MikeTrizna/Voucher counts in GenBank.ipynb

## Voucher counts in GenBank.ipynb
{
  "cells": [
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Notebook to back up assertion from TDWG abstract: *there has actually been a decrease in the percentage of sequence records that are backed by voucher specimens*"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "import genetic_collections as gc\nimport pandas as pd\n\n%matplotlib inline",
      "execution_count": 1,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Here we use the genetic_collections Python library (https://github.com/MikeTrizna/genetic_collections) to run queries on GenBank to count the number of total records each year (*all_records*), and the number of records that have a voucher (*vouchered_records*)."
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "voucher = '\"src specimen voucher\"[Properties]'\n\nresult_list = []\nfor year in range(2006,2018):\n    date_restraint = '\"{}/01/01\"[PDAT] : \"{}/12/31\"[PDAT]'.format(year, year)\n    all_results = gc.gb_search(raw_query=date_restraint)\n    voucher_query = '({}) AND ({})'.format(voucher, date_restraint)\n    voucher_results = gc.gb_search(raw_query=voucher_query)\n    result = {'year':year,\n              'all_records':all_results.result_count,\n              'vouchered_records':voucher_results.result_count}\n    result_list.append(result)",
      "execution_count": 2,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Then put these results into a Pandas DataFrame for calculations and plotting."
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "count_df = pd.DataFrame(result_list)\ncount_df.head()",
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 3,
          "data": {
            "text/plain": "   all_records  vouchered_records  year\n0      1854913             118279  2006\n1      6919532             107450  2007\n2      5849346             235843  2008\n3      8313765             196068  2009\n4     10971664             309853  2010",
            "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>all_records</th>\n      <th>vouchered_records</th>\n      <th>year</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1854913</td>\n      <td>118279</td>\n      <td>2006</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>6919532</td>\n      <td>107450</td>\n      <td>2007</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>5849346</td>\n      <td>235843</td>\n      <td>2008</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>8313765</td>\n      <td>196068</td>\n      <td>2009</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>10971664</td>\n      <td>309853</td>\n      <td>2010</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "count_df['Percentage Vouchered'] = count_df['vouchered_records'] / count_df['all_records'] * 100\ncount_df.head()",
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 4,
          "data": {
            "text/plain": "   all_records  vouchered_records  year  Percentage Vouchered\n0      1854913             118279  2006              6.376525\n1      6919532             107450  2007              1.552851\n2      5849346             235843  2008              4.031955\n3      8313765             196068  2009              2.358354\n4     10971664             309853  2010              2.824120",
            "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>all_records</th>\n      <th>vouchered_records</th>\n      <th>year</th>\n      <th>Percentage Vouchered</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1854913</td>\n      <td>118279</td>\n      <td>2006</td>\n      <td>6.376525</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>6919532</td>\n      <td>107450</td>\n      <td>2007</td>\n      <td>1.552851</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>5849346</td>\n      <td>235843</td>\n      <td>2008</td>\n      <td>4.031955</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>8313765</td>\n      <td>196068</td>\n      <td>2009</td>\n      <td>2.358354</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>10971664</td>\n      <td>309853</td>\n      <td>2010</td>\n      <td>2.824120</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "ax = count_df.plot.line(x='year',y='Percentage Vouchered',\n                   legend=False, figsize = (10,6),\n                   title='Percentage of GenBank records with a specimen_voucher field (from 2006-2017)')\nax.set_ylabel('Percentage of GenBank records')\nax.set_xlabel('Year')",
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 9,
          "data": {
            "text/plain": "Text(0.5,0,'Year')"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 720x432 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "",
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3",
      "language": "python"
    },
    "language_info": {
      "name": "python",
      "version": "3.6.2",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "gist": {
      "id": "",
      "data": {
        "description": "Voucher counts in GenBank",
        "public": true
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
}