Cyclistic_Data_Analysis.ipynb

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        "<a href=\"https://colab.research.google.com/github/AnnLivio/Cyclistic/blob/main/Cyclistic_Data_Analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "# CYCLISTIC · Data Analysis\n",
        "\n",
        "**Cyclistic** es un programa de bicicletas compartidas que incluye 5,800 bicicletas y más de 600 estaciones en Chicago. Se destaca por ofrecer también bicicletas reclinadas, triciclos manuales y bicicletas de carga que ofrecen un uso más inclusivo para los ciclistas que no pueden utilizar una bicicleta estándar.\n",
        "Uno de los enfoques que ayudó a hacer esto posible fue la flexibilidad de sus planes de precios: `pases de un solo viaje`, `pases de un día completo` y `membresías anuales`. A los clientes que compran pases de un solo viaje o pases de un día completo se los llama ciclistas ocasionales. Los clientes con membresías anuales se llaman miembros de Cyclistic.\n",
        "\n",
        "### Pregunta inicial:\n",
        "¿En qué se diferencian los socios anuales y los ciclistas ocasionales con respecto al uso de las bicicletas de Cyclistic?\n",
        "\n"
      ],
      "metadata": {
        "id": "GMhFb40yY0u2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Cargar librerías y paquetes"
      ],
      "metadata": {
        "id": "ngt27qrGZP79"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#import libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n"
      ],
      "metadata": {
        "id": "lyJHkM1qaqWU"
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      "execution_count": 1,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        "# Set palette for graphics\n",
        "custom_palette = [\"#696960\", \"#b0dd60\",  \"#ebeb93\"]\n",
        "sns.set_palette(custom_palette)"
      ],
      "metadata": {
        "id": "Oy4yWa9pX9hH"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Cargar datos para el análisis"
      ],
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        "id": "tNjyRfC4ZaIs"
      }
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    {
      "cell_type": "code",
      "source": [
        "#Load the full dataset\n",
        "df = pd.read_csv(\"/content/drive/MyDrive/Colab Notebooks/Capstone/data/cyclistic_clean_data.csv\")\n"
      ],
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 5851552 entries, 0 to 5851551\n",
            "Data columns (total 15 columns):\n",
            " #   Column              Dtype  \n",
            "---  ------              -----  \n",
            " 0   ride_id             object \n",
            " 1   rideable_type       object \n",
            " 2   started_at          object \n",
            " 3   ended_at            object \n",
            " 4   start_station_name  object \n",
            " 5   start_station_id    object \n",
            " 6   end_station_name    object \n",
            " 7   end_station_id      object \n",
            " 8   start_lat           float64\n",
            " 9   start_lng           float64\n",
            " 10  end_lat             float64\n",
            " 11  end_lng             float64\n",
            " 12  member_casual       object \n",
            " 13  ride_lenght         object \n",
            " 14  weekday             object \n",
            "dtypes: float64(4), object(11)\n",
            "memory usage: 669.7+ MB\n"
          ]
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    {
      "cell_type": "markdown",
      "source": [
        "## Seleccionar columnas necesarias"
      ],
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      "source": [
        "# Select column of interest\n",
        "my_cols = ['rideable_type', 'started_at', 'ended_at', 'start_station_name', 'end_station_name', 'member_casual', 'ride_lenght', 'weekday']\n",
        "df = df[my_cols]\n",
        "df.head()"
      ],
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              "   rideable_type           started_at             ended_at  \\\n",
              "0   classic_bike  2023-10-08 10:36:26  2023-10-08 10:49:19   \n",
              "1  electric_bike  2023-10-11 17:23:59  2023-10-11 17:36:08   \n",
              "2  electric_bike  2023-10-12 07:02:33  2023-10-12 07:06:53   \n",
              "3   classic_bike  2023-10-24 19:13:03  2023-10-24 19:18:29   \n",
              "4   classic_bike  2023-10-09 18:19:26  2023-10-09 18:30:56   \n",
              "\n",
              "                     start_station_name             end_station_name  \\\n",
              "0  Orleans St & Chestnut St (NEXT Apts)  Sheffield Ave & Webster Ave   \n",
              "1             Desplaines St & Kinzie St  Sheffield Ave & Webster Ave   \n",
              "2  Orleans St & Chestnut St (NEXT Apts)        Franklin St & Lake St   \n",
              "3             Desplaines St & Kinzie St        Franklin St & Lake St   \n",
              "4             Desplaines St & Kinzie St        Franklin St & Lake St   \n",
              "\n",
              "  member_casual      ride_lenght    weekday  \n",
              "0        member  0 days 00:12:53     Sunday  \n",
              "1        member  0 days 00:12:09  Wednesday  \n",
              "2        member  0 days 00:04:20   Thursday  \n",
              "3        member  0 days 00:05:26    Tuesday  \n",
              "4        member  0 days 00:11:30     Monday  "
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        "df.info()"
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      "outputs": [
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 5851552 entries, 0 to 5851551\n",
            "Data columns (total 8 columns):\n",
            " #   Column              Dtype \n",
            "---  ------              ----- \n",
            " 0   rideable_type       object\n",
            " 1   started_at          object\n",
            " 2   ended_at            object\n",
            " 3   start_station_name  object\n",
            " 4   end_station_name    object\n",
            " 5   member_casual       object\n",
            " 6   ride_lenght         object\n",
            " 7   weekday             object\n",
            "dtypes: object(8)\n",
            "memory usage: 357.2+ MB\n"
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    {
      "cell_type": "markdown",
      "source": [
        "## Convertir columnas al tipo correcto\n",
        "\n",
        "`started_at` y `ended_at` a tipo *datetime* y `ride_lenght` a *timedelta*"
      ],
      "metadata": {
        "id": "VoFlC_qrZ0bU"
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      "source": [
        "# Convert the `started_at` and `ended_at` column to datetime.\n",
        "df['started_at'] = pd.to_datetime(df['started_at'], format='ISO8601')\n",
        "df['ended_at'] = pd.to_datetime(df['ended_at'],  format='ISO8601')\n",
        "\n",
        "# Convert the 'ride_lenght' to timedelta\n",
        "df['ride_lenght'] = pd.to_timedelta(df['ride_lenght'])"
      ],
      "metadata": {
        "id": "VVrGZJ6Q-cNR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create column month\n",
        "df['month'] = df['started_at'].dt.month"
      ],
      "metadata": {
        "id": "I4pfs3gtCIEp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create column ride_in_seconds\n",
        "df['ride_in_seconds'] = df['ride_lenght'].dt.total_seconds()"
      ],
      "metadata": {
        "id": "VGX-omCvROlR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Create list of weekday in order\n",
        "weekday_order = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']\n"
      ],
      "metadata": {
        "id": "pjFUUFrcALxU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Dividir dataset en 2 subsets: members y casual"
      ],
      "metadata": {
        "id": "aJg4C68ZaSOf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Group data for members\n",
        "df_members = df.query(\"member_casual == 'member'\")\n",
        "\n",
        "# Group data for casuals\n",
        "df_casual = df.query(\"member_casual == 'casual'\")"
      ],
      "metadata": {
        "id": "fTKTJxrdBiUn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Viajes por día de la semana\n",
        "**Member users**"
      ],
      "metadata": {
        "id": "-G5_5AckEWtt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_members.groupby('weekday')['ride_lenght'].count().reset_index(name='counts').sort_values('counts', ascending=False).style.background_gradient()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "NyU1NthrDt4Z",
        "outputId": "0c8299d6-0437-4bdc-c706-f0dc5fe0d195"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7d4493d07790>"
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              "#T_3c904_row0_col1 {\n",
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              "#T_3c904_row4_col1 {\n",
              "  background-color: #6da6cd;\n",
              "  color: #f1f1f1;\n",
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              "  background-color: #cacee5;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_3c904_row6_col1 {\n",
              "  background-color: #fff7fb;\n",
              "  color: #000000;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_3c904\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_3c904_level0_col0\" class=\"col_heading level0 col0\" >weekday</th>\n",
              "      <th id=\"T_3c904_level0_col1\" class=\"col_heading level0 col1\" >counts</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_3c904_level0_row0\" class=\"row_heading level0 row0\" >6</th>\n",
              "      <td id=\"T_3c904_row0_col0\" class=\"data row0 col0\" >Wednesday</td>\n",
              "      <td id=\"T_3c904_row0_col1\" class=\"data row0 col1\" >612889</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_3c904_level0_row1\" class=\"row_heading level0 row1\" >4</th>\n",
              "      <td id=\"T_3c904_row1_col0\" class=\"data row1 col0\" >Thursday</td>\n",
              "      <td id=\"T_3c904_row1_col1\" class=\"data row1 col1\" >583618</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_3c904_level0_row2\" class=\"row_heading level0 row2\" >5</th>\n",
              "      <td id=\"T_3c904_row2_col0\" class=\"data row2 col0\" >Tuesday</td>\n",
              "      <td id=\"T_3c904_row2_col1\" class=\"data row2 col1\" >570822</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_3c904_level0_row3\" class=\"row_heading level0 row3\" >1</th>\n",
              "      <td id=\"T_3c904_row3_col0\" class=\"data row3 col0\" >Monday</td>\n",
              "      <td id=\"T_3c904_row3_col1\" class=\"data row3 col1\" >539980</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_3c904_level0_row4\" class=\"row_heading level0 row4\" >0</th>\n",
              "      <td id=\"T_3c904_row4_col0\" class=\"data row4 col0\" >Friday</td>\n",
              "      <td id=\"T_3c904_row4_col1\" class=\"data row4 col1\" >519689</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_3c904_level0_row5\" class=\"row_heading level0 row5\" >2</th>\n",
              "      <td id=\"T_3c904_row5_col0\" class=\"data row5 col0\" >Saturday</td>\n",
              "      <td id=\"T_3c904_row5_col1\" class=\"data row5 col1\" >473132</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_3c904_level0_row6\" class=\"row_heading level0 row6\" >3</th>\n",
              "      <td id=\"T_3c904_row6_col0\" class=\"data row6 col0\" >Sunday</td>\n",
              "      <td id=\"T_3c904_row6_col1\" class=\"data row6 col1\" >421667</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Casual users**"
      ],
      "metadata": {
        "id": "7eAc7fImEgK1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_casual.groupby(['weekday'])['ride_lenght'].count().reset_index(name='counts').sort_values('counts', ascending=False).style.background_gradient()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "b0WkTWofD4EJ",
        "outputId": "f736c807-27b9-4950-9076-18703accffe3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7d445c71df00>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_e243c_row0_col1 {\n",
              "  background-color: #023858;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_e243c_row1_col1 {\n",
              "  background-color: #2383ba;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_e243c_row2_col1 {\n",
              "  background-color: #a9bfdc;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_e243c_row3_col1 {\n",
              "  background-color: #dedcec;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_e243c_row4_col1 {\n",
              "  background-color: #e2dfee;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_e243c_row5_col1 {\n",
              "  background-color: #f0eaf4;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_e243c_row6_col1 {\n",
              "  background-color: #fff7fb;\n",
              "  color: #000000;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_e243c\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_e243c_level0_col0\" class=\"col_heading level0 col0\" >weekday</th>\n",
              "      <th id=\"T_e243c_level0_col1\" class=\"col_heading level0 col1\" >counts</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_e243c_level0_row0\" class=\"row_heading level0 row0\" >2</th>\n",
              "      <td id=\"T_e243c_row0_col0\" class=\"data row0 col0\" >Saturday</td>\n",
              "      <td id=\"T_e243c_row0_col1\" class=\"data row0 col1\" >429983</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e243c_level0_row1\" class=\"row_heading level0 row1\" >3</th>\n",
              "      <td id=\"T_e243c_row1_col0\" class=\"data row1 col0\" >Sunday</td>\n",
              "      <td id=\"T_e243c_row1_col1\" class=\"data row1 col1\" >366448</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e243c_level0_row2\" class=\"row_heading level0 row2\" >0</th>\n",
              "      <td id=\"T_e243c_row2_col0\" class=\"data row2 col0\" >Friday</td>\n",
              "      <td id=\"T_e243c_row2_col1\" class=\"data row2 col1\" >305888</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e243c_level0_row3\" class=\"row_heading level0 row3\" >6</th>\n",
              "      <td id=\"T_e243c_row3_col0\" class=\"data row3 col0\" >Wednesday</td>\n",
              "      <td id=\"T_e243c_row3_col1\" class=\"data row3 col1\" >271100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e243c_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "      <td id=\"T_e243c_row4_col0\" class=\"data row4 col0\" >Thursday</td>\n",
              "      <td id=\"T_e243c_row4_col1\" class=\"data row4 col1\" >267578</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e243c_level0_row5\" class=\"row_heading level0 row5\" >1</th>\n",
              "      <td id=\"T_e243c_row5_col0\" class=\"data row5 col0\" >Monday</td>\n",
              "      <td id=\"T_e243c_row5_col1\" class=\"data row5 col1\" >254554</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e243c_level0_row6\" class=\"row_heading level0 row6\" >5</th>\n",
              "      <td id=\"T_e243c_row6_col0\" class=\"data row6 col0\" >Tuesday</td>\n",
              "      <td id=\"T_e243c_row6_col1\" class=\"data row6 col1\" >234204</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Boxplot para members por día de la semana"
      ],
      "metadata": {
        "id": "XhnIwvFKavds"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8, 4))\n",
        "sns.boxplot(x=df_members[\"weekday\"],y=df_members[\"ride_lenght\"], order=weekday_order, showfliers=False, color='#b0dd60')\n",
        "plt.title('Members count rides per weekday')\n",
        "plt.xlabel('Weekday')\n",
        "plt.ylabel('Ride lenght')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "id": "KatJn3tZBvBN",
        "outputId": "5c066c53-5db1-4bc2-8e6f-9f89c76baf50"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Rides por día de la semana para Casual"
      ],
      "metadata": {
        "id": "9fhLSAXla_jQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8, 4))\n",
        "sns.boxplot(x=df_casual[\"weekday\"],y=df_casual[\"ride_lenght\"], order=weekday_order, showfliers=False, color='#908585')\n",
        "plt.title('Casuals ride count per weekday')\n",
        "plt.xlabel('Weekday')\n",
        "plt.ylabel('Ride lenght')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "id": "EMPiojRBCTFa",
        "outputId": "2d756677-6466-4b46-a5ac-6fc7f6a45ebd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## La moda en días de la semana"
      ],
      "metadata": {
        "id": "5ZgkHpU_EU2-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "stats_dict = {}\n",
        "for i in ['members', 'casual']:\n",
        "  # 1. Convert `started_at` and `ended_at` in datetime\n",
        "  exec(f\"median = df_{i}['ride_lenght'].median()\")\n",
        "  exec(f\"max = df_{i}['ride_lenght'].max()\")\n",
        "  exec(f\"min = df_{i}['ride_lenght'].min()\")\n",
        "  exec(f\"mean = df_{i}['ride_lenght'].mean()\")\n",
        "  exec(f\"std = df_{i}['ride_lenght'].std()\")\n",
        "  exec(f\"count = df_{i}['ride_lenght'].count()\")\n",
        "  exec(f\"mode = df_{i}['weekday'].mode()\")\n",
        "  exec(f\"Q1 = df_{i}['ride_lenght'].quantile(0.25)\")\n",
        "  exec(f\"Q3 = df_{i}['ride_lenght'].quantile(0.75)\")\n",
        "\n",
        "  stats_dict[i] = {'median': median, 'mean': mean, 'max': max, 'min': min, 'std':std, 'mode': mode[0], 'count':count, 'Q1':Q1, 'Q3':Q3}\n",
        "\n",
        "stats_df = pd.DataFrame(stats_dict)\n",
        "stats_df\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331
        },
        "id": "qDJh1aD-D3Ig",
        "outputId": "a608d0a0-d6ae-4d21-b33c-a69b2bb3c29d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                          members                     casual\n",
              "median     0 days 00:08:40.467000            0 days 00:12:00\n",
              "mean    0 days 00:12:49.850469939  0 days 00:25:08.303247889\n",
              "max               1 days 01:59:48            1 days 01:59:56\n",
              "min               0 days 00:00:00            0 days 00:00:00\n",
              "std     0 days 00:37:21.164462338  0 days 01:30:05.247969503\n",
              "mode                    Wednesday                   Saturday\n",
              "count                     3721797                    2129755\n",
              "Q1                0 days 00:05:05            0 days 00:06:39\n",
              "Q3                0 days 00:14:46     0 days 00:22:37.929000"
            ],
            "text/html": [
              "\n",
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              "    }\n",
              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>members</th>\n",
              "      <th>casual</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>median</th>\n",
              "      <td>0 days 00:08:40.467000</td>\n",
              "      <td>0 days 00:12:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0 days 00:12:49.850469939</td>\n",
              "      <td>0 days 00:25:08.303247889</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1 days 01:59:48</td>\n",
              "      <td>1 days 01:59:56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0 days 00:00:00</td>\n",
              "      <td>0 days 00:00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0 days 00:37:21.164462338</td>\n",
              "      <td>0 days 01:30:05.247969503</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mode</th>\n",
              "      <td>Wednesday</td>\n",
              "      <td>Saturday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>3721797</td>\n",
              "      <td>2129755</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Q1</th>\n",
              "      <td>0 days 00:05:05</td>\n",
              "      <td>0 days 00:06:39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Q3</th>\n",
              "      <td>0 days 00:14:46</td>\n",
              "      <td>0 days 00:22:37.929000</td>\n",
              "    </tr>\n",
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              "  @keyframes spin {\n",
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              "      const quickchartButtonEl =\n",
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              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "        width: 32px;\n",
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              "\n",
              "      .colab-df-generate:hover {\n",
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              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('stats_df')\"\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "stats_df",
              "summary": "{\n  \"name\": \"stats_df\",\n  \"rows\": 9,\n  \"fields\": [\n    {\n      \"column\": \"members\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          \"0 days 00:05:05\",\n          \"0 days 00:12:49.850469939\",\n          \"Wednesday\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"casual\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          \"0 days 00:06:39\",\n          \"0 days 00:25:08.303247889\",\n          \"Saturday\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Agrupa por mes y cuenta los viajes\n",
        "rides_per_month_casual = df_casual.query('month < 10').groupby('month')['member_casual'].count().reset_index()\n",
        "rides_per_month_member = df_members.query('month < 10').groupby('month')['member_casual'].count().reset_index()\n",
        "\n",
        "# Crea el linechart\n",
        "plt.figure(figsize=(6, 4))\n",
        "plt.plot(rides_per_month_casual['month'], rides_per_month_casual['member_casual'])\n",
        "plt.plot(rides_per_month_member['month'], rides_per_month_member['member_casual'])\n",
        "\n",
        "# Personaliza el gráfico\n",
        "plt.title('Número de viajes a lo largo del año')\n",
        "plt.xlabel('Mes')\n",
        "plt.ylabel('Número de viajes')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        },
        "id": "urYHhn_YKqqS",
        "outputId": "30cf81ad-19be-4774-b94c-cf865c64b1f9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ],
            "image/png": 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9/qGnpO78lXzvX8Wff/75Qx8vLCwMGo0GH3/8MYqLi++bOT8/v9TMn0aNGsHBwaHUdPzKylYWb29vtG7dGqtWrTIrrM+fP4+dO3fi6aefrtDxKiNf7969YWVlVWoRy6+//rpU26effhpHjx5FZGSkaVteXh6WL18Of39/s7Fi9+rfvz8A4Msvv3xgVoVCgSFDhuCnn37C+fPnSx2nPD+H91IoFKVeow0bNjzytO2QkBB4eHhg6dKlZj8/27dvx4ULF8qcAUbViz03ZNH++c9/4vvvv0dsbCyCg4PN9k2cOBHz58/HxIkTERISggMHDuDixYuVnmHUqFFYv349Jk2ahL1796JLly4wGAyIiYnB+vXrsWPHDoSEhDzwGB999BF27dqFrl274rXXXoOVlRWWLVsGnU6HhQsXPvC+AwYMQJcuXTBr1ixcuXIFzZo1w6ZNm8oca7Bo0SJ07doVLVq0wMsvv4yAgACkpqYiMjIS165dK7UuSFmGDRuGr776CnPnzkWLFi1M4zHuR6PRmMbAFBcXo169eti5cycSEhIe+lgajQZLlizBqFGj0LZtW7z44otwd3dHYmIitm3bhi5duuDrr7/GxYsX0bt3bwwdOhTNmjWDlZUVNm/ejNTU1AdOUX+cbPfzySefoH///ggNDcWECRNMU8EdHR0rfH2lysjn6emJadOm4dNPP8XAgQPx1FNP4cyZM9i+fTvc3NzMehtnzZqFNWvWoH///pg6dSpcXFywatUqJCQk4KeffnrgKr6tW7fG8OHDsXjxYmRnZ6Nz587YvXt3mevHzJ8/H3v37kXHjh3x8ssvo1mzZsjIyMDJkyfxxx9/ICMjo0Kv0zPPPIN//etfGDduHDp37oxz587hxx9/NBugXxFKpRILFizAuHHj0KNHDwwfPtw0Fdzf3x8zZsx4pONSJZJqmhZRZbp7Kvi9xowZIwCYTQUXomQK7YQJE4Sjo6NwcHAQQ4cOFWlpafedCp6enl7quHZ2dqUe795p50KUTB1dsGCBCA4OFmq1Wjg7O4t27dqJDz74QGRnZ5vaARARERFlPseTJ0+KsLAwYW9vL2xtbUWvXr3E4cOHH/raCCHErVu3xKhRo4RGoxGOjo5i1KhR4tSpU2VOo42LixOjR48WXl5eQqlUinr16olnnnlGbNy4sVyPZTQahZ+fnwAgPvroo1L7y5q+e+3aNfHss88KJycn4ejoKF544QWRnJxc6ntx71TwO/bu3SvCwsKEo6OjsLa2Fo0aNRJjx44Vx48fF0IIcfPmTRERESGCgoKEnZ2dcHR0FB07dhTr169/6PMpb7ay3G+q8h9//CG6dOkibGxshEajEQMGDBDR0dEPzfI4r939fo6FKFkaYM6cOcLLy0vY2NiIJ598Uly4cEG4urqKSZMmmbWNi4sTzz//vHBychLW1taiQ4cOYuvWrQ/NLoQQBQUFYurUqcLV1VXY2dmJAQMGiKSkpDJfy9TUVBERESH8/PyEUqkUXl5eonfv3mL58uUPfD3KUlhYKN544w3h7e0tbGxsRJcuXURkZKTo0aOH6NGjh6ndnangGzZsMLv//R5n3bp1ok2bNkKtVgsXFxcxYsQIce3atXK9FlS1ZEJwhBQR1Q7//e9/MXHiRCQlJcHX11fqOBYtKysLzs7O+Oijjx46boqopuGYGyKqNW7cuAGZTGa2Rgw9voKCglLb7oyF6dmzZ/WGIaoEHHNDRDVeamoqNm7ciKVLlyI0NBS2trZSR7Io69atw8qVK/H000/D3t4eBw8exJo1a9CvXz906dJF6nhEFcbihohqvAsXLuCtt95Chw4dypx1Ro+nZcuWsLKywsKFC6HVak2DjD/66COpoxE9Eo65ISIiIovCMTdERERkUVjcEBERkUVhcUNEREQWhQOKq5HRaERycjIcHBwe6RpDREREdZUQAjk5OfDx8XngatgAi5tqlZyc/MALHBIREdGDlWcRTxY31cjBwQFAyTfmQRc6JCIiInNarRZ+fn6mz9IHYXFTje6citJoNCxuiIiIHkF5hnVwQDERERFZFBY3REREZFFY3BAREZFFYXFDREREFoXFDREREVkUFjdERERkUVjcEBERkUVhcUNEREQWhcUNERERWRQWN0RERFSpCvSZuJS1DUIYJXl8Xn6BiIiIKkVGYRzis3chOe84BAywV/rA265NtedgcUNERESPzCj0SM47gfjsXcjUxZm2u1oHQim3lSQTixsiIiKqsCJDLq5o9yFBuweFhkwAgBxWqGffEQGOfeGkbiBZNhY3REREVG7aouuIz96FpNzDMIpiAIBaoYG/phf8HXrB2spR4oQsboiIiOghhDAiNf8s4rW7kF4QbdruqKqPAMd+qGffAQqZUsKE5ljcEBERUZmKjQVIyjmEeO0fyCtOvb1VBm+7dmik6QsX6yaQyWSSZiwLixsiIiIyk1ecjgTtH7iq/RN6UQAAsJLbwN+hBxpqesNW6SZxwgdjcUNEREQQQuBWYSzisnchJf8UAAEAsFd6IUDTF34OnWElt5Y2ZDmxuCEiIqrDDMZiXMs7gvjsXdAWJZm2e9g0R4BjX3jYNIdMVrvW/GVxQ0REVAcV6rOQoN2LK9q9KDLmAAAUMhX87LsgwLEPHFQ+Eid8dCxuiIiI6pAs3RXEZe/E9dyjEDAAAGwULmjo2BsNHLpDpbCXOOHjY3FDRERk4YzCgJS8k4jL3oUM3SXTdhd1EwQ49oW3XVvIZQoJE1YuFjdEREQWqsiQh6s5B5Cg3Y0C/S0AgAwK1LPvgADHvnBWN5Q4YdVgcUNERGRhcoqSEZ/9B5JyD8EgigAAKrkD/DW90FDTC9ZWTtIGrGIsboiIiCyAEEakFUQhPnsn0grOm7ZrVH4IcOwLX7tOUMhrzirCVYnFDRERUS2mN+qQlHsI8dm7kFuccnurDF62bdDIsS9crQNr5CrCVYnFDRERUS2UX3wTCdrduJpzAMXGfACAlcwGDTTd0FDTG3ZKD4kTSofFDRERUS0hhECG7hLisnfhRt4J3FlF2M7KAwGOfeHn0AVKuY20IWsAFjdEREQ1nEEU43ruUcRn70J20VXTdnebZgjQ9IWnbctat4pwVWJxQ0REVEMV6rNxJadkFWGdQQsAkMuU8LPvjADHPtCofCVOWDOxuCEiIqphsnRXEZ+9C9dz/4IRegCAtcIJDTW90UDTA2qFg8QJazYWN0RERDWAEEbcyD+F+OxduFUYa9rurG6EAMe+8LFrB7mMH9vlwVeJiIhIAkahR6E+C4WGTGQUxiFBuxv5+psASlYR9rELQYBjX7hYN5I4ae3D4oaIiKgSCSGgNxagwJCJQn2m6V+z/xuyTGNo7qaS26OBpicaap6EjZWzBOktA4sbIiKicjIKA3SGbBToM1FoyEShPuv2/zNK/n+7eLlzyYOHkcMK1lZOsLVyg699J/jah0IhV1Xxs7B8LG6IiIgAFBsLTKeJCkr1tGSiQJ8FnSEbd9aWeRil3A7WCifYWDnD2soZNoqSf//+vxNUcoc6t3pwdWBxQ0REFk0II3QG7V2nh+70sGSgwJBlOmWkF4XlOp4MClgrHO8pVJxhY+UE6zsFjMIJVnJ1FT8zuh8WN0REVGvpjbp7Tg/d7nEx9bZkQmfIhoCxXMezktncLlScYa24XbCY/l/yr1rhwAXzajgWN0REVOMVGXKRqYtDRmEcsnRXUKDPQKEh03RNpYeTwVrhdLunxanU6aE7xYuV3LpKnwdVDxY3RERUowhhhLboOjJ1l5FRGIcMXRzyTFe7Lk0hszY7JWT2/9v/qhUayGWKanwWJCUWN0REJKkiQy4ydHHILCwpZjJ1CTCUMf7FTukFF3UjOFs3gp2VB6ytnGBj5cILRVIpLG6IiKjalPTKXPu7mNHFl9kro5BZw9k6AC7qRnCxbgRndSOoFPYSJKbaiMUNERFVGZ0hB5m3Ty1lPqBXxl7pBWd1Y1Mho1HV46BdemQsboiIqFIYhQE5Rdfv6pWJQ15xaql2Vrd7ZZzZK0NVhMUNERE9EvNemcu3e2V0pdrd3SvjYt0IDkr2ylDVYnFDREQP9XevzO1Bv4WXkadPK9Xu716ZO6eYAtgrQ9WOxQ0REZWiM2iRWRiPDN3lu8bKlNUr4317BlPj270yPuyVIcmxuCEiquOMwgBt0bXbp5hKipmye2VsTDOYnK0b3+6VsZMgMdGDsbghIqpjdAbt7fVkLiOzMP4BvTI+JeNkbq8tw14Zqi1Y3BARWbCSXpkks1NMD+6VaQxn01gZ9spQ7cTihojIwghhxK3Ci0jKPYzk3OPQi4JSbRyUPnA29co0hoPSm70yZDFY3BARWYjcohQk5R5GUu5hFOhvmbZbyW3grC7plbmzroxSYSthUqKqxeKGiKgWKzLk4nruUSTlHkamLs603UpmAx/7EPjZd4GrdRP2ylCdUqN+2ufPnw+ZTIbp06ebthUWFiIiIgKurq6wt7fHkCFDkJpqvuJlYmIiwsPDYWtrCw8PD7z11lvQ6/Vmbfbt24e2bdtCrVajcePGWLlyZanHX7RoEfz9/WFtbY2OHTvi6NGjZvvLk4WIqKoZhR438k7iaMpX+P3qdJy99T0ydXGQQQ5Pm5YI8ZiEpxp8jjbu4+FmE8jChuqcGtNzc+zYMSxbtgwtW7Y02z5jxgxs27YNGzZsgKOjI15//XU899xzOHToEADAYDAgPDwcXl5eOHz4MG7cuIHRo0dDqVTi448/BgAkJCQgPDwckyZNwo8//ojdu3dj4sSJ8Pb2RlhYGABg3bp1mDlzJpYuXYqOHTvi888/R1hYGGJjY+Hh4VGuLEREVUUIgUxdPJJyD+N67l8oNuaZ9jmq6sPPvjPq2XeCtZWjhCmJaghRA+Tk5IgmTZqIXbt2iR49eohp06YJIYTIysoSSqVSbNiwwdT2woULAoCIjIwUQgjx22+/CblcLlJSUkxtlixZIjQajdDpdEIIId5++20RHBxs9pjDhg0TYWFhptsdOnQQERERptsGg0H4+PiIefPmlTvLw2RnZwsAIjs7u1ztiYjyitJFTMYvYlfiLLElbqzpa/uV6eL8zXUiW5codUSialGRz9Aa0VcZERGB8PBw9OnTx2z7iRMnUFxcbLY9KCgI9evXR2RkJAAgMjISLVq0gKenp6lNWFgYtFotoqKiTG3uPXZYWJjpGEVFRThx4oRZG7lcjj59+pjalCfLvXQ6HbRardkXEdHDFBsLcFV7AIeSF2BX0luIydyEvOIUKGQq+Np3QqjXGwir/ymCXYdCo/KTOi5RjSP5aam1a9fi5MmTOHbsWKl9KSkpUKlUcHJyMtvu6emJlJQUU5u7C5s7++/se1AbrVaLgoICZGZmwmAwlNkmJiam3FnuNW/ePHzwwQcPePZERCWMwoD0gmgk5RzCjfyTMIri23tkcLMOgp9DZ3jbtYNSbiNpTqLaQNLiJikpCdOmTcOuXbtgbW0tZZQqMXv2bMycOdN0W6vVws+Pf2UR0d+ydYlIyj2Ma7lHoDNkm7bbK73hZ98Zvg6hsLVylTAhUe0jaXFz4sQJpKWloW3btqZtBoMBBw4cwNdff40dO3agqKgIWVlZZj0mqamp8PLyAgB4eXmVmtV0ZwbT3W3undWUmpoKjUYDGxsbKBQKKBSKMtvcfYyHZbmXWq2GWq2uwCtCRHVBoT4L13KPICn3MLRFSabtKrk96tl3gp9DZzip/CGTySRMSVR7STrmpnfv3jh37hxOnz5t+goJCcGIESNM/1cqldi9e7fpPrGxsUhMTERoaCgAIDQ0FOfOnUNa2t/Lie/atQsajQbNmjUztbn7GHfa3DmGSqVCu3btzNoYjUbs3r3b1KZdu3YPzUJEdD96ow7XciMReeM/2JE4E1EZ66AtSoIcVvC2C0EHz6kIa/AZWrqNgLO6IQsboscgac+Ng4MDmjdvbrbNzs4Orq6upu0TJkzAzJkz4eLiAo1GgylTpiA0NBSdOnUCAPTr1w/NmjXDqFGjsHDhQqSkpODdd99FRESEqddk0qRJ+Prrr/H2229j/Pjx2LNnD9avX49t27aZHnfmzJkYM2YMQkJC0KFDB3z++efIy8vDuHHjAACOjo4PzUJEdDfTZRByDiE57zj0otC0z0XdGL4OnVHPrj1UCnsJUxJZHskHFD/MZ599BrlcjiFDhkCn0yEsLAyLFy827VcoFNi6dSsmT56M0NBQ2NnZYcyYMfjXv/5latOwYUNs27YNM2bMwBdffAFfX198++23pjVuAGDYsGFIT0/He++9h5SUFLRu3Rq///672SDjh2UhIgKAnKIbuJZ7GEm5kWaXQbC1cjONo7FXln06m4gen0wIIaQOUVdotVo4OjoiOzsbGo1G6jhEVImKDLm4lvsXknIPI0sXb9puJbNBPfv28LPvAhfrxlwtmOgRVeQztMb33BAR1VQGUYzU/DNIyolEav4ZCBgAADLI4WHbAn72neFl2xoKuUripER1C4sbIqIKEKbLIBzC9dyjpS+D4NAF9ew68jIIRBJicUNEVA75xTeRlHsYSbmHkVf897IR1gon+NqHws+hMzQqXwkTEtEdLG6IiO6j2JiP5NzjSMo9jFuFsabtCpkK3nbt4GffBe42TTmOhqiGYXFDRHSXkssgnEdSTmTpyyDYNIWffWd427XlZRCIajAWN0REKFmTJjbzZ1zJ2Qed4e+L3NorfeDn0Bl+9p1gw8sgENUKLG6IqM4TQuDcrR+RoN0DgJdBIKrtWNwQUZ13Ofs3U2HTym006jt0g1zGt0ei2oq/vURUpyXlHEZ0xkYAQHPX4fDX9JI4ERE9Lg7xJ6I6Ky0/CqfSvwMANHIMQyPHfhInIqLKwOKGiOqkLN1VHE39GgIG1LPriGCXoVJHIqJKwuKGiOqcvOJ0HEn5DAZRCDfrILTxmMC1aogsCH+biahO0RlyEJnyH+gM2dCofNHBawoUMqXUsYioErG4IaI6Q2/U4a+UL5BXnAIbK1d08poJpdxW6lhEVMlY3BBRnWAUBpxIW4pMXRyUcjuEes2EjZWz1LGIqAqwuCEiiyeEwLmbPyAl/zTkMit09JoKB5WP1LGIqIqwuCEii3cxayuu5OwDIEM7j1fhav2ExImIqCqxuCEii3Y150/EZG4CALRwfQk+diESJyKiqsbihogsVmr+WZxJXwkAaOL0NAIc+0gbiIiqBYsbIrJImYXxOJa6CAJG+Np3RlPn56WORETVhMUNEVmcvOI0HEn5HAZRBHebYLRxH8crexPVISxuiMii6AxaRN74FEXGHDiq6qO9ZwSv8E1Ux7C4ISKLoTfqcCTlc+Tp02Br5XZ7kT4bqWMRUTVjcUNEFsEoDDieuhhZugQo5Xbo5DUT1laOUsciIgmwuCGiWk8IgTM3VyG14CzkMiU6eU2Hg8pb6lhEdZoQQrLHZnFDRLVebObPSMz5E4AMIR6T4WLdWOpIRHWSEAIJCXH46ae1eO+9d5CVlSlJDo6yI6Ja7Yp2P2KzfgYAtHIbBW+7NhInIqpbjEYjrlyJx8mTx3Dq1HFkZmaY9p06dQK9elX/+lIsboio1krJO40zN1cBAJ5wGgh/TS+JExHVDUajEQkJcaaC5u4eGrXaGi1atELbtu3RrFkLSfJVuLhZtWoV3NzcEB4eDgB4++23sXz5cjRr1gxr1qxBgwYNKj0kEdG9MgrjcDxtCQCB+vZdEeQ8WOpIRBbNaDQiPv6yqaDJzs4y7bO2tkaLFq3Rpk17NGvWHCqVSrqgAGSigiN+AgMDsWTJEjz55JOIjIxEnz598Nlnn2Hr1q2wsrLCpk2bqiprrafVauHo6Ijs7GxoNBqp4xDVWrlFKfgz+d8oMubCw6YFOnpN5Vo2RFXAaDTi8uWLOHXqGE6fPnlPQWODli3boG3bEDRt2hxKpbJKs1TkM7TC7wZJSUlo3LhksN6WLVswZMgQvPLKK+jSpQt69uz5SIGJiMqrUJ+NyJT/oMiYCye1P9p7vsbChqgSGY1GXLoUi1OnjuP06RPQarNN+2xs7hQ07REUFFzlBc2jqvA7gr29PW7duoX69etj586dmDlzJoCSLqmCgoJKD0hEdEexsQBHUj5Dvj4ddlYe6OQ1A1Zya6ljEdV6BoPBrKDJydGa9tnY2KJVq7Zo2zYEQUHBsLKq+X9MVDhh3759MXHiRLRp0wYXL17E008/DQCIioqCv79/ZecjIgIAGIUex1IXIbvoKlRyB3Tyngm1gqd3iR6VwWDAxYsxplNOubk5pn12dnZo2bIt2rZtj8DAprWioLlbhdMuWrQI7777LpKSkvDTTz/B1dUVAHDixAkMHz680gMSEQkhcDp9BdILoqCQqdDJazrslZ5SxyKqdQwGPWJj/y5o8vJyTfvs7OzRunVbtGnTHoGBQVAoaldBc7cKDyimR8cBxUSPJjrjJ1zK2goZ5OjgNRVetq2kjkRUa+j1esTGXsDJk8dw9uxJ5OXlmfbZ29ujdet2aNu2PZo0CazRBU2VDigGgD///BPLli1DfHw8NmzYgHr16uH7779Hw4YN0bVr10cKTURUloTsPbiUtRUA0MptDAsbonLQ6/WIiYnCyZPHcfbsKeTn/13QODhobhc0IWjcOBAKhULCpFWjwsXNTz/9hFGjRmHEiBE4efIkdDodACA7Oxsff/wxfvvtt0oPSUR1U3LeCZy99QMAIMh5MBpoukuciKjmKi4uvl3QHMPZs6fMJvloNBq0bh2CNm1C0KRJIORyy776UoWLm48++ghLly7F6NGjsXbtWtP2Ll264KOPPqrUcERUd90qvIQTacsACDRw6IEnnAZKHYmoxikuLsaFC+dvFzSnUVh4d0HjiDZt2qFNm/Zo3PgJiy9o7lbh4iY2Nhbdu5f+68nR0RFZWVmVkYmI6ricomT8lfIFjKIYXrat0dJtFGQymdSxiGqEoqIiREefx6lTx3Du3GkUFhaa9jk6OqFNmxC0bdseAQGN61RBc7cKFzdeXl64fPlyqWnfBw8eREBAQGXlIqI6qkCficiU/6DYmAdndQDaeUyCXGZ5YwKIKqKoqAhRUWdx6tRxnDt3Bjrd3wWNk5OzqaBp2LBRnS1o7lbh4ubll1/GtGnT8N1330EmkyE5ORmRkZF48803MWfOnKrISER1xJ1F+gr0t2Cn9ERHr+mwkquljkUkiaIiHc6fP4uTJ48hKuqsaYwrADg7u5gKGn//ABY096hwcTNr1iwYjUb07t0b+fn56N69O9RqNd58801MmTKlKjISUR1gFHocTf0a2qIkqBUahHrNhFrhIHUsqiGMRiNSUpIRF3cJSUmJEELAysrK9KVQlPyrVCqhUChgZaUstV+ptIKV1d37y25nZWUlWbGg0+lw/vwZnDp1HOfPn0FRUZFpn4uLq6mgadCgIQuaB3jkdW6Kiopw+fJl5ObmolmzZrC3t6/sbBaH69wQlU0II06mf4NruUegkFmjq887cFL7Sx2LJFRYWIirV+MRF3cZ8fGXkZBwuVov8SOXKx5Y/JQunJT33W9+X2WZ+/Lz83D69EmcP38WxcV/FzSurm5o06Y92rYNQYMGDev02LMqX+cGAFQqFZo1a/aodyciMonO2IhruUcggwIdPCNY2NRBGRm3EB9fUsjExV3C9etJMBqNZm3UajX8/RvB378hVCoV9Hp9GV/FMBgM0OuL77NfD4Oh7PvczWg0oKjIYNZzUl3c3NzRtm17tGkTgvr1/et0QfOoylXcPPfcc1i5ciU0Gg2ee+65B7a1t7dHcHAwJk2aBEdHx0oJSUSWKy57Jy5nbwcAtHYfBw/b5hInoqpmMBhw/XrS7V6ZS4iLu4SsrMxS7VxcXBEQ0BgBAY3RqFET+Pj4VtmCc0KI+xQ+fxdAD95/Z195iyrzdjKZDEFBzdC2bXv4+TVgQfOYylXcODo6ml7ohxUsOp0OS5cuxaFDh/DLL788fkIisljXc4/h/K2S9bKaOg9BfYcuEieiqpCfn4+EhDhTIXPlSnypHhG5XA5f3/qmQiYgoDGcnV2qLaNMJoNSqYRSqay2x6SqUyXXloqOjkb79u3Nrl9BHHNDdLebBTGIvPEpjNDDX/MkWrqO5F+rFkAIgZs30xEXdwnx8ZcQH38ZN24k496PGhsbWwQENEJAQEkh4+8fALWaM+Po/qplzM2DBAYG4vDhw1VxaCKyANqia/gr9UsYoYeXbVu0dB3BwqaWKi4uRlLS1dvFTMnAX61WW6qdu7uHqUcmIKAJvLy8OduHqswjFTfHjx/H+vXrkZiYWKprcdOmTVAoFGjVihe3I6LSCvQZiLzxH+iNBXBRN0GIx6uQyfghV1vk5GhNA3/j4y/j6tUE6PV6szZWVlaoX9/fVMg0atQYDg7srabqU+HiZu3atRg9ejTCwsKwc+dO9OvXDxcvXkRqaiqeffbZqshIRBai2JCPyBv/QaEhE/ZKb3T0mgqFXCV1LLoPo9GI1NQbt2cwlRQzaWkppdrZ2zvc1SvTGPXr+3PsCkmqwsXNxx9/jM8++wwRERFwcHDAF198gYYNG+LVV1+Ft7d3VWQkIgtgEMX4K/VL5BRfh1rhhFCvmVApuD5WTVJUpMOVKwmm6dgJCXHIzy89dtLb28fUIxMQ0Bju7p48rUg1SoX7guPi4hAeHg6gZK2bvLw8yGQyzJgxA8uXL6/QsZYsWYKWLVtCo9FAo9EgNDQU27dvN+0vLCxEREQEXF1dYW9vjyFDhiA1NdXsGImJiQgPD4etrS08PDzw1ltvleoi3bdvH9q2bQu1Wo3GjRtj5cqVpbIsWrQI/v7+sLa2RseOHXH06FGz/eXJQkRlE8KIk2nf4FZhLKxk1gj1mgFbpZvUseq8rKxMnDx5DBs2rMaCBf/CzJkR+PzzBfjll58QFXUW+fl5UCpVaNIkCE899Qxee206/u//vsacOf/GiBFj0alTV3h4eLGwoRqnwj03zs7OyMnJAQDUq1cP58+fR4sWLZCVlYX8/PwKHcvX1xfz589HkyZNIITAqlWrMGjQIJw6dQrBwcGYMWMGtm3bhg0bNsDR0RGvv/46nnvuORw6dAhAyVoJ4eHh8PLywuHDh3Hjxg2MHj0aSqUSH3/8MQAgISEB4eHhmDRpEn788Ufs3r0bEydOhLe3N8LCwgAA69atw8yZM7F06VJ07NgRn3/+OcLCwhAbGwsPDw8AeGgWIrq/qIx1SM47VrJIn9cUOKrrSx2pzjEajbh+/ZppBlNc3CVkZNwq1c7JydlsbRlfXz8oFFUy94SoylR4KvhLL72EkJAQzJw5Ex9++CG++uorDBo0CLt27ULbtm2xadOmxwrk4uKCTz75BM8//zzc3d2xevVqPP/88wCAmJgYNG3aFJGRkejUqRO2b9+OZ555BsnJyfD09AQALF26FO+88w7S09OhUqnwzjvvYNu2bTh//rzpMV588UVkZWXh999/BwB07NgR7du3x9dffw2g5E3Az88PU6ZMwaxZs5Cdnf3QLOXBqeBUF13O2oGojJK1bNp5vAJf+1CJE9UdxcXF2Lt3J2JiopGQEG92JWmgZG0XX1+/uwb+NoGzswt7YqhGqtKp4F9//TUKC0t+Qf75z39CqVTi8OHDGDJkCN59991HS4ySXpgNGzYgLy8PoaGhOHHiBIqLi9GnTx9Tm6CgINSvX99UUERGRqJFixamwgYAwsLCMHnyZERFRaFNmzaIjIw0O8adNtOnTwdQco2sEydOYPbs2ab9crkcffr0QWRkJACUK0tZdDqd2VVcy5oeSWTJruX+ZSpsgl2GsrCpRtnZWVi27CtcuRJv2mZtbXN7bZmSYsbfvyGsrW0kTElUNSpc3Li4/L1ipFwux6xZsx4rwLlz5xAaGorCwkLY29tj8+bNaNasGU6fPg2VSgUnJyez9p6enkhJKRmtn5KSYlbY3Nl/Z9+D2mi1WhQUFCAzMxMGg6HMNjExMaZjPCxLWebNm4cPPvigfC8EkYVJL4jGybRvAAABmr5o5PiUxInqjqtXE7Bs2VfIysqEra0dwsMHo0mTQPj41OPaMlQnlKu40Wq1pi6gh/U+VPR0S2BgIE6fPo3s7Gxs3LgRY8aMwf79+yt0jJpq9uzZmDlzpum2VquFn5+fhImIqke2LhFHU76GgAE+du3R3PVFnuqoJseP/4Xvv/8viouL4eXlg0mTpsLDw/PhdySyIOUqbpydnXHjxg14eHjAycmpzDcpIQRkMlmpK6s+jEqlQuPGjQEA7dq1w7Fjx/DFF19g2LBhKCoqQlZWllmPSWpqKry8vAAAXl5epWY13ZnBdHebe2c1paamQqPRwMbGBgqFAgqFosw2dx/jYVnKolaruZw41Tn5xTcRmfIZ9KIArtaBaOv+MhfpqwZGoxHbtv2M7dtLrukXHNwS48e/ChsbW4mTEVW/chU3e/bsMZ2O2rNnT5X+BWY0GqHT6dCuXTsolUrs3r0bQ4YMAQDExsYiMTERoaEl5+1DQ0Px73//G2lpaaZZTbt27YJGo0GzZs1MbX777Tezx9i1a5fpGCqVCu3atcPu3bsxePBgU4bdu3fj9ddfB4ByZSEioMiQi8iUz6AzZMFBWQ8dPKdAIediblWtsLAQq1Z9gzNnTgIA+vR5CoMHv8BTUFR3CQnNmjVL7N+/XyQkJIizZ8+KWbNmCZlMJnbu3CmEEGLSpEmifv36Ys+ePeL48eMiNDRUhIaGmu6v1+tF8+bNRb9+/cTp06fF77//Ltzd3cXs2bNNbeLj44Wtra146623xIULF8SiRYuEQqEQv//+u6nN2rVrhVqtFitXrhTR0dHilVdeEU5OTiIlJcXU5mFZyiM7O1sAENnZ2Y/6khHVWHqDThy49m+xJW6s+P3KDJFffFPqSHXCzZvp4t//niMmTx4rpkyZKCIj/5Q6ElGVqMhnaIWLm8aNG4u5c+eKixcvPlK4u40fP140aNBAqFQq4e7uLnr37m0qbIQQoqCgQLz22mvC2dlZ2NraimeffVbcuHHD7BhXrlwR/fv3FzY2NsLNzU288cYbori42KzN3r17RevWrYVKpRIBAQFixYoVpbJ89dVXon79+kKlUokOHTqII0eOmO0vT5aHYXFDlspoNIi/bnwltsSNFVsTJotsXZLUkeqEy5cvirffniomTx4r3n57qrh8+fHfl4lqqop8hlZ4nZvPPvsMq1evxsmTJ9G2bVuMHDkSw4YNe+DYEyrBdW7IEgkhcO7WD0jQ7oEcVgj1fgNuNkFSx7J4kZF/YvXqVTAYDPD19cOkSdPg4uIqdSyiKlORz9AKFzd3XLx4ET/++CPWrFmDhIQE9OrVCyNHjsTo0aMfKXRdwOKGLNGlrG2IztgIQIYQj0moZ99B6kgWzWg0YvPm9di9ewcAoE2bEIwePZGTF8jiVUtxc7cjR45g8uTJOHv2bIVnS9UlLG7I0iTlHMbJ9JK1bJq7Dkcjx34SJ7JsBQX5+O9/lyI6+hwA4OmnB+Lppwdx4DDVCVW6QvHdjh49itWrV2PdunXQarV44YUXHudwRFSLpOVH4VT6dwCAxo5PsbCpYmlpKViy5Eukpt6AUqnC6NET0K4de8mIylLh4ube01FPPvkkFixYgOeeew729vZVkZGIahAhBK7k7MX5m2sgYEA9u45o5sI/bKpSTEw0vv12MfLz8+Dk5IxJk6aifn1/qWMR1VgVLm6CgoLQvn17RERE4MUXXyx12QIislzFxgKcSV+J63kli2d62bZFG48JXKSvigghcODAHmzYsBpGoxH+/gF49dUpcHR0kjoaUY1W4eImNjYWTZo0qYosRFSDZeuScCxtMfKKUyCDAs1cnkcjxzBeVqGKGAx6rF+/Gn/+uRcA0KFDZ4wYMRZKJRdFJHqYChc3LGyI6hYhBBJzDuDsrR9hFMWwVjijvedrcLFuLHU0i5Wbm4tvvlmES5diIJPJMGjQ8+jbtz8LSaJyeqwBxURk2fTGQpy5+T9cy40EAHjYtEBbj5ehVjhInMxyJSdfx9KlX+DmzXSo1dYYN+5VtGzZWupYRLUKixsiKpO26DqOpS5CbvENyCBHkMtzaOLYn+NrqtC5c6exYsUyFBYWwtXVHZMnT4WPj6/UsYhqHRY3RFRKYs5BnL35PQyiCNYKJ4R4TIarzRNSx7JYQgj88cfv2LJlA4QQaNIkEC+/HAF7e/aQET2KRy5uioqKkJCQgEaNGsHKijUSkSXQG3U4d/MHJOYeBAC42wSjnccrUCu46GRVKS4uxo8/rsTRo4cBAF279sDQoSP5vkr0GCr825Ofn48pU6Zg1apVAErWvQkICMCUKVNQr149zJo1q9JDElHVyym6gWOpi5BTfB2ADEHOg/GE0zM8DVWFsrOzsXz5V0hIiINcLsfzzw9Hjx69OXCY6DFV+F1r9uzZOHPmDPbt2wdra2vT9j59+mDdunWVGo6IqkdSTiT2X/8AOcXXoVY4orP3Wwh0HsjCpgolJl7BggUfICEhDjY2tnj99Zno2bMPCxuiSlDhnpstW7Zg3bp16NSpk9kvYXBwMOLi4io1HBFVLYOxCOdurcbVnP0AADfrILTzmARrK0eJk1m2kyePYdWqb1FcXARPTy9MnjwNHh5eUscishgVLm7S09Ph4eFRanteXh7/4iCqRXKLUnAsbTG0RUkAZAh0GoBA50HsralCRqMRv/32C3777WcAQLNmzTF+/GTY2tpKnIzIslS4uAkJCcG2bdswZcoUADAVNN9++y1CQ0MrNx0RVYnruUdxOn0F9KIQKrkD2nm8Cg/bYKljWTSdTof//e9bnDp1HADw5JP98OyzQ6FQKCRORmR5KlzcfPzxx+jfvz+io6Oh1+vxxRdfIDo6GocPH8b+/furIiMRVRKDsRjnM9biinYPAMDVOhDtPF6FjZWzxMksW0bGLSxd+iWuXUuEQqHA8OGj0blzd6ljEVmsCvc/d+3aFadPn4Zer0eLFi2wc+dOeHh4IDIyEu3atauKjERUCfKK0/Bn8r9NhU0Tp2fQ2fstFjZVLD7+MhYu/BeuXUuEvb0Dpk17m4UNURWTCSGE1CHqCq1WC0dHR2RnZ0Oj4bohVH2S847jVNp30IsCqOT2aOvxCjxtW0gdy+IdOXIQq1evgl6vR716fpg0aSpcXd2kjkVUK1XkM7Rcp6W0Wm25H5wf2kQ1h1HoEXVrPeK1uwAALurGCPGcBBsrV4mTWTaj0YgtWzbgjz9+BwC0atUWY8a8bLZ8BhFVnXIVN05OTuWeCWUwGB4rEBFVjvzimziWtgRZungAQGPH/mjq8hzkMq58W5UKCgrw3XdLERV1FgDQv/8AhIcPhlzOWWhE1aVc73J79+41/f/KlSuYNWsWxo4da5odFRkZiVWrVmHevHlVk5KIKuRG3imcSv8WxcZ8KOV2aOs+EV52raWOZfHS09OwZMkXSElJhlKpxMiR49G+fSepYxHVORUec9O7d29MnDgRw4cPN9u+evVqLF++HPv27avMfBaFY26oqhmFHtEZGxGXvQMA4KwOQIjHZNgqOc6jqsXGXsC33y5CXl4eHB2d8OqrU+DvHyB1LCKLUZHP0AoXN7a2tjhz5gyaNGlitv3ixYto3bo18vPzK564jmBxQ1WpQH8Lx1KXIFNXslJ4gKYfgl1f4GmoanDgwB6sX78aRqMBDRo0xKuvToGTE2ehEVWmSh9QfDc/Pz988803WLhwodn2b7/9Fn5+fhU9HBFVgtT8MziR9g2KjXmwktugjfsE+NhxaYaqZjDosWHDGhw4UDK9vn37ThgxYhxUKpXEyYjqtgoXN5999hmGDBmC7du3o2PHjgCAo0eP4tKlS/jpp58qPSAR3Z9RGBCTuQmXsn4DADiq/NHeczLslKUvkUKVKy8vF99+uxixsRcAAAMHDkFYWDgvQ0NUAzzSOjfXrl3DkiVLcOFCyS9106ZNMWnSJPbcPARPS1FlKtBn4kTaUtwqvAgAaKjpjWDXYVDIlBIns3w3biRj6dIvkJ6eBrVajbFjX0GrVm2ljkVk0ap0zA09OhY3VFnS8s/jRNpyFBlzYCWzRmv38ahn317qWHXC+fNn8N13y1BYWABXVzdMmjQV9erxDzuiqlalY26ISDpCGBGTuQUXs7YCEHBU1UeI52uwV3pKHc3iCSGwe/cObN68HkIING78BF5+OQIODvxDhaimYXFDVEsU6rNxIm0pbhbGAAD8HXqiuetLUMh5GqqqFRcXY82a/+HIkYMAgC5dumPYsFGwsuJbKFFNxN9MologvSAaJ9KWQWfQQiFTo7X7WPjac3G46qDVZmP58q8RH38ZMpkMzz8/HD179uHAYaIajMUNUQ0mhBEXs35FTObPAAQ0Kl+EeLwGB5W31NHqhKSkq1i69EtkZmbAxsYGEya8hmbNmksdi4ge4pGLm/T0dMTGxgIAAgMD4e7uXmmhiAjQGbQ4kbYM6QXRAID6Dt3QwnUErORqiZPVDadOHceqVd+gqKgIHh6emDx5Gjw9WVQS1QYVLm7y8vIwZcoUfP/996aLZCoUCowePRpfffUVbG1tKz0kUV1zsyAGx9OWQWfIgkKmQku30ajv0EXqWHWCEALbt/+KrVs3AwCCgoIxceJk2NraSZyMiMqrwpepnTlzJvbv349ffvkFWVlZyMrKws8//4z9+/fjjTfeqIqMRHWGEEZczNyKQzcWQmfIgoPSB93rvcfCppoUFenw3/8uMRU2vXr1RUTEDBY2RLVMhde5cXNzw8aNG9GzZ0+z7Xv37sXQoUORnp5emfksCte5oQfRGXJwMu0bpBWcAwD42XdGS7fRPA1VTQoK8vHFF58gMfEK5HIFXnxxFLp27SF1LCK6rUrXucnPz4enZ+k1NTw8PHjRTKJHdKvwEo6nLkGhIRNymRItXUeivkM3zsipJgaDHt9+uxiJiVdgZ2ePl1+OwBNPBEkdi4geUYVPS4WGhmLu3LkoLCw0bSsoKMAHH3yA0NDQSg1HZOmEMOJS1nYcSp6PQkMm7JVe6FFvDhpourOwqSZCCKxb9yMuXIiCSqXClClvsLAhquUq3HPz+eef46mnnoKvry9atWoFADhz5gysra2xY8eOSg9IZKmKDLk4mf4tUvPPAAB87TuhpdtoKOU2EierW/7443ccPLgPMpkM48a9ivr1/aWORESPqcLFTYsWLXDp0iX8+OOPiIkpWSl1+PDhGDFiBGxs+KZMVB4ZhXE4nrYEBfpbkMus0ML1JTRw6Mnemmp2+vQJbNmyAQDw3HPDePFLIgtRoeKmuLgYQUFB2Lp1K15++eWqykRksYQQiNfuQtSt9RAwwM7KAyGer8FJ3UDqaHXOlSvxWLFiOYQQ6NatF558sp/UkYioklSouFEqlWZjbYio/IoN+TiV/l/cyD8JAPCxa4/W7uN4GkoCGRm3sHTplyguLkKzZi0wdOgI9poRWZAKDyiOiIjAggULoNfrqyIPkUW6VXgJ+66/jxv5JyGHFVq4jkSIx2QWNhIoKCjA4sWfQavNho+PLyZMmAyFQiF1LCKqRBUec3Ps2DHs3r0bO3fuRIsWLWBnZ7641aZNmyotHFFtl6lLQGzGFqQWnAUA2Fq5I8RzMpzVDSVOVjcZDAb8979LkJx8HRqNI157bTrHChJZoAoXN05OThgyZEhVZCGyGNm6RMRkbkZK/mkAgAxy1HfoimCXYVAqeIkSKQghsH79j4iOPgelUoXJk6fBxcVV6lhEVAUqXNysWLGiKnIQWQRtURJiMn7GjfwTt7fI4GffGYHOA2Gn9JA0W123Z89O/PnnXtOU7wYN2HtGZKke6arger0e+/btQ1xcHF566SU4ODggOTkZGo0G9vb2lZ2RqMbTFl1HbObPSM47dnuLDL72HRHoNAj2Ki9JsxFw5sxJbNq0DgDw7LND0bo1p3wTWbIKFzdXr17FU089hcTEROh0OvTt2xcODg5YsGABdDodli5dWhU5iWqknKIbuJj1C67l/gWg5DJt9ew64AnngdCo6kkbjgAAiYlXsGLFMggh0LVrT/TuHSZ1JCKqYhUubqZNm4aQkBCcOXMGrq5/n69+9tlnufYN1Rm5xam4mPkLknIjcaeo8bYLQZDzQGhUftKGI5OMjFtYsuQLFBUVoWnTYAwbxinfRHVBhYubP//8E4cPH4ZKpTLb7u/vj+vXr1daMKKaKK84HRezfkVSziEIGAEAXrZtEOQ8GI7q+hKno7sVFhZgyZIvkJ2dBW/vepg48TUoFI90Jp6IapkK/6YbjUYYDIZS269duwYHB4dKCUVU0+Trb+Fi5q9IzDkIgZKff0/bVgh0HsRp3TXQnSnf168nQaPR3J7yzVlqRHVFhRfx69evHz7//HPTbZlMhtzcXMydOxdPP/10hY41b948tG/fHg4ODvDw8MDgwYMRGxtr1qawsBARERFwdXWFvb09hgwZgtTUVLM2iYmJCA8Ph62tLTw8PPDWW2+VWmRw3759aNu2LdRqNRo3boyVK1eWyrNo0SL4+/vD2toaHTt2xNGjRyuchSxLgT4TZ25+jz8S38HVnP0QMMDDpjm6+byLTl7TWdjUQEIIbNy4GlFRJVO+J02aBldXN6ljEVE1qnBx8+mnn+LQoUNo1qwZCgsL8dJLL5lOSS1YsKBCx9q/fz8iIiJw5MgR7Nq1C8XFxejXrx/y8vJMbWbMmIFff/0VGzZswP79+5GcnIznnnvOtN9gMCA8PBxFRUU4fPgwVq1ahZUrV+K9994ztUlISEB4eDh69eqF06dPY/r06Zg4caLZVczXrVuHmTNnYu7cuTh58iRatWqFsLAwpKWllTsLWY5CfRbO3fwRfyS9jSvaPRAwwM2mGbr6/AOh3m/AxbqR1BHpPvbu3YX9+/dAJpNh7NiX4e8fIHUkIqpmMiGEqOid9Ho91q5di7NnzyI3Nxdt27atlKuCp6enw8PDA/v370f37t2RnZ0Nd3d3rF69Gs8//zwAICYmBk2bNkVkZCQ6deqE7du345lnnkFycjI8PT0BAEuXLsU777yD9PR0qFQqvPPOO9i2bRvOnz9veqwXX3wRWVlZ+P333wEAHTt2RPv27fH1118DKDn95ufnhylTpmDWrFnlyvIwWq0Wjo6OyM7OhkajeazXiqqGzqDFpazfkKDdA6MoBgC4WgciyHkw3GyCJE5HD3P27GksW/YlhBB49tmh6Nu3v9SRiKiSVOQz9JFG11lZWWHkyJGPFO5BsrOzAQAuLi4AgBMnTqC4uBh9+vQxtQkKCkL9+vVNBUVkZCRatGhhKmwAICwsDJMnT0ZUVBTatGmDyMhIs2PcaTN9+nQAQFFREU6cOIHZs2eb9svlcvTp0weRkZHlznIvnU4HnU5nuq3Vah/1paEqpjPk4HLWdiRod8MgigAALurGCHJ5Fm7WTTnDphZISrqKFSuWQgiBLl16oE+fp6SOREQSeaTiJjk5GQcPHkRaWhqMRqPZvqlTpz5SEKPRiOnTp6NLly5o3rw5ACAlJQUqlQpOTk5mbT09PZGSkmJqc3dhc2f/nX0PaqPValFQUIDMzEwYDIYy28TExJQ7y73mzZuHDz74oJyvAEmhyJCLy9k7EJ/9Bwyi5Ir3TuoANHUeDHeb5ixqaonMzAwsXvw5dDodgoKC8eKLI/m9I6rDKlzcrFy5Eq+++ipUKhVcXV3N3kBkMtkjFzcRERE4f/48Dh48+Ej3r4lmz56NmTNnmm5rtVr4+XENlJqg2JCPuOwdiMveBb0oAAA4qhogyOVZeNq05AdjLVJYWHjXlG8fvPwyp3wT1XUVfgeYM2cO3nvvPcyePRtyeYXHI5fp9ddfx9atW3HgwAH4+vqatnt5eaGoqAhZWVlmPSapqanw8vIytbl3VtOdGUx3t7l3VlNqaio0Gg1sbGygUCigUCjKbHP3MR6W5V5qtRpqtboCrwRVtWJjAeKzdyEueweKjfkAAI3KD0HOg+Fl24ZFTS1jNBrx3XdLce1aIhwcNHjttRmc8k1EFZ8tlZ+fjxdffLFSChshBF5//XVs3rwZe/bsQcOG5tNq27VrB6VSid27d5u2xcbGIjExEaGhoQCA0NBQnDt3zmxW065du6DRaNCsWTNTm7uPcafNnWOoVCq0a9fOrI3RaMTu3btNbcqThWouvbEQFzO3YlfiW4jJ3IxiYz4clPXQ3iMCPeu9D2+7tixsaqGNG9fg/PkzUCqVmDRpKqd8ExGAR+i5mTBhAjZs2IBZs2Y99oNHRERg9erV+Pnnn+Hg4GAau+Lo6AgbGxs4OjpiwoQJmDlzJlxcXKDRaDBlyhSEhoaaBvD269cPzZo1w6hRo7Bw4UKkpKTg3XffRUREhKnXZNKkSfj666/x9ttvY/z48dizZw/Wr1+Pbdu2mbLMnDkTY8aMQUhICDp06IDPP/8ceXl5GDdunCnTw7JQzaM36pCg3YPLWb+hyJgLALBXeiPIeRB87NpDJquc3keqfvv2/YF9+/4AAIwZ8zIaNuT0fCIqUeGp4AaDAc888wwKCgrQokULKJVKs/3/+c9/yv/g9/lLecWKFRg7diyAkvPpb7zxBtasWQOdToewsDAsXrzY7FTQ1atXMXnyZOzbtw92dnYYM2YM5s+fDyurv2u3ffv2YcaMGYiOjoavry/mzJljeow7vv76a3zyySdISUlB69at8eWXX6Jjx46m/eXJ8iCcCl59DMYiXNHuxaXs36AzlMxSs1N6ItBpEHztO7KoqeXOnTuDpUu/gBACgwc/j379wqWORERVrCKfoRUubj766CO89957CAwMhKenZ6kBxXv27Hm01HUAi5uqZzAW42rOflzM2gadIQsAYGvljkDngfC1D4VcppA2ID22pKRE/Oc/H0On06Fz524YMWIcTykS1QFVus7Np59+iu+++65UrweRlAyiGIk5f+Ji5lYUGjIBADZWrgh0Ggg/h86Qyzh7xhJkZWViyZKSKd+Bgc0wfPhoFjZEVEqF3/HVajW6dOlSFVmIKswo9EjMOYSLWb+iQH8LAGCtcEag8wDUd+jGosaClEz5/hxZWZnw8vLByy9HcMo3EZWpwu8M06ZNw1dffYUvv/yyKvIQlYtRGJCUexgXM39Fvj4dAKBWOOEJp2fQQNMdCpnyIUeg2sRoNGLFimVISkqEvb0DXnttOmxtOeWbiMpW4eLm6NGj2LNnD7Zu3Yrg4OBSA4o3bdpUaeGI7iWEEddyjyA282fk6Uum/6sVGjRxCoe/Q08o5CqJE1JV2LRpHc6dOw0rKytMmjQVbm7uUkciohqswsWNk5MTr4RN1U4II67nHUNs5hbkFpcsGaCS25cUNZpesJJzsURLtX//HuzZsxNAyZTvgIDGEiciopquwsXNihUrqiIHUZmEMOJG3gnEZP6MnOLrAACl3A5NnPqjoaY3rOTWEiekqhQVdRbr1/8AABg4cAjatesgcSIiqg04Go9qJCEEUvJPISZzC7RFSQAApdwWjRyfQoBjHyjlNhInpKp27VoSvv12CYQQCA3tirAwrmVDROVT4eKmYcOGD5x6GR8f/1iBiG4WxOD8rTXILkoEAFjJbNDIsR8aOfaDUsFBpHVBdnbW7SnfhXjiiSAMHz6GU76JqNweWtxs3LgRnTp1Ml3Qcvr06Wb7i4uLcerUKfz+++946623qiQk1Q3FxgJE31qPKzn7AAAKmTUaOfZBI8cwqBT2kmaj6qPT6bBkyRfIzMyAp6c3XnnldbPVxomIHuah7xhWVlbo1q0btmzZglatWmHatGlltlu0aBGOHz9e6QGpbkjNP4vT6StNC/D5O/REkMtzUCscJE5G1enOlO/ExCuwt7dHRMR02NraSR2LiGqZcl1+4ejRo3jllVdw+vTp+7aJj49H69atodVqKzOfReHlF0orMuTi/K01SMo9DKDkUglt3MfDzSZI4mQkhZ9+Wovdu3fAysoK06a9jUaNmkgdiYhqiEq//EKHDh1w4MCBB7bZuHEjXFxcyp+S6rzkvOM4e/P72xe2lKGRY18EOT/Had111IEDe7F79w4AwOjRE1nYENEjK/eJ7DtVUps2bcwG9gkhkJKSgvT0dCxevLjyE5LFKdRn49ytH5GcdwwAYK/0QRv3cXCx5voldVV09DnTlO8BA55FSEhHiRMRUW1W4VF6gwcPNrstl8vh7u6Onj17IiiIpxLo/oQQuJZ7BOdu/YhiYx5kkKOx09MIdBoIhZyXS6irrl9PwrffLobRaESnTl3w1FMDpI5ERLVchYubuXPnVkUOsnAF+kycubkKqflnAAAalR/auE+Ak7qBxMlIStnZ2Viy5AsUFhaiSZNAvPTSWE75JqLHxvmVVKWEEEjMOYDzt9ZBLwoghxWecB6IJk79ecXuOq6oSIelS79ARsYteHh4cco3EVWacr+TyOXyh/5FJZPJoNfrHzsUWYa84nScvrkSNwuiAQBO6gC0cR8PjaqexMlIakajEStXfoOrVxNgZ2ePiIgZsLPjWkZEVDnKXdxs3rz5vvsiIyPx5Zdfwmg0Vkooqt2EMCJBuwfRGRtgEEWQy5Ro6jwEjRz7QiaTSx2PaoAtWzbi9OkTsLKywquvToG7u4fUkYjIgpS7uBk0aFCpbbGxsZg1axZ+/fVXjBgxAv/6178qNRzVPjlFN3A6fQUydJcAAK7WgWjtPg72Sk+Jk1FNcfDgfvzxx3YAwKhR49G48RMSJyIiS/NIJ7iTk5Mxd+5crFq1CmFhYTh9+jSaN29e2dmoFjEKA+Kyf0dM5hYYhR4KmTWCXV+Av0NP9taQyYULUVi79n8AgGeeGYz27UMlTkRElqhCxU12djY+/vhjfPXVV2jdujV2796Nbt26VVU2qiW0RUk4mfYdsouuAAA8bJqjlftY2Fq5ShuMapTk5Ov45ptFMBqN6NChM/r3Hyh1JCKyUOUubhYuXIgFCxbAy8sLa9asKfM0FdUtRqHHxcytuJi1FQIGKOW2aO46HH72XTidl8xotdlYvPgzFBYWoHHjQIwYwSnfRFR1ynVtKaBktpSNjQ369OkDhUJx33abNm2qtHCWxpKuLZWpS8Dp9O+gLboGAPCybYtWbqNgbeUkbTCqcYqKdPj884W4ciUeHh6eePPNd2Fvz5lRRFQxlX5tKQAYPXo0/9IiGIxFiMncgsvZvwMQUMkd0NJtJHzs2vPng0oxGo1YtepbXLkSDzs7O7z22nQWNkRU5cpd3KxcubIKY1BtcKvwIk6lr0BecQoAwNe+E5q7vgS1wkHiZFRT/fLLTzh16vjtKd9T4eHhJXUkIqoDuBwoPZTeWIjojJ+QoN0NQECtcEIrt9HwtmsjdTSqwQ4d2o+dO38DAIwYMY5Tvomo2rC4oQdKy4/CmZsrka+/CQCo79ANzV1ehFJhK3EyqsliYqKxZs33AICnnx6Ejh07S5yIiOoSFjdUpmJjPqJurcPVnAMAABsrV7R2GwcP22CJk1FNd+PGdXzzzdcwGg1o374TwsM5s5KIqheLGyolJe80ztxchUJDFgCgoaY3mrk8Dyu5tbTBqMbLydFi8eLPUVBQgEaNmmDkyPEcaE5E1Y7FDZnoDDk4f2s1ruUeAQDYKT3Rxm08XG04VoIerqioCEuXfolbt27C3d0Dr746BUqlUupYRFQHsbghCCGQnHccZ29+jyJjDgAZGjs+hSDnwVDIVVLHo1rAaDTif//7FgkJcbC1tcNrr82AvT1n0RGRNFjc1HGF+mycvfk/3Mg/CQBwUNZDG/fxcLYOkDgZ1Sa//roZJ08eg0KhwCuvvA5PT075JiLpsLipo4QQSMo9jPO31qDYmAcZFHjCKRxNnJ+BQsZTCVR+kZF/YseOrQBKpnw/8USQxImIqK5jcVMH5etv4Uz6KqQVnAMAOKoaoI37eDiq60ucjGqb2Nho/PjjKgBA//4D0alTF4kTERGxuKlThDDias5+RN1aD70ohFxmhUDnwWjs+BTksvtfL4yoLCkpN7B8+SIYjQaEhHTEM88MljoSEREAFjd1Rl5xGk6nr8DNwhgAgIu6MVq7j4eDylviZFTbGI1GHD16GFu2bEBBQT4aNWqCUaMmcMo3EdUYLG4snBBGxGfvwoXMTTCIIihkKjR1eR4Bmt6QyeRSx6NaJiEhDhs2rMaVK/EAAG/venjlldc55ZuIahQWNxYspygZp9K/Q6YuDgDgZt0Urd3Hwk7pIXEyqm2ysjKxZctGHD16GABgbW2N/v0HomfPPixsiKjGYXFjgYxCj8tZvyM282cYoYeVzBrBri+igUN3njqgCikuLsbu3TuwY8dW6HQ6AEBoaFcMHPg8HB0dJU5HRFQ2FjcWJluXiFPp/0V2USIAwNOmJVq5j4GNlYvEyag2EULgzJlT2LRpLW7eTAcANGzYCC+88BL8/bkGEhHVbCxuLIRBFONi5q+4lPUbBAxQyu3QwvUl+NqHsreGKiQ5+To2blyNmJhoAICjoxMGD34BHTrwZ4mIagcWNxYgozAOp9O/Q05xMgDAxy4ELVxHwtqKpw2o/PLycrF16xb8+edeGI1GWFlZoU+fp9CvXzisrXnRVCKqPVjc1HIxmVsQm/kLAAG1QoOWrqPgYx8idSyqRQwGAw4d2o9ff92MvLxcAEDr1u3w3HND4ebGwedEVPuwuKnlrBXOAAR87TujhetwqBT2UkeiWuTixRhs2LAa168nAQB8fOrh+edfQlBQM4mTERE9OhY3tVwDh+5wUPnA1bqJ1FGoFrl16yY2bVqHU6eOAwBsbe0wYMCz6Nq1JxQKrlZNRLUbi5taTiaTsbChctPpdNi5cxt27doOvV4PmUyG7t17ITz8Wdjbs9ePiCwDixuiOkAIgePHj2Dz5g3IysoEAAQGNsXzzw9HvXp+EqcjIqpcLG6ILFxi4hVs2LAacXGXAACurm547rlhaN26Had2E5FFYnFDZKG02mz88stPiIw8CCEEVCoVnnpqAHr3DuMlE4jIorG4IbIwer0e+/b9gd9++wWFhQUAgPbtO+HZZ4fCyclZ4nRERFWPxQ2RBTl//gw2blyLtLQUAED9+v544YWX0KgRB50TUd0hlzrAgQMHMGDAAPj4+EAmk2HLli1m+4UQeO+99+Dt7Q0bGxv06dMHly5dMmuTkZGBESNGQKPRwMnJCRMmTEBubq5Zm7Nnz6Jbt26wtraGn58fFi5cWCrLhg0bEBQUBGtra7Ro0QK//fZbhbMQSSE19QYWLfoMixd/jrS0FGg0GowaNR5vvz2HhQ0R1TmSFzd5eXlo1aoVFi1aVOb+hQsX4ssvv8TSpUvx119/wc7ODmFhYSgsLDS1GTFiBKKiorBr1y5s3boVBw4cwCuvvGLar9Vq0a9fPzRo0AAnTpzAJ598gvfffx/Lly83tTl8+DCGDx+OCRMm4NSpUxg8eDAGDx6M8+fPVygLUXUqKMjHTz+txYcfzkFU1FkoFAr06dMfc+fOR2hoN8jlkv+KExFVP1GDABCbN2823TYajcLLy0t88sknpm1ZWVlCrVaLNWvWCCGEiI6OFgDEsWPHTG22b98uZDKZuH79uhBCiMWLFwtnZ2eh0+lMbd555x0RGBhouj106FARHh5ulqdjx47i1VdfLXeWh8nOzhYARHZ2drnaE92PwWAQBw/uF2+/PVVMnjxWTJ48Vixa9JlISbkhdTQioipRkc/QGv1nXUJCAlJSUtCnTx/TNkdHR3Ts2BGRkZEAgMjISDg5OSEk5O/rKfXp0wdyuRx//fWXqU337t2hUqlMbcLCwhAbG4vMzExTm7sf506bO49TnixE1SEu7hIWLPgXfvxxBXJytPD09EJExEy89tp0eHp6SR2PiEhyNXpAcUpKyaBIT09Ps+2enp6mfSkpKfDwML+4n5WVFVxcXMzaNGzYsNQx7uxzdnZGSkrKQx/nYVnupdPpoNPpTLe1Wu1DnjHR/WVmZmDz5vU4frykaLexscHTTw9Cz569oVDU6F9lIqJqxXfEKjRv3jx88MEHUsegWq6oqAh//PE7du7chqKiIshkMnTu3B0DBz4HBweN1PGIiGqcGl3ceHmVdLGnpqbC29vbtD01NRWtW7c2tUlLSzO7n16vR0ZGhun+Xl5eSE1NNWtz5/bD2ty9/2FZ7jV79mzMnDnTdFur1cLPj0vdU/kIIXDq1HFs2rQOGRm3AACNGjXB0KEj4OfXQOJ0REQ1V40ec9OwYUN4eXlh9+7dpm1arRZ//fUXQkNDAQChoaHIysrCiRMnTG327NkDo9GIjh07mtocOHAAxcXFpja7du1CYGAgnJ2dTW3ufpw7be48Tnmy3EutVkOj0Zh9EZXHtWtJ+Pzzhfj228XIyLgFZ2cXjB8/CTNnzmZhQ0T0EJL33OTm5uLy5cum2wkJCTh9+jRcXFxQv359TJ8+HR999BGaNGmChg0bYs6cOfDx8cHgwYMBAE2bNsVTTz2Fl19+GUuXLkVxcTFef/11vPjii/Dx8QEAvPTSS/jggw8wYcIEvPPOOzh//jy++OILfPbZZ6bHnTZtGnr06IFPP/0U4eHhWLt2LY4fP26aLi6TyR6ahehx5ebm4NdfN+PgwX0QQkCpVKJv36fRr19/qFRqqeMREdUOVT536yH27t0rAJT6GjNmjBCiZAr2nDlzhKenp1Cr1aJ3794iNjbW7Bi3bt0Sw4cPF/b29kKj0Yhx48aJnJwcszZnzpwRXbt2FWq1WtSrV0/Mnz+/VJb169eLJ554QqhUKhEcHCy2bdtmtr88WR6EU8HpfvT6YrFnzy4xc+Zrpqnd33yzSNy8mS51NCKiGqEin6EyIYSQsLaqU7RaLRwdHZGdnc1TVGQSExOFDRtW48aNZACAr68fnn/+JTzxRJDEyYiIao6KfIZKflqKqK5KT0/DTz+txdmzpwAAdnb2GDjwOXTp0oMrCxMRPQYWN0TVrLCwAL//vhV79uyEXq+HXC5Hjx69ER4+CLa2dlLHIyKq9VjcEFUTo9GIo0cj8fPPG5GdnQUAaNo0GM8/Pxze3vWkDUdEZEFY3BBVMSEELl6Mwc8/b8SVK/EAAHd3DwwZ8iJatGgNmUwmcUIiIsvC4oaoihQWFuCvvyJx4MAe3LhxHQCgVlujf/8B6NWrL5RKpcQJiYgsE4sbokqWnHwdBw7swV9/HYZOVwgAUKlU6NixC55+eiAcHZ2kDUhEZOFY3BBVAoNBjzNnTmH//t24dCnWtN3T0wvduz+JTp26wMbGVsKERER1B4sboseQlZWJgwf349Ch/aZBwjKZDC1btkGPHr0RGNiUY2qIiKoZixuiChJC4NKlWOzfvxtnzpyE0WgEAGg0GnTp0gNduvSAi4urxCmJiOouFjdE5VRQUICjRw/fHiCcbNreqFET9OjRG61bt4OVFX+liIikxndioocoa4CwWq1G+/ah6N79Sfj6+kmckIiI7sbihqgMBoMep0+fxIEDe+4ZIOx9e4BwZw4QJiKqoVjcEN2lrAHCcrn89gDhJ/HEExwgTERU07G4oTrvwQOEe6Jr1x5wdnaROCUREZUXixuqs+43QLhx4yfQvfuTHCBMRFRL8Z2b6pzk5GvYv38Pjh6N5ABhIiILxOKG6oQ7A4T379+Dy5c5QJiIyJKxuCGLVjJAeB8OHTrAAcJERHUEixuyOEIIXLwYgwMH9nCAMBFRHcTihixGQUEB/vrrEA4c2IuUFA4QJiKqq/hOT7Xe/QYId+jQGd2790K9ehwgTERUl7C4oVpJr9fj9OkTOHBgb6kBwj16PImOHTlAmIiormJxQ7XKnQHCBw/uh1abDaBkgHCrVm3RvXsvDhAmIiIWN1Tz3X+AsCO6du2BLl04QJiIiP7G4oZqrPsPEA5E9+69OECYiIjKxE8GqlGKi4sRH38ZJ08ew9Gjh6HT6QBwgDAREZUfixuSlNFoRHLyNcTERCMmJgqXLl1EcXGRab+Xlw+6d++Fjh27wMbGRsKkRERUW7C4oWqXkXELMTFRiImJRmzsBeTkaM32azQaBAUFIzS0G554IogDhImIqEJY3FCVKyjIR2xsDGJjo3DhQjTS0lLM9qvVajRuHIigoGYICmoGHx9fFjRERPTIWNxQpdPr9UhIiDOdarpyJR5CCNN+mUwGf/+A28VMMBo2bMSBwUREVGn4iUKPTQiBGzeu48KFklNNly/HmgYC3+Hh4WUqZp54Igi2tlxgj4iIqgaLG3okWVmZpnEzMTHRpgX17rC3dzCdZgoKCoaLi6tESYmIqK5hcUPlUlhYgIsXY00Fzd3rzgCAUqlCkyZPIDCwGZo2DYaPjy/kcrlEaYmIqC5jcUNlMhj0uHIlARcuRCE2NhoJCfEwGg2m/TKZDPXr+yMoKBhBQc0QENAYSqVSwsREREQlWNwQgJJxMykpN0w9M5cuxaCwsNCsjbu7h6mYeeKJINjZ2UuUloiI6P5Y3NRh2dlZphlNMTHRyM7OMttvZ2ePwMCmaNo0GEFBwXB1dZMmKBERUQWwuKlDCgsLcflyrKmgSU6+brZfqVSiUaMnTAOBfX3rc9wMERHVOixuLJjBYMDVqwmmGU0JCZdhMJiPm/Hza2Ca0RQQ0BgqlUrCxERERI+PxY0FEUIgLS0FMTHRuHAhChcvxqCwsMCsjaurG4KCgtG0acl6M/b2DhKlJSIiqhosbmq5nByt2biZzMwMs/22tnYIDGyKoKCSKdpubh4SJSUiIqoeLG5quV9/3YSDB/ebbltZWaFRoyamWU1+fg04boaIiOoUFje1XFBQMK5cSTAVM40bN4FKpZY6FhERkWRY3NRybdu2R9u27aWOQUREVGPwfAURERFZFBY3REREZFFY3BAREZFFYXFDREREFoXFDREREVkUFjdERERkUVjcEBERkUVhcUNEREQWhcUNERERWRQWN0RERGRRWNwQERGRReG1paqREAIAoNVqJU5CRERUu9z57LzzWfogLG6qUU5ODgDAz89P4iRERES1U05ODhwdHR/YRibKUwJRpTAajUhOToaDgwNkMlmlHVer1cLPzw9JSUnQaDSVdlypWNrzAficags+p9rB0p6TpT0foGqekxACOTk58PHxgVz+4FE17LmpRnK5HL6+vlV2fI1GYzG/GIDlPR+Az6m24HOqHSztOVna8wEq/zk9rMfmDg4oJiIiIovC4oaIiIgsCosbC6BWqzF37lyo1Wqpo1QKS3s+AJ9TbcHnVDtY2nOytOcDSP+cOKCYiIiILAp7boiIiMiisLghIiIii8LihoiIiCwKixsiIiKyKCxuarEDBw5gwIAB8PHxgUwmw5YtW6SO9FjmzZuH9u3bw8HBAR4eHhg8eDBiY2OljvVYlixZgpYtW5oWsgoNDcX27duljlVp5s+fD5lMhunTp0sd5bG8//77kMlkZl9BQUFSx3os169fx8iRI+Hq6gobGxu0aNECx48flzrWI/P39y/1PZLJZIiIiJA62iMzGAyYM2cOGjZsCBsbGzRq1Agffvhhua6dVJPl5ORg+vTpaNCgAWxsbNC5c2ccO3asWjNwheJaLC8vD61atcL48ePx3HPPSR3nse3fvx8RERFo37499Ho9/vGPf6Bfv36Ijo6GnZ2d1PEeia+vL+bPn48mTZpACIFVq1Zh0KBBOHXqFIKDg6WO91iOHTuGZcuWoWXLllJHqRTBwcH4448/TLetrGrv22NmZia6dOmCXr16Yfv27XB3d8elS5fg7OwsdbRHduzYMRgMBtPt8+fPo2/fvnjhhRckTPV4FixYgCVLlmDVqlUIDg7G8ePHMW7cODg6OmLq1KlSx3tkEydOxPnz5/H999/Dx8cHP/zwA/r06YPo6GjUq1evekIIsggAxObNm6WOUanS0tIEALF//36po1QqZ2dn8e2330od47Hk5OSIJk2aiF27dokePXqIadOmSR3pscydO1e0atVK6hiV5p133hFdu3aVOkaVmjZtmmjUqJEwGo1SR3lk4eHhYvz48WbbnnvuOTFixAiJEj2+/Px8oVAoxNatW822t23bVvzzn/+sthw8LUU1VnZ2NgDAxcVF4iSVw2AwYO3atcjLy0NoaKjUcR5LREQEwsPD0adPH6mjVJpLly7Bx8cHAQEBGDFiBBITE6WO9Mh++eUXhISE4IUXXoCHhwfatGmDb775RupYlaaoqAg//PADxo8fX6kXIa5unTt3xu7du3Hx4kUAwJkzZ3Dw4EH0799f4mSPTq/Xw2AwwNra2my7jY0NDh48WG05am+/K1k0o9GI6dOno0uXLmjevLnUcR7LuXPnEBoaisLCQtjb22Pz5s1o1qyZ1LEe2dq1a3Hy5MlqP4delTp27IiVK1ciMDAQN27cwAcffIBu3brh/PnzcHBwkDpehcXHx2PJkiWYOXMm/vGPf+DYsWOYOnUqVCoVxowZI3W8x7ZlyxZkZWVh7NixUkd5LLNmzYJWq0VQUBAUCgUMBgP+/e9/Y8SIEVJHe2QODg4IDQ3Fhx9+iKZNm8LT0xNr1qxBZGQkGjduXH1Bqq2PiKoULOy01KRJk0SDBg1EUlKS1FEem06nE5cuXRLHjx8Xs2bNEm5ubiIqKkrqWI8kMTFReHh4iDNnzpi2WcJpqXtlZmYKjUZTa08fKpVKERoaarZtypQpolOnThIlqlz9+vUTzzzzjNQxHtuaNWuEr6+vWLNmjTh79qz43//+J1xcXMTKlSuljvZYLl++LLp37y4ACIVCIdq3by9GjBghgoKCqi0DixsLYUnFTUREhPD19RXx8fFSR6kSvXv3Fq+88orUMR7J5s2bTW9Yd74ACJlMJhQKhdDr9VJHrDQhISFi1qxZUsd4JPXr1xcTJkww27Z48WLh4+MjUaLKc+XKFSGXy8WWLVukjvLYfH19xddff2227cMPPxSBgYESJapcubm5Ijk5WQghxNChQ8XTTz9dbY/NMTdUYwgh8Prrr2Pz5s3Ys2cPGjZsKHWkKmE0GqHT6aSO8Uh69+6Nc+fO4fTp06avkJAQjBgxAqdPn4ZCoZA6YqXIzc1FXFwcvL29pY7ySLp06VJqGYWLFy+iQYMGEiWqPCtWrICHhwfCw8OljvLY8vPzIZebfwwrFAoYjUaJElUuOzs7eHt7IzMzEzt27MCgQYOq7bE55qYWy83NxeXLl023ExIScPr0abi4uKB+/foSJns0ERERWL16NX7++Wc4ODggJSUFAODo6AgbGxuJ0z2a2bNno3///qhfvz5ycnKwevVq7Nu3Dzt27JA62iNxcHAoNQbKzs4Orq6utXps1JtvvokBAwagQYMGSE5Oxty5c6FQKDB8+HCpoz2SGTNmoHPnzvj4448xdOhQHD16FMuXL8fy5culjvZYjEYjVqxYgTFjxtTqqfp3DBgwAP/+979Rv359BAcH49SpU/jPf/6D8ePHSx3tsezYsQNCCAQGBuLy5ct46623EBQUhHHjxlVfiGrrI6JKt3fvXgGg1NeYMWOkjvZIynouAMSKFSukjvbIxo8fLxo0aCBUKpVwd3cXvXv3Fjt37pQ6VqWyhDE3w4YNE97e3kKlUol69eqJYcOGicuXL0sd67H8+uuvonnz5kKtVougoCCxfPlyqSM9th07dggAIjY2VuoolUKr1Ypp06aJ+vXrC2traxEQECD++c9/Cp1OJ3W0x7Ju3ToREBAgVCqV8PLyEhERESIrK6taM8iEqOVLIRIRERHdhWNuiIiIyKKwuCEiIiKLwuKGiIiILAqLGyIiIrIoLG6IiIjIorC4ISIiIovC4oaIiIgsCosbIiIisigsbojIYo0dOxYymQyTJk0qtS8iIgIymQxjx46t/mBEVKVY3BCRRfPz88PatWtRUFBg2lZYWIjVq1fXymuwEdHDsbghIovWtm1b+Pn5YdOmTaZtmzZtQv369dGmTRvTNqPRiHnz5qFhw4awsbFBq1atsHHjRtP+zMxMjBgxAu7u7rCxsUGTJk2wYsWKan0uRFQ+LG6IyOKNHz/erBD57rvvSl2heN68efjf//6HpUuXIioqCjNmzMDIkSOxf/9+AMCcOXMQHR2N7du348KFC1iyZAnc3Nyq9XkQUfnwwplEZLHGjh2LrKwsfPPNN/Dz80NsbCwAICgoCElJSZg4cSKcnJywbNkyuLi44I8//kBoaKjp/hMnTkR+fj5Wr16NgQMHws3NDd99951UT4eIyslK6gBERFXN3d0d4eHhWLlyJYQQCA8PN+t1uXz5MvLz89G3b1+z+xUVFZlOXU2ePBlDhgzByZMn0a9fPwwePBidO3eu1udBROXD4oaI6oTx48fj9ddfBwAsWrTIbF9ubi4AYNu2bahXr57ZPrVaDQDo378/rl69it9++w27du1C7969ERERgf/7v/+rhvREVBEsboioTnjqqadQVFQEmUyGsLAws33NmjWDWq1GYmIievTocd9juLu7Y8yYMRgzZgy6deuGt956i8UNUQ3E4oaI6gSFQoELFy6Y/n83BwcHvPnmm5gxYwaMRiO6du2K7OxsHDp0CBqNBmPGjMF7772Hdu3aITg4GDqdDlu3bkXTpk2leCpE9BAsboioztBoNPfd9+GHH8Ld3R3z5s1DfHw8nJyc0LZtW/zjH/8AAKhUKsyePRtXrlyBjY0NunXrhrVr11ZXdCKqAM6WIiIiIovCdW6IiIjIorC4ISIiIovC4oaIiIgsCosbIiIisigsboiIiMiisLghIiIii8LihoiIiCwKixsiIiKyKCxuiIiIyKKwuCEiIiKLwuKGiIiILAqLGyIiIrIo/w91CAx6/2x1agAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Comparativas entre miembros vs ocasionales"
      ],
      "metadata": {
        "id": "-Ow_u3fLAPCr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Percentage of members by category\n",
        "df['member_casual'].value_counts(normalize=True)*100"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "id": "MGa5sKiDkCtS",
        "outputId": "9458917d-91aa-4afb-f657-c5fd23e9c139"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "member_casual\n",
              "member    63.603588\n",
              "casual    36.396412\n",
              "Name: proportion, dtype: float64"
            ],
            "text/html": [
              "<div>\n",
              "<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>proportion</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>member_casual</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>member</th>\n",
              "      <td>63.603588</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>casual</th>\n",
              "      <td>36.396412</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Percentage of members by category\n",
        "df.groupby('member_casual')['ride_lenght'].mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "id": "3yeQymfwT59h",
        "outputId": "ff39b084-b063-4235-99a2-e2c1779d278a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "member_casual\n",
              "casual   0 days 00:25:08.303247889\n",
              "member   0 days 00:12:49.850469939\n",
              "Name: ride_lenght, dtype: timedelta64[ns]"
            ],
            "text/html": [
              "<div>\n",
              "<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>ride_lenght</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>member_casual</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>casual</th>\n",
              "      <td>0 days 00:25:08.303247889</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>member</th>\n",
              "      <td>0 days 00:12:49.850469939</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> timedelta64[ns]</label>"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Generate bar chart\n",
        "def generate_graph(dft, title, y_col, x_col):\n",
        "  # Crea el gráfico de barras (el resto del código es igual)\n",
        "  plt.figure(figsize=(10, 6))\n",
        "  sns.barplot(x=x_col, y=y_col, hue='member_casual', data=dft, order=weekday_order)\n",
        "\n",
        "  # Personaliza el gráfico\n",
        "  plt.title(title)\n",
        "  plt.xlabel(x_col)\n",
        "  plt.ylabel(y_col)\n",
        "\n",
        "  plt.title(title)\n",
        "  plt.xlabel('Day of week')\n",
        "  plt.ylabel('Total ride_lenght')\n",
        "  plt.legend(title='Member type')\n",
        "\n",
        "  # Show graph\n",
        "  plt.show()"
      ],
      "metadata": {
        "id": "YVoyiOm3ENxe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Group data by 'weekday' y 'member_casual', and calculate mean of 'ride_lenght'\n",
        "grouped_data = df.groupby(['weekday', 'member_casual'], observed=False)['ride_in_seconds'].mean().reset_index()\n",
        "\n",
        "# Sort DataFrame by 'weekday'\n",
        "grouped_data = grouped_data.sort_values('weekday')\n",
        "\n",
        "# Call function to generate graph\n",
        "generate_graph(grouped_data, 'AVG ride_lenght per weekday and member type', y_col='ride_in_seconds', x_col='weekday')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "9RunlSKVJCJm",
        "outputId": "5776ba32-cc7b-46e8-fb00-9835154f6ff6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Group data by 'weekday' y 'member_casual', and calculate mean of 'ride_lenght'\n",
        "grouped_data = df.groupby(['weekday', 'member_casual'], observed=False)['member_casual'].value_counts().reset_index()\n",
        "\n",
        "# Call function to generate graph\n",
        "generate_graph(grouped_data, 'Count rides per weekday and member type', y_col='count', x_col='weekday')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "Nh5YSuYWDvJ6",
        "outputId": "9ce74de9-2e3f-4c5d-f1b4-bacd22bb0e4b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Preferencia de tipo de bicicleta por tipo de usuario"
      ],
      "metadata": {
        "id": "Trca81ruS3Fd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "group_data3 = df.groupby('member_casual')['rideable_type'].value_counts(normalize=True)*100\n",
        "group_data3 = group_data3.reset_index()\n",
        "\n",
        "  # Crea el gráfico de barras (el resto del código es igual)\n",
        "plt.figure(figsize=(8, 4))\n",
        "sns.barplot(x='rideable_type', y='proportion', hue='member_casual', data=group_data3)\n",
        "\n",
        "  # Personaliza el gráfico\n",
        "plt.title('Porcentaje de viajes por tipo de bicicleta y membresía')\n",
        "plt.xlabel('Rideable Type')\n",
        "plt.ylabel('Total ride_lenght')\n",
        "plt.legend(title='Member type')\n",
        "\n",
        "# Muestra el gráfico\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        },
        "id": "o2BjfFqfSKro",
        "outputId": "549b1b11-e9c3-4043-c4ab-7d8ac0feea4c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Estaciones más frequentes para Casuals"
      ],
      "metadata": {
        "id": "0PlvycdJg3CJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "end_stations = df_casual['end_station_name'].value_counts().reset_index(name='count')\n",
        "start_stations = df_casual['start_station_name'].value_counts().reset_index(name='count')\n",
        "\n",
        "\n",
        "st1 = start_stations.sort_values('count', ascending=False).reset_index(drop=True)\n",
        "st2 = end_stations.sort_values('count', ascending=False).reset_index(drop=True)\n",
        "about_stations = pd.concat([st1.iloc[0:10,], st2.iloc[0:10,]],\n",
        "                           axis=1)\n",
        "about_stations"
      ],
      "metadata": {
        "id": "VQu7D33qxAMw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "outputId": "bdf4060d-5985-4057-8b13-76e2a954ac3f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   start_station_name  count  \\\n",
              "0             Streeter Dr & Grand Ave  49103   \n",
              "1   DuSable Lake Shore Dr & Monroe St  32903   \n",
              "2               Michigan Ave & Oak St  24675   \n",
              "3  DuSable Lake Shore Dr & North Blvd  22684   \n",
              "4                     Millennium Park  21833   \n",
              "5                      Shedd Aquarium  20519   \n",
              "6                      Dusable Harbor  18021   \n",
              "7                 Theater on the Lake  16459   \n",
              "8               Michigan Ave & 8th St  13264   \n",
              "9                   Adler Planetarium  12862   \n",
              "\n",
              "                     end_station_name  count  \n",
              "0             Streeter Dr & Grand Ave  52616  \n",
              "1   DuSable Lake Shore Dr & Monroe St  30636  \n",
              "2  DuSable Lake Shore Dr & North Blvd  25963  \n",
              "3               Michigan Ave & Oak St  25174  \n",
              "4                     Millennium Park  23498  \n",
              "5                      Shedd Aquarium  18517  \n",
              "6                 Theater on the Lake  17627  \n",
              "7                      Dusable Harbor  16157  \n",
              "8               Michigan Ave & 8th St  11965  \n",
              "9              Wabash Ave & Grand Ave  11382  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-fd1c79cc-29be-480f-a709-1d58499f7175\" 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",
              "        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>start_station_name</th>\n",
              "      <th>count</th>\n",
              "      <th>end_station_name</th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Streeter Dr &amp; Grand Ave</td>\n",
              "      <td>49103</td>\n",
              "      <td>Streeter Dr &amp; Grand Ave</td>\n",
              "      <td>52616</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>DuSable Lake Shore Dr &amp; Monroe St</td>\n",
              "      <td>32903</td>\n",
              "      <td>DuSable Lake Shore Dr &amp; Monroe St</td>\n",
              "      <td>30636</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Michigan Ave &amp; Oak St</td>\n",
              "      <td>24675</td>\n",
              "      <td>DuSable Lake Shore Dr &amp; North Blvd</td>\n",
              "      <td>25963</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>DuSable Lake Shore Dr &amp; North Blvd</td>\n",
              "      <td>22684</td>\n",
              "      <td>Michigan Ave &amp; Oak St</td>\n",
              "      <td>25174</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Millennium Park</td>\n",
              "      <td>21833</td>\n",
              "      <td>Millennium Park</td>\n",
              "      <td>23498</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Shedd Aquarium</td>\n",
              "      <td>20519</td>\n",
              "      <td>Shedd Aquarium</td>\n",
              "      <td>18517</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Dusable Harbor</td>\n",
              "      <td>18021</td>\n",
              "      <td>Theater on the Lake</td>\n",
              "      <td>17627</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Theater on the Lake</td>\n",
              "      <td>16459</td>\n",
              "      <td>Dusable Harbor</td>\n",
              "      <td>16157</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Michigan Ave &amp; 8th St</td>\n",
              "      <td>13264</td>\n",
              "      <td>Michigan Ave &amp; 8th St</td>\n",
              "      <td>11965</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Adler Planetarium</td>\n",
              "      <td>12862</td>\n",
              "      <td>Wabash Ave &amp; Grand Ave</td>\n",
              "      <td>11382</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "      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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              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('about_stations')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "about_stations",
              "summary": "{\n  \"name\": \"about_stations\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"start_station_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"Michigan Ave & 8th St\",\n          \"DuSable Lake Shore Dr & Monroe St\",\n          \"Shedd Aquarium\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10824,\n        \"min\": 12862,\n        \"max\": 49103,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          13264,\n          32903,\n          20519\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"end_station_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"Michigan Ave & 8th St\",\n          \"DuSable Lake Shore Dr & Monroe St\",\n          \"Shedd Aquarium\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12020,\n        \"min\": 11382,\n        \"max\": 52616,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          11965,\n          30636,\n          18517\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "end_st = df_members['end_station_name'].value_counts().reset_index(name='count')\n",
        "start_st = df_members['start_station_name'].value_counts().reset_index(name='count')\n",
        "\n",
        "\n",
        "stm1 = start_st.sort_values('count', ascending=False).reset_index(drop=True)\n",
        "stm2 = end_st.sort_values('count', ascending=False).reset_index(drop=True)\n",
        "about_stationsm = pd.concat([st1.iloc[0:10,0], st2.iloc[0:10,0], stm1.iloc[0:8,0], stm2.iloc[0:8,0]],\n",
        "                           axis=1)\n",
        "about_stationsm"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "bxGsXzrD9YAT",
        "outputId": "01f411d1-1145-43cc-91a9-ac1609c3b540"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   start_station_name                    end_station_name  \\\n",
              "0             Streeter Dr & Grand Ave             Streeter Dr & Grand Ave   \n",
              "1   DuSable Lake Shore Dr & Monroe St   DuSable Lake Shore Dr & Monroe St   \n",
              "2               Michigan Ave & Oak St  DuSable Lake Shore Dr & North Blvd   \n",
              "3  DuSable Lake Shore Dr & North Blvd               Michigan Ave & Oak St   \n",
              "4                     Millennium Park                     Millennium Park   \n",
              "5                      Shedd Aquarium                      Shedd Aquarium   \n",
              "6                      Dusable Harbor                 Theater on the Lake   \n",
              "7                 Theater on the Lake                      Dusable Harbor   \n",
              "8               Michigan Ave & 8th St               Michigan Ave & 8th St   \n",
              "9                   Adler Planetarium              Wabash Ave & Grand Ave   \n",
              "\n",
              "             start_station_name              end_station_name  \n",
              "0      Kingsbury St & Kinzie St  Clinton St & Washington Blvd  \n",
              "1  Clinton St & Washington Blvd      Kingsbury St & Kinzie St  \n",
              "2             Clark St & Elm St       Clinton St & Madison St  \n",
              "3       Clinton St & Madison St             Clark St & Elm St  \n",
              "4         Wells St & Concord Ln         Wells St & Concord Ln  \n",
              "5             Wells St & Elm St             Wells St & Elm St  \n",
              "6     Clinton St & Jackson Blvd     Clinton St & Jackson Blvd  \n",
              "7        State St & Chicago Ave        State St & Chicago Ave  \n",
              "8                           NaN                           NaN  \n",
              "9                           NaN                           NaN  "
            ],
            "text/html": [
              "\n",
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              "      <th></th>\n",
              "      <th>start_station_name</th>\n",
              "      <th>end_station_name</th>\n",
              "      <th>start_station_name</th>\n",
              "      <th>end_station_name</th>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Streeter Dr &amp; Grand Ave</td>\n",
              "      <td>Streeter Dr &amp; Grand Ave</td>\n",
              "      <td>Kingsbury St &amp; Kinzie St</td>\n",
              "      <td>Clinton St &amp; Washington Blvd</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>DuSable Lake Shore Dr &amp; Monroe St</td>\n",
              "      <td>DuSable Lake Shore Dr &amp; Monroe St</td>\n",
              "      <td>Clinton St &amp; Washington Blvd</td>\n",
              "      <td>Kingsbury St &amp; Kinzie St</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Michigan Ave &amp; Oak St</td>\n",
              "      <td>DuSable Lake Shore Dr &amp; North Blvd</td>\n",
              "      <td>Clark St &amp; Elm St</td>\n",
              "      <td>Clinton St &amp; Madison St</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>DuSable Lake Shore Dr &amp; North Blvd</td>\n",
              "      <td>Michigan Ave &amp; Oak St</td>\n",
              "      <td>Clinton St &amp; Madison St</td>\n",
              "      <td>Clark St &amp; Elm St</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Millennium Park</td>\n",
              "      <td>Millennium Park</td>\n",
              "      <td>Wells St &amp; Concord Ln</td>\n",
              "      <td>Wells St &amp; Concord Ln</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Shedd Aquarium</td>\n",
              "      <td>Shedd Aquarium</td>\n",
              "      <td>Wells St &amp; Elm St</td>\n",
              "      <td>Wells St &amp; Elm St</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Dusable Harbor</td>\n",
              "      <td>Theater on the Lake</td>\n",
              "      <td>Clinton St &amp; Jackson Blvd</td>\n",
              "      <td>Clinton St &amp; Jackson Blvd</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Theater on the Lake</td>\n",
              "      <td>Dusable Harbor</td>\n",
              "      <td>State St &amp; Chicago Ave</td>\n",
              "      <td>State St &amp; Chicago Ave</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Michigan Ave &amp; 8th St</td>\n",
              "      <td>Michigan Ave &amp; 8th St</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Adler Planetarium</td>\n",
              "      <td>Wabash Ave &amp; Grand Ave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      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",
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              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-82052d2f-fe0b-4838-9c7a-26319ec5b050 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-82052d2f-fe0b-4838-9c7a-26319ec5b050');\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",
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              "\n",
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              "            title=\"Suggest charts\"\n",
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              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </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",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
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              "      --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",
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              "    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",
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              "    30% {\n",
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              "      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-3f681906-d88c-4a61-ad92-64ed99b8e66d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_ab8b3b91-e55b-49c9-8a28-4bd09bda0160\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('about_stationsm')\"\n",
              "            title=\"Generate code using this dataframe.\"\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",
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              "    </button>\n",
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              "      (() => {\n",
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              "        document.querySelector('#id_ab8b3b91-e55b-49c9-8a28-4bd09bda0160 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('about_stationsm');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "about_stationsm",
              "summary": "{\n  \"name\": \"about_stationsm\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"start_station_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"Michigan Ave & 8th St\",\n          \"DuSable Lake Shore Dr & Monroe St\",\n          \"Shedd Aquarium\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"end_station_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"Michigan Ave & 8th St\",\n          \"DuSable Lake Shore Dr & Monroe St\",\n          \"Shedd Aquarium\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"start_station_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"Clinton St & Washington Blvd\",\n          \"Wells St & Elm St\",\n          \"Kingsbury St & Kinzie St\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"end_station_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"Kingsbury St & Kinzie St\",\n          \"Wells St & Elm St\",\n          \"Clinton St & Washington Blvd\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Identify the top 20 locations with most days of lightning.\n",
        "df_casual.start_station_name.value_counts()[:10].rename_axis('unique_values').reset_index(name='counts').style.background_gradient()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "FHpAEy2VkCSX",
        "outputId": "979860b5-e0f9-47cc-b749-4e558145453d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7bdcffad3040>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_09a8e_row0_col1 {\n",
              "  background-color: #023858;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_09a8e_row1_col1 {\n",
              "  background-color: #5a9ec9;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_09a8e_row2_col1 {\n",
              "  background-color: #b7c5df;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_09a8e_row3_col1 {\n",
              "  background-color: #c9cee4;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_09a8e_row4_col1 {\n",
              "  background-color: #d1d2e6;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_09a8e_row5_col1 {\n",
              "  background-color: #d9d8ea;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_09a8e_row6_col1 {\n",
              "  background-color: #e8e4f0;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_09a8e_row7_col1 {\n",
              "  background-color: #f0eaf4;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_09a8e_row8_col1 {\n",
              "  background-color: #fef6fa;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_09a8e_row9_col1 {\n",
              "  background-color: #fff7fb;\n",
              "  color: #000000;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_09a8e\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_09a8e_level0_col0\" class=\"col_heading level0 col0\" >unique_values</th>\n",
              "      <th id=\"T_09a8e_level0_col1\" class=\"col_heading level0 col1\" >counts</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_09a8e_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_09a8e_row0_col0\" class=\"data row0 col0\" >Streeter Dr & Grand Ave</td>\n",
              "      <td id=\"T_09a8e_row0_col1\" class=\"data row0 col1\" >49103</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_09a8e_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_09a8e_row1_col0\" class=\"data row1 col0\" >DuSable Lake Shore Dr & Monroe St</td>\n",
              "      <td id=\"T_09a8e_row1_col1\" class=\"data row1 col1\" >32903</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_09a8e_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_09a8e_row2_col0\" class=\"data row2 col0\" >Michigan Ave & Oak St</td>\n",
              "      <td id=\"T_09a8e_row2_col1\" class=\"data row2 col1\" >24675</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_09a8e_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "      <td id=\"T_09a8e_row3_col0\" class=\"data row3 col0\" >DuSable Lake Shore Dr & North Blvd</td>\n",
              "      <td id=\"T_09a8e_row3_col1\" class=\"data row3 col1\" >22684</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_09a8e_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "      <td id=\"T_09a8e_row4_col0\" class=\"data row4 col0\" >Millennium Park</td>\n",
              "      <td id=\"T_09a8e_row4_col1\" class=\"data row4 col1\" >21833</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_09a8e_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "      <td id=\"T_09a8e_row5_col0\" class=\"data row5 col0\" >Shedd Aquarium</td>\n",
              "      <td id=\"T_09a8e_row5_col1\" class=\"data row5 col1\" >20519</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_09a8e_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
              "      <td id=\"T_09a8e_row6_col0\" class=\"data row6 col0\" >Dusable Harbor</td>\n",
              "      <td id=\"T_09a8e_row6_col1\" class=\"data row6 col1\" >18021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_09a8e_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
              "      <td id=\"T_09a8e_row7_col0\" class=\"data row7 col0\" >Theater on the Lake</td>\n",
              "      <td id=\"T_09a8e_row7_col1\" class=\"data row7 col1\" >16459</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_09a8e_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
              "      <td id=\"T_09a8e_row8_col0\" class=\"data row8 col0\" >Michigan Ave & 8th St</td>\n",
              "      <td id=\"T_09a8e_row8_col1\" class=\"data row8 col1\" >13264</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_09a8e_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
              "      <td id=\"T_09a8e_row9_col0\" class=\"data row9 col0\" >Adler Planetarium</td>\n",
              "      <td id=\"T_09a8e_row9_col1\" class=\"data row9 col1\" >12862</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {},
          "execution_count": 184
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Cantidad de viajes por tipo de bicicleta"
      ],
      "metadata": {
        "id": "OnEwWMlgRMNA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "group_by_rideable_type = df_casual[['rideable_type']].value_counts(normalize=True)*100\n",
        "group_by_rideable_type = group_by_rideable_type.reset_index()\n",
        "\n",
        "# Create the pie chart with labels\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.pie(group_by_rideable_type['proportion'], labels=group_by_rideable_type['rideable_type']) # Add labels and percentage format\n",
        "\n",
        "# Add a legend\n",
        "\n",
        "plt.legend(title=\"Rideable Type\", loc='upper left')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        },
        "id": "E7Cel4lcDDL9",
        "outputId": "35537ed5-5458-406d-f573-57bf02da1fcc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_members[['rideable_type']].value_counts(normalize=True)*100"
      ],
      "metadata": {
        "id": "I_OGft5zlb3s",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 209
        },
        "outputId": "d7784dd5-f30b-44fc-cfbc-c45a42b66a2e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "rideable_type   \n",
              "electric_bike       49.67689\n",
              "classic_bike        48.73506\n",
              "electric_scooter     1.58805\n",
              "Name: proportion, dtype: float64"
            ],
            "text/html": [
              "<div>\n",
              "<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>proportion</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rideable_type</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>electric_bike</th>\n",
              "      <td>49.67689</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>classic_bike</th>\n",
              "      <td>48.73506</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>electric_scooter</th>\n",
              "      <td>1.58805</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "datag = df_casual.groupby('rideable_type')['weekday'].value_counts().reset_index(name='counts')"
      ],
      "metadata": {
        "id": "Z2lYjBi4StDL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "sns.barplot(y='counts', x='weekday', hue='rideable_type', data=datag, dodge=True, order=weekday_order)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 559
        },
        "id": "okdVwTxRrcmB",
        "outputId": "fc491521-d802-44e9-94b4-c4fe5dfa70ec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='weekday', ylabel='counts'>"
            ]
          },
          "metadata": {},
          "execution_count": 81
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Set palette for graphics\n",
        "custom_palette = [\"#696960\", \"#c0ed60\",  \"#8ba950\"]\n",
        "sns.set_palette(custom_palette)"
      ],
      "metadata": {
        "id": "A__bFF1YsfkL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "kcdS3ZZP9Fxf"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}