Intro to Python for Data Science 5 & 6

intro-to-python-for-data-science-5-6.ipynb

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        "<a href=\"https://colab.research.google.com/gist/syamdev/a56e8f475522d7643886ca5973edb12f/intro-to-python-for-data-science-5-6.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
      "execution_count": 0,
      "outputs": []
    },
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      "cell_type": "code",
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        "id": "24bAxJIXkaUC",
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      "source": [
        "data_bike = pd.read_csv('http://bit.ly/dwp-data-bike')"
      ],
      "execution_count": 0,
      "outputs": []
    },
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          "height": 204
        }
      },
      "source": [
        "data_bike.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tripduration</th>\n",
              "      <th>starttime</th>\n",
              "      <th>stoptime</th>\n",
              "      <th>start station name</th>\n",
              "      <th>end station name</th>\n",
              "      <th>bikeid</th>\n",
              "      <th>usertype</th>\n",
              "      <th>birth year</th>\n",
              "      <th>gender</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>261</td>\n",
              "      <td>2019-08-01 00:14:55.9900</td>\n",
              "      <td>2019-08-01 00:19:17.4780</td>\n",
              "      <td>JC Medical Center</td>\n",
              "      <td>Liberty Light Rail</td>\n",
              "      <td>26268</td>\n",
              "      <td>Subscriber</td>\n",
              "      <td>1980</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>172</td>\n",
              "      <td>2019-08-01 00:23:06.9910</td>\n",
              "      <td>2019-08-01 00:25:59.1480</td>\n",
              "      <td>Dixon Mills</td>\n",
              "      <td>Grove St PATH</td>\n",
              "      <td>26162</td>\n",
              "      <td>Subscriber</td>\n",
              "      <td>1996</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>525</td>\n",
              "      <td>2019-08-01 00:23:28.6170</td>\n",
              "      <td>2019-08-01 00:32:13.7000</td>\n",
              "      <td>Newport Pkwy</td>\n",
              "      <td>Hamilton Park</td>\n",
              "      <td>29279</td>\n",
              "      <td>Subscriber</td>\n",
              "      <td>1991</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>219</td>\n",
              "      <td>2019-08-01 00:32:36.1410</td>\n",
              "      <td>2019-08-01 00:36:15.2730</td>\n",
              "      <td>Warren St</td>\n",
              "      <td>City Hall</td>\n",
              "      <td>29598</td>\n",
              "      <td>Subscriber</td>\n",
              "      <td>1988</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>262</td>\n",
              "      <td>2019-08-01 00:41:26.6700</td>\n",
              "      <td>2019-08-01 00:45:49.3530</td>\n",
              "      <td>Grove St PATH</td>\n",
              "      <td>Jersey &amp; 3rd</td>\n",
              "      <td>26162</td>\n",
              "      <td>Subscriber</td>\n",
              "      <td>1960</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   tripduration                 starttime  ... birth year gender\n",
              "0           261  2019-08-01 00:14:55.9900  ...       1980      1\n",
              "1           172  2019-08-01 00:23:06.9910  ...       1996      1\n",
              "2           525  2019-08-01 00:23:28.6170  ...       1991      1\n",
              "3           219  2019-08-01 00:32:36.1410  ...       1988      1\n",
              "4           262  2019-08-01 00:41:26.6700  ...       1960      1\n",
              "\n",
              "[5 rows x 9 columns]"
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          "base_uri": "https://localhost:8080/",
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        }
      },
      "source": [
        "data_bike.groupby('usertype', as_index=False)['gender'].count()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>usertype</th>\n",
              "      <th>gender</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Customer</td>\n",
              "      <td>6149</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Subscriber</td>\n",
              "      <td>40081</td>\n",
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              "     usertype  gender\n",
              "0    Customer    6149\n",
              "1  Subscriber   40081"
            ]
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      },
      "source": [
        "data_bike.info()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 46230 entries, 0 to 46229\n",
            "Data columns (total 9 columns):\n",
            "tripduration          46230 non-null int64\n",
            "starttime             46230 non-null datetime64[ns]\n",
            "stoptime              46230 non-null object\n",
            "start station name    46230 non-null object\n",
            "end station name      46230 non-null object\n",
            "bikeid                46230 non-null int64\n",
            "usertype              46230 non-null object\n",
            "birth year            46230 non-null int64\n",
            "gender                46230 non-null int64\n",
            "dtypes: datetime64[ns](1), int64(4), object(4)\n",
            "memory usage: 3.2+ MB\n"
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      "cell_type": "code",
      "metadata": {
        "id": "d4aoA1xGrQhH",
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      "source": [
        "# Fill NA dgn mean untuk tipe object\n",
        "#data_bike['usertype'] = data_bike['usertype'].fillna(data_bike['usertype'].mean())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UHMEvDAVl1Gi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Datetime Data Type\n",
        "data_bike['starttime'] = pd.to_datetime(data_bike['starttime'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XJ6kXi-wmn96",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data_bike['hour'] = data_bike['starttime'].dt.hour"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "klhZQ2fATVC6",
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        "colab": {}
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      "source": [
        "data_bike['usertype'] = data_bike['usertype'].fillna(data_bike['usertype'].mode())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YtiCzqZNm4YI",
        "colab_type": "code",
        "outputId": "88a02e87-a0d6-4b33-d25d-3fcafc01ba22",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "data_bike.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tripduration</th>\n",
              "      <th>starttime</th>\n",
              "      <th>stoptime</th>\n",
              "      <th>start station name</th>\n",
              "      <th>end station name</th>\n",
              "      <th>bikeid</th>\n",
              "      <th>usertype</th>\n",
              "      <th>birth year</th>\n",
              "      <th>gender</th>\n",
              "      <th>hour</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>261</td>\n",
              "      <td>2019-08-01 00:14:55.990</td>\n",
              "      <td>2019-08-01 00:19:17.4780</td>\n",
              "      <td>JC Medical Center</td>\n",
              "      <td>Liberty Light Rail</td>\n",
              "      <td>26268</td>\n",
              "      <td>Subscriber</td>\n",
              "      <td>1980</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>172</td>\n",
              "      <td>2019-08-01 00:23:06.991</td>\n",
              "      <td>2019-08-01 00:25:59.1480</td>\n",
              "      <td>Dixon Mills</td>\n",
              "      <td>Grove St PATH</td>\n",
              "      <td>26162</td>\n",
              "      <td>Subscriber</td>\n",
              "      <td>1996</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>525</td>\n",
              "      <td>2019-08-01 00:23:28.617</td>\n",
              "      <td>2019-08-01 00:32:13.7000</td>\n",
              "      <td>Newport Pkwy</td>\n",
              "      <td>Hamilton Park</td>\n",
              "      <td>29279</td>\n",
              "      <td>Subscriber</td>\n",
              "      <td>1991</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>219</td>\n",
              "      <td>2019-08-01 00:32:36.141</td>\n",
              "      <td>2019-08-01 00:36:15.2730</td>\n",
              "      <td>Warren St</td>\n",
              "      <td>City Hall</td>\n",
              "      <td>29598</td>\n",
              "      <td>Subscriber</td>\n",
              "      <td>1988</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>262</td>\n",
              "      <td>2019-08-01 00:41:26.670</td>\n",
              "      <td>2019-08-01 00:45:49.3530</td>\n",
              "      <td>Grove St PATH</td>\n",
              "      <td>Jersey &amp; 3rd</td>\n",
              "      <td>26162</td>\n",
              "      <td>Subscriber</td>\n",
              "      <td>1960</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   tripduration               starttime  ... gender hour\n",
              "0           261 2019-08-01 00:14:55.990  ...      1    0\n",
              "1           172 2019-08-01 00:23:06.991  ...      1    0\n",
              "2           525 2019-08-01 00:23:28.617  ...      1    0\n",
              "3           219 2019-08-01 00:32:36.141  ...      1    0\n",
              "4           262 2019-08-01 00:41:26.670  ...      1    0\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bHWpzyJLnWsj",
        "colab_type": "code",
        "outputId": "262186c2-0999-48f3-95bf-919f9805ba2b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "source": [
        "data_bike['starttime'].dt.strftime('%m/%d/%Y')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        08/01/2019\n",
              "1        08/01/2019\n",
              "2        08/01/2019\n",
              "3        08/01/2019\n",
              "4        08/01/2019\n",
              "            ...    \n",
              "46225    08/31/2019\n",
              "46226    08/31/2019\n",
              "46227    08/31/2019\n",
              "46228    08/31/2019\n",
              "46229    08/31/2019\n",
              "Name: starttime, Length: 46230, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fFHfWaAao3DR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qYJCHtJRqam7",
        "colab_type": "text"
      },
      "source": [
        "# Data Visualization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SMm_Zn-SqOPU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data_bike = pd.read_csv('http://bit.ly/dwp-data-bike')\n",
        "\n",
        "data_bike['age'] = 2020 - data_bike['birth year']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8aAGZFfosF5G",
        "colab_type": "text"
      },
      "source": [
        "#### Histogram"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5pv4grdiqPEy",
        "colab_type": "code",
        "outputId": "5f5b1b65-b2a7-4839-951b-9434017a3023",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        }
      },
      "source": [
        "viz = sns.distplot(data_bike['age'], kde=False, bins=10)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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1S+AKYEWf+yRJs8bBcmnpfGBb1/p24OS9GyVZDaxuqz9LsnUa+jaeY4Af9fH9p8tsGKdj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\nCcNAkgT8HxAoy2+FEYeLAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sGOGEV8hsB10",
        "colab_type": "text"
      },
      "source": [
        "#### Bar Chart"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HdX-3tFCqQHC",
        "colab_type": "code",
        "outputId": "2d1dfcb7-de47-482b-f07a-fedcbc81636f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        }
      },
      "source": [
        "trip_per_age = data_bike.groupby('age', as_index=False)['usertype'] \\\n",
        "                        .count() \\\n",
        "                        .rename(columns={'usertype': 'total'})\n",
        "\n",
        "plt.figure(figsize=(15,5))\n",
        "viz = sns.barplot(data=trip_per_age, x='age', y='total')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ybezcW+bauLG75Nq4+Fa5NvIY84BbgN/qkmujtzl/OGhmfhO4fdy6iAjgIKoN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\nGVSctPgeAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wHv10_KS247L",
        "colab_type": "code",
        "outputId": "e5b8f6ba-82b1-4852-c8b1-8f07b6aa196d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        }
      },
      "source": [
        "trip_per_age = data_bike.groupby(['age','usertype'], as_index=False)['gender'] \\\n",
        "                        .count() \\\n",
        "                        .rename(columns={'gender': 'total'})\n",
        "\n",
        "plt.figure(figsize=(15,5))\n",
        "viz = sns.barplot(data=trip_per_age, x='age', y='total', hue='usertype')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DRwFzgCnAmGy2McCt2fMpwKlRcTCwvOqwUUmSJElSEzricNDtgMmVOz/QE7g+\npfSHiHgQuDEiTgeeAU7K5r+dyu0h5lO5RcSn23/IkiRJak+eAyq1TrsXgSml/wb2rdG+FDiiRnsC\nPt8OQ1Mn5oZAkiRJahud6RYRkiRJkqQWswiUJEmSpBKxCJQkSZKkEumo+wRKkqQS8hzv5rnOJLU1\n9wRKkiRJUolYBEqSJElSiXg4qCRJkrodD6OV8rknUJIkSZJKxD2BkiRJagn3xkmdk3sCJUmSJKlE\nLAIlSZIkqUQsAiVJkiSpRCwCJUmSJKlEvDCM1MUd8OVra7ZP3rydByJJ6pS8OIukDbknUJIkSZJK\nxD2BUhtwb5wkSZK6CotAFebhJJIkSZ2bn9dUhIeDSpIkSVKJWARKkiRJUol4OKiUKet5fWV935Ik\ntYqHZKqzswhUt2ExI0mSJDXm4aCSJEmSVCLuCZQkSVJNHtYodU8WgZIkSZLeMb806Do8HFSSJEmS\nSsQ9gVIn4EVtJEmS1F7cEyhJkiRJJeKeQHUq7hGTJEmSWssiUO3CE4W7J4t2SZKkrsciUFKHsYiU\nJOmt/OJ847jemmMRKKlLsoCUJEnaOBaBalN+MJckSZI6N4tASZIkqYqHFqq78xYRkiRJklQi7gmU\npHbmYdOSJKkjWQTqbfyAKnVe/n1KktS2ynj4r0WgpFIqazFV1vctSZLe5DmBkiRJklQi7gmUpCa5\nN639uc4lSWo7FoGSJEmSSnluXFlZBEqSCnmne+M6cm+eexIlSZ1RRxXeXaYIjIhjgO8DPYCfpZQu\na3WfeR8aZk44tdVd1/nAMqFme3WidOS4Jam7sYCU1Ar+b3kr90K2ry5RBEZED+Aq4EhgEfBgRExJ\nKT3WsSOTJKl1/JAoSZ1fVyxgu0QRCBwIzE8p/TdARNwAjAY6bRHo3jhJEnTdw2AtQNUM80XqWrpK\nEbgjsLDq9SLgoEZBnbEQ68hvCrritxSSpK6powrQvNii8e9URxbeXbXoL+v7ltrCxn6+j5RSK8bT\npiLi48AxKaUzstenAAellM6pmucs4Kzs5R7AE3UWuQ2w5B0M6Z3E27d927d927d927d927d927d9\nt7rvXVJKA2pOSSl1+gdwCHBH1euLgIvewfJmvMPxbHS8fdu3fdu3fdu3fdu3fdu3fdt3R/a9yYZF\nYSf1ILBbRAyOiHcBJwNTOnhMkiRJktTldIlzAlNKqyPiHOAOKreImJhSerSDhyVJkiRJXU6XKAIB\nUkq3A7e30eKu7sB4+7Zv+7Zv+7Zv+7Zv+7Zv+7bvDuu7S1wYRpIkSZLUNrrKOYGSJEmSpLbwTq5G\n0xUewERgMTCnqu03wMPZY/7V228AAA9+SURBVAHwcJPxQ4H7s/gZwIFNxO4L/BmYDfwHsEVO7M7A\nPcBjwKPAv2Tt/YG7gHnZz35Nxp+YvV4LDGsydgLwODALmAxs1WT8N7PYh4E7gR2KxlZN/xKQgG2a\n7Hsc8FzV7/3YZvoGvpC990eBf2uy74b5Vie2aK7lxTfMN6A3MB14JIu9JGsfDDwAzM/ew7ty+s6L\nPyeLrff7yov9FZXbvMyh8nfUq8n4n2dts4CbgL5FY6umXwmsqPO/Ia/va4Cnq37nQ5uIDWA88CQw\nFzi3yb7vrer3r8AtTcQeATyUxd4HvLfJvg/P4ucAk4CeddZdD+AvwG3N5FpObMM8axBfKNdyYhvm\nWb34ormW03fDPGsQXyjXcmIb5lmD+EK5lhPbTJ4toPK/72Gyq+dRcBtaJ77hNrRObKFtaJ34htvQ\nvNiqaXW3oXX6HkeDbWi9vimwDa3Td6HPbDmxhbahdeKLfmbbisr/gMep/D0d0mSu1Yovmmu1YpvJ\ntVrxRXPtbbFN5lqtvovmWs2+m8i1Wn0XzbVasc3kWq34Ip/X9qga38PA34Dzmsm1tyyvyExd+QEc\nCuxPVSG2wfTvAGObic/+ID6cPT8WmNpE7IPAYdnzzwDfzIndHtg/e745lQ31XsC/ARdm7RcC324y\nfs8siaaSXwTmxR5FtsEFvr0RfW9RNc+5wI+Lxmavd6ZycaBnyC8q8voeB1zQIFfyYkcC/wlsmk3b\ntpn4IvlWp++iuZYX3zDfqHwY7Js970Xlw/jBwI3AyVn7j4F/zuk7L34/YBCVf6R5v6+82GOzaQH8\neiP6rs6175L9zRSJzV4PA35J/SIwr+9rgI83yLW82E8D1wKbNMi13LFXzXMzcGoTfT8J7Jm1fw64\npom+/z9gIbB71n4pcHqd9/9F4Hre/GBfKNdyYhvmWYP4QrmWE9swz+rFF821nL4b5lmD+EK5ljfu\nRnnWoO9CubZhLJWjl5rJs7flBAW3oXXiG25D68QW2obWiW+4Dc2LzdobbkPr9D2OBtvQOrGFtqH1\nxl41PfczW07fhbahdeKLfmabBJyRPX8XlQ/5zeRarfiiuVYrtplcqxVfNNfeFttkrtXqu2iu1Ypt\nJtdqjr1grtXqu5lcqxVfKNeqltEDeAHYpZlcq350+8NBU0rTgJdrTYuIAE6isuFvJj4BW2TPt6Ty\nTWjR2N2Badnzu4CP5cQ+n1J6KHv+KpVvCnYERlNJHrKfxzcTn1Kam1J6olZMgdg7U0qrs9nuB3Zq\nMv5vVbNtRmU9Fn3fAN8DvlIrrmB8XXVi/xm4LKW0Kpu2eGP6rpdvdWKL5lpefMN8SxUrspe9skei\n8o37TVl7vVyrGZ9S+ktKaUGtmAKxt2fTEpW9Tnm5lhf/N1i/zvtQO9dqxkZEDyrfon5lY8ZeL6ZA\n7D8Dl6aU1mbz5eVa3b4jYgsqv79bmogtmmu14tcAf08pPZm15/5vi4idgI8AP8teBwVzbcPYbDwN\n86xBfKFcy4ltmGf14ovmWq3YZuTEF8q1en3Xy7MG8YVyrUbs1hTMszoKbUPzFNmG1okttA2tE99w\nG9pAw21oixTahjZS5DNbDYVyrY6G29CI2JLKl/4/B0gp/T2ltIyCuZYXXyTX6sQWyrU68Q1zrc77\nhgK51iC+rjqxhXKtUd/1cq1ObNH/a3nxheqDKkcAT6WUnmEj/691+yKwgX8EXkwpzWsy7jxgQkQs\nBC6ncvP6oh6l8suCyq7+nRsFRMQgKt92PwBsl1J6Ppv0ArBdk/FNqRP7GeD3zcZHxPhsvX0KGFs0\nNiJGA8+llB55B2M/JyJmRcTEiOjXROzuwD9GxAMR8X8jYvhG9A0F822D2KZzbYP4QvkWET0i4mEq\nhy/fBTwFLKvaiCyiTjG9YXxKqXCu1YuNiF7AKcAfmo2PiF9Q+Rt5H/CDJmLPAaZU/Z1tzNjHZ7n2\nvYjYtInY9wCfiIgZEfH7iNhtI/qGygbg7g025I1izwBuj4hFVNb5ZUX7plI89YyIYdksHyf/f9sV\nVD4crM1eb03xXNswtlm58QVyrWZskTyrE1801/LG3TDP6sQXzbV667xuntWJL5prG8YuoXieQeVD\n2Z0RMTMizsramtmG1oovqlFso21ozfiC29C3xTa5Dc0be5FtaK3YZrah9dZbo21ordhmtqG14ots\nQwcDLwG/iIi/RMTPImIziudaXnwRRWLr5VpufIFcqxnbRK7VG3ujXMuLLZprjdZbvVzLiy2aa3nx\nzdYHJ/Nmkdp0bQB0/8NBK1/uMogah4MCPwK+1Gw8lfM3PpY9Pwn4zyZi30dll/FM4BvA0gZ9983m\n/Wj2etkG019pJr6qfSp1Di9oEPs1KseYx8bEZ9MuYoNzsPJigXdTKWi2zKYtoMFhXzXW23ZUdp1v\nQuU8mIlNxM6h8uEugAOpnIeT+97rrLeG+Vaj78K5lhPfbL5tReXcwg8C86vad671N1Qnfu+qtoa/\nrzqxPwWuaBRbJ74H8O/ApwvGHkrlHKV1h9HUPUSvVt9UDs0NYFMq38jlHm5eI3bFuhzJcv/ejXzf\nv1+XN030/VvgoKz9y8DPmow/hMq5YtOBf6X2ua+jgH/Pno+gcojfNkVyrVbsBtPr5lmB+NxcKxBb\nN89y3vcORXItr++ieVYnvmGuFXjfdfOsTt8Nc61ObMM8q1rGjtnPbamcu3koTWxDa8VXTZtK/UP0\n6sU23IbWi8/ac7ehOe+78DY0J77QNjQntvA2tMF6q7sNzem7mc9rteIbbkOpHNK9uiqnv0/lnLpC\nuZYXXyTXCsTWzbVG8fVyLSd2QtFcq7PeGuZandhCuVZgveXmWp2+C+VanfjCn9eoHEK6hErxR9Fc\ne9tyiszU1R/UKAKp3CPxRWCnZuOB5euSKku0vzXTd9W03YHpdWJ7UTmm+otVbU8A22fPtweeaCa+\natpU6m/AasYCp1E5cfXdDdZZbt/Z9IF11stbYoF9qOxxWJA9VgPPAv+wkX3X+53UWud/AEZWvX4K\nGNDkemuYbzl9N5Nrjd533Xyrmm8slQ9mS3jzA+ohwB2NYqviL6h6vYACReCGsVT+Cd5Cds7SxvSd\ntR1KjXOZcmK/QeVbtHW5tpaqAmUj+h7RRN8XUDlJfHDV73v5Rqy3bYClQO8mYr9M5bCS6r/Px97B\n+z4KuLHGvN+isqdvQbaeX6dyYZaGuZYTe13RPKsX3yjXGvXdKM9y4l8pkmsF+87Ns7z4IrnWYJ01\nzLOc+N8VybWC77tmnuWMZRyVv7HC29Ba8VWvp9Lgi9RasRTchtbru2q9FflibhxwMU1sQwv0PaiJ\nvi+giW1onfVW+DPbBn0X3oYWeN81t6HAPwALql7/Y5bnhXItL75IrtWLLZJrjfqul2s5sXcXzbWC\nfdfMtTrrvFCuNVhvdXOtTt+Fcq3g+25UH4wG7qx6vVH/18p8OOiHgMdTSos2IvavwGHZ88OpXI2n\nkIjYNvu5CfB1KhdBqDVfUDleeG5K6btVk6YAY7LnY4Bbm4wvMsaasRFxDJVDc45LKb2+EfHVhxuN\npvIhpGFsSml2SmnblNKglNIgKh8M9k8pvdBE39tXzXYClW+LCsVS+XA4Mptnd978BqZoPDTItzqx\nhXKtzvtumG8RMSAitsqe9wGOpHJO4T1UDreC+rlWK/5tv9tmYiPiDOBo4JMpO2epifgnIuK9Vevl\nuFrjyYmdmVL6h6pcez2l9N4mx759Vd/HUzvX8tbZ+lyj8nt/csPYBvFQ+Z3dllJa2UTsXGDLLL+p\namvmfa/LtU2Br1Ij11JKF6WUdsrW7cnAH1NKn6JAruXE/lOtMdaSF18k12rFAqcUybM6ffcrkmt1\nxt0wz+rFUyDXGqzzunlWZ72NpkCu1XnfDfMsm75ZRGy+7jmVgnEOxbehefEN5cU2sQ3Niy+yDa0V\n+2AT29C8votsQ/PWWdFtaL113mgbmhdbdBua974bbkOz9bgwIvbImo6gcqXuQrlWJ76hvNiiuVYn\nvmGu5cQ+VDTX6vTdMNfqrLNCudZgndfNtTqxhXKtzvsuVB9kPslbz1cslGu1BtOtH9lKeh54g0oy\nnp61XwN8dmPiqRwqN5PK4QIPAAc0EfsvVDa2T1I5DyJvF/0HqRyfvu4SvQ9TudrQ1lS+aZlH5QpI\n/ZuMPyEbyyoq33TU+sY9L3Y+lSuzrWvLu1pUXvzNVP6YZ1G5/O2ORWM3mGcB+YcX5PX9SyqX3Z1F\n5Y9l+yZi30Xlm/M5VC5NfngzfRfJtzp9F821vPiG+QYMoXIJ9lnZexybte9K5ZCr+cD/IbvaVhPx\n52a5tprKP8dah3zlxa6m8g3euveSd6jb2+KpHELyp+z3PYfKnqZal1qu2fcG89S7Omje2P9Y1fd1\n1L49RV7sVlS+UZxN5RvcfZvpO5s2FThmI8Z9QtbvI9kydm0yfgKVD/NPAOcV+N86gjcP8SuUazmx\nDfOsQXyhXNswtmie1eu7aK7ljLthnjWIL5RreeNulGcN+i6UazmxhfIsy6lHePNWJl/L2otuQ/Pi\ni2xD82KLbkPz4otsQ2vGbjDPAvK3oXl9F9mG5sUW3Ybmjp3G29C8votuQ/Pii35mG0rltgCzqBQi\n/YrmWp34hrlWJ7ZQrtWJb5hrebFFc61O3w1zrU5soVyrN/ZGuVan70K5Vie+aK5tRuUojC2r2grn\nWvVj3W5LSZIkSVIJlPlwUEmSJEkqHYtASZIkSSoRi0BJkiRJKhGLQEmSJEkqEYtASZIkSSoRi0BJ\nkiRJKhGLQEmSJEkqEYtASZI2QkTcEhEzI+LRiDgrazs9Ip6MiOkR8dOI+GHWPiAibo6IB7PHBzp2\n9JKkMvNm8ZIkbYSI6J9Sejki+gAPAkcDfwL2B14F/gg8klI6JyKuB/49pXRfRAwE7kgp7dlhg5ck\nlVrPjh6AJEld1LkRcUL2fGfgFOD/ppReBoiI/wPsnk3/ELBXRKyL3SIi+qaUVrTngCVJAotASZKa\nFhEjqBR2h6SUXo+IqcDjQN7evU2Ag1NKK9tnhJIk5fOcQEmSmrcl8EpWAL4POBjYDDgsIvpFRE/g\nY1Xz3wl8Yd2LiBjarqOVJKmKRaAkSc37A9AzIuYClwH3A88B/z8wncq5gQuA5dn85wLDImJWRDwG\nfLbdRyxJUsYLw0iS1EbWneeX7QmcDExMKU3u6HFJklTNPYGSJLWdcRHxMDAHeBq4pYPHI0nS27gn\nUJIkSZJKxD2BkiRJklQiFoGSJEmSVCIWgZIkSZJUIhaBkiRJklQiFoGSJEmSVCIWgZIkSZJUIv8P\n53ulR/JEBAkAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rXbsIv7Z5Pl6",
        "colab_type": "code",
        "outputId": "ea051e39-7d51-4cd2-a299-6437ba42eab4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# filter dataframe yang umurnya 51\n",
        "# plot kan trip per day nya\n",
        "\n",
        "age_51 = data_bike[(data_bike['age'] == 51) & (data_bike['usertype'] == 'Customer')]\n",
        "\n",
        "age_51.groupby('date', as_index=False)['usertype'].count()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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>date</th>\n",
              "      <th>usertype</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019-08-01</td>\n",
              "      <td>51</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2019-08-02</td>\n",
              "      <td>70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2019-08-03</td>\n",
              "      <td>141</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2019-08-04</td>\n",
              "      <td>155</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2019-08-05</td>\n",
              "      <td>66</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2019-08-06</td>\n",
              "      <td>45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2019-08-07</td>\n",
              "      <td>37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2019-08-08</td>\n",
              "      <td>65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2019-08-09</td>\n",
              "      <td>53</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2019-08-10</td>\n",
              "      <td>175</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>2019-08-11</td>\n",
              "      <td>138</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>2019-08-12</td>\n",
              "      <td>98</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>2019-08-13</td>\n",
              "      <td>48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>2019-08-14</td>\n",
              "      <td>55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>2019-08-15</td>\n",
              "      <td>42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>2019-08-16</td>\n",
              "      <td>104</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>2019-08-17</td>\n",
              "      <td>92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>2019-08-18</td>\n",
              "      <td>89</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>2019-08-19</td>\n",
              "      <td>57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>2019-08-20</td>\n",
              "      <td>69</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>2019-08-21</td>\n",
              "      <td>54</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>2019-08-22</td>\n",
              "      <td>59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>2019-08-23</td>\n",
              "      <td>70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>2019-08-24</td>\n",
              "      <td>157</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>2019-08-25</td>\n",
              "      <td>150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>2019-08-26</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>2019-08-27</td>\n",
              "      <td>61</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>2019-08-28</td>\n",
              "      <td>59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>2019-08-29</td>\n",
              "      <td>62</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>2019-08-30</td>\n",
              "      <td>72</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>2019-08-31</td>\n",
              "      <td>168</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          date  usertype\n",
              "0   2019-08-01        51\n",
              "1   2019-08-02        70\n",
              "2   2019-08-03       141\n",
              "3   2019-08-04       155\n",
              "4   2019-08-05        66\n",
              "5   2019-08-06        45\n",
              "6   2019-08-07        37\n",
              "7   2019-08-08        65\n",
              "8   2019-08-09        53\n",
              "9   2019-08-10       175\n",
              "10  2019-08-11       138\n",
              "11  2019-08-12        98\n",
              "12  2019-08-13        48\n",
              "13  2019-08-14        55\n",
              "14  2019-08-15        42\n",
              "15  2019-08-16       104\n",
              "16  2019-08-17        92\n",
              "17  2019-08-18        89\n",
              "18  2019-08-19        57\n",
              "19  2019-08-20        69\n",
              "20  2019-08-21        54\n",
              "21  2019-08-22        59\n",
              "22  2019-08-23        70\n",
              "23  2019-08-24       157\n",
              "24  2019-08-25       150\n",
              "25  2019-08-26        40\n",
              "26  2019-08-27        61\n",
              "27  2019-08-28        59\n",
              "28  2019-08-29        62\n",
              "29  2019-08-30        72\n",
              "30  2019-08-31       168"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xb9DjoETqQkh",
        "colab_type": "text"
      },
      "source": [
        "#### Line Chart"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n3xGryBmqQ2Z",
        "colab_type": "code",
        "outputId": "3608fcfa-e5ca-4359-fdc8-19e59af6b0f8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        }
      },
      "source": [
        "data_bike['starttime'] = pd.to_datetime(data_bike['starttime'])\n",
        "\n",
        "data_bike['date'] = data_bike['starttime'].dt.date\n",
        "#data_bike['date'] = data_bike['starttime'].dt.strftime('%d/%m/%Y')\n",
        "\n",
        "trip_per_date = data_bike.groupby('date', as_index=False)['usertype'] \\\n",
        "                        .count() \\\n",
        "                        .rename(columns={'usertype': 'total'})\n",
        "#trip_per_date\n",
        "\n",
        "plt.figure(figsize=(15,5))\n",
        "viz = sns.lineplot(data=trip_per_date, x='date', y='total')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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M69pNJsX8rDSqWrqku3IQSBIZ4WxxFiaPSToxkFx8qKKp88TKQKhkpiZiMSlK\nDVYXWdLQyeT0kdVDDkVSfAx3L8nh/e9eyLc+M5n3SprYUNQYtPeLBN19A82O/E11Tsdui+OlOxdg\nT4pjxdPbddmKV97YyY1PbgVg9Z0LTsxMjTaZqYmYFFIXGSAv76jBbFJ8IUgNdU7l+jkZTB+fzC/e\nPEx3n9zoEsFT2tDJ9X/cQlefm5fuXMDMzNEnPeeO87L5zqX5vLb3KN/9yz68IU4kV22upLvfw1cu\nCL9VSL/cdBser0al7AAJOEkio8DMzBT2VreG/OQSzvrcXo60dpMd4tWQGLMJp91qqJXInn4PVc0u\nJg9xvMeZSIg189WlOSTFWdhQ1BD09zOykoYONO2jTXVOZ0xyPC/eMZ+k+BhufmrbiRrXUKhscrH8\nya14vBqr75x/Yl5qNIq1mJg4OlGSyADodXv4065aPjM1nfQQzhY1mxT3XzWdo209PL6xLGTvK6JL\nYV07y57YgleDl+9ayFkTRp32ufcszeWbn8njz7tq+X+vHgjZVmtXr5tnN1dyybQxI266Fwr+vzlG\n7Iwf7iSJjAIzM0bT3uOmXC5cTqhu6cKrfbi9LJRyHFZDrUSeaWfW4Yoxmzhvsp0NRY1Sd/QJCn1d\nbfPHJg/p+RNHJ/LiHfOJMZu48cltlIfgZ7C6uYvlT26l36Px0p0LwvpCI1Sy7Fa5Ix4Abx2qp8XV\nF/SGOqcyLyuVK88Zx+Mby2TurQi4/bVtLHtiKxaTibV3LRjSaK1vXJTHV31NZH78emhqdldvr6at\nu5+7l+QE/b3OhL+Xg5Fu3huFJJFRoCDT11xHtrSe4F8J0KMuKzfdRnVzl+4d1YaqpMHXmTUEK5F+\nSyanU9fecyJREicrPNZBfIyJzGF0N3Xarbx053w0TeOLK7dR09IVtPhqWgYSyO5+Dy/cPp/8IayY\nRoMsu5WKRpfcIDlDq7dXMyElgfPyHLq8//evmIpS8Is3D+vy/iIy7apq5caVW7HFWVh710Kyh7j1\nXynFdy7N587zsli1pYoH3jgc1HNMr9vDyvcqWJiddspttuEkMdbChJQEQ928N4qgJZFKqaeVUg1K\nqQMfO/51pVShUuqgUurBQce/r5QqVUoVKaUuHXT8Mt+xUqXU94IVbyTLddhIirPo2lQj3FTqmETm\nOGy4vRpVzcG7gA+k4voOzCYV0v9WF+QPXBhKXeTpFdW3M3lMEuZhNhTJTU/i+dvn09Xn4caVWznW\nFviVlNrWgQSys9fNC7fPZ9r4oa2WRoMsuxVXn4dGGVo/YpVNLjaVNrNsbsawf/4DZUJKAl+5IIe/\n7zsWFp2PhfFtK2/mlqe2kdMgL74AACAASURBVGaNZe1dC8lMG974I6UUP7hiKrcucrLy/QoeXFcU\ntETytT1HqWvvCftVSL+cdJtsZw2CYK5EPgtcNviAUmopcA0wQ9O06cBDvuPTgGXAdN/3PKqUMiul\nzMAfgMuBacBy33PFMJhMihkZKdKhdZDyJhejE2NISYwN+Xv7m4oY5YRWXN+JMwidWT/JmOR4po9P\nZr3URZ5WUV3HkOohT2Xa+GSev30ex139fPHJbTR09AQsrqPHu1n+5Fbauvt54fb5n1jLE438W+gr\nDDbmJ5ys8TfUmZOhaxx3nZ/D+FHx/ORvh3TpjCkix3sljax4ZjvjUhJYe9dCxo+w06lSivuvmsaN\n8zN5bEMZv/t3SYAjBY9X4/GNZUwfn8x5ecEfrRMIuQ4bZY2d0hskwIKWRGqa9i7w8YFvdwO/1DSt\n1/cc/xXiNcAaTdN6NU2rAEqBeb5HqaZp5Zqm9QFrfM8Vw1SQkUJRfQddfW69QwkLlU0uXeohYdCY\nD4NsrSip7xhSTUagLcl3sKuqlbbu/pC/d7hr6uylqbNvyPWQp3LOxBSeuW0ude093LRyGy2uvjOO\nq66th+VPbuW4ayCBPHuiJJAf52/mJXWRI9Pn9vLnXTVcOCWdsaNC11DnVBJizfzgs1M5dKydtTtr\ndI1FGNfbh+u5fdVOnGlW1nx5wRk3ilJK8bNrzuILsyfy8Nsl/GF9aYAiHfCvg3WUN7n46pLckI5I\nOxO56TZ6+r1Swxxgoa6JnAycp5TappTaqJSa6zs+ARh8Bq71HTvdcTFMMzNT8Hg19teGvr1/OKpo\ncuk2p84WZ2Fscrwhirx7+j1UtXTp0hBlaX46Hq/GptKmkL93uCvy1YqOdCXSb44zlZUr5lDV3MXN\nT22jrWvkCXtD+0AC2dzZx6rb5zEjI+WMYotU41MSiDWbpNHZCP37cD1NnX3cqENDnVP57NnjmOdM\n5VfriuSGlxi2fx44xlde2MWUsUms+fIC7La4gLyuyaT45efP4T8KxvOrdUU8+W55QF5X0zQe3VBG\nlt3KZWeNDchrhsKJDq0GuXlvFKFOIi1AKrAA+A6wVgXoNoZS6stKqZ1KqZ2NjVJH9XEFvgu6PdJc\nh64+N3XtPWSFeEbkYLnpNkOsRJY1dqJphGS8x8cVZKSQHG9hfaFsaf24w8faAQLSrGZRjp0/3jyb\n4voOVjyznc7e4e9WaOjoYdmTW2lo72HVl+YyK8wbLejJbFJkpiWeqMsWw7N6ezXjR8Vz/mR9Gup8\nnFKKH101jdauPv7v7cBvHRSR67W9R7jnpT2cMzGFF+6YH/DyGrNJ8dAXZvDZs8fxwJuHeXZTxRm/\n5qbSZvYfaeOu87N1q0ceCX8SaYSb90YS6iSyFnhFG7Ad8AJ24AgwuLhhou/Y6Y6fRNO0JzRNm6Np\n2hyHIzz+uISTNFscmamJ7JW6SCqbBhraZDn0SyJzHFbKDNChsaR+4ISrx3ZWi9nE+ZMdbChulDqG\njymq68BuiwvYXesl+ek8cuMs9h9p40vP7hjWEPWmzl5ufHIbdW09PPulecyelBqQmCKZM80qsyJH\noLq5i/dKmrhhbmZYXcCeNWEUy+ZmsmpzpWFq3YW+1u6o4Zsv72WuczTPfWkeyfExQXkfi9nE75YV\ncMm0Mfz4b4d4cVvVGb3eoxtKGZMcx7WzjLUpMNUaS6o11hA3740k1Enkq8BSAKXUZCAWaAJeB5Yp\npeKUUllAHrAd2AHkKaWylFKxDDTfeT3EMUeMmZkp7K5uDfvEJdj8tUhOHVcic9JtdPa6qW8P7w6N\nxfUdWExKt/9WS/PTaezo5ZBv5U0MKKofeVOd07l0+lh+d0MBOytbuPO5nfT0f3oi2dzZy41PbqW2\ntYunb53LXKckkEOR7bBS2dwlN0eGac2OakwKrp87Ue9QTvKfl0wmIdbMz944pHcoIsw9v6WS//rL\nPhbn2nnm1nlY4yxBfb8Ys4lHbpzFhVPS+X9/PTDi+t29NcfZXNbMHYuzQ9poL1ByHFa5yRNgwRzx\nsRrYAuQrpWqVUrcDTwPZvrEfa4AVvlXJg8Ba4BDwT+AeTdM8mqa5ga8B64DDwFrfc8UIzMxIoaGj\nl2NtgevEaER6zoj0y3UYo7lOcX0nTruVWIs+I2X9W9Y2SJfWEzxejeL6jqDMXbxqxngevG4G75c2\n8dUXd9PnPv0s0xZXH19cuY2q5i6eXjGXBdlpAY8nUjnTrPS5vRwNwniVSNXv8bJ2Zy0XTkln3KiR\nda4MpjRbHN+4KI8NRY28U1ivdzgiTK18r5wfvnaQz0xNZ+WKOSTEhiYZi7WYePSLszgvz853/7KP\nv+6pHfZrPLahlFEJMSyfHx71yMOVK2M+Ai5otz80TVt+mi/ddJrnPwA8cIrjbwJvBjC0qFXgq1Pa\nW3N8xO2jI0FFk4v0pLig3/37JP4OraUNnZybG74tsksaOpiu44w/R1Ic50wcxYaiRr52YZ5ucYST\nqmYXPf3eoCSRANfNnkhPv4f/fvUA31izh98vn4nF/NGbCMe7+rhp5TYqmlw8tWIui8L4Zzgc+W9g\nVTZ1MXH08GbBRau3D9fT1NnL8jBpqHMqtyx08tL2av7n74dZnOsgxqzodXvp7vPQ4/bQ0++lp99D\nd7+Hnn4PvR/5/MOPe/s99Lh9n/d9+PGHj4HPz544it9cX6D3/20xRI+8U8JD/yrmirPH8rsbZob8\n5mx8jJknb5nDbc/s4L61HxBjNnHlOeOH9L2lDR2sO1jPvRfmYtPx2ulM5DhstHb109zZS1qASkGi\nnTF/EsSITBuXTKzFxJ7qVq44e5ze4ehGz86sfulJcSTFWcJ6JbK7z0N1Sxf/UaBv7cOS/HQeeaeE\n4119usz1DDeB6sz6SW5aMIlet5f/+fsh/vNPH/Dr6wtO1KC1dfVz01PbKG3s5Mlb5rDYIHPCwon/\n/FPR1Cn//YZo9fYaxo2K54IwaahzKrEWEz+6chq3PrODs+5fR5/n9Cv5nyY+xkRCjJn4jzxMxFvM\nOJJiaO/u55XdR7hlofNE4zwRnjRN4zdvFfP7d0q5duYEfnXdOSfdmAuV+BgzT906h1uf3sE31uzF\nYjINqcvq4xvLiY8xsWKRM/hBBknuoJv3kkQGhiSRUSTWYuKs8cnsjfIOrZVNLi6eNkbXGJRSZId5\nh9YPO7OGvqnOYEvyHfzf2yW8W9LE1TOGdtc0khXWdWBSkJce3H+X2xdn0dPv4VfrioiPMfPza8+m\no9fNzU9vo7iukz/ePDusL+jD2ZjkOBJizFT4mnyJT1bT0sW7JY3ce2GebhffQ7UkP50HP38OZY2d\nxPkSv4SPJYHxseaB/40xkXDiYzMJMWbiYkzEWUyfOn+vo6efhb94h1WbKym4QVYjw5Wmafz8zcM8\n+V4Fy+Zm8MC1Z+veFCox1sLTt83l5qe28fXVu3n8ptlcNPX010RHj3fz6p4j3LRgkqGTr8FjPuZL\n+UVASBIZZQoyRvPitir6PV5iwvyPcTC0dffT7OrDqfNKJAwUeYfzDMSShoEVLz3Geww2Y2IKoxNj\n2FDUIEkkAyuRzjRrSGpp7lmaS0+/h9+/U4rZpDh4tJ3Dx9p5/KbZLJ2SHvT3j1RKKZx2KxVN4XsT\nKZys3VmDAq6fm/Gpzw0HoYgzKT6G62ZP5MVtVXz/8ilnPKBeBJ7Xq/Hjvx3kuS1VrFg4ifuvmo4p\nTLoK2+IsPHvbPG5+aht3v7CbJ1fMOe1NwSffG5gxeef52aEMMeDGj0ogIcYsdZEBFH1ZRJSbmZlC\nr9tL4bEOvUPRRWUYNNXxy023Ud/eS0dPeA6oLq7vxGJSTNKxiy0MzLo6f7KDjUUy6gMGOrMGqx7y\nVL598WTuPC+LF7dVc+BIG3+4cdYn3rUWQ5NlT6SyWVYiP43b4+XlHTUsyU9nQhTX8p/KrYucuL0a\nL26r1jsU8TEer8b3X9nPc1uquOv8bH58dfgkkH6jEmJ47kvzyE238eXndrL5FDe1W1x9rNlewzUF\nEwz/+2cyKXLSB8aricCQJDLKzMwcqJ3YW9OqcyT68I/3CIckMsfXobU8TE9oJfUdZOnYmXWwpfnp\nNLv62H+kTe9QdNXV56ay2RXSJFIpxQ+umMoPr5zG07fO5ZLpn14/Iz5dlt1KdUsX/WdQNxcN3ils\noKEjvBvq6MVpt7I0P50Xt1XR6x76bFcRXG6Pl/vW7uXlnTXce1Ee37t8yqduT9ZLSmIsL9wxH2ea\nldtX7WR7RctHvv7s5kq6+z185QJjr0L65ThslMlKZMDof3UoQmpCSgJ2Wxx7qqOzLrK80YVSkJmq\nf0fEwUXe4ai4vlP3eki/8yc7UAo2FDXqHYquSuoH6lSD2VTnVJRS3L4468TIFXHmnGlWPF6N2lYZ\n8/FJVm+vZkxyHEvz5WfvVG5d5KSps4839h3TOxQB9Lm93LtmD6/uPcp3Ls3n2xdPDtsE0i/VOpBI\njk+J57ZntrOramCRwdXrZtXmSi6ZNoa8MLkWOFO5DhtHjnfj6nXrHUpEkCQyyiilmJmZwp4oba5T\n2exi/KgE4mP0H5SbmZqIxaTCsrlOd5+HmtYu8nSuh/RLtcYyY2IK66N8XuSHnVn1G7siAiPb4R/z\nEZ47EcLBkePdbChu5Po5GWHfUEcv5+XZyU238cymSjRNtvvryePV+OqLu3lzfx0/vHIa9yzN1Tuk\nIXMkxfHSnQtwJMVx69Pb2Vd7nNXbq2nr7ufuJTl6hxcw/pv34boDzGjkrByFZmamUNHkotXVp3co\nIVfR5Dpx8aa3GLOJSWmJYbkSGS6dWQdbmp/OB7XHae7s1TsU3RTWdZAQYw6LlXRxZpy+WuNySSJP\n6+UdNQBcP8cYDXX0oJRixSIn+4+0sbs6OstUwsXOyhb+fbie710+hdsXZ+kdzrCNSY7npTsXkGKN\n4aaV23h8YzkLs9OY6ZsxHgk+7NAanX1BAk2SyCjknym1tza6ViM1TaOiyXXi4i0c5IbpmI/i+vDo\nzDrY0ikONA3eKwnfjrbBVljXzuQxtrBr0CCGL9UaS3K8RVYiT8Pt8bJ2Rw3n5znIkJsmn+jzsyaQ\nFG/h6U2VeocS1Xb5kngj3/QYn5LAS3cswBZnoamzN6JWIQEmpVkxm1RY3rw3Ikkio9A5E1MwKaKu\nLrLZ1UdHjzssmur45ThsVDWHX3ON4vpOYsz6d2Yd7Kzxo7DbYqN6S2tRXWg7s4rgUUqRZbdSIUnk\nKW0oaqSuvUca6gxBYqyFZXMz+OeBOo61SY2tXnZVtpLtsJJqjdU7lDOSkZrI2q8s5NdfmMF5eXa9\nwwmoWEv47gAzIkkio5AtzsLkMUnsjbK6yHAa7+GXm27D7dWoCrNW//7OrOE0S9TkG/XxbnEjnigc\n9dHY0Uuzq498qYeMGJJEnt7q7dU4kuK4aKrMIx2KWxY60TSN57dU6R1KVNI0jV3VrcyZFBlbPyeO\nTuTzsyeGfVOgkch12GTMR4CEzxWiCKmZmSnsrW6Nqrl75WGYRPrHfITbltbiho6w7Ma2ND+d1q5+\nPoiyrdjwYVOdqbISGTGcditH27rp6ZfxDIMdPd7N+qIGrp8zMaxuZIWzjNREPjN1DKu3V8vPkw7K\nm1wc7+pndoQkkZEsJ91GZZMr7HaAGZGcnaPUzIzRtPe4o6qpQ2WTC4tJMXF0+AzM9Tf5CaetFV19\nbmpaupmcHn7Jynl5dkwKNhRG35bWwrp2ANnOGkGy7FY0Dapbwmsngt7W7qzBq8GyubKVdThuOzeL\n1q5+Xt97VO9Qoo5/LIYkkeEv1xGeO8CMSJLIKDUz09dcJ4q2tFY0uQbGaoTRne2k+BjGJseH1Upk\nWcPAjYVwaqrjl5IYy6zM0Wwojr55kYV1HdhtcaTZ4vQORQSIf1eEtJv/kMer8fKOGs7Ls0tDnWFa\nkJ3KlLFJPL2pQsZ9hNiuylZGJcSQbQ+/v5vio8J9RreRhM/VtAipHIeNpDgLe6KoJXhFkwtnGG1l\n9ctJt4bV/nx/Z9Zw3M4KsCTfwb7aNho7omvUR1FdB1NkFTKi+M9Hlc3h8/uvt43FDRxr6+FGaagz\nbEopbjvXSWFdB9sqWvQOJ6rsqm5l9qTR0jnbAHLSw7OMyIgkiYxSJpNiRkZK1HRo9Xo1KptdYVUP\n6ZfrsFHW0Bk2d46LGzqIMSucaeG5CrAkf6DRxsYoWo30eDWK6yWJjDTJ8THYbbFUhNFNJL29tK0G\nuy2Oz0wbo3cohnRNwQRGJ8bwzKYKvUOJGse7+iht6JStrAZhi7MwblS8rEQGgCSRUWxmZgpF9R10\n9bn1DiXo6jt66On3hulKpI3OXjcNYbKyVlLfSbbdFlbbfgebPj6Z9KQ4NkTRqI+qZhe9bq/UQ0Yg\nZ5qVClmJBKCurYd3Cuv5gjTUGbH4GDPL52Xy1qF6aqTWNiR2+3Z0zcqUJNIoctNtkkQGgJylo1hB\nRgoer8b+2ja9Qwk6fxv97HBMIh3htT+/uL6DvDCsh/RTSnGBb9SHO0q6qxX6OrNOkfEeEUfGfHzo\nw4Y6xh3WHg5uWjAJpRTPb5VxH6Gwq6oVs0lRkJGidyhiiHIcNsoaw2cHmFFJEhnF/Ce8PVHQXMd/\nkRaOK5G5YbQ/39Xrpra1m8lhWg/pt3RKOu097qj42YWBJNKkCOvkXoyM026lsaOXzt7I3xHySfwN\ndRbn2pmUFn7naSMZn5LAZWeNZc326qjYaaS3XVWtTB+fTEKsWe9QxBDlptvo6vNwrK1H71AMTZLI\nKJZmi2NSWiJ7o6AusrLJRZzFxLjkeL1DOUl6Uhy2OAtlYbAS6V8NDcfOrIMtzrNjNqmo2dJaVNeO\nM81KfIxcpEQa/+6IyihfjXy3pJEjx7tZLg11AuK2RU7ae9y8svuI3qFEtH6Plw9q2mQrq8GE2w4w\no5IkMsoVZKSwu7o14pf0K5pcONOsYdk5TSlFTrqN0jBYiSzxnVDDtTOrX3J8DLMnjWZ9YXQ01ymq\n62DKuPD+NxEj498dEe1bWl/ZfYQ0aywXS0OdgJg9aTRnTUjm2c2VEf/3XU+Hj7XT3e+RpjoGI2M+\nAkOSyCg3MyOFho7eiF/SHxjvEZ7dRgFyHNYT8xn1VFLfQazZxCQDzGdbmp/OoWPt1LdH9s9uV5+b\nqpYu8sdIPWQkcqbJSiTAoaNtzHGOJtYilyWBoJTitkVZlDZ08n5pk97hRKxdVQNNdeY4JYk0Erst\nllEJMWFx897Igna2Vko9rZRqUEodGHTsx0qpI0qpvb7HFYO+9n2lVKlSqkgpdemg45f5jpUqpb4X\nrHijVYFvC8beCK4tc3u8VLd0kRXGQ4BzHDbq2nvo6OnXNY7i+g6yHdaw7cw62JJ8BwAbiyJ7NbK4\nvhNNQzqzRqiEWDPjRsVH9Upkv+8c7d9iJgLjyhnjsNtieXZTpd6hRKxdVa2MHxXPuFEJeocihkEp\nJR1aAyCYV4rPAped4vhvNU0r8D3eBFBKTQOWAdN93/OoUsqslDIDfwAuB6YBy33PFQEybVwysRYT\ne3wtqiPR0eM99Hs0ssJ4JdK/taJc53lxxfWdYb+V1W/K2CTGJsezPsLrIovq2gFkRmQEy7JH95iP\nmpYu+j0a2ZJEBlScxcyN8yfxTlFD1K90B8vuqlZmyVZWQ/LP6BYjF7QkUtO0d4GWIT79GmCNpmm9\nmqZVAKXAPN+jVNO0ck3T+oA1vueKAIm1mDhrfDJ7Iri5TnnTwEki3FciQd8Ora5eN0eOdzM5PXz/\nOw2mlGLpFAfvlzTRH8GjPgrrOkiIMZNpgC3GYmScUT7mw3/zLNshXVkD7ab5mVhMilVbKvUOJeIc\nPd7N0bYeqYc0qNx0G82uPlpdfXqHYlh67Fn7mlJqn2+7q/83bwJQM+g5tb5jpzt+EqXUl5VSO5VS\nOxsbI3t7W6AVZIxm/5G2iL0Qrzwx3iN8L8InpSViMSldt1YYpanOYBdMTqej132iLiUSFdV1MHls\nUlg2hRKBkW23cryrP2ovZvw3z3LC+EafUaUnx/PZs8fxp521UT9GJtBO1ENOStU5EjES4TRezahC\nnUQ+BuQABcAx4NeBemFN057QNG2OpmlzHA5HoF42KszMTKHX7aXwWIfeoQRFRZMLW5wFhy1O71BO\nK8ZsYlJaoq4ns5L6gX//cB/vMdi5uWnEmFXEbmnVNI3Cug6mGCixF8Pnb64TrVtayxtdA40uEmP0\nDiUi3XpuFp29bv68s+bTnyyGbFdVKwkxZumcbVAy5uPMhTSJ1DStXtM0j6ZpXuBJBrarAhwBMgY9\ndaLv2OmOiwCamZkCwN6ayFzNqWjuwmlPRKnwXsnJcehb5F3S0EmsxWSoQd9J8THMdaZGbHOdxs5e\nWlx90lQnwmU5ortDa1ljp9RDBlFBRgozM1NYtaUKr1fGfQTK7upWZmSMIsYAjejEySaMTiDOYpIk\n8gyE9CdfKTVu0KfXAv7Ora8Dy5RScUqpLCAP2A7sAPKUUllKqVgGmu+8HsqYo8GElATstriIrYus\naOoM63pIv9x0G1XNXbptKy6u7yDHYcNssG2TS/IdFNZ1cPR4t96hBFxR3cDqsDTViWwZoxMxqeid\nFVne5CJH6iGD6tZFTiqaXGwsjswbbqHW1efm4NF2qYc0MLNJke0IjxndRhXMER+rgS1AvlKqVil1\nO/CgUmq/UmofsBT4FoCmaQeBtcAh4J/APb4VSzfwNWAdcBhY63uuCCClFDMzU9gTgWM++txejrR2\nk5UWvvWQfjkOG26vRnVLly7vX1LfSZ5BmuoMtjQ/HYANEbga6U8iZSUyssVaTGSkJkZlEtnq6qPF\n1SfjPYLsirPHMSY5jmc2V+odSkT4oKYNj1eTekiDkzEfZyaY3VmXa5o2TtO0GE3TJmqa9pSmaTdr\nmna2pmnnaJp2taZpxwY9/wFN03I0TcvXNO0fg46/qWnaZN/XHghWvNFuZmYKFU2uiGvsUN3ShVf7\ncLtYOPMXeetxQuv0d2Y1UD2kX266jQkpCWyIwLrIwroOHElxpIVxPa8IDGdadHZo9XfPls6swRVj\nNnHT/Em8W9woF80BsNs3Fs1fDiSMKddh48jxbrr7PHqHYkiykVsAAzUTAHtrI2s10n9R5jRAnZ//\nIkqP5jr+pjpG6szqp5RiSb6DTaVN9Lkjq8NwYV27bGWNEll2K5VNLjQtumrWyvzjPQxQcmB0N87P\nJNZiYpWsRp6xXVWt5KbbSEmM1TsUcQZy021o2oc3s8TwSBIpADhnYgomRcTVRfobVWTZwz+JTIqP\nYUxyHGUNoV+N8I/3mGzAJBIGtrS6+jzsrBzqaNrw5/FqlNR3km/QfxMxPFl2K64+D40dvXqHElJl\njZ3Emk1MHJ2gdygRL80Wx9UzxvOX3bW0dffrHY5heb0au6pamZ0p9ZBGp+cOsEggSaQAwBZnYfKY\nJPZGWF1keZOL0YkxhrlbmJuuT5F3SX0HcRaTYQfaL8pNI9ZsiqhRH5XNLnrdXqmHjBJO342uaNvS\nWt7oGpiTKx0uQ+LWRU66+jz8ScZ9jFh5Uydt3f3MdkoSaXRO+0BTszJJIkdEztrihJmZKeytbo2o\nFuCVTa4TF2dGkOOwUd7QGfItbcX1nYbszOqXGGthfnYq6yOouY6/qc7Ucck6RyJCITtqk8hOaaoT\nQmdNGMU8ZyrPbq7EE0F/60NpV9VAPaR0ZjW+OIuZzNRE6dA6QpJEihNmZoymvcdNeQRdxFQ0uQyx\nldUvx2Gjo9dNQ4i3tJXUd5BnwKY6gy3JT6e0oZManbrbBlphXQcm9eF2GxHZxqckEGs2UdEcOeff\nT9Pv8VLV3CVNdULs1nOd1LZ28/bher1DMaRdVa2MTow5ceNHGJt0aB05SSLFCf4uY5GypbWrz01d\new9ZBmiq4+dPGEK5taKjp5+jbT2GrYf0W5LvAGBDhMxBKzzWjtNuJT7GrHcoIgTMJkVmWuKJOu5o\nUNPShdurkS0rkSF1ybQxjB8VzzObKvUOxZB2VrUye9JolDLmzh3xUTnpNiqaXLh1mtFtZJJEihNy\nHDaS4izs8bWuNrrKpoEVKSOM9/Dzb+sKZYdWf1MdI86IHCzbbiUzNZENhZFRF1lU3yGdWaNMtI35\n8HdmzTHQOToSWMwmbl7oZEt5M4V17XqHYygtrj7KG13Mkq2sESPXYaPfo9+MbiOTJFKcYDIpZmSk\nREyH1spm44z38BuTHIctzhLSrRWl9cbuzOqnlGJpvoPNZc309Bt75lNXn5vqli7yx0g9ZDTJdlip\nbO6KqLr0T1Le6J8RaewbWEa0fF4G8TEy7mO4/DfZpTNr5DixA6wxem7gBYokkeIjZmamUFTfQVef\nW+9QzliFgcZ7+CmlyHFYQ3oyK/Z1Zs0waGfWwZbkp9Pd72F7hbFHfRTXd6JpMGWcsRN7MTzONCt9\nbi9H27r1DiUkyho7sdviGJUQo3coUSclMZZrZ07gld1HaHX16R2OYeyqasViUpwzMUXvUESA5MiY\njxGTJFJ8REFGCh6vxv7aNr1DOWMVTS7Sk+Kwxln0DmVYchyhLfIubugkN924nVkHW5CdRpzFxAaD\nd2ktPDawxUy2s0YX/w0v/1b8SFfe6JKmOjq6dVEWvW4va3bIuI+h2lnVyvQJo0iIlVr1SJEcH0N6\nUpwkkSMgSaT4iIKMgbtreyKguY7ROrP65aTbqGvvobM3NKvBJfUdhq+H9EuINbMgO40NBp8XWVjX\nQWKsmYzRxl8dFkOXdWLMR3RczJQ3uWS8h47yxyaxKCeN57dUSlORIej3ePmg5rhsZY1Aes3oNjpJ\nIsVHpNnimJSWyN4IqIusNGoS6buoKg/BCa29p59jbT3kGbwecrCl+Q7Km1xUGXhUQlFdB3ljkjBF\nwOqwGLoxyXEkxJipiIKVyFZXHy2uPmmqo7Pbzs3iaFsP/zok4z4+zaGj7fS6vTIfMgLlptso02FG\nt9FJEilOUpCRwu7q3AHuygAAIABJREFUVkP/MrV199Ps6jNkEpkbwv35JRHSVGewJfnpAIbd0qpp\nGkX1HUyVraxRRymF026NipXI8iZ/Ux3jnaMjyYVT0slITeCZTRV6hxL2dlX5mupIEhlxctNtdPa6\nqW8P7Yxuo5MkUpxkZkYKDR29HGvr0TuUEfPPWnMaMImclJaIxaRCMuajpL4DgMljImdLmdNuJctu\nZb1Bt7Q2dvbS4uojX5LIqJRtH+jQGunKGvzjPSLn3GNEZpNixUInOypbOXDE+L0QgmlXVSsTUhIY\nOype71BEgOU6pLnOSEgSKU4y07fff6+B6yL94z2yDZhExphNZKYlhmYlsqGT+BhTxNXeLcl3sMWg\noz4Kjw0k9pJERienPZHqli76I7xGraypk1iziYkRdu4xoi/MySAx1swzmyr1DiVsaZrGzqoWWYWM\nUB+O+ZAkcjgkiRQnmToumViL6cQ8JCMqb3ShFIYdW5HrsIVkzEdxfQe56baIq71bmp9Or9vLlvJm\nvUMZtqK6gSRyyliZERmNnGlWPF6N2tbIHvNR3ujCaU+MiK7QRjcqIYbrZk/kbx8cpalTtvOdytG2\nHurbeyWJjFCOpDiS4kM7ozsSSBIpThJrMXHW+GT2GLi5TmWziwkpCcTHGLMNd066japmV9BXI0rq\nO5mcHnkrXvOyUkmIMbOh0HhbWgvrOnAkxZFqjdU7FKEDf41gpNdFljV2km2XrazhYsUiJ30eLy9t\nq9Y7lLAk9ZCRbWBGd2jHq0UCSSLFKc3MHM3+I22G3VJl1PEefrkOG/0ejeqW4NVGtXX3U9feQ24E\n1UP6xceYWZSTxvqiRsM1iCqqb5f5kFHMmeZPIiO3LrLf46W6uUua6oSRHIeNCyY7eGFrFX1uY/7d\nD6ZdlS0kxprl3BzBZMzH8EkSKU6pICOFXrf3RH2WkWiaZvgkMse/Pz+Id8VKG3xNdSJwJRIG6iKr\nW7qoaDLOqA+3x0txfadcqESxVGssyfGWiF6JrG7pwu3VpKlOmLn1XCcNHb3848AxvUMJO7uqWynI\nSMFilsvmSJWbbqOxo5e27n69QzEM+W0QpzQzMwWAPTXGq4tsdvXR0eM+cUffiPx36IN5V6w4Asd7\nDOYf9bHeQKM+Kpu76HN7yZd6yKillCLLbqUyglciy3313rISGV4uyHOQbbdKg52PcfW6OXysQ7ay\nRjjp0Dp8kkSKU5qQkoAjKY69BqyL9I/3MPJKZHJ8DGOS4060wQ+GkvpOEmLMTBydELT30FNGaiK5\n6TY2GGjUx4dNdSIzsRdDk2W3GmoFfbj8HRCzZSUyrJhMihWLnOytOW7oxnqB9kHtcTxejVmSREa0\n3BDsAIs0QUsilVJPK6UalFIHTvG1+5RSmlLK7vtcKaX+TylVqpTap5SaNei5K5RSJb7HimDFKz5K\nKUVBRgp7DDjmozwCkkgYqFEJZrvpkobI7Mw62JLJDraVt9DV59Y7lCEpqmvHpD78Yyaik9Nu5Whb\ntyFH1AxFeWMndlscoxJi9A5FfMznZ08kKc7Cs5sr9Q4lbOyqHEioZ2VKEhnJMlITibWYZMzHMJw2\niVRK7fcldB9/7FdK7RvCaz8LXHaK180ALgEGtwC7HMjzPb4MPOZ7bipwPzAfmAfcr5SS3+IQmZmZ\nQkWTi1ZXn96hDEtlkwuLSRl+hS033UZZQ2fQGsMU13eQF4FNdQZbOiWdPo+XzaXGGPVRWNdBlt1q\n2K7CIjCy7FY0jaA21tJTeaOLHNnKGpZscRa+MCeDN/Ydo769R+9wwsKu6lYmj7HJTY8IZzYpsu1W\n2c46DJ+0EnklcNUpHv7jn0jTtHeBllN86bfAfwGDr4yvAZ7TBmwFUpRS44BLgbc0TWvRNK0VeItT\nJKYiOGZmDOTre2uNtRpZ0eQiMzXR8AXwOQ4bHb1uGjsCP7errbuf+vZe8iK0qY7fHOdorLFmNhQb\nY0trYV2HzIcUJ3ZRlIdgVqweyho7ZStrGFuxaBIeTePFrVV6h6I7r1djd1Wr1ENGiRyHdGgdjtNe\nZWuaVvVJj5G8mVLqGuCIpmkffOxLE4CaQZ/X+o6d7rgIgXMmjsKkMNy8yIomF06Db2UFTnQuDMZd\nsZJ6X2fWCF+JjLOYWZRrZ31h+I/6cPW6qW7pIl/qIaOe//xV2Rx5SWSLq4/Wrn5ZiQxjk9KsXDQl\nnRe3VdP7/9u78/i4rvr+/6/PaKTRLtnabNmStXnJajt2HAhNAxSyAHHYQh2gSdpQvmVpKf0ChS+l\n7Q++tFC6shQKJIQUSFj7JYGEECBhJ4ntOI6d2Ja1etMuaxlZ+/n9MXdkxZaskTSjGc28n4/HPDK6\nc++d4znK1XzuOefzGU/OKdWRaugcpH94nG3rVsa7KbIEaktzOdYzlLRLCaJtzqEaM3uRmT1lZoNm\nNmpmE2bWP983MrNs4P8Af7uQhkZw/reb2W4z293ZuXyyMSaynICfDWV57FtG6yInJx3N3cu7vEfY\n1CLvGNwVS/bMrNO9bGMpJ06fSfgpKke8wF5BpORnplOcm0FTEo5ENnrXM5X3SGx3XF1Nd3CUB59J\n7XIfu1tC6yE1Epka6kpzmXQkdWKzaIpkvt9ngVuBeiALeBvwuQW8Vy1QDTxjZs3AWmCvma0CTgAV\n0/Zd622bbft5nHNfdM5td85tLykpWUDzZCZbKwvZ19rL5GRij+KEtQ8MMzw2mRQjkWX5AXIDfhpi\n8EWyvmOArPQ01hQu73WjkXjpxtD14PEEL/URzsx6kaazCl6G1iQciVR5j+XhJXVFrC/N5Su/bkr4\nWRyxtKell5U5GVQVZce7KbIEVOZjfiJaNOacOwqkOecmnHNfYQHrEp1zzzrnSp1zVc65KkJTU69w\nzrUBDwC3eVlaXwT0OedOAY8A15nZCi+hznXeNlkiWytW0D88PpXxNNGF7x7VJEEQaWbUlsRmkXd9\n+yDry5I7M2tYeWEWG8vyeCzBS30cahsgOyN5S67I/FQVJWeZj4auQTLSfKxdoS/liczMuOMlVRw8\n2c9398547z4l7G3p5YrKFZgl/99KCd3cMlMQGalIgsghM8sA9pnZP5nZeyM5zszuA34LbDSz42Z2\n5wV2fwhoBI4CXwLeCeCc6wE+BjzlPT7qbZMlsrWyEGDZTGkNf+lKhpFIiF2ZjyPtA0mfVGe6l24q\n4anmHgZHErfUx6G2fjaU5aVEYC9zqy7JoXNgJKF/ZxeioSNIVXE2afo9T3hv3LaWq2uL+Ovv7ueR\ng23xbs6S6x4cobEryPYqTWVNFZnpaVSsyFaZjwhFEkT+kbffu4Egoemlr5/rIOfcrc651c65dOfc\nWufcXee8XuWc6/KeO+fcu5xztc65y5xzu6ftd7dzrs57fGU+/zhZvNqSXPIC/mVTeLi5K0jA72N1\nfma8mxIVtaW5nOobjuoXyb6hMToGRpK+vMd0L91QytiE49dHu+LdlBk55zjcNsAmrYcUT3WRl1wn\nyUYjG7sGtR5ymQj40/jibdu5bE0Bf/6Np/lVfWJeP2Nlr5dUUOshU0tdaa5GIiMUSRD5WufcsHOu\n3zn3/znn/opQmQ9JAT6fsbmicNlkaG3qClJVlJM0oznhL1uNUbwrdqQjNTKzTre9agW5AT+PJ+iU\n1s6BEXqHxpRUR6aEZ1Mk05TWsYlJWruHtB5yGckN+Lnnj6+kpiSHP713N3talscN5WjY09JLeppx\n2ZqCeDdFllBdaS6NXUEmlkkukHiKJIi8fYZtd0S5HZLAtlYWcrh9gKHRxJ9W1dSVHJlZw+pKQ/+W\naE6tCGcBTaXprOlpPq5ZX8zjhxOz1MchL6mOakRKWFVR8gWRrT1DjE86aopT5wZWMijMzuDeO3dQ\nlh/gj7/yJM+dnHeC/mVpb0svl5QXkJmeFu+myBKqLclhdHyS471D8W5Kwps1iDSzW83sQaDazB6Y\n9ngc0LrEFLKlopCJScezx/vi3ZQLGp+YpLVnKGnWQ0KoXpffZ1GdWlHfPkh2RmpkZp3upRtLONU3\nzGEviE4kh9pCX8o0nVXCsjLSWF2QmVTTWRu861htqYLI5aY0L5Ovve0qcgJ+brv7iajOjklEo+OT\nPHP8NNs1lTXlhMuraUrr3C40Evkb4F+AQ95/w4+/Aq6PfdMkUWypCCXXeTrBk+ucPD3M2IRLisys\nYelpPiqLsmnoiN4XyfqOAdaXpkZm1uleurEUgMcOJV6pj0NtA5TmBViRkxHvpkgCqS7OWTaZsSMR\n/rdoOuvytHZFNl9721U4B2/98hOcOH0m3k2KmYMn+xgZn9R6yBRUVxK6masgcm6zBpHOuRbn3OPO\nuRcTCiTzvMdx51ziz2uUqCnKDbCuKJt9Cb4usrEr9D98Mo1EQvQztB5pH2R9WeqNeJXlZ3LR6vyE\nXBd5uG1A6yHlPFXFOTQnUa3Ixs5BSvIC5Gemx7spskC1Jbnce+cOBkbGeeuXn6BzYCTeTYqJ8NrP\nKxREppyC7HSKcwMKIiMQSamOW4AngVuANwFPmNkbY90wSSxbKgrZ29qbkOvJwpqnynskV/2xutJc\nmruDjE9MLvpcp4dG6RwYSamkOtO9bGMJu1t66R8ei3dTpoxPTFLfMaiprHKemuIcTg+N0RscjXdT\noqKhM5hUM0VS1SXlBdzzx1fS1jfMH931BH1DiXM9jZa9rb2sXZFFWZJkepf5qSvNUZmPCESSWOdv\ngCudc7c7524DdgAfiW2zJNFsrSikY2CEU33D8W7KrJq6guQG/JTkBuLdlKiqLcllbMLR2rP4Rd5H\n2kMXxVRKqjPdyzaVMjHpEipVfXP3EKPjk0qqI+eZSq6TJKORjZ2D1Ki8R1LYtm4lX7xtG42dQe64\n50mCSVTP1DnH7uZerYdMYeEyH4k8cJIIIgkifc656fO/uiM8TpLI1srQxXRfAq+LbOoeoqo4G7Pk\nWusXzUXeU5lZU3QkcmtFIfmZiVXqI5xUR9NZ5VzVJclTK7InOErv0Bi1Wg+ZNK5ZX8Knb93K/uN9\n/Om9uxkem4h3k6LieO8ZOgZGtB4yhdWV5NI/PE7nYHJO146WSILBh83sETO7w8zuAH4IPBTbZkmi\nuWh1Phl+H0+3Jm6NqKauQaqTMHV8OAlFQ+fiv0jWtw+Qk4KZWcP8aT6u2VDCYwlU6uNw2wBpPpu6\nWSASVrEiG58lR5mPcDbPWo1EJpUbLl3FP73hcn7T0M27v/E0Y1FYdhFve1u1HjLV1SpDa0QiCSId\n8F/A5d7jizFtkSSkDL+PS8vzeTpBk+uMjk9yovcM1UXJtR4SID8zndK8QFTm59d3DFJXlpd0o7Xz\n8bKNpXQOjHAwQWqdHWoboKooW7XI5DwZfh8VK7OTIohsUBCZtN6wbS0fvfkSfvJ8O+//9jNMLvMi\n7XtaesnJSNMSgxQWvqnboCDygiIJIl/pnPuec+6vvMf/ADfGumGSeLZWruDZE30JeaextWeISXd2\n+leyCc/PX6wj7YNsSPERr2s3lADw8yOJUerjcNsAm1bry4rMrKooJymCyMbOIBl+H2tWpOYsiGR3\n24ureP/1G/l/+07yke8fSJiZHguxu7mXrZUrSEuxMlhy1qr8THIDfo1EzmHWINLM3mFmzwIbzWz/\ntEcTsH/pmiiJYktFISPjkxw6lXjF2sNfssKJKJJNuMzHYv4w9wZH6RocYUMKlveYriQvwGVrCnjs\nUPzXRQ6OjNPaM8SmFO8TmV11cQ7NXcFl/aUcQtPxq4ty9MU8ib3zpbX8r2tr+PoTrXzyR4fj3ZwF\nGRwZ51Bbv6aypjgzo7Ykh6PK0HpBFxqJ/AZwE/CA99/wY5tz7q1L0DZJMFsrCwF4+ljirYsMJ56o\nTtL08XWluQwMjy+qJlc4qU5diibVme5lG0vY29ob99T04T5RUh2ZTXVxDsHRiWVfjy+UmTU5r88S\nYmZ88IZNvOWqSr7w8wY+99jReDdp3p45dppJh5LqCLWluTR0LP9ZILE0axDpnOtzzjU75251zrVM\ne/QsZQMlcawpzKIkL8C+BFwX2dgVZEV2OoXZGfFuSkyE1xEt5q7YEW9aRqqPRAJcu7GUSQc/r4/v\nlNbDbaEgUmtvZDbhG2PLeUrr2MQkrT1DCiJTgJnxsZsv5eYt5XzqkcPc+9vmeDdpXva09GJ29qa5\npK660lza+ocZSKC60olGpTokYmbGlopCnk7AMh/NXcGkHYUEqC1dfIbW+vYBcgN+ygtUPHlLRSGr\n8jP5yq+b4jpN8HDbANkZaazVOjGZRTIEkS3dQ4xPOiXVSRE+n/HPt2zmFReV8bffP8j39h6Pd5Mi\ntrull41leeRnpse7KRJndd71KhqZ8ZOVgkiZl62VhTR1BekNjsa7KS/Q1BWkKomDyFX5meRkpC0q\nU1h9+yB1pbkpnZk1LM1n/NUrN/B062keerYtbu041NbPxlV5+LROTGZRXphFRpqPpu7l+0UmXN6j\nRkFkykhP8/HZN2/l6toi3v+d/fzoQPyus5GanHQ83dKr9ZACqMxHJBREyrxsrQhdXPcdT5zRyKHR\ncdr6h6lJ4iDSzELz8xcxnbW+Y4ANWg855Q3b1rJpVR6f/NEhRseXPuOwc45DbQNs0npIuYA0n1FZ\nlE3TMr4b3uiNomo6a2rJTE/jS7dt57I1BfzFfU/zyzgvH5hLfccgAyPjbKtUECmwbmU26WmmIPIC\nFETKvFy+tgCfkVD1Ipu7hgCSeiQSQlMrFnox6wmO0jU4qvWQ06T5jA+96iJae4b479+1LPn7dwyM\ncHpojI3qE5lDVVEOzct4JLKhY5CSvICmCKagnICfe/74SmpKcnj7vXvY05K4aTX2tISSBm6vUhAp\n4E/zUVWUoyDyAhREyrzkBPxsKMtjXwKtiwx/uUrmNZEQmlpxqm+YwZHxeR87lZk1xWtEnuvaDSVc\ns76Yz/ysnr4zS7t4/lBbODOrkurIhdWU5NDcPbRsi7g3dgWTeqaIXFhhdgb33rmDsvwAd3zlKQ6e\n7It3k2a0u6WH4twMKldmx7spkiDqFjkDLNkpiJR521q5gqdbexmbWPopgDNJ9hqRYbXeVLCFTGur\n94JIjUSe70M3XkTfmTH+c4nT0R9u6wfQdFaZU1VRDqPjk5zsOxPvpixIQ+fg1PoiSU2leZl87W1X\nkRvwc9tdTybkF/O9Lb1cUblCeQNkSl1pLq09Q4yMT8S7KQlJQaTM28s3lTIwPM6vjnbFuylAKIgs\nyw+QE/DHuykxFR5FPNo5MO9jj7QPkhfws1qZWc9zcXk+b7hiLV/5dTPHeoaW7H0PnRqgLD/Aipzk\nLEsj0bOcM7T2BEc5PTSmkUhh7Ypsvva2qwB465ef4Hjv0l1v59I1OEJz95DqQ8oL1JXmMjHpaOlO\nnN/VRKIgUubt9zcUk5/p58F9J+PdFMDLzJrko5AAlStzSPPZgorf1ncMUFemzKyz+d/XbcDng3/+\n8eEle89DbQOayioRCQeRzcswiAyPOGkkUiBU8/jeO3cwODLOW7/8BB0Dw/FuEhAahQSth5QXmqrR\nrXWRM4pZEGlmd5tZh5kdmLbtY2a238z2mdmPzazc225m9mkzO+q9fsW0Y243s3rvcXus2iuRC/jT\nuPHS1TxysI3hsfgP8Sd7jciwDL+PdUXZC7qY1bcPsqFU0yZns7ogi7f9Xg3f33eS/UuQeXh8YpKj\nnYOayioRKcsPkJWeRlPX8rsbHi7vUVusIFJCLikv4J4/vpL2/hFuu+tJTg/Fv2TYnpZeMtJ8XFJe\nEO+mSAIJZ5RWEDmzWI5E3gPccM62TznnLnfObQF+APytt/1GYL33eDvweQAzWwn8HXAVsAP4OzPT\nbaIEsHNLOcHRCX52qCOu7eg7M0Z3cDQlgkgI3RWb71qS7sERuoOjrFd5jwv6X9fWUJSTwcd/+DzO\nxTaBSXN3kNHxSWVmlYiYGVXFOTR1Lb8vMo2dQTL8PtasyIp3UySBbFu3ki/eto3GziB3fOWpBSWM\ni6Y9Lb1cuiafzPS0uLZDEkt2hp81hVkKImcRsyDSOfcLoOecbf3TfswBwt/UbgbudSG/AwrNbDVw\nPfCoc67HOdcLPMr5ganEwYtqiijJC/BAnKe0hqd3JXt5j7Daklyau4OMzyOp0ZH20MVPSXUuLC8z\nnb98xXqeaOrhp8/H9ubI2cys6hOJTE1xKEPrctPQOUh1UWgqvsh016wv4dO3buXZE328576n45Z9\neGR8gv0n+rQeUmZUV7rw8mrJbsnXRJrZx83sGPAWzo5ErgGOTdvtuLdttu0SZ2k+49WXreZnhzvo\nH17a0gjThct7pErShrrSXMYmHK3zSABT3xEKWDQSObddOyqpKcnhHx9+fl6B+nwdOjVAms9UckUi\nVlWcTWvPUMJkxY5UY2dwakqYyLluuHQVH3n1Rfz0UAef/3lDXNpw8GQ/o+OTbFu3Mi7vL4mtrjSX\nxq7BZVtiKZaWPIh0zn3YOVcBfB14d7TOa2ZvN7PdZra7s7MzWqeVC9i5pZzR8Ul+fLA9bm1o7Axi\nBhUpUtcpXOajYR5lPo60D5AX8LMqX5lZ55Ke5uODN2yioTPI/U8dm/uABTrUNkB1cY6mTknEqotD\nWQKP9y6fMh+j45O09AxNJacQmcntV1fxmstX8y8/PsxvGpY+6/ue5lBSnSvWFS75e0viqyvNZXhs\nkhOnl8+1d6nEMzvr14E3eM9PABXTXlvrbZtt+3mcc190zm13zm0vKSmJQXPlXFsrCqlYmcUDz8Rv\nSmtzd5A1hVkp82U8nOFwPusi69sHWa/MrBF75cVl7Kheyb//5EjM1ukcbu/XVFaZl+ri0I2y5bQu\nsrVniIlJp5FIuSAz4xNvuJzq4hz+4r6nae9f2oyte1p6qVyZTWmebrTK+c6WV1s+196lsqRBpJmt\nn/bjzcAh7/kDwG1eltYXAX3OuVPAI8B1ZrbCS6hznbdNEoCZsXNzOb8+2kXX4Ehc2tCUIplZw/Iz\n0ynNC8xrfn59x6DWQ86DmfHhV11E1+Ao/xWD6VWDI+Mc6znDJvWJzEO1l910OWVoncrMqpFImUNu\nwM/n37qN4MgEf/6Np5ds2rZzjj2tvWzXekiZRZ13/WrQusjzxLLEx33Ab4GNZnbczO4EPmFmB8xs\nP6GA8D3e7g8BjcBR4EvAOwGccz3Ax4CnvMdHvW2SIHZuXsPEpOOhZ08t+Xs751IuiIT5ZWjtGhyh\nJzjKegUs87K5opCbNpfzpV820tYX3bvih72kOptWq0akRG5Fdjr5mf5lNRIZnnavkUiJxIayPP7x\n9ZfxZHMP//zI0tTsPd57hs6BEa5QECmzWJGTwcqcDCXXmUEss7Pe6pxb7ZxLd86tdc7d5Zx7g3Pu\nUq/Mx03OuRPevs459y7nXK1z7jLn3O5p57nbOVfnPb4Sq/bKwmxclcfGsry4ZGntDo4yMDxOVVFq\nfUEJZwqLpAzFkfZQwLJBSXXm7QPXb2RyEv710eh+mZkKIjWdVebBzKguzqF5mY1EluQFyMtMj3dT\nZJl47dY1vOWqSv7rF408crAt5u+3uyU0LqHMrHIhdSXK0DqTeK6JlCSxc0s5u1t6l3zRcbi8R3WK\n3eWuLclhYHiczgimENd75T3Wlypgma+KldncfvU6vr3nOM+f6p/7gAgdbusnJyONNYWqmyfzU12c\nQ1NX5Em14q2hc3AqGZhIpP72pou5fG0B7/v2M7R0x/b3fU9LL3kBv5Z8yAXVluZytDOym/epREGk\nLNpNl5cD8OASJ9hpDAeRKTYSOZVcp2PuP65H2gfIy/RTlh+IdbOS0rtftp78zHT+8eFDc+8coUNt\nA2xYlYdPdfNknqqKczjZd4bhsYl4N2VOzjkaOoPUaD2kzFPAn8bn3nwFPjPe8bW9Mf1939Nymi2V\nhapjKhdUV5rL6aExuoOj8W5KQlEQKYtWWZTNlorCJZ/S2twVxO8z1q5IrRGd+WQKq28PJdVRZtaF\nKchO589fXscvjnTyiyOLLx3knONw+4CmssqCVBfn4By0dCf+lNae4Ch9Z8aUVEcWpGJlNv/2h5t5\n7lQ/f/f9gzF5j4HhMQ639Wsqq8yprlTJdWaiIFKiYufmcp471b+kc8abuoJUrszGn5Zav8ar8jPJ\nyUib82LmnONIx4DWQy7SH714HRUrs/iHh55nYpHFhtv7Rzg9NMamVUqqI/MXTiK2HKa0hmeKKKmO\nLNTLN5XxrpfV8s3dx/jW7ujX7d137DSTTushZW4q8zGz1Pr2LTHzmstX4zOWtGZkKmZmhVCCjdrS\nuTO0dg2OcnpoTOshFyngT+Ovb9jEobYBvrf3+KLOdagttLZSNSJlIaqWUxDpXZ/qNBIpi/DeV2zg\nxTVFfOT/HeC5k9Fbmw6h9ZA+gy0VhVE9rySf1fmZZGekKbnOORRESlSU5mfyopoiHnzm5JIsPJ6c\ndDR3B6e+VKWa2pLcOUci66cysypgWaxXX7aaLRWF/POPD3NmdOHrc5SZVRYjPzOd4tyMqaRiiayh\nM0iG30e5EkjJIvjTfHz61q0UZKXzzq/voX94LGrn3tPSy8ZV+coeLHPy+YyakhwFkedQEClRs3Nz\nOU1dQQ6ciO7dwpm0DwwzPDaZkiOREMrQerJvmODI+Kz7hMt7rNd01kUzMz786oto7x/hrl81Lvg8\nh9sGKMsPUJidEcXWSSqpLs6hKcYZK6OhsXOQ6qIcJSyRRSvJC/DZN1/Bsd4zfODb+6Nyo3pi0rGv\n9TTb1mkUUiJTF8HN+1SjIFKi5sZLV5OeZnx/34mYv1d4OleqBpHh+fmNnbN/mTzSMUh+pp/SPGVm\njYYrq1Zy/SVlfP7xBjoH5i6vMpNDbQNs1HpIWYSqouVR5qOhM0htaWpenyX6dlSv5IM3bOJHB9u4\n61dNiz7fkfYBBkbGtR5SIlZXmjvnzftUoyBSoqYgO51rN5Tyg/2nmFxkApK5hL9EpfJ0VuCC6yLr\n2weUmTXK/vpL5miPAAAgAElEQVSGTYyMT/IfPz0y72PHJiY52jHIRZrKKotQXZJD58AIgwn8RWZ0\nfJLWniFqijULQqLnbddUc93FZXzi4UPsbu5Z1Ln2tPQCsK1yZTSaJilgKkOrkutMURApUbVzSzlt\n/cM8ucgL/Fyau4IE/D5W52fG9H0S1Tpvmths8/OdcxxpH2S91kNGVU1JLm++qpL7njw277URzV1B\nRicmlVRHFiVcFzeR10W29gwxMek0EilRZWZ86pbNrFmRxbu+sZeuwYXNCAHY29JLSV6AipVasyuR\nURB5PgWRElWvuKiUrPS0mGdpbeoKUlWUk7IF2zP8PtatzJ71YtY5OELfmTGV94iB9/zBerLS0/jE\nw4fmddwhL6mOgkhZjOqSxM/QGr4uaSRSoq0gK53/fMsVnB4a4z33P73gsku7W3rZVrlCM3UkYuuK\ncvBf4OZ9KlIQKVGVneHnlReX8fCzpxibmIzZ+6RqeY/pakpmL/NR3x7arsys0VeUG+AdL63lJ8+3\n80Rjd8THHW4bIM1nU3czRRZi3crEDyLDa7VVI1Ji4ZLyAj5286X8+mg3//6T+S8t6BgYprVnSOsh\nZV7S03ysK8pWEDmNgkiJup2by+kdGuNX9V0xOf/4RGi9TaquhwyrK82lqSvI+AzBujKzxtadv1fN\n6oJM/uGh5yNe/3uorZ+a4hwC/rQYt06SWVZGGuUFmQk9nbWhc5DSvIBKJ0jMvOnKCt60fS2f+dlR\nHjvcMa9j97acBmBblYJImZ+60lwFkdMoiJSo+/0NJRRkpcdsSuvJ08OMTThqUjyIrC3JYWzCcaz3\nzHmvHWkfpCArnZJcZWaNhcz0NN533UaeOd7Hg/sj+z0PZWbVyLAsXlVxDo0JHEQ2dg5qFFJi7qM3\nX8qmVXm895v7ON47FPFxe1t7yfD7uKRcmbJlfmpLcmnpHorpTLvlREGkRF2G38eNl67ixwfbFlWY\nfTaNXaG7QBqJDI0yznRXLJSZNVfrPWLodVvXcPHqfD71yGFGxi/8ez44Ms7x3jNsUhApUVBVnENz\ngtaKdM6FynuUaBaExFZmehpfeOs2JiYc7/r63jmvw2G7m3u4fE2BZoXIvNWV5jI+6WhJ0OvvUlMQ\nKTGxc3M5wdEJfnZoftNMItGc4jUiw2pmKfPhnKO+Q5lZY83nMz786os43nuGe3/TcsF9D08l1dGd\nb1m8muIcTg+N0RscjXdTztMTHKXvzNjU9UkklqqKc/jULZt55ngfH//h83PuPzw2wYET/VoPKQty\noZv3qUhBpMTEVTVFlOYF+P6+E1E/d1NXkNyAn+LcjKifezkpyEqnJC9AwzkXs84BLzOrErjE3Evq\ninnpxhI+87N6Tg/N/oU+HERqJFKiocor89GUgHfDw9NsazWdVZbIDZeu4k+vqebe37bM+Z3j4Mk+\nRicmFUTKgpyt0Z141954UBApMZHmM15zeTmPH+6k78xYVM/d1D1EdXGOpmoCdSW5HD1nJPKIMrMu\nqQ/deBGDI+N85mdHZ93nUFs/uQE/awpVk0wWb6rMRwJ+kQnf1NJ0VllKH7hhE1dWreBD33uWei+x\n3Ez2tPQCcIWCSFmAnICf8oJMjUR6FERKzOzcUs7oxCSPHGyL6nmbugZTfj1kWG1pDg0dgzh3NkPo\n2cysCiKXwsZVebxpewX3/rZ51nUSh9pCa1RTta6pRFfFimx8RkKui2zsCpLh91GuGyayhNLTfHz2\nzVeQnZHGO76+l+DI+Iz77W7upaoom2IlnZMFqlWG1ikKIiVmNq8tYF1RNg9GMUvr6PgkJ3rPpPx6\nyLC6klz6h8fpHByZ2lbfMUBhdnrKT/ddSn/1yg34fT7+6ZHD573mnONw24DWQ0rUZPh9VKzMTsgM\nrQ0dg9QU55CmGyayxMryM/n0rq00dg7ywe89+4KbqxC6Fu9t7dUopCxKXWmoRnek5b2SmYJIiRkz\n46bLy/n10S46B0bmPiACrT1DTDqoLs6OyvmWu1pv3WNDx9kvk0faB9lQmqfpvkuoND+Tt/9+DT/c\nf4q9rb0veK29P7RGVeshJZqqinISslZkY1dQ5T0kbq6uK+Z/X7eRB585ydd+98KEZ609Q3QNjrJ9\n3co4tU6SQW1JLkOjE5zqH453U+IuZkGkmd1tZh1mdmDatk+Z2SEz229m/2NmhdNe+5CZHTWzw2Z2\n/bTtN3jbjprZB2PVXomNnVvKmXTw0LOnonK+pqnMrFpvA9MXeYemVjjnONI+wPoyfT5L7e2/X0NJ\nXoB/+OHzL7gD/nxbP4BqREpUVRfn0NQVPG+0JZ5Gxydp7RnSekiJq3dcW8vLN5Xy0R88x75jp6e2\nh9dDKqmOLIYytJ4Vy5HIe4Abztn2KHCpc+5y4AjwIQAzuxjYBVziHfOfZpZmZmnA54AbgYuBW719\nZZnYUJbHplV5PBClKa1T5T2KdKcbYHVBJtkZaVMXs46BEQaGx5VUJw5yAn7e+4oN7G7p5ZGD7VPb\nlZlVYqG6OIeh0YmozfKIhtaeIBOTTiORElc+n/Gvb9pMaV4m7/r63qlSOLtbeskL+FmvzOWyCAoi\nz4pZEOmc+wXQc862Hzvnwqudfwes9Z7fDNzvnBtxzjUBR4Ed3uOoc67ROTcK3O/tK8vITZvL2dPS\ny/HeoUWfq7EryIrsdAqy06PQsuXPzKgtyZ0aiTybVEd/JOPhTdvXsr40l0/+6BBjE5NAKIhclZ9J\nYbbWqEr0hNeFNyXQlNZw2nuNREq8FWZn8Pm3XkHnwAjv/dY+Jicde1t62bpuhRKcyaIU5WRQmJ1+\nXo3uVBTPNZF/AjzsPV8DHJv22nFv22zbZRnZubkcgAefWfyU1uauoJLqnKOuNJdG78ubynvElz/N\nx4detYmmriDfeKIVCGVm1VRWibbEDCJD1x9doyURXL62kI/cdDGPH+7kkz86xOH2AbZrKqsskpmF\nyqtpJDI+QaSZfRgYB74exXO+3cx2m9nuzs7OaJ1WoqBiZTZbKwvnLAIciaauoMp7nKO2JIcTp88Q\nHBmnvn2AFdnpFOVo1CteXraxlKtri/iPn9bTGxyloWNQU1kl6soLs8hI89GUQGU+GjuDlOYFyMvU\nTBFJDG+9qpLXbinnv37RiHNaDynRUVeaO1UTN5UteRBpZncArwHe4s5mBDgBVEzbba23bbbt53HO\nfdE5t905t72kpCTq7ZbF2bm5nENtAxcsAjyXodFx2vqHqVEQ+QLhqWNNXUEvqY4ys8aTmfF/XnUR\nPcFR3v+d/YxOTLJptYJIia40n1FZlE1TZ+IEkQ2dg5rKKgnFzPiH11/G+tJc0nzG5orCuQ8SmUNd\naS7dwdGp9bapakmDSDO7AfgAsNM5N32B3APALjMLmFk1sB54EngKWG9m1WaWQSj5zgNL2WaJjldf\nvhqfsagEO81doV8ZjUS+0PRF3vXtg2zQesi4u3RNAa/buoafPB9KsLOxTDUiJfqqi3NoTpCRSOcc\njZ0q7yGJJzvDz7137uCu27eTG/DHuzmSBMI3y46m+LrIWJb4uA/4LbDRzI6b2Z3AZ4E84FEz22dm\nXwBwzh0EvgU8B/wIeJdzbsJLwvNu4BHgeeBb3r6yzJTmZXJ1bTEPPHNywSnpw1+WtN7mhdYVhQp7\n//poFwMjysyaKN53/UYy/D7SfEZtqX5nJfpCQeQQEwlQ9LonOErfmTGNREpCWl2QxUs3lsa7GZIk\nlKE1JGa3ZJxzt86w+a4L7P9x4OMzbH8IeCiKTZM42bm5nA98dz/7j/ctaEpJOIFElcp7vECG38e6\nldk86o16rS9VEJkI1hRm8YHrN/L8qQEC/rR4N0eS0GVrChgdn+SJxm6uriuOa1vCmVk1EikiyW5N\nYRaZ6b6UDyLjmZ1VUsz1l64iI8234CmtTV1ByvID5Gg6ynlqSnI5PTQGoOmsCeRt19TwL2/aHO9m\nSJJ65cVlFGSlc99Tx+beOcYavWldGokUkWTn8xk1xbkpX+ZDQaQsmYKsdK7dWMIP9p9c0PSrpq6g\nRiFnEZ4uWZSTQVFuIM6tEZGlkJmexuu2ruGRA230xDnBQ0PnIAG/j/LCrLi2Q0RkKdSVqsyHgkhZ\nUjs3l9PeP8KTTT3zPra5S0kbZlPn3f0Pz9MXkdRw645KRicm+d7e43FtR2NnqIZvmgq5i0gKqCvN\n5cTpM5wZnYh3U+JGQaQsqVdcVEZ2Rtq8p7T2nRmjOziqkchZ1HrBo5LqiKSWjavy2FpZyP1PHVtw\n0rJoaOwKaiqriKSMutJcnCOlp7QqiJQllZWRxisvLuPhA6cYHZ+M+LjmLmVmvZANZXkUZKVzVc3K\neDdFRJbYrVdWcrRjkD0tvXF5/9HxSVp7hjRTRERSRvimmYJIkSW0c3M5p4fG+GV9Z8THqLzHheUG\n/Oz5m1fw6stWx7spIrLEXrN5NbkBP/c9GZ8EO609QSYmnUYiRSRlVBVn47PULvOhIFKW3DXrSyjM\nTp/XlNbGziBmUFmUHcOWLW/+NB9mWo8kkmqyM/zs3FLOD589Sd+ZsSV//6MdKu8hIqkl4E9jXVGO\ngkiRpZTh93Hjpat59Ln2iBckN3cHWVOYpXp7IiIzuPXKSobHJnlg34klf+/GrtCXKM0UEZFUUluS\n2mU+FERKXOzcXM7Q6AQ/eb49ov2buoL6giIiMovL1hZwSXk+9z259Al2GjpCNXzzMtOX9H1FROKp\nrjSXpq4g4xOR5/hIJgoiJS52VK+kLD8Q0ZRW55yCSBGROezaUclzp/p59kTfkr5vY9cgNcVaDyki\nqaWuNJexCUdrz1C8mxIXCiIlLtJ8xmsuL+fnhzvnXMPTHRxlYHhc5T1ERC7g5i3lZKWnLWmCHecc\njZ1Bakt1fRaR1BKuzZ2q6yIVRErc7NxczujEJI8caLvgflPlPZS0QURkVvmZ6bz68tU8sO8EwZHx\nJXnP7uAofWfGNBIpIiknnEzsaIqui1QQKXFz+doC1hVlzzmltTEcRGokUkTkgm7dUUFwdIIf7I88\n+/ViNHaGrs+1pQoiRSS15GemU5Yf0EikyFIzM3ZuLuc3DV10DAzPul9zVxC/z1i7ImsJWycisvxc\nUbmC9aW5SzalNZyZsEZr1kUkBdWV5tKgIFJk6e3cXM6kgx/uPzXrPk1dQSpXZuNP06+riMiFmBm7\ndlSy79hpDrX1x/z9GjsHCfh9rCnUTT4RST11Jbk0dAaXPCt2ItC3comr9WV5bFqVd8EprcrMKiIS\nuddvXUNGmo/7l2A0srEzdH32+Szm7yUikmjqSnMZHBmnvX8k3k1ZcgoiJe5u3rKGp1tPc2yGFMmT\nk47m7iBVCiJFRCKyIieDGy5dxff2Hmd4bCKm79XQOUhtidZDikhqqk3hDK0KIiXubtq8GmDG0cj2\ngWGGxyY1EikiMg+7dlTQPzzOwwdmXyqwWCPjExzrPUOtMmeLSIo6W+ZjIM4tWXoKIiXu1q7IZtu6\nFTw4QxDZFM7MqiBSRCRiL64poqooO6YJdlq7h5iYdNRoJFJEUlRJboC8TH9KlvlQECkJYefmcg61\nDXCk/YV3chREiojMn5nxh1dW8mRTz1QG1Whr8Mp71GgkUkRSlJlRV5qr6awi8fKqy1bjM3hg3wtH\nI5u7ggT8PlblZ8apZSIiy9Mbt63F7zO++VRsRiOnyntoJFJEUlhdSS5HO4LxbsaSUxApCaEkL8BL\n6op54JmTL0iTHM7Mqsx/IiLzU5IX4BUXlfHdPccZHZ+M+vkbO4OU5QfIDfijfm4RkeWirjSXrsER\n+obG4t2UJRWzINLM7jazDjM7MG3bLWZ20MwmzWz7Oft/yMyOmtlhM7t+2vYbvG1HzeyDsWqvxN9N\nm8tp7RnimeN9U9uauoJUFWmqlIjIQuzaUUF3cJRHn2uP+rkbu5SZVURkw6o8qoqy6RxMrTIfsRyJ\nvAe44ZxtB4DXA7+YvtHMLgZ2AZd4x/ynmaWZWRrwOeBG4GLgVm9fSULXX7KKjDQf3993AoDxiUla\ne4ao1nobEZEFuWZ9CWsKs7j/qdaontc5R0PHoNZDikjKe9nGUh5//8umMrWmipgFkc65XwA952x7\n3jl3eIbdbwbud86NOOeagKPADu9x1DnX6JwbBe739pUkVJCVzks3lvCD/aeYmHScPD3M2ISjWiOR\nIiILkuYz3rS9gl/Wd81Yi3ehuoOj9A+PayRSRCRFJcqayDXA9JX/x71ts22XJLVzSzmdAyM80dhN\nY1coaYNGIkVEFu6W7WvxGVFNsNPQoaQ6IiKpLFGCyEUzs7eb2W4z293Z2Rnv5sgC/cGmMnIy0njg\nmZM0e+U9tCZSRGThyguzuHZDCd/ec4zxiegk2Gn0rs81Kr8kIpKSEiWIPAFUTPt5rbdttu3ncc59\n0Tm33Tm3vaSkJGYNldjKykjjuktW8fCBNg63D5Ib8FOcmxHvZomILGu7dlTS3j/CY4ejc5O1sXOQ\ngN/HmsKsqJxPRESWl0QJIh8AdplZwMyqgfXAk8BTwHozqzazDELJdx6IYztlCezcXE7fmTEe2HeC\n6uIczFTeQ0RkMV6+qZSSvAD3PxmdBDsNnSq/JCKSymJZ4uM+4LfARjM7bmZ3mtnrzOw48GLgh2b2\nCIBz7iDwLeA54EfAu5xzE865ceDdwCPA88C3vH0lif3e+mJWZKcTHJ2gSlOlREQWLT3Nxy3b1vLY\n4Q5O9Z1Z9PkaOwepTbFMhCIiclYss7Pe6pxb7ZxLd86tdc7d5Zz7H+95wDlX5py7ftr+H3fO1Trn\nNjrnHp62/SHn3AbvtY/Hqr2SONLTfNx42WoAqhVEiohExR9eWcGkg2/vPr6o84yMT9DaM0Strs8i\nIikrUaazirzAa7eEkvCu151uEZGoWFeUw0vqivjmU8eYnHQLPk9r9xCTTplZRURSmYJISUg7qlfy\n7T97MTdeuireTRERSRq7rqzkxOkz/PJo14LP0dAZKu+hGpEiIqlLQaQkrCurVuJP06+oiEi0XHdJ\nGSuy0xeVYKehM1TeQzV8RURSl76hi4iIpIiAP403XLGWR59rp3NgZEHnaOwMsio/k9yAP8qtExGR\n5UJBpIiISArZtaOC8UnHd/cuLMFOQ+cgNRqFFBFJaQoiRUREUkhdaR5XVq3gm08dw7n5JdhxzoXK\ne2g9pIhISlMQKSIikmJ2XVlJU1eQ3zX2zOu4rsFR+ofHNRIpIpLiFESKiIikmFddtpq8TD/3PzW/\nBDuNXmZWlfcQEUltCiJFRERSTFZGGq/buoaHD7Rxemg04uMau0KZWWs1EikiktIURIqIiKSgXVdW\nMjo+yff2noj4mIaOQTLTfZQXZMWwZSIikugURIqIiKSgi8vz2by2gPufao04wU5jV5Dq4lx8Potx\n60REJJEpiBQREUlRu3ZUcqR9kL2tpyPaX+U9REQEFESKiIikrJs2l5Odkcb9T86dYGdkfIJjPUMq\n7yEiIgoiRUREUlVuwM/OzeX8YP8pBobHLrhvS/cQk05JdUREREGkiIhIStu1o5IzYxN8f9/JC+43\nVd6jWCORIiKpTkGkiIhICtu8toBNq/LmrBnZ0Bkq76E1kSIioiBSREQkhZkZt+6o5MCJfg6c6Jt1\nv4bOQVblZ5IT8C9h60REJBEpiBQREUlxr92yhoDfx30XSLDT2BmktlSjkCIioiBSREQk5RVkp/Pq\ny1bz/X0nGRodP+9151yovIfWQ4qICAoiRUREhFCCncGRcX6w/9R5r3UNjjIwPK71kCIiAiiIFBER\nEeDKqhXUluTMWDMynJlVNSJFRAQURIqIiAihBDu7rqxkb+tpjrQPvOA1ZWYVEZHpYhZEmtndZtZh\nZgembVtpZo+aWb333xXedjOzT5vZUTPbb2ZXTDvmdm//ejO7PVbtFRERSXWvv2IN6Wl2XoKdxs5B\nMtN9lBdkxallIiKSSGI5EnkPcMM52z4I/NQ5tx74qfczwI3Aeu/xduDzEAo6gb8DrgJ2AH8XDjxF\nREQkuopyA1x3ySr+5+kTDI9NTG1v6BykujgXn8/i2DoREUkUMQsinXO/AHrO2Xwz8FXv+VeB107b\nfq8L+R1QaGargeuBR51zPc65XuBRzg9MRUREJEpuvbKS00NjPHKwbWpbY1eQWk1lFRERz1KviSxz\nzoXTvrUBZd7zNcCxafsd97bNtv08ZvZ2M9ttZrs7Ozuj22oREZEUcXVtERUrs6amtI6MT3CsZ4ga\nJdURERFP3BLrOOcc4KJ4vi8657Y757aXlJRE67QiIiIpxecLJdj5XWMPTV1BWrqHmHRoJFJERKYs\ndRDZ7k1Txftvh7f9BFAxbb+13rbZtouIiEiM3LJtLWk+4/6nWlXeQ0REzrPUQeQDQDjD6u3A96dt\nv83L0voioM+b9voIcJ2ZrfAS6lznbRMREZEYKc3P5OWbSvnunuMcaguV+6gu1kikiIiExLLEx33A\nb4GNZnbczO4EPgG80szqgVd4PwM8BDQCR4EvAe8EcM71AB8DnvIeH/W2iYiISAzduqOCrsFRvva7\nVlYXZJIT8Me7SSIikiBi9hfBOXfrLC/9wQz7OuBds5znbuDuKDZNRERE5nDthlJWF2Ryqm+Yl9QV\nxbs5IiKSQOKWWEdEREQSV5rPuGV7KC1BTbHWQ4qIyFkKIkVERGRGf3hlBQG/j8vWFsS7KSIikkC0\nwEFERERmtKYwi9988OUUZmfEuykiIpJAFESKiIjIrIpyA/FugoiIJBhNZxUREREREZGIKYgUERER\nERGRiCmIFBERERERkYgpiBQREREREZGIKYgUERERERGRiCmIFBERERERkYgpiBQREREREZGIKYgU\nERERERGRiCmIFBERERERkYgpiBQREREREZGImXMu3m2IOjPrBFri3Y4ZFANd8W6EnEf9knjUJ4lJ\n/ZJ41CeJSf2SeNQniUn9Mj/z/bzWOedKYtGQpAwiE5WZ7XbObY93O+SF1C+JR32SmNQviUd9kpjU\nL4lHfZKY1C/zk0ifl6azioiIiIiISMQURIqIiIiIiEjEFEQurS/GuwEyI/VL4lGfJCb1S+JRnyQm\n9UviUZ8kJvXL/CTM56U1kSIiIiIiIhIxjUSKiIiIiIhIxFI6iDSzCjN7zMyeM7ODZvYeb/tKM3vU\nzOq9/67wtm8ys9+a2YiZve+cc73HzA545/nLC7znDWZ22MyOmtkHp23/AzPba2b7zOxXZlY3y/Hb\nzOxZ7/hPm5l522/x3nvSzBIia9NCJVm//L2ZnfCO32dmr4rGZ7TUkqxPNntte9bMHjSz/Gh8RvGw\nTPvl42Z2zMwGz9n+Z16fhI+/eDGfTbwkWZ/827Rr1xEzO72Yzyaellu/mFm2mf3QzA557/OJaa/9\nvnf8uJm9MRqfTzwkWZ/cYWad0/5/eVs0PqOllmR9ss7Mfmpm+83scTNbG43PaIY2JNJn9nLvMztg\nZl81M/8sx1eb2RPe8d80swxv+/yuLc65lH0Aq4ErvOd5wBHgYuCfgA962z8IfNJ7XgpcCXwceN+0\n81wKHACyAT/wE6BuhvdLAxqAGiADeAa42HvtCHCR9/ydwD2ztPlJ4EWAAQ8DN3rbLwI2Ao8D2+P9\n2apfpvrl76e3abk+kqxPngKu9Z7/CfCxeH++KdYvL/LaPXjO9vxpz3cCP4r355vqfXLOPn8O3B3v\nzzdV+sU7/8u85xnALzl7DasCLgfuBd4Y789WfeIA7gA+G+/PVH3ygj75NnC79/zlwH8n82dGaGDw\nGLDB2++jwJ2ztPlbwC7v+ReAd3jPq5jHtSWlRyKdc6ecc3u95wPA88Aa4Gbgq95uXwVe6+3T4Zx7\nChg751QXAU8454acc+PAz4HXz/CWO4CjzrlG59wocL/3XgAOCI+IFAAnzz3YzFYT+qL1Oxfq7Xun\nte1559zh+X4GiSiZ+iVZJFmfbAB+4T1/FHhDZJ9C4llu/eK14XfOuVMzbO+f9mOOd75lJ5n65By3\nAvfNsU/CWm794p3/Me/5KLAXWOv93Oyc2w9Mzu9TSCzJ1CfJIsn65GLgZ97zx6adN6oS6DMrAkad\nc0e8/Wb8fmNmRiio/s4MbZvXtSWlg8jpzKwK2Ao8AZRN+4PaBpTNcfgB4BozKzKzbOBVQMUM+60h\ndJcg7Li3DeBtwENmdhz4I+ATnG+Nd8xMxyelJOmXd3vTKe4OT2dYzpKgTw5y9o/JLbO8/7KzTPpl\nrn/Du8ysgdAd3L+Y7/GJJhn6BELTwoBqzn4hW9aWW7+YWSFwE/DTOdq2bCVJn7zB+1v/HTNb9n9X\nkqBPnuFsEPY6IM/MiuZo96LE+TPrAvx2dknbG2c5vgg47QWq04+fNwWRgJnlAt8F/vKcu+F4oxgX\nvCPunHse+CTwY+BHwD5gYp7NeC/wKufcWuArwL/O8/ikkyT98nmgFtgCnAL+ZZ7HJ5Qk6ZM/Ad5p\nZnsITT0ZnefxCSdJ+gXn3Oecc7XAXwN/M9/jE0my9IlnF/Ad59x83z/hLLd+8dY03Qd82jnXOM/3\nWRaSpE8eBKqcc5cTGgH66mzHLwdJ0ifvA641s6eBa4ETC2hDxOL9mXnvsQv4NzN7EhiYz/ELkfJB\npJmlE+r0rzvnvudtbvemw4WnxXXMdR7n3F3OuW3Oud8HeoEj3mLb8CLrPyP0Czz9rsBa4ISZlQCb\nnXNPeNu/CVxtZmnTjv+od/zac49f8D8+gSVLvzjn2p1zE865SeBLhKYhLEtJ1CeHnHPXOee2Efqj\n07CgDyRBLLN+idT9LOMp4UnYJ7tYxlNZw5Zpv3wRqHfO/fsi/ukJK1n6xDnX7Zwb8X78MrBtnh9F\nwkiiPjnpnHu9c24r8GFvW0ySgyXCZ+Yd/1vn3DXOuR2Elu0c8d7/Ee/4LwPdQKGdTbqz4Fhixqw9\nqcLMDLgLeN45N/0OxwPA7YSGzm8Hvh/BuUqdcx1mVklo+PxF3i/rlmn7+IH1ZlZNqMN2AW8m9ItS\nYGYbXI9ZE90AAAOZSURBVGgu8yu9Nk1MP947R7+ZvYjQUPltwGcW9q9PXMnUL2a2etp0htcRmq6w\n7CRZn4Tf30dotOsL8/9EEsNy7JcLvP9651y99+OrgfoL7Z+okqlPvPNvAlYAv430mES0HPvFzP4v\nobVgyzLT51ySqU/O+Vu/k9C6uGUnyfqkGOjxbuJ/CLg78k8icgn0mU0/PkBoRs/HAZxz15/zPo8R\nmu56f6Rtm5FLgGxQ8XoAv0doeHk/oWHjfYTmIBcRmlNdTyg70kpv/1WE5g73A6e95/nea78EniM0\nB/sPLvCeryJ0Z6AB+PC07a8DnvWOfxyomeX47YQCkQbgs4BNO/44MAK0A4/E+/NVvziA//aO30/o\ngrI63p+v+oT3eOc9QujibvH+fFOsX/7Je99J779/723/D0LrVfcRSoJwSbw/31TvE++1vwc+Ee/P\nNdX6hdDogCMUjITb+zbvtSu99gQJjSocjPfnqz7hHwldv54hdP3aFO/PV33CG732HiE0OhxIgc/s\nU95ncZjQtNrZjq8hlMH+KKEstgFv+7yuLeEvVSIiIiIiIiJzSvk1kSIiIiIiIhI5BZEiIiIiIiIS\nMQWRIiIiIiIiEjEFkSIiIiIiIhIxBZEiIiIiIiISMQWRIiIiC2Rmf29m77vA6681s4uXsk0iIiKx\npiBSREQkdl4LKIgUEZGkojqRIiIi82BmHwZuBzqAY8AeoA94O5BBqIDzHwFbgB94r/UBb/BO8Tmg\nBBgC/tQ5d2gp2y8iIrJYCiJFREQiZGbbgHuAqwA/sBf4AvAV51y3t8//Bdqdc58xs3uAHzjnvuO9\n9lPgz5xz9WZ2FfCPzrmXL/2/REREZOH88W6AiIjIMnIN8D/OuSEAM3vA236pFzwWArnAI+ceaGa5\nwNXAt80svDkQ8xaLiIhEmYJIERGRxbsHeK1z7hkzuwN46Qz7+IDTzrktS9guERGRqFNiHRERkcj9\nAnitmWWZWR5wk7c9DzhlZunAW6btP+C9hnOuH2gys1sALGTz0jVdREQkOhREioiIRMg5txf4JvAM\n8DDwlPfSR4AngF8D0xPl3A+838yeNrNaQgHmnWb2DHAQuHmp2i4iIhItSqwjIiIiIiIiEdNIpIiI\niIiIiERMQaSIiIiIiIhETEGkiIiIiIiIRExBpIiIiIiIiERMQaSIiIiIiIhETEGkiIiIiIiIRExB\npIiIiIiIiERMQaSIiIiIiIhE7P8HmCq2iL/h8jYAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PwwrQzXk08fe",
        "colab_type": "code",
        "outputId": "9b02b76b-6614-4e96-8927-439276cc64b8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        }
      },
      "source": [
        "trip_per_date = data_bike.groupby(['date', 'usertype'], as_index=False)['gender'] \\\n",
        "                        .count() \\\n",
        "                        .rename(columns={'gender': 'total'})\n",
        "\n",
        "plt.figure(figsize=(15,5))\n",
        "viz = sns.lineplot(data=trip_per_date, x='date', y='total', hue='usertype')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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6z551X3TBH6FFTxndoCfMnpE5DwqPy3xj5Z2ii+dFbtY9w67izu6s11hrm1lr\nQ621Udba90/5fGtr7e7iP1tr7X3W2lhrbRdr7eJS9/vAWtu2+ONDd8Xr9+JGwN4NUs4ZyLJTZRXS\nG0ukOl4ipbYbHeqkm3dILjTEj5a9tN6k9fmyn2F1gF9Z375C9qrEaimrTzJGSloz5+se9VN9+ydJ\neMZ+Bg1aOx2NbwsOlbLWwnz4+h79WfOEtJkQXk8a5SnvdE4XCKujzXVcSC9lB4oOw+U2kEtaj+6V\nJNob5kOWJXaQdG90qqR17TQoOAZdr3Hm+c8kOAQ6XSpv1MePOB2NczLmyG3sIGfjUFUXkyizT7ev\ncDoS71FUCOmzIOEqiPayrQa+qmEsDHtWZuwueMvpaPxbUZFUybQdpCXY3iwoWF5fdF+ky2gSGSjq\nNpfylnXfOB2Jc3IWya237YcsERoB7YbI/1FRoeeff/l4aNDGuzrXltZ5DOQflU7DgWrD93BOAtRp\n6nQkqqpiEuVWS1pP2L5CqjBiEp2OxL/0uBE6jIDkp2H7Kqej8V/bl8Ph7dBex5h7vVYDYNc6OLLH\n6Uj8giaRgSRuhAxuPrjV6UickZ0KJhia93A6kvJ1GiXlip6u2T+wBbJSpKGON5b6guxnqH0OrJ7s\ndCTOyD0gjaF0tIdvq9MUmsQXd9lVgLz2gJStK9cxBka9JvvcJ90B+blOR+Sf0mYBRl+bfUHJvEjd\nF+kSmkQGkg4j5Hb9DGfjcEr2QmiWAGE1nY6kfG0HQ0gErJnq2eddORGwUk7mrYKCZb9m+mzIPeh0\nNJ6XlQJFBXqi4g9iEmHzAu02XCIrBRrH6Qq7O9RqBJe9BTvXQPLfnI7GP6V9B1G95HutvFvz7hAc\nriWtLqJJZCBp3AEiYwOzpLWwQFZhvW0+5KnCa0vTlLXTPNcMwVpYPkG+N97WtfZUncdAYV5gXgjJ\nmCNNAby13FhVXGyS/Bzr1XDpRr3pFxlzpNyj3WDofQcseFNXwF3t8E7YutT7OpqrsoWEQ1Rv2KxJ\npCtoEhlIjJGS1qwUOLbf6Wg8a8cq2U/nCyfgnUbBoa3yxuQJ21fArrXQdaxnnq86onpDvZaBN//M\nWshIhpiB2rjBH7QaILN79YReXufyj2gS6W6D/waNOki31qM6Kc1l0mfJrY728B2tBsC25dKRXlWL\nJpGBJm6klMSVdHkMFN7eVKe09sNkVtqaKZ55vuUTIDhMSkW9nTEQf5nMSgykE6HdaXAgG9rqaA+/\nEFZLXosy5zkdifOyUgBzYq+Sco+wmjDmHTiyG6b9TmdGu0raTKjTXMZHKN/Qqj/YIumToapFk8hA\nE9ULajUJvFEf2Qvlhb5elJZ+UNEAACAASURBVNORnF2N+tBmoJS0uvuNvrAAVn4hXWFrRrr3uVwl\nfoxcCAmkn+GMZLnV+ZD+IzaxeO7nbqcjcVZWCjTr6juvP76seTcY9BdYOxWWf+50NL6v4LhUE7Qf\n4r0N6dTpovpIk0XdF1ltmkQGmqBg6HCxNCcpyHM6Gs/JXijzIX3lhb7TKNiXJWW47pQ5F47s9I1S\n1hLNu8sokkAqac2YA43aQ4NWTkeiXCUmSW6z5jsbh5Pyj8lrs5ayes6AB2XVd8YfYW+W09H4ts0/\nw/FDOtrD14TXlgtXmkRWmyaRgShuJBw/fKKtur87tB32b/aNUtYSHUaACXJ/l9bl46FGA1mJ9BXG\nSIOdrJTAWMXJPwabftKurP6meXeIqBfY+yKzF0Lhcam8UJ4RFAyj/yMrMZPvlmoUVTVps6TTp14E\n8T2tBkizRR17Uy2aRAaiNhdAWO3AKQcsqXv3pSSydmOIHiBlR+6Se1A69caPkY5lviR+DNhCz+0b\nddLGn6AgV0tZ/U1QsLwWZ84L3P1pWSmy/zu6n9ORBJb60TDiJcheAD++4nQ0vivtu+LzqVpOR6Iq\nq9W50iHbUw0M/ZQmkYEoNEJWNdZ/67kxEk7KXihXC89JcDqSyuk0Cnatg11p7nn8tVOh4JhvlbKW\naBov5Z2rJzsdiftlzJHZoa218YjfiUmUhkl7M52OxBmZ86FFLykvU56VcCV0vgLmPQs5S5yOxvfs\nzoC9G3S0h68quXC16Sdn4/BxmkQGqriRcHiHLOf7u+xUKR0LCXM6ksrpeIncums1cvl4iIyRsRm+\nxhhZjdz4Ixzc5nQ07pUxR66ahtZwOhLlaiX7Ijd872wcTsg9IKsAWgronBEvQp1mMOkOOH7E6Wh8\nS/pMufWlrSDqhJqR0KSTzKhVVaZJZKBqN1jKiPy9pLUgD7Yt8435kKeq21wSPHckkQdyJAFLuNp3\nmg2dqvMYwPp3Seu+TbAnXfdD+qvIGCktDMRRH5t+kTb7mkQ6p0YDGP22rITP/IvT0fiWtJnQuKM2\nO/NlrQYU78vWfcFVpUlkoKpRH1qfL3vi/Nm25dK4wZf2Q5bW8RL5N+zb5NrHXTERsJJE+qrGHaBp\nZ1jtx11aNxSP9tAk0j8ZIyWtWT8E3olMVoqUaftiJYQ/aXMBDHgAlnwoW1zU2eUelDLI9roK6dOi\n+0uTyR0rnY7EZ2kSGcjiRsgqh7v23HmD7IVy64srkQAdR8nt2mmue0xrYcUEaNkPItu47nGdED9a\n/o8P5DgdiXtkJEO9ltCondORKHeJSYK8A7D1V6cj8aysFLm4FxrhdCRq0F/hnC4w5X44vNPpaLxf\n5lyZVayjPXxbqwFyq6M+qkyTyEDWYbjc+nNJa/ZCaNAaajdxOpKqiWwjb+6uLGndtkwa9nT14VXI\nEp3HyK0/NtgpOC6NR9pe6Lslx+rs2gwEjJyYBooje+Tqv5ayeoeQcBjznqzKTLkvcLsFV1TaTIio\nL0Prle+q21zODzWJrDJNIgNZvRbQvIf/lrRaK011fLWUtUTHUZIMu6qBzPIJEBwmq3i+LjIGmnWD\nVV85HYnr5aTKIGstZfVvtRpCs4TA2he58Qe51fmQ3qNJHAz+G6TPgsXvOx2N9yoqku9R24sgOMTp\naFR1tTpXkki9cFIlmkQGurjhsGWxf3a43L9ZOtD6ailriZKSVlesGBcWwKovpS15jQbVfzxv0HmM\nlAL625iEjDnS/EpXa/xfTJJc8Mo77HQknpGVAmF1pGu28h6975B5tDP/6t/bXKpj269wZJeO9vAX\nrQbAsb2wa73TkfgkTSIDXdxIuV0/w9k43CE7VW59fSWySZzMRHRFSeuG7+UNsOs11X8sb1Gyoupv\nJa0ZyfKzG1HP6UiUu8UkQlF+4JRVZaXIyZuu5HiXoCC47C0ZJzTpdimpVydLmwkmSCtE/EV0f7nV\neZFVoklkoGscJyWB/ljSmr0QwmrLLCBf13EUbPxJ9hJVx/LPoUYktB3smri8Qf1o2Zuyyo+SyEM7\nYPsK2Q+p/F90f+lUGgj7Ig9ulYZuusLuneqcA6Nek67g859zOhrvkzZT3m9qRjodiXKFyBiofQ5s\n1nmRVaFJZKAzRrq0ZqXI8Gd/kr0QWvSEoGCnI6m+jpeALazeinHuAfn6zmMgJMx1sXmDzmOkUcfu\ndKcjcY2S4fOxmkQGhNAISSQ3BEASmVWyH1KTSK/V8RLofgP88HLgrI5XxKHt0phOR3v4D2OkKkL3\nRVaJJpFKSlqL8iF9ttORuE7eYdix2vdLWUs06yorbtUpaV0zFQpyIWGs6+LyFp0uAwys8pOZkRlz\noFZjOCfB6UiUp8Qkwq61cqLqz7JSZD92085OR6LOZNhz0rly0l3+d4G5qtJnya2O9vAvrQbAwS3S\nR0NVituSSGPMB8aYncaYVaWOvWCMWWeMWWGMmWyMqV/qc48ZYzKMMeuNMUNLHR9WfCzDGPOou+IN\naFG95YTVn0paty6VlTt/SSKNkZLWDXOr/oa+YgJExkJUL9fG5g3qNpM3glVf+f7VxKJCWYmMvVD2\nKKnAEJskt/7cpdVayJoPrc/Xn21vF14bxrwLB3NgztNOR+Md0mZC3Sj/2CKjTtB5kVXmzlfxccCp\nl2tmA52ttQlAGvAYgDGmEzAWiC/+mreMMcHGmGDgTeBioBNwTfF9lSsFBUOHi2UlsiDP6WhcI3uh\n3Eb1dDYOV+p0qawYp82q/Nfu3yxt9buO9d+Zg53HwO71sHON05FUz7Zl0i1OGzcElqZdoGZD/y5p\n3bcRDmRDjI728Akte0Pfe2Tkx6YA3zNWkCe/m+2H+u97aKBq3FHmfmpznUpzWxJprU0B9p5ybJa1\ntqD4rwuAqOI/XwqMt9bmWWuzgAygT/FHhrU201p7HBhffF/lanEjZSZdyX4VX5edKk2D/GWMBUCL\nXlCnGaydUvmvXTFRbhOucm1M3qTjpdI1z9dLWjOSAXNiZUoFhqAgmZuYOc/3V9PLk5Uitzof0ncM\n+otspZj2IOTnOh2Nczb+CPlHdLSHPwoKkj3p2lyn0pysJ7kV+Lb4zy2A7FKfyyk+Vt5x5WptBkJo\nLVjvByWtRUWQs8j350OeKihIkv30OXD8SMW/zlopZY3uL3tc/FXtxtKsY/Uk3z4Jz5gj8/NqNXI6\nEuVpsUlweDvsWud0JO6RlSIXwhq2dToSVVFhtWDkK7A7DX54yelonJM+C0JqaEMof9VqAOzJkM7o\nqsIcSSKNMX8BCoBPXfiYdxpjFhtjFu/atctVDxs4QiOg3UWwboYkYb5sTwYc2ydtuP1Np1FQcEwS\njYra+qucACRc7b64vEX8GNibKe3pfdGxfXIBREd7BKaY4tVnfyxptVaSyDYXaDmgr2l7kTRk+/Fl\naVgXaKyFtO/kZze0htPRKHco2Repq5GV4vEk0hhzMzASuM7a35YLtgAtS90tqvhYecdPY619x1rb\ny1rbq3Hjxi6POyDEjZSr4FuXOh1J9ZTsh/SXpjqlRQ+QOY9rp1X8a1ZMgOBwiL/MfXF5i46XQFCI\nNNjxRZnzwBbpfshAVb+lNL/yx+Y6u9bBkZ26kuOrhv4TIurB1Ael+Vcg2Z0u+3m1lNV/NesKoTW1\nuU4leTSJNMYMA/4EjLLWHi31qanAWGNMuDGmDdAOSAUWAe2MMW2MMWFI851qzDhQZ9RusJyAr5vu\ndCTVk71Q9kL6Y8lUcIjM9UybWbEmSIX5sPJL6DDMv/aHlqdmpKzmrP7aN0taM+ZAeD3Z/6oCU2yS\n7L8qOO50JK71235ITSJ9Uq2GMOxfsGUxpL7rdDSelT5TbtvpfEi/FRwqW6A0iawUd474+Bz4Behg\njMkxxtwGvAHUAWYbY5YZY/4DYK1dDUwE1gDfAfdZawuLm/DcD8wE1gITi++r3KFGA2h9nu+P+shZ\nJKWs/tpCvtOlkHewYqsVGclwdLd/zoYsT+fL4cBmyFnsdCSVYy1kfA+xiXKxQAWmmCRp4LHFx35+\nzyYrRfZk1492OhJVVV2ugLaDIflvgTVTL20mNImXSgHlv6IHwI5VcGy/05H4DHd2Z73GWtvMWhtq\nrY2y1r5vrW1rrW1pre1W/HF3qfs/Y62NtdZ2sNZ+W+r4DGtt++LPPeOueFWxuJGyf25XmtORVM2x\nfVI21bK305G4T5uBEF4X1lZgUX7FeCl/DaTyyLjhEBwmDXZ8yc61cGhrYP1fqdO1Pk+6DPvTvsii\nQhkxpKuQvs0YGPmy/Hna732z2qOyju2XfXJayur/Wg0A7IktUeqs/HSpRlVZh4vl1le7tJasPvnj\nfsgSIWHQfpg0QSosKP9+uQfkPp0vl68JFBH15Gr56q99q0lUSbOkWG2qE9Bq1IcWPSHTj5LI7Svk\n9UhHe/i++tFw0ZOwIRlWfuF0NO634XsoKtAkMhBE9YKgUJ0XWQmaRKqT1YuCZt18t6Q1eyGYYGje\nw+lI3KvTKBlIv+nH8u+zZgoU5kHXazwXl7foPEZW9bIXOB1JxWXMkaHH9XSKUcCLSYQtSyTx8gcl\n+yFbn+9sHMo1et8OUb3h2/+DI7udjsa90mfJVp8oP65uUiK0BrToAZu0Q2tFaRKpThc3UvYVHtru\ndCSVl50K53SG8NpOR+JesRdKJ7E1ZyhpXT5emgu18POEuizth8lML1/p0pp3WEqmdLSHAtkXaYsg\n6wenI3GNrBRoHAd1mjodiXKFoGAY9TrkHYKZf3Y6GvcpKpQksu1g+Tcr/9dqgEwoOH707PdVmkSq\nMsSNkNv1M5yNo7IKC+TqvT/OhzxVWE3ZO7duetklm/s2SUlGwtjAnMkWXlvKj9ZMOXPJr7fY+CMU\nHtf9kEpE9YbQWv5R0lpwXK7s635I/9KkI5z/BxkhlV6JucW+ZMtSOLpHS1kDSfQAKV/2t8ZmbqJJ\npDpdk47QoI3vlbTuXAPHD/v3fsjSOl0Kh3dATurpn1s5UW4TrvJsTN6k8xg4suvMJb/eYkOyrCxH\n93c6EuUNQsKg9bn+MS9y61LpNqtJpP85/2Fo1B6mPyTVFP4mfaZsj9EKkcAR3RcwOuqjgjSJVKcz\nRlYjM+dD7kGno6m4kmSqZQCsRILMrAoOO72k1VpYPkGuqDVo5Uxs3qDdEAirDat8oEtrxhzZLxYa\n4XQkylvEJMGeDNif7XQk1ZOVAhhoda7TkShXCwmXstYDm2GuHzbPT/sOovsFxoxlJSLqwTldtLlO\nBWkSqcoWNxKK8iFjttORVFx2KtQ+J3DmkEXUhdhBsHbaya3WtyyFPenQNYBmQ5YltIZ0G147FQrz\nnY6mfHs2wN5MvdqtThabJLe+XtKalQLNEqBmpNORKHeI7ieNdha87Xuzec/k4FbYvlIuRqrA0moA\nZC+SUnx1RppEqrK17AM1G/lWSWv2QpkPGUh7ADteIleBty07cWzFeAgOl3LXQBc/RmaHZs53OpLy\nbfhebnU/pCqtcZxcFPPlktb8Y/K6rKWs/u3CJ6FOM5j6oP+ceKfNlFvdDxl4Wg2AgmOwbbnTkXg9\nTSJV2YKCZRUnfbZvvCkc3gn7NgbOfsgSHYbLno2SktbCfOlI2uFimTcX6NpeCOH1vLtLa8YcaNAa\nImOcjkR5E2Nk1EfmPN+ad1pa9kJpGKXzIf1bRF0Y8RLsXA0/v+p0NK6RPkuqmhrHOR2J8rSS3gRa\n0npWmkSq8sWNhLyDsNEH2sxnl+yHDLAksmYktDlfSjatlYTk6B4tZS0REg4dR8qKekGe09GcriBP\nyv3aXhRYK+iqYmKT5Pd5xyqnI6marBQICtGGUYEgbjjEj4b5z8OuNKejqZ78XLl4026ovi4HotpN\noGE7GbulzkiTSFW+mIHSZt4XSlqzF0qTmWZdnY7E8zqOkgYcu9bJbMiaDbU0srT4MZB3ADKSnY7k\ndJsXQP5R/f9SZStZwfPVfZFZKdCil//P7VXi4uely/S03/nu6jnIyKX8ozJvWAWmVgMkifTln2MP\n0CRSlS+0hpQDrp/h/b9I2anQrJusPAWauJGAgaX/g/XfQucrIDjU6ai8R8xAqBEJq72wS2vGHAgK\nlc6sSp2qbjNo3BE2+GASmXtQmnzpfsjAUbsJDP0nbP4Zlo5zOpqqS/tOkuHW5zkdiXJKqwGQe0BG\nx6lyaRKpzixuJBzaBlt/dTqS8hUcl/gCZbTHqeo0lQ55C9+GwjzoerXTEXmX4FBpQLT+Wzh+1Olo\nTpaRLP93ulKjyhOTKFfE83OdjqRyNv0MtlCTyEDT7VpZQZ/9pHQ49TXWynzImEQduRTIWg2QW50X\neUaaRKozaz9EGresm+50JOXbvkKSp0DbD1lax1Fgi6SOv3kPp6PxPp3HwPHD0izBWxzcKo0otJRV\nnUlsEhTkQvYCpyOpnKwUCImAqN5OR6I8yRi45N/S5O2bR04eP+ULdq2D/Zt1tEegqx8N9Vpqc52z\n0CRSnVmNBlLS4c37IrMXym2grkSCrLQFhUD367QRQFlanw+1mnhXSWvJHk1NItWZtDpXfrd9raQ1\nK0Uu7OlqTuCJjIGkP8P6b2DNFKejqRwd7aFKRPeXKhBfuxDiQZpEqrOLGwG718PudKcjKVv2Qrlq\nVOccpyNxTv2WcF8q9H/A6Ui8U1CwzM1MmwV5h52ORmxIljmATeOdjkR5s/DaENXHt+ZFHtkDO1Zq\nKWsg63evNLqb8UeZ1esr0mfBOV2gbnOnI1FOazUADu+AvZlOR+K1NIlUZ9dhuNx642qktdJUJ5BL\nWUs0jIXgEKej8F6dx8gA4bTvnI4ECgtkZUlHe6iKiE2SwddH9zodScWUjIXS+ZCBKzgERr0uI2pm\nPe50NBVzbJ90zG6nq5CKUvsitaS1PJpEqrOr31KuKHpjEnkgRxr/aBKpzqZlP6jTHFZ5QUnr1qWQ\nux/aDnI6EuULYpIA6zurkVkpEFYHmnd3OhLlpGZdYcAD8Ov/IHO+09GcXUayNIPS0R4KoFF7GZm2\nSedFlkeTSFUxcSMhZxEc2u50JCfT/ZCqooKCIP4yyJgtrbudlDEHTFBxcqDUWTTvDuH1fCuJbDVA\nKyMUJD4KDdrI7Mj8Y05Hc2ZpMyVpaKHN6RRSJdRqgK5EnoEmkapi4kYAVsYkeJPsVAitBU10X5mq\ngPgxUHjc+VX1jDnQoifUjHQ2DuUbgkOgzfmQOdf7mzwc3Ap70nU/pBKhNWDUa7AvC+Y953Q05Ssq\nlAuM7YbIHnqlAKIHwP5NcGCL05F4JU0iVcU06QQNWjt/8n2q7IVy1VCveKuKiOoF9aKdLWk9skeG\nsGtXVlUZMYkyemBfltORnFlWyX5ITSJVsTYXQPcb4OfXZW+vN8pZJHsidbSHKq1kX+RmLWktiyaR\nqmKMkZLWrPmQe9DpaMTxI7B9pe6HVBVnDHQeLSs6TjUpyZwLWE0iVeWUlD57+6iPrBQZDdW0s9OR\nKG8y5O9SKjr1AWks5m3SZsoonVjdp65KOaeL7O/WktYyaRKpKi5uhJQCZsxxOhKx9VfZBK9JpKqM\n+DFQVABrpznz/BnJcpKtTUdUZTSMleHXmV6cRForFxpbny97kJUqUaMBDH9BViIXvOV0NKdLmylz\nAWvUdzoS5U2CgiG6rzbXKYfbXuWNMR8YY3YaY1aVOhZpjJltjEkvvm1QfNwYY14zxmQYY1YYY3qU\n+pqbiu+fboy5yV3xqgpo2VeuJHpLSWtJU52oXs7GoXxLs64yDHu1AyWtRUVyESYmSffdqMoxRkpa\ns1Jk/5Y32rcRDmRrKasqW6dLpaJp7j+9a/be/mzYuRra62gPVYZWA2DXWtmKok7izkuF44BT+yQ/\nCiRba9sBycV/B7gYaFf8cSfwNkjSCTwJ9AX6AE+WJJ7KAUHB0OFiSJ8NBcedjkaa6jRqr81JVOUY\nI6uRWSlweKdnn3vHKjiyU0tZVdXEJEpn4a3LnI6kbFkpcqvzIVVZjJHVyOBQmPZ772kSlT5LbnU+\npCpLtO6LLI/bkkhrbQpw6qajS4GPiv/8EXBZqeMfW7EAqG+MaQYMBWZba/daa/cBszk9MVWeFDcS\n8g7Aph+djcNaSSJ1tIeqis6Xgy2C7/8OeYc997wlpeBtL/Tccyr/EZMot5nfOxlF+bJSoPY50Kid\n05Eob1W3OQx+Wsqel33mdDQibaY0DtSfW1WWFj0gOFyTyDJ4uqVlU2vttuI/bweaFv+5BZBd6n45\nxcfKO66cEpMIoTWlpNXJDeh7NsCxvbofUlVN007Q61ZY/AGkzYKLnoSEse7fx5WRDE27QJ1z3Ps8\nyj/VaiSNHjLnwwV/dDqak1krSWRskqw4KVWeHjfDii9g5p+h3WCo3cS5WI4flYS25836c+tm+fn5\n5OTkkJub63QolTfsC7nwvHat05GUKyIigqioKEJDQz32nI7NRbDWWmOMy2oZjDF3IqWwREdHu+ph\n1alCa8gqytppcNHTEF7bmTh+2w+pK5Gqika+Al2vhe8eha/vgYX/hWHPQav+7nm+vEOQvQD63++e\nx1eBISYJFrwt3anDajkdzQm71kmptu6HVGcTFCSzI98eAN/+Ca4c51wsG3+Aglwd7eEBOTk51KlT\nh9atW2N8LWE/WA8O74Bz2ntlPwNrLXv27CEnJ4c2bdp47Hk93T5tR3GZKsW3JRuStgAtS90vqvhY\necdPY619x1rby1rbq3Hjxi4PXJXS7144sgtmPOJcDNkLIaKe7IlUqqpa9obbZsOYd2V/5IfD4Iub\nYd8m1z9XVop0hdVSVlUdsUlQlA+bfnY6kpP9th9Sk0hVAY3awcA/werJsG6Gc3GkzYTQWtD6POdi\nCBC5ubk0bNjQ9xJIgLDiBZPjR5yNoxzGGBo2bOjxVV5PJ5FTgZIOqzcBU0odv7G4S2s/4EBx2etM\nYIgxpkFxQ50hxceUk1oNgAv+BMs/d25PQ84iWYXUNvKquoKCIOEqeGAxJD4G67+DN3pD8t9k9dBV\nMubIyUrLfq57TBV4ovvL/pzMeU5HcrKsFNlXVl8rgVQFDfgdNImHbx52Zv60tZJExiZBSLjnnz8A\n+WQCCSeqPo57sIdCJTnxvXXniI/PgV+ADsaYHGPMbcBzwGBjTDpwUfHfAWYAmUAG8C5wL4C1di/w\nd2BR8cffio8ppw38k8wC++Zh2JXm2ec+th92rtX9kMq1wmpB4qPwwBKIvwx+eAle7wm/fiKjOarD\n2uLRHgMhJMw18arAFFoDovvBBi+aF1lUKGWBugqpKiMkDEa9Doe2wcQbPd8te+caOJijoz3U2QUF\nSz+QMlYiN27cyGefeUmTKA9zZ3fWa6y1zay1odbaKGvt+9baPdbaC6217ay1F5UkhMVdWe+z1sZa\na7tYaxeXepwPrLVtiz8+dFe8qpKCgqUEMLSGlP/lH/Pcc29ZDFgpRVTK1eq1gDHvwO3Jsqoy5T54\nN7F65YN7MmD/Zi1lVa4RmyRz7Q7tcDoSsX2FjB7R0R6qsqJ6wiWvSufLtwdIozNPSftObnU/pKqI\nsNqSRJa6qFxQUKBJpFJVUrcZjP6vnMzM/LPnnjc7FUwQtOjpuedUgSeql+yXvPx9GTL84cUw8SYZ\nqF5ZJaM9YjWJVC4Qkyi3WfOdjOKEkv2Qrc93Ng7lm3reBHfOg9pN4bMr4ZtHPHNhOm0WNOum3bID\nyMaNG+ncufNvf3/xxRd56qmneO211+jUqRMJCQmMHTsWgCNHjnDrrbfSp08funfvzpSZ8wHLuPff\nYdSoUQwaNIgLL7yQRx99lB9++IFu3brxyiuvcMEFF7Bs2YlZvueddx7Lly/nqaee4oYbbqB///60\na9eOd99997f7vPDCC/Tu3ZuEhASefPJJj30/qsux7qzKT7QbDAMehJ9fk1Km+NHuf87sVGgaD+F1\n3P9cKrAZA12ugA7D4Zc34MdXYP230P8+OP8PFf8ZzEiGhm0h0nNd05QfO6cr1IiUktaEq5yORpLI\nxnFQp+nZ76tUWZp0hDu+l73ov7wh5dGXvycjbdzh6F7ISfW+UTnKEc899xxZWVmEh4ezf/9+AJ55\n5hkGDRrEBx98wP79++nTpw8XzRgHhXksXbqUFStWEBkZybx583jxxReZPn06AJGRkYwbN45///vf\npKWlkZubS9euXZk8eTIrVqxgwYIFHDlyhO7duzNixAhWrVpFeno6qampWGsZNWoUKSkpXHCB928P\n0JVIVX0XPgFRvWHqg7A3y73PVVQIOYt1P6TyrLCasg/4gSXQeQz8+DK81gOW/u/s+yXzj8HGH3UV\nUrlOUJDsr82cJ/ttnVRwHDb9ovshVfWFhMPQZ+CGydL74N1B8PMb1d+TXpaMOTL3r53uh1SQkJDA\nddddxyeffEJIiKyvzZo1i+eee45u3bqRmJhIbm4um7fvhYJcBg8eTGRkZJmPdeWVVzJ9+nTy8/P5\n4IMPuPnmm3/73KWXXkqNGjVo1KgRSUlJpKamMmvWLGbNmkX37t3p0aMH69atIz093RP/7GrTJFJV\nX3ColPwZA1/eIicV7rJzLRw/pPMhlTPqNofR/4Hbv5dOlFPvh3cGwsafyv+aTT9DwTFoe5HHwlQB\nICYRDm2F3R5ubHaqrUsh/4gmkcp1YgfBPT/LXsVZf4FPRsPBba59jrSZUKsxNO/u2sdVXi0kJISi\nUhclSkZifPPNN9x3330sXbqU3r17U1BQgLWWr776imXLlrFs2TI2b95Mx85doSCPWjVrlvscNWvW\nZPDgwUyZMoWJEydy3XXX/fa5UzuoGmOw1vLYY4/99jwZGRncdtttLv6Xu4cmkco1GrSCUW/A1l8h\n+Wn3PU/2QrltqUmkclBUT7htllw8OboXxg2X7oJl7ZfMSJaRDK3P9XiYyo/FJMmt011as1IAA630\n51u5UK2GcPUn0nQnOxXe7g9rp7nmsQsLIGO2JKk6JiygNG3alJ07d7Jnzx7y8vKYPn06RUVFZGdn\nk5SUxL/+9S8OHDjAItlDOQAAIABJREFU4cOHGTp0KK+//jq2uNrj119/lS7u1kpVXLE6depw6NDJ\n48Buv/12HnzwQXr37k2DBg1+Oz5lyhRyc3PZs2cP8+bNo3fv3gwdOpQPPviAw4dlfMiWLVvYudPD\nnYqrSH97lOt0GgW975D9DOu/c89z5CyCWk1kFUgpJ5Xsl3xgMST9FdJny3zJOU+dPPMsY47MVi2Z\nM6WUKzRoBQ3aOD8vMisFmiVAzbJLu5SqMmOg581w1w9QvxVMuB6mPgB51ZzVl5Mq3YR1tEfACQ0N\n5YknnqBPnz4MHjyYuLg4CgsLuf766+nSpQvdu3fnwQcfpH79+jz++OPk5+eTkJBAfHw8jz/+OITX\nlgcqyv/tMRMSEggODqZr16688sorAPTs2ZO6detyyy23nPT8CQkJJCUl0a9fPx5//HGaN2/OkCFD\nuPbaa+nfvz9dunThiiuuOC0p9VbGOr2fwg169eplFy9efPY7KtfLz4X3L4IDOXD3TzIuwZVe6w5N\nOsHYT137uEpV18GtkPx3WP6ZXOi48HEZefBqAgx5Bgbc73SEyt9MfwhWTITfrZCVG0/LPwbPRUPf\nu2DIPzz//CpwFByHec9Kc7PIGLj83ap3aJ/9BPzyFvwpEyLqujZOVa61a9fSsWNHp8Oovh2rZbxd\nZEy5d9m6dSuJiYmsW7eOoOLV7qeeeoratWvzyCOPuC20sr7Hxpgl1tpe7ng+XYlUrhUaAVeMkxf8\nr26TshFXObwL9mZqKavyTnWbw+i3pcNgZIxcMX8nUT6n8yGVO/S4EYoK4NPLT1799pTshVB4XOdD\nKvcLCYOLnoSbp0NBHrw/BH546aSywgpLmyXVIZpAqqoIqy2r4eUswn388cf07duXZ5555rcE0l/5\n979OOaNRW7jk3zI8eP5zrnvcnFS51c6sypu16Am3fgdXfCAlrI3ay/gDpVyteXe46mPYvhI+H+uZ\n2XqlZaVAUAhE9/Ps86rA1fo8uOdH6DhKxoF8dAnsz6741+/bBLvWaimrqrqw2mALoSC3zE/feOON\nZGdnc+WVV550/KmnnnLrKqQTyk0ijTErjTEryvhYaYxZ4ckglQ9KuAq6XQ8pL7qu8UN2KgSFynBg\npbyZMdD5cnjwVxmifUpHNqVcpv1QGP1f6QI88Ub3dsc+VVaKXDTRmb3Kk2o0kIt0o/8L21bA2+fC\nqq8q9rXps+S2/TD3xaf8W3hxf4PjR5yNwwucaSVyJHBJGR8lx5U6s+HPyyrMpDvhsAs6TWWnQvNu\nUjKrlC8IDtWGOsr9ulwBI1+RE+TJd1WtxK+ycg/ClqU62kM5wxjoOhbu/gEad4Avb4VJd529rDtt\nJkTGQsNYz8Sp/E9wuCxoHK9mgyc/UG4Saa3ddKYPTwapfFRYLbhyHOQdlESyOgODC47LPDKdD6mU\nUqfrdQsM/husngTf/KHc/Tous+lnKenS/ZDKSZFt4JZvYeCjsHIi/Oc82Lyw7PsePyKr51rKqqrD\nGDm/PcO+yEBx1j2Rxph+xphFxpjDxpjjxphCY4wDO/iVT2raCS7+F2TOhZ9eqfrjbF8p9efaVEcp\npcp27u/g/IdhyTjpQOnOE5ysFAiJgKje7nsOpSoiOASSHoNbikeLfTgM5j57emO/rBQozNMkUlVf\neG0Z81Howe0DXqgijXXeAK4B0oEawO3Am+4MSvmZHjdB/Bj4/hnYvKBqj6FNdZRS6uwGPS7zen9+\nTbpXuktWirwe6/YC5S2i+8LdP0LC1dLU78NhsDfrxOfTvoOwOhA9wLkYleO2b9/O2LFjiY2NpWfP\nngwfPpy0tLRKPcbX337PmrTMgC9prVB3VmttBhBsrS201n4I6I5kVXHGwCWvQv1o+PI2OLq38o+R\nvRDqRUPdZq6PTyml/IUxcPHzciL9/d8h9V3XP8eRPbBjpe6HVN4noi6M/g9c/j7sSpPy1mWfyap8\n2iyITZJxISogWWsZPXo0iYmJbNiwgSVLlvDss8+yY8eOSj3O19O/ZU36Ro821yks9MBe90qqSBJ5\n1BgTBiwzxjxvjHmogl+n1AkRdaWb2uEdMOW+ypdZZadCSy2bUkqpswoKgkvfgg4jYMYjsHy8ax9/\n4w9yq/shlbfqcgXc8xM06wpf3yOjQA5t1VLWADd37lxCQ0O5++67fzvWtWtXCgsLGTly5G/H7r//\nfsaNGwfAo48+SqdOnUhISOCRRx7h559/ZurUqfzxH/+m23lD2LBhA8uWLaNfv34kJCQwevRo9u3b\nB0BiYiIPPfQQvXr1omPHjixatIgxY8bQrl07/vrXv/72fJ988gl9+vShW7du3HXXXb8ljLVr1+bh\nhx+ma9eu/PLLLx74DlVOSAXucwOSNN4PPAS0BMa4Myjlp1r0gCF/h+8ehYX/gX73VOzrDuTAwS1a\nyqqUUhUVHCIX7j67Er6+V2abdRx59q+riKwUKQts3t01j6eUO9RvCTdNg59ehbnPyLG2g52NSQHw\n9LTVrNnq2vYqnZrX5clL4s94n1WrVtGzZ88KP+aePXuYPHky69atwxjD/v37qV+/PqNGjWLkRedx\nxaCe0DSahO49ef311xk4cCBPPPEETz/9NP/+978BCAsLY/Hixbz66qtceumlLFmyhMjISGJjY3no\noYfYuXMnEyZM4KeffiI0NJR7772XTz/9lBtvvJEjR47Qt29fXnrJjVsTqqEiK4qXWWtzrbUHrbVP\nW2v/gIz5UKry+t4NHYbDrMelPXxFZJfsh9SmOkopVWGhETD2c0n2vrwFMue55nGzUqDVAElUlfJm\nQcFw/h/gju/hqo+hTlOnI1I+pF69ekRERHDbbbcxadIkataseeKTIbIf/MCurezfv5+BA6Uy46ab\nbiIlJeW3u40aNQqALl26EB8fT7NmzQgPDycmJobs7GySk5NZsmQJvXv3plu3biQnJ5OZmQlAcHAw\nl19+uYf+tZVXkXeAm4BXTzl2cxnHlDo7Y+DSN+E/58tcp7tSpNT1TLJTIbQmNO3smRiVUspfhNeG\n676AcSPh82vhxinV2xpwcCvsSYeeN7ssRKXcrllX+VBe4Wwrhu4SHx/Pl19+edrxkJAQikqNocvN\nzf3teGpqKsnJyXz55Ze88cYbfP/993Kn4DAwQWfdFxkeHg5AUFDQb38u+XtBQQHWWm666SaeffbZ\n0742IiKC4ODgSv87PaXclUhjzDXGmGlAG2PM1FIf84AqdEZRqljNSLjifdi/Gab97uz7I7MXQvMe\nMrhdKaVU5dSMhBsmQ+0m8OnlsH1V1R8rq2Q/pDbVUUr5lkGDBpGXl8c777zz27EVK1ZgrWXNmjXk\n5eWxf/9+kpOTATh8+DD/396dx0dVHfwf/5zsGyFkIQIBElZBQJZEARVwqbZq1brhLlVxeVpbf22f\ntj7W6tOntlr7uLVWRcG1dUP7aN2tioiyhV3ZEiCBsGUPWcgyM+f3x70JAVkmkGQmk+/79cpr7tz1\n5B6YzPeec8+tqqri3HPP5eGHH2bVqlUA9OjRg+qaWoiMo2dMGL169eKLL5zPxhdffLGlVdIfZ555\nJnPnzqW4uBiA8vJyCgsL2+tX7lCHa4n8CtgJpAKtO+NWA6s7slDSDQyYCGfcBZ/8DgZNPfRV7aa9\nsGs1TP5JpxZPRCSk9Eh3WiHnfBde/AHc8AGkDG77frbMh9he6hkiIl2OMYZ//vOf3HHHHTzwwAPE\nxMSQmZnJI488wuWXX86oUaPIyspi3Djnfu/q6mouvPBC6uvrsdby0EMPAXDFFVcwc+ZMHnvkf5n7\nxB94/tnZ3PofP6auro5Bgwbx7LPP+l2mkSNH8vvf/56zzz4bn89HZGQkjz/+OAMHDuyQc9CejPVj\nlExjTDrQ3P9libW2uENLdYyys7Ntbm5uoIshR+LzwUsXw9aFMPMzSB/57XUKv4JnvwdXvgrD9WQZ\nEZFjUrLReX5eZJwTJHtm+L+ttfDIaOcey+kvdlwZRSTkrFu3jhEjRgS6GO2roRrK8iF5EMT0DHRp\nDnqOjTHLrLXZHXG8Iw6sY4y5DFgCXAZcDiw2xlzaEYWRbiYsDC6eBdGJ8PqMg/cr37bYec3Q4z1E\nRI5Z2jC45k2or4IXLoKaEv+3rSiAqm3qyioiAhAZDxhorAl0SQLCn9FZfwPkWGuvt9ZeB5wE3N2x\nxZJuI6E3XPI0lG6E93/57eXblkDKEIhP6fyyiYiEor5j4apXnccnvfQD2Fvp33Zb3BEH9XxIERGn\nMSQyDhoOP7hOqPInRIYd0H21zM/tDskY8/+MMd8YY742xrxsjIkxxmQZYxYbY/KNMa8aY6LcdaPd\n9/nu8sxjObYEoUHTYMovYMVLsPq1ffOtdVoi9XxIEZH2NXAyTH8JitfDP6ZDY92Rt9kyHxKOg9Sh\nHV8+EZGuIDoemurA5w10STqdP2HwfWPMh8aYGcaYGcC7wHtHe0BjTD/gJ0C2tXYUEA5cATwAPGyt\nHQJUADe6m9wIVLjzH3bXk1Az9dcwYDK88/+gNN+ZV74Z6sr0fEgRkY4w9CynJ0jREnj1GvA0Hnpd\na50QmTXFeVSTiIhAVAJgnSDZzfgTIi3wFDDG/Zl1+NX9EgHEGmMigDicUWDPAJof3vI8cJE7faH7\nHnf5mcboL1jICY+AS55xnrszdwY01TtdWUEtkSIiHeWEH8D3H4VNn8CbN4HXc/D1SjZAbbHuhxQR\naS0q3nnthvdF+hMiv2OtfdNa+zP355/A9472gNba7cCfga044bEKWAZUWmub/3oVAf3c6X7ANndb\nj7v+t26QM8bcbIzJNcbklpS0YaAACR49+8FFT8CuNfDx3U5X1uiekDo80CUTEQld46+Dc/4Aa99y\nnt3b6qHbLbZ87rwqRIqI7BMWARGx0KAQ2cIYc5sxZg0w3BizutXPFo7hOZHGmF44rYtZQF8gHjjm\nZzdYa2dZa7OttdlpaWnHujsJlOHfhUk/hiWz4Os3ICPbuXFZREQ6zqQfwdRfwcqX4KO7nO6rrW2Z\nD70yoVfwP7tMRORQ7rvvPk444QTGjBnD2LFjWbx48SHXvffee/nzn/985J1GJzj3lduDXIA7QG5u\nLj/5yU/atv8gFXGYZf8A3gf+CPy61fxqa235MRzzLGCLtbYEwBjzJnAKkGSMiXBbGzOA7e7624H+\nQJHb/bUnzuA+EqrOvMd5PuSO5erKKiLSWabd6Tz6Y9HfICYJpv3Kme/zQsEXMPLCwJZPROQYLFy4\nkHfeeYfly5cTHR1NaWkpjY2HuRfcX1EJUFvi3IoVFXfI1TweD9nZ2WRnH/tjG621WGsJC2BDyyGP\nbK2tstYWWGuvtNYWtvo5lgAJTjfWicaYOPfexjOBtcBnQPPzJ68H3nKn33bf4y7/1NoDL5FKSImI\ngkvnOAFyxPmBLo2ISPdgDJzzRxh7Ncz7Ayx6wpm/a7UTLvVoDxHpwnbu3ElqairR0dEApKam0rdv\nXzIzMyktLQWclsJp06a1bLNq1SomTZrE0KFDefrpp1v2M2XKFMaOHcuoUaP4YslKSB/NB5/OZ/z4\n8Zx44omceeaZgNPaeO2113LKKadw7bXXMm/ePM4///zD7h/gwQcfJCcnhzFjxnDPPfcAUFBQwPDh\nw7nuuusYNWoU27Zt69DzdSSHa4nsENbaxcaYucBywAOswBms513gFWPM7915s91NZgMvGmPygXKc\nkVwl1CVnwY0fBboUIiLdS1gYfP8xaNgDH/waons4o2QDZJ4W2LKJSGh4/9fO+Bft6bjR8L37D7vK\n2Wefze9+9zuGDRvGWWedxfTp05k69fAXx1avXs2iRYuora1l3LhxnHfeebz88succ8453HXXXXi9\nXurq6igpr2DmzJnMnz+frKwsysv3tbmtXbuWBQsWEBsby7x58464/6+//pq8vDyWLFmCtZYLLriA\n+fPnM2DAAPLy8nj++eeZOHHiUZ+q9tLpIRLAWnsPcM8BszcD33qWg7W2HrisM8olIiLS7YVHwCWz\nnedHvn07JPaDtOOhR3qgSyYictQSEhJYtmwZX3zxBZ999hnTp0/n/vsPHzwvvPBCYmNjiY2N5fTT\nT2fJkiXk5ORwww030NTUxEUXXcTYsWOZN28eU6ZMISsrC4Dk5OSWfVxwwQXExsb6vf8FCxbw0Ucf\nMW7cOABqamrIy8tjwIABDBw4MCgCJAQoRIqIiEgQi4iGK/4OL/7AGSn7pJsDXSIRCRVHaDHsSOHh\n4UybNo1p06YxevRonn/+eSIiIvC5o1LX19fvt/6BTxU0xjBlyhTmz5/Pu+++y4wZM/jZz35Gr169\nDnnM+Pj4Qy472P6ttdx5553ccsst+y0rKCg47L46m4a9FBERkW+LioerXoMJP4ScmYEujYjIMdmw\nYQN5eXkt71euXMnAgQPJzMxk2bJlALzxxhv7bfPWW29RX19PWVkZ8+bNIycnh8LCQtLT05k5cyY3\n3XQTy5cvZ+LEicyfP58tW7YA7Ned9XAOtv9zzjmHOXPmUFPjPDZk+/btFBcXt8cpaFdqiRQREZGD\ni02C7z8S6FKIiByzmpoabr/9diorK4mIiGDIkCHMmjWLdevWceONN3L33XfvN6gOwJgxYzj99NMp\nLS3l7rvvpm/fvjz//PM8+OCDREZGkpCQwAsvvEBaWhqzZs3i4osvxufz0bt3bz7++OMjlulg++/b\nty/r1q1j0qRJgNMN96WXXiI8PLwjTstRM6E40Gl2drbNzc0NdDFERERERLq9devWMWLEiEAXI6Qd\n7BwbY5ZZa4/9mSIHoe6sIiIiIiIi4jeFSBEREREREfGbQqSIiIiIiIj4TSFSREREREQ6VCiOwxIs\nAnFuFSJFRERERKTDxMTEUFZWpiDZAay1lJWVERMT06nH1SM+RERERESkw2RkZFBUVERJSUmgixKS\nYmJiyMjI6NRjKkSKiIiIiEiHiYyMJCsrK9DFkHak7qwiIiIiIiLiN4VIERERERER8ZtCpIiIiIiI\niPhNIVJERERERET8phApIiIiIiIiflOIFBEREREREb8pRIqIiIiIiIjfFCJFRERERETEbwqRIiIi\nIiIi4jeFSBEREREREfGbQqSIiIiIiIj4TSFSRERERERE/BaQEGmMSTLGzDXGrDfGrDPGTDLGJBtj\nPjbG5Lmvvdx1jTHmMWNMvjFmtTFmfCDKLCIiIiIiIoFriXwU+MBaezxwIrAO+DXwibV2KPCJ+x7g\ne8BQ9+dm4InOL66IiIiIiIhAAEKkMaYnMAWYDWCtbbTWVgIXAs+7qz0PXOROXwi8YB2LgCRjTJ9O\nLraIiIiIiIgQmJbILKAEeNYYs8IY84wxJh5It9budNfZBaS70/2Aba22L3LniYiIiIiISCcLRIiM\nAMYDT1hrxwG17Ou6CoC11gK2LTs1xtxsjMk1xuSWlJS0W2FFRERERERkn0CEyCKgyFq72H0/FydU\n7m7upuq+FrvLtwP9W22f4c7bj7V2lrU221qbnZaW1mGFFxERERER6c46PURaa3cB24wxw91ZZwJr\ngbeB69151wNvudNvA9e5o7ROBKpadXsVERERERGRThQRoOPeDvzdGBMFbAZ+iBNoXzPG3AgUApe7\n674HnAvkA3XuuiIiIiIiIhIAAQmR1tqVQPZBFp15kHUt8KMOL5SIiIiIiIgcUaCeEykiIiIiIiJd\nkEKkiIiIiIiI+E0hUkRERERERPymECkiIiIiIiJ+U4gUERERERERvylEioiIiIiIiN8UIkVERERE\nRMRvCpEiIiIiIiLiN4VIERERERER8ZtCpIiIiIiIiPhNIVJERERERET8phApIiIiIiIiflOIFBER\nEREREb8pRIqIiIiIiIjfFCJFRERERETEbwqRIiIiIiIi4jeFSBEREREREfGbQqSIiIiIiIj4TSFS\nRERERERE/KYQKSIiIiIiIn5TiBQRERERERG/KUSKiIiIiIiI3xQiRURERERExG8BC5HGmHBjzApj\nzDvu+yxjzGJjTL4x5lVjTJQ7P9p9n+8uzwxUmUVERERERLq7QLZE/hRY1+r9A8DD1tohQAVwozv/\nRqDCnf+wu56IiIiIiIgEQEBCpDEmAzgPeMZ9b4AzgLnuKs8DF7nTF7rvcZef6a4vIiIiIiIinSxQ\nLZGPAL8EfO77FKDSWutx3xcB/dzpfsA2AHd5lbu+iIiIiIiIdLJOD5HGmPOBYmvtsnbe783GmFxj\nTG5JSUl77lpERERERERcgWiJPAW4wBhTALyC0431USDJGBPhrpMBbHentwP9AdzlPYGyA3dqrZ1l\nrc221manpaV17G8gIiIiIiLSTXV6iLTW3mmtzbDWZgJXAJ9aa68GPgMudVe7HnjLnX7bfY+7/FNr\nre3EIouIiIiIiIgrmJ4T+SvgZ8aYfJx7Hme782cDKe78nwG/DlD5REREREREur2II6/Scay184B5\n7vRm4KSDrFMPXNapBRMREREREZGDCqaWSBEREREREQlyCpEiIiIiIiLiN4VIERERERER8ZtCpIiI\niIiIiPhNIVJERERERET8FtDRWUVEAsHnszyzYDMp8dF8b/RxxEXpo1BEJJCstXh9lkavjyaPpcHr\npclrafT4aPL6aPT4aHRfm1q9Nnh8fq3XPzmOG07JIizMBPpXlRDk9VnCu9m/LX1zkjb7Mr+Uj9fu\npn9yHEN6JzCkdwJ9EmP0wSxdxh/eW8czC7YAcM/b33D+mD5clp3B+AG9MEb/jkVEOkNZTQO3vLiM\nNduraPT6sLZ99x8ZbogMDyMizLCn3sOOynruPn+EPuelXX2RV8Lv/rWWF288meN6xgS6OJ1GIVL8\n5vNZ/jYvn//9eCMRYYYm775P+9jIcAb3jmdwWgJD0pxgObh3Apkp8URFqNe0BI9Z8zfxzIItzJic\nybmj+/Ba7jbeXrWDV5ZuY1BaPJdN6M/F4/uRnth9/hCIiHS2spoGrn5mMVtKa7l24kDiosKJDA8j\nKiKs5TXqgPeR4aZlfut1ow+yTmRYWMvFbWstv3tnLXO+3EJKQhQ/On1IgH97CRWvLt3KXf/8miG9\nE/C191WQIGdsCP7C2dnZNjc3N9DFCCl76pv4+Wur+Hjtbi44sS/3XzKaukYv+cU15BfXsKnEed1c\nUsv2yr0t24WHGQYmxzGoOVimxbcEzMSYyAD+RtIdvbm8iJ+9torzxvThL1eMa/mCUdPg4b3VO3l9\n2TaWFlQQZmDa8N5cNiGDM0ek60KIiEg7ah0g58zI4ZQhqR1+TJ/P8vPXV/HPFdu57wejuPrkgR1+\nTAldPp/lzx9t4G/zNjFlWBqPXzWOHkH4vdYYs8xam90h+1aIlCPZuLuaW15cxtbyOu46dwQ/PCXz\nsF1Bahs8bC6pbQmWza8FZbX7tV727hHtBsuElm6xg9MSSE+MVlcTaXefbyzhxueWclJWMs/+MIfo\niPCDrre5pIa5y4p4Y3kRu/c0kBwfxYVj+3LZhP6M7JvYyaUWEQkt5bWNXPX0ok4NkM2avD5ueXEZ\nn20o5q9Xjue8MX067dgSOuqbvPzi9VW8s3onV540gN9deAKR4cF5sVkhso0UItvPO6t38Mu5q4mL\niuDxq8Zx8qCUo96Xx+tja3mdGyxrWwLmpuIaqhs8LeslREcwOC2eYek9+NHpQ8hMjW+PX0W6sVXb\nKrny6UUMTInntVsm+nW10OuzzM8rYW5uER+v3U2j18eofolcNqE/F47tS1JcVCeUXEQkdLQOkLOv\nz+HUoZ0XIJvtbfRy3ZzFrNxWyZwZOZw2NK3TyyBdV1lNAze/uIxlhRXc+b3juXnKoKBu+FCIbCOF\nyGPn8fp44IP1PP3FFsYPSOKJayZ02D1i1lqKqxvYVFxDvhsq80tqWLWtiohww5PXTGDiMYRX6d62\nlNZyyRNfERcVzpu3Tab3Ufw7rqht5K2V23l9WRHf7NhDVHgY3zkhncsmZHDa0LRuNyKbiEhbBUOA\nbFa1t4npTy1ka3kdf7/pZMYN6BWwskjXsbmkhh8+t5RdVfU8PH0s544O/pZshcg2Uog8NqU1Dfz4\nH8tZtLmc6yYN5DfnjQzIPWGFZbXc8NxStpbXcd8PRnN5dv9OL4N0bcXV9VzyxFfUNniZe+skBqUl\nHPM+v9lRxeu5Rby1cjsVdU306RnDxeP7cdmE/mo1FxE5iPLaRq5+ZjGbS2p45vrsoGj9K66u57In\nF1K1t4nXb5nE0PQegS6SBLElW8q5+cVcwo3h6euzGd9FLjwoRLaRQuTRW7G1gtteWk5FXSN/+MFo\nLpmQEdDyVO1t4kd/X86C/FJumTqIX51zvB4lIn6prm9i+lPOVe+Xb57I2P5J7br/Bo+XT9YV83ru\nNj7fWILPwkmZyVyWncG5o/sQH63Br0VEKmobucoNkE9fl82UYYEPkM22ltVxyZNfEW4Mc2+bREav\nuEAXSYLQ/63Yzi/nriYjOZbnZpzEgJSu8+9EIbKNFCLbzlrLP5Zs5b/fXkvvxGievGYCo/r1DHSx\nAOdG+Hvf/oa/L97K2SPTeeSKsXo4vBxWg8fLDc8tZfHmcp65Pptpw3t36PF2VdXz5ooiXs8tYktp\nLXFR4Zw3ug+X5/Qne6CePSki3VNzgNxUUsMzQRYgm63buYfpTy0kNSGa126dRGpCdKCLJEHCWstf\nPs3noY83cnJWMk9dO6HLjYegENlGCpFtU9/k5bdvfc1ruUVMHZbGo1eMDbr/JNZanvuqgP95Zy0j\n+iTyzPXZ9OkZG+hiSRDy+Sw/eWUF76zeyUOXn8jF4zuvNd1ay7LCCl7L3ca7q3dS2+jl+ON68Nvz\nRzK5E0cgFBEJtK4QIJvlFpRzzezFDOmdwMsz/Rt8TUJbo8fHnW+u4Y3lRVw8rh/3XzKmSz7uSyGy\njRQi/VdUUcdtLy1nzfYqfnLGEH561rCgHiTks/XF3P7yCuKiwnnm+mzGZLRvF0Xp2pofKP3slwX8\n+nvHc+vUwQG3H+XFAAAgAElEQVQrS22Dh3fX7OQvn+axrXwv543uw3+dN4J+Sbr4IUfP57Psrq6n\noLSOreW11DR4ycnsxQl9ewb1Z7d0LxXuPZD5XSBANvtsfTEzX8glO7MXz/3wJGIiD/4YKAl9VXVN\n3PrSMhZuLuOOs4by0zOHdtkeRQqRbaQQ6Z8v8kr4ycsr8HgtD00fy3dGpge6SH5Zv2sPNz6XS1lt\nAw9fPpbvdYHRsaRzPPn5Ju5/fz03nJLF3eePCIoP/fomL7Pmb+Zv8/IB+NG0IcycMkhfUOSQPF4f\nOyrrKSirpbC8jsLSWgrKnNBYWFZHg8f3rW2S4iKZPDiFU4akcuqQVAYkxwXFv3/pfloHyKevy2Zq\nFwiQzd5auZ2fvrKS74xM54mrxxMRpM/+k46zrbyOGc8uYWt5HQ9cMqZTezN1BIXINlKIPDxrLU98\nvok/f7iBIb0TeOrabLK62KiSJdUN3PxiLiu2VvKf5wznP6YN1hembu6NZUX8/PVVfP/Evjw6fWzQ\nDcBUVFHHH95bx3trdtE/OZa7zxvJd0am699tN9Xg8bKtfC9by2spKK2jsKw5KNaxrbwOj2/f3+bo\niDAGpsQxMCWeTPd1YEocmSnxREWEsWhzGQvySlmQX8rOqnoAMnrFcuqQVE5xf5Ljg+sWBQlNlXVO\ngMwr7noBstlzX27h3n+t5dIJGTx46Rh9RncjK7ZWMPOFXBo9Pp66NptJg7v+4+UUIttIIfLQquub\n+MXrq/jwm92cP6YPD1wypsuOIlnf5OWXc1fz9qodXDy+H3+8eDTREWrd6Y4+21DMTc/nMnFQMnNm\n5AT1v4Mv80u59+1vyCuuYcqwNO75/kgGt8OjRyT41DV6KCyrc3+ckFhY5rQm7qjaS+s/vwnRES3B\nsPl1gPvau0e0XxdFrLVsKa1lQX4pC/JKWbi5jOp6DwAn9E1sCZU5mcnERgXv/xHpmloHyFnXTujw\nAc060sMfb+TRT/KYeVoW/3VucPRqkY71wdc7+ekrK0lPjGHOjByG9A6Nv8sKkW2kEHlweburueWl\nZRSW1XHn947nxlOzuvwHo7WWxz7J5+F/byQnsxdPXZutK+7dzMptlVw5axGD0uJ55eauMSBCk9fH\n818V8Oi/86j3eLnhlCxuP3MoCV30go7sb0FeKX/6cD2ri6r2m98rLvJbrYnN75Pjo9r989jj9bFm\nexVf5jutlMsKK2jyWqLCw5gwsBenDnW6vo7qp/sp5diEUoAE57vFPW9/wwsLC/nVd4/ntmmBu79e\nOpa1lme+2MIf3l/H2P5JPHNdNikhNEKvQmQbKUR+23trdvKfr68iNiqcv1w5PiSa6Ft7e9UOfvH6\nKo5LjGHOjGyG9NZDg7uDzSU1XPrkQhKiI3jjtsmk9ehaH/wl1Q088MF65i4ronePaO4893guGtuv\ny1/c6a427Krmj++vY96GEvolxTI9pz9ZqfEtrYo9YwN7gaOu0cOSLeVuqCxj3c49ACTGRDB5cCqn\nuKEyM0X3U4r/KusauWb2YjbuqmHWdV0/QDbz+Sx3vLqSt1ft4I8Xj+bKkwYEukjSzjxeH/e4j5A7\nd/RxPHT52JAbr0Ahso0UIvfxeH08+OEGnpq/mXEDkvjb1eND9tEYy7dWcPMLy2jweHn8qvFdYjQ4\nOXrFe+q5+Imv2Nvo5Y3bJpPZxe7rbW351gruffsbVhdVkT2wF/decELQPKdVjmz3nnoe+mgjry/b\nRnx0BLefMYTrJmUG/ZeR0poGvtpUxoK8EhbklbLDvZ+yX5J7P+XQVCYPTtFz8+SQWgfIp66bwOkh\nEiCbNXp8zHwhly/ySnj8qvEayC+E1DR4+PE/ljNvQwm3TB3Er845PujGUmgPIRUijTH9gReAdMAC\ns6y1jxpjkoFXgUygALjcWlthnMuhjwLnAnXADGvt8sMdQyHSUVbTwO0vr+CrTWVcffIAfvv9kUF9\nr1h72F65lxufW0pecQ33XnAC104cGOgiSQfYU9/E9KcWUVhWyys3TwyJR734fJbXcrfxpw83UFnX\nyJUnDeAXZw+nl7pnB63aBg9Pzd/M0/M34/H5uHZiJrefMaRL1pm1loKyOhbkl/JlXilfbSplj3s/\n5Yg+iVw2IYMrTupPXJS6XIujqq6Jq2cvCtkA2Wxvo5drZi9mTVEVz/4wh1P0zN8ub2fVXn74rPNd\n8X8uHMVVJ4duK3Oohcg+QB9r7XJjTA9gGXARMAMot9beb4z5NdDLWvsrY8y5wO04IfJk4FFr7cmH\nO4ZCJKzaVsltLy2jtLaR+y4axWXZ/QNdpE5T0+Dhpy+v4JP1xcyYnMlvzhuhYbpDSIPHy/VzlpBb\nUMGcGTkh1+JcVdfEw//eyIuLCukRE8HPzx7OVScN0D1rQcTj9fFq7jYe/jiP0poGzhvTh1+eM5yB\nKV23NfxAXp9tuZ/y3+t2s2JrJUlxkVw3KZPrJw0MqXuGpO32C5DXTuD040MzQDarqmvi8qcWUlRR\nxz9mTuTE/l3/wmV39fX2Km58fim1DV4ev3p8lxxBuC1CKkR+qwDGvAX81f2ZZq3d6QbNedba4caY\np9zpl931NzSvd6h9dvcQ+fKSrdzz1jek9YjmyWsmMDqj+3WL8/osf3xvHc8s2MK04Wn85cpxXWLA\nFTk8n89y+8sreHfNTh6ZPpaLxvULdJE6zPpde7jnrW9YvKWckX0S+e8LTyAnMznQxerWrLV8ur6Y\nP76/nvziGrIH9uK/zhvB+AG9Al20DressIKnPt/ER2t3ExMZxuXZ/bnp1EEMSIkLdNGkk1XVNXHN\n7MVs2FXdLQJks9176rn0ya+oqffw+q2TQ2b0zu7ks/XF/Ogfy+kZG8mcGTmM6JMY6CJ1uJANkcaY\nTGA+MArYaq1NcucboMJam2SMeQe431q7wF32CfAra+0hU2J3DJHN97a8v2Yn73+9i9OGpvLYFeO6\nZLeq9vSPxVv57VtfMygtntnX59A/WV94uiprLf/9r7U891UBd507gplTBgW6SB3OWss7q3fyh/fW\nsbOqnovG9uXOc0eQnhgT6KJ1O2uKqrjvvbUs2lxOVmo8v/ru8ZxzQvd7zmd+cQ1Pz9/MmyuK8Pos\n543pyy1TBuke3m6idYB88trxnHF8eqCL1KkKSmu59MmFRIYb5t42mX5JoTnGRCh6cWEB97z9DSP6\nJDJnRk63+TsakiHSGJMAfA7cZ6190xhT2Rwi3eUV1tpe/oZIY8zNwM0AAwYMmFBYWNhpv0sgHG6U\nvRmTM/npWcPU/c31ZX4pt720jMjwMGZdN4EJA9Wa0xU9/lk+D364gZmnZXHXeSMDXZxOVdfo4fHP\n8nl6/hYiww23nzmUG07JIipC3bQ7WlFFHX/+cAP/t3IHyfFR3HHWUK48aQCR3byL/O499cz5cgv/\nWLSV6gYPpw1N5ZYpgzllSEq3C9bdRdXeJq6dvZj1O7tngGy2dsceps9aSFqPaF6/ZZK6dge51j3T\nzjy+N49dOa7LPh/9aIRciDTGRALvAB9aax9y57V0U1V31m878HlfywsrafT69LwvP20qqeHG55ay\no7KeP106JqS7QYai13O38Z9zV3PR2L48dPnYkBxBzR8FpbX8zztr+WR9MYNS4/nt90eGzHD6waZq\nbxN/+yyfZ78qwAA3nprFrdMGk6hu8fvZU9/EPxZvZc6CLRRXNzC6X09umTqI755wnO5FDyGtA+QT\n14znzBHdM0A2W7KlnGtnL2ZYeg9evnminvEbZKrrm1haUM7CTWXM31jKht3VXD9pIL/9/gnd7jty\nSIVIt6vq8ziD6NzRav6DQFmrgXWSrbW/NMacB/yYfQPrPGatPelwxwiFEGmtZUtpLV/ml/JFXikL\nN5dR7Y6Ud0LfRGf49SGp5GQmExsV2iOutpeK2kZufWkZi7eU85MzhnDHWcO6bRjpSj5dv5uZLyxj\n8uAUZl+fo9Y3nPs6/vtf31BQVsdZI9L57fkjdW9aO2n0+HhpUSGPfZpH1d4mLh6Xwc/PHkZfdVs7\nrAaPl/9bsZ2n5m9mc0ktA5LjmDllEJdNyAj6R53I4TUHyHU79/DkNRO6fYBs9sm63dz84jJOzkpm\nzowc/TsPoNoGD0sLylm0uZyFm8v4ensVXp8lKjyMsQOSuHR8BpfndJ8BJlsLtRB5KvAFsAbwubP/\nC1gMvAYMAApxHvFR7obOvwLfxXnExw8Pdz8kdN0QWVLdwFebSlmQV8qX+fs/s+u0oU5onDw4RV0n\njkGjx8dv/m8Nr+UWcd6YPvzvZSfqgz+ILd9awVVPL2Job13tPVCDx8vsBVv466f5NHl9nJyVwpRh\nqUwd1pth6QnqUthG1lreW7OLP324nsKyOk4dksqd5x7PCX11r19b+HyWj9ft5snPN7FiayUp8VHM\nmJzJtZMGkhTXve/R74qq9jZx3ezFrFWAPKg3lxfxs9dWcc4J6Tx+1Xi1vneSvY1elhVWsHBzKQs3\nlbG6qAqPzxIRZjixfxKTBqUwaXAK4wf06vYNLSEVIjtDVwmRdY0eFm8p58s8p4vq+l3VAPSMjWTy\n4JSWLqoDkuP0hbAdWWuZNX8z93+wnjEZSfz1ynEacCcI5RfXcOmTX9EzNpI3bpusB54fwq6qemYv\n2My8DSXkFdcAcFxiDFOGpTJlWBqnDknVl/cjWFZYzn3vrmP51kqGp/fgznOPZ+qwNH3uHgNrLUsL\nKnjy8018ur6YuKhwrsgZwI2nZXXaYCRen2V7xV62lNWypaSGgrI6iqvrSUuIpk9SLH16xtA3KZa+\nSbGk94hWAHBZayksq2P19iqe+WIz63bu4YmrJ3DWSAXIg5mzYAu/e2ct07P7c/8lo/W50QHqm7ys\n2FrJws1lLNpUxsptzi1d4WGG0f16MtENjdkDe3Wr+x39oRDZRsEaIj1eH6u3V7WExuVbK2jyWqIi\nwsjJ7MUpQ5zQeEJf3dfYGT78Zhd3vLKSvU1e+ifHkjMwmezMZHIyezE4LUFdXQNo9556Lv7bVzR4\nvLxx2+SQev5eR9pRuZf5G0uYn1fCF3mlVNd7CDNwYv8kpgxNY+rwNE7MSArazxevz1Jd30RMZDjR\nEWEd/mVsS2ktf/pgPe9/vYvePaL5+dnDuHRC/6A9P13Vhl3VPDV/E2+v3AHABSf25eapgzj+uGMf\nXt/ns+zaU09Baa0bFmspKKtlS2ktW8vraPLu+44THxVOemIMJTUNLbeHNAsz0LtHDH2S3GDZM4Y+\nPWPpm9T8GktKfFTI/V2w1lJUsZfVRVWs2V7Fmu2VrCmqYo97fhKiI3hk+lgFyCP434828JdP87ll\nyiBumzaYiPAwIsIMkeFhhBmCPlhaa/FZaPL68PgsHq/TUTAhOiIgF1caPT5WFVWycFMZCzeVsXxr\nBQ0eH8bAqL49mTQ4hUmDUsjO7KXHtx2BQmQbBWOI/Cq/lFteWkZ1vQdjnPsam0Nj9kDd1xgohWW1\nfLx2N7kFFeQWllNa0whAUlwk2QN7tYTKUf16Eh2hOtpWXsemkhqsBZ+1La8+C+C8tp4P7nIfWJqX\nNS9330Orec4+Xs/dxrbyOl69ZZIeHXCUPF7nj/DnG0r4PK+U1UWVWOv0dDh1aCpTh6YxZVgax/Xs\n/GHOPV4fheV15O2uIW93NXnFNeQV17CppIZGj/PlJTzMEBcZTmxUOPHREcRGhhMfHU5sVARxkeHE\nRYcTFxVOXFSE+3rw6diocOJbTcdFRVC1t4nHPsnjpUWFREWEcevUwdx0WhZxUbqC3ZF2VO5l9oIt\nvLxkK3WNXk4fnsYtUwdzclbyYb9kW2sprWlkS2ntt8JiQVkt9U2+lnWjI8LITIknMzWOrNQEslLj\nyEyJJystnrSE6JbjVNc3sbOqnh2Ve1ted1TWs7Nq3/sGj2+/ckSFh3Fcz5hWLZjfDpqJMRFBGxis\nteysqncDY2VLcKysawIgMtwwok8io/v1ZExGT0b3S2JoekK3H4nYH9ZafvN/X/P3xVsPujwy3BAR\nFkZEuBMsmwNmRLghPMwQ6S6LCA8jMszst154WJizfatl1joX3ZrcwNfktXh8PjxeS5PXt98yj9fS\n5C7z+mxLUGxyl3l8vv0uthwoITqCxJgIEmMjSYyJJDG29XQkiTER9IyN3H95TCQ94yJJiIrw68JL\ncyPLwk1lLNpcRm5BBXubvACM6JPY0j31pKxkesYqNLaFQmQbBWOI3L2nnkf+vdG9rzGV5G7+/MZg\nZK2loKyOpQXl5BaUk1tQwebSWsD5YnJi/yRyMp1gOX5Ar27zQdbg8fLRN7t5ZelWvswv65RjxkWF\nM+vabE4dmtopx+sOymsbWZBfyucbnJbKkuoGAIan92Dq8DSmDE0jO7NXu94j3OjxUVhW64TE3TVs\nLK4mf3cNW0prafTu+4LeLymWoekJDO2dQHpiDA0eH3WNHmobvOxt9FLX5KWuwUPdAdN7m7zUNni+\n9WX/SIyBMGOYntOfO84aSu8e3eN5YcGiqq6JFxcV8OyXBZTVNjK2fxK3Th3ESVkpFLqtiE5YrGNL\naQ0FpXXUNOxrOYwIMwxIiSMrJZ7M1Hiy3J/M1Hj6JMa0S2uhtZby2sb9g2aVGzTd97v21OP17f8d\nKj4qnD5JsaQnRtO7RwxpPaJJS4imd6LzmtbDmZ8Y2/Fhs3iPExhXb69iTVEla7ZXtVwojQgzDEvv\n4YTFjJ6M6ZfEsOMSdLH0GHh9lndW76CsprElmLUOaZ7mVr6WsLdvumXeQYJfc8tg6+BnDE7oDHOC\nZkS4GzRbTYeH7Qud+wJo8zJn28gDlkU07yMsDAvU1HvYU99E1d4m9uxtYk99E3v27pt3YIv+gYyB\nHtFO6Ox5QMhMjI0kNjKcr3dUsXRLObWNTmgclp7QEhpPzkrp9s87P1YKkW0UjCFSuqaS6gaWFVaQ\nW1DO0oJyvt6xB6/P+QAfnt6DnMxksjN7kZOZHHKjN+YXV/Pykm28ubyIirom+iXFMj2nP6cMSSHM\nmJYfY/Z9KW95xem+09yNJ8ydDxAW5iwPc+dj2Lcvd350ZJgGPOpA1lrW76rm840lzN9YwtKCcpq8\nlpjIMCYNSmHKsDSmDksjKzXery+6DR4vBaV1bHRbFfOLq8lzw6LH/ZJtDPTvFcfQ3gkMSU9gaO8e\nDO2dwODeCcc8YJLXZ9nbOmg2eqlrPNi08+r1WS4c25chvXsc03Hl2NQ3eZm7rIinv9hMYVndfsvC\nDGT0inNCYkpcS0jMSo2nX1JsUNy/6PVZSqob2F6512nBrHSC5s7Keoqr6ymubqC4uqGldb21qPAw\nJ2D2aA6Wradj9i1LiPZrROrSmgbWuC2LzS2Nu/c4F4rCDAxL78Hofk5gHN2vJyP6JOozVo6Z12ep\nafCwZ68bNFuFzD0twdPTEkCdMLpveW2jl8Fp8S33NE4clKLxD9qZQmQbKURKR6lr9LByayVL3e6v\nywsrWq6e9UuKJTtzXxfYYb17dLn7Z/Y2enln9Q5eXbqN3MIKIsIMZ5+QzvScAZw6JFX3ioWo2gYP\nizaXtYTKAvcLfUavWKYOc7q9Th6cQmR4GJtLaslzQ2JesRMaC8vqWlpkwgwMSI5jaLoTEoe6gXFQ\nWry6i8pBeX2Wj9fuoqhir9sVNZ7+ybEh0SpmraW6wUPxngZKqhsorq6npLqBkpoGSva4r27YLK9t\nPOg+kuIi92vN7J0YQ1pCNI1eH6uLnHsYm0dzNwYGpyUwxg2MYzKcwKj/exKMfD7b5b4ndTUKkW2k\nECmdxeP1sX5XtdsFtoIlBeUt3QQTYyKY0HJfZTJjMnoG7ZXfr7dX8crSrby1YgfVDR4GpcYzPac/\nl0zI0FXBbqiwrJb5G0v4fGMpX20qpa7RS3iYaRl8AZx7FgemOC2Lw9J7MKT3vrAYrP/ORYJZk9dH\nWU1jS9AsrnYC5oHhs3hPQ0sX7qzU+Fb3MPbkhH499SgkEWmhENlGCpESKNZatpXvdUJlYTlLCyrI\ndx+7EBUexpiMni0tlRMG9grooxf21Dfx9sodvLJ0K19v30N0RBjnju7DFTn9OekIA11I99Ho8bGs\nsIIF+SWEh4UxzG1ZzEyNC4mWIpGuprl1EyBRI1OKyGEoRLaRQqQEk/LaRnILyllWWMHSgnLWbK9q\nGQltWHpCS6jMHphMRq/YDg1v1lqWb63g5SXbeHf1TvY2eTn+uB5cedIALhrbj55x+kIiIiIiEgoU\nIttIIVKC2d5GL6uKKt3BeipYXljRclX5uMSYloF6cjKTGX5cj3a5D7G8tpE3lxfx6tJt5BXXEB8V\nzgVj+zI9ZwAnZvRUq6OIiIhIiOnIEKmO8yKdLDYqnImDnFHIwBlUYsOu6pbur0u3lPPO6p2AMzT2\n+IG9Wh4tMrZ/kt/3m/l8loWby3h5yVY++mY3jV4fY/sncf/Fozn/xL66b0ZEREREjopaIkWCjLWW\n7ZV7yS2oaBmwZ8PuasB5YPGofj2dR4u4g/Yc+MzR4j31vL7MaXXcWl5HYkwEF4/PYHpOf0b0SQzE\nryQiIiIinUzdWdtIIVJCTWVdo3tPpfPMytVFVS0Pax+cFk9OZjIj+yYyf2Mpn20oxuuznJyVzJUn\nDeC7o47TaJkiIiIi3YxCZBspREqoq2/ysmZ7VUtLZW5BOXvqPaQmRHHJhAymZ/dnUFpCoIspIiIi\nIgGieyJFZD8xkeEtg++Ac//jtoo6+vSMJSoiLMClExEREZFQphApEgLCwgwDU+IDXQwRERER6QbU\nZCEiIiIiIiJ+U4gUERERERERvylEioiIiIiIiN8UIkVERERERMRvCpEiIiIiIiLiN4VIERERERER\n8ZtCpIiIiIiIiPhNIVJERERERET8phApIiIiIiIiflOIFBEREREREb8Za22gy9DujDElQGGgy3EQ\nqUBpoAsh36J6CT6qk+Ckegk+qpPgpHoJPqqT4KR6aZu2nq+B1tq0jihISIbIYGWMybXWZge6HLI/\n1UvwUZ0EJ9VL8FGdBCfVS/BRnQQn1UvbBNP5UndWERERERER8ZtCpIiIiIiIiPhNIbJzzQp0AeSg\nVC/BR3USnFQvwUd1EpxUL8FHdRKcVC9tEzTnS/dEioiIiIiIiN/UEikiIiIiIiJ+69Yh0hjT3xjz\nmTFmrTHmG2PMT935ycaYj40xee5rL3f+8caYhcaYBmPMLw7Y10+NMV+7+7njMMf8rjFmgzEm3xjz\n61bzzzTGLDfGrDTGLDDGDDnE9hOMMWvc7R8zxhh3/mXusX3GmKAYtelohVi93GuM2e5uv9IYc257\nnKPOFmJ1cqJbtjXGmH8ZYxLb4xwFQhetl/uMMduMMTUHzL/VrZPm7Ucey7kJlBCrk4dbfXZtNMZU\nHsu5CaSuVi/GmDhjzLvGmPXuce5vtWyKu73HGHNpe5yfQAixOplhjClp9f/lpvY4R50txOpkoDHm\nE2PMamPMPGNMRnuco4OUIZjO2RnuOfvaGPO8MSbiENtnGWMWu9u/aoyJcue37bPFWtttf4A+wHh3\nugewERgJ/An4tTv/18AD7nRvIAe4D/hFq/2MAr4G4oAI4N/AkIMcLxzYBAwCooBVwEh32UZghDv9\nH8BzhyjzEmAiYID3ge+580cAw4F5QHagz63qpaVe7m1dpq76E2J1shSY6k7fAPxPoM9vN6uXiW65\naw6Yn9hq+gLgg0Cf3+5eJwesczswJ9Dnt7vUi7v/093pKOAL9n2GZQJjgBeASwN9blUnFmAG8NdA\nn1PVyX518jpwvTt9BvBiKJ8znIbBbcAwd73fATceosyvAVe4008Ct7nTmbThs6Vbt0Raa3daa5e7\n09XAOqAfcCHwvLva88BF7jrF1tqlQNMBuxoBLLbW1llrPcDnwMUHOeRJQL61drO1thF4xT0WgAWa\nW0R6AjsO3NgY0wfni9Yi69T2C63Kts5au6Gt5yAYhVK9hIoQq5NhwHx3+mPgEv/OQvDpavXilmGR\ntXbnQebvafU23t1flxNKdXKAK4GXj7BO0Opq9eLu/zN3uhFYDmS47wustasBX9vOQnAJpToJFSFW\nJyOBT93pz1rtt10F0TlLARqttRvd9Q76/cYYY3BC9dyDlK1Nny3dOkS2ZozJBMYBi4H0Vn9QdwHp\nR9j8a+A0Y0yKMSYOOBfof5D1+uFcJWhW5M4DuAl4zxhTBFwL3M+39XO3Odj2ISlE6uXHbneKOc3d\nGbqyEKiTb9j3x+SyQxy/y+ki9XKk3+FHxphNOFdwf9LW7YNNKNQJON3CgCz2fSHr0rpavRhjkoDv\nA58coWxdVojUySXu3/q5xpgu/3clBOpkFftC2A+AHsaYlCOU+5gE+JyVAhFm3y1tlx5i+xSg0g2q\nrbdvM4VIwBiTALwB3HHA1XDcVozDXhG31q4DHgA+Aj4AVgLeNhbj/wHnWmszgGeBh9q4fcgJkXp5\nAhgMjAV2Av/bxu2DSojUyQ3AfxhjluF0PWls4/ZBJ0TqBWvt49bawcCvgN+0dftgEip14roCmGut\nbevxg05Xqxf3nqaXgcestZvbeJwuIUTq5F9AprV2DE4L0POH2r4rCJE6+QUw1RizApgKbD+KMvgt\n0OfMPcYVwMPGmCVAdVu2PxrdPkQaYyJxKv3v1to33dm73e5wzd3iio+0H2vtbGvtBGvtFKAC2Oje\nbNt8k/WtOP+AW18VyAC2G2PSgBOttYvd+a8Ck40x4a22/527fcaB2x/1Lx/EQqVerLW7rbVea60P\neBqnG0KXFEJ1st5ae7a1dgLOH51NR3VCgkQXqxd/vUIX7hIegnVyBV24K2uzLlovs4A8a+0jx/Cr\nB61QqRNrbZm1tsF9+wwwoY2nImiEUJ3ssNZebK0dB9zlzuuQwcGC4Zy52y+01p5mrT0J57adje7x\nP3S3fwYoA5LMvkF3jjpLHHTUnu7CGGOA2cA6a23rKxxvA9fjNJ1fD7zlx756W2uLjTEDcJrPJ7r/\nWMe2WlWbJqAAAAOpSURBVCcCGGqMycKpsCuAq3D+ofQ0xgyzTl/m77hl8rbe3t3HHmPMRJym8uuA\nvxzdbx+8QqlejDF9WnVn+AFOd4UuJ8TqpPn4YTitXU+2/YwEh65YL4c5/lBrbZ779jwg73DrB6tQ\nqhN3/8cDvYCF/m4TjLpivRhjfo9zL1iXHOnzSEKpTg74W38Bzn1xXU6I1UkqUO5exL8TmOP/mfBf\nEJ2z1ttH4/TouQ/AWnvOAcf5DKe76yv+lu2gbBCMBhWoH+BUnObl1TjNxitx+iCn4PSpzsMZHSnZ\nXf84nL7De4BKdzrRXfYFsBanD/aZhznmuThXBjYBd7Wa/wNgjbv9PGDQIbbPxgkim4C/AqbV9kVA\nA7Ab+DDQ51f1YgFedLdfjfOB0ifQ51d1wk/d/W7E+XA3gT6/3axe/uQe1+e+3uvOfxTnftWVOIMg\nnBDo89vd68Rddi9wf6DPa3erF5zWAYsTRprLe5O7LMctTy1Oq8I3gT6/qhP+iPP5tQrn8+v4QJ9f\n1QmXuuXdiNM6HN0NztmD7rnYgNOt9lDbD8IZwT4fZxTbaHd+mz5bmr9UiYiIiIiIiBxRt78nUkRE\nRERERPynECkiIiIiIiJ+U4gUERERERERvylEioiIiIiIiN8UIkVERERERMRvCpEiIiJHyRhzrzHm\nF4dZfpExZmRnlklERKSjKUSKiIh0nIsAhUgREQkpek6kiIhIGxhj7gKuB4qBbcAyoAq4GYjCeYDz\ntcBY4B13WRVwibuLx4E0oA6Yaa1d35nlFxEROVYKkSIiIn4yxkwAngNOBiKA5cCTwLPW2jJ3nd8D\nu621fzHGPAe8Y62d6y77BLjVWptnjDkZ+KO19ozO/01ERESOXkSgCyAiItKFnAb801pbB2CMedud\nP8oNj0lAAvDhgRsaYxKAycDrxpjm2dEdXmIREZF2phApIiJy7J4DLrLWrjLGzACmHWSdMKDSWju2\nE8slIiLS7jSwjoiIiP/mAxcZY2KNMT2A77vzewA7jTGRwNWt1q92l2Gt3QNsMcZcBmAcJ3Ze0UVE\nRNqHQqSIiIifrLXLgVeBVcD7wFJ30d3AYuBLoPVAOa8A/2mMWWGMGYwTMG80xqwCvgEu7Kyyi4iI\ntBcNrCMiIiIiIiJ+U0ukiIiIiIiI+E0hUkRERERERPymECkiIiIiIiJ+U4gUERERERERvylEioiI\niIiIiN8UIkVERERERMRvCpEiIiIiIiLiN4VIERERERER8dv/B8l3tc4/HbMuAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XLWatQbE2Ezz",
        "colab_type": "code",
        "outputId": "0aa0339a-92f9-4ae6-a952-18a77b537415",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        }
      },
      "source": [
        "trip_per_date = data_bike.groupby(['date', 'gender'], as_index=False)['usertype'] \\\n",
        "                        .count() \\\n",
        "                        .rename(columns={'usertype': 'total'})\n",
        "\n",
        "plt.figure(figsize=(15,5))\n",
        "viz = sns.lineplot(data=trip_per_date, x='date', y='total', hue='gender')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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EeokkkUFAp9eRsT2fjlP1so3P0qDzCfeI5guUlbvWjrONbP7oPp8689d1rpm5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\nrkJggg==\n",
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MogZNvOhqRFr",
        "colab_type": "text"
      },
      "source": [
        "#### Scatter Plot"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FqW-wsui0E6A",
        "colab_type": "code",
        "outputId": "159f2f74-4b26-4d96-e663-4d7322359aa5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        }
      },
      "source": [
        "viz = sns.scatterplot(data=data_bike, x='age', y='tripduration')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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3nJQELZSFbQ9qLmAIlARV1NdmhvxQSpkXxQYJnZ0exa1/UqGzIUv/3PRN5CsH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\nw0e78I2FDx8+fPhoF/8fRoSpNFjZT6oAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8FuiFWPt0Gyk",
        "colab_type": "code",
        "outputId": "9d6f127c-c3ca-48ab-8817-94029045d6a7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "data_bike.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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>tripduration</th>\n",
              "      <th>starttime</th>\n",
              "      <th>stoptime</th>\n",
              "      <th>start station name</th>\n",
              "      <th>end station name</th>\n",
              "      <th>bikeid</th>\n",
              "      <th>usertype</th>\n",
              "      <th>birth year</th>\n",
              "      <th>gender</th>\n",
              "      <th>age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>261</td>\n",
              "      <td>2019-08-01 00:14:55.9900</td>\n",
              "      <td>2019-08-01 00:19:17.4780</td>\n",
              "      <td>JC Medical Center</td>\n",
              "      <td>Liberty Light Rail</td>\n",
              "      <td>26268</td>\n",
              "      <td>Subscriber</td>\n",
              "      <td>1980</td>\n",
              "      <td>1</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>172</td>\n",
              "      <td>2019-08-01 00:23:06.9910</td>\n",
              "      <td>2019-08-01 00:25:59.1480</td>\n",
              "      <td>Dixon Mills</td>\n",
              "      <td>Grove St PATH</td>\n",
              "      <td>26162</td>\n",
              "      <td>Subscriber</td>\n",
              "      <td>1996</td>\n",
              "      <td>1</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>525</td>\n",
              "      <td>2019-08-01 00:23:28.6170</td>\n",
              "      <td>2019-08-01 00:32:13.7000</td>\n",
              "      <td>Newport Pkwy</td>\n",
              "      <td>Hamilton Park</td>\n",
              "      <td>29279</td>\n",
              "      <td>Subscriber</td>\n",
              "      <td>1991</td>\n",
              "      <td>1</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>219</td>\n",
              "      <td>2019-08-01 00:32:36.1410</td>\n",
              "      <td>2019-08-01 00:36:15.2730</td>\n",
              "      <td>Warren St</td>\n",
              "      <td>City Hall</td>\n",
              "      <td>29598</td>\n",
              "      <td>Subscriber</td>\n",
              "      <td>1988</td>\n",
              "      <td>1</td>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>262</td>\n",
              "      <td>2019-08-01 00:41:26.6700</td>\n",
              "      <td>2019-08-01 00:45:49.3530</td>\n",
              "      <td>Grove St PATH</td>\n",
              "      <td>Jersey &amp; 3rd</td>\n",
              "      <td>26162</td>\n",
              "      <td>Subscriber</td>\n",
              "      <td>1960</td>\n",
              "      <td>1</td>\n",
              "      <td>60</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   tripduration                 starttime  ... gender age\n",
              "0           261  2019-08-01 00:14:55.9900  ...      1  40\n",
              "1           172  2019-08-01 00:23:06.9910  ...      1  24\n",
              "2           525  2019-08-01 00:23:28.6170  ...      1  29\n",
              "3           219  2019-08-01 00:32:36.1410  ...      1  32\n",
              "4           262  2019-08-01 00:41:26.6700  ...      1  60\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z3sVxwoo0HYq",
        "colab_type": "text"
      },
      "source": [
        "# Final Project"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NMgVwoL50Hog",
        "colab_type": "text"
      },
      "source": [
        "## Analyze Rental Bike Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T3HIak1V_kCy",
        "colab_type": "text"
      },
      "source": [
        "Final project ini akan membahas ......."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TiP8Xmaf_zdZ",
        "colab_type": "text"
      },
      "source": [
        "### Questions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R1S7NS6-AFlC",
        "colab_type": "text"
      },
      "source": [
        "1. Dari station mana ke mana?\n",
        "2. Peminjaman tertinggi tgl brp?\n",
        "3. Rata2 durasi di rute favorit?\n",
        "4. Rata2 umur, rute favorit & non favorit?\n",
        "5. Tren total peminjaman tiap hari & tiap jamnya?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FlavwoTQCQhC",
        "colab_type": "text"
      },
      "source": [
        "### Wrangle"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u7jesA9ICODl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Import data\n",
        "data_bike = pd.read_csv('http://bit.ly/dwp-data-bike')\n",
        "\n",
        "# Add age column\n",
        "data_bike['age'] = 2020 - data_bike['birth year']\n",
        "\n",
        "# Add route column\n",
        "data_bike['route'] = data_bike['start station name'] + ' - ' + data_bike['end station name'] \n",
        "\n",
        "# Convert time\n",
        "data_bike['starttime'] = pd.to_datetime(data_bike['starttime'])\n",
        "\n",
        "# Add date column\n",
        "data_bike['date'] = data_bike['starttime'].dt.date\n",
        "\n",
        "# Add dayname and time(hour)\n",
        "data_bike['dayname'] = data_bike['starttime'].dt.strftime('%A')\n",
        "data_bike['dayname'] = pd.Categorical(data_bike['dayname'],\n",
        "                                      ordered=True,\n",
        "                                      categories=['Monday',\n",
        "                                                  'Tuesday',\n",
        "                                                  'Wednesday',\n",
        "                                                  'Thursday',\n",
        "                                                  'Friday',\n",
        "                                                  'Saturday',\n",
        "                                                  'Sunday'])\n",
        "data_bike['hour'] = data_bike['starttime'].dt.hour"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rtwSEu1ZFTQE",
        "colab_type": "code",
        "outputId": "e9b7769a-3c9d-46fa-cac9-510e4618966a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        }
      },
      "source": [
        "data_bike.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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>tripduration</th>\n",
              "      <th>starttime</th>\n",
              "      <th>stoptime</th>\n",
              "      <th>start station name</th>\n",
              "      <th>end station name</th>\n",
              "      <th>bikeid</th>\n",
              "      <th>usertype</th>\n",
              "      <th>birth year</th>\n",
              "      <th>gender</th>\n",
              "      <th>age</th>\n",
              "      <th>route</th>\n",
              "      <th>date</th>\n",
              "      <th>dayname</th>\n",
              "      <th>hour</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>261</td>\n",
              "      <td>2019-08-01 00:14:55.990</td>\n",
              "      <td>2019-08-01 00:19:17.4780</td>\n",
              "      <td>JC Medical Center</td>\n",
              "      <td>Liberty Light Rail</td>\n",
              "      <td>26268</td>\n",
              "      <td>Subscriber</td>\n",
              "      <td>1980</td>\n",
              "      <td>1</td>\n",
              "      <td>40</td>\n",
              "      <td>JC Medical Center - Liberty Light Rail</td>\n",
              "      <td>2019-08-01</td>\n",
              "      <td>Thursday</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>172</td>\n",
              "      <td>2019-08-01 00:23:06.991</td>\n",
              "      <td>2019-08-01 00:25:59.1480</td>\n",
              "      <td>Dixon Mills</td>\n",
              "      <td>Grove St PATH</td>\n",
              "      <td>26162</td>\n",
              "      <td>Subscriber</td>\n",
              "      <td>1996</td>\n",
              "      <td>1</td>\n",
              "      <td>24</td>\n",
              "      <td>Dixon Mills - Grove St PATH</td>\n",
              "      <td>2019-08-01</td>\n",
              "      <td>Thursday</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>525</td>\n",
              "      <td>2019-08-01 00:23:28.617</td>\n",
              "      <td>2019-08-01 00:32:13.7000</td>\n",
              "      <td>Newport Pkwy</td>\n",
              "      <td>Hamilton Park</td>\n",
              "      <td>29279</td>\n",
              "      <td>Subscriber</td>\n",
              "      <td>1991</td>\n",
              "      <td>1</td>\n",
              "      <td>29</td>\n",
              "      <td>Newport Pkwy - Hamilton Park</td>\n",
              "      <td>2019-08-01</td>\n",
              "      <td>Thursday</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>219</td>\n",
              "      <td>2019-08-01 00:32:36.141</td>\n",
              "      <td>2019-08-01 00:36:15.2730</td>\n",
              "      <td>Warren St</td>\n",
              "      <td>City Hall</td>\n",
              "      <td>29598</td>\n",
              "      <td>Subscriber</td>\n",
              "      <td>1988</td>\n",
              "      <td>1</td>\n",
              "      <td>32</td>\n",
              "      <td>Warren St - City Hall</td>\n",
              "      <td>2019-08-01</td>\n",
              "      <td>Thursday</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>262</td>\n",
              "      <td>2019-08-01 00:41:26.670</td>\n",
              "      <td>2019-08-01 00:45:49.3530</td>\n",
              "      <td>Grove St PATH</td>\n",
              "      <td>Jersey &amp; 3rd</td>\n",
              "      <td>26162</td>\n",
              "      <td>Subscriber</td>\n",
              "      <td>1960</td>\n",
              "      <td>1</td>\n",
              "      <td>60</td>\n",
              "      <td>Grove St PATH - Jersey &amp; 3rd</td>\n",
              "      <td>2019-08-01</td>\n",
              "      <td>Thursday</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   tripduration               starttime  ...   dayname hour\n",
              "0           261 2019-08-01 00:14:55.990  ...  Thursday    0\n",
              "1           172 2019-08-01 00:23:06.991  ...  Thursday    0\n",
              "2           525 2019-08-01 00:23:28.617  ...  Thursday    0\n",
              "3           219 2019-08-01 00:32:36.141  ...  Thursday    0\n",
              "4           262 2019-08-01 00:41:26.670  ...  Thursday    0\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L1FiOOiXCYTz",
        "colab_type": "text"
      },
      "source": [
        "### Explore"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NPcVG1g1C0Ig",
        "colab_type": "text"
      },
      "source": [
        "#### 1. Favourite Route"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kvVqi9EEC7CP",
        "colab_type": "code",
        "outputId": "b339c783-c23b-4b5e-d1c4-f291be9c90ac",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "favorite_route = data_bike.groupby('route', as_index=False)['usertype'] \\\n",
        "                        .count() \\\n",
        "                        .sort_values('usertype', ascending=False) \\\n",
        "                        .rename(columns={'usertype': 'total'}) \\\n",
        "                        .head()\n",
        "favorite_route\n",
        "\n",
        "#plt.figure(figsize=(15,5))\n",
        "#viz = sns.barplot(data=favorite_route, x='route', y='total')\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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>route</th>\n",
              "      <th>total</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>729</th>\n",
              "      <td>Hamilton Park - Grove St PATH</td>\n",
              "      <td>1006</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>681</th>\n",
              "      <td>Grove St PATH - Hamilton Park</td>\n",
              "      <td>722</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>697</th>\n",
              "      <td>Grove St PATH - Marin Light Rail</td>\n",
              "      <td>628</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1323</th>\n",
              "      <td>Marin Light Rail - Grove St PATH</td>\n",
              "      <td>590</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>170</th>\n",
              "      <td>Brunswick &amp; 6th - Grove St PATH</td>\n",
              "      <td>530</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                 route  total\n",
              "729      Hamilton Park - Grove St PATH   1006\n",
              "681      Grove St PATH - Hamilton Park    722\n",
              "697   Grove St PATH - Marin Light Rail    628\n",
              "1323  Marin Light Rail - Grove St PATH    590\n",
              "170    Brunswick & 6th - Grove St PATH    530"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rMyvk8WagJLA",
        "colab_type": "text"
      },
      "source": [
        "#### 2. Trips per Day"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fNaM8bwQHAOB",
        "colab_type": "code",
        "outputId": "fd4265d0-d8c8-4139-b500-b84ebbad8ebe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "trip_dayname = data_bike.groupby(['date','dayname'], as_index=False)['usertype'] \\\n",
        "                        .count() \\\n",
        "                        .sort_values('usertype', ascending=False) \\\n",
        "                        .rename(columns={'usertype': 'total'}) \\\n",
        "                        .head(10)\n",
        "trip_dayname['dayname'].value_counts()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Thursday     4\n",
              "Tuesday      2\n",
              "Friday       2\n",
              "Monday       1\n",
              "Wednesday    1\n",
              "Name: dayname, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rwf62rPag1aB",
        "colab_type": "text"
      },
      "source": [
        "#### 3. Avarage Tripduration"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dmd4620-aCxA",
        "colab_type": "code",
        "outputId": "33ec798e-6a62-4490-f88b-b144e6cbe4b5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        }
      },
      "source": [
        "# 1. groupby route, cari mean dari tripduration\n",
        "avg_tripduration = data_bike.groupby('route', as_index=False)['tripduration'].mean()\n",
        "\n",
        "# 2. Ambil rute yg favorit\n",
        "# count route nya\n",
        "# ambil top 10\n",
        "top_10_route = data_bike.groupby('route', as_index=False)['usertype'] \\\n",
        "                        .count() \\\n",
        "                        .sort_values('usertype', ascending=False) \\\n",
        "                        .rename(columns={'usertype': 'total'}) \\\n",
        "                        .head(10)\n",
        "\n",
        "# ambil nilai route nya\n",
        "routes = list(top_10_route['route'].values)\n",
        "\n",
        "# jadikan nilai route itu sebagai filter untuk df 1\n",
        "avg_tripduration[avg_tripduration['route'].isin(routes)]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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>route</th>\n",
              "      <th>tripduration</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>170</th>\n",
              "      <td>Brunswick &amp; 6th - Grove St PATH</td>\n",
              "      <td>362.515094</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>209</th>\n",
              "      <td>Brunswick St - Grove St PATH</td>\n",
              "      <td>284.954217</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>491</th>\n",
              "      <td>Dixon Mills - Grove St PATH</td>\n",
              "      <td>192.486486</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>681</th>\n",
              "      <td>Grove St PATH - Hamilton Park</td>\n",
              "      <td>316.189751</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>697</th>\n",
              "      <td>Grove St PATH - Marin Light Rail</td>\n",
              "      <td>289.402866</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>729</th>\n",
              "      <td>Hamilton Park - Grove St PATH</td>\n",
              "      <td>326.728628</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1047</th>\n",
              "      <td>Jersey &amp; 6th St - Grove St PATH</td>\n",
              "      <td>238.914530</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1323</th>\n",
              "      <td>Marin Light Rail - Grove St PATH</td>\n",
              "      <td>270.786441</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1395</th>\n",
              "      <td>McGinley Square - Sip Ave</td>\n",
              "      <td>243.854586</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1412</th>\n",
              "      <td>Monmouth and 6th - Grove St PATH</td>\n",
              "      <td>310.130864</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                 route  tripduration\n",
              "170    Brunswick & 6th - Grove St PATH    362.515094\n",
              "209       Brunswick St - Grove St PATH    284.954217\n",
              "491        Dixon Mills - Grove St PATH    192.486486\n",
              "681      Grove St PATH - Hamilton Park    316.189751\n",
              "697   Grove St PATH - Marin Light Rail    289.402866\n",
              "729      Hamilton Park - Grove St PATH    326.728628\n",
              "1047   Jersey & 6th St - Grove St PATH    238.914530\n",
              "1323  Marin Light Rail - Grove St PATH    270.786441\n",
              "1395         McGinley Square - Sip Ave    243.854586\n",
              "1412  Monmouth and 6th - Grove St PATH    310.130864"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lqMx1SE1ndOA",
        "colab_type": "text"
      },
      "source": [
        "#### 4. Avarage Age"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lsABz1K6nZHq",
        "colab_type": "code",
        "outputId": "36215288-a1f5-495e-fbda-e612910aa937",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "# ambil top 10\n",
        "# ambil nilai rutenya\n",
        "# filter data_bike dgn nilai rute yg telah diambil\n",
        "# cari rata2 age nya\n",
        "\n",
        "top_10_route = data_bike.groupby('route', as_index=False)['usertype'] \\\n",
        "                        .count() \\\n",
        "                        .sort_values('usertype', ascending=False) \\\n",
        "                        .rename(columns={'usertype': 'total'}) \\\n",
        "                        .head(10)\n",
        "\n",
        "routes = list(top_10_route['route'].values)\n",
        "\n",
        "# filter\n",
        "top_10_df = data_bike[data_bike['route'].isin(routes)]\n",
        "exclude_top_10_df = data_bike[~data_bike['route'].isin(routes)]\n",
        "\n",
        "print('Rata-rata umur top 10 routes: ' + str(int(top_10_df['age'].mean())) + ' tahun')\n",
        "print('Rata-rata umur non top 10 routes: ' + str(int(exclude_top_10_df['age'].mean())) + ' tahun')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Rata-rata umur top 10 routes: 37 tahun\n",
            "Rata-rata umur non top 10 routes: 38 tahun\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VD8ihtmkuK_5",
        "colab_type": "text"
      },
      "source": [
        "#### 5. Trip per Hour per Day"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QdrzpITOueSe",
        "colab_type": "code",
        "outputId": "e88040b7-0455-47a9-8248-40d1c459ef02",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 608
        }
      },
      "source": [
        "trip_dayname1 = data_bike.groupby(['dayname','hour'], as_index=False)['usertype'] \\\n",
        "                        .count() \\\n",
        "                        .rename(columns={'usertype': 'total'})\n",
        "#trip_dayname1\n",
        "#trip_dayname1['dayname'].value_counts()\n",
        "\n",
        "plt.figure(figsize=(20,10))\n",
        "viz = sns.barplot(data=trip_dayname1, x='dayname', y='total', hue='hour')\n",
        "viz.legend_.remove()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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F3AOmqqokb0ny+dbab09suibJ85P8xvDzXRP186rqnVlY3Pub1l8CYB7O3H71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\n0khsYJIkSZIkSdJIbGCSJEmSJEnSSP4DeaAtKsSBDFgAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y3hIw1ifaDM9",
        "colab_type": "text"
      },
      "source": [
        "### Conclution"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1DJoQzIraIxn",
        "colab_type": "text"
      },
      "source": [
        "1. Dilihat dari top 5 rute favorit, terlihat bahwa Grove St PATH adalah stasiun tersibuk baik stausiun asal maupun tujuan, oleh karena itu ada baiknya jika manajemen selalu memastikan stok sepeda di stasiun tersebut terpenuhi."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "To77pBwdaMp8",
        "colab_type": "text"
      },
      "source": [
        "2. Hari dengan total peminjaman tertinggi jatuh pada hari kamis tanggal 1 Agustus 2018 dengan total 1784 peminjaman, dan diketahui juga bahwa dari 10 hari pertama terdapat 4 kali kemunculan hari kamis."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BLA6lRD3gExA",
        "colab_type": "text"
      },
      "source": [
        "3. Rata-rata tripduration pada rute favorit berkisar antara 192 detik (3 menit) - 362 detik (6 menit)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fw90hPNgnPtu",
        "colab_type": "text"
      },
      "source": [
        "4. Rata-rata umur di 10 rute favorit dan non favorit adalah 37 dan 38 tahun"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bB38YsPW28Bt",
        "colab_type": "text"
      },
      "source": [
        "5. Dilihat dari visualisasi peminjaman di tiap jam-nya, diketahui bahwa peak hours terjadi pada jam 6-8 pagi dan 5-7 malam. Karena diketahui pengguna sepeda pada weekdays adalah pekerja kantoran, maka diperlukan stasiun tambahan di area perkantoran New York."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jnPINvl_3qXM",
        "colab_type": "text"
      },
      "source": [
        "## PandaSQL"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HV-V79Fv33WB",
        "colab_type": "code",
        "outputId": "7f418a7d-25e0-4ef8-b1e6-707b42e41a60",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        }
      },
      "source": [
        "!pip install pandasql"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting pandasql\n",
            "  Downloading https://files.pythonhosted.org/packages/6b/c4/ee4096ffa2eeeca0c749b26f0371bd26aa5c8b611c43de99a4f86d3de0a7/pandasql-0.7.3.tar.gz\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from pandasql) (1.17.5)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.6/dist-packages (from pandasql) (0.25.3)\n",
            "Requirement already satisfied: sqlalchemy in /usr/local/lib/python3.6/dist-packages (from pandasql) (1.3.13)\n",
            "Requirement already satisfied: python-dateutil>=2.6.1 in /usr/local/lib/python3.6/dist-packages (from pandas->pandasql) (2.6.1)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas->pandasql) (2018.9)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil>=2.6.1->pandas->pandasql) (1.12.0)\n",
            "Building wheels for collected packages: pandasql\n",
            "  Building wheel for pandasql (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pandasql: filename=pandasql-0.7.3-cp36-none-any.whl size=26819 sha256=518c268bde34000a106f2b1572364c93a5bba81577aae7b91947b41c2bb9b395\n",
            "  Stored in directory: /root/.cache/pip/wheels/53/6c/18/b87a2e5fa8a82e9c026311de56210b8d1c01846e18a9607fc9\n",
            "Successfully built pandasql\n",
            "Installing collected packages: pandasql\n",
            "Successfully installed pandasql-0.7.3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IhD10q033trg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandasql"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ItjDRINJ3v3z",
        "colab_type": "code",
        "outputId": "52c6aa14-46b6-4aa3-aa89-71a90711e2f8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "source": [
        "query = \"\"\"\n",
        "        SELECT\n",
        "            tripduration,\n",
        "            age\n",
        "        FROM\n",
        "            data_bike\n",
        "        \"\"\"\n",
        "\n",
        "result = pandasql.sqldf(query)\n",
        "result"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>tripduration</th>\n",
              "      <th>age</th>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>46229</th>\n",
              "      <td>690</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>46230 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       tripduration  age\n",
              "0               261   40\n",
              "1               172   24\n",
              "2               525   29\n",
              "3               219   32\n",
              "4               262   60\n",
              "...             ...  ...\n",
              "46225           384   32\n",
              "46226           633   23\n",
              "46227           324   23\n",
              "46228           126   22\n",
              "46229           690   28\n",
              "\n",
              "[46230 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "28d1cDe15Jkt",
        "colab_type": "code",
        "outputId": "1881a67f-25ee-4fea-e7ff-d51af3d92cef",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "source": [
        "query = \"\"\"\n",
        "        SELECT\n",
        "            tripduration,\n",
        "            age\n",
        "        FROM\n",
        "            data_bike\n",
        "        WHERE\n",
        "            age > 20\n",
        "            AND tripduration > 300\n",
        "        \"\"\"\n",
        "\n",
        "result = pandasql.sqldf(query)\n",
        "result"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tripduration</th>\n",
              "      <th>age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <td>35</td>\n",
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              "      <th>3</th>\n",
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              "      <td>42</td>\n",
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              "      <th>4</th>\n",
              "      <td>646</td>\n",
              "      <td>33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27659</th>\n",
              "      <td>1543</td>\n",
              "      <td>43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27660</th>\n",
              "      <td>384</td>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27661</th>\n",
              "      <td>633</td>\n",
              "      <td>23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27662</th>\n",
              "      <td>324</td>\n",
              "      <td>23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27663</th>\n",
              "      <td>690</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>27664 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       tripduration  age\n",
              "0               525   29\n",
              "1               820   39\n",
              "2               543   35\n",
              "3               478   42\n",
              "4               646   33\n",
              "...             ...  ...\n",
              "27659          1543   43\n",
              "27660           384   32\n",
              "27661           633   23\n",
              "27662           324   23\n",
              "27663           690   28\n",
              "\n",
              "[27664 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R3DY8vHn6i3J",
        "colab_type": "code",
        "outputId": "7415059e-1326-4c1a-f25e-d4a9020175c5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "source": [
        "query = \"\"\"\n",
        "        SELECT\n",
        "            tripduration,\n",
        "            age\n",
        "        FROM\n",
        "            data_bike\n",
        "        WHERE\n",
        "            age > 20\n",
        "            OR tripduration > 300\n",
        "        ORDER BY\n",
        "            age DESC\n",
        "        \"\"\"\n",
        "\n",
        "result = pandasql.sqldf(query)\n",
        "result"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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>tripduration</th>\n",
              "      <th>age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>224</td>\n",
              "      <td>70</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>607</td>\n",
              "      <td>70</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>860</td>\n",
              "      <td>70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>609</td>\n",
              "      <td>70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>117</td>\n",
              "      <td>69</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46203</th>\n",
              "      <td>516</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46204</th>\n",
              "      <td>497</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46205</th>\n",
              "      <td>639</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46206</th>\n",
              "      <td>491</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46207</th>\n",
              "      <td>788</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>46208 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       tripduration  age\n",
              "0               224   70\n",
              "1               607   70\n",
              "2               860   70\n",
              "3               609   70\n",
              "4               117   69\n",
              "...             ...  ...\n",
              "46203           516   17\n",
              "46204           497   17\n",
              "46205           639   17\n",
              "46206           491   17\n",
              "46207           788   17\n",
              "\n",
              "[46208 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DsgQ-Aa97Bxf",
        "colab_type": "code",
        "outputId": "ab72170a-c674-40d8-8384-84a3549ea761",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "query = \"\"\"\n",
        "        SELECT\n",
        "            tripduration,\n",
        "            age\n",
        "        FROM\n",
        "            data_bike\n",
        "        WHERE\n",
        "            age > 20\n",
        "            OR tripduration > 300\n",
        "        ORDER BY\n",
        "            age DESC\n",
        "        LIMIT\n",
        "            5\n",
        "        \"\"\"\n",
        "\n",
        "result = pandasql.sqldf(query)\n",
        "result"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\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>tripduration</th>\n",
              "      <th>age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>224</td>\n",
              "      <td>70</td>\n",
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              "      <th>1</th>\n",
              "      <td>607</td>\n",
              "      <td>70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>860</td>\n",
              "      <td>70</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>609</td>\n",
              "      <td>70</td>\n",
              "    </tr>\n",
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              "      <th>4</th>\n",
              "      <td>117</td>\n",
              "      <td>69</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   tripduration  age\n",
              "0           224   70\n",
              "1           607   70\n",
              "2           860   70\n",
              "3           609   70\n",
              "4           117   69"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lKQPtV2z7RII",
        "colab_type": "code",
        "outputId": "bd2a49b1-1168-4c51-d7d7-5a6bf8c3124f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "query = \"\"\"\n",
        "        SELECT\n",
        "            age,\n",
        "            COUNT(*) total\n",
        "        FROM\n",
        "            data_bike\n",
        "        GROUP BY\n",
        "            age\n",
        "        \"\"\"\n",
        "\n",
        "result = pandasql.sqldf(query)\n",
        "result"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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>age</th>\n",
              "      <th>total</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>17</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>18</td>\n",
              "      <td>43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>19</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>20</td>\n",
              "      <td>72</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>21</td>\n",
              "      <td>50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>22</td>\n",
              "      <td>388</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>23</td>\n",
              "      <td>298</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>24</td>\n",
              "      <td>639</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>25</td>\n",
              "      <td>830</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>26</td>\n",
              "      <td>1301</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>27</td>\n",
              "      <td>1802</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>28</td>\n",
              "      <td>1701</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>29</td>\n",
              "      <td>2101</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>30</td>\n",
              "      <td>2045</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>31</td>\n",
              "      <td>2449</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>32</td>\n",
              "      <td>2506</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>33</td>\n",
              "      <td>2489</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>34</td>\n",
              "      <td>2004</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>35</td>\n",
              "      <td>1723</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>36</td>\n",
              "      <td>1995</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>37</td>\n",
              "      <td>2121</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>38</td>\n",
              "      <td>1617</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>39</td>\n",
              "      <td>1426</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>40</td>\n",
              "      <td>1328</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>41</td>\n",
              "      <td>1031</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>42</td>\n",
              "      <td>948</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>43</td>\n",
              "      <td>1105</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>44</td>\n",
              "      <td>869</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>45</td>\n",
              "      <td>692</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>46</td>\n",
              "      <td>540</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>47</td>\n",
              "      <td>547</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>48</td>\n",
              "      <td>435</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>49</td>\n",
              "      <td>519</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>50</td>\n",
              "      <td>789</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>51</td>\n",
              "      <td>3460</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>52</td>\n",
              "      <td>548</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>53</td>\n",
              "      <td>426</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>54</td>\n",
              "      <td>326</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>55</td>\n",
              "      <td>231</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>56</td>\n",
              "      <td>386</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>57</td>\n",
              "      <td>481</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>58</td>\n",
              "      <td>397</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>59</td>\n",
              "      <td>138</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>60</td>\n",
              "      <td>263</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>61</td>\n",
              "      <td>199</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>62</td>\n",
              "      <td>290</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>63</td>\n",
              "      <td>61</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>64</td>\n",
              "      <td>215</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>65</td>\n",
              "      <td>44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>66</td>\n",
              "      <td>160</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>67</td>\n",
              "      <td>95</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>68</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>69</td>\n",
              "      <td>45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>70</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    age  total\n",
              "0    17      8\n",
              "1    18     43\n",
              "2    19     21\n",
              "3    20     72\n",
              "4    21     50\n",
              "5    22    388\n",
              "6    23    298\n",
              "7    24    639\n",
              "8    25    830\n",
              "9    26   1301\n",
              "10   27   1802\n",
              "11   28   1701\n",
              "12   29   2101\n",
              "13   30   2045\n",
              "14   31   2449\n",
              "15   32   2506\n",
              "16   33   2489\n",
              "17   34   2004\n",
              "18   35   1723\n",
              "19   36   1995\n",
              "20   37   2121\n",
              "21   38   1617\n",
              "22   39   1426\n",
              "23   40   1328\n",
              "24   41   1031\n",
              "25   42    948\n",
              "26   43   1105\n",
              "27   44    869\n",
              "28   45    692\n",
              "29   46    540\n",
              "30   47    547\n",
              "31   48    435\n",
              "32   49    519\n",
              "33   50    789\n",
              "34   51   3460\n",
              "35   52    548\n",
              "36   53    426\n",
              "37   54    326\n",
              "38   55    231\n",
              "39   56    386\n",
              "40   57    481\n",
              "41   58    397\n",
              "42   59    138\n",
              "43   60    263\n",
              "44   61    199\n",
              "45   62    290\n",
              "46   63     61\n",
              "47   64    215\n",
              "48   65     44\n",
              "49   66    160\n",
              "50   67     95\n",
              "51   68     29\n",
              "52   69     45\n",
              "53   70      4"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4N6Rwj4q8EnO",
        "colab_type": "code",
        "outputId": "8470023d-8d1f-4b0f-8c5e-7e0cb640f7b9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "source": [
        "query = \"\"\"\n",
        "        SELECT\n",
        "            dayname AS day,\n",
        "            hour,\n",
        "            COUNT(*) AS total,\n",
        "            AVG(tripduration) AS average_tripduration\n",
        "        FROM\n",
        "            data_bike\n",
        "        GROUP BY\n",
        "            dayname,\n",
        "            hour\n",
        "        \"\"\"\n",
        "\n",
        "result = pandasql.sqldf(query)\n",
        "result"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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>day</th>\n",
              "      <th>hour</th>\n",
              "      <th>total</th>\n",
              "      <th>average_tripduration</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Friday</td>\n",
              "      <td>0</td>\n",
              "      <td>64</td>\n",
              "      <td>553.937500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Friday</td>\n",
              "      <td>1</td>\n",
              "      <td>26</td>\n",
              "      <td>590.384615</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Friday</td>\n",
              "      <td>2</td>\n",
              "      <td>12</td>\n",
              "      <td>458.083333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Friday</td>\n",
              "      <td>3</td>\n",
              "      <td>11</td>\n",
              "      <td>464.090909</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Friday</td>\n",
              "      <td>4</td>\n",
              "      <td>11</td>\n",
              "      <td>291.636364</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>163</th>\n",
              "      <td>Wednesday</td>\n",
              "      <td>19</td>\n",
              "      <td>457</td>\n",
              "      <td>443.555799</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>164</th>\n",
              "      <td>Wednesday</td>\n",
              "      <td>20</td>\n",
              "      <td>286</td>\n",
              "      <td>457.926573</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>165</th>\n",
              "      <td>Wednesday</td>\n",
              "      <td>21</td>\n",
              "      <td>218</td>\n",
              "      <td>519.004587</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>166</th>\n",
              "      <td>Wednesday</td>\n",
              "      <td>22</td>\n",
              "      <td>124</td>\n",
              "      <td>490.822581</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>167</th>\n",
              "      <td>Wednesday</td>\n",
              "      <td>23</td>\n",
              "      <td>68</td>\n",
              "      <td>597.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>168 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "           day  hour  total  average_tripduration\n",
              "0       Friday     0     64            553.937500\n",
              "1       Friday     1     26            590.384615\n",
              "2       Friday     2     12            458.083333\n",
              "3       Friday     3     11            464.090909\n",
              "4       Friday     4     11            291.636364\n",
              "..         ...   ...    ...                   ...\n",
              "163  Wednesday    19    457            443.555799\n",
              "164  Wednesday    20    286            457.926573\n",
              "165  Wednesday    21    218            519.004587\n",
              "166  Wednesday    22    124            490.822581\n",
              "167  Wednesday    23     68            597.000000\n",
              "\n",
              "[168 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GGcFVNOn87Vb",
        "colab_type": "code",
        "outputId": "338714b5-e465-47b6-f172-c54ab8d8f944",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        }
      },
      "source": [
        "# mencari total trip di tiap jam dan dan tiap umur tetapi hanya umur 21\n",
        "query = \"\"\"\n",
        "        SELECT\n",
        "            age,\n",
        "            hour,\n",
        "            COUNT(*) AS total_trips\n",
        "        FROM\n",
        "            data_bike\n",
        "        WHERE\n",
        "            age = 21\n",
        "        GROUP BY\n",
        "            age,\n",
        "            hour\n",
        "        \"\"\"\n",
        "\n",
        "result = pandasql.sqldf(query)\n",
        "plt.figure(figsize=(7,3))\n",
        "viz = sns.barplot(data=result, x='age', y='total_trips', hue='hour')\n",
        "viz.legend_.remove()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAbEAAADQCAYAAACJD1/6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nUrMMMUlSswwxSVKz/hck1C9hRNIj+gAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 504x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yz_O4sz_-wh1",
        "colab_type": "code",
        "outputId": "32c98994-d6f7-4b0a-d5a0-4dfbd91b6d15",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        }
      },
      "source": [
        "# mencari total trip di tiap jam dan dan tiap umur tetapi hanya umur 21\n",
        "query = \"\"\"\n",
        "        SELECT\n",
        "            age,\n",
        "            hour,\n",
        "            COUNT(*) AS total_trips\n",
        "        FROM\n",
        "            data_bike\n",
        "        WHERE\n",
        "            age IN (25, 30, 35, 40, 45)\n",
        "        GROUP BY\n",
        "            age,\n",
        "            hour\n",
        "        \"\"\"\n",
        "\n",
        "result = pandasql.sqldf(query)\n",
        "plt.figure(figsize=(15,4))\n",
        "viz = sns.barplot(data=result, x='age', y='total_trips', hue='hour')\n",
        "viz.legend_.remove()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1080x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dy5SuhuYAhEg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}