analisis_exploratorio.ipynb

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        "id": "view-in-github",
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        "<a href=\"https://colab.research.google.com/github/AnnLivio/runkeeper_data_analysis/blob/main/analisis_exploratorio.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "source": [
        "# Don't stop me now 🏃\n",
        "En esta parte analizaremos y responderemos preguntas sobre mi primer año que va desde: Julio de 2023 a Junio de 2024.\n"
      ],
      "metadata": {
        "id": "OmW4WcYXyBYE"
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      "cell_type": "code",
      "source": [
        "# Import libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\") # do not distube mode"
      ],
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      "outputs": []
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      "source": [
        "# Load data\n",
        "path = '/content/drive/MyDrive/Data Analysis/01_runkeeper_data/running_202307202406.csv'\n",
        "df = pd.read_csv(path)"
      ],
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        "id": "iBaucKNYyaBz"
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      "execution_count": 61,
      "outputs": []
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 215 entries, 0 to 214\n",
            "Data columns (total 11 columns):\n",
            " #   Column     Non-Null Count  Dtype  \n",
            "---  ------     --------------  -----  \n",
            " 0   Date       215 non-null    object \n",
            " 1   Type       215 non-null    object \n",
            " 2   Distance   215 non-null    float64\n",
            " 3   Duration   215 non-null    float64\n",
            " 4   Avg_speed  215 non-null    float64\n",
            " 5   Climb      215 non-null    int64  \n",
            " 6   Steps      214 non-null    float64\n",
            " 7   Month      215 non-null    int64  \n",
            " 8   WeekDay    215 non-null    int64  \n",
            " 9   Intensity  215 non-null    int64  \n",
            " 10  Weekend    215 non-null    int64  \n",
            "dtypes: float64(4), int64(5), object(2)\n",
            "memory usage: 18.6+ KB\n"
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      "source": [
        "df['Date'] = pd.to_datetime(df['Date'], format='%Y-%m-%d %H:%M:%S')\n",
        "\n",
        "# Drop unnecessary columns\n",
        "df.drop(columns=['Intensity','Weekend'], inplace=True)"
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              "                 Date     Type  Distance  Duration  Avg_speed  Climb   Steps  \\\n",
              "0 2024-06-22 19:31:10  Running      6.10     38.27       9.57     60  6434.0   \n",
              "1 2024-05-31 10:06:04  Running      7.20     41.80      10.33     47  7365.0   \n",
              "2 2024-05-29 10:55:14  Running      9.22     53.63      10.31     67  9077.0   \n",
              "3 2024-05-28 19:18:04  Running      6.37     35.95      10.63     33  6305.0   \n",
              "4 2024-05-27 19:02:08  Running      7.12     39.87      10.72     70  6681.0   \n",
              "\n",
              "   Month  WeekDay  \n",
              "0      6        5  \n",
              "1      5        4  \n",
              "2      5        2  \n",
              "3      5        1  \n",
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 215,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2023-07-02 09:13:52\",\n        \"max\": \"2024-06-22 19:31:10\",\n        \"num_unique_values\": 215,\n        \"samples\": [\n          \"2023-07-28 08:52:31\",\n          \"2023-07-06 09:44:29\",\n          \"2023-10-23 16:34:36\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Running\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Distance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.7488533493072482,\n        \"min\": 5.07,\n        \"max\": 15.91,\n        \"num_unique_values\": 135,\n        \"samples\": [\n          10.04\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Duration\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.582849226381125,\n        \"min\": 31.2,\n        \"max\": 95.08,\n        \"num_unique_values\": 196,\n        \"samples\": [\n          35.35\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Avg_speed\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8175478413772891,\n        \"min\": 7.57,\n        \"max\": 11.55,\n        \"num_unique_values\": 135,\n        \"samples\": [\n          9.51\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Climb\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 29,\n        \"min\": 23,\n        \"max\": 220,\n        \"num_unique_values\": 72,\n        \"samples\": [\n          70\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Steps\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1961.0814694653097,\n        \"min\": 2382.0,\n        \"max\": 16729.0,\n        \"num_unique_values\": 201,\n        \"samples\": [\n          9884.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Month\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 1,\n        \"max\": 12,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"WeekDay\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 6,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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            "text/plain": [
              "                                Date    Distance    Duration   Avg_speed  \\\n",
              "count                            215  215.000000  215.000000  215.000000   \n",
              "mean   2023-12-21 03:03:30.288371968    7.398000   43.498093   10.244326   \n",
              "min              2023-07-02 09:13:52    5.070000   31.200000    7.570000   \n",
              "25%       2023-09-25 06:42:28.500000    6.120000   35.965000   10.040000   \n",
              "50%              2023-12-16 10:52:17    6.580000   39.780000   10.540000   \n",
              "75%       2024-03-20 04:45:14.500000    8.155000   47.065000   10.795000   \n",
              "max              2024-06-22 19:31:10   15.910000   95.080000   11.550000   \n",
              "std                              NaN    1.748853   10.582849    0.817548   \n",
              "\n",
              "            Climb         Steps       Month     WeekDay  \n",
              "count  215.000000    214.000000  215.000000  215.000000  \n",
              "mean    62.386047   7343.257009    6.293023    2.781395  \n",
              "min     23.000000   2382.000000    1.000000    0.000000  \n",
              "25%     46.000000   6232.750000    3.000000    1.000000  \n",
              "50%     55.000000   6798.500000    7.000000    3.000000  \n",
              "75%     69.000000   7919.250000    9.000000    4.000000  \n",
              "max    220.000000  16729.000000   12.000000    6.000000  \n",
              "std     29.116825   1961.081469    3.403228    1.970253  "
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              "      <td>214.000000</td>\n",
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              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2023-12-21 03:03:30.288371968</td>\n",
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              "      <td>43.498093</td>\n",
              "      <td>10.244326</td>\n",
              "      <td>62.386047</td>\n",
              "      <td>7343.257009</td>\n",
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              "      <th>min</th>\n",
              "      <td>2023-07-02 09:13:52</td>\n",
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              "      <td>7.570000</td>\n",
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              "      <td>2382.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2023-09-25 06:42:28.500000</td>\n",
              "      <td>6.120000</td>\n",
              "      <td>35.965000</td>\n",
              "      <td>10.040000</td>\n",
              "      <td>46.000000</td>\n",
              "      <td>6232.750000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2023-12-16 10:52:17</td>\n",
              "      <td>6.580000</td>\n",
              "      <td>39.780000</td>\n",
              "      <td>10.540000</td>\n",
              "      <td>55.000000</td>\n",
              "      <td>6798.500000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>3.000000</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2024-03-20 04:45:14.500000</td>\n",
              "      <td>8.155000</td>\n",
              "      <td>47.065000</td>\n",
              "      <td>10.795000</td>\n",
              "      <td>69.000000</td>\n",
              "      <td>7919.250000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>4.000000</td>\n",
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              "      <th>max</th>\n",
              "      <td>2024-06-22 19:31:10</td>\n",
              "      <td>15.910000</td>\n",
              "      <td>95.080000</td>\n",
              "      <td>11.550000</td>\n",
              "      <td>220.000000</td>\n",
              "      <td>16729.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>6.000000</td>\n",
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              "      <th>std</th>\n",
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              "      <td>10.582849</td>\n",
              "      <td>0.817548</td>\n",
              "      <td>29.116825</td>\n",
              "      <td>1961.081469</td>\n",
              "      <td>3.403228</td>\n",
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              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"1970-01-01 00:00:00.000000215\",\n        \"max\": \"2024-06-22 19:31:10\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"215\",\n          \"2023-12-21 03:03:30.288371968\",\n          \"2024-03-20 04:45:14.500000\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Distance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 73.54841304313568,\n        \"min\": 1.7488533493072482,\n        \"max\": 215.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          7.397999999999999,\n          8.155000000000001,\n          215.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Duration\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 65.21517173439184,\n        \"min\": 10.582849226381125,\n        \"max\": 215.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          43.49809302325581,\n          47.065,\n          215.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Avg_speed\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 72.9865782048983,\n        \"min\": 0.8175478413772891,\n        \"max\": 215.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          10.244325581395348,\n          10.795,\n          215.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Climb\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 80.25429270145025,\n        \"min\": 23.0,\n        \"max\": 220.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          62.38604651162791,\n          69.0,\n          215.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Steps\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5125.004145811028,\n        \"min\": 214.0,\n        \"max\": 16729.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          7343.257009345794,\n          7919.25,\n          214.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Month\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 73.99185614414782,\n        \"min\": 1.0,\n        \"max\": 215.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          6.293023255813954,\n          9.0,\n          215.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"WeekDay\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 75.08912698680166,\n        \"min\": 0.0,\n        \"max\": 215.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          2.781395348837209,\n          4.0,\n          215.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Answering questions"
      ],
      "metadata": {
        "id": "ZmOVg6TPVtA9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1. Top 3 carreras más largas"
      ],
      "metadata": {
        "id": "5ZT5r3dbW2k8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.sort_values('Distance', ascending=False).head(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "w75xwp4TVwt8",
        "outputId": "bbe0cd2f-c6a3-4ae3-e34d-19651fc9b972"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   Date     Type  Distance  Duration  Avg_speed  Climb  \\\n",
              "85  2024-02-05 15:50:36  Running     15.91     95.08      10.04    167   \n",
              "167 2023-09-11 08:57:28  Running     14.08     92.12       9.17    155   \n",
              "91  2024-01-28 15:45:15  Running     12.25     69.52      10.58    121   \n",
              "\n",
              "       Steps  Month  WeekDay  \n",
              "85   16729.0      2        0  \n",
              "167  16113.0      9        0  \n",
              "91   12079.0      1        6  "
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              "      <th>85</th>\n",
              "      <td>2024-02-05 15:50:36</td>\n",
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              "      <td>2023-09-11 08:57:28</td>\n",
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              "      <td>14.08</td>\n",
              "      <td>92.12</td>\n",
              "      <td>9.17</td>\n",
              "      <td>155</td>\n",
              "      <td>16113.0</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>91</th>\n",
              "      <td>2024-01-28 15:45:15</td>\n",
              "      <td>Running</td>\n",
              "      <td>12.25</td>\n",
              "      <td>69.52</td>\n",
              "      <td>10.58</td>\n",
              "      <td>121</td>\n",
              "      <td>12079.0</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
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              "\n",
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              "    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",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2023-09-11 08:57:28\",\n        \"max\": \"2024-02-05 15:50:36\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"2024-02-05 15:50:36\",\n          \"2023-09-11 08:57:28\",\n          \"2024-01-28 15:45:15\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Running\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Distance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.83,\n        \"min\": 12.25,\n        \"max\": 15.91,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          15.91\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Duration\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13.981149213613786,\n        \"min\": 69.52,\n        \"max\": 95.08,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          95.08\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Avg_speed\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7114070564732965,\n        \"min\": 9.17,\n        \"max\": 10.58,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          10.04\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Climb\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 23,\n        \"min\": 121,\n        \"max\": 167,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          167\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Steps\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2525.704918103723,\n        \"min\": 12079.0,\n        \"max\": 16729.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          16729.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Month\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 1,\n        \"max\": 9,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"WeekDay\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 0,\n        \"max\": 6,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2. Top 3 carreras más rápidas"
      ],
      "metadata": {
        "id": "D79rQC2eW_AE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.sort_values('Avg_speed', ascending=False).head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "BQ8kWWnuV9Y8",
        "outputId": "5d337d90-9967-4dba-d32a-90a23208e4d1"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  Date     Type  Distance  Duration  Avg_speed  Climb   Steps  \\\n",
              "36 2024-04-12 16:27:51  Running      6.37     33.08      11.55     27  4964.0   \n",
              "38 2024-04-09 15:16:02  Running      6.25     32.85      11.41     24  4863.0   \n",
              "37 2024-04-11 10:58:38  Running      6.25     33.07      11.35     25  5979.0   \n",
              "39 2024-04-08 11:25:27  Running      6.12     32.38      11.34     26  4754.0   \n",
              "87 2024-02-02 15:43:09  Running      6.36     33.75      11.30     55  5937.0   \n",
              "\n",
              "    Month  WeekDay  \n",
              "36      4        4  \n",
              "38      4        1  \n",
              "37      4        3  \n",
              "39      4        0  \n",
              "87      2        4  "
            ],
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              "      <td>Running</td>\n",
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-451ad028-f1a2-4cec-b350-fac34aa5a07b')\"\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",
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              "    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-451ad028-f1a2-4cec-b350-fac34aa5a07b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2024-02-02 15:43:09\",\n        \"max\": \"2024-04-12 16:27:51\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"2024-04-09 15:16:02\",\n          \"2024-02-02 15:43:09\",\n          \"2024-04-11 10:58:38\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Running\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Distance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.10173494974687908,\n        \"min\": 6.12,\n        \"max\": 6.37,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          6.25\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Duration\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4942974812802499,\n        \"min\": 32.38,\n        \"max\": 33.75,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          32.85\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Avg_speed\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.09772410142846051,\n        \"min\": 11.3,\n        \"max\": 11.55,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          11.41\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Climb\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13,\n        \"min\": 24,\n        \"max\": 55,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          24\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Steps\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 605.9680684656577,\n        \"min\": 4754.0,\n        \"max\": 5979.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          4863.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Month\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 2,\n        \"max\": 4,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"WeekDay\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3. Top 5 carreras con mayor desnivel positivo"
      ],
      "metadata": {
        "id": "6R06OjloXEfU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.sort_values('Climb', ascending=False).head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "MxsONX89WeF0",
        "outputId": "522bf837-f955-4f59-d9ce-5dc4e7e50065"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   Date     Type  Distance  Duration  Avg_speed  Climb  \\\n",
              "162 2023-09-22 10:06:31  Running      8.07     55.08       8.79    220   \n",
              "105 2023-12-24 09:46:08  Running     10.08     61.58       9.82    207   \n",
              "104 2024-01-01 10:23:07  Running     10.69     63.37      10.12    196   \n",
              "163 2023-09-21 09:43:28  Running      7.05     49.42       8.56    178   \n",
              "85  2024-02-05 15:50:36  Running     15.91     95.08      10.04    167   \n",
              "\n",
              "       Steps  Month  WeekDay  \n",
              "162   9486.0      9        4  \n",
              "105   6798.0     12        6  \n",
              "104  11372.0      1        0  \n",
              "163   8326.0      9        3  \n",
              "85   16729.0      2        0  "
            ],
            "text/html": [
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              "      <th>Date</th>\n",
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              "      <th>162</th>\n",
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              "      <td>2023-09-21 09:43:28</td>\n",
              "      <td>Running</td>\n",
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              "      <td>8.56</td>\n",
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              "    <tr>\n",
              "      <th>85</th>\n",
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              "      <td>Running</td>\n",
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              "\n",
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              "      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",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-dafb240a-0d1d-47e0-a0ac-41c7c4b7f082 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
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              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
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              "\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
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              "    }\n",
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              "\n",
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              "    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-90a5c03b-3625-483c-882e-9bf8916e1bb5 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2023-09-21 09:43:28\",\n        \"max\": \"2024-02-05 15:50:36\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"2023-12-24 09:46:08\",\n          \"2024-02-05 15:50:36\",\n          \"2024-01-01 10:23:07\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Running\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Distance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.434748899119119,\n        \"min\": 7.05,\n        \"max\": 15.91,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          10.08\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Duration\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17.750058591452593,\n        \"min\": 49.42,\n        \"max\": 95.08,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          61.58\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Avg_speed\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7349013539244567,\n        \"min\": 8.56,\n        \"max\": 10.12,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          9.82\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Climb\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 21,\n        \"min\": 167,\n        \"max\": 220,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          207\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Steps\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3840.9372814457674,\n        \"min\": 6798.0,\n        \"max\": 16729.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          6798.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Month\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 1,\n        \"max\": 12,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          12\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"WeekDay\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 0,\n        \"max\": 6,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 4. Total kms corridos en el primer año"
      ],
      "metadata": {
        "id": "Dcgm5DRhXRHy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Total Distance in kms\n",
        "df.Distance.sum().round(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wuzXfWPke_gn",
        "outputId": "7c418f6a-ac12-4dad-d517-30be42909313"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1590.57"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 5. Desnivel positivo acumulado en todo el año"
      ],
      "metadata": {
        "id": "ZXLTZt0jvVBG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Total Climb in kms\n",
        "df.Climb.sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7qreXQc7auhp",
        "outputId": "b14206e4-aaab-430a-9405-f8ebdfd56fac"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "13413"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 6. Extracción de datos y cálculos para evaluar la evolución"
      ],
      "metadata": {
        "id": "aNXI7lofvh02"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.groupby('Month').agg({'Distance':'sum','Climb':['sum', 'max'], 'Avg_speed':['max','min']})"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "tWCbkhZXfa31",
        "outputId": "0ce43f41-c8c0-4b4a-85eb-f41b8622442b"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Distance Climb      Avg_speed       \n",
              "           sum   sum  max       max    min\n",
              "Month                                     \n",
              "1       143.92  1199  196     11.08  10.12\n",
              "2       160.24  1244  167     11.30  10.04\n",
              "3       146.19  1112   69     11.22  10.22\n",
              "4       160.08  1054   91     11.55  10.44\n",
              "5       163.83  1148   72     10.93  10.15\n",
              "6         6.10    60   60      9.57   9.57\n",
              "7       110.60  1270  122      9.28   7.57\n",
              "8       148.14  1304  114     10.11   8.51\n",
              "9       134.43  1575  220     10.45   8.56\n",
              "10      161.88  1268   74     10.87  10.03\n",
              "11      152.70  1170   87     10.96  10.17\n",
              "12      102.46  1009  207     11.24   9.82"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-26b3147c-c688-41a1-aa90-12551449e6bc\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>Distance</th>\n",
              "      <th colspan=\"2\" halign=\"left\">Climb</th>\n",
              "      <th colspan=\"2\" halign=\"left\">Avg_speed</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>sum</th>\n",
              "      <th>sum</th>\n",
              "      <th>max</th>\n",
              "      <th>max</th>\n",
              "      <th>min</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Month</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>143.92</td>\n",
              "      <td>1199</td>\n",
              "      <td>196</td>\n",
              "      <td>11.08</td>\n",
              "      <td>10.12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>160.24</td>\n",
              "      <td>1244</td>\n",
              "      <td>167</td>\n",
              "      <td>11.30</td>\n",
              "      <td>10.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>146.19</td>\n",
              "      <td>1112</td>\n",
              "      <td>69</td>\n",
              "      <td>11.22</td>\n",
              "      <td>10.22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>160.08</td>\n",
              "      <td>1054</td>\n",
              "      <td>91</td>\n",
              "      <td>11.55</td>\n",
              "      <td>10.44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>163.83</td>\n",
              "      <td>1148</td>\n",
              "      <td>72</td>\n",
              "      <td>10.93</td>\n",
              "      <td>10.15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>6.10</td>\n",
              "      <td>60</td>\n",
              "      <td>60</td>\n",
              "      <td>9.57</td>\n",
              "      <td>9.57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>110.60</td>\n",
              "      <td>1270</td>\n",
              "      <td>122</td>\n",
              "      <td>9.28</td>\n",
              "      <td>7.57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>148.14</td>\n",
              "      <td>1304</td>\n",
              "      <td>114</td>\n",
              "      <td>10.11</td>\n",
              "      <td>8.51</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>134.43</td>\n",
              "      <td>1575</td>\n",
              "      <td>220</td>\n",
              "      <td>10.45</td>\n",
              "      <td>8.56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>161.88</td>\n",
              "      <td>1268</td>\n",
              "      <td>74</td>\n",
              "      <td>10.87</td>\n",
              "      <td>10.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>152.70</td>\n",
              "      <td>1170</td>\n",
              "      <td>87</td>\n",
              "      <td>10.96</td>\n",
              "      <td>10.17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>102.46</td>\n",
              "      <td>1009</td>\n",
              "      <td>207</td>\n",
              "      <td>11.24</td>\n",
              "      <td>9.82</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "\n",
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              "        document.querySelector('#df-26b3147c-c688-41a1-aa90-12551449e6bc button.colab-df-convert');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "                                                    [key], {});\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "  @keyframes spin {\n",
              "    0% {\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "+ Los máximos en Climb se corresponden con viajes a Luxemburgo, Francia y Valencia donde pude hacer rutas con mayor desnivel positivo que en la región en la que vivo.\n",
        "+ De Julio a Septiembre se puede ver una evolución positiva y significativa en la velocidad promedio.\n",
        "+ El mes con mayor acumulación de kms es mayo, tras 11 meses de entrenamiento continuo.\n",
        "+ Junio pasó factura con una contractura que me obligó a descansar y sólo hacer bicicleta y caminata.\n",
        "\n",
        "### Nos vemos el año próximo para comparar resultados y si nos cruzamos en alguna ruta... say *Hi!*\n",
        "\n",
        "### Author: Anastasia Livio"
      ],
      "metadata": {
        "id": "-sR5mhLQvzDO"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "g0ey8leZz3V5"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}