00-data-validation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Validation and some simple Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "import json\n",
    "import matplotlib.pyplot as plt\n",
    "from glob import glob"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First read in the table with the generally available metadata from the CODH."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "overview = pd.read_csv(\"bukan-overview.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, I drop all the lines where I manually decided to not consider the book. This is mostly because is was not a book to begin with or the scan was useless."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = overview[overview[\"K?\"] == \"x\"].drop(\"K?\", 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just checking if the `metadata.json` and the actual number of images is consistent. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING book 200019503: 181 images, 180 manifest pages\n"
     ]
    }
   ],
   "source": [
    "label_mapping = {}\n",
    "for idx in data[\"国文研書誌ID\"]:\n",
    "    idx = int(idx)\n",
    "    with open(f\"data/{idx}/manifest.json\", \"r\", encoding=\"utf-8\") as fd:\n",
    "        manifest = json.load(fd)\n",
    "        label = manifest[\"label\"]\n",
    "        alt_label = manifest[\"metadata\"][3][\"value\"]\n",
    "        if label in label_mapping and label_mapping[label] != alt_label:\n",
    "            print(f\"Inconsistent alternative label in {idx}: {label} <-> {alt_label}\")\n",
    "        label_mapping[label] = alt_label\n",
    "        nr_pages = len(manifest[\"sequences\"][0][\"canvases\"])\n",
    "    images = glob(f\"data/{idx}/image/*\")\n",
    "    if len(images) != nr_pages: print(f\"WARNING book {idx}: {len(images)} images, {nr_pages} manifest pages\")\n",
    "    # print(f\"book {idx}: {len(images)} images, {nr_pages} manifest pages\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mapping the names to latin letters\n",
    "\n",
    "An hiragana reading is already included in the `metadata.json` but it's easier for me and for visualization to have latin letters. Therefore I created a manual mapping:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "hiragana_mapping = {\n",
    "    'ほんちょうぶかん': \"honchōbukan\",\n",
    "    'たいへいぶかん': \"taihenbukan\",\n",
    "    'たいへいぶかんたいぜん': \"taihenbukantaizen\",\n",
    "    'せいとうぶかん': \"seitōbukan\",\n",
    "    'げんろくぶかんたいぜん': \"genrokubukantaizen\",\n",
    "    'ほうえいぶかんたいせい': \"hōeibukantaisen\",\n",
    "    'ごりんぶかん': \"gorinbukan\",\n",
    "    'ほうえいぶかん': \"hōeibukan\",\n",
    "    'しょうふうぶかん': \"shōfūbukan\",\n",
    "    'しょうえんぶかん': \"shōenbukan\",\n",
    "    'しょうとくぶかん': \"shōtokubukan\",\n",
    "    'きょうほうぶかん': \"kyōhōbukan\",\n",
    "    'えいせいぶかん': \"eiseibukan\", \n",
    "    'げんぶんぶかん': \"genbunbukan\",\n",
    "    'かんぽうぶかん': \"kanpōbukan\",\n",
    "    'えんきょうぶかん': \"enkyōbukan\",\n",
    "    'かんえんぶかん': \"kanenbukan\",\n",
    "    'ゆうしぶかん': \"yūshibukan\",\n",
    "    'ほうりゃくぶかん': \"hōryakubukan\",\n",
    "    'たいせいぶかん': \"taiseibukan\",\n",
    "    'めいわぶかん': \"meiwabukan\",\n",
    "    'たいへいりゃくぶかん': \"taiheiryakubukan\",\n",
    "    'しゅうぎょくぶかん': \"shūgyokubukan\",\n",
    "    'あんえいぶかん': \"aneibukan\",\n",
    "    'しゅうちんぶかん': \"shūchinbukan\",\n",
    "    'てんめいぶかん': \"tenmeibukan\",\n",
    "    'まんじゅぶかん': \"manjubukan\",\n",
    "    'かんせいぶかん': \"kanseibukan\",\n",
    "    'きょうわぶかん': \"kyōwabukan\",\n",
    "    'ぶんかぶかん': \"bunkabukan\",\n",
    "    'ぶんせいぶかん': \"bunseibukan\",\n",
    "    'かまくらぶかん': \"kamakurabukan\",\n",
    "    'かいほうりゃくぶかん': \"kaihōryakubukan\", \n",
    "    'てんぽうぶかん': \"tenpōbukan\",\n",
    "    'おうにんぶかん': \"ōninbukan\",\n",
    "    'こうかぶかん': \"kōkabukan\",\n",
    "    'ばんせいぶかん': \"banseibukan\",\n",
    "    'かえいぶかん': \"kaeibukan\",\n",
    "    'しょうえいぶかん': \"shōeibukan\",\n",
    "    'あんせいぶかん': \"anseibukan\",\n",
    "    'ぶんきゅうぶかん': \"bunkyūbukan\",\n",
    "    'けいおうぶかん': \"keiōbukan\",\n",
    "    'おくにわけぶかん': \"okuniwakebukan\",\n",
    "    'かいほうおくにわけりゃくぶかん': \"kaihō okuniwake ryakubukan\"\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How many of the same Bukan do we have?\n",
    "There are many editions of the same bukan. But how are these distributed exactly? Let's plot the book title and the number of books!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "count_by_name = data.groupby([\"書名（統一書名）\", \"Aspect\"]).count()[\"国文研書誌ID\"]\n",
    "count_by_name_index = count_by_name.index.map(lambda idx: (hiragana_mapping[label_mapping[idx[0]]], idx[1]))\n",
    "count_by_name_index = count_by_name_index.set_names([\"Title\", \"Aspect\"])\n",
    "count_by_name_index.name = \"Title\"\n",
    "count_by_name.index = count_by_name_index\n",
    "count_by_name_sorted = count_by_name.sort_values(ascending=False)\n",
    "axes = count_by_name_sorted.plot(kind=\"bar\", figsize=(15,5), grid=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test = count_by_name_sorted[count_by_name_sorted>2]\n",
    "bars = plt.bar(range(len(test)), test)\n",
    "plt.ylabel(\"number of editions\")\n",
    "plt.xticks(range(len(test)), list(test.index), rotation=\"vertical\")\n",
    "plt.grid(axis=\"y\")\n",
    "bars[1].set_color(\"#ff7f0e\")\n",
    "bars[3].set_color(\"#ff7f0e\")\n",
    "#plt.savefig(\"bukan-distribution.pdf\", transparent=True, bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Filtering out books\n",
    "\n",
    "We can not compare books when there are not multiple editions available. Therefore I remove these lines from the overview table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(338, 13)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_by_name_jap = data.groupby(\"書名（統一書名）\").count()[\"国文研書誌ID\"]\n",
    "thresholded_larger_1 = data[data[\"書名（統一書名）\"].map(lambda x: x in set(count_by_name_jap[count_by_name_jap > 1].index))]\n",
    "thresholded_larger_1.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Shūgyoku Bukan"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And this is the Bukan with the most editions:\n",
    "\n",
    "The **袖玉武鑑** (しゅうぎょくぶかん) (shūgyokubukan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "shuugyokubukan = data[data[\"書名（統一書名）\"] == \"袖玉武鑑\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "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>国文研書誌ID</th>\n",
       "      <th>原本請求記号</th>\n",
       "      <th>刊年・書写年</th>\n",
       "      <th>（西暦）</th>\n",
       "      <th>Pages per Scan</th>\n",
       "      <th>Aspect</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>200019397</td>\n",
       "      <td>ＭＹ－１２０１－２２５</td>\n",
       "      <td>宝暦１４</td>\n",
       "      <td>1764</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>200019400</td>\n",
       "      <td>ＭＹ－１２０１－２２７</td>\n",
       "      <td>天明８</td>\n",
       "      <td>1788</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>200019401</td>\n",
       "      <td>ＭＹ－１２０１－２２８</td>\n",
       "      <td>天明９</td>\n",
       "      <td>1789</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>200019406</td>\n",
       "      <td>ＭＹ－１２０１－２３０</td>\n",
       "      <td>寛政１０</td>\n",
       "      <td>1798</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>200019408</td>\n",
       "      <td>ＭＹ－１２０１－２３１</td>\n",
       "      <td>文化３</td>\n",
       "      <td>1806</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348</th>\n",
       "      <td>200019639</td>\n",
       "      <td>ＭＹ－１２０１－３１２</td>\n",
       "      <td>慶応３</td>\n",
       "      <td>1867</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>200019641</td>\n",
       "      <td>ＭＹ－１２０１－３１２</td>\n",
       "      <td>慶応３</td>\n",
       "      <td>1867</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>352</th>\n",
       "      <td>200019643</td>\n",
       "      <td>ＭＹ－１２０１－３１２</td>\n",
       "      <td>慶応３</td>\n",
       "      <td>1867</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>354</th>\n",
       "      <td>200019646</td>\n",
       "      <td>ＭＹ－１２０１－３１３</td>\n",
       "      <td>慶応３</td>\n",
       "      <td>1867</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>356</th>\n",
       "      <td>200019649</td>\n",
       "      <td>ＭＹ－１２０１－３１３</td>\n",
       "      <td>慶応３</td>\n",
       "      <td>1867</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       国文研書誌ID       原本請求記号 刊年・書写年  （西暦）  Pages per Scan     Aspect\n",
       "170  200019397  ＭＹ－１２０１－２２５   宝暦１４  1764             1.0  Landscape\n",
       "172  200019400  ＭＹ－１２０１－２２７    天明８  1788             1.0  Landscape\n",
       "173  200019401  ＭＹ－１２０１－２２８    天明９  1789             1.0  Landscape\n",
       "175  200019406  ＭＹ－１２０１－２３０   寛政１０  1798             1.0  Landscape\n",
       "176  200019408  ＭＹ－１２０１－２３１    文化３  1806             1.0  Landscape\n",
       "..         ...          ...    ...   ...             ...        ...\n",
       "348  200019639  ＭＹ－１２０１－３１２    慶応３  1867             1.0  Landscape\n",
       "350  200019641  ＭＹ－１２０１－３１２    慶応３  1867             1.0  Landscape\n",
       "352  200019643  ＭＹ－１２０１－３１２    慶応３  1867             1.0  Landscape\n",
       "354  200019646  ＭＹ－１２０１－３１３    慶応３  1867             1.0  Landscape\n",
       "356  200019649  ＭＹ－１２０１－３１３    慶応３  1867             1.0  Landscape\n",
       "\n",
       "[100 rows x 6 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Output CSV for easier analysis in other software\n",
    "shuugyokubukan.drop(\n",
    "    [\"書名（統一書名）\", \"Comment\", \"(単位)\", \"冊数等\", \"刊・写\", \"オープンデータ分類\", \"公開時期\"], 1).astype(\n",
    "    {\"国文研書誌ID\":int, \"（西暦）\":int})#.to_csv(\n",
    "#    \"shuugyokubukan.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Shūchin Bukan"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since most of the Bukan from above are really bad quality (better to look at this later) let's inspect the second most frequent:\n",
    "\n",
    "The **袖珍武鑑** (しゅうちんぶかん) (shūchinbukan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "shuuchinbukan = data[data[\"書名（統一書名）\"] == \"袖珍武鑑\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "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>国文研書誌ID</th>\n",
       "      <th>原本請求記号</th>\n",
       "      <th>刊年・書写年</th>\n",
       "      <th>（西暦）</th>\n",
       "      <th>Pages per Scan</th>\n",
       "      <th>Aspect</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>200019399</td>\n",
       "      <td>ＭＹ－１２０１－２２６</td>\n",
       "      <td>安永７</td>\n",
       "      <td>1778</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>200019413</td>\n",
       "      <td>ＭＹ－１２０１－２３６</td>\n",
       "      <td>文化６</td>\n",
       "      <td>1809</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>200019419</td>\n",
       "      <td>ＭＹ－１２０１－２４２</td>\n",
       "      <td>文政７</td>\n",
       "      <td>1824</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>200019423</td>\n",
       "      <td>ＭＹ－１２０１－２４６</td>\n",
       "      <td>天保８</td>\n",
       "      <td>1837</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>200019429</td>\n",
       "      <td>ＭＹ－１２０１－２５１</td>\n",
       "      <td>天保１１</td>\n",
       "      <td>1840</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>200019430</td>\n",
       "      <td>ＭＹ－１２０１－２５２</td>\n",
       "      <td>天保１２</td>\n",
       "      <td>1841</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>200019451</td>\n",
       "      <td>ＭＹ－１２０１－２６６</td>\n",
       "      <td>嘉永４</td>\n",
       "      <td>1851</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>200019461</td>\n",
       "      <td>ＭＹ－１２０１－２７４</td>\n",
       "      <td>安政４</td>\n",
       "      <td>1857</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>200019464</td>\n",
       "      <td>ＭＹ－１２０１－２７６</td>\n",
       "      <td>安政５</td>\n",
       "      <td>1858</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>200019509</td>\n",
       "      <td>ＭＹ－１２０１－２９１</td>\n",
       "      <td>文久３</td>\n",
       "      <td>1863</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>200019525</td>\n",
       "      <td>ＭＹ－１２０１－２９４</td>\n",
       "      <td>文久４</td>\n",
       "      <td>1864</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>200019527</td>\n",
       "      <td>ＭＹ－１２０１－２９４</td>\n",
       "      <td>文久４</td>\n",
       "      <td>1864</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266</th>\n",
       "      <td>200019533</td>\n",
       "      <td>ＭＹ－１２０１－２９６</td>\n",
       "      <td>元治１</td>\n",
       "      <td>1864</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>200019538</td>\n",
       "      <td>ＭＹ－１２０１－２９７</td>\n",
       "      <td>元治１</td>\n",
       "      <td>1864</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>200019541</td>\n",
       "      <td>ＭＹ－１２０１－２９８</td>\n",
       "      <td>元治１</td>\n",
       "      <td>1864</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273</th>\n",
       "      <td>200019543</td>\n",
       "      <td>ＭＹ－１２０１－２９８</td>\n",
       "      <td>元治１</td>\n",
       "      <td>1864</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275</th>\n",
       "      <td>200019545</td>\n",
       "      <td>ＭＹ－１２０１－２９８</td>\n",
       "      <td>元治１</td>\n",
       "      <td>1864</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>200019548</td>\n",
       "      <td>ＭＹ－１２０１－２９９</td>\n",
       "      <td>元治１</td>\n",
       "      <td>1864</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279</th>\n",
       "      <td>200019550</td>\n",
       "      <td>ＭＹ－１２０１－２９９</td>\n",
       "      <td>元治１</td>\n",
       "      <td>1864</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281</th>\n",
       "      <td>200019552</td>\n",
       "      <td>ＭＹ－１２０１－２９９</td>\n",
       "      <td>元治１</td>\n",
       "      <td>1864</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>200019556</td>\n",
       "      <td>ＭＹ－１２０１－３０１</td>\n",
       "      <td>元治２</td>\n",
       "      <td>1865</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>200019558</td>\n",
       "      <td>ＭＹ－１２０１－３０１</td>\n",
       "      <td>元治２</td>\n",
       "      <td>1865</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>200019560</td>\n",
       "      <td>ＭＹ－１２０１－３０１</td>\n",
       "      <td>元治２</td>\n",
       "      <td>1865</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>200019565</td>\n",
       "      <td>ＭＹ－１２０１－３０２</td>\n",
       "      <td>慶応１</td>\n",
       "      <td>1865</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>200019568</td>\n",
       "      <td>ＭＹ－１２０１－３０２</td>\n",
       "      <td>慶応１</td>\n",
       "      <td>1865</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>200019570</td>\n",
       "      <td>ＭＹ－１２０１－３０２</td>\n",
       "      <td>慶応１</td>\n",
       "      <td>1865</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>200019572</td>\n",
       "      <td>ＭＹ－１２０１－３０２</td>\n",
       "      <td>慶応１</td>\n",
       "      <td>1865</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>200019576</td>\n",
       "      <td>ＭＹ－１２０１－３０３</td>\n",
       "      <td>慶応１</td>\n",
       "      <td>1865</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>200019579</td>\n",
       "      <td>ＭＹ－１２０１－３０３</td>\n",
       "      <td>慶応１</td>\n",
       "      <td>1865</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>200019581</td>\n",
       "      <td>ＭＹ－１２０１－３０３</td>\n",
       "      <td>慶応１</td>\n",
       "      <td>1865</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>304</th>\n",
       "      <td>200019585</td>\n",
       "      <td>ＭＹ－１２０１－３０４</td>\n",
       "      <td>慶応１</td>\n",
       "      <td>1865</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306</th>\n",
       "      <td>200019587</td>\n",
       "      <td>ＭＹ－１２０１－３０４</td>\n",
       "      <td>慶応１</td>\n",
       "      <td>1865</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308</th>\n",
       "      <td>200019589</td>\n",
       "      <td>ＭＹ－１２０１－３０４</td>\n",
       "      <td>慶応１</td>\n",
       "      <td>1865</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311</th>\n",
       "      <td>200019593</td>\n",
       "      <td>ＭＹ－１２０１－３０５</td>\n",
       "      <td>慶応２</td>\n",
       "      <td>1866</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>312</th>\n",
       "      <td>200019594</td>\n",
       "      <td>ＭＹ－１２０１－３０５</td>\n",
       "      <td>慶応２</td>\n",
       "      <td>1866</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314</th>\n",
       "      <td>200019596</td>\n",
       "      <td>ＭＹ－１２０１－３０５</td>\n",
       "      <td>慶応２</td>\n",
       "      <td>1866</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316</th>\n",
       "      <td>200019599</td>\n",
       "      <td>ＭＹ－１２０１－３０６</td>\n",
       "      <td>慶応２</td>\n",
       "      <td>1866</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>200019601</td>\n",
       "      <td>ＭＹ－１２０１－３０６</td>\n",
       "      <td>慶応２</td>\n",
       "      <td>1866</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>200019604</td>\n",
       "      <td>ＭＹ－１２０１－３０６</td>\n",
       "      <td>慶応２</td>\n",
       "      <td>1866</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>322</th>\n",
       "      <td>200019607</td>\n",
       "      <td>ＭＹ－１２０１－３０７</td>\n",
       "      <td>慶応２</td>\n",
       "      <td>1866</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>200019609</td>\n",
       "      <td>ＭＹ－１２０１－３０７</td>\n",
       "      <td>慶応２</td>\n",
       "      <td>1866</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326</th>\n",
       "      <td>200019611</td>\n",
       "      <td>ＭＹ－１２０１－３０７</td>\n",
       "      <td>慶応２</td>\n",
       "      <td>1866</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328</th>\n",
       "      <td>200019616</td>\n",
       "      <td>ＭＹ－１２０１－３０８</td>\n",
       "      <td>慶応２</td>\n",
       "      <td>1866</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>332</th>\n",
       "      <td>200019620</td>\n",
       "      <td>ＭＹ－１２０１－３０８</td>\n",
       "      <td>慶応２</td>\n",
       "      <td>1866</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335</th>\n",
       "      <td>200019624</td>\n",
       "      <td>ＭＹ－１２０１－３１０</td>\n",
       "      <td>慶応３</td>\n",
       "      <td>1867</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337</th>\n",
       "      <td>200019626</td>\n",
       "      <td>ＭＹ－１２０１－３１０</td>\n",
       "      <td>慶応３</td>\n",
       "      <td>1867</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>200019628</td>\n",
       "      <td>ＭＹ－１２０１－３１０</td>\n",
       "      <td>慶応３</td>\n",
       "      <td>1867</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>200019631</td>\n",
       "      <td>ＭＹ－１２０１－３１１</td>\n",
       "      <td>慶応３</td>\n",
       "      <td>1867</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>343</th>\n",
       "      <td>200019633</td>\n",
       "      <td>ＭＹ－１２０１－３１１</td>\n",
       "      <td>慶応３</td>\n",
       "      <td>1867</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345</th>\n",
       "      <td>200019635</td>\n",
       "      <td>ＭＹ－１２０１－３１１</td>\n",
       "      <td>慶応３</td>\n",
       "      <td>1867</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347</th>\n",
       "      <td>200019638</td>\n",
       "      <td>ＭＹ－１２０１－３１２</td>\n",
       "      <td>慶応３</td>\n",
       "      <td>1867</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349</th>\n",
       "      <td>200019640</td>\n",
       "      <td>ＭＹ－１２０１－３１２</td>\n",
       "      <td>慶応３</td>\n",
       "      <td>1867</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>351</th>\n",
       "      <td>200019642</td>\n",
       "      <td>ＭＹ－１２０１－３１２</td>\n",
       "      <td>慶応３</td>\n",
       "      <td>1867</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>353</th>\n",
       "      <td>200019645</td>\n",
       "      <td>ＭＹ－１２０１－３１３</td>\n",
       "      <td>慶応３</td>\n",
       "      <td>1867</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>355</th>\n",
       "      <td>200019648</td>\n",
       "      <td>ＭＹ－１２０１－３１３</td>\n",
       "      <td>慶応３</td>\n",
       "      <td>1867</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>357</th>\n",
       "      <td>200019650</td>\n",
       "      <td>ＭＹ－１２０１－３１４</td>\n",
       "      <td>慶応３</td>\n",
       "      <td>1867</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       国文研書誌ID       原本請求記号 刊年・書写年  （西暦）  Pages per Scan     Aspect\n",
       "171  200019399  ＭＹ－１２０１－２２６    安永７  1778             1.0  Landscape\n",
       "181  200019413  ＭＹ－１２０１－２３６    文化６  1809             1.0  Landscape\n",
       "187  200019419  ＭＹ－１２０１－２４２    文政７  1824             1.0  Landscape\n",
       "191  200019423  ＭＹ－１２０１－２４６    天保８  1837             1.0  Landscape\n",
       "195  200019429  ＭＹ－１２０１－２５１   天保１１  1840             1.0  Landscape\n",
       "196  200019430  ＭＹ－１２０１－２５２   天保１２  1841             1.0  Landscape\n",
       "209  200019451  ＭＹ－１２０１－２６６    嘉永４  1851             1.0  Landscape\n",
       "216  200019461  ＭＹ－１２０１－２７４    安政４  1857             1.0  Landscape\n",
       "218  200019464  ＭＹ－１２０１－２７６    安政５  1858             1.0  Landscape\n",
       "251  200019509  ＭＹ－１２０１－２９１    文久３  1863             1.0  Landscape\n",
       "261  200019525  ＭＹ－１２０１－２９４    文久４  1864             1.0  Landscape\n",
       "263  200019527  ＭＹ－１２０１－２９４    文久４  1864             1.0  Landscape\n",
       "266  200019533  ＭＹ－１２０１－２９６    元治１  1864             1.0  Landscape\n",
       "270  200019538  ＭＹ－１２０１－２９７    元治１  1864             1.0  Landscape\n",
       "271  200019541  ＭＹ－１２０１－２９８    元治１  1864             1.0  Landscape\n",
       "273  200019543  ＭＹ－１２０１－２９８    元治１  1864             1.0  Landscape\n",
       "275  200019545  ＭＹ－１２０１－２９８    元治１  1864             1.0  Landscape\n",
       "277  200019548  ＭＹ－１２０１－２９９    元治１  1864             1.0  Landscape\n",
       "279  200019550  ＭＹ－１２０１－２９９    元治１  1864             1.0  Landscape\n",
       "281  200019552  ＭＹ－１２０１－２９９    元治１  1864             1.0  Landscape\n",
       "284  200019556  ＭＹ－１２０１－３０１    元治２  1865             1.0  Landscape\n",
       "286  200019558  ＭＹ－１２０１－３０１    元治２  1865             1.0  Landscape\n",
       "288  200019560  ＭＹ－１２０１－３０１    元治２  1865             1.0  Landscape\n",
       "290  200019565  ＭＹ－１２０１－３０２    慶応１  1865             1.0  Landscape\n",
       "292  200019568  ＭＹ－１２０１－３０２    慶応１  1865             1.0  Landscape\n",
       "294  200019570  ＭＹ－１２０１－３０２    慶応１  1865             1.0  Landscape\n",
       "296  200019572  ＭＹ－１２０１－３０２    慶応１  1865             1.0  Landscape\n",
       "298  200019576  ＭＹ－１２０１－３０３    慶応１  1865             1.0  Landscape\n",
       "300  200019579  ＭＹ－１２０１－３０３    慶応１  1865             1.0  Landscape\n",
       "302  200019581  ＭＹ－１２０１－３０３    慶応１  1865             1.0  Landscape\n",
       "304  200019585  ＭＹ－１２０１－３０４    慶応１  1865             1.0  Landscape\n",
       "306  200019587  ＭＹ－１２０１－３０４    慶応１  1865             1.0  Landscape\n",
       "308  200019589  ＭＹ－１２０１－３０４    慶応１  1865             1.0  Landscape\n",
       "311  200019593  ＭＹ－１２０１－３０５    慶応２  1866             1.0  Landscape\n",
       "312  200019594  ＭＹ－１２０１－３０５    慶応２  1866             1.0  Landscape\n",
       "314  200019596  ＭＹ－１２０１－３０５    慶応２  1866             1.0  Landscape\n",
       "316  200019599  ＭＹ－１２０１－３０６    慶応２  1866             1.0  Landscape\n",
       "318  200019601  ＭＹ－１２０１－３０６    慶応２  1866             1.0  Landscape\n",
       "320  200019604  ＭＹ－１２０１－３０６    慶応２  1866             1.0  Landscape\n",
       "322  200019607  ＭＹ－１２０１－３０７    慶応２  1866             1.0  Landscape\n",
       "324  200019609  ＭＹ－１２０１－３０７    慶応２  1866             1.0  Landscape\n",
       "326  200019611  ＭＹ－１２０１－３０７    慶応２  1866             1.0  Landscape\n",
       "328  200019616  ＭＹ－１２０１－３０８    慶応２  1866             1.0  Landscape\n",
       "332  200019620  ＭＹ－１２０１－３０８    慶応２  1866             1.0  Landscape\n",
       "335  200019624  ＭＹ－１２０１－３１０    慶応３  1867             1.0  Landscape\n",
       "337  200019626  ＭＹ－１２０１－３１０    慶応３  1867             1.0  Landscape\n",
       "339  200019628  ＭＹ－１２０１－３１０    慶応３  1867             1.0  Landscape\n",
       "341  200019631  ＭＹ－１２０１－３１１    慶応３  1867             1.0  Landscape\n",
       "343  200019633  ＭＹ－１２０１－３１１    慶応３  1867             1.0  Landscape\n",
       "345  200019635  ＭＹ－１２０１－３１１    慶応３  1867             1.0  Landscape\n",
       "347  200019638  ＭＹ－１２０１－３１２    慶応３  1867             1.0  Landscape\n",
       "349  200019640  ＭＹ－１２０１－３１２    慶応３  1867             1.0  Landscape\n",
       "351  200019642  ＭＹ－１２０１－３１２    慶応３  1867             1.0  Landscape\n",
       "353  200019645  ＭＹ－１２０１－３１３    慶応３  1867             1.0  Landscape\n",
       "355  200019648  ＭＹ－１２０１－３１３    慶応３  1867             1.0  Landscape\n",
       "357  200019650  ＭＹ－１２０１－３１４    慶応３  1867             1.0  Landscape"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Output CSV for easier analysis in other software\n",
    "shuuchinbukan.drop(\n",
    "    [\"書名（統一書名）\", \"Comment\", \"(単位)\", \"冊数等\", \"刊・写\", \"オープンデータ分類\", \"公開時期\"], 1).astype(\n",
    "    {\"国文研書誌ID\":int, \"（西暦）\":int})#.to_csv(\n",
    "    #\"shuuchinbukan.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Yūshi Bukan"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This looks better; but for completeness let's also process the third most frequent:\n",
    "\n",
    "The **有司武鑑** (ゆうしぶかん) (yūshibukan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "yuushibukan = data[data[\"書名（統一書名）\"] == \"有司武鑑\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "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>国文研書誌ID</th>\n",
       "      <th>原本請求記号</th>\n",
       "      <th>刊年・書写年</th>\n",
       "      <th>（西暦）</th>\n",
       "      <th>Pages per Scan</th>\n",
       "      <th>Aspect</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>200019391</td>\n",
       "      <td>ＭＹ－１２０１－２２２</td>\n",
       "      <td>寛延１</td>\n",
       "      <td>1748</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>200019394</td>\n",
       "      <td>ＭＹ－１２０１－２２３</td>\n",
       "      <td>宝暦２</td>\n",
       "      <td>1752</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>200019421</td>\n",
       "      <td>ＭＹ－１２０１－２４４</td>\n",
       "      <td>文政１３</td>\n",
       "      <td>1830</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>200019428</td>\n",
       "      <td>ＭＹ－１２０１－２５０</td>\n",
       "      <td>天保９</td>\n",
       "      <td>1838</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>200019439</td>\n",
       "      <td>ＭＹ－１２０１－２５７</td>\n",
       "      <td>天保１５</td>\n",
       "      <td>1844</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>200019443</td>\n",
       "      <td>ＭＹ－１２０１－２６１</td>\n",
       "      <td>弘化４</td>\n",
       "      <td>1847</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>200019444</td>\n",
       "      <td>ＭＹ－１２０１－２６２</td>\n",
       "      <td>嘉永２</td>\n",
       "      <td>1849</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>200019446</td>\n",
       "      <td>ＭＹ－１２０１－２６４</td>\n",
       "      <td>嘉永３</td>\n",
       "      <td>1850</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>200019448</td>\n",
       "      <td>ＭＹ－１２０１－２６５</td>\n",
       "      <td>嘉永３</td>\n",
       "      <td>1850</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>200019456</td>\n",
       "      <td>ＭＹ－１２０１－２７０</td>\n",
       "      <td>安政２</td>\n",
       "      <td>1855</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>200019459</td>\n",
       "      <td>ＭＹ－１２０１－２７３</td>\n",
       "      <td>安政３</td>\n",
       "      <td>1856</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>200019480</td>\n",
       "      <td>ＭＹ－１２０１－２８２</td>\n",
       "      <td>安政５</td>\n",
       "      <td>1858</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>200019501</td>\n",
       "      <td>ＭＹ－１２０１－２８８</td>\n",
       "      <td>文久１</td>\n",
       "      <td>1861</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>200019557</td>\n",
       "      <td>ＭＹ－１２０１－３０１</td>\n",
       "      <td>元治２</td>\n",
       "      <td>1865</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>200019559</td>\n",
       "      <td>ＭＹ－１２０１－３０１</td>\n",
       "      <td>元治２</td>\n",
       "      <td>1865</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>200019561</td>\n",
       "      <td>ＭＹ－１２０１－３０１</td>\n",
       "      <td>元治２</td>\n",
       "      <td>1865</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>200019566</td>\n",
       "      <td>ＭＹ－１２０１－３０２</td>\n",
       "      <td>慶応１</td>\n",
       "      <td>1865</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>200019569</td>\n",
       "      <td>ＭＹ－１２０１－３０２</td>\n",
       "      <td>慶応１</td>\n",
       "      <td>1865</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>200019571</td>\n",
       "      <td>ＭＹ－１２０１－３０２</td>\n",
       "      <td>慶応１</td>\n",
       "      <td>1865</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>200019573</td>\n",
       "      <td>ＭＹ－１２０１－３０２</td>\n",
       "      <td>慶応１</td>\n",
       "      <td>1865</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>200019578</td>\n",
       "      <td>ＭＹ－１２０１－３０３</td>\n",
       "      <td>慶応１</td>\n",
       "      <td>1865</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>200019580</td>\n",
       "      <td>ＭＹ－１２０１－３０３</td>\n",
       "      <td>慶応１</td>\n",
       "      <td>1865</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>317</th>\n",
       "      <td>200019600</td>\n",
       "      <td>ＭＹ－１２０１－３０６</td>\n",
       "      <td>慶応２</td>\n",
       "      <td>1866</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>200019602</td>\n",
       "      <td>ＭＹ－１２０１－３０６</td>\n",
       "      <td>慶応２</td>\n",
       "      <td>1866</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>200019605</td>\n",
       "      <td>ＭＹ－１２０１－３０６</td>\n",
       "      <td>慶応２</td>\n",
       "      <td>1866</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323</th>\n",
       "      <td>200019608</td>\n",
       "      <td>ＭＹ－１２０１－３０７</td>\n",
       "      <td>慶応２</td>\n",
       "      <td>1866</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325</th>\n",
       "      <td>200019610</td>\n",
       "      <td>ＭＹ－１２０１－３０７</td>\n",
       "      <td>慶応２</td>\n",
       "      <td>1866</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331</th>\n",
       "      <td>200019619</td>\n",
       "      <td>ＭＹ－１２０１－３０８</td>\n",
       "      <td>慶応２</td>\n",
       "      <td>1866</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Landscape</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       国文研書誌ID       原本請求記号 刊年・書写年  （西暦）  Pages per Scan     Aspect\n",
       "167  200019391  ＭＹ－１２０１－２２２    寛延１  1748             2.0  Landscape\n",
       "168  200019394  ＭＹ－１２０１－２２３    宝暦２  1752             2.0  Landscape\n",
       "189  200019421  ＭＹ－１２０１－２４４   文政１３  1830             1.0  Landscape\n",
       "194  200019428  ＭＹ－１２０１－２５０    天保９  1838             1.0  Landscape\n",
       "201  200019439  ＭＹ－１２０１－２５７   天保１５  1844             1.0  Landscape\n",
       "204  200019443  ＭＹ－１２０１－２６１    弘化４  1847             1.0  Landscape\n",
       "205  200019444  ＭＹ－１２０１－２６２    嘉永２  1849             1.0  Landscape\n",
       "207  200019446  ＭＹ－１２０１－２６４    嘉永３  1850             1.0  Landscape\n",
       "208  200019448  ＭＹ－１２０１－２６５    嘉永３  1850             1.0  Landscape\n",
       "213  200019456  ＭＹ－１２０１－２７０    安政２  1855             1.0  Landscape\n",
       "215  200019459  ＭＹ－１２０１－２７３    安政３  1856             1.0  Landscape\n",
       "228  200019480  ＭＹ－１２０１－２８２    安政５  1858             1.0  Landscape\n",
       "245  200019501  ＭＹ－１２０１－２８８    文久１  1861             1.0  Landscape\n",
       "285  200019557  ＭＹ－１２０１－３０１    元治２  1865             1.0  Landscape\n",
       "287  200019559  ＭＹ－１２０１－３０１    元治２  1865             1.0  Landscape\n",
       "289  200019561  ＭＹ－１２０１－３０１    元治２  1865             1.0  Landscape\n",
       "291  200019566  ＭＹ－１２０１－３０２    慶応１  1865             1.0  Landscape\n",
       "293  200019569  ＭＹ－１２０１－３０２    慶応１  1865             1.0  Landscape\n",
       "295  200019571  ＭＹ－１２０１－３０２    慶応１  1865             1.0  Landscape\n",
       "297  200019573  ＭＹ－１２０１－３０２    慶応１  1865             1.0  Landscape\n",
       "299  200019578  ＭＹ－１２０１－３０３    慶応１  1865             1.0  Landscape\n",
       "301  200019580  ＭＹ－１２０１－３０３    慶応１  1865             1.0  Landscape\n",
       "317  200019600  ＭＹ－１２０１－３０６    慶応２  1866             1.0  Landscape\n",
       "319  200019602  ＭＹ－１２０１－３０６    慶応２  1866             1.0  Landscape\n",
       "321  200019605  ＭＹ－１２０１－３０６    慶応２  1866             1.0  Landscape\n",
       "323  200019608  ＭＹ－１２０１－３０７    慶応２  1866             1.0  Landscape\n",
       "325  200019610  ＭＹ－１２０１－３０７    慶応２  1866             1.0  Landscape\n",
       "331  200019619  ＭＹ－１２０１－３０８    慶応２  1866             1.0  Landscape"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Output CSV for easier analysis in other software\n",
    "yuushibukan.drop(\n",
    "    [\"書名（統一書名）\", \"Comment\", \"(単位)\", \"冊数等\", \"刊・写\", \"オープンデータ分類\", \"公開時期\"], 1).astype(\n",
    "    {\"国文研書誌ID\":int, \"（西暦）\":int})#.to_csv(\n",
    "    #\"yuushibukan.csv\", index=False)"
   ]
  }
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