ddds-schedule-FT.ipynb

ddds-schedule-ft.ipynb

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        "<a href=\"https://colab.research.google.com/gist/rwcitek/fbfdc7fa357f432dec4e1e8500c82018/ddds-schedule-ft.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 Full-Time Data Science Boot Camp"
      ],
      "metadata": {
        "id": "-PiCN-LePwmu"
      }
    },
    {
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      "execution_count": 1,
      "metadata": {
        "id": "lIYdn1woOS1n"
      },
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      "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[\"Lecture (32hrs)\"] = [0, 8, 8, 8, 8, 0, 0]\n",
        "df[\"Practice (32hrs)\"] = [ 8, 2, 2, 2, 2, 8, 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": "f58d76ec-0197-4cc6-bc15-aa5fe9320ee5"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Sleep  Hygiene  Meals  Commute  Exercise  Lecture (32hrs)  \\\n",
              "Sunday         8        1      2        2         1                0   \n",
              "Monday         8        1      2        2         1                8   \n",
              "Tuesday        8        1      2        2         1                8   \n",
              "Wednesday      8        1      2        2         1                8   \n",
              "Thursday       8        1      2        2         1                8   \n",
              "Friday         8        1      2        2         1                0   \n",
              "Saturday       8        1      2        2         1                0   \n",
              "\n",
              "           Practice (32hrs)  Free  Total  \n",
              "Sunday                    8     2     24  \n",
              "Monday                    2     0     24  \n",
              "Tuesday                   2     0     24  \n",
              "Wednesday                 2     0     24  \n",
              "Thursday                  2     0     24  \n",
              "Friday                    8     2     24  \n",
              "Saturday                  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\": \"Lecture (32hrs)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\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 (32hrs)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 2,\n        \"max\": 8,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          2\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": "markdown",
      "source": [
        "- [matplotlib colors]( https://matplotlib.org/stable/gallery/color/named_colors.html )"
      ],
      "metadata": {
        "id": "AeOGPCmB_d3f"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "colors = dict(zip(\n",
        "  df.iloc[:,:-1].columns,\n",
        "  '''\n",
        "    gold\n",
        "    darkslategrey\n",
        "    orange\n",
        "    silver\n",
        "    blue\n",
        "    lime\n",
        "    purple\n",
        "    red\n",
        "  '''.split() )\n",
        ")\n",
        "colors"
      ],
      "metadata": {
        "id": "dGk5Xd0v6w1L",
        "outputId": "e170a9ee-3490-44fc-d9ec-47d084e77c1d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Sleep': 'gold',\n",
              " 'Hygiene': 'darkslategrey',\n",
              " 'Meals': 'orange',\n",
              " 'Commute': 'silver',\n",
              " 'Exercise': 'blue',\n",
              " 'Lecture (32hrs)': 'lime',\n",
              " 'Practice (32hrs)': 'purple',\n",
              " 'Free': 'red'}"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sp = df.iloc[:,:-1].plot(\n",
        "    figsize=(12, 8),\n",
        "    kind='bar',\n",
        "    stacked=True,\n",
        "    # colormap='tab20c',\n",
        "    color=colors,\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 Full-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": 670
        },
        "id": "NabYF1286y1E",
        "outputId": "0fc63a17-d0ff-4b92-cf86-3a57edd92247"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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XL140pk+fbhQpUsSQZPTv3z/NfjO7fudUHTczcuRIsw0DBgww4uLibmn7xMREo379+ubvsQkTJhinT5821x87dsyYOnWqMWTIEIftMutf2bk+pj6nBQsWNOrXr29s2rTJbOuKFSuM0NBQM7TLqeOx8ppuGJm/bi9evGhMmzbN/KOM/Y90Nxo4cKB57R82bJhx4cIFwzAM4/z588bQoUPN8zZ48OA02/74448O4Zv9D38JCQnGl19+aV6r69Wrl+a9W078Tvntt9/M+m/1D3U3atWqlTFv3jzjxIkT5rLLly8bCxYsMMqXL29IcgiAU0sdSFarVs34/fffDcNI+cPn119/bRQoUMCQZPTu3dsoUaKE8fDDDxs7duwwDCPl2vV///d/5rXks88+S7N/+/slPz8/w2azGaNHjzavfadPnzZ69+5ttuHDDz/M1nkAAKsQQCFP+fnnn81frhs3bnRYZx9ZcezYMcMwDOPLL79M98Op/a+AHh4e5gdSwzCM559/3pBkPPvssxnW/9577xmSjAceeMBh+a+//mpIMgoXLuwwiiW1I0eOmCOvtm7d6rDuxg+dCQkJZqDm5+eX6QfWzGQ1gGrcuHG6H/ImTZpkSDI8PT2NxMTEW6r7VgOozD4816hRw5BkdO/e3WF5UlKSUbZsWUOSMXXq1AzreOKJJwxJRt++fW/pGJwVQGWnL97M3LlzzbalNyosMTHRDKjuv//+NOudEUDdTv988803zYAoo7/YL1iwwJBSRpfcav+2v2bz58+f5q/29ke5cuVu+bjvpgBKkjFmzJg062NiYsxroSRj1qxZacrs27fPXL927VqHddm93t7MrQZQhmGY16H69evfUl2bNm0ypJRREleuXHFYl9UAKjt13MyZM2fMAMu+j+bNmxvDhg0zfvjhhwxHgthNmzbNDDWWLFmS5Xoz61/ZuT6mPqcVKlRId1TYokWLzDJHjhzJkeOx8ppuGNdftx4eHg7XoNQjicuXL2+MHTs23ZE1R48eNQOwG8MzuwEDBpjXvBtHMlWsWNEM7dLbf+pzeuMf03Lid4r95yLJWL9+/W3v52aOHj1quLu7GzabLd0/aNnbEBwcbJw9ezbN+mHDhpllKleu7PAe1O65554zpPRHcqV+vzRs2LB022gP5wMCAm759Q4AdwJzQCFPqV+/vtzc3CQ5TjC+c+dOnTp1SmXLllWRIkUkSREREZLSzhdl365u3bpyd3eXlDIfjv2++cGDB2dYf+fOnSVJ27dv16lTp8zln3/+uSSpY8eO5rdI3ahYsWLmROrLly/PsI7Y2Fg1b95c8+fPV2hoqH799VeH+T6sMHToULm4pL0ctG7dWlLK14/v3bvX0jYUL15cXbp0SXfdE088IUn666+/HJb/+uuv2rt3rwIDA9W9e/cM923/uWV23nOL7PbFm5k3b56klP7ftGnTNOtdXV01YsQISdKOHTvMb71zptvpn/bX5IABA5Q/f/5099umTRv5+vrq7Nmz2rJly221LTExUadOncrwcS/z8PBI92vVfX19VbduXUlSWFiYnn322TRl7rvvPpUpU0ZS2td9Tl5vc4r92yxvnAPqZmrWrKnChQvr0qVL6c69lhOyU0dgYKDWrVunRx99VJJ06dIlLVu2TKNHj1abNm0UHBysmjVrasaMGenO0/fFF19Ikh577DE99thj2T6WnLw+Dhw4MN15q1q0aGG+z7jx+nc7x2P1Nf3GulJff6Kjo81158+f15kzZ8wvkkjtu+++07Vr1+Th4aHXXnst3X2/8cYbcnd3V2Jior799ltz+V9//WVOav7GG28oX758abZt1aqVateuLSllfqacdu7cOfP/Vn6zbNGiRfXAAw/IMAxt2LAhw3IvvviiChUqlGZ5s2bNzP8PGDDAfA+aXpkbr3upeXp66pVXXkl33fDhwyWl/LxXrFiR4T4AwFlcnd0A4FYUKFBAtWrV0vr16xUZGWm+mbOHTPbQSUr5YFOyZEkdPHhQUVFRuu+++3T16lVzok/7hxNJ2rJli+Lj4yUp3Q/l6Tl06JCCg4MlSevXr5eU8sEoswkgY2JizG3Tc+LECUVERGjbtm0qV66cli9fnukkrDnlwQcfTHe5PcyTbv2D1a2qVatWht9GY2/HjW2wn/eYmBiHtt7IPhlxRuc9N8luX7yZzZs3S5IeeeSRDMs0btxY+fLlU1JSkjZv3pxmktY77Vb757Fjx8yf9QsvvJDuByI7+4Syhw4dyrCezERERGQ6qfi9rFKlSvLy8kp3nb2/1qxZM8PXfXBwsPbt26cLFy44LM+p6+2dcvXqVX3xxRdasGCBduzYYX4d/I2y83XwVtZRqlQp/fzzz9q1a5cWLVqk3377TVu3bjW/3GHLli3q1q2bvv76ay1cuFAeHh6SpGvXrmnTpk2SUgKInJCT18eMXu+urq4KCgrSsWPHHK4rt3s8Vl/TU+vSpYtmzJhhPjcMQxcuXND69es1ZMgQvf/++1q5cqVWrVqloKAgs5z990KtWrXk6+ub7r4LFiyomjVrav369Wb51Nu6uro6vAe70aOPPqo//vjDYdvcKDk5WXPnztXcuXO1bds2nTlzxvz5pZbZa8kett0o9c+0Vq1amZa58bqXWs2aNTP8OZUtW1bFihXT0aNHtXnz5hx77QFATiGAQp7TuHFjrV+/XuvWrdO1a9fk6upqfgC8caRQRESEDh48qMjISN133336448/zL/+Pfzww2a548ePm//P6l8eU/8V0b59bGysYmNjb2nb1D799FNJKSMHVq5cmeFf93Oaj49Pusvt3zoo6ba/RSm7bUjdjhvbYD/v9lEoN3PlypVstPDOyG5fvJnTp09LSvlLbkY8PDwUGBioU6dOmeWd6Vb7Z+pzePbs2SzVcSvnMC8JCQlJd3n79u314YcfWlp3Vl7T2XndZ/d6m5PsQcWNox5Onz6tRx55xGEkjf31ZQ9Gz5w5o+TkZF26dOm26r4TdUhSxYoVVbFiRfP5yZMntXDhQo0bN06HDh3Szz//rDfeeEPvvPOOpJRRKfafXYkSJW673tRy8vp4q33vdo/H6mt6Zmw2mwICAtSqVSuFh4ercuXK+vvvvzV48GBzNJeUtd8LUsrIwtTlU/8/MDAw3RE9mW2bU1K/7rLzx7LLly+rZcuWDiPs3dzcFBAQYI6kPX/+vBITEzN9LWXld9bNyly7di3D/d/s51S0aFEdPXo0V/z+BoAbcQse8hz7yKW4uDjzL2lr1qyRpDR/fbM/t7+ZsP/r5eXl8BeqpKQk8/9XrlyRkTI/WqaP1GGXffspU6ZkadvUf6FMrWXLlvLz81N8fLy6det2134ozin28/7ggw9m6bwbhuHkFt9cdvsiHM/hrl27snQOu3bt6rwGWyijWwPto4Pyopy63uaUuLg47d+/X1LKrYOp9e/fX3///bcKFSqkL774QidOnNCVK1d05swZnTx5UidPnjRH8t3u9elO1JGekJAQ9ezZUxs3blThwoUlpdyiZr8VL6ORbdnhzOvj7R5PbrmmFytWTM2bN5ckzZ8/P9OAI6+pXLmy+f+tW7fe9n7eeustrV69Wp6ennr//fd16NAhxcfH69y5c+ZryT5yLi+8nwCA3IgACnlOvXr1zL+yRUZGaufOnTp9+rTuu+8+8y9sdjfOA2X/t379+g7zwqQeJXA7t2vYt8/urR41atTQypUrVbBgQa1atUqPP/54tv5ifbfLqfOem2S3L96M/YNiZrcP2N9wpy6fl1h9Dm+XfTRKerdz2OV0MOSsUMZKue11v2zZMjNkSB0cJCYmasGCBZKkyZMnq1u3bmlGpCUlJWV5lF567kQdNxMcHGzOx3bhwgWdOXNGkhxGjeTUz8qZr+3bPZ7cdD2yj9y6dOmS+XOSsvZ7IfX61L8X7P8/e/asEhISbmnbnFKzZk35+flJkr7//vvb3s/cuXMlpcyj1K9fP4WFhaUJHk+ePHn7Dc0hx44dy9L6vPj7G8DdjwAKeY6Hh4fq1KkjKWVEU3rzP9mVLl1axYoV07Fjx7Rjx45053+SUu7Ft086+uOPP95ym+rXry9JWrx48S1ve6OaNWtq1apVCggIUGRkpFq0aGHOU5OX2CeNtvKvhPbzfvLkyVw/r0RWZbcv3kzNmjUlSatWrcqwTGRkpPnX8YzmqcgO+xt6q/pGyZIlzVsUrDiHt6tgwYKSpCNHjqS7/uLFi+ZkvshYTl5vs+vq1asaO3asJMnPz09t2rQx16WeOyY8PDzd7detW5dhIJl64v2MXivZrSOneHt7m/+3/4HI1dXVHGmcU69Dq6+Pmbnd43Fmm2+UOmBKPT+b/ffC5s2bMwzBo6OjHeaKunHba9eumaPR07Ny5co020o5817B1dVVPXr0kJTyu+3XX3/N8rapJ8+3X5szei0dPHhQ+/btu+125pTNmzdn+L5w37595s/Z/rMBgNyEAAp5kj1AWr9+vfmmJqMh6/ZgasKECeYcQDcGUF5eXuY3MU2YMMGcXDUjN84xYH/js2PHDk2ZMiXTbS9dupTu5LCphYeH65dfflFgYKDWrl2r5s2b6+LFi5luk9vYJ8hM/S08Oa1x48bmN2X179//pufV6onUc0J2++LNPPPMM5Kk3377TT///HOa9deuXdObb74pSbr//vt1//3339L+s+JO9I0XX3xRUspE1Te7JeNO9YsHHnhAUso3TqXnnXfeyXQEAVLk9PX2dl25ckVdu3Y1+9eQIUPk7+9vrvf19TXD1u3bt6fZ/tq1a3r99dcz3H/qSYYzeq1kt46bWbt27U1vBY+LizNHYZUqVcrhHLzwwguSpKVLl2rp0qW33Q47q6+PN3M7x+PsNtudPXvW/Fa0smXLOvSvtm3bytXVVfHx8ZowYUK6248dO1YJCQnKnz+/2rZtay6vWrWqKlWqJEkaM2aMwy2HdkuXLtXGjRslSR06dHBYl1O/DwYNGmTeatqhQwf9888/mZa/cuW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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "8a90LE60NJi7"
      },
      "execution_count": 4,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "name": "ddds-schedule-FT.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}