distribution-gve

distribution-gve.ipynb

{
  "cells": [
    {
      "metadata": {
        "trusted": true
      },
      "id": "2bec0f14",
      "cell_type": "code",
      "source": "import pandas as pd\n\n\ndf = pd.read_csv(\"GVE_only-06-16_14_13_48.csv\")",
      "execution_count": 1,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "id": "c2fa68ba",
      "cell_type": "code",
      "source": "def score_programming(data):\n    \"\"\"https://github.com/oceanhackweek/admin/issues/41#issuecomment-1157692167\"\"\"\n    nbox = len(data.split(\"\\n\"))\n    score = 0\n    if nbox <= 3:\n        score = 1\n    elif nbox >=4 and nbox < 6:\n        score = 2\n    elif nbox >=6:\n        score = 3\n    else:\n        raise ValueError(f\"Could not evalute score for {nbox}.\")\n    return score\n\n\ncol = \"For the primary programming language you listed above, please check the boxes of the tasks you can execute.  Check all that apply.\"\n\nscores = [score_programming(data) for data in df[col]]\n\ndf[\"score programming\"] = scores",
      "execution_count": 2,
      "outputs": []
    },
    {
      "metadata": {},
      "id": "9ef72a79",
      "cell_type": "markdown",
      "source": "### Oceanographic Subfields"
    },
    {
      "metadata": {
        "trusted": true
      },
      "id": "f7bda223",
      "cell_type": "code",
      "source": "import re\n\n\nsubfields = [re.split(\"\\n|, \", s.lower()) for s in df[\"In which field(s) does your research interest fall under? Select all that apply.\"]]\nflat_list = [answer for applicant in subfields for answer in applicant]\n\nunique = sorted(set(flat_list))\n\nunique",
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 3,
          "data": {
            "text/plain": "['artscience',\n 'biological oceanography',\n 'chemical oceanography',\n 'coastal morphology',\n 'data science',\n 'education',\n 'geology and geophysics',\n 'geomorphology',\n 'meteorology',\n 'ocean engineering',\n 'ocean literacy',\n 'physical oceanography',\n 'resource management',\n 'statistics']"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "id": "c11b1f03",
      "cell_type": "code",
      "source": "compose = {}\n\nfor k, applicant in enumerate(subfields):\n    compose.update({k: [True if e in sorted(applicant) else False for e in unique]})\n\n\nsubfields = pd.DataFrame(compose).T\nsubfields.columns = unique",
      "execution_count": 4,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "id": "deae8572",
      "cell_type": "code",
      "source": "subfields.sum().plot.bar();",
      "execution_count": 5,
      "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"
          }
        }
      ]
    },
    {
      "metadata": {},
      "id": "926aed42",
      "cell_type": "markdown",
      "source": "### Diversity"
    },
    {
      "metadata": {
        "scrolled": false,
        "trusted": true
      },
      "id": "1b81a35d",
      "cell_type": "code",
      "source": "diversity = \"In terms of ethnic identity, do you consider yourself a minority with respect to your research field?\"\ngender = \"In terms of gender identity, do you consider yourself a minority with respect to your research field?\"\n\ndf[[diversity, gender]].describe()",
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 6,
          "data": {
            "text/plain": "       In terms of ethnic identity, do you consider yourself a minority with respect to your research field?  \\\ncount                                                  27                                                      \nunique                                                  2                                                      \ntop                                                    No                                                      \nfreq                                                   21                                                      \n\n       In terms of gender identity, do you consider yourself a minority with respect to your research field?  \ncount                                                  26                                                     \nunique                                                  2                                                     \ntop                                                    No                                                     \nfreq                                                   16                                                     ",
            "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>In terms of ethnic identity, do you consider yourself a minority with respect to your research field?</th>\n      <th>In terms of gender identity, do you consider yourself a minority with respect to your research field?</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>27</td>\n      <td>26</td>\n    </tr>\n    <tr>\n      <th>unique</th>\n      <td>2</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>top</th>\n      <td>No</td>\n      <td>No</td>\n    </tr>\n    <tr>\n      <th>freq</th>\n      <td>21</td>\n      <td>16</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "id": "7b65b392",
      "cell_type": "markdown",
      "source": "### Language"
    },
    {
      "metadata": {
        "trusted": true
      },
      "id": "cf8050a6",
      "cell_type": "code",
      "source": "col = \"Please rank the programming languages, up to 3, that you are most familiar with.\"\ndf[col].describe()",
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 7,
          "data": {
            "text/plain": "count         27\nunique        24\ntop       Python\nfreq           2\nName: Please rank the programming languages, up to 3, that you are most familiar with., dtype: object"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "id": "12e033a0",
      "cell_type": "code",
      "source": "ax = df[\"score programming\"].T.plot.hist(bins=4)\nax.set_xticks([1, 2, 3]);",
      "execution_count": 8,
      "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"
          }
        }
      ]
    }
  ],
  "metadata": {
    "_draft": {
      "nbviewer_url": "https://gist.github.com/3100d7b04ca56ff99cc0431fc10ebb3d"
    },
    "gist": {
      "id": "3100d7b04ca56ff99cc0431fc10ebb3d",
      "data": {
        "description": "distribution-gve",
        "public": true
      }
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3 (ipykernel)",
      "language": "python"
    },
    "language_info": {
      "name": "python",
      "version": "3.10.5",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}