Visualize_data_with_Matplotlib.ipynb

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            "--2023-03-21 21:48:23--  https://raw.githubusercontent.com/MicrosoftDocs/mslearn-introduction-to-machine-learning/main/Data/ml-basics/grades.csv\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.109.133, 185.199.110.133, 185.199.111.133, ...\n",
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            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 322 [text/plain]\n",
            "Saving to: ‘grades.csv’\n",
            "\n",
            "\rgrades.csv            0%[                    ]       0  --.-KB/s               \rgrades.csv          100%[===================>]     322  --.-KB/s    in 0s      \n",
            "\n",
            "2023-03-21 21:48:23 (11.8 MB/s) - ‘grades.csv’ saved [322/322]\n",
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              "         Name  StudyHours  Grade   Pass\n",
              "0         Dan       10.00   50.0  False\n",
              "1       Joann       11.50   50.0  False\n",
              "2       Pedro        9.00   47.0  False\n",
              "3       Rosie       16.00   97.0   True\n",
              "4       Ethan        9.25   49.0  False\n",
              "5       Vicky        1.00    3.0  False\n",
              "6    Frederic       11.50   53.0  False\n",
              "7      Jimmie        9.00   42.0  False\n",
              "8      Rhonda        8.50   26.0  False\n",
              "9    Giovanni       14.50   74.0   True\n",
              "10  Francesca       15.50   82.0   True\n",
              "11      Rajab       13.75   62.0   True\n",
              "12    Naiyana        9.00   37.0  False\n",
              "13       Kian        8.00   15.0  False\n",
              "14      Jenny       15.50   70.0   True\n",
              "15     Jakeem        8.00   27.0  False\n",
              "16     Helena        9.00   36.0  False\n",
              "17      Ismat        6.00   35.0  False\n",
              "18      Anila       10.00   48.0  False\n",
              "19       Skye       12.00   52.0  False\n",
              "20     Daniel       12.50   63.0   True\n",
              "21      Aisha       12.00   64.0   True"
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              "      <th>9</th>\n",
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              "      <th>11</th>\n",
              "      <td>Rajab</td>\n",
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              "      <td>62.0</td>\n",
              "      <td>True</td>\n",
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              "      <th>12</th>\n",
              "      <td>Naiyana</td>\n",
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              "      <td>False</td>\n",
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              "      <th>13</th>\n",
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              "      <td>8.00</td>\n",
              "      <td>15.0</td>\n",
              "      <td>False</td>\n",
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      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Load data from a text file\n",
        "!wget https://raw.githubusercontent.com/MicrosoftDocs/mslearn-introduction-to-machine-learning/main/Data/ml-basics/grades.csv\n",
        "df_students = pd.read_csv('grades.csv',delimiter=',',header='infer')\n",
        "\n",
        "# Remove any rows with missing data\n",
        "df_students = df_students.dropna(axis=0, how='any')\n",
        "\n",
        "# Calculate who passed, assuming '60' is the grade needed to pass\n",
        "passes  = pd.Series(df_students['Grade'] >= 60)\n",
        "\n",
        "# Save who passed to the Pandas dataframe\n",
        "df_students = pd.concat([df_students, passes.rename(\"Pass\")], axis=1)\n",
        "\n",
        "\n",
        "# Print the result out into this notebook\n",
        "df_students "
      ]
    },
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      "source": [
        "# Ensure plots are displayed inline in the notebook\n",
        "%matplotlib inline\n",
        "\n",
        "from matplotlib import pyplot as plt\n",
        "\n",
        "# Create a bar plot of name vs grade\n",
        "plt.bar(x=df_students.Name, height=df_students.Grade)\n",
        "\n",
        "# Display the plot\n",
        "plt.show()"
      ],
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              "<Figure size 432x288 with 1 Axes>"
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            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a bar plot of name vs grade\n",
        "plt.bar(x=df_students.Name, height=df_students.Grade, color='orange')\n",
        "\n",
        "# Customize the chart\n",
        "plt.title('Student Grades')\n",
        "plt.xlabel('Student')\n",
        "plt.ylabel('Grade')\n",
        "plt.grid(color='#95a5a6', linestyle='--', linewidth=2, axis='y', alpha=0.7)\n",
        "plt.xticks(rotation=90)\n",
        "\n",
        "# Display the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "id": "5NNn87-RJYaz",
        "outputId": "3c37b712-8eae-4f1c-8ebf-fffe80e7aa8f"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a Figure\n",
        "fig = plt.figure(figsize=(8,3))\n",
        "\n",
        "# Create a bar plot of name vs grade\n",
        "plt.bar(x=df_students.Name, height=df_students.Grade, color='orange')\n",
        "\n",
        "# Customize the chart\n",
        "plt.title('Student Grades')\n",
        "plt.xlabel('Student')\n",
        "plt.ylabel('Grade')\n",
        "plt.grid(color='#95a5a6', linestyle='--', linewidth=2, axis='y', alpha=0.7)\n",
        "plt.xticks(rotation=90)\n",
        "\n",
        "# Show the figure\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "6XL57G0uJa-0",
        "outputId": "689b7e71-d516-4efb-c767-ab89f3b56c70"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x216 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a figure for 2 subplots (1 row, 2 columns)\n",
        "fig, ax = plt.subplots(1, 2, figsize = (10,4))\n",
        "\n",
        "# Create a bar plot of name vs grade on the first axis\n",
        "ax[0].bar(x=df_students.Name, height=df_students.Grade, color='orange')\n",
        "ax[0].set_title('Grades')\n",
        "ax[0].set_xticklabels(df_students.Name, rotation=90)\n",
        "\n",
        "# Create a pie chart of pass counts on the second axis\n",
        "pass_counts = df_students['Pass'].value_counts()\n",
        "ax[1].pie(pass_counts, labels=pass_counts)\n",
        "ax[1].set_title('Passing Grades')\n",
        "ax[1].legend(pass_counts.keys().tolist())\n",
        "\n",
        "# Add a title to the Figure\n",
        "fig.suptitle('Student Data')\n",
        "\n",
        "# Show the figure\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        },
        "id": "bwH0_0MKJsy3",
        "outputId": "8fb27961-5bf0-4db2-9b35-ed4d0b242091"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-5-4eea5c60d58f>:7: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
            "  ax[0].set_xticklabels(df_students.Name, rotation=90)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 2 Axes>"
            ],
            "image/png": 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w1KlTBwDMmzev7tlnnx3Q3XjUQiQiIlJjipkm39OOPPLI9/fYY48NN95447Gbb775gvXWW29Rbpm77rprpfPOO2+NhoYGHzRo0KcTJ058fa211lp68cUXtxxwwAHrf/LJJwZw2mmnzdp8880XdyceC2spJmv8+PE+adKkpMMojqbdi5SFmU129/FJxyFSC5555pmWcePGvZt0HL3pmWeeGT5u3LhUseXVZSYiIiI1TwmRiIiI1DwlRCIiIiVKpTMDUunMcoOApXppULWIiNS8VDozDNgEWAdYK7qtHf27GrASMBhYAegfe54Di2K3hcD7wOzY7a3o9hLwaktz46e9clFSEiVEIiJSM1LpTD0wDtgS2Cx2W6OLVRohSVohdmz9DsovSqUzLwLPAy8AzwKPtDQ3vtfF80uZKCESEZEuM7PLgT2BOe6+WXSsCfgeMDcqdqq735FEfFG31rbAF6LbjoTWnqQMJCRk42LHPJXOTAceBh4CHm5pbizrooPSOSVEIiLSHX8FLgCuyjn+B3f/be+HA6l0ZnVgb+AbwC6EJKSSGaG7bhNCIkkqnXkVuBW4hZAglbebrWnI1uWtr7XDdY3efvvt+l122WU0wLvvvtuvrq7Ohw0bthTg6aefnjZw4MDE16tRQiQiIl3m7g+aWSrpOFLpzAbAN4EJwHZU/6ShDYATo9t7qXQmA9wM3NHS3PhJopF1wRprrPFpdh+zk046aa3Bgwd/esYZZ7yTfXzJkiX069cvuQBRQiQiIj3jODM7BJgEnOzuH5T7BKl0ZhCwH3AE8Ply119BVgUOiW7vptKZicDlLc2NzyYbVvd885vfTA0YMKBt6tSpg7bddtuPV1555bZ4orTRRhttevvtt788evToT/70pz8Nu+iii1ZfsmSJbbXVVvOvuuqqGQ0N5U1hqj2DFhGRynMRoYVjC8Isq9+Vs/JUOjM+lc78Oar7r/TtZCjXcOB44JlUOjMplc78IJXOrJx0UF01e/bs/lOmTJl+2WWXzSxUZsqUKQNvuOGGYZMmTZo+ffr0F+rq6vzPf/7zquWORS1EIiJSVu7+WVeImV0K3N7dOlPpjAFfB04hDIwW2Dq6nZVKZy4Gzm1pbpydcEwl2WeffT7orKXnzjvvXGnq1KmDxo0btwnAokWL6lZbbbWl5Y5FCZGIiJSVma3p7tkv5m8AU7taVyqd6QccBPwEGFuG8PqiIYTX54RUOnM18JuW5sbpCcdUlMGDB7dlf25oaPC2ts/usnjxYgNwd9tvv/3eu/DCC2f1ZCzqMhMRkS4zs2uBR4HRZjbTzI4AzjGz58zsWeBLhIHBJUmlMw2pdOYY4FVCt5iSoc71Bw4HXkilMzek0plNkg6oFKlUavHTTz+9IsDDDz88aNasWQMAdt9993m33377KrNmzWoAeOedd+pfeumlsq8SrhYiERHpMnc/MM/hv3SnzlQ6sxdwNjCmO/XUMCOacZdKZ64CfnHLgSPbl+hkmnwSDjnkkA8mTpy46oYbbrjplltuOX/UqFGLALbeeutFP//5z2ftuuuuG7e1tdGvXz8/77zz3th4443LOtvO3BOf+s/48eN90qRJSYdRnGus8zIHJf+ailQ6M5vs7uOTjkMqRyqd2Qb4LWEBRSmfBRO/udaS7bfe4rX6OmvrvHjf8MwzzwwfN25cqtjyaiESEZFEpdKZEYSZaN8mtG5IeQ1asMR58e2PNltz6MAZqwzq35p0QJVIY4hERCQxqXTmMGA68B2UDPWopW1t/d58f8GGr787f/1PlrapQSSHXhAREel1qXRmFHAZ8JWkY6kFjuPumBkfLVqyykvvLF159ZUHzhw+uP+7Zn0vD21razOgpO5BtRCJiEivSqUzRxKm4isZ6iUzPlzC0gXzyI4bbnOvn926cNSrc+dv/MnStmT3zCiztrY2mzt37hBKXO5BLUQiItIrUunMSsDFQL6ZadKDzn/8A34IjBr6Lta+Z3KlGcZmKw2oe3dAvS1MKLxyawOmLl269MhSnqSESEREelwqndkSuB7YMOlYatG8xW38+sH3Cj1cB4wAfg/8tKW5cUmvBVZB1GUmIiI9KpXOHEdYvFHJUOUy4GTgv9H4rpqjhEhERHpEKp3pl0pn/gKcDwxIOh4pyjbAE6l0ZvukA+ltSohERKTsUunMKsBdhK0kpLqsBtyXSmdqaqyXxhBJz9CK3iI1K5XObABkgNFJxyJdNhC4JpXOjGlpbjwt6WB6Q7daiMzsRDN73symmtm1ZjbQzNYzs8fN7BUzu87Myr4Bm4iIVKZUOrMD8BhKhvqKX6bSmatT6Uyfb0DpckJkZmsDPwLGu/tmQD1wAGFDvj+4+4bAB8AR5QhUREQqWyqd2QW4BxiecChSXgcD16XSmT7dwNHdMUQNwApm1gAMAmYDXwZuiB6/EpjQzXOIiEiFS6UzuwF3ACsmHYv0iH2Am1PpzMCkA+kpXU6I3H0WYVfiNwiJUCswGfjQ3ZdGxWYCa+d7vpkdZWaTzGzS3LlzuxqGiIgkLJXOfA24DVgh6VikR30NuD2VzgxKOpCe0J0us1WAvYH1gLUIfxXsXuzz3f0Sdx/v7uNHjBjR1TBERCRBqXRmL+BmNK2+VuwK/CuVzvS55Lc7XWZfAV5397nuvgS4CdgJGBp1oQGMBGZ1M0YREalAqXTm88B1QJ8eWyLL+QLw91Q6U590IOXUnYToDWB7MxtkYavcXYEXgPuAfaMyhwK3dC9EERGpNKl0ZjPgVsL0bKk9exH2peszujOG6HHC4OkpwHNRXZcApwAnmdkrwKrAX8oQp4iIVIhUOrMO8C9gaMKhSLKOSKUzv046iHLp1iwzdz/N3ce4+2bu/h13X+zur7n7tu6+obvv5+6LyxWsiIgkK1qB+k7CkAiRU6O96qqetu4QEZGiRGNGrgfGJh2LVJRzU+nMl5MOoruUEImISLF+RZhQIxJXT1i4cd2kA+kOJUQiItKpVDrzDSCddBxSsYYDN1bzwo1KiEREpEOpdGY0YecBkY6MB/6UdBBdpYRIREQKilYlvglYKelYpCoclkpnDk86iK5QQiQiIh05Gw2iltKcm0pnUkkHUSolRCIiklc0c+jYpOOQqrMScGUqnamqHKOh8yIiMddYx48f5L0Th4j0qFQ6szJwBdDJf3qRvL4AnAj8LulAilVV2ZuIiPSac4GqnkYtift1Kp3ZNOkgiqWESERE2kmlM7sDhyUdh1S9AcDl1dJ1VhVBiohI70ilM/2B85KOQ/qMbYHvJR1EMZQQiYhI3InARkkHIX3K/6XSmeFJB9EZJUQiIgJAKp1ZC/h50nFIn7MKYduXiqaESEREss4BBicdhPRJ30ulM+OSDqIjSohERIRUOrMdcHDScUifVUdY5LNiaR0iEa2tJAJwVtIBSJ/3P6l0ZseW5sZHkg4kH7UQiYjUuFQ6swvw5aTjkJpwetIBFKKESEREmpIOQGrGV1LpzOeTDiIfJUQiIjUs+nL6YtJxSE2pyFYiJUQiIrVN0+ylt30plc7slHQQuZQQiYjUqFQ6swnw1aTjkJr0w6QDyKWESESkdv0g6QCkZn0zWgi0YighEhGpQal0ZjBwSNJxSM1qAI5JOog4JUQiIrXpEGDlpIOQmnZUtJlwRVBCJCJSm45NOgCpeasD+yYdRJYSIhGRGpNKZ7YFxiYdhwjwnaQDyFJCJCJSew5MOgCRyFdS6czwpIMAJUQiIjUllc4YsF/ScYhEGqiQ96M2dxWR4mgT3L7i88DaSQchEnMgcFHSQaiFSESktnwr6QBEcuycSmfWSToItRCJ1LLOWn1ALT99SNRd9s2k4xDJYcAE4Pwkg1ALkYhI7diSMNVZpNL8T9IBqIVIkqexKSK9ZbekAxApYJdUOtO/pbnxk6QC6FYLkZkNNbMbzGy6mU0zsx3MbJiZ3WNmL0f/rlKuYEVEpFuUEEmlWhHYMckAuttl9kfgTncfA4wDpgFp4F533wi4N7ovIiIJSqUzKwA7Jx2HSAe+muTJu5wQmdkQ4AvAXwDc/RN3/xDYG7gyKnYlYaCUiIgk6wvAgKSDEOlAdSZEwHrAXOAKM3vKzC4zsxWB1d19dlTmbTSAT0SkEnwh6QBEOjEuaslMRHcSogZgK+Aid98SmE9O95i7O5B3RKyZHWVmk8xs0ty5c7sRhoiIFGHrpAMQ6UQDYSZkIrqTEM0EZrr749H9GwgJ0jtmtiZA9O+cfE9290vcfby7jx8xYkQ3whARkSIoIZJqMD6pE3c5IXL3t4E3zWx0dGhX4AXgVuDQ6NihwC3dilBERLollc6sC1TEBpoinUgsIeruOkQ/BCaaWX/gNeAwQpJ1vZkdAcwA9u/mOUREpHvUOiTVojoTInd/mvzB79qdekVEpKy2SjoAkSKNTqUzg1qaGxf09om1dYeISN83JukARIpUB6yf1IlFRKRvS+QLRqSLlBCJiEiPWC/pAERKsEESJ1VCJCLSh6XSmSGA9pSUaqIWIhERKTt1l0m1UQuRiIiU3aikAxAp0TpJnFQJkYhI36YFGaXaJNLFq4RIRKRvG5p0ACIlUkIkIiJlNzTpAERKNCiVzgzo7ZMqIRIR6ds0w0yqUa+/b7u7l5n0tmus8zIHec/HISLVYmjSAYh0wSrA2715QrUQiYj0bYOTDkCkCwb19gnVQiTSF3XWkqhWxFpSRLOySMXp9fxECZGISN+m7LeHDGTxwjra2pKOoy/6lPpeT+SVEImI9G36wu4BK7Lwo6kDjmgzY0jSsfRd3+jVs2kMkYhI36YWoh7ws4aJU5QM9aglvX3C6mwhKnZ8hGZkiYjoQ67MGli6ZP/6+zdOOo4+7pPePqFaiERE+rZe/2Lp646qzzzRYG1rJh1HH6cWoj5FM31EJHnzkg6gr/lhw80jko6hBqiFSEREyurDpAPoS75e98jkFewTdZf1vAW9fUK1EIlUC7U4Std8kHQAfclp/a7Suk49bzHwTm+fVC1EIiJ929ykA+grtrKXpg+3eVslHUcNeIOm1l7/C08tRFn661tE+iYlRGVydr9L3ks6hhrRksRJ1UIkItK3zUw6gL5gHZsza0N7a7uk46gRLUmcVAmRiEjf9mrSAfQFZzdc8oqZelV6yetJnFQJkYhIH9bS3NgKqKunG1bm49Yd6l7YOuk4akhLEidVQiQi0ve9knQA1ey0fn97yozBScdRQ1qSOKkSIhGRvk8JURf1Z8niCXUPj006jhrTksRJlRCJiPR9LycdQLU6tuGfT9Sbr5Z0HDVkEfB2EidWQiQi0vc9m3QA1cn9mPrb1k46ihozI4k1iEAJkYhILXgi6QCq0X71Dzw5wJaun3QcNeb5pE6shEhEpI9raW6cBcxOOo5qc2rDxIFJx1CD7kvqxEqIRERqw5NJB1BNtq97/vlVbP7mScdRgxJLiLTIVF+m7UhEZJkngL2SDqJaNDdcNi/pGGrQHJpaq7fLzMzqzewpM7s9ur+emT1uZq+Y2XVm1r/7YYqISDc9lnQA1WJ9e2vGKHtH23T0vsRah6A8XWbHA9Ni988G/uDuGwIfAEeU4RwiItI9/wUWJh1ENTin3yUzzDSkJAHVmxCZ2UigEbgsum/Al4EboiJXAhO6cw4REem+lubGRcCDScdR6YbR+t7W9tI2ScdRo6o3IQLOBX4CtEX3VwU+dPel0f2ZQN41HMzsKDObZGaT5s6d280wRESkCHcmHUClO6PfX58zY4Wk46hBs2hqfSnJALqcEJnZnsAcd5/clee7+yXuPt7dx48YMaKrYYiISPHuSjqASjaQxQu/VvfE55KOo0Yl2joE3Wsh2gnYy8xagL8Tusr+CAw1s+zstZHArG5FKCIiZdHS3DgNeCPpOCrViQ03PFlnvmrScdSo6k2I3P2n7j7S3VPAAcB/3P1gwkXtGxU7FLil21GKiEi56DM5D6Ot7bD6O0clHUcNuzfpAHpiFP0pwElm9gphTNFfeuAcIiLSNdcmHUAl+nb9v5/ob58qIUrGQzS1zkg6iLIkRO5+v7vvGf38mrtv6+4buvt+7r64HOcQEZHua2lufBR4Pek4Ks1PGq5bKekYathlSQcA2rpDRKQWXZN0AJXkS3VPPbuSLdw06Thq1IfAP5IOArR1h1QbbUciUg4TgZ8lHUSl+FW/yxclHUMNu4am1opYMFQtRCIiNSaabfZU0nFUgjH2xmtr8Z4WYkzOpUkHkKUWIpGeoJYsqXx/ooK+jJJyTr+LZ5mxftJx1KhJNLU+nXQQWWohEhGpTROB95IOIkmr8/6cz9nr2yYdRw2riMHUWWohEklSZy1JoNYk6REtzY0LU+nMZYSlUmrSr/pd/oIZuyQdR42aT4UN7lcLkYhI7boQ+DTpIJKwIgs/3rVuyhZJx1HDrqep9aOkg4hTQiQiUqNamhvfBG5OOo4k/KThusl1xtCk46hhFTd+TQmRiEhta046gN5Wz6dLD67/9wZJx1HD/kNT66NJB5FLCZGISA1raW6cTI3tb3Z4/Z1PNFjbyKTjqGEVuQaWEiIREfklUDOj909suGFY0jHUsNtpan0s6SDyUUIkIlLjWpobnwVuSDqO3rB73eNTBtniMUnHUaMc+HnSQRSihEhERACagLakg+hpp/e7smZawirQ9TS1PpN0EIUoIRIREVqaG18ALk86jp60ub368ur24dZJx1GjFgHppIPoiBIiERHJ+inwQdJB9JRz+l3yTtIx1LA/0NTaknQQHVFCJCIiALQ0N75Lhc4A6q61mTt7tL25XdJx1KjZwFlJB9EZJUQiIhJ3MTA56SDK7f/6XfaSGf2SjqNGnUpT68dJB9EZJUQiIvKZlubGNuBY+tA0/MEsmPf5uue2TDqOGvVf4MqkgyiGEiIREWmnpbnxceCipOMol583XD3FjJWTjqMGtQIH09RaFcm1EiIREcnnJ8CrSQfRXQ0sXbJf/YOjk46jRh1NU+uMpIMolhIiERFZTktz43zgu1T52kTH1N/2eL21rZl0HDXoCppar0s6iFIoIRIRkbxamhsfpso3fz2u4Z+rJx1DDXoJ+GHSQZSqIekARKrGNdZ5mYOqoqtcpBSnAbsB2yQdSKkm1D08aaAtGZ90HDXmE+AgmlrnJx1IqdRCJCIiBbU0Ny4F9gfeSzqWUv2i39/0R3/v+xlNrVW5bIPeLKKWDxHpUEtzY0sqnfkWcBdQn3Q8xRhvL05b1T7aIuk4aszdwO+SDqKr1EIkIiKdamluvBc4Jek4inV2v0v67BYkFWoucGi1TLHPRwmRiIgUpaW58XfAtUnH0ZlR9vbM9W22tunoPYuA/WlqfTvpQLpDCZGIiJTiSCp8a4+z+136qll1dO31AUuBb9HUen/SgXSXEiIRESlaS3PjAmAP4OWkY8lnKB99sJ1N08yy3uHA4TS13pp0IOWghEhERErS0tw4F/gq8FbSseQ6rd9Vz5ixYtJx1IgTaGr9W9JBlIsSIhERKVlLc2ML8D9AxQxe7s+SxXvVPbJp0nHUiNNpaj0v6SDKSQmRiIh0SUtz41RgT2BB0rEA/KjhpifqzUckHUcNOI+m1qakgyg3JUQiItJlLc2NjxBaiuYlG4n79+ozI5ONoSZcBZyQdBA9QQmRiIh0S7Tn2VdIsPvsgPr7nhhgS9dL6vw14hbgiGpea6gjXU6IzGwdM7vPzF4ws+fN7Pjo+DAzu8fMXo7+XaV84YqISCVqaW58EtgFmJPE+X/acM0KSZy3hkwkTK9fmnQgPaU7LURLgZPdfSywPXCsmY0F0sC97r4RcG90X0RE+riW5sZngS8AM3vzvDvXPffcEFuweW+es4Y4YX+yb9PUujjpYHpSlxMid5/t7lOinz8CpgFrA3sDV0bFrgQmdDNGERGpEi3NjS8C2wGTeuucZzX8pep2Vq8S84F9aWo9K+lAekNZxhCZWQrYEngcWN3dZ0cPvQ2sXuA5R5nZJDObNHfu3HKEISIiFaClufEtQkvR9T19ro1sZss6Nmfbnj5PDZoJfJ6m1puSDqS3dDshMrPBwI3ACe7ebpaBuzuhuW057n6Ju4939/EjRmiWpIhIX9LS3LgQOAA4vSfPc3a/S94w0wShMnsC2Iam1qeSDqQ3detNZGb9CMnQRHfPZpHvmNma0eNrktAAOxERSVZLc6O3NDc2Ad8CPi53/avS+u6W9opah8rr78AXq32j1q7oziwzA/4CTHP338ceuhU4NPr5UMI0PRERqVEtzY3XE4ZVlHVT2DP7XTHVjIHlrLOGOXAaTa0H0tS6KOlgktCdFqKdgO8AXzazp6Pb14BmYDcze5mwLkVzGeIUEZEq1tLc+AqwA/A7CgylKMUKLF6we92TmllWHrOAr9HUekbSgSSpoatPdPeHASvw8K5drVdERPqmlubGJcCPU+nMPYRZyHkn3RTj5IZ/TKoz/0LZgqtdVxI2af0w6UCSpoFoIiLSq1qaG+8CNmXZEi0lqaPt00Pr70qVNaja8zawF02t31UyFCghEhGRXtfS3PheS3Pjdwk9Ci+X8tzv1N/9RD/7dN0eCazvc+BSYFOaWm9LOphKooRIREQS09Lc+B9gc+DXwJJinvO/DdcP6dGg+q6ngR1paj2Kptb3kw6m0ighEhGRRLU0Ny5qaW78ObAZYSmXgnatm/z0YFs0tnci6zM+Ak4ExtPU+ljSwVQqJUQiIlIRWpobX2ppbtyXsD/mg/nK/KrfFUW1IgkAHwK/AtanqfVcmlo/7ewJZjY6NnP8aTObZ2Yn9HSglaDLs8xEKto1hSZAxhzU7Zm/ItIDWpobHwe+mEpn9gTOAj4HsInNeHUN3h+faHDVYQ7wB+BPNLXO66xwnLu/CGwBYGb1hCn5N5c7wEqkFiIREalILc2NtwPjCJuGP/qbfhe/ZVZwuReBN4AfASmaWptLTYby2BV41d1ndD+0yqcWIhERqVgtzY1O2AHh1qWnfXsH4ARgH/T9FfcSYRHkq2lqLWeX4gHAtWWsr6LpDSUiIlWh4fQPHgUepWnISOAHwFHAqslGlRgHHgHOA26gqbWtnJWbWX9gL+Cn5ay3kikhEhGR6tLUOhM4laYhZwATgG8AewArJRlWL8gmQf8gJEGzevBcewBT3P2dHjxHRVFCJCIi1SlsQvp34O80DelPGPMygdCysUaCkZVTbyZBcQdSQ91loIRIRET6gqbWT4B/Af+iacgxhKn7E6LbxskF1iVJJUEAmNmKwG7A0b153qQpIRIRkb6lqdWBR6PbKTQN2QTYE9iasCr2xkB9cgEuZyYwOXabRFPrnKSCcff51ODYLCVEIiLStzW1TgOmLbs/ZCAwljClf/PYbXgvRPMm7ZOfyUkmP7KMEiIREaktYezRlOgWOz5kTUKStD4whDBIe+XYLd99I2yNEb/NIyyO+Hbs9g4wjabWuT16bdJlSohEREQAmlpnA7OTDkOSoZWqRUREpOYpIRIREZGap4RIREREap4SIhEREal5SohERESk5ikhEhERkZqnhEhERERqnhIiERERqXlKiERERKTmKSESERGRmqeESERERGqeEiIRERGpeUqIREREpOYpIRIREZGa15B0AAJcY52XOch7Pg4REZEapRYiERERqXlKiERERKTm9UhCZGa7m9mLZvaKmaV74hwiIiIi5VL2hMjM6oELgT2AscCBZja23OcRERERKZeeaCHaFnjF3V9z90+AvwN798B5RERERMqiJxKitYE3Y/dnRsdEREREKpK5l3c6t5ntC+zu7kdG978DbOfux+WUOwo4Kro7GnixG6cdDrxbxnI9VbaW60z6/NVSZ9Ln76lrymeUu4/oxvNFRMqmJ9YhmgWsE7s/MjrWjrtfAlxSjhOa2SR3H1+ucj1VtpbrTPr81VJn0ufvqWsSEal0PdFl9iSwkZmtZ2b9gQOAW3vgPCIiIiJlUfYWIndfambHAXcB9cDl7v58uc8jIiIiUi49snWHu98B3NETdRdQbNdbKV10PVG2lutM+vzVUmfS5++paxIRqWhlH1QtIiIiUm20dYeIiIjUPCVEIiIiUvOUEPVhZrazmR0W/TzCzNZLOqZKY2afSzoGERFJXp8eQ2RmOwFNwCjCAHID3N3X7+A544DPR3cfcvdnOjnHIHdf0EmZemB1YoPY3f2NYq6hQH3bA8+7+0fR/ZWBTdz98ViZ04DxwGh339jM1gL+4e47dfW8Pc3MtgJ2Bhz4r7tP6aT8asDA7P2uvKZm9hAwAPgrMNHdWzsoWw80Aina/y5/X+p589R9vLv/sbNjXai30/eniIhUeUJkZjuy/JfTVbHHpwMnApOBT2Nl3itQ3/HA94CbokPfAC5x9/MLnPsyYLC7rxslUke7+w9yyv0QOA14B2hbFoJvnlNuMnA5cI27f9DJdT8FbOXRL8/M6oBJ7r5VrMzTwJbAFHffMjr2bO55c+pdBdiI9knGg7HHv+zu/zGzffI9391vyne8s3qjMr8E9mPZaz+BkMD9Kk99ewG/A9YC5hAS3mnuvmlOuX2As4HVCMlwNiFeOafcRsDh0fmfAK5w93vynPcOYBHwHMt+l7j76fmuu9hrj8pNif/+omNPZX93Xaiv0/dnlIAWlJuQmtlA4Ahg05zzH95RPSIi1aBHpt33BjP7G7AB8DTLkh0HrooVa3X3f5VQ7RGEbUbmR+c4G3gUWC4hAv4A/A/RopPu/oyZfSFPueMJrTR5k7CYbwGHAU+a2STgCuDubNKTw+LH3b3NzHJ/l5+4u5tZNmlasaOTm9mRUawjCa/p9oRr/3Ks2BeB/wBfz1OFsyyZKbVegIOBce6+KHpec1R+uYQIODOq59/uvqWZfQn4dp5y5wBfd/dpeR5bFrj7y2b2c2AScB6wpZkZcGpOkjeyo4QyVzHXbmYHAgcB65lZfAHTlYD3S60vppj35+86CN/z1Ps3YHpU7xmE31mHr62ISNVw96q8ET6IrZMyzcBvgB2ArbK3Dso/BwyM3R8IPFeg7OPRv0/Fjj2Tp9x9QEMJ11UH7EXY7uQN4HRgWE6Zm4AfAf2i2/HAP3PK/Bi4GHiN0Or1KPDDzq4deDq6Pwa4qQy/p6LqjV6nobH7Q4H/FKhzUvb1Buo6eO3/W0R8mxOSh5eAC7PvD0Lr04ycsmcDXy3ntRNat3aJfj9fjN22yn3flPI7Kvb9WeLv8qno32ejf/sBj3X3PaKbbrrpVgm3qm0hAqYCawCzOyizXfRvfL+lfH/5Zl0BPG5mN0f3JwB/KVD2zahbws0sm5Tk+2v5NeB+M8sAiz8LIs+4EzPbnNBK9DXgRmAiYUzNf4AtYkWPIbRk/Dy6nntZtlFutv7fmtluwDzC5rm/9DzdQDGL3H2RmWFmA9x9upmNzlfQzAYA32T57sozSq3XzM6PrqEVeN7M7onu70bovsrnQzMbDDwITDSzOcD8POUmmdl1wD9p/9rHW33OJ/yOT3X3hbEyb0WtRnGPATdHXZRLKNAFV+y1R+eZAcwgJO2dKfp3RPHvT8xsEHASsK67HxV1IY5299tzii6J/v3QzDYD3iZ0R4qIVL1qToiGAy+Y2RO0/7LbK/bzl4qtLPqSewy4n5CEABzm7k8VeMoxwB+BtQmtOXcDx+Yp90Z06x/dCp1/MvAh4cs57e7Za3o8Ghz+GXefQ9gjrkNRAtRREhQ308yGEpKHe8zsA8IXdT63EBKYycRe+y7WOyn6dzJwc+z4/R3UuTdhLM+JhG6bIYQunFwrAwuAr8aOtevac/cvFjqJu/8t59DvCYnLc+5ezOC7ol/TaKD8+cAmhPdJPTA/J9kq5XdU7PsTwh8Ck4Edo/uzgH8AuQnRJdEYpl8QuuIGA78sUKeISFWp2kHVZpb3i8zdH4iVKaUlI+8g1t5iZtu6+xM5x/aM/5VuZj9x93NirSrtuPuPzOxhd9/ZzD7KKdNZa0b8vF8kJBl3uvsneR6f6u6bFX91xdWbhFJmIprZg8Au7t6W+1gR5+nsNZ1ESHL/QWjRPATY2N1/2sX6VvXOx619dm53Hx9//5vZM+4+rsjLExGpelXbQhRPfDpQSksGwL1m9k3CuIy8mWIxSUlO+RHAT1h+Zk5ut92fzewQd58aPe9A4ATa/5We7fKYRAHuvnP070qFyuTENyzP4eeifweTM7A38oiZfc7dn8vzWG79ny0R4O4PWFgiYEvg8ZxyGwH/B4yl/euULzEpdvZYMbOi/kKemYgFZLs//0Un3Z+xGLJLLrweHVqD0GK4HHd/xczq3f1T4IpoNuFPu/g7eiyaaXg5IWnq6C+fT8xsBaL3s5ltQJ7/L6X+gSEiUk2qNiEqsothpLvvXkK1RxPGUiw1s0Xk/6LtNCnJMRG4DtiT0I1xKDA3T7l9gRvM7CDCOkiH0L6rB3e/LfqC/Zy7/7izE0fdG+vQ/ssrd22fyYQvQgPWBT6Ifh5K+OLOt5jjzsB3zex1whdn9nXKNwPrIsIA4ayP8xyD0G1zGmGA85cIY6kKLRxa1OwxipsVVcpMxNejW4fdn1lWYMkFwkDuXAvMrD/wtJmdQxgbl73++O8olwP51tXaGPgKYTmB883seuCv7v5SnrKnAXcC65jZRGAn4Lt5ypX6B4aISNWo5i6zTrsYzOwS4PxiWjK6GUsdYb2XeXkem+zuW1tsDSAze9Ldt8lTdmPC+JA3gG/EB/nmlHvU3TschGtmZxK+1F6j/fpHeQeUm9mlwM3ufkd0fw9ggrsfnafsqHx1RAOEc8s+7e5b5Bxbbj2k2Ov0nLt/Ln4sT53/9SIWmMx2AWXPFw0ufsjdt4+VaSYk0zfRvtWn4KKQVuRih2b2CmEZh067rqLXdA5h5taJhO6wP7n7K509t4i6vwRcDaxImJmXdvdHc8qsSpjGb4SZY+/mqadLXaUiItWgaluIoHAXQ6xIUS0ZVuICddFzriG0+HwKPAmsbGZ/dPff5BTNzsyZbWaNwFvAsFg9z9G+620Y4Qv6cTOjQKvL0xbWrPkHsdlVObOn9gc2KGGszvbu/r1YXf+KWiqW4+4zLM/q2wW8ZmY/IrQKAfyAkKTlWhwlli+b2XGEgb2DC9RZzOwxKG5WVNEzEc1sB0IX22Cg4GKcMW8SWlQ6FUsmFxKWWoifd0w0oyzv+7TA+3NVwtpM3yG0UP2QMBB6C8L7Zr089WZnbK5rZuvmqbforlIRkWpTzQlRR10MWXsUWVd2gbqBhC/GZwjJ0+aErrF8rTFj3X2emR0M/AtIE7oSchOiX5nZEOBkQhffyoQWgKw9i4wxbiDwHu2/tHMXRpxK6PaaU2Sd2WnmV0f3DyYkb8spsSuo0yUCIscDgwjrK51JuLZDC8Ta6eyxSKezokqZiQicS3GLcWaVsuTCnoTrzh3cvTKhG/co8i+kWGgZiUcJXYYT3H1m7PgkM/tz9HNR9caS9gbgMDN7jc67SkVEqko1d5mNInwh96eTLgYrcs8rM7sJOC37F3DUqtDk7vvmKfs84a/ta4ALogHDXZ6ZY0XsT1ZifeMJYz6mUmBZgpzywwhJTvYL/kHgdHdfbsBuKV1Bla6UgcJm9ri7b1fsbCwL+8ktx/Ns9RG9pvtQ/JT+DpnZUe5+Sc6xZndPd6GuvF2kWfm6SkVEqk3VthBF3TYjop/z7iVlBfa8Isw6ymd0vDvA3aea2SYFyl4MtBBakx6MvjTyjSEaQVgpOkX7L9zc/Z+KHXycHWt0EbC6u29mYUHHvbz9vl9XEmZitdt3q5Ao8Tm+s3KRoruCota7XxG6gu4ktCKd6O5XR4+f6+4nmNltLD9rzwkzqC5298didXY4e8zMvu3uV5vZSfliymmhKWWgcNGLHUbnOT2Kp5gxR28CUztLhqyT/ftiJpjZfHefGD3vQmKvVSn1ZhOeaPbZTHdfbGa7EH6X+c4tIlJ1qi4hMjMjtGQcR+giMzNbShg8nftXfbF7XmU9a2aX0b7b6Nl8Bd39PEJXUNaMqP5ctwAPAf+m42ndxexPlnUp8L+EpAx3fzYa0xRPiBZEMRYlSrJ+zPJfivm6Y4ruCiJsdfETM/sGIYHch9D6lH2Ns4sf/rZAaMMJU8fHxo51Nnssu29bvqUHchOOUmYilrLYYaljjn4C3GFmD1DgNbXi9u/L+iZwq5m1AbsDH7r7EQXiLLbeG4HxZrYhcAnhvX0NYWV1EZGqVnUJEaF7bCdgG3d/HcDM1gcuMrMT3f0PsbJL3P09M6szszp3v8/Mzu2g7sOA77OspeRBlg0GbicaFxTvYnqA8OWc23IyyN1PKeK6ih18nK3ziZAbfmZpTpmHzOz/CONdipk99Q/gz4Qd0jtbj6eo1bcj2fdYI2H3+tZ43O4+Ofq34LpSZpY7MHxDd9/PzPZ29yujZPCh2ON3RHXm65rKHbNV9EDhaObVwZ2VizmX4scc/ZrQKjiQwq/peMLYtYKtSNZ+zaIjCUnLw8DpZjYsXxdoMfVG2tx9qYV1oM539/MtTGQQEal61ZgQfQfYLT4t2N1fM7NvE/5ijydE2T2vHqLjPa+y9SyKBpze4e4vdhLH5YTxOfvH4rqC0AISd7uZfc2j6ewdKHbwMcC7UfdFdiG9fVl+T7fsitvbx451tI/bUnfPm/zlKtRFWcDtZjad0GX2/agLcVFuIetgYUZ3vy2neGezx+4xs93dvSXnHIcRXt/4YpdFr6lUZFdlO+7+Zk7iWijZXMs7n9JezP598TWLsv9+jWWtOPnWLCqmXoAlFhYMPQT4enSsXyfPERGpDl4BO8yWciOMsyjqMULXST0h8TuUMINp1Q6evxfwIvB6dH8L4NYCZZ/u6BjwEWFM0UeEMTwLY/fndfM1WJ/QBbeA0HXzMJDqZp1NhFapNQlT/4cBwwqUHUGYTXcHYePZ/1BgZ/qo/DCgPvY7WSNPmYeBXQldlKOieM4oUN+RwCqEXeFfI4wPOzr2+NcIu9dvFDv2U8J4qpE5dY3Kdytw3geAbWm/g3xH78cbCPuDTSEkDj8G/l6g7DmE7sWOfkf3ERbOvIvQ6nQrcEtOmW2BNWP3D43KndfB7zNfvcu97wnJ6nnAgdH99YBTuvO+00033XSrlFvVzTIzsynunnc9lnyPmdnqQHYRxCc8bIxaqO7JhBaU+33ZLKLPFgrMKfso8L/u/nB0fyfgt97Jgol56ilpK5Cc564I1Hk0My3nsaGEv+RTtB8TlLe+qIUkz+nzbp1xN2H17R8TW33b83QNWpE7qVtpCzNm154qyMx2JYyxmkBIoLYFGt39gzxldyYkT1dELViDPeqOzSn3pLtvkzPL7GnPWXgyVn44YczRVwgtNXcDx3ue2XkW9p5bkdBKtYQ8q6Rb+/37jLCi+QHuvmmszBTgK+7+ftQ993fCGkRbEGYt5psx+cXcY1D09jgiIn1CNXaZjTOz5WZzEb4g2s2iMbP9CS0Z90ePn29m/+vuNxSoe4nnjHEhT5IS+T5wZTSWyAizoZZbN8fM7nX3XTs4doyZPUIRW4EUmjWVjdfbD2q+A3iM4meZ5duio5BV3f0vZnZ89KX5gJk9WaDsFRS3k3opCzO+bGY3Ale4+wsFrufeqIvsfuAR4Mvunq+r7jTCGJrRUaz9CAO+862EXUxXZTyGoscceRF7z3lY2mFL4CBgP8I2In/OKVbvy8YJfQu4xN1vBG60sLdZ3no7Oq+ZXe/u+9vyi4gaYVxRl5aaEBGpJFWXELl7fQnFf0YYfD0HPpsC/29CV0Y+z1vYS6w+asn4EeHLNF8cTxOSs+xf8PMJW4k8G51rIOEv/uEWFgfMZlkrE2YpZZ1HSNrWBK4HrnX3QgNVs1+aowmtXrdG978OPJFTdqC7502g8rEwjfz7LBskfj9huvuSPMU7XH07xwbu/q1o7AnuvsByMs5IvoUZDylQ5zjCa31ZlERdTuiKmhddy0csGz8zgNAVNyc6b7tWF+AbhPFWU6L43jKzQsnJsYTZVWPMbBYhIVku4SnU2peVr5UuamF82t3nR+PhtgLOdfc3orFLB0a3dwmtc+b5F5WsN7MGd18aXXd8HFre/+/W+b6A2UkG8QHpRtgnL74yvIhI1aq6hKhEdTldZO9ReMNQCF0LPyN0W1xDGFPRbsBslAAdS0hqbiEkWMcSVqJ+lrCZK4SNYk8grIEUn9k1D7gge8fdzwXOtbCO0QHA5RZ2Hr+GkBy9HCubXdfmQWArX7aIYxOQybmWv5nZ9wgtMfFZZvlmGUEYLNwP+FN0/zvRsSPzlO1s9e24onZSd/dsC9PHhNWQ6wmvx3ILU0bXfSlwadTdcw3wBzO7ATizmNaWeHzu7maWjW/FDsqu4u5fiXdVRrPWchcmLHbj37iLCAn2OMLrehlheYEvEpYYeAjY06OFR82s0Ot9LaHF7l3CuLWHovIbUnjtqAvIsy9g9kF3nx39OyNPC9WNXbhWEZHKk/Qgpp68EVpe7iJscvpdwsKA5+QpN5CQvFxASGQaOqjzFuCvUbnrCS0pDwBbFCj/wy7EvSXwFPBpgcdfBAbE7g8AXswpcyzwIWHtn9ej22sdnPOZYo5Fx0eUcC27Ra/PXEKy2ALsEnt8ZUIrwwWErTiMsMbU6+QMGI49p54wAP7m6HU6ibCv2r7ASyW+1j8mjDV6jbCA5mPAjwqUnQJsFrt/APB4EecYVESZKdG/vwSOyDk2gTAW6E1CIrgr0cD/AnVtT2j5WjF2bGNCEp2v/KTo32djx57Kee5phMTsYcIfDjNKfV/rpptuulXyreoGVZcqWjMlOx7kIXf/Z54y1xG6gR4i7H/W4u4nFKgvPui3njCGZF3PGZ+SHSwd/byfu/8j9thZ7n5qTvmG6NwHEL7w7ie0EN2SJ4afEab73xwdmgBc7+5nxcq8BmzreXYtL3BdU4D93P3V6P76wA2eZwC7mb1ESGyuA27yPAOVc8oX3EndzG4hzHB6NLru1aJyx3volsxX32uEmVF/cfdHch47zzsYiF6gvt1Yti/aXe7+7wLl1id0tx5EGNB8CKHVJm/Li8UWZnT3DhdmtLAg452EtbC+QJg594zHBvRHLVN7E7rOvkxYOPFmd7+7lOvNc+4HCQO/LyMsYTAb+K5HY4MsLO74ECFRy7ZQveZ5BtyLiFStpDOynrixbMp7dpp7/DaX0Aqwa6z8c7GfG4j+Mi9Q95SO7uc73tFzCC0olxO+iG4lfNmu2NH1Rc/bijC243hgyzyP300RLROx8rsSFlu8n9Ci0wJ8qYPy2wK/J7Ss3A58u0C5bwBDYveHEjYczffa1xMSgYGdxDq4t98jsedtDLxASF5W6OQcjxPG2TwVO5Z3mj5hHaCTgM9H99cFDumg7lUI44PuLcNrMYrQSroyoSXo94TFL7OPT6CEFirddNNNt2q89fkWolxRq85mwESPFsKznOn6ufdznv8pyxZ3NGAFwnpA7Qbs5kzN/uznPI/9hzAG5kbvpKUlJ44Op4qb2c2Evb7uo/0Yoo6m8Q8gDNiG0AXX2d5e2anlvwcO9jwD3vNNS8+5/lJe++wSBXm3JOno2kqR+x7JM7tqNcJ4nMXRefPu9m4lbgabJFu2L+DcDsr0SAuViEgl6OuDqpfjYf2aZ6KZQFnxqfwGrBDdX25WUr4v/UKnKvBzu/uef6+wDhU5Vfyf0a0UW7Ns3aItzAzPs3FoNLD8G4TuvQ0IXXfbFqgz3yD2+Puu6NeeZfuVTS7ucromz3skd7uPYnW6GWxsRlyufNdfVtGsu9Mobl9A3H0+IXm/Jpo5uR9wCqE1UkSkqtVcC1FvibUkxVuRiO4PdPcub3kQrSezJaHrLdvy8Gyhlooi68y7wWe+VhcLizj+kzBu6dFO6r2cMLj7wujQsYQVk7/b1ViTZmar0X57kTcKlCt6YcYkWFjXag/gKM/ZFxC409vvCygi0qcpIapCZvaEu2+b7V6KujIedffNreNF9LyD7p1pFLfBJ2Zm7u4W9onD3T/uoOyKwC8ISQHAPcCvotaGLom6d05h+X3PSm5tK/G8ewG/IyylMIcw9maax1aKriYWNmZtty9gdHwEcHe8m1dEpK+ruS6zPuJ6M7sYGBqtNXQ4YbArhIUNIXTzLJcQdVBnsRt8AmwatSgNI+RHc4FD3X1qbsEo8UkXUWcpJhJmuDUS2zqkzOfI50zCbLl/u/uWZvYl4Nu5hawLCzMmpF9uMgRhHFHUxSciUjOUEFWZaNzHdcAYwgyp0cAv3f2eqMjL2UUG81hsZq8CP3P3e6P6biN8ea8EvGBmT9B+EPZeeeq5BDjJ3e+L6tglOrZjbkELqyz/mOX3VOtOa04pW4eU0xJ3f8/M6syszt3vM7Nz85SLL8x4OmGcTiX6pIuPiYj0OUqIqkzUVXWHh/Vp7snzeMFVmuOzp6J/AX7bhTBWzCZD0Tnvt8IrPP+DsN/WZSwbm9RdpWwdUk4fRt2EDwETzWwOy2Ycfsbdr8z+bGYnxO9XmKL3BRQR6euUEFWnKWa2jS/b7qIo+WbYeYGNPS3sEXZggapeM7NfELaWgNBt9FqBskvd/aJS4ixCKVuHlNNewCLCbLFvR+c9vZPnVOwgvRJmTIqI9HkaVF2FzGw6sBFh8cTsTLaCA6Y7qSu+N9uthFanYwndXM+4+955nrMKIRHYOTr0ENCUbx0lC/uszSFMzS9mT7WKU2BqfHaD2kVAu27InOcWXFdJREQqhxKiKmJm63rY/XxUvsfdPXeT0WLqLHnrjBLrfz3PYfcubPtgZr/s4GF39zNLrbO7Ciz0GU+gBtF+yYUeXVtIRES6RglRFYm3NpjZje7+zTLUWdTebNHj57r7CbGB2O0UGIBdNmZ2cp7DKwJHEAZaD+7J83fEzI5294uTOr+IiHSPxhBVF4v9XK6NNbMDlHH3T81sZr5kKJIdMxQfiJ1NjIwCzGwzll8zaLkVsDvj7r+L1bkSYSzPYYR9tn5X6Hm9QcmQiEh1U0JUXTraDqSrStk6Y6SZbe/uF0JYIBIYEcVySr7Ko21GdiEkRHcQVkZ+mLAPVsnMbBhhE9SDgSuBrfKNXRIRESmFEqLqMi6WrKyQk8h0aWxKiTONfkLYvyyrP2FPtRUJe6r9I89z9gXGEXZ8P8zMVifsu1YyM/sNsA9hzaPPdbRCtoiISCmUEFWRCpgm3d/d34zdfzjal+u9DtYhWujubWa2NJrRNgdYp4vnP5kwU+3nwM/CGpWABiuLiEg3KSGSUqwSv+Pux8XujijwnElmNpSwtchk4GPCjLaSuXtdV54nIiLSGc0yk6KZ2UTgfne/NOf40cAu7n5gznEDRmZblcwsBazs7s/2UsgiIiJFUUIkRTOz1YB/ErqtpkSHtwYGABPc/Z08z/lsWr+IiEilUkIkJTOzLwObRnefd/f/dFD2SuCCUrcZERER6U1KiKRHlXObERERkZ6ihEh6RE9sMyIiItJTlBBJj+iJbUZERER6iqYxS0/piW1GREREeoQSIukpPbHNiIiISI9Ql5n0CDP7lGWDqFcAFmQfQqtKi4hIhVFCJCIiIjVPXWYiIiJS85QQiYiISM1TQiQiIiI1TwmRiIiI1Lz/B+qYhmzyoTr/AAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_students.plot.bar(x='Name', y='StudyHours', color='teal', figsize=(6,4))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        },
        "id": "JFWOC-NbJxJw",
        "outputId": "ec474304-ffaa-461e-d938-61da5f93fae0"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Name'>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Get the variable to examine\n",
        "var_data = df_students['Grade']\n",
        "\n",
        "# Create a Figure\n",
        "fig = plt.figure(figsize=(10,4))\n",
        "\n",
        "# Plot a histogram\n",
        "plt.hist(var_data)\n",
        "\n",
        "# Add titles and labels\n",
        "plt.title('Data Distribution')\n",
        "plt.xlabel('Value')\n",
        "plt.ylabel('Frequency')\n",
        "\n",
        "# Show the figure\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "oOYNyNWJJ5dt",
        "outputId": "71e1c68d-4602-41ff-9d8f-eb0d7ce4a259"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Get the variable to examine\n",
        "var = df_students['Grade']\n",
        "\n",
        "# Get statistics\n",
        "min_val = var.min()\n",
        "max_val = var.max()\n",
        "mean_val = var.mean()\n",
        "med_val = var.median()\n",
        "mod_val = var.mode()[0]\n",
        "\n",
        "print('Minimum:{:.2f}\\nMean:{:.2f}\\nMedian:{:.2f}\\nMode:{:.2f}\\nMaximum:{:.2f}\\n'.format(min_val,\n",
        "                                                                                        mean_val,\n",
        "                                                                                        med_val,\n",
        "                                                                                        mod_val,\n",
        "                                                                                        max_val))\n",
        "\n",
        "# Create a Figure\n",
        "fig = plt.figure(figsize=(10,4))\n",
        "\n",
        "# Plot a histogram\n",
        "plt.hist(var)\n",
        "\n",
        "# Add lines for the statistics\n",
        "plt.axvline(x=min_val, color = 'gray', linestyle='dashed', linewidth = 2)\n",
        "plt.axvline(x=mean_val, color = 'cyan', linestyle='dashed', linewidth = 2)\n",
        "plt.axvline(x=med_val, color = 'red', linestyle='dashed', linewidth = 2)\n",
        "plt.axvline(x=mod_val, color = 'yellow', linestyle='dashed', linewidth = 2)\n",
        "plt.axvline(x=max_val, color = 'gray', linestyle='dashed', linewidth = 2)\n",
        "\n",
        "# Add titles and labels\n",
        "plt.title('Data Distribution')\n",
        "plt.xlabel('Value')\n",
        "plt.ylabel('Frequency')\n",
        "\n",
        "# Show the figure\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        },
        "id": "5yI9jr1QKAgN",
        "outputId": "48f60ba1-8944-4484-8a67-8c52f5a65da3"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Minimum:3.00\n",
            "Mean:49.18\n",
            "Median:49.50\n",
            "Mode:50.00\n",
            "Maximum:97.00\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Get the variable to examine\n",
        "var = df_students['Grade']\n",
        "\n",
        "# Create a Figure\n",
        "fig = plt.figure(figsize=(10,4))\n",
        "\n",
        "# Plot a histogram\n",
        "plt.boxplot(var)\n",
        "\n",
        "# Add titles and labels\n",
        "plt.title('Data Distribution')\n",
        "\n",
        "# Show the figure\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "SU4joLlwKHnD",
        "outputId": "97ea4a57-3c98-4f31-eba4-ef919bb7f132"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a function that we can re-use\n",
        "def show_distribution(var_data):\n",
        "    from matplotlib import pyplot as plt\n",
        "\n",
        "    # Get statistics\n",
        "    min_val = var_data.min()\n",
        "    max_val = var_data.max()\n",
        "    mean_val = var_data.mean()\n",
        "    med_val = var_data.median()\n",
        "    mod_val = var_data.mode()[0]\n",
        "\n",
        "    print('Minimum:{:.2f}\\nMean:{:.2f}\\nMedian:{:.2f}\\nMode:{:.2f}\\nMaximum:{:.2f}\\n'.format(min_val,\n",
        "                                                                                            mean_val,\n",
        "                                                                                            med_val,\n",
        "                                                                                            mod_val,\n",
        "                                                                                            max_val))\n",
        "\n",
        "    # Create a figure for 2 subplots (2 rows, 1 column)\n",
        "    fig, ax = plt.subplots(2, 1, figsize = (10,4))\n",
        "\n",
        "    # Plot the histogram   \n",
        "    ax[0].hist(var_data)\n",
        "    ax[0].set_ylabel('Frequency')\n",
        "\n",
        "    # Add lines for the mean, median, and mode\n",
        "    ax[0].axvline(x=min_val, color = 'gray', linestyle='dashed', linewidth = 2)\n",
        "    ax[0].axvline(x=mean_val, color = 'cyan', linestyle='dashed', linewidth = 2)\n",
        "    ax[0].axvline(x=med_val, color = 'red', linestyle='dashed', linewidth = 2)\n",
        "    ax[0].axvline(x=mod_val, color = 'yellow', linestyle='dashed', linewidth = 2)\n",
        "    ax[0].axvline(x=max_val, color = 'gray', linestyle='dashed', linewidth = 2)\n",
        "\n",
        "    # Plot the boxplot   \n",
        "    ax[1].boxplot(var_data, vert=False)\n",
        "    ax[1].set_xlabel('Value')\n",
        "\n",
        "    # Add a title to the Figure\n",
        "    fig.suptitle('Data Distribution')\n",
        "\n",
        "    # Show the figure\n",
        "    fig.show()\n",
        "\n",
        "# Get the variable to examine\n",
        "col = df_students['Grade']\n",
        "# Call the function\n",
        "show_distribution(col)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 412
        },
        "id": "V4ybo1ydKLdw",
        "outputId": "f4ffd04d-113e-4152-ec38-d5053ba5abe9"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Minimum:3.00\n",
            "Mean:49.18\n",
            "Median:49.50\n",
            "Mode:50.00\n",
            "Maximum:97.00\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def show_density(var_data):\n",
        "    from matplotlib import pyplot as plt\n",
        "\n",
        "    fig = plt.figure(figsize=(10,4))\n",
        "\n",
        "    # Plot density\n",
        "    var_data.plot.density()\n",
        "\n",
        "    # Add titles and labels\n",
        "    plt.title('Data Density')\n",
        "\n",
        "    # Show the mean, median, and mode\n",
        "    plt.axvline(x=var_data.mean(), color = 'cyan', linestyle='dashed', linewidth = 2)\n",
        "    plt.axvline(x=var_data.median(), color = 'red', linestyle='dashed', linewidth = 2)\n",
        "    plt.axvline(x=var_data.mode()[0], color = 'yellow', linestyle='dashed', linewidth = 2)\n",
        "\n",
        "    # Show the figure\n",
        "    plt.show()\n",
        "\n",
        "# Get the density of Grade\n",
        "col = df_students['Grade']\n",
        "show_density(col)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "ys_nvPNnKOwA",
        "outputId": "0a2e4dad-bc66-4ca8-f4e2-ecaa10b9c7c7"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "reaywQfxKTiB"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}