analyse_dois.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import rc\n",
    "rc('font', family='sans-serif')\n",
    "rc('font', size=13.0)\n",
    "rc('text', usetex=False)\n",
    "rc('figure', figsize=(11.69,8.27))\n",
    "\n",
    "from matplotlib.font_manager import FontProperties\n",
    "\n",
    "panel_label_font = FontProperties().copy()\n",
    "panel_label_font.set_weight(\"bold\")\n",
    "panel_label_font.set_size(14.0)\n",
    "panel_label_font.set_family(\"sans-serif\")\n",
    "\n",
    "# Seaborn styles\n",
    "sns.set_style(\"whitegrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/asura/.virtualenvs/altmetrics/lib/python3.5/site-packages/numpy/lib/arraysetops.py:472: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  mask |= (ar1 == a)\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv(\"out.csv\", dtype={'cr_works':str})\n",
    "df.set_index(\"id\", inplace=True)\n",
    "df['timestamp'] = pd.to_datetime(df['timestamp'])\n",
    "df['date'] = pd.to_datetime(df['date'], errors = 'coerce')\n",
    "\n",
    "df = df[~df.date.isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1670795"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "200                66.6\n",
       "404                33.0\n",
       "ReadTimeout         0.3\n",
       "503                 0.0\n",
       "500                 0.0\n",
       "ConnectionError     0.0\n",
       "Name: cr_works, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "100*df.cr_works.value_counts().div(len(df)).round(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "EOL while scanning string literal (<ipython-input-6-e057c411a0ec>, line 13)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-6-e057c411a0ec>\"\u001b[0;36m, line \u001b[0;32m13\u001b[0m\n\u001b[0;31m    pdf = pdf.replace(\"404\", \"CoulI'm in until 5ish\u001b[0m\n\u001b[0m                                                   ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m EOL while scanning string literal\n"
     ]
    }
   ],
   "source": [
    "pdf = pd.DataFrame(columns=[\"date\", \"doi\", \"cr_works\"])\n",
    "a = df[df.cr_works==\"200\"].set_index(\"date\").groupby(pd.Grouper(freq=\"M\")).doi.count().reset_index()\n",
    "a['cr_works'] = \"200\"\n",
    "pdf = pdf.append(a)\n",
    "\n",
    "a = df[df.cr_works==\"404\"].set_index(\"date\").groupby(pd.Grouper(freq=\"M\")).doi.count().reset_index()\n",
    "a['cr_works'] = \"404\"\n",
    "pdf = pdf.append(a)\n",
    "pdf.index = range(len(pdf))\n",
    "pdf.date = pd.to_datetime(pdf.date)\n",
    "\n",
    "pdf = pdf.replace(\"200\", \"Found DOI\")\n",
    "pdf = pdf.replace(\"404\", \"CoulI'm in until 5ish\n",
    "￼dn't find DOI\")\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = plt.gca()\n",
    "ax.xaxis_date()\n",
    "\n",
    "sns.lineplot(x=\"date\", y=\"doi\", hue=\"cr_works\", style=\"cr_works\", data=pdf, markers=True)\n",
    "\n",
    "# ax.xaxis.set_major_formatter(plt.FixedFormatter(pdf.date.dt.strftime(\"%Y\")))\n",
    "\n",
    "plt.gcf().autofmt_xdate()\n",
    "sns.despine(left=True, right=True, top=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/asura/.virtualenvs/altmetrics/lib/python3.5/site-packages/pandas/plotting/_converter.py:129: FutureWarning: Using an implicitly registered datetime converter for a matplotlib plotting method. The converter was registered by pandas on import. Future versions of pandas will require you to explicitly register matplotlib converters.\n",
      "\n",
      "To register the converters:\n",
      "\t>>> from pandas.plotting import register_matplotlib_converters\n",
      "\t>>> register_matplotlib_converters()\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 841.68x595.44 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pdf = pd.DataFrame(columns=[\"date\", \"doi\", \"cr_works\"])\n",
    "a = df[df.cr_works==\"200\"].set_index(\"date\").groupby(pd.Grouper(freq=\"Y\")).doi.count().reset_index()\n",
    "a['cr_works'] = \"200\"\n",
    "pdf = pdf.append(a)\n",
    "\n",
    "a = df[df.cr_works==\"404\"].set_index(\"date\").groupby(pd.Grouper(freq=\"Y\")).doi.count().reset_index()\n",
    "a['cr_works'] = \"404\"\n",
    "pdf = pdf.append(a)\n",
    "pdf.index = range(len(pdf))\n",
    "pdf.date = pd.to_datetime(pdf.date)\n",
    "\n",
    "pdf = pdf.replace(\"200\", \"Found DOI\")\n",
    "pdf = pdf.replace(\"404\", \"Couldn't find DOI\")\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = plt.gca()\n",
    "ax.xaxis_date()\n",
    "\n",
    "sns.lineplot(x=\"date\", y=\"doi\", hue=\"cr_works\", style=\"cr_works\", data=pdf, markers=True)\n",
    "\n",
    "# ax.xaxis.set_major_formatter(plt.FixedFormatter(pdf.date.dt.strftime(\"%Y\")))\n",
    "# plt.xlim(1960, 2022)\n",
    "\n",
    "plt.gcf().autofmt_xdate()\n",
    "sns.despine(left=True, right=True, top=True)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "altmetrics",
   "language": "python",
   "name": "altmetrics"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}