scoring-gve-distribution

distribution-floripa.ipynb

{
  "cells": [
    {
      "metadata": {
        "trusted": true
      },
      "id": "2bec0f14",
      "cell_type": "code",
      "source": "import pandas as pd\n\n\ndf = pd.read_csv(\"Brazil-06-16_14_09_22.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": "['biological oceanography',\n 'chemical oceanography',\n 'data science',\n 'geology and geophysics',\n 'meteorology',\n 'ocean engineering',\n 'physical oceanography',\n 'resource management']"
          },
          "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                                                  21                                                      \nunique                                                  2                                                      \ntop                                                    No                                                      \nfreq                                                   17                                                      \n\n       In terms of gender identity, do you consider yourself a minority with respect to your research field?  \ncount                                                  21                                                     \nunique                                                  2                                                     \ntop                                                    No                                                     \nfreq                                                   13                                                     ",
            "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>21</td>\n      <td>21</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>17</td>\n      <td>13</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Looking at the name we have 12 female 9 male and 13 said they **do not** consider themselves minority in their research field."
    },
    {
      "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     22\nunique    21\ntop        2\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"
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    "_draft": {
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    "gist": {
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      "language": "python"
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      "nbconvert_exporter": "python",
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