Updated EDA

.gitattributes

@@ -55,3 +55,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.webp filter=lfs diff=lfs merge=lfs -text
 data/v1-dev-names.csv filter=lfs diff=lfs merge=lfs -text
 notebooks/BioCLIP_taxa_viz_bySource.ipynb filter=lfs diff=lfs merge=lfs -text
+statistics.csv filter=lfs diff=lfs merge=lfs -text

README.md

@@ -10,6 +10,11 @@ This repo contains the analysis of the TreeOfLife10M dataset as it's being craft
 ### Data
 
 The `data` folder contains
+ - `statistics.csv`: the metadata and identifiers for all images in the dataset. This includes original data source, unique identifier within TreeOfLife10M, and the 
+associated taxa information for the image. This is the file that will be updated, while preserving the `v1-dev-names.csv` file for ease of comparison.
+- `stats_avg_std_byClass.csv`: average and standard distribution of images given by class in `statistics.csv`. This is for both all 
+images, and images that have labels.
+
  - `v1-dev-names.csv`: the metadata and identifiers for all images in the dataset. This includes original data source, unique identifier within TreeOfLife10M, and the 
 associated taxa information for the image.
  - `taxa_counts.csv`: count of distinct lower taxa within each higher taxon from `kingdom` down to 
@@ -19,9 +24,13 @@ images, and images that have labels. Note that kingdoms have not been merged and
 been performed on the taxonomic hierarchy prior to creation of this file.
  - `tol_hierarchy_test.csv`: Subset of `v1-dev-names.csv` for testing the [`check_taxa` script](https://github.com/Imageomics/open_clip/tree/main/scripts/evobio10m) to ensure the hierarchy is properly filled in after data generation. 
 
+
 ### Notebooks
 
 The `notebooks` folder contains
+ - `ToL_stats_EDA.ipynb`: more full EDA of TreeOfLife10M dataset using `statistics.csv`.
+ - `ToL_stats_EDA.py`: py file paired to `ToL_stats_EDA.ipynb` to facilitate diff checking in case of cell text changes in notebook.
+
  - `BioCLIP_data_viz.ipynb`: notebook with quick basic stats for `v1-dev-names.csv`, generates 
 `taxa_counts.csv`.
  - `BioCLIP_taxa_viz_bySource.ipynb`: generates data visualizations, in particular, the generation of 

data/missing_taxa_output.txt

@@ -0,0 +1,44 @@
+Output of Data Check for First Full Version of BioCLIP training data (initial submission):
+
+[2023-10-25 10:20:12,498] [WARNING] [root] There are 5 kingdoms instead of 3.
+[2023-10-25 10:20:13,673] [WARNING] [root] 1300744 entries are missing rank kingdom, but have genus label.
+[2023-10-25 10:20:13,918] [WARNING] [root] 1302578 entries are missing rank phylum, but have genus label.
+[2023-10-25 10:20:14,168] [WARNING] [root] 2368981 entries are missing rank class, but have genus label.
+[2023-10-25 10:20:14,402] [WARNING] [root] 1344781 entries are missing rank order, but have genus label.
+[2023-10-25 10:20:14,637] [WARNING] [root] 1310177 entries are missing rank family, but have genus label.
+[2023-10-25 10:20:15,065] [WARNING] [root] 24 entries are missing rank phylum, but have family label.
+[2023-10-25 10:20:15,094] [WARNING] [root] 8759 entries are missing rank class, but have family label.
+[2023-10-25 10:20:15,123] [WARNING] [root] 111 entries are missing rank order, but have family label.
+[2023-10-25 10:20:15,233] [WARNING] [root] 67 entries are missing rank class, but have order label.
+[2023-10-25 10:20:15,254] [WARNING] [root] 253 entries have kingdom and species labels but no genus.
+[2023-10-25 10:20:15,254] [WARNING] [root] 253 entries have phylum and species labels but no genus.
+[2023-10-25 10:20:15,255] [WARNING] [root] 173 entries have class and species labels but no genus.
+[2023-10-25 10:20:15,255] [WARNING] [root] 253 entries have order and species labels but no genus.
+[2023-10-25 10:20:15,255] [WARNING] [root] 253 entries have family and species labels but no genus.
+[2023-10-25 10:20:16,644] [WARNING] [root] There are 755133 samples for which the species column may have genus and species.
+[2023-10-25 10:20:17,199] [WARNING] [root] 2470 species are labeled as '(unidentified)'.
+
+
+Output of Data Check for next iteration where taxon matching was performed (statistics.csv):
+
+[2023-10-25 10:25:28,242] [WARNING] [root] There are 7 kingdoms instead of 3.
+[2023-10-25 10:25:29,234] [WARNING] [root] 14795 entries are missing rank kingdom, but have genus label.
+[2023-10-25 10:25:29,477] [WARNING] [root] 5824 entries are missing rank phylum, but have genus label.
+[2023-10-25 10:25:29,719] [WARNING] [root] 15763 entries are missing rank class, but have genus label.
+[2023-10-25 10:25:29,958] [WARNING] [root] 10550 entries are missing rank order, but have genus label.
+[2023-10-25 10:25:30,203] [WARNING] [root] 7539 entries are missing rank family, but have genus label.
+[2023-10-25 10:25:30,470] [WARNING] [root] 296 entries are missing rank kingdom, but have family label.
+[2023-10-25 10:25:30,495] [WARNING] [root] 272 entries are missing rank phylum, but have family label.
+[2023-10-25 10:25:30,520] [WARNING] [root] 753 entries are missing rank class, but have family label.
+[2023-10-25 10:25:30,545] [WARNING] [root] 395 entries are missing rank order, but have family label.
+[2023-10-25 10:25:30,590] [WARNING] [root] 156 entries are missing rank kingdom, but have order label.
+[2023-10-25 10:25:30,591] [WARNING] [root] 100 entries are missing rank phylum, but have order label.
+[2023-10-25 10:25:30,592] [WARNING] [root] 1187 entries are missing rank class, but have order label.
+[2023-10-25 10:25:30,637] [WARNING] [root] 74 entries have kingdom and species labels but no genus.
+[2023-10-25 10:25:30,644] [WARNING] [root] 74 entries have phylum and species labels but no genus.
+[2023-10-25 10:25:30,650] [WARNING] [root] 74 entries have class and species labels but no genus.
+[2023-10-25 10:25:30,656] [WARNING] [root] 74 entries have order and species labels but no genus.
+[2023-10-25 10:25:30,662] [WARNING] [root] 74 entries have family and species labels but no genus.
+[2023-10-25 10:25:31,992] [WARNING] [root] There are 158279 samples for which the species column may have genus and species.
+Added check for nulls in common column (wasn't an issue in the first iteration):
+[2023-10-25 11:39:11,384] [WARNING] [root] 915509 entries have null common. They are from ['EOL' 'BIOSCAN'].

data/statistics.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:995cfb557048441d0426da39c0eece02493208ed92bb615d6a50d683dca9616b
+size 1646503457

data/stats_avg_std_byClass.csv

@@ -0,0 +1,9 @@
+class,average_all_imgs,standard_deviation,avg_labeled,std_dev_labeled
+kingdom,1490931.5714285714,1221.0370884737988,1313721.0,1146.1766879499862
+phylum,115961.34444444445,340.53097428052627,102281.77777777778,319.8152244308857
+class,37008.93971631206,192.3770768992815,32562.521276595744,180.45088328017613
+order,7858.826054216867,88.65002004634216,6912.875753012048,83.14370543229384
+family,1344.5659623808297,36.66832369199374,1178.8904921412006,34.33497476540795
+genus,146.0490770931583,12.085076627525302,116.09587315803468,10.77477949463629
+species,62.11105754924716,7.881056880218995,49.31926441706838,7.0227675753272925
+common,23.720875419344868,4.8704081368346195,19.653807515023683,4.4332614986061545

notebooks/ToL_EDA.ipynb

@@ -22,7 +22,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/tmp/ipykernel_414099/3694103411.py:1: DtypeWarning: Columns (4,5,6) have mixed types. Specify dtype option on import or set low_memory=False.\n",
+      "/var/folders/nv/f0fq1p1n1_3b11x579py_0q80000gq/T/ipykernel_88978/3694103411.py:1: DtypeWarning: Columns (4,5,6) have mixed types. Specify dtype option on import or set low_memory=False.\n",
       "  df = pd.read_csv(\"../data/v1-dev-names.csv\")\n"
      ]
     }
@@ -176,22 +176,22 @@
        "</div>"
       ],
       "text/plain": [
-       "                          treeoflife_id  eol_content_id  eol_page_id  \\\n",
-       "0  0824741f-cc1c-4881-b292-15fd3f7964cd      29538374.0   65414274.0   \n",
+       "                          treeoflife_id  eol_content_id  eol_page_id   \n",
+       "0  0824741f-cc1c-4881-b292-15fd3f7964cd      29538374.0   65414274.0  \\\n",
        "1  5ca08f6b-9396-4cb9-9283-8dee158aac18      27793900.0     888015.0   \n",
        "2  f8c0f271-d8e5-4299-92d3-920508f74bf0      29121641.0    5618956.0   \n",
        "3  1f53e9d1-527f-42fd-b813-9f62fa2c2372      27596176.0     607817.0   \n",
        "4  a05bc2a8-5453-4683-903e-ed44f0fe7245      20300703.0     267922.0   \n",
        "\n",
-       "   bioscan_part bioscan_filename inat21_filename inat21_cls_name  \\\n",
-       "0           NaN              NaN             NaN             NaN   \n",
+       "   bioscan_part bioscan_filename inat21_filename inat21_cls_name   \n",
+       "0           NaN              NaN             NaN             NaN  \\\n",
        "1           NaN              NaN             NaN             NaN   \n",
        "2           NaN              NaN             NaN             NaN   \n",
        "3           NaN              NaN             NaN             NaN   \n",
        "4           NaN              NaN             NaN             NaN   \n",
        "\n",
-       "   inat21_cls_num         kingdom        phylum         class        order  \\\n",
-       "0             NaN             NaN           NaN           NaN          NaN   \n",
+       "   inat21_cls_num         kingdom        phylum         class        order   \n",
+       "0             NaN             NaN           NaN           NaN          NaN  \\\n",
        "1             NaN         Metazoa    Arthropoda  Pancrustacea  Lepidoptera   \n",
        "2             NaN  Archaeplastida  Tracheophyta           NaN   Sapindales   \n",
        "3             NaN         Metazoa    Arthropoda  Pancrustacea  Trichoptera   \n",
@@ -345,9 +345,328 @@
     "`Metazoa` and `Animalia` overlap, as do `Archaeplastida` and `Plantae`. They are sometimes used interchangably, though the former of each is a newer (more refined?) designation. Later we'll see this distinction cuts is by our data sources."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "One more oddity to note: we have about 84K images with genus labeled \"UNKNOWN\"."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 84025 entries, 30 to 10436423\n",
+      "Data columns (total 16 columns):\n",
+      " #   Column            Non-Null Count  Dtype  \n",
+      "---  ------            --------------  -----  \n",
+      " 0   treeoflife_id     84025 non-null  object \n",
+      " 1   eol_content_id    84025 non-null  float64\n",
+      " 2   eol_page_id       84025 non-null  float64\n",
+      " 3   bioscan_part      0 non-null      float64\n",
+      " 4   bioscan_filename  0 non-null      object \n",
+      " 5   inat21_filename   0 non-null      object \n",
+      " 6   inat21_cls_name   0 non-null      object \n",
+      " 7   inat21_cls_num    0 non-null      float64\n",
+      " 8   kingdom           0 non-null      object \n",
+      " 9   phylum            0 non-null      object \n",
+      " 10  class             0 non-null      object \n",
+      " 11  order             0 non-null      object \n",
+      " 12  family            0 non-null      object \n",
+      " 13  genus             84025 non-null  object \n",
+      " 14  species           7 non-null      object \n",
+      " 15  common            84025 non-null  object \n",
+      "dtypes: float64(4), object(12)\n",
+      "memory usage: 10.9+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.loc[df.genus.str.lower() == \"unknown\"].info(show_counts = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Check in on the few non-null species values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>treeoflife_id</th>\n",
+       "      <th>eol_content_id</th>\n",
+       "      <th>eol_page_id</th>\n",
+       "      <th>bioscan_part</th>\n",
+       "      <th>bioscan_filename</th>\n",
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+       "      <th>inat21_cls_name</th>\n",
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+       "      <th>kingdom</th>\n",
+       "      <th>phylum</th>\n",
+       "      <th>class</th>\n",
+       "      <th>order</th>\n",
+       "      <th>family</th>\n",
+       "      <th>genus</th>\n",
+       "      <th>species</th>\n",
+       "      <th>common</th>\n",
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+       "  </thead>\n",
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+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                treeoflife_id  eol_content_id  eol_page_id   \n",
+       "302277   1295e061-064b-4616-a10f-4a546489eb1a      14046024.0   62660090.0  \\\n",
+       "662535   79159008-2108-4690-a9b2-9812e0247ea5      14046023.0   62660090.0   \n",
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+       "4526299  be856027-d6c7-4321-b0d3-a155ad5b49ba      14046029.0   62660090.0   \n",
+       "\n",
+       "         bioscan_part bioscan_filename inat21_filename inat21_cls_name   \n",
+       "302277            NaN              NaN             NaN             NaN  \\\n",
+       "662535            NaN              NaN             NaN             NaN   \n",
+       "1133612           NaN              NaN             NaN             NaN   \n",
+       "1490749           NaN              NaN             NaN             NaN   \n",
+       "3686711           NaN              NaN             NaN             NaN   \n",
+       "4036030           NaN              NaN             NaN             NaN   \n",
+       "4526299           NaN              NaN             NaN             NaN   \n",
+       "\n",
+       "         inat21_cls_num kingdom phylum class order family    genus   \n",
+       "302277              NaN     NaN    NaN   NaN   NaN    NaN  Unknown  \\\n",
+       "662535              NaN     NaN    NaN   NaN   NaN    NaN  Unknown   \n",
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+       "4036030             NaN     NaN    NaN   NaN   NaN    NaN  Unknown   \n",
+       "4526299             NaN     NaN    NaN   NaN   NaN    NaN  Unknown   \n",
+       "\n",
+       "            species              common  \n",
+       "302277   pleasehelp  Unknown pleasehelp  \n",
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+       "1133612  pleasehelp  Unknown pleasehelp  \n",
+       "1490749  pleasehelp  Unknown pleasehelp  \n",
+       "3686711  pleasehelp  Unknown pleasehelp  \n",
+       "4036030  pleasehelp  Unknown pleasehelp  \n",
+       "4526299  pleasehelp  Unknown pleasehelp  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_unknown_genus = df.loc[df.genus.str.lower() == \"unknown\"]\n",
+    "df_unknown_genus.loc[df_unknown_genus.species.notna()]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Would be interesting to see what the model thinks of these once we re-train. They're all some kind of blue dragonfly-like bug ([eol page](https://eol.org/pages/62660090/media))."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(df.loc[df.species.str.lower() == \"pleasehelp\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Good only these 7."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -356,7 +675,7 @@
        "['kingdom', 'phylum', 'class', 'order', 'family', 'genus', 'species']"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -551,7 +870,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -637,7 +956,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -782,7 +1101,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1778,211 +2097,1249 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### EOL"
+    "Let's check this to be sure."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 17,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "Index: 6621365 entries, 0 to 10436520\n",
-      "Data columns (total 9 columns):\n",
-      " #   Column       Non-Null Count    Dtype \n",
-      "---  ------       --------------    ----- \n",
-      " 0   data_source  6621365 non-null  object\n",
-      " 1   kingdom      3919403 non-null  object\n",
-      " 2   phylum       3917533 non-null  object\n",
-      " 3   class        2842328 non-null  object\n",
-      " 4   order        3875193 non-null  object\n",
-      " 5   family       3906994 non-null  object\n",
-      " 6   genus        5119837 non-null  object\n",
-      " 7   species      4408570 non-null  object\n",
-      " 8   common       6621365 non-null  object\n",
-      "dtypes: object(9)\n",
-      "memory usage: 505.2+ MB\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "eol_df.info(show_counts = True)"
+    "def check_sci_name(df):\n",
+    "    \"\"\"\n",
+    "    This function checks the number of words in the species column for each sample.\n",
+    "    Logs a warning with the number that have more than one word indicating the potential for both genus and species to be recorded.\n",
+    "    Warning is printed to terminal, not saved to file.\n",
+    "\n",
+    "    Parameters:\n",
+    "    -----------\n",
+    "    df - DataFrame with taxonomic hierarchy as columns.\n",
+    "\n",
+    "    Returns:\n",
+    "    --------\n",
+    "    df - DataFrame with taxonomic hierarchy and length of species entry as columns.\n",
+    "    \"\"\"\n",
+    "    # Set length of species column with default = 1\n",
+    "    df[\"len_species\"] = 1\n",
+    "\n",
+    "    # Check for scientific name in species column (i.e., genus speices in species column, may correspond to missing genus)\n",
+    "    for species in list(df.loc[df[\"species\"].notna(), \"species\"].unique()):\n",
+    "        len_species = len(species.split(\" \"))\n",
+    "        if len_species > 1:\n",
+    "            df.loc[df[\"species\"] == species, \"len_species\"] = len_species\n",
+    "    \n",
+    "    return df\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "data_source         1\n",
-       "kingdom             3\n",
-       "phylum             49\n",
-       "class             119\n",
-       "order             679\n",
-       "family           5446\n",
-       "genus           89115\n",
-       "species        180738\n",
-       "common         518118\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/nv/f0fq1p1n1_3b11x579py_0q80000gq/T/ipykernel_65691/1898242669.py:16: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  df[\"len_species\"] = 1\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 1128313 entries, 4319650 to 8403834\n",
+      "Data columns (total 10 columns):\n",
+      " #   Column       Non-Null Count    Dtype \n",
+      "---  ------       --------------    ----- \n",
+      " 0   data_source  1128313 non-null  object\n",
+      " 1   kingdom      1128313 non-null  object\n",
+      " 2   phylum       1128313 non-null  object\n",
+      " 3   class        1128313 non-null  object\n",
+      " 4   order        1128313 non-null  object\n",
+      " 5   family       1112922 non-null  object\n",
+      " 6   genus        254149 non-null   object\n",
+      " 7   species      84450 non-null    object\n",
+      " 8   common       1128313 non-null  object\n",
+      " 9   len_species  1128313 non-null  int64 \n",
+      "dtypes: int64(1), object(9)\n",
+      "memory usage: 94.7+ MB\n"
+     ]
     }
    ],
    "source": [
-    "eol_df.nunique()"
+    "bioscan_species_len_df = check_sci_name(bioscan_df)\n",
+    "bioscan_species_len_df.info(show_counts = True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "data_source         1\n",
-       "kingdom             3\n",
-       "phylum             31\n",
-       "class              66\n",
-       "order             284\n",
-       "family           1480\n",
-       "genus           48126\n",
-       "species             0\n",
-       "common         148959\n",
-       "duplicate           2\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 84450 entries, 4401381 to 8403808\n",
+      "Data columns (total 10 columns):\n",
+      " #   Column       Non-Null Count  Dtype \n",
+      "---  ------       --------------  ----- \n",
+      " 0   data_source  84450 non-null  object\n",
+      " 1   kingdom      84450 non-null  object\n",
+      " 2   phylum       84450 non-null  object\n",
+      " 3   class        84450 non-null  object\n",
+      " 4   order        84450 non-null  object\n",
+      " 5   family       84450 non-null  object\n",
+      " 6   genus        84441 non-null  object\n",
+      " 7   species      84450 non-null  object\n",
+      " 8   common       84450 non-null  object\n",
+      " 9   len_species  84450 non-null  int64 \n",
+      "dtypes: int64(1), object(9)\n",
+      "memory usage: 7.1+ MB\n"
+     ]
     }
    ],
    "source": [
-    "eol_df.loc[eol_df.species.isna()].nunique()"
+    "bioscan_long_species = bioscan_species_len_df.loc[bioscan_species_len_df[\"len_species\"] > 1]\n",
+    "bioscan_long_species.info(show_counts = True)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "There are 570,515 unique page IDs from EOL, which clearly represent varying levels of taxa.\n",
-    "\n",
-    "Unique species + unique common where species is null does not reach this number."
+    "Yes, all species indicated are of length greater than 1 (indicating they may be genus-species)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "kingdom\n",
-       "Metazoa           2207522\n",
-       "Archaeplastida    1481804\n",
-       "Fungi              230077\n",
+       "len_species\n",
+       "2    81156\n",
+       "3     2679\n",
+       "4      598\n",
+       "5       17\n",
        "Name: count, dtype: int64"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "eol_df['kingdom'].value_counts()"
+    "bioscan_long_species.len_species.value_counts()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "EOL uses `Metazoa` and `Archaeplastida` in place of `Animalia` and `Plantae`, respectively. These designations will need to be merged.\n",
-    "\n",
-    "We have already observed that not all ranks are filled in at the higher levels, sometimes having just one gap. It seems this is particularly common for `class` (it has the least non-null values of any taxa by far)."
+    "Note that some are as long as 5 words, so this may require some more caution."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/tmp/ipykernel_414099/52183210.py:2: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  eol_df['duplicate'] = eol_df.duplicated(subset = taxa, keep = 'first')\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>data_source</th>\n",
+       "      <th>kingdom</th>\n",
+       "      <th>phylum</th>\n",
+       "      <th>class</th>\n",
+       "      <th>order</th>\n",
+       "      <th>family</th>\n",
+       "      <th>genus</th>\n",
+       "      <th>species</th>\n",
+       "      <th>common</th>\n",
+       "      <th>len_species</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>7249466</th>\n",
+       "      <td>BIOSCAN</td>\n",
+       "      <td>Animalia</td>\n",
+       "      <td>Arthropoda</td>\n",
+       "      <td>Insecta</td>\n",
+       "      <td>Lepidoptera</td>\n",
+       "      <td>Nepticulidae</td>\n",
+       "      <td>Stigmella</td>\n",
+       "      <td>stigmella pruinosa</td>\n",
+       "      <td>Stigmella stigmella pruinosa</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5561508</th>\n",
+       "      <td>BIOSCAN</td>\n",
+       "      <td>Animalia</td>\n",
+       "      <td>Arthropoda</td>\n",
+       "      <td>Insecta</td>\n",
+       "      <td>Diptera</td>\n",
+       "      <td>Chironomidae</td>\n",
+       "      <td>Tanytarsus</td>\n",
+       "      <td>tanytarsus hastatus</td>\n",
+       "      <td>Tanytarsus tanytarsus hastatus</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5590255</th>\n",
+       "      <td>BIOSCAN</td>\n",
+       "      <td>Animalia</td>\n",
+       "      <td>Arthropoda</td>\n",
+       "      <td>Insecta</td>\n",
+       "      <td>Diptera</td>\n",
+       "      <td>Psychodidae</td>\n",
+       "      <td>Pneumia</td>\n",
+       "      <td>pneumia mutua</td>\n",
+       "      <td>Pneumia pneumia mutua</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7204744</th>\n",
+       "      <td>BIOSCAN</td>\n",
+       "      <td>Animalia</td>\n",
+       "      <td>Arthropoda</td>\n",
+       "      <td>Insecta</td>\n",
+       "      <td>Diptera</td>\n",
+       "      <td>Sphaeroceridae</td>\n",
+       "      <td>Bifronsina</td>\n",
+       "      <td>bifronsina bifrons</td>\n",
+       "      <td>Bifronsina bifronsina bifrons</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6194418</th>\n",
+       "      <td>BIOSCAN</td>\n",
+       "      <td>Animalia</td>\n",
+       "      <td>Arthropoda</td>\n",
+       "      <td>Insecta</td>\n",
+       "      <td>Diptera</td>\n",
+       "      <td>Tachinidae</td>\n",
+       "      <td>Paradidyma</td>\n",
+       "      <td>paradidyma melania</td>\n",
+       "      <td>Paradidyma paradidyma melania</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4513431</th>\n",
+       "      <td>BIOSCAN</td>\n",
+       "      <td>Animalia</td>\n",
+       "      <td>Arthropoda</td>\n",
+       "      <td>Insecta</td>\n",
+       "      <td>Diptera</td>\n",
+       "      <td>Ceratopogonidae</td>\n",
+       "      <td>Forcipomyia</td>\n",
+       "      <td>forcipomyia sp. db 11421</td>\n",
+       "      <td>Forcipomyia forcipomyia sp. db 11421</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7813982</th>\n",
+       "      <td>BIOSCAN</td>\n",
+       "      <td>Animalia</td>\n",
+       "      <td>Arthropoda</td>\n",
+       "      <td>Insecta</td>\n",
+       "      <td>Diptera</td>\n",
+       "      <td>Sciaridae</td>\n",
+       "      <td>Corynoptera</td>\n",
+       "      <td>corynoptera breviformis</td>\n",
+       "      <td>Corynoptera corynoptera breviformis</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        data_source   kingdom      phylum    class        order   \n",
+       "7249466     BIOSCAN  Animalia  Arthropoda  Insecta  Lepidoptera  \\\n",
+       "5561508     BIOSCAN  Animalia  Arthropoda  Insecta      Diptera   \n",
+       "5590255     BIOSCAN  Animalia  Arthropoda  Insecta      Diptera   \n",
+       "7204744     BIOSCAN  Animalia  Arthropoda  Insecta      Diptera   \n",
+       "6194418     BIOSCAN  Animalia  Arthropoda  Insecta      Diptera   \n",
+       "4513431     BIOSCAN  Animalia  Arthropoda  Insecta      Diptera   \n",
+       "7813982     BIOSCAN  Animalia  Arthropoda  Insecta      Diptera   \n",
+       "\n",
+       "                  family        genus                   species   \n",
+       "7249466     Nepticulidae    Stigmella        stigmella pruinosa  \\\n",
+       "5561508     Chironomidae   Tanytarsus       tanytarsus hastatus   \n",
+       "5590255      Psychodidae      Pneumia             pneumia mutua   \n",
+       "7204744   Sphaeroceridae   Bifronsina        bifronsina bifrons   \n",
+       "6194418       Tachinidae   Paradidyma        paradidyma melania   \n",
+       "4513431  Ceratopogonidae  Forcipomyia  forcipomyia sp. db 11421   \n",
+       "7813982        Sciaridae  Corynoptera   corynoptera breviformis   \n",
+       "\n",
+       "                                       common  len_species  \n",
+       "7249466          Stigmella stigmella pruinosa            2  \n",
+       "5561508        Tanytarsus tanytarsus hastatus            2  \n",
+       "5590255                 Pneumia pneumia mutua            2  \n",
+       "7204744         Bifronsina bifronsina bifrons            2  \n",
+       "6194418         Paradidyma paradidyma melania            2  \n",
+       "4513431  Forcipomyia forcipomyia sp. db 11421            4  \n",
+       "7813982   Corynoptera corynoptera breviformis            2  "
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "#number of unique 7-tuples in EOL\n",
-    "eol_df['duplicate'] = eol_df.duplicated(subset = taxa, keep = 'first')\n",
-    "eol_df_unique_taxa = eol_df.loc[~eol_df['duplicate']]"
+    "bioscan_long_species.sample(7)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "Index: 422190 entries, 0 to 10436512\n",
-      "Data columns (total 10 columns):\n",
-      " #   Column       Non-Null Count   Dtype \n",
-      "---  ------       --------------   ----- \n",
-      " 0   data_source  422190 non-null  object\n",
-      " 1   kingdom      256471 non-null  object\n",
-      " 2   phylum       256242 non-null  object\n",
-      " 3   class        192666 non-null  object\n",
-      " 4   order        253217 non-null  object\n",
-      " 5   family       255792 non-null  object\n",
-      " 6   genus        421118 non-null  object\n",
-      " 7   species      372306 non-null  object\n",
-      " 8   common       422190 non-null  object\n",
-      " 9   duplicate    422190 non-null  bool  \n",
-      "dtypes: bool(1), object(9)\n",
-      "memory usage: 32.6+ MB\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>data_source</th>\n",
+       "      <th>kingdom</th>\n",
+       "      <th>phylum</th>\n",
+       "      <th>class</th>\n",
+       "      <th>order</th>\n",
+       "      <th>family</th>\n",
+       "      <th>genus</th>\n",
+       "      <th>species</th>\n",
+       "      <th>common</th>\n",
+       "      <th>len_species</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>4426608</th>\n",
+       "      <td>BIOSCAN</td>\n",
+       "      <td>Animalia</td>\n",
+       "      <td>Arthropoda</td>\n",
+       "      <td>Insecta</td>\n",
+       "      <td>Diptera</td>\n",
+       "      <td>Psychodidae</td>\n",
+       "      <td>Psychoda</td>\n",
+       "      <td>psychoda sp. 11gmk</td>\n",
+       "      <td>Psychoda psychoda sp. 11gmk</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7173904</th>\n",
+       "      <td>BIOSCAN</td>\n",
+       "      <td>Animalia</td>\n",
+       "      <td>Arthropoda</td>\n",
+       "      <td>Insecta</td>\n",
+       "      <td>Diptera</td>\n",
+       "      <td>Sciaridae</td>\n",
+       "      <td>Cosmosciara</td>\n",
+       "      <td>cosmosciara sp. saevg morph0042</td>\n",
+       "      <td>Cosmosciara cosmosciara sp. saevg morph0042</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7266486</th>\n",
+       "      <td>BIOSCAN</td>\n",
+       "      <td>Animalia</td>\n",
+       "      <td>Arthropoda</td>\n",
+       "      <td>Insecta</td>\n",
+       "      <td>Coleoptera</td>\n",
+       "      <td>Curculionidae</td>\n",
+       "      <td>Hypothenemus</td>\n",
+       "      <td>hypothenemus sp. h291</td>\n",
+       "      <td>Hypothenemus hypothenemus sp. h291</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5594884</th>\n",
+       "      <td>BIOSCAN</td>\n",
+       "      <td>Animalia</td>\n",
+       "      <td>Arthropoda</td>\n",
+       "      <td>Insecta</td>\n",
+       "      <td>Diptera</td>\n",
+       "      <td>Psychodidae</td>\n",
+       "      <td>Psychoda</td>\n",
+       "      <td>psychoda sp. 11gmk</td>\n",
+       "      <td>Psychoda psychoda sp. 11gmk</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5716363</th>\n",
+       "      <td>BIOSCAN</td>\n",
+       "      <td>Animalia</td>\n",
+       "      <td>Arthropoda</td>\n",
+       "      <td>Insecta</td>\n",
+       "      <td>Diptera</td>\n",
+       "      <td>Psychodidae</td>\n",
+       "      <td>Psychoda</td>\n",
+       "      <td>psychoda sp. 11gmk</td>\n",
+       "      <td>Psychoda psychoda sp. 11gmk</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4949127</th>\n",
+       "      <td>BIOSCAN</td>\n",
+       "      <td>Animalia</td>\n",
+       "      <td>Arthropoda</td>\n",
+       "      <td>Insecta</td>\n",
+       "      <td>Diptera</td>\n",
+       "      <td>Psychodidae</td>\n",
+       "      <td>Psychoda</td>\n",
+       "      <td>psychoda sp. 11gmk</td>\n",
+       "      <td>Psychoda psychoda sp. 11gmk</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4434473</th>\n",
+       "      <td>BIOSCAN</td>\n",
+       "      <td>Animalia</td>\n",
+       "      <td>Arthropoda</td>\n",
+       "      <td>Insecta</td>\n",
+       "      <td>Diptera</td>\n",
+       "      <td>Psychodidae</td>\n",
+       "      <td>Psychoda</td>\n",
+       "      <td>psychoda sp. 11gmk</td>\n",
+       "      <td>Psychoda psychoda sp. 11gmk</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5541057</th>\n",
+       "      <td>BIOSCAN</td>\n",
+       "      <td>Animalia</td>\n",
+       "      <td>Arthropoda</td>\n",
+       "      <td>Insecta</td>\n",
+       "      <td>Diptera</td>\n",
+       "      <td>Chironomidae</td>\n",
+       "      <td>Thienemanniella</td>\n",
+       "      <td>thienemanniella sp. 3lm</td>\n",
+       "      <td>Thienemanniella thienemanniella sp. 3lm</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4459500</th>\n",
+       "      <td>BIOSCAN</td>\n",
+       "      <td>Animalia</td>\n",
+       "      <td>Arthropoda</td>\n",
+       "      <td>Insecta</td>\n",
+       "      <td>Hymenoptera</td>\n",
+       "      <td>Eupelmidae</td>\n",
+       "      <td>Brasema</td>\n",
+       "      <td>brasema sp. gg14</td>\n",
+       "      <td>Brasema brasema sp. gg14</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6623861</th>\n",
+       "      <td>BIOSCAN</td>\n",
+       "      <td>Animalia</td>\n",
+       "      <td>Arthropoda</td>\n",
+       "      <td>Insecta</td>\n",
+       "      <td>Diptera</td>\n",
+       "      <td>Chironomidae</td>\n",
+       "      <td>Tanytarsus</td>\n",
+       "      <td>tanytarsus sp. 3te</td>\n",
+       "      <td>Tanytarsus tanytarsus sp. 3te</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        data_source   kingdom      phylum    class        order   \n",
+       "4426608     BIOSCAN  Animalia  Arthropoda  Insecta      Diptera  \\\n",
+       "7173904     BIOSCAN  Animalia  Arthropoda  Insecta      Diptera   \n",
+       "7266486     BIOSCAN  Animalia  Arthropoda  Insecta   Coleoptera   \n",
+       "5594884     BIOSCAN  Animalia  Arthropoda  Insecta      Diptera   \n",
+       "5716363     BIOSCAN  Animalia  Arthropoda  Insecta      Diptera   \n",
+       "4949127     BIOSCAN  Animalia  Arthropoda  Insecta      Diptera   \n",
+       "4434473     BIOSCAN  Animalia  Arthropoda  Insecta      Diptera   \n",
+       "5541057     BIOSCAN  Animalia  Arthropoda  Insecta      Diptera   \n",
+       "4459500     BIOSCAN  Animalia  Arthropoda  Insecta  Hymenoptera   \n",
+       "6623861     BIOSCAN  Animalia  Arthropoda  Insecta      Diptera   \n",
+       "\n",
+       "                family            genus                          species   \n",
+       "4426608    Psychodidae         Psychoda               psychoda sp. 11gmk  \\\n",
+       "7173904      Sciaridae      Cosmosciara  cosmosciara sp. saevg morph0042   \n",
+       "7266486  Curculionidae     Hypothenemus            hypothenemus sp. h291   \n",
+       "5594884    Psychodidae         Psychoda               psychoda sp. 11gmk   \n",
+       "5716363    Psychodidae         Psychoda               psychoda sp. 11gmk   \n",
+       "4949127    Psychodidae         Psychoda               psychoda sp. 11gmk   \n",
+       "4434473    Psychodidae         Psychoda               psychoda sp. 11gmk   \n",
+       "5541057   Chironomidae  Thienemanniella          thienemanniella sp. 3lm   \n",
+       "4459500     Eupelmidae          Brasema                 brasema sp. gg14   \n",
+       "6623861   Chironomidae       Tanytarsus               tanytarsus sp. 3te   \n",
+       "\n",
+       "                                              common  len_species  \n",
+       "4426608                  Psychoda psychoda sp. 11gmk            3  \n",
+       "7173904  Cosmosciara cosmosciara sp. saevg morph0042            4  \n",
+       "7266486           Hypothenemus hypothenemus sp. h291            3  \n",
+       "5594884                  Psychoda psychoda sp. 11gmk            3  \n",
+       "5716363                  Psychoda psychoda sp. 11gmk            3  \n",
+       "4949127                  Psychoda psychoda sp. 11gmk            3  \n",
+       "4434473                  Psychoda psychoda sp. 11gmk            3  \n",
+       "5541057      Thienemanniella thienemanniella sp. 3lm            3  \n",
+       "4459500                     Brasema brasema sp. gg14            3  \n",
+       "6623861                Tanytarsus tanytarsus sp. 3te            3  "
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bioscan_long_species.loc[bioscan_long_species['len_species'] > 2].sample(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([nan, 'psychoda albipennis', 'psychoda trinodulosa',\n",
+       "       'psychoda sp. 11gmk', 'psychoda satchelli', 'psychoda alternata',\n",
+       "       'psychoda phalaenoides', 'psychoda sigma', 'psychoda grisescens',\n",
+       "       'psychoda minuta', 'psychoda uncinula', 'psychoda divaricata',\n",
+       "       'psychoda erminea', 'psychoda mycophila', 'psychoda uniformata',\n",
+       "       'psychoda setigera', 'psychoda lativentris', 'psychoda lobata',\n",
+       "       'psychoda gemina', 'psychoda cinerea'], dtype=object)"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bioscan_df.loc[bioscan_df[\"genus\"] == \"Psychoda\", \"species\"].unique()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It would seem the \"sp. ...\" is used to indicate they are likely the same species, though the actual species is not designated (either unnamed or undetermined). This is information we should retain, but the duplicated genus can be removed."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### EOL"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 6621365 entries, 0 to 10436520\n",
+      "Data columns (total 9 columns):\n",
+      " #   Column       Non-Null Count    Dtype \n",
+      "---  ------       --------------    ----- \n",
+      " 0   data_source  6621365 non-null  object\n",
+      " 1   kingdom      3919403 non-null  object\n",
+      " 2   phylum       3917533 non-null  object\n",
+      " 3   class        2842328 non-null  object\n",
+      " 4   order        3875193 non-null  object\n",
+      " 5   family       3906994 non-null  object\n",
+      " 6   genus        5119837 non-null  object\n",
+      " 7   species      4408570 non-null  object\n",
+      " 8   common       6621365 non-null  object\n",
+      "dtypes: object(9)\n",
+      "memory usage: 505.2+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "eol_df.info(show_counts = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "data_source         1\n",
+       "kingdom             3\n",
+       "phylum             49\n",
+       "class             119\n",
+       "order             679\n",
+       "family           5446\n",
+       "genus           89115\n",
+       "species        180738\n",
+       "common         518118\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "eol_df.nunique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "data_source         1\n",
+       "kingdom             3\n",
+       "phylum             31\n",
+       "class              66\n",
+       "order             284\n",
+       "family           1480\n",
+       "genus           48126\n",
+       "species             0\n",
+       "common         148959\n",
+       "duplicate           2\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "eol_df.loc[eol_df.species.isna()].nunique()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are 570,515 unique page IDs from EOL, which clearly represent varying levels of taxa.\n",
+    "\n",
+    "Unique species + unique common where species is null does not reach this number."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "kingdom\n",
+       "Metazoa           2207522\n",
+       "Archaeplastida    1481804\n",
+       "Fungi              230077\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "eol_df['kingdom'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "EOL uses `Metazoa` and `Archaeplastida` in place of `Animalia` and `Plantae`, respectively. These designations will need to be merged.\n",
+    "\n",
+    "We have already observed that not all ranks are filled in at the higher levels, sometimes having just one gap. It seems this is particularly common for `class` (it has the least non-null values of any taxa by far)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_414099/52183210.py:2: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  eol_df['duplicate'] = eol_df.duplicated(subset = taxa, keep = 'first')\n"
+     ]
+    }
+   ],
+   "source": [
+    "#number of unique 7-tuples in EOL\n",
+    "eol_df['duplicate'] = eol_df.duplicated(subset = taxa, keep = 'first')\n",
+    "eol_df_unique_taxa = eol_df.loc[~eol_df['duplicate']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 422190 entries, 0 to 10436512\n",
+      "Data columns (total 10 columns):\n",
+      " #   Column       Non-Null Count   Dtype \n",
+      "---  ------       --------------   ----- \n",
+      " 0   data_source  422190 non-null  object\n",
+      " 1   kingdom      256471 non-null  object\n",
+      " 2   phylum       256242 non-null  object\n",
+      " 3   class        192666 non-null  object\n",
+      " 4   order        253217 non-null  object\n",
+      " 5   family       255792 non-null  object\n",
+      " 6   genus        421118 non-null  object\n",
+      " 7   species      372306 non-null  object\n",
+      " 8   common       422190 non-null  object\n",
+      " 9   duplicate    422190 non-null  bool  \n",
+      "dtypes: bool(1), object(9)\n",
+      "memory usage: 32.6+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "eol_df_unique_taxa.info(show_counts = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The actual number of distinct creatures is likely higher, as evidenced below where we have unique common names listed in `common` but all taxa are null.\n",
+    "\n",
+    "We should be able to fill all in for all values of `species` that also have `genus` indicated. Is `genus` labeled for all entries with `species` labeled? "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 4408570 entries, 1 to 10436520\n",
+      "Data columns (total 10 columns):\n",
+      " #   Column       Non-Null Count    Dtype \n",
+      "---  ------       --------------    ----- \n",
+      " 0   data_source  4408570 non-null  object\n",
+      " 1   kingdom      3562320 non-null  object\n",
+      " 2   phylum       3560488 non-null  object\n",
+      " 3   class        2496336 non-null  object\n",
+      " 4   order        3526489 non-null  object\n",
+      " 5   family       3555902 non-null  object\n",
+      " 6   genus        4408326 non-null  object\n",
+      " 7   species      4408570 non-null  object\n",
+      " 8   common       4408570 non-null  object\n",
+      " 9   duplicate    4408570 non-null  bool  \n",
+      "dtypes: bool(1), object(9)\n",
+      "memory usage: 340.6+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "eol_df.loc[eol_df.species.notna()].info(show_counts = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It looks like we do have just under 250 images for which there's a `species` label, but no `genus` label. This is a sufficiently low number that we could go in to manually check them and hopefully match the the proper higher taxonomic ranks (considering some species names get reused across different genera)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's get a quick sample of the `common` column for images both with and without `species` labels. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "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>data_source</th>\n",
+       "      <th>kingdom</th>\n",
+       "      <th>phylum</th>\n",
+       "      <th>class</th>\n",
+       "      <th>order</th>\n",
+       "      <th>family</th>\n",
+       "      <th>genus</th>\n",
+       "      <th>species</th>\n",
+       "      <th>common</th>\n",
+       "      <th>duplicate</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>7109927</th>\n",
+       "      <td>EOL</td>\n",
+       "      <td>Archaeplastida</td>\n",
+       "      <td>Tracheophyta</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Fabales</td>\n",
+       "      <td>Polygalaceae</td>\n",
+       "      <td>Polygala</td>\n",
+       "      <td>leendertziae</td>\n",
+       "      <td>Polygala leendertziae</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1169608</th>\n",
+       "      <td>EOL</td>\n",
+       "      <td>Fungi</td>\n",
+       "      <td>Ascomycota</td>\n",
+       "      <td>Eurotiomycetes</td>\n",
+       "      <td>Pyrenulales</td>\n",
+       "      <td>Pyrenulaceae</td>\n",
+       "      <td>Pyrenula</td>\n",
+       "      <td>dermatodes</td>\n",
+       "      <td>Pyrenula dermatodes</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2955438</th>\n",
+       "      <td>EOL</td>\n",
+       "      <td>Archaeplastida</td>\n",
+       "      <td>Tracheophyta</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Magnoliales</td>\n",
+       "      <td>Annonaceae</td>\n",
+       "      <td>Asimina</td>\n",
+       "      <td>obovata</td>\n",
+       "      <td>bigflower pawpaw</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1551580</th>\n",
+       "      <td>EOL</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Numenius</td>\n",
+       "      <td>hudsonicus</td>\n",
+       "      <td>Numenius hudsonicus</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3845702</th>\n",
+       "      <td>EOL</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Urtica</td>\n",
+       "      <td>gracilis holosericea</td>\n",
+       "      <td>stinging nettle</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3499959</th>\n",
+       "      <td>EOL</td>\n",
+       "      <td>Metazoa</td>\n",
+       "      <td>Chordata</td>\n",
+       "      <td>Gnathostomata</td>\n",
+       "      <td>Gymnophiona</td>\n",
+       "      <td>Ichthyophiidae</td>\n",
+       "      <td>Ichthyophis</td>\n",
+       "      <td>bombayensis</td>\n",
+       "      <td>Bombay Caecilian</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4155103</th>\n",
+       "      <td>EOL</td>\n",
+       "      <td>Fungi</td>\n",
+       "      <td>Basidiomycota</td>\n",
+       "      <td>Agaricomycetes</td>\n",
+       "      <td>Boletales</td>\n",
+       "      <td>Suillaceae</td>\n",
+       "      <td>Suillus</td>\n",
+       "      <td>placidus</td>\n",
+       "      <td>Slippery white bolete</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        data_source         kingdom         phylum           class  \\\n",
+       "7109927         EOL  Archaeplastida   Tracheophyta             NaN   \n",
+       "1169608         EOL           Fungi     Ascomycota  Eurotiomycetes   \n",
+       "2955438         EOL  Archaeplastida   Tracheophyta             NaN   \n",
+       "1551580         EOL             NaN            NaN             NaN   \n",
+       "3845702         EOL             NaN            NaN             NaN   \n",
+       "3499959         EOL         Metazoa       Chordata   Gnathostomata   \n",
+       "4155103         EOL           Fungi  Basidiomycota  Agaricomycetes   \n",
+       "\n",
+       "               order          family        genus               species  \\\n",
+       "7109927      Fabales    Polygalaceae     Polygala          leendertziae   \n",
+       "1169608  Pyrenulales    Pyrenulaceae     Pyrenula            dermatodes   \n",
+       "2955438  Magnoliales      Annonaceae      Asimina               obovata   \n",
+       "1551580          NaN             NaN     Numenius            hudsonicus   \n",
+       "3845702          NaN             NaN       Urtica  gracilis holosericea   \n",
+       "3499959  Gymnophiona  Ichthyophiidae  Ichthyophis           bombayensis   \n",
+       "4155103    Boletales      Suillaceae      Suillus              placidus   \n",
+       "\n",
+       "                        common  duplicate  \n",
+       "7109927  Polygala leendertziae       True  \n",
+       "1169608    Pyrenula dermatodes       True  \n",
+       "2955438       bigflower pawpaw       True  \n",
+       "1551580    Numenius hudsonicus       True  \n",
+       "3845702        stinging nettle       True  \n",
+       "3499959       Bombay Caecilian       True  \n",
+       "4155103  Slippery white bolete       True  "
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# existing species label\n",
+    "eol_df.loc[eol_df.species.notna()].sample(7)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Good, some of these do have common names. We could check numbers of `common` where it doesn't match the `genus-species` form for a better count, but our random sample is promising."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "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>data_source</th>\n",
+       "      <th>kingdom</th>\n",
+       "      <th>phylum</th>\n",
+       "      <th>class</th>\n",
+       "      <th>order</th>\n",
+       "      <th>family</th>\n",
+       "      <th>genus</th>\n",
+       "      <th>species</th>\n",
+       "      <th>common</th>\n",
+       "      <th>duplicate</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2635269</th>\n",
+       "      <td>EOL</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>tāranga</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4404459</th>\n",
+       "      <td>EOL</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Trans-Pecos thimblehead</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>575560</th>\n",
+       "      <td>EOL</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Cremastobaeus</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>456164</th>\n",
+       "      <td>EOL</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>American red raspberry</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10332391</th>\n",
+       "      <td>EOL</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Straight-lined Vaxi Moth</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6082438</th>\n",
+       "      <td>EOL</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Rock ptarmigan</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3443067</th>\n",
+       "      <td>EOL</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Catapion meieri</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         data_source kingdom phylum class order family genus species  \\\n",
+       "2635269          EOL     NaN    NaN   NaN   NaN    NaN   NaN     NaN   \n",
+       "4404459          EOL     NaN    NaN   NaN   NaN    NaN   NaN     NaN   \n",
+       "575560           EOL     NaN    NaN   NaN   NaN    NaN   NaN     NaN   \n",
+       "456164           EOL     NaN    NaN   NaN   NaN    NaN   NaN     NaN   \n",
+       "10332391         EOL     NaN    NaN   NaN   NaN    NaN   NaN     NaN   \n",
+       "6082438          EOL     NaN    NaN   NaN   NaN    NaN   NaN     NaN   \n",
+       "3443067          EOL     NaN    NaN   NaN   NaN    NaN   NaN     NaN   \n",
+       "\n",
+       "                            common  duplicate  \n",
+       "2635269                    tāranga       True  \n",
+       "4404459    Trans-Pecos thimblehead       True  \n",
+       "575560               Cremastobaeus       True  \n",
+       "456164      American red raspberry       True  \n",
+       "10332391  Straight-lined Vaxi Moth       True  \n",
+       "6082438             Rock ptarmigan       True  \n",
+       "3443067            Catapion meieri       True  "
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# No species label\n",
+    "eol_df.loc[eol_df.species.isna()].sample(7)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "All `common` values are filled in with predominently common names. It seems we could map these back to some level of taxa with the taxon-common matching? Though why would these EOL images have common without any other designation?\n",
+    "\n",
+    "Good example of strange inconsistency: the `American red raspberry` is `Rubus strigosus`, and a quick Google search easily provides the entire taxonomy.\n",
+    "\n",
+    "`Cremastobaeus` seems to be a genus of wasp."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's check the `species` length in EOL as well, we know there are some that have genus-species."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/nv/f0fq1p1n1_3b11x579py_0q80000gq/T/ipykernel_65691/1392676569.py:16: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  df[\"len_species\"] = 1\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 6621365 entries, 0 to 10436520\n",
+      "Data columns (total 10 columns):\n",
+      " #   Column       Non-Null Count    Dtype \n",
+      "---  ------       --------------    ----- \n",
+      " 0   data_source  6621365 non-null  object\n",
+      " 1   kingdom      3919403 non-null  object\n",
+      " 2   phylum       3917533 non-null  object\n",
+      " 3   class        2842328 non-null  object\n",
+      " 4   order        3875193 non-null  object\n",
+      " 5   family       3906994 non-null  object\n",
+      " 6   genus        5119837 non-null  object\n",
+      " 7   species      4408570 non-null  object\n",
+      " 8   common       6621365 non-null  object\n",
+      " 9   len_species  6621365 non-null  int64 \n",
+      "dtypes: int64(1), object(9)\n",
+      "memory usage: 555.7+ MB\n"
      ]
     }
    ],
    "source": [
-    "eol_df_unique_taxa.info(show_counts = True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The actual number of distinct creatures is likely higher, as evidenced below where we have unique common names listed in `common` but all taxa are null.\n",
-    "\n",
-    "We should be able to fill all in for all values of `species` that also have `genus` indicated. Is `genus` labeled for all entries with `species` labeled? "
+    "eol_species_len = check_sci_name(eol_df)\n",
+    "eol_species_len.info(show_counts = True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -1990,46 +3347,84 @@
      "output_type": "stream",
      "text": [
       "<class 'pandas.core.frame.DataFrame'>\n",
-      "Index: 4408570 entries, 1 to 10436520\n",
+      "Index: 670683 entries, 13 to 10436519\n",
       "Data columns (total 10 columns):\n",
-      " #   Column       Non-Null Count    Dtype \n",
-      "---  ------       --------------    ----- \n",
-      " 0   data_source  4408570 non-null  object\n",
-      " 1   kingdom      3562320 non-null  object\n",
-      " 2   phylum       3560488 non-null  object\n",
-      " 3   class        2496336 non-null  object\n",
-      " 4   order        3526489 non-null  object\n",
-      " 5   family       3555902 non-null  object\n",
-      " 6   genus        4408326 non-null  object\n",
-      " 7   species      4408570 non-null  object\n",
-      " 8   common       4408570 non-null  object\n",
-      " 9   duplicate    4408570 non-null  bool  \n",
-      "dtypes: bool(1), object(9)\n",
-      "memory usage: 340.6+ MB\n"
+      " #   Column       Non-Null Count   Dtype \n",
+      "---  ------       --------------   ----- \n",
+      " 0   data_source  670683 non-null  object\n",
+      " 1   kingdom      129237 non-null  object\n",
+      " 2   phylum       129231 non-null  object\n",
+      " 3   class        101987 non-null  object\n",
+      " 4   order        129237 non-null  object\n",
+      " 5   family       129114 non-null  object\n",
+      " 6   genus        670439 non-null  object\n",
+      " 7   species      670683 non-null  object\n",
+      " 8   common       670683 non-null  object\n",
+      " 9   len_species  670683 non-null  int64 \n",
+      "dtypes: int64(1), object(9)\n",
+      "memory usage: 56.3+ MB\n"
      ]
     }
    ],
    "source": [
-    "eol_df.loc[eol_df.species.notna()].info(show_counts = True)"
+    "eol_long_species = eol_species_len.loc[eol_species_len[\"len_species\"] > 1]\n",
+    "eol_long_species.info(show_counts = True)"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 20,
    "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "len_species\n",
+       "2     534380\n",
+       "3      95127\n",
+       "4      20029\n",
+       "5      13807\n",
+       "7       3820\n",
+       "6       2043\n",
+       "9        583\n",
+       "8        447\n",
+       "10       195\n",
+       "11        95\n",
+       "13        44\n",
+       "15        31\n",
+       "12        28\n",
+       "16        16\n",
+       "14         8\n",
+       "36         7\n",
+       "17         6\n",
+       "21         6\n",
+       "18         5\n",
+       "22         3\n",
+       "50         1\n",
+       "26         1\n",
+       "19         1\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "It looks like we do have just under 250 images for which there's a `species` label, but no `genus` label. This is a sufficiently low number that we could go in to manually check them and hopefully match the the proper higher taxonomic ranks (considering some species names get reused across different genera)."
+    "eol_long_species.len_species.value_counts()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's get a quick sample of the `common` column for images both with and without `species` labels. "
+    "That's quite a lot, and why are some so long?!"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -2062,154 +3457,176 @@
        "      <th>genus</th>\n",
        "      <th>species</th>\n",
        "      <th>common</th>\n",
-       "      <th>duplicate</th>\n",
+       "      <th>len_species</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>7109927</th>\n",
+       "      <th>6442342</th>\n",
        "      <td>EOL</td>\n",
-       "      <td>Archaeplastida</td>\n",
-       "      <td>Tracheophyta</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>Fabales</td>\n",
-       "      <td>Polygalaceae</td>\n",
-       "      <td>Polygala</td>\n",
-       "      <td>leendertziae</td>\n",
-       "      <td>Polygala leendertziae</td>\n",
-       "      <td>True</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Echinocereus</td>\n",
+       "      <td>engelmannii subsp. fasciculatus (engelm. ex wa...</td>\n",
+       "      <td>Echinocereus engelmannii subsp. fasciculatus (...</td>\n",
+       "      <td>9</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1169608</th>\n",
+       "      <th>4033534</th>\n",
        "      <td>EOL</td>\n",
-       "      <td>Fungi</td>\n",
-       "      <td>Ascomycota</td>\n",
-       "      <td>Eurotiomycetes</td>\n",
-       "      <td>Pyrenulales</td>\n",
-       "      <td>Pyrenulaceae</td>\n",
-       "      <td>Pyrenula</td>\n",
-       "      <td>dermatodes</td>\n",
-       "      <td>Pyrenula dermatodes</td>\n",
-       "      <td>True</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Amanita</td>\n",
+       "      <td>pseudobreckonii n. siegel &amp; c.f. schwarz nom. ...</td>\n",
+       "      <td>Amanita pseudobreckonii N. Siegel &amp; C.F. Schwa...</td>\n",
+       "      <td>8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2955438</th>\n",
+       "      <th>2942755</th>\n",
        "      <td>EOL</td>\n",
-       "      <td>Archaeplastida</td>\n",
-       "      <td>Tracheophyta</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>Magnoliales</td>\n",
-       "      <td>Annonaceae</td>\n",
-       "      <td>Asimina</td>\n",
-       "      <td>obovata</td>\n",
-       "      <td>bigflower pawpaw</td>\n",
-       "      <td>True</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Lotus</td>\n",
+       "      <td>unifoliolatus (hook.) benth. var. helleri (bri...</td>\n",
+       "      <td>Lotus unifoliolatus (Hook.) Benth. var. heller...</td>\n",
+       "      <td>9</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1551580</th>\n",
+       "      <th>4229094</th>\n",
        "      <td>EOL</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>Numenius</td>\n",
-       "      <td>hudsonicus</td>\n",
-       "      <td>Numenius hudsonicus</td>\n",
-       "      <td>True</td>\n",
+       "      <td>Bulinus</td>\n",
+       "      <td>versicolor broderip in broderip and sowerby i ...</td>\n",
+       "      <td>Bulinus versicolor Broderip in Broderip and So...</td>\n",
+       "      <td>8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3845702</th>\n",
+       "      <th>7151573</th>\n",
        "      <td>EOL</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>Urtica</td>\n",
-       "      <td>gracilis holosericea</td>\n",
-       "      <td>stinging nettle</td>\n",
-       "      <td>True</td>\n",
+       "      <td>Rubus</td>\n",
+       "      <td>revealii a. beek &amp; m. p. widrlechner 2021</td>\n",
+       "      <td>Rubus revealii A. Beek &amp; M. P. Widrlechner 2021</td>\n",
+       "      <td>8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3499959</th>\n",
+       "      <th>92767</th>\n",
        "      <td>EOL</td>\n",
-       "      <td>Metazoa</td>\n",
-       "      <td>Chordata</td>\n",
-       "      <td>Gnathostomata</td>\n",
-       "      <td>Gymnophiona</td>\n",
-       "      <td>Ichthyophiidae</td>\n",
-       "      <td>Ichthyophis</td>\n",
-       "      <td>bombayensis</td>\n",
-       "      <td>Bombay Caecilian</td>\n",
-       "      <td>True</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Scadoxus</td>\n",
+       "      <td>multiflorus (martyn) raf. ssp. katherinae (bak...</td>\n",
+       "      <td>Scadoxus multiflorus (Martyn) Raf. ssp. kather...</td>\n",
+       "      <td>9</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>4155103</th>\n",
+       "      <th>7707570</th>\n",
        "      <td>EOL</td>\n",
-       "      <td>Fungi</td>\n",
-       "      <td>Basidiomycota</td>\n",
-       "      <td>Agaricomycetes</td>\n",
-       "      <td>Boletales</td>\n",
-       "      <td>Suillaceae</td>\n",
-       "      <td>Suillus</td>\n",
-       "      <td>placidus</td>\n",
-       "      <td>Slippery white bolete</td>\n",
-       "      <td>True</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Heliotropium</td>\n",
+       "      <td>baclei dc. &amp; a. dc.&lt;br &gt; var. rostratum i.m. j...</td>\n",
+       "      <td>Heliotropium baclei DC. &amp; A. DC.&lt;br &gt; var. ros...</td>\n",
+       "      <td>10</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "        data_source         kingdom         phylum           class  \\\n",
-       "7109927         EOL  Archaeplastida   Tracheophyta             NaN   \n",
-       "1169608         EOL           Fungi     Ascomycota  Eurotiomycetes   \n",
-       "2955438         EOL  Archaeplastida   Tracheophyta             NaN   \n",
-       "1551580         EOL             NaN            NaN             NaN   \n",
-       "3845702         EOL             NaN            NaN             NaN   \n",
-       "3499959         EOL         Metazoa       Chordata   Gnathostomata   \n",
-       "4155103         EOL           Fungi  Basidiomycota  Agaricomycetes   \n",
+       "        data_source kingdom phylum class order family         genus   \n",
+       "6442342         EOL     NaN    NaN   NaN   NaN    NaN  Echinocereus  \\\n",
+       "4033534         EOL     NaN    NaN   NaN   NaN    NaN       Amanita   \n",
+       "2942755         EOL     NaN    NaN   NaN   NaN    NaN         Lotus   \n",
+       "4229094         EOL     NaN    NaN   NaN   NaN    NaN       Bulinus   \n",
+       "7151573         EOL     NaN    NaN   NaN   NaN    NaN         Rubus   \n",
+       "92767           EOL     NaN    NaN   NaN   NaN    NaN      Scadoxus   \n",
+       "7707570         EOL     NaN    NaN   NaN   NaN    NaN  Heliotropium   \n",
        "\n",
-       "               order          family        genus               species  \\\n",
-       "7109927      Fabales    Polygalaceae     Polygala          leendertziae   \n",
-       "1169608  Pyrenulales    Pyrenulaceae     Pyrenula            dermatodes   \n",
-       "2955438  Magnoliales      Annonaceae      Asimina               obovata   \n",
-       "1551580          NaN             NaN     Numenius            hudsonicus   \n",
-       "3845702          NaN             NaN       Urtica  gracilis holosericea   \n",
-       "3499959  Gymnophiona  Ichthyophiidae  Ichthyophis           bombayensis   \n",
-       "4155103    Boletales      Suillaceae      Suillus              placidus   \n",
+       "                                                   species   \n",
+       "6442342  engelmannii subsp. fasciculatus (engelm. ex wa...  \\\n",
+       "4033534  pseudobreckonii n. siegel & c.f. schwarz nom. ...   \n",
+       "2942755  unifoliolatus (hook.) benth. var. helleri (bri...   \n",
+       "4229094  versicolor broderip in broderip and sowerby i ...   \n",
+       "7151573          revealii a. beek & m. p. widrlechner 2021   \n",
+       "92767    multiflorus (martyn) raf. ssp. katherinae (bak...   \n",
+       "7707570  baclei dc. & a. dc.<br > var. rostratum i.m. j...   \n",
        "\n",
-       "                        common  duplicate  \n",
-       "7109927  Polygala leendertziae       True  \n",
-       "1169608    Pyrenula dermatodes       True  \n",
-       "2955438       bigflower pawpaw       True  \n",
-       "1551580    Numenius hudsonicus       True  \n",
-       "3845702        stinging nettle       True  \n",
-       "3499959       Bombay Caecilian       True  \n",
-       "4155103  Slippery white bolete       True  "
+       "                                                    common  len_species  \n",
+       "6442342  Echinocereus engelmannii subsp. fasciculatus (...            9  \n",
+       "4033534  Amanita pseudobreckonii N. Siegel & C.F. Schwa...            8  \n",
+       "2942755  Lotus unifoliolatus (Hook.) Benth. var. heller...            9  \n",
+       "4229094  Bulinus versicolor Broderip in Broderip and So...            8  \n",
+       "7151573    Rubus revealii A. Beek & M. P. Widrlechner 2021            8  \n",
+       "92767    Scadoxus multiflorus (Martyn) Raf. ssp. kather...            9  \n",
+       "7707570  Heliotropium baclei DC. & A. DC.<br > var. ros...           10  "
       ]
      },
-     "execution_count": 41,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# existing species label\n",
-    "eol_df.loc[eol_df.species.notna()].sample(7)"
+    "eol_long_species.loc[eol_long_species[\"len_species\"] > 7].sample(7)"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 22,
    "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "data_source        1\n",
+       "kingdom            3\n",
+       "phylum            10\n",
+       "class             14\n",
+       "order             97\n",
+       "family           635\n",
+       "genus          18911\n",
+       "species        68321\n",
+       "common         77520\n",
+       "len_species       23\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "Good, some of these do have common names. We could check numbers of `common` where it doesn't match the `genus-species` form for a better count, but our random sample is promising."
+    "eol_long_species.nunique()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -2242,144 +3659,148 @@
        "      <th>genus</th>\n",
        "      <th>species</th>\n",
        "      <th>common</th>\n",
-       "      <th>duplicate</th>\n",
+       "      <th>len_species</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>2635269</th>\n",
+       "      <th>118415</th>\n",
        "      <td>EOL</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>tāranga</td>\n",
-       "      <td>True</td>\n",
+       "      <td>Amida</td>\n",
+       "      <td>tricolor formosana weise</td>\n",
+       "      <td>Amida tricolor formosana Weise</td>\n",
+       "      <td>3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>4404459</th>\n",
+       "      <th>814971</th>\n",
        "      <td>EOL</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Trans-Pecos thimblehead</td>\n",
-       "      <td>True</td>\n",
+       "      <td>Retifusus</td>\n",
+       "      <td>saginatus (tiba</td>\n",
+       "      <td>Retifusus saginatus (Tiba</td>\n",
+       "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>575560</th>\n",
+       "      <th>8158583</th>\n",
        "      <td>EOL</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Cremastobaeus</td>\n",
-       "      <td>True</td>\n",
+       "      <td>Olearia</td>\n",
+       "      <td>rani var. colorata (colenso) kirk</td>\n",
+       "      <td>Olearia rani var. colorata (Colenso) Kirk</td>\n",
+       "      <td>5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>456164</th>\n",
+       "      <th>1663463</th>\n",
        "      <td>EOL</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>American red raspberry</td>\n",
-       "      <td>True</td>\n",
+       "      <td>Speyeria</td>\n",
+       "      <td>zerene zerene</td>\n",
+       "      <td>Speyeria zerene zerene</td>\n",
+       "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>10332391</th>\n",
+       "      <th>1362286</th>\n",
        "      <td>EOL</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Straight-lined Vaxi Moth</td>\n",
-       "      <td>True</td>\n",
+       "      <td>Catharylla</td>\n",
+       "      <td>coronata t. léger &amp; b. landry</td>\n",
+       "      <td>Catharylla coronata T. Léger &amp; B. Landry</td>\n",
+       "      <td>6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>6082438</th>\n",
+       "      <th>6482276</th>\n",
        "      <td>EOL</td>\n",
+       "      <td>Archaeplastida</td>\n",
+       "      <td>Tracheophyta</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Rock ptarmigan</td>\n",
-       "      <td>True</td>\n",
+       "      <td>Piperales</td>\n",
+       "      <td>Aristolochiaceae</td>\n",
+       "      <td>Asarum</td>\n",
+       "      <td>asarum fauriei</td>\n",
+       "      <td>Asarum fauriei takaoi</td>\n",
+       "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3443067</th>\n",
+       "      <th>7549534</th>\n",
        "      <td>EOL</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Catapion meieri</td>\n",
-       "      <td>True</td>\n",
+       "      <td>Metazoa</td>\n",
+       "      <td>Arthropoda</td>\n",
+       "      <td>Pancrustacea</td>\n",
+       "      <td>Orthoptera</td>\n",
+       "      <td>Pyrgomorphidae</td>\n",
+       "      <td>Phymateus</td>\n",
+       "      <td>phymateus leprosus</td>\n",
+       "      <td>Phymateus leprosus leprosus</td>\n",
+       "      <td>2</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "         data_source kingdom phylum class order family genus species  \\\n",
-       "2635269          EOL     NaN    NaN   NaN   NaN    NaN   NaN     NaN   \n",
-       "4404459          EOL     NaN    NaN   NaN   NaN    NaN   NaN     NaN   \n",
-       "575560           EOL     NaN    NaN   NaN   NaN    NaN   NaN     NaN   \n",
-       "456164           EOL     NaN    NaN   NaN   NaN    NaN   NaN     NaN   \n",
-       "10332391         EOL     NaN    NaN   NaN   NaN    NaN   NaN     NaN   \n",
-       "6082438          EOL     NaN    NaN   NaN   NaN    NaN   NaN     NaN   \n",
-       "3443067          EOL     NaN    NaN   NaN   NaN    NaN   NaN     NaN   \n",
+       "        data_source         kingdom        phylum         class       order   \n",
+       "118415          EOL             NaN           NaN           NaN         NaN  \\\n",
+       "814971          EOL             NaN           NaN           NaN         NaN   \n",
+       "8158583         EOL             NaN           NaN           NaN         NaN   \n",
+       "1663463         EOL             NaN           NaN           NaN         NaN   \n",
+       "1362286         EOL             NaN           NaN           NaN         NaN   \n",
+       "6482276         EOL  Archaeplastida  Tracheophyta           NaN   Piperales   \n",
+       "7549534         EOL         Metazoa    Arthropoda  Pancrustacea  Orthoptera   \n",
        "\n",
-       "                            common  duplicate  \n",
-       "2635269                    tāranga       True  \n",
-       "4404459    Trans-Pecos thimblehead       True  \n",
-       "575560               Cremastobaeus       True  \n",
-       "456164      American red raspberry       True  \n",
-       "10332391  Straight-lined Vaxi Moth       True  \n",
-       "6082438             Rock ptarmigan       True  \n",
-       "3443067            Catapion meieri       True  "
+       "                   family       genus                            species   \n",
+       "118415                NaN       Amida           tricolor formosana weise  \\\n",
+       "814971                NaN   Retifusus                    saginatus (tiba   \n",
+       "8158583               NaN     Olearia  rani var. colorata (colenso) kirk   \n",
+       "1663463               NaN    Speyeria                      zerene zerene   \n",
+       "1362286               NaN  Catharylla      coronata t. léger & b. landry   \n",
+       "6482276  Aristolochiaceae      Asarum                     asarum fauriei   \n",
+       "7549534    Pyrgomorphidae   Phymateus                 phymateus leprosus   \n",
+       "\n",
+       "                                            common  len_species  \n",
+       "118415              Amida tricolor formosana Weise            3  \n",
+       "814971                   Retifusus saginatus (Tiba            2  \n",
+       "8158583  Olearia rani var. colorata (Colenso) Kirk            5  \n",
+       "1663463                     Speyeria zerene zerene            2  \n",
+       "1362286   Catharylla coronata T. Léger & B. Landry            6  \n",
+       "6482276                      Asarum fauriei takaoi            2  \n",
+       "7549534                Phymateus leprosus leprosus            2  "
       ]
      },
-     "execution_count": 42,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# No species label\n",
-    "eol_df.loc[eol_df.species.isna()].sample(7)"
+    "eol_long_species.loc[eol_long_species[\"len_species\"] < 7].sample(7)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "All `common` values are filled in with predominently common names. It seems we could map these back to some level of taxa with the taxon-common matching? Though why would these EOL images have common without any other designation?\n",
-    "\n",
-    "Good example of strange inconsistency: the `American red raspberry` is `Rubus strigosus`, and a quick Google search easily provides the entire taxonomy.\n",
-    "\n",
-    "`Cremastobaeus` seems to be a genus of wasp."
+    "### Label Overlap Check"
    ]
   },
   {
@@ -2690,7 +4111,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "venv",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -2704,9 +4125,8 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10"
-  },
-  "orig_nbformat": 4
+   "version": "3.11.3"
+  }
  },
  "nbformat": 4,
  "nbformat_minor": 2

notebooks/ToL_stats_EDA.py

@@ -0,0 +1,534 @@
+# ---
+# jupyter:
+#   jupytext:
+#     formats: ipynb,py:percent
+#     text_representation:
+#       extension: .py
+#       format_name: percent
+#       format_version: '1.3'
+#       jupytext_version: 1.15.2
+#   kernelspec:
+#     display_name: Python 3 (ipykernel)
+#     language: python
+#     name: python3
+# ---
+
+# %%
+import pandas as pd
+import seaborn as sns
+
+sns.set_style("whitegrid")
+sns.set(rc = {'figure.figsize': (10,10)})
+
+# %%
+df = pd.read_csv("../data/statistics.csv")
+
+# %%
+df.head()
+
+# %%
+df.info(show_counts = True)
+
+# %% [markdown]
+# Original version had 10,436,521 entries; was the validation set left out (there does seem to be about 5-10% less of each source).
+#
+# Labeling definitely has far better coverage now.
+
+# %%
+df.nunique()
+
+# %% [markdown]
+# There are 498,053 unique EOL page IDs, suggesting 498,053 unique classes among the 5,938,235 images pulled from EOL (and maintained here). Presumably this would represent the number of species or other lowest rank taxa covered.
+
+# %% [markdown]
+# Notice that we have 7 unique kingdoms, when there are only...
+
+# %%
+df['kingdom'].value_counts()
+
+# %% [markdown]
+# `Metazoa` and `Archaeplastida` have been replaced by `Animalia` and `Plantae`. 
+#
+# We now have other single-celled organisms for kingdom
+
+# %%
+taxa = list(df.columns[8:15])
+taxa
+
+# %% [markdown]
+# Check the number of images with all 7 taxonomic labels.
+
+# %%
+df_all_taxa = df.dropna(subset = taxa)
+df_all_taxa[taxa].info(show_counts = True)
+
+# %% [markdown]
+# We have 7,901,259 images with full taxonomic labels.
+#
+# Notice that we have gaps in the taxonomic hierarchy (both higher and lower values), as this is less than the number of species labels in the dataset and species had the least non-null values. 
+#
+# We did hit the 7M+ entries with full taxonomic labels expectation, so that's good.
+
+# %% [markdown]
+# More detail on these missing values from [`check_taxa` script](https://github.com/Imageomics/open_clip/blob/main/scripts/evobio10m/check_taxa.py)
+#
+# ```
+# [2023-10-25 10:25:28,242] [WARNING] [root] There are 7 kingdoms instead of 3.
+# [2023-10-25 10:25:29,234] [WARNING] [root] 14795 entries are missing rank kingdom, but have genus label.
+# [2023-10-25 10:25:29,477] [WARNING] [root] 5824 entries are missing rank phylum, but have genus label.
+# [2023-10-25 10:25:29,719] [WARNING] [root] 15763 entries are missing rank class, but have genus label.
+# [2023-10-25 10:25:29,958] [WARNING] [root] 10550 entries are missing rank order, but have genus label.
+# [2023-10-25 10:25:30,203] [WARNING] [root] 7539 entries are missing rank family, but have genus label.
+# [2023-10-25 10:25:30,470] [WARNING] [root] 296 entries are missing rank kingdom, but have family label.
+# [2023-10-25 10:25:30,495] [WARNING] [root] 272 entries are missing rank phylum, but have family label.
+# [2023-10-25 10:25:30,520] [WARNING] [root] 753 entries are missing rank class, but have family label.
+# [2023-10-25 10:25:30,545] [WARNING] [root] 395 entries are missing rank order, but have family label.
+# [2023-10-25 10:25:30,590] [WARNING] [root] 156 entries are missing rank kingdom, but have order label.
+# [2023-10-25 10:25:30,591] [WARNING] [root] 100 entries are missing rank phylum, but have order label.
+# [2023-10-25 10:25:30,592] [WARNING] [root] 1187 entries are missing rank class, but have order label.
+# [2023-10-25 10:25:30,637] [WARNING] [root] 74 entries have kingdom and species labels but no genus.
+# [2023-10-25 10:25:30,644] [WARNING] [root] 74 entries have phylum and species labels but no genus.
+# [2023-10-25 10:25:30,650] [WARNING] [root] 74 entries have class and species labels but no genus.
+# [2023-10-25 10:25:30,656] [WARNING] [root] 74 entries have order and species labels but no genus.
+# [2023-10-25 10:25:30,662] [WARNING] [root] 74 entries have family and species labels but no genus.
+# [2023-10-25 10:25:31,992] [WARNING] [root] There are 158279 samples for which the species column may have genus and species.
+# ```
+
+# %% [markdown]
+# Can we get some more information on those 74 entries that are missing genus?
+
+# %%
+missing_genus = df.dropna(subset = ['kingdom', 'phylum', 'class', 'order', 'family', 'species'])
+missing_genus = missing_genus.loc[missing_genus.genus.isna()]
+missing_genus[taxa].nunique()
+
+# %% [markdown]
+# So it's a handful of taxa, let's take a look.
+
+# %%
+print("The taxa missing genus are: ")
+for taxon in taxa:
+    print(taxon, ": ", missing_genus[taxon].unique())
+
+# %% [markdown]
+# Let's add a column indicating the original data source so we can also get some stats by datasource.
+
+# %%
+# Add data_source column for easier slicing
+df.loc[df['inat21_filename'].notna(), 'data_source'] = 'iNat21'
+df.loc[df['bioscan_filename'].notna(), 'data_source'] = 'BIOSCAN'
+df.loc[df['eol_content_id'].notna(), 'data_source'] = 'EOL'
+
+# %%
+df.loc[df['common'].isna(), 'data_source'].unique()
+
+# %%
+missing_genus.data_source.unique()
+
+# %% [markdown]
+# Missing genus with all other taxa occurs in both BIOSCAN and EOL.
+
+# %% [markdown]
+# First, check their unique class values (`common`).
+
+# %%
+df.loc[df['data_source'] == 'EOL', 'common'].nunique()
+
+# %%
+df.loc[df['data_source'] == 'iNat21', 'common'].nunique()
+
+# %%
+df.loc[df['data_source'] == 'BIOSCAN', 'common'].nunique()
+
+# %% [markdown]
+# iNat's number of unique values in `common` has gone up by 6...why?
+#
+# BIOSCAN and EOL's counts went down, as expected (this is just training set and the common mapping was not done if scientific name was not provided, so we wouldn't see many for BIOSCAN).
+
+# %% [markdown]
+# Make `df_taxa` with just taxa columns (+ `common` & `data_source`) so it's smaller to process faster.
+
+# %%
+taxa_com = list(df.columns[8:16]) # taxa + common
+taxa_com.insert(0, 'data_source')
+df_taxa = df[taxa_com]
+
+# %%
+df_taxa.head()
+
+# %% [markdown]
+# Let's look a little closer at each of our three data sources.
+
+# %%
+inat21_df = df_taxa.loc[df_taxa.data_source == 'iNat21']
+bioscan_df = df_taxa.loc[df_taxa.data_source == 'BIOSCAN']
+eol_df = df_taxa.loc[df_taxa.data_source == 'EOL']
+
+# %% [markdown]
+# ### iNat21
+
+# %%
+inat21_df.info(show_counts = True)
+
+# %% [markdown]
+# iNat21 isn't missing anything, as expected, and we have 2,552,501 of 2,686,843 images (training, validation set is separate).
+#
+# Quick view of diversity in iNat21.
+
+# %%
+inat21_df.nunique()
+
+# %% [markdown]
+# Again, 6 more common values were added, but the diversity has not been altered (same numbers of unique values).
+
+# %%
+inat21_df['kingdom'].value_counts()
+
+# %% [markdown]
+# iNat21 uses `Animalia` and `Plantae`.
+
+# %%
+#number of unique 7-tuples in iNat21
+inat21_df['duplicate'] = inat21_df.duplicated(subset = taxa, keep = 'first')
+inat21_df_unique_taxa = inat21_df.loc[~inat21_df['duplicate']]
+
+# %%
+inat21_df_unique_taxa.info(show_counts = True)
+
+# %% [markdown]
+# There's the 10K unique `species` that we expect. Let's check the same information across BIOSCAN and EOL.
+
+# %% [markdown]
+# ### BIOSCAN
+
+# %%
+bioscan_df.info(show_counts = True)
+
+# %% [markdown]
+# All images are labeled down to `order`, most (98.6%) are labeled to the `family` level, but we only have 22.5% and 7.5% with `genus` or `species` level designation, respectively. These proportions were maintained when the validation set was removed.
+
+# %%
+bioscan_df.nunique()
+
+# %% [markdown]
+# We did lose 2 families, 37 genera, and 1,354 species to the validation set. Note, they may be (likely are) represented in EOL and/or iNat.
+
+# %%
+bioscan_df['kingdom'].value_counts()
+
+# %% [markdown]
+# BIOSCAN is all `Animalia`, as expected.
+
+# %% [markdown]
+# Check we're not missing `family` designation when we have `genus`.
+
+# %%
+bioscan_df.loc[bioscan_df['genus'].notna()].info()
+
+# %%
+bioscan_df.loc[bioscan_df['genus'].notna()].sample(7)
+
+# %% [markdown]
+# We should not have instances where `common` is labeled as `Genus genus species` this time.
+
+# %%
+bioscan_df.loc[bioscan_df['genus'].isna()].sample(7)
+
+# %% [markdown]
+# When the `genus` is null, we no longer get `common` of all higher order taxa available; though we are going to go back to this with the next iteration it seems.
+
+# %%
+#number of unique 7-tuples in BIOSCAN
+bioscan_df['duplicate'] = bioscan_df.duplicated(subset = taxa, keep = 'first')
+bioscan_df_unique_taxa = bioscan_df.loc[~bioscan_df['duplicate']]
+
+# %%
+bioscan_df_unique_taxa.info(show_counts = True)
+
+# %% [markdown]
+# We should be able to fill all in for all values of `species` that also have `genus` indicated since they are all in `Animalia`. Is `genus` labeled for all entries with `species` labeled? 
+
+# %%
+bioscan_df.loc[bioscan_df.species.notna()].info(show_counts = True)
+
+# %% [markdown]
+# There are only 8 images where the `species` is labeled, but the `genus` isn't. There must be 1 in the validation set.
+
+# %%
+bioscan_df.loc[(bioscan_df.species.notna()) & (bioscan_df.genus.isna())]
+
+
+# %% [markdown]
+# It seems the `genus` is `tetramalaise01` ([genus page for tetraMalaise01](https://v3.boldsystems.org/index.php/Taxbrowser_Taxonpage?taxid=1074204)).
+
+# %% [markdown]
+# In general, when the species is listed in BIOSCAN it is listed as `genus-species`. This is certainly true here and would work to fill the 9 missing genera.
+#
+# Let's check those stats again, we did confirm it earlier, but would also like to check EOL as it had a similar issue for some entries.
+
+# %%
+def check_sci_name(df):
+    """
+    This function checks the number of words in the species column for each sample.
+    Logs a warning with the number that have more than one word indicating the potential for both genus and species to be recorded.
+    Warning is printed to terminal, not saved to file.
+
+    Parameters:
+    -----------
+    df - DataFrame with taxonomic hierarchy as columns.
+
+    Returns:
+    --------
+    df - DataFrame with taxonomic hierarchy and length of species entry as columns.
+    """
+    # Set length of species column with default = 1
+    df["len_species"] = 1
+
+    # Check for scientific name in species column (i.e., genus speices in species column, may correspond to missing genus)
+    for species in list(df.loc[df["species"].notna(), "species"].unique()):
+        len_species = len(species.split(" "))
+        if len_species > 1:
+            df.loc[df["species"] == species, "len_species"] = len_species
+    
+    return df
+
+
+
+# %%
+bioscan_species_len_df = check_sci_name(bioscan_df)
+
+# %%
+bioscan_species_len_df.info(show_counts = True)
+
+# %%
+bioscan_long_species = bioscan_species_len_df.loc[bioscan_species_len_df["len_species"] > 1]
+bioscan_long_species.info(show_counts = True)
+
+# %% [markdown]
+# Not all species indicated have length greater than 1 now.
+
+# %%
+bioscan_species_len_df.len_species.value_counts()
+
+# %% [markdown]
+# It seems the genus may have been removed, but the remaining string retained as they previously ranged from 2 to 5 "words" long.
+
+# %%
+bioscan_long_species.sample(7)
+
+# %% [markdown]
+# Ah but the whole species indicator was moved into genus for some of these.
+
+# %%
+bioscan_long_species.loc[bioscan_long_species['len_species'] > 2].sample(10)
+
+# %%
+bioscan_df.loc[bioscan_df["genus"] == "Psychoda", "species"].unique()
+
+# %% [markdown]
+# We can see that the case of "Psychoda" was resolved appropriately.
+
+# %% [markdown]
+# It would seem the "sp. ..." is used to indicate they are likely the same species, though the actual species is not designated (either unnamed or undetermined). This is information we should retain, but the duplicated genus can be removed.
+
+# %% [markdown]
+# ### EOL
+
+# %%
+eol_df.info(show_counts = True)
+
+# %%
+eol_df.nunique()
+
+# %%
+eol_df.loc[eol_df.species.isna()].nunique()
+
+# %% [markdown]
+# There are 498,053 (training + val 570,515) unique page IDs from EOL in this training set, which clearly represent varying levels of taxa. 
+#
+# Unique species + unique common where species is null (197,301) does not reach this number.
+
+# %%
+eol_df['kingdom'].value_counts()
+
+# %% [markdown]
+# We have much greater kingdom variety here.
+#
+# We have already observed that not all ranks are filled in at the higher levels, sometimes having just one gap. This has been greatly improved from the first iteration.
+
+# %%
+#number of unique 7-tuples in EOL
+eol_df['duplicate'] = eol_df.duplicated(subset = taxa, keep = 'first')
+eol_df_unique_taxa = eol_df.loc[~eol_df['duplicate']]
+
+# %%
+eol_df_unique_taxa.info(show_counts = True)
+
+# %% [markdown]
+# The actual number of distinct creatures is likely higher, as evidenced below where we have unique common names listed in `common` but all taxa are null.
+#
+# We should be able to fill all in for all values of `species` that also have `genus` indicated. Is `genus` labeled for all entries with `species` labeled? 
+
+# %%
+eol_df.loc[eol_df.species.notna()].info(show_counts = True)
+
+# %% [markdown]
+# Not all labeled species have higher order taxa, but they do all have `common` (likely because if there is no common name, it pulls scientific name (genus-species)).
+
+# %% [markdown]
+# Let's get a quick sample of the `common` column for images both with and without `species` labels. 
+
+# %%
+# existing species label
+eol_df.loc[eol_df.species.notna()].sample(7)
+
+# %% [markdown]
+# Good, some of these do have common names. We could check numbers of `common` where it doesn't match the `genus-species` form for a better count, but our random sample is promising.
+
+# %%
+# No species label
+eol_df.loc[eol_df.species.isna()].sample(7)
+
+# %% [markdown]
+# Here are a lot of higher-order taxa missing species...let's check for the details noted below.
+#
+# From original check:
+# All `common` values are filled in with predominently common names. It seems we could map these back to some level of taxa with the taxon-common matching? Though why would these EOL images have common without any other designation?
+#
+# Good example of strange inconsistency: the `American red raspberry` is `Rubus strigosus`, and a quick Google search easily provides the entire taxonomy.
+#
+# `Cremastobaeus` seems to be a genus of wasp.
+
+# %%
+eol_df.loc[eol_df.common == "American red raspberry"]
+
+# %%
+eol_df.loc[eol_df.common == "Rubus strigosus"]
+
+# %% [markdown]
+# We seem to have lost the raspberry...
+
+# %%
+eol_df.loc[eol_df.species == "strigosus"]
+
+# %%
+eol_df_unique_taxa.loc[eol_df_unique_taxa.species == "strigosus"]
+
+# %% [markdown]
+# It seems likely the row with the null values is our lost raspberry.
+#
+# Also good evidence of the duplication of species names across kingdoms and other taxa.
+
+# %% [markdown]
+# Rubus strigosus is apparently a subspecies of "Rubus idaeus", and we are not looking at the subspecies level.
+
+# %%
+eol_df_unique_taxa.loc[eol_df_unique_taxa.species == "idaeus"]
+
+# %% [markdown]
+# Though the question remains, why did we lose the common name?
+
+# %% [markdown]
+# Let's check the `species` length in EOL as well, we know there are some that have genus-species.
+
+# %%
+eol_species_len = check_sci_name(eol_df)
+eol_species_len.info(show_counts = True)
+
+# %%
+eol_long_species = eol_species_len.loc[eol_species_len["len_species"] > 1]
+eol_long_species.info(show_counts = True)
+
+# %% [markdown]
+# That's quite a lot with species name longer than 1 word.
+
+# %%
+eol_long_species.len_species.value_counts()
+
+# %% [markdown]
+# Observe, we did not wind up with any of length 2, so this issue was partially resolved. 
+#
+# Why are some more than 10 words long?
+
+# %%
+eol_long_species.loc[eol_long_species["len_species"] > 7].sample(7)
+
+# %%
+eol_long_species.nunique()
+
+# %%
+eol_long_species.loc[eol_long_species["len_species"] < 7].sample(7)
+
+# %% [markdown]
+# Quick search: "Adelpha godmani" is a butterfly with species name given by Fruhstorfer in 1913 ([source](https://butterfliesofamerica.com/L/t/Adelpha_godmani_a.htm)).
+
+# %% [markdown]
+# ### Label Overlap Check
+
+# %% [markdown]
+# Checking for overlap between the three data sources should give pretty good results, now that most inconsistencies have been addressed.
+#
+# For now, let's just take a quick look at genera across the datasets since they are more standardized (and listed more often in BIOSCAN).
+
+# %%
+eol_genera = list(eol_df.loc[eol_df['genus'].notna(), 'genus'].unique())
+inat21_genera = list(inat21_df.loc[inat21_df['genus'].notna(), 'genus'].unique())
+bioscan_genera = list(bioscan_df.loc[bioscan_df['genus'].notna(), 'genus'].unique())
+
+print(f"there are {len(eol_genera)} genera in EOL")
+print(f"there are {len(inat21_genera)} genera in inat21")
+print(f"there are {len(bioscan_genera)} genera in bioscan")
+
+# %%
+gen_overlap = list(set(eol_genera) & set(inat21_genera))
+print(f"There are {len(gen_overlap)} genera shared between EOL and iNat21.")
+print(f"There are {len(list(set(eol_genera) & set(bioscan_genera)))} genera shared between EOL and BIOSCAN.")
+print(f"There are {len(list(set(inat21_genera) & set(bioscan_genera)))} genera shared between iNat21 and BIOSCAN.")
+print(f"There are {len(list(set(gen_overlap) & set(bioscan_genera)))} genera shared between all three data sources.")
+
+# %% [markdown]
+# BIOSCAN and iNat21's overlap of genera is completely contained in EOL.
+
+# %% [markdown]
+# ## Overall Stats
+#
+# Keep in mind, this is without fixing remaining inconsistencies observed above.
+
+# %%
+import numpy as np
+
+# %%
+avgs_all_images = []
+std_all_images = []
+avgs_labeled_images = []
+std_labeled_images = []
+for taxon in taxa_com[1:]: #taxa + common
+    num_taxon = df[taxon].nunique()
+    num_img_taxon = len(df.loc[df[taxon].notna()])
+    avg_all = 10436521/num_taxon
+    std_all = np.sqrt(10436521/num_taxon)
+    avg_labeled = num_img_taxon/num_taxon
+    std_labeled = np.sqrt(num_img_taxon/num_taxon)
+    avgs_all_images.append(avg_all)
+    std_all_images.append(std_all)
+    avgs_labeled_images.append(avg_labeled)
+    std_labeled_images.append(std_labeled)
+
+# %%
+avg_std = pd.DataFrame(data = {'class': taxa_com[1:], 'average_all_imgs': avgs_all_images, 'standard_deviation': std_all_images,
+                                'avg_labeled': avgs_labeled_images, 'std_dev_labeled': std_labeled_images })
+avg_std
+
+# %%
+avg_std.to_csv("../data/stats_avg_std_byClass.csv", index = False)
+
+# %% [markdown]
+# Observe that the Plant and Animal `kingdom`s are actually much more heavily represented than Fungi.
+
+# %%
+sns.set(rc = {'figure.figsize': (10,6)})
+
+# %%
+sns.histplot(df_taxa, x = 'kingdom')
+
+# %%