ddds-schedule-pt.ipynb

ddds-schedule-pt.ipynb

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        "<a href=\"https://colab.research.google.com/gist/rwcitek/19e7a99744d63ac56372fa486127d29b/ddds-schedule-pt.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
      "cell_type": "markdown",
      "source": [
        "# A Week in the Life of the Part-Time Data Science Boot Camp"
      ],
      "metadata": {
        "id": "-PiCN-LePwmu"
      }
    },
    {
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      "execution_count": 1,
      "metadata": {
        "id": "YCKOni_NbgZp"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.DataFrame()\n",
        "df.index = \"Sunday Monday Tuesday Wednesday Thursday Friday Saturday\".split()\n",
        "df[\"Sleep\"] = 8\n",
        "df[\"Hygiene\"] = 1\n",
        "df[\"Meals\"] = 2\n",
        "df[\"Commute\"] = 2\n",
        "df[\"Exercise\"] = 1\n",
        "df[\"Work (40hrs)\"] = [0, 10, 10, 10, 10, 0, 0]\n",
        "df[\"Lecture (8hrs)\"] = [0, 0, 0, 0, 0, 8, 0]\n",
        "df[\"Practice (16hrs)\"] = [ 8, 0, 0, 0, 0, 0, 8]\n",
        "df[\"Free\"] = 24 - df.sum(axis=1)\n",
        "df[\"Total\"] = df.sum(axis=1)\n",
        "\n",
        "df\n"
      ],
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        "outputId": "75c98b8d-d80f-4402-8454-67746a2a41ee"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Sleep  Hygiene  Meals  Commute  Exercise  Work (40hrs)  \\\n",
              "Sunday         8        1      2        2         1             0   \n",
              "Monday         8        1      2        2         1            10   \n",
              "Tuesday        8        1      2        2         1            10   \n",
              "Wednesday      8        1      2        2         1            10   \n",
              "Thursday       8        1      2        2         1            10   \n",
              "Friday         8        1      2        2         1             0   \n",
              "Saturday       8        1      2        2         1             0   \n",
              "\n",
              "           Lecture (8hrs)  Practice (16hrs)  Free  Total  \n",
              "Sunday                  0                 8     2     24  \n",
              "Monday                  0                 0     0     24  \n",
              "Tuesday                 0                 0     0     24  \n",
              "Wednesday               0                 0     0     24  \n",
              "Thursday                0                 0     0     24  \n",
              "Friday                  8                 0     2     24  \n",
              "Saturday                0                 8     2     24  "
            ],
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              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 7,\n  \"fields\": [\n    {\n      \"column\": \"Sleep\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 8,\n        \"max\": 8,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Hygiene\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 1,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Meals\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 2,\n        \"max\": 2,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Commute\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 2,\n        \"max\": 2,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Exercise\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 1,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Work (40hrs)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5,\n        \"min\": 0,\n        \"max\": 10,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          10\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Lecture (8hrs)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Practice (16hrs)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Free\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Total\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 24,\n        \"max\": 24,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          24\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sp = df.iloc[:,:-1].plot(\n",
        "    figsize=(12, 8),\n",
        "    kind='bar',\n",
        "    stacked=True,\n",
        "    colormap='tab20c',\n",
        "    ylim=(0,25),\n",
        ")\n",
        "plt.xticks(rotation=0, ha='center', fontsize=12)\n",
        "plt.yticks(fontsize=12)\n",
        "plt.title(\"Week in the Life of the Part-Time Data Science Boot Camp\", fontsize=18)\n",
        "plt.ylabel(\"Hours\", fontsize = 14)\n",
        "plt.xlabel('')\n",
        "\n",
        "plt.legend(\n",
        "    *(zip(*(list(zip(*sp.get_legend_handles_labels()))[::-1]))), # reverse legend order\n",
        "    bbox_to_anchor=(1.2, .5),\n",
        "    loc='center right'\n",
        ")\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 708
        },
        "id": "NabYF1286y1E",
        "outputId": "57647a09-72f1-4ba7-bf89-bbd4f219a199"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "8a90LE60NJi7"
      },
      "execution_count": 3,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "name": "ddds-schedule-pt.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}