notebooks/analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from wordcloud import WordCloud, STOPWORDS\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import nltk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "stopwords = set(STOPWORDS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "RESOURCE_DIR = \"../resources/\"\n",
    "CSV_FILE = f\"{RESOURCE_DIR}/Reproducibility Survey EACL'21 (public document) - 35 papers to produce Figure 1.csv\"\n",
    "EXCEL_FILE = f\"{RESOURCE_DIR}/Reproducibility Survey EACL'21 (public document).xlsx\"\n",
    "SAVE_DIR = f\"../figures/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_df(csv_path):\n",
    "    df = pd.read_csv(csv_path)\n",
    "    return df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pandas helpers from \n",
    "# https://gist.github.com/shubhamagarwal92/13e2c41d09156c3810740d7697a883d1\n",
    "def get_column_names(df):\n",
    "    return df.columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['bibtex', 'Authors', 'Title', 'Venue', 'Year', 'Abbr venue',\n",
       "       'Abstract'], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = get_df(CSV_FILE)\n",
    "# print(df)\n",
    "get_column_names(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n"
     ]
    },
    {
     "data": {
      "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>Year</th>\n",
       "      <th>Venue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010</td>\n",
       "      <td>WS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013</td>\n",
       "      <td>ACL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016</td>\n",
       "      <td>WS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017</td>\n",
       "      <td>WS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017</td>\n",
       "      <td>WS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2017</td>\n",
       "      <td>TACL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2017</td>\n",
       "      <td>EACL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2017</td>\n",
       "      <td>EMNLP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2017</td>\n",
       "      <td>EMNLP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2018</td>\n",
       "      <td>WS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2018</td>\n",
       "      <td>TACL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2018</td>\n",
       "      <td>LREC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2018</td>\n",
       "      <td>LREC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2018</td>\n",
       "      <td>LREC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2018</td>\n",
       "      <td>LREC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2018</td>\n",
       "      <td>CL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2018</td>\n",
       "      <td>EMNLP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2018</td>\n",
       "      <td>COLING</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2018</td>\n",
       "      <td>WS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2019</td>\n",
       "      <td>INLG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2019</td>\n",
       "      <td>WS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2019</td>\n",
       "      <td>WS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2019</td>\n",
       "      <td>ACL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2020</td>\n",
       "      <td>ACL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2020</td>\n",
       "      <td>LREC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2020</td>\n",
       "      <td>LREC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2020</td>\n",
       "      <td>LREC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2020</td>\n",
       "      <td>LREC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2020</td>\n",
       "      <td>LREC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2020</td>\n",
       "      <td>LREC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2020</td>\n",
       "      <td>LREC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2020</td>\n",
       "      <td>LREC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2020</td>\n",
       "      <td>LREC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2020</td>\n",
       "      <td>LREC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2020</td>\n",
       "      <td>WS</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Year   Venue\n",
       "0   2010      WS\n",
       "1   2013     ACL\n",
       "2   2016      WS\n",
       "3   2017      WS\n",
       "4   2017      WS\n",
       "5   2017    TACL\n",
       "6   2017    EACL\n",
       "7   2017   EMNLP\n",
       "8   2017   EMNLP\n",
       "9   2018      WS\n",
       "10  2018    TACL\n",
       "11  2018    LREC\n",
       "12  2018    LREC\n",
       "13  2018    LREC\n",
       "14  2018    LREC\n",
       "15  2018      CL\n",
       "16  2018   EMNLP\n",
       "17  2018  COLING\n",
       "18  2018      WS\n",
       "19  2019    INLG\n",
       "20  2019      WS\n",
       "21  2019      WS\n",
       "22  2019     ACL\n",
       "23  2020     ACL\n",
       "24  2020    LREC\n",
       "25  2020    LREC\n",
       "26  2020    LREC\n",
       "27  2020    LREC\n",
       "28  2020    LREC\n",
       "29  2020    LREC\n",
       "30  2020    LREC\n",
       "31  2020    LREC\n",
       "32  2020    LREC\n",
       "33  2020    LREC\n",
       "34  2020      WS"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# https://www.datacamp.com/community/tutorials/wordcloud-python\n",
    "# SA: last is NaN\n",
    "year_venue_df = df[[\"Year\", \"Abbr venue\"]] #[:-1]\n",
    "year_venue_df.columns = [\"Year\", \"Venue\"]\n",
    "print(type(year_venue_df[\"Year\"]))\n",
    "year_venue_df = year_venue_df.dropna()\n",
    "\n",
    "year_venue_df['Year'] = pd.to_datetime(year_venue_df['Year'], format='%Y')  \n",
    "year_venue_df['Year'] = pd.DatetimeIndex(year_venue_df['Year']).year\n",
    "# year_venue_df.Year = year_venue_df.Year.astype(int)\n",
    "year_venue_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = year_venue_df.groupby('Year').apply(lambda x: x['Venue'].value_counts())\n",
    "df = df.unstack().fillna(0)\n",
    "\n",
    "fig = df.plot(kind='bar', stacked=True,  figsize=(10, 10)).get_figure()\n",
    "save_filepath = f\"{SAVE_DIR}/stacked_bar.png\"\n",
    "fig.savefig(save_filepath)   # save the figure to file\n",
    "\n",
    "# -------------------------------------------------------------------------\n",
    "# SA: Uncomment below to create plot with cubehelix palette\n",
    "# http://rajatrajatg.blogspot.com/2017/01/session7-plotting-graphs-with.html\n",
    "# Create a cubehelix colormap \n",
    "# -------------------------------------------------------------------------\n",
    "# cmap = sns.cubehelix_palette(start=1, light=0.5, as_cmap=True)\n",
    "# fig = df.plot(colormap=cmap, kind='bar', stacked=True,  figsize=(10, 5)).get_figure()\n",
    "# save_filepath = f\"{SAVE_DIR}/stacked_bar_5.png\"\n",
    "# fig.savefig(save_filepath)   # save the figure to file"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}