive/Datasets/pandas_data_analysis/covid.csv')\n",
        "fif_ds = pd.read_csv('/content/drive/MyDrive/Datasets/pandas_data_analysis/players_19.csv')\n",
        "hap_ds = pd.read_csv('/content/drive/MyDrive/Datasets/pandas_data_analysis/happy/2019.csv')\n",
        "sup_ds = pd.read_csv('/content/drive/MyDrive/Datasets/pandas_data_analysis/train.csv')"
      ],
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        "id": "ljpBIGnaNiQE"
      },
      "execution_count": 280,
      "outputs": []
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      "cell_type": "code",
      "source": [
        "# 1\n",
        "hap_ds.sort_values(by='Score', ascending=False).head(5)"
      ],
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      "execution_count": 16,
      "outputs": [
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            "text/plain": [
              "   Overall rank Country or region  Score  GDP per capita  Social support  \\\n",
              "0             1           Finland  7.769           1.340           1.587   \n",
              "1             2           Denmark  7.600           1.383           1.573   \n",
              "2             3            Norway  7.554           1.488           1.582   \n",
              "3             4           Iceland  7.494           1.380           1.624   \n",
              "4             5       Netherlands  7.488           1.396           1.522   \n",
              "\n",
              "   Healthy life expectancy  Freedom to make life choices  Generosity  \\\n",
              "0                    0.986                         0.596       0.153   \n",
              "1                    0.996                         0.592       0.252   \n",
              "2                    1.028                         0.603       0.271   \n",
              "3                    1.026                         0.591       0.354   \n",
              "4                    0.999                         0.557       0.322   \n",
              "\n",
              "   Perceptions of corruption  \n",
              "0                      0.393  \n",
              "1                      0.410  \n",
              "2                      0.341  \n",
              "3                      0.118  \n",
              "4                      0.298  "
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              "      <th>Social support</th>\n",
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              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Finland</td>\n",
              "      <td>7.769</td>\n",
              "      <td>1.340</td>\n",
              "      <td>1.587</td>\n",
              "      <td>0.986</td>\n",
              "      <td>0.596</td>\n",
              "      <td>0.153</td>\n",
              "      <td>0.393</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Denmark</td>\n",
              "      <td>7.600</td>\n",
              "      <td>1.383</td>\n",
              "      <td>1.573</td>\n",
              "      <td>0.996</td>\n",
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              "      <th>2</th>\n",
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              "      <td>Norway</td>\n",
              "      <td>7.554</td>\n",
              "      <td>1.488</td>\n",
              "      <td>1.582</td>\n",
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              "      <td>0.341</td>\n",
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              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Iceland</td>\n",
              "      <td>7.494</td>\n",
              "      <td>1.380</td>\n",
              "      <td>1.624</td>\n",
              "      <td>1.026</td>\n",
              "      <td>0.591</td>\n",
              "      <td>0.354</td>\n",
              "      <td>0.118</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Netherlands</td>\n",
              "      <td>7.488</td>\n",
              "      <td>1.396</td>\n",
              "      <td>1.522</td>\n",
              "      <td>0.999</td>\n",
              "      <td>0.557</td>\n",
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              "summary": "{\n  \"name\": \"hap_ds\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Overall rank\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2,\n          5,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Country or region\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Denmark\",\n          \"Netherlands\",\n          \"Norway\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.11473011810331234,\n        \"min\": 7.488,\n        \"max\": 7.769,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          7.6,\n          7.488,\n          7.554\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GDP per capita\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.054806933867896666,\n        \"min\": 1.34,\n        \"max\": 1.488,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1.383,\n          1.396,\n          1.488\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Social support\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.03665105728352188,\n        \"min\": 1.522,\n        \"max\": 1.624,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1.573,\n          1.522,\n          1.582\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Healthy life expectancy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0188944436276912,\n        \"min\": 0.986,\n        \"max\": 1.028,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.996,\n          0.999,\n          1.028\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Freedom to make life choices\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.017852170736355814,\n        \"min\": 0.557,\n        \"max\": 0.603,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.592,\n          0.557,\n          0.603\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Generosity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.07709280122034741,\n        \"min\": 0.153,\n        \"max\": 0.354,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.252,\n          0.322,\n          0.271\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Perceptions of corruption\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.11708757406317719,\n        \"min\": 0.118,\n        \"max\": 0.41,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.41,\n          0.298,\n          0.341\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          "metadata": {},
          "execution_count": 16
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    {
      "cell_type": "code",
      "source": [
        "# 2\n",
        "cov_ds[['New_cases', 'New_deaths']].corr()"
      ],
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          "height": 112
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      "execution_count": 35,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "            New_cases  New_deaths\n",
              "New_cases    1.000000    0.288404\n",
              "New_deaths   0.288404    1.000000"
            ],
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              "      <td>1.000000</td>\n",
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              "  <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-9a4ad98f-181c-476a-b3ea-5f042c7adf10 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-9a4ad98f-181c-476a-b3ea-5f042c7adf10');\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=\"df-4826e999-8c95-4905-832d-ff7935c7ecc9\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4826e999-8c95-4905-832d-ff7935c7ecc9')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-4826e999-8c95-4905-832d-ff7935c7ecc9 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"cov_ds[['New_cases', 'New_deaths']]\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"New_cases\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5031741231910333,\n        \"min\": 0.2884043307480503,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.2884043307480503,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"New_deaths\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5031741231910333,\n        \"min\": 0.2884043307480503,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0,\n          0.2884043307480503\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 3\n",
        "x = sup_ds['Order Date'].dropna(axis=0, how='any')\n",
        "x = pd.to_datetime(x, format='%d/%m/%Y')\n",
        "sup_ds.insert(3, 'Year', x.dt.year)\n",
        "\n",
        "yearly_revenue = sup_ds.groupby('Year')['Sales'].sum()\n",
        "yearly_revenue.pct_change().idxmax()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rsekri2JdH-J",
        "outputId": "bc780c30-18ff-4ffd-edf2-264073edc46c"
      },
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2017"
            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 4\n",
        "fif_ds[['weight_kg', 'overall']].corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "PJ0HJ5BKiIK4",
        "outputId": "67970fe6-b336-48fd-df3d-cacb4ffaf821"
      },
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           weight_kg  overall\n",
              "weight_kg    1.00000  0.15461\n",
              "overall      0.15461  1.00000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-64385c3c-e9d1-4afd-9173-d6de0e11b718\" 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",
              "    .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>weight_kg</th>\n",
              "      <th>overall</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>weight_kg</th>\n",
              "      <td>1.00000</td>\n",
              "      <td>0.15461</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>overall</th>\n",
              "      <td>0.15461</td>\n",
              "      <td>1.00000</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-64385c3c-e9d1-4afd-9173-d6de0e11b718')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\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-64385c3c-e9d1-4afd-9173-d6de0e11b718 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-64385c3c-e9d1-4afd-9173-d6de0e11b718');\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=\"df-158d158e-d856-43df-9853-fa4f1598b7bd\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-158d158e-d856-43df-9853-fa4f1598b7bd')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-158d158e-d856-43df-9853-fa4f1598b7bd button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"fif_ds[['weight_kg', 'overall']]\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"weight_kg\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5977812375706854,\n        \"min\": 0.1546096664953636,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.1546096664953636,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"overall\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5977812375706854,\n        \"min\": 0.1546096664953636,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0,\n          0.1546096664953636\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 5\n",
        "fif_ds['skill_moves'].fillna(fif_ds['skill_moves'].mean(), inplace=True)\n",
        "fif_ds['dribbling'].fillna(fif_ds['dribbling'].mean(), inplace=True)\n",
        "\n",
        "players = fif_ds[['skill_moves', 'dribbling']]\n",
        "kmeans = KMeans(n_clusters=3)\n",
        "\n",
        "fif_ds.insert(1, 'Cluster', kmeans.fit_predict(players))\n",
        "fif_ds['Cluster'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yWDMqQRkixVS",
        "outputId": "dc8ee959-7240-45f6-fbaa-f0a88596e74c"
      },
      "execution_count": 153,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Cluster\n",
              "0    9381\n",
              "2    5782\n",
              "1    2922\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 153
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 6\n",
        "date = pd.to_datetime(cov_ds['Date_reported'], format='%Y-%m-%d')\n",
        "cov_ds.insert(0, 'Month', date.dt.to_period('M'))\n",
        "\n",
        "cov_ds.groupby('Month').sum().agg({'New_cases':' mean', 'New_deaths':'mean'})\n",
        "# the columns have nan values. fill with the mean will not maintain data fidelity"
      ],
      "metadata": {
        "id": "QzhL6DmjvGiF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 7\n",
        "cat_sal_grp = sup_ds.groupby('Category')['Sales'].mean()\n",
        "print(f'Highest average profit margin are from {cat_sal_grp.idxmax()}. \\nLowest average profit margin are from {cat_sal_grp.idxmin()}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2RK7MEs26wER",
        "outputId": "7b92f8b4-a250-411c-aaa6-4bd007f4dc7f"
      },
      "execution_count": 190,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Highest average profit margin are from Technology. \n",
            "Lowest average profit margin are from Office Supplies\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 8\n",
        "hap2018_ds = pd.read_csv('/content/drive/MyDrive/Datasets/pandas_data_analysis/happy/2018.csv')\n",
        "\n",
        "hap_18_19_ds = pd.DataFrame(hap2018_ds['Score'])\n",
        "hap_18_19_ds['2019'] = hap_ds['Score']\n",
        "hap_18_19_ds.rename(columns = {'Score': '2018'}, inplace=True)\n",
        "\n",
        "mean_18 = hap_18_19_ds['2018'].mean()\n",
        "mean_19 = hap_18_19_ds['2019'].mean()\n",
        "\n",
        "print(f'Average score in 2018: {mean_18}')\n",
        "print(f'Average score in 2019: {mean_19}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y7jhTQH9-Jp8",
        "outputId": "2b352ad5-36f0-4ffd-e45d-4961c0d2fd36"
      },
      "execution_count": 221,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Average score in 2018: 5.375916666666667\n",
            "Average score in 2019: 5.407096153846155\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 9\n",
        "x = fif_ds[['movement_sprint_speed', 'movement_agility']]\n",
        "y = fif_ds['power_stamina']\n",
        "\n",
        "model = LinearRegression()\n",
        "model.fit(x, y)\n",
        "print('Regression coefficients:', model.coef_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gccjvjfKIdpO",
        "outputId": "90316199-5541-4159-b358-1872ad31d00e"
      },
      "execution_count": 232,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Regression coefficients: [0.47296342 0.25139398]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 10\n",
        "head = hap_ds.nlargest(10, 'Score')\n",
        "tail = hap_ds.nsmallest(10, 'Score')\n",
        "\n",
        "head['Score'].mean() - tail['Score'].mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W_Kug8PoJxSc",
        "outputId": "41b822e0-e640-4d63-99a0-8b14eb0ace7c"
      },
      "execution_count": 236,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4.1518"
            ]
          },
          "metadata": {},
          "execution_count": 236
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# adding season by month column\n",
        "months = pd.to_datetime(sup_ds['Order Date'], format='%d/%m/%Y')\n",
        "sup_ds.insert(1, 'Month', months.dt.to_period('M'))\n",
        "\n",
        "def season_type(month):\n",
        "  temp = int(str(month).split('-')[1])\n",
        "  if (12 <= temp <= 2):\n",
        "    return 'Winter'\n",
        "  elif (3 <= temp <= 5):\n",
        "    return 'Spring'\n",
        "  elif (6 <= temp <= 8):\n",
        "    return 'Summer'\n",
        "  else:\n",
        "    return 'Fall'\n",
        "\n",
        "seasons = sup_ds['Month'].apply(season_type)\n",
        "\n",
        "sup_ds.insert(1, 'Season', seasons)\n",
        "# it works! :D"
      ],
      "metadata": {
        "id": "qyiw4PBaK6PZ"
      },
      "execution_count": 281,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 11\n",
        "sup_ds.groupby('Month')['Sales'].mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RmBoh7dgSnxB",
        "outputId": "dc402412-1ba3-43da-bd19-1c8f93e6aff4"
      },
      "execution_count": 283,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Month\n",
              "2015-01    184.489701\n",
              "2015-02     98.258522\n",
              "2015-03    358.479201\n",
              "2015-04    214.668115\n",
              "2015-05    195.407463\n",
              "2015-06    262.007142\n",
              "2015-07    237.898190\n",
              "2015-08    185.736551\n",
              "2015-09    305.706093\n",
              "2015-10    197.820082\n",
              "2015-11    254.600198\n",
              "2015-12    248.784885\n",
              "2016-01    316.964168\n",
              "2016-02    186.740797\n",
              "2016-03    252.650925\n",
              "2016-04    214.807978\n",
              "2016-05    209.507206\n",
              "2016-06    182.940884\n",
              "2016-07    210.354846\n",
              "2016-08    233.027482\n",
              "2016-09    221.521425\n",
              "2016-10    189.095960\n",
              "2016-11    234.421805\n",
              "2016-12    239.690036\n",
              "2017-01    208.342596\n",
              "2017-02    276.853193\n",
              "2017-03    317.795398\n",
              "2017-04    230.236708\n",
              "2017-05    256.366100\n",
              "2017-06    202.675949\n",
              "2017-07    195.514199\n",
              "2017-08    174.526859\n",
              "2017-09    198.261865\n",
              "2017-10    310.328297\n",
              "2017-11    216.028677\n",
              "2017-12    283.251837\n",
              "2018-01    304.031287\n",
              "2018-02    191.548052\n",
              "2018-03    248.368830\n",
              "2018-04    177.709551\n",
              "2018-05    182.608259\n",
              "2018-06    205.066926\n",
              "2018-07    201.009435\n",
              "2018-08    293.634804\n",
              "2018-09    190.182976\n",
              "2018-10    263.429018\n",
              "2018-11    258.636305\n",
              "2018-12    180.894093\n",
              "Freq: M, Name: Sales, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 283
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 12\n",
        "brazil = cov_ds[cov_ds['Country'] == 'Brazil']['New_cases'].pct_change()\n",
        "india = cov_ds[cov_ds['Country'] == 'India']['New_cases'].pct_change()\n",
        "\n",
        "print('Brazil:', brazil.mean())\n",
        "print('India:', india.mean())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cKD200P0TCLg",
        "outputId": "0a6efcb7-1306-41c0-c76f-ddb5477565dc"
      },
      "execution_count": 295,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Brazil: 0.20187233276010455\n",
            "India: 0.15583467961716777\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 13\n",
        "fif_ds.groupby('player_positions')['shooting'].mean().sort_values(ascending=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b_oDByq-VZF3",
        "outputId": "943f9e9d-fba5-46e5-e390-68956b25a815"
      },
      "execution_count": 299,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "player_positions\n",
              "CF, RW, ST         91.0\n",
              "LM, CAM, ST, LW    86.0\n",
              "CF, RW, CAM, ST    83.0\n",
              "LW, CF, ST         81.0\n",
              "LW, LM, ST, CAM    80.5\n",
              "                   ... \n",
              "CB, RB, CM         27.0\n",
              "RWB, CM, ST        27.0\n",
              "LB, RB, LM         26.5\n",
              "RB, RM, LM, CB     26.0\n",
              "GK                  NaN\n",
              "Name: shooting, Length: 850, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 299
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 14\n",
        "hap_ds[['Score', 'GDP per capita']].corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "k8XhpA5-XJPV",
        "outputId": "5b50c6f9-f1fc-4183-a15b-13c4b5619313"
      },
      "execution_count": 301,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   Score  GDP per capita\n",
              "Score           1.000000        0.793883\n",
              "GDP per capita  0.793883        1.000000"
            ],
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              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f23b95cd-707e-4f1e-862f-939ee827fcd1 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"hap_ds[['Score', 'GDP per capita']]\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.14574682184209956,\n        \"min\": 0.7938828678781276,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.7938828678781276,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GDP per capita\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.14574682184209956,\n        \"min\": 0.7938828678781276,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0,\n          0.7938828678781276\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 301
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 15\n",
        "model = ARIMA(sup_ds['Sales'], order=(1, 1, 1))\n",
        "model_fit = model.fit()\n",
        "forecast = model_fit.forecast(steps=3)[0]\n",
        "forecast"
      ],
      "metadata": {
        "id": "sG-7A6U8XY5N"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}