Met_collection_clean_18.03.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "#from tabulate import tabulate\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "import math\n",
    "import operator\n",
    "import functools\n",
    "import re\n",
    "\n",
    "pd.set_option('mode.chained_assignment', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Customize matplotlib\n",
    "matplotlib.rcParams.update({\n",
    "    'text.usetex': False,\n",
    "    'font.family': 'serif',\n",
    "    'font.serif': 'cmr10',\n",
    "    'mathtext.fontset': 'cm',\n",
    "    'axes.unicode_minus': False,\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Due to mixed values, we will be treating most fields as text for now and see if there\n",
    "# is anything to be done such that numerical values could be extracted out of these.\n",
    "# There are however a few Boolean and Numerical types exceptions.\n",
    "\n",
    "df = pd.read_csv(r\"MetObjects.csv\", dtype={\n",
    "    'Gallery Number': str, 'Period': str,\n",
    "    'Portfolio': str, 'AccessionYear': str,\n",
    "    'River': str, 'Subregion': str,\n",
    "    'Dynasty': str, 'Excavation': str,\n",
    "    'Locale': str, 'Locus': str, 'Reign': str,\n",
    "\n",
    "}, low_memory=False, nrows=9980)\n",
    "\n",
    "#df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Re-order the columns such that <Object ID> is the first column.\n",
    "# Other reorders should follow\n",
    "df = df[['Object Number', 'Is Public Domain','Object ID', 'Department', 'AccessionYear', 'Object Name', 'Title', 'Culture', 'Period',\n",
    "         'Dynasty', 'Reign', 'Artist Display Name', 'Artist Nationality', 'Artist Begin Date', 'Artist End Date', 'Artist Gender', 'Artist Wikidata URL',\n",
    "         'Object Begin Date', 'Object End Date', 'Medium', 'Dimensions', 'Credit Line',\n",
    "         'City', 'Country','Classification','Object Wikidata URL', 'Tags']]\n",
    "\n",
    "#df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Object Number 0\n",
      "Is Public Domain 0\n",
      "Object ID 0\n",
      "Department 0\n",
      "AccessionYear 246\n",
      "Object Name 0\n",
      "Title 0\n",
      "Culture 1709\n",
      "Period 9980\n",
      "Dynasty 9980\n",
      "Reign 9980\n",
      "Artist Display Name 5208\n",
      "Artist Nationality 8437\n",
      "Artist Begin Date 5583\n",
      "Artist End Date 5246\n",
      "Artist Gender 9287\n",
      "Artist Wikidata URL 7526\n",
      "Object Begin Date 0\n",
      "Object End Date 0\n",
      "Medium 1\n",
      "Dimensions 3\n",
      "Credit Line 2\n",
      "City 5424\n",
      "Country 1997\n",
      "Classification 0\n",
      "Object Wikidata URL 9428\n",
      "Tags 6833\n"
     ]
    }
   ],
   "source": [
    "#Number of rows that have a value NaN\n",
    "for column in df:\n",
    "    print(column, len(df[pd.isnull(df[column])]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# In case we can fill the NaN values with 0\n",
    "#df1 = df.copy()\n",
    "# Fill all NaN values with 0\n",
    "#df1['Object Number'] = df1['Object Number'].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Displaying all values of the dataframe if necessary\n",
    "pd.set_option(\"display.max_rows\", None, \"display.max_columns\", None)\n",
    "#df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Displaying all unique values in every column\n",
    "#for col in df:\n",
    "#    print(col, df[col].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Clean up of the Culture column \n",
    "df['Culture'] = df['Culture'].str.replace('for export', '')\n",
    "df['Culture'] = df['Culture'].str.replace(', for Swedish market', ' (Swedish market)')\n",
    "df['Culture'] = df['Culture'].str.replace(', for American market', ' (American market)')\n",
    "df['Culture'] = df['Culture'].str.replace('or', ',')\n",
    "df['Culture'] = df['Culture'].str.replace(', probably', '')\n",
    "df['Culture'] = df['Culture'].str.replace(', possibly', '')\n",
    "df['Culture'] = df['Culture'].str.replace('probably', '')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Department'].value_counts().plot(kind='bar')\n",
    "plt.title(\"Counts for items in each one of the departments\")\n",
    "plt.xlabel(\"Department\")\n",
    "plt.ylabel(\"Number of stored items\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df['Medium'].value_counts()\n",
    "#df['Department'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[:,'Covered Area'] = 0\n",
    "df.loc[:,'Covered Volume'] = 0\n",
    "#print(tabulate(df, headers = 'keys', tablefmt = 'psql'))\n",
    "#print(df.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df['Object Number'].notna()]\n",
    "df = df[df['Dimensions'].notna()]\n",
    "df = df[df['Object ID'].notna()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Several field support multiple values, they should look similarly to the values\n",
    "# at the <Tags> field in order to reduce the confusion.\n",
    "df['Medium'] = df['Medium'].str.lower()\n",
    "df['Medium'] = (df['Medium'].str.replace(\", \", \"|\"))\n",
    "df['Artist Nationality'] = (df['Artist Nationality'].str.replace(\", \", \"|\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Originally, the dimensions of each object are stored both in inches and centimeters\n",
    "# Should only keep the centimeters dimensions\n",
    "# i.e. 15 3/4 x 11 3/4 in. (40 x 29.8 cm) - > 40 x 29.8\n",
    "df['Dimensions'] = df['Dimensions'].apply(lambda st: st[st.find(\"(\") + 1:st.find(\")\")])\n",
    "df['Dimensions'] = (df['Dimensions'].str.replace(\"cm\", \"\"))\n",
    "df['Dimensions'] = (df['Dimensions'].str.replace(\" \", \"\"))\n",
    "df['Dimensions'] = (df['Dimensions'].str.replace(\"×\", \"x\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We can see that some of the items do not have a specified dimension, would rather delete\n",
    "# those rows since there's not much we can do with badly catalogued items like these.\n",
    "df = df[~df['Dimensions'].str.contains(\"unavailabl\", na = False)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For now, it might not be the best idea to turn the <Dimensions> column into numerical types\n",
    "# since there is a mixture of 2-dimensional and 3-dimensional items (e.g. 1 x 5.3 VS 2 x 4 x 8)\n",
    "\n",
    "# besides 2- and 3-dimensional items, there are: irregular, trimmed, bookletclosed, necklace, includingfur, eachscroll,\n",
    "# Left, withbase, N.A, Eachapprox., each, bottomsection, variabl, topsection, trimmedtoplate, \n",
    "# also in these forms: 511/16in., SealFace;1.12x0.74\\r\\nHeight:0.56\\r\\nStringHole..., [nodimensionsavailable\n",
    "\n",
    "# below we look for each object's dimensions, and if it doesn't contain a digit, we remove the row\n",
    "#re.search(r'\\d', inputString)\n",
    "for index, dimensions in enumerate(df['Dimensions']):\n",
    "    string = df.iloc[index]['Dimensions']\n",
    "    nr_d = 0\n",
    "    for c in string:\n",
    "        if c.isdigit():\n",
    "            nr_d += 1\n",
    "    if (nr_d == 0):\n",
    "        df.at[index,'Dimensions'] = 'DELETE'\n",
    "        \n",
    "df = df[~df['Dimensions'].str.contains(\"DELETE\", na = False)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2       2.0\n",
       "3       2.0\n",
       "4       2.0\n",
       "5       2.0\n",
       "6       2.0\n",
       "7       2.0\n",
       "8       2.0\n",
       "14      2.0\n",
       "15      2.0\n",
       "16      2.0\n",
       "17      2.0\n",
       "18      2.0\n",
       "19      2.0\n",
       "20      2.0\n",
       "21      2.0\n",
       "22      2.0\n",
       "23      2.0\n",
       "24      2.0\n",
       "25      1.0\n",
       "26      1.0\n",
       "27      1.0\n",
       "28      1.0\n",
       "29      2.0\n",
       "30      2.0\n",
       "31      1.0\n",
       "32      2.0\n",
       "33      2.0\n",
       "34      3.0\n",
       "35      3.0\n",
       "36      2.0\n",
       "37      2.0\n",
       "38      2.0\n",
       "39      2.0\n",
       "40      2.0\n",
       "41      2.0\n",
       "42      2.0\n",
       "43      2.0\n",
       "44      2.0\n",
       "45      2.0\n",
       "46      2.0\n",
       "47      2.0\n",
       "48      2.0\n",
       "49      2.0\n",
       "50      1.0\n",
       "51      1.0\n",
       "52      1.0\n",
       "53      1.0\n",
       "54      2.0\n",
       "55      2.0\n",
       "56      1.0\n",
       "57      2.0\n",
       "58      2.0\n",
       "59      3.0\n",
       "60      3.0\n",
       "61      1.0\n",
       "62      1.0\n",
       "63      3.0\n",
       "64      3.0\n",
       "65      3.0\n",
       "66      3.0\n",
       "67      2.0\n",
       "68      2.0\n",
       "69      2.0\n",
       "70      2.0\n",
       "71      2.0\n",
       "72      2.0\n",
       "73      3.0\n",
       "74      3.0\n",
       "75      3.0\n",
       "76      3.0\n",
       "77      3.0\n",
       "78      3.0\n",
       "79      3.0\n",
       "80      3.0\n",
       "81      2.0\n",
       "82      2.0\n",
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       "1900    2.0\n",
       "1901    2.0\n",
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       "1903    3.0\n",
       "1904    1.0\n",
       "1905    1.0\n",
       "1906    1.0\n",
       "1907    1.0\n",
       "1908    1.0\n",
       "1910    2.0\n",
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       "1914    2.0\n",
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       "1916    3.0\n",
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       "1918    2.0\n",
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       "1920    2.0\n",
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       "1930    2.0\n",
       "1932    2.0\n",
       "1934    2.0\n",
       "1935    2.0\n",
       "1936    2.0\n",
       "1937    2.0\n",
       "1938    3.0\n",
       "1939    1.0\n",
       "1940    2.0\n",
       "1941    1.0\n",
       "1942    1.0\n",
       "1943    1.0\n",
       "1944    1.0\n",
       "1945    2.0\n",
       "1946    2.0\n",
       "1947    2.0\n",
       "1948    2.0\n",
       "1949    2.0\n",
       "1950    2.0\n",
       "1951    2.0\n",
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       "1954    2.0\n",
       "1955    2.0\n",
       "1956    2.0\n",
       "1957    2.0\n",
       "1958    1.0\n",
       "1959    2.0\n",
       "1960    2.0\n",
       "1961    1.0\n",
       "1962    2.0\n",
       "1963    2.0\n",
       "1964    2.0\n",
       "1965    2.0\n",
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       "1968    2.0\n",
       "1969    2.0\n",
       "1970    2.0\n",
       "1971    2.0\n",
       "1972    1.0\n",
       "1973    2.0\n",
       "1974    2.0\n",
       "1975    2.0\n",
       "1976    2.0\n",
       "1977    2.0\n",
       "1978    2.0\n",
       "1979    2.0\n",
       "1980    1.0\n",
       "1981    3.0\n",
       "1982    2.0\n",
       "1983    3.0\n",
       "1984    1.0\n",
       "1985    2.0\n",
       "1986    2.0\n",
       "1987    2.0\n",
       "1988    2.0\n",
       "1989    2.0\n",
       "1990    2.0\n",
       "1991    1.0\n",
       "1992    1.0\n",
       "1993    2.0\n",
       "1994    2.0\n",
       "1995    2.0\n",
       "1996    2.0\n",
       "1997    2.0\n",
       "1998    1.0\n",
       "1999    1.0\n",
       "2000    3.0\n",
       "2001    2.0\n",
       "2002    1.0\n",
       "2003    2.0\n",
       "2004    2.0\n",
       "2005    2.0\n",
       "2006    3.0\n",
       "2007    1.0\n",
       "2008    1.0\n",
       "2009    1.0\n",
       "2010    2.0\n",
       "2011    1.0\n",
       "2012    1.0\n",
       "2013    1.0\n",
       "2014    1.0\n",
       "2015    1.0\n",
       "2016    1.0\n",
       "2017    2.0\n",
       "2018    2.0\n",
       "2019    2.0\n",
       "2020    2.0\n",
       "2021    2.0\n",
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       "2030    1.0\n",
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       "2037    1.0\n",
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       "2040    1.0\n",
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       "Name: Dimensionality, dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# now we have only numerical values (at least for the first 9980 rows, because the code above was running for idk how long\n",
    "# using all the rows)\n",
    "df['Dimensionality']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Dimensionality'] = df['Dimensions'].apply(lambda st: str.count(st, \"x\") + 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Some items are being characterized by the diameter instead of its length or width\n",
    "# We need to standardize the dimensions:\n",
    "# 1.5 - > 1.5x1.5\n",
    "for index, (dimensionality, dimensions) in enumerate(zip(df['Dimensionality'], df['Dimensions'])):\n",
    "    if(df.iloc[index]['Dimensionality']) == 1:\n",
    "        df.at[index,'Dimensionality'] = 2\n",
    "        df.at[index,'Dimensions'] = df.iloc[index]['Dimensions'] + \"x\" + df.iloc[index]['Dimensions']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Object ID'] = df['Object ID'].astype('Int64')\n",
    "df['Object Begin Date'] = df['Object Begin Date'].astype('Int64')\n",
    "df['Object End Date'] = df['Object End Date'].astype('Int64')\n",
    "df['Dimensionality'] = df['Dimensionality'].fillna(0).astype('Int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'Subregion'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2894\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2895\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2896\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'Subregion'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-45-9ffdd2e8e886>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     12\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[1;31m# We already have the <Region> field so this seems like an overkill\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 14\u001b[1;33m \u001b[1;32mdel\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Subregion'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m__delitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   3709\u001b[0m             \u001b[1;31m# there was no match, this call should raise the appropriate\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3710\u001b[0m             \u001b[1;31m# exception:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3711\u001b[1;33m             \u001b[0mloc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3712\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_mgr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0midelete\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3713\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2895\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2896\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2897\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2898\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2899\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mtolerance\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'Subregion'"
     ]
    }
   ],
   "source": [
    "# commented the rows below because these columns were already not included in the dataframe\n",
    "\n",
    "# the <Artist Display Bio> looks useless. It's more likely we are interested in the years the artist lived. \n",
    "# del df['Artist Display Bio']\n",
    "\n",
    "# there's some sort of redundancy in the <Object Date> field\n",
    "#del df['Object Date']\n",
    "\n",
    "# The object number is a string stored like it would be some sort of IP address\n",
    "# df['Object Number'] = (df['Object Number'].str.split()).apply(lambda x: (x[0].replace('.', '')))\n",
    "\n",
    "# We already have the <Region> field so this seems like an overkill\n",
    "#del df['Subregion']\n",
    "\n",
    "# There is a \"Primary Key\" <Object ID> that has the same scope as <Object Number>\n",
    "del df['Object Number']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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