recently_digitised_update.ipynb

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    "# Files digitised in the last week\n",
    "\n",
    "Each Sunday I'm automatically harvesting details of files digitised by the NAA in the previous week. You can view the results [in this repository](https://github.com/wragge/naa-recently-digitised). This notebook analyses the most recent harvest to provide a summary of the results."
   ]
  },
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   "execution_count": 19,
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   "outputs": [],
   "source": [
    "import datetime\n",
    "from urllib.error import HTTPError\n",
    "\n",
    "import arrow\n",
    "import pandas as pd\n",
    "from IPython.display import display\n",
    "from recordsearch_data_scraper.scrapers import RSSeries\n",
    "from tqdm.auto import tqdm"
   ]
  },
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   "cell_type": "code",
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     "name": "stdout",
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     "text": [
      "Harvested on Sunday, 15 January 2023.\n"
     ]
    }
   ],
   "source": [
    "# Find the date of the most recent Sunday\n",
    "today = arrow.now().to(\"Australia/Sydney\")\n",
    "# Today is Sunday and it's past 2pm so the harvest should have run\n",
    "if today.weekday() == 6 and today.time() >= datetime.time(14, 0, 0, 0):\n",
    "    harvest_day = today\n",
    "# Otherwise get last Sunday\n",
    "else:\n",
    "    harvest_day = arrow.now().to(\"Australia/Sydney\").shift(weekday=6).shift(weeks=-1)\n",
    "\n",
    "print(f'Harvested on {harvest_day.format(\"dddd, D MMMM YYYY\")}.')"
   ]
  },
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   "cell_type": "code",
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    "try:\n",
    "    df = pd.read_csv(\n",
    "        f'https://raw.githubusercontent.com/wragge/naa-recently-digitised/master/data/digitised-week-ending-{harvest_day.format(\"YYYYMMDD\")}.csv'\n",
    "    )\n",
    "except HTTPError:\n",
    "    df = pd.DataFrame()"
   ]
  },
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   "cell_type": "code",
   "execution_count": 22,
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     "execution_count": 22,
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   "source": [
    "df.shape"
   ]
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   "cell_type": "code",
   "execution_count": 23,
   "id": "eba145be-32d6-4bb0-a49d-83b9264052a5",
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   "source": [
    "if not df.empty:\n",
    "    df[\"series\"].value_counts()[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1383b682-c3da-435c-b07c-8b6674ffabca",
   "metadata": {},
   "outputs": [],
   "source": [
    "if not df.empty:\n",
    "    series_list = list(df[\"series\"].unique())\n",
    "\n",
    "    cited_series = []\n",
    "    for series in tqdm(series_list):\n",
    "        data = RSSeries(\n",
    "            series, include_number_digitised=False, include_access_status=False\n",
    "        ).data\n",
    "        cited_series.append({\"series\": series, \"series_title\": data[\"title\"]})\n",
    "\n",
    "    df_titles = pd.merge(df, pd.DataFrame(cited_series), how=\"left\", on=\"series\")\n",
    "\n",
    "    with pd.option_context(\"display.max_colwidth\", 100):\n",
    "        df_titles = (\n",
    "            df_titles.value_counts([\"series\", \"series_title\"]).to_frame().reset_index()\n",
    "        )\n",
    "        df_titles.columns = [\"series\", \"series_title\", \"total\"]\n",
    "        display(df_titles[:20])\n",
    "        totals = \"\"\n",
    "        for title in df_titles[:20].itertuples():\n",
    "            totals += (\n",
    "                f\"{title.series}, {title.series_title}, {title.total} files digitised; \"\n",
    "            )\n",
    "        print(totals)"
   ]
  }
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