Descriptive_statistics_.ipynb

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        "import pandas as pd\n",
        "# Read CSV file\n",
        "df = pd.read_csv('datos.csv')"
      ],
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        "df.describe()"
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              "         Errores\n",
              "count  75.000000\n",
              "mean    2.186667\n",
              "std     1.768289\n",
              "min     0.000000\n",
              "25%     0.000000\n",
              "50%     2.000000\n",
              "75%     4.000000\n",
              "max     5.000000"
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Errores\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 25.819210999258445,\n        \"min\": 0.0,\n        \"max\": 75.0,\n        \"samples\": [\n          75.0,\n          2.1866666666666665,\n          4.0\n        ],\n        \"num_unique_values\": 7,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "# Convert the 'Time' column to seconds\n",
        "df['Tiempo'] = pd.to_timedelta(df['Tiempo']).dt.total_seconds()\n",
        "\n",
        "# Calculate statistics for times\n",
        "media_tiempos = np.mean(df['Tiempo'])\n",
        "mediana_tiempos = np.median(df['Tiempo'])\n",
        "moda_tiempos = df['Tiempo'].mode()[0]\n",
        "desviacion_estandar_tiempos = np.std(df['Tiempo'], ddof=1)\n",
        "\n",
        "# Calculate statistics for errors\n",
        "media_errores = np.mean(df['Errores'])\n",
        "mediana_errores = np.median(df['Errores'])\n",
        "moda_errores = df['Errores'].mode()[0]\n",
        "desviacion_estandar_errores = np.std(df['Errores'], ddof=1)\n",
        "\n",
        "# Print the results\n",
        "print(\"Statistics for times:\")\n",
        "print(f\"Mean: {media_tiempos:.2f} seconds\")\n",
        "print(f\"Median: {mediana_tiempos:.2f} seconds\")\n",
        "print(f\"Mode: {moda_tiempos:.2f} seconds\")\n",
        "print(f\"Standard_deviation: {desviacion_estandar_tiempos:.2f} seconds\\n\")\n",
        "\n",
        "print(\"Statistics for errors:\")\n",
        "print(f\"Mean: {media_errores:.2f}\")\n",
        "print(f\"Median: {mediana_errores:.2f}\")\n",
        "print(f\"Mode: {moda_errores}\")\n",
        "print(f\"Standard_deviation: {desviacion_estandar_errores:.2f}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-KWLzcNeQIAb",
        "outputId": "1b928c45-1771-43d4-d704-e77f1b5b9e97"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Statistics for times:\n",
            "Mean: 797.51 seconds\n",
            "Median: 759.28 seconds\n",
            "Mode: 10.62 seconds\n",
            "Standard_deviation: 531.25 seconds\n",
            "\n",
            "Statistics for errors:\n",
            "Mean: 2.19\n",
            "Median: 2.00\n",
            "Mode: 0\n",
            "Standard_deviation: 1.77\n"
          ]
        }
      ]
    },
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      "cell_type": "code",
      "source": [
        "# Get the minimum value of time in seconds\n",
        "min_tiempo = df['Tiempo'].min()\n",
        "\n",
        "# Get the maximum value of the time in seconds\n",
        "max_tiempo = df['Tiempo'].max()\n",
        "\n",
        "print(\"Minimum time value:\", min_tiempo, \"seconds\")\n",
        "print(\"Maximum time value:\", max_tiempo, \"seconds\")\n"
      ],
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        "id": "yhYPPnADVs29",
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      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Minimum time value: 10.62 seconds\n",
            "Maximum time value: 1772.56 seconds\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# Calculate statistics\n",
        "media_tiempos = np.mean(df['Tiempo'])\n",
        "mediana_tiempos = np.median(df['Tiempo'])\n",
        "desviacion_estandar_tiempos = np.std(df['Tiempo'], ddof=1)\n",
        "\n",
        "# Set the Seaborn style\n",
        "sns.set(style=\"whitegrid\")\n",
        "\n",
        "# Histogram or density chart\n",
        "plt.subplot(1, 1, 1)\n",
        "sns.histplot(data=df, x='Tiempo', bins=30, kde=True, color='skyblue')\n",
        "plt.axvline(media_tiempos, color='red', linestyle='dashed', linewidth=2, label='Mean')\n",
        "plt.axvline(mediana_tiempos, color='green', linestyle='dashdot', linewidth=2, label='Median')\n",
        "\n",
        "# Add lines for standard deviation\n",
        "plt.axvline(media_tiempos + desviacion_estandar_tiempos, color='violet', linestyle='dotted', linewidth=2, label='Standard Deviation +')\n",
        "plt.axvline(media_tiempos - desviacion_estandar_tiempos, color='violet', linestyle='dotted', linewidth=2, label='Standard Deviation -')\n",
        "plt.title('Statistics for times')\n",
        "plt.xlabel('Time (seconds)')\n",
        "plt.ylabel('Frequency')\n",
        "plt.legend()\n",
        "\n",
        "# Adjust the layout and display the graphic\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
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        },
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      "execution_count": 50,
      "outputs": [
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              "<Figure size 640x480 with 1 Axes>"
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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert the 'Time' column to seconds\n",
        "df['Tiempo_Segundos'] = pd.to_timedelta(df['Tiempo']).dt.total_seconds()\n",
        "\n",
        "# Set the Seaborn style\n",
        "sns.set(style=\"whitegrid\")\n",
        "\n",
        "# Create the figure and axes for the subgraphs\n",
        "fig, ax = plt.subplots(figsize=(14, 6))\n",
        "\n",
        "# Create boxplots for the distribution of times for each number of errors\n",
        "sns.boxplot(x='Errores', y='Tiempo_Segundos', data=df, ax=ax, palette='Set3')\n",
        "\n",
        "# Improve labels and title\n",
        "ax.set_title('Time Distribution by Number of Errors')\n",
        "ax.set_xlabel('Number of Errors')\n",
        "ax.set_ylabel('Time (seconds)')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 650
        },
        "id": "uiUKUq_TTInc",
        "outputId": "02576266-0a1f-45af-d3b3-2694764661b3"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-51-d7cf8d4ac0ce>:11: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.boxplot(x='Errores', y='Tiempo_Segundos', data=df, ax=ax, palette='Set3')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x600 with 1 Axes>"
            ],
            "image/png": 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          "metadata": {}
        }
      ]
    }
  ]
}