Generated CSV for testing check_taxa script (auto-check for filled-in taxonomic hierarchy).

README.md

@@ -12,6 +12,7 @@ associated taxa information for the image.
  - `avg_std_byClass.csv`: average and standard distribution of images given by class. This is for both all 
 images, and images that have labels. Note that kingdoms have not been merged and no standardization has 
 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
 
@@ -22,6 +23,7 @@ The `notebooks` folder contains
 visualizations in visuals and some histograms. The treemaps produced in the notebook are interactive.
  - `ToL_EDA.ipynb`: more full EDA of TreeOfLife10M dataset. Explores the labeling inconsistencies for 
 direction of standardization efforts.
+ - `missing_taxa_testGen.ipynb`: generates `tol_hierarchy_test.csv` to test `check_taxa` script. Also observes species labeled as `(unidentified)` in EOL data.
 
 Note: run `pip install -r requirements.txt` before starting the notebooks.
 

notebooks/missing_taxa_testGen.ipynb

@@ -0,0 +1,897 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/nv/f0fq1p1n1_3b11x579py_0q80000gq/T/ipykernel_70089/3694103411.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"
+     ]
+    }
+   ],
+   "source": [
+    "df = pd.read_csv(\"../data/v1-dev-names.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Add data_source column for easier slicing\n",
+    "df.loc[df['inat21_filename'].notna(), 'data_source'] = 'iNat21'\n",
+    "df.loc[df['bioscan_filename'].notna(), 'data_source'] = 'BIOSCAN'\n",
+    "df.loc[df['eol_content_id'].notna(), 'data_source'] = 'EOL'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's get the data that's not in iNat for testing since we know all issues found were in BIOSCAN and EOL (plus the combination of the two for more than 3 kingdoms)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 1000 entries, 7986767 to 8659983\n",
+      "Data columns (total 17 columns):\n",
+      " #   Column            Non-Null Count  Dtype  \n",
+      "---  ------            --------------  -----  \n",
+      " 0   treeoflife_id     1000 non-null   object \n",
+      " 1   eol_content_id    655 non-null    float64\n",
+      " 2   eol_page_id       655 non-null    float64\n",
+      " 3   bioscan_part      93 non-null     float64\n",
+      " 4   bioscan_filename  93 non-null     object \n",
+      " 5   inat21_filename   252 non-null    object \n",
+      " 6   inat21_cls_name   252 non-null    object \n",
+      " 7   inat21_cls_num    252 non-null    float64\n",
+      " 8   kingdom           728 non-null    object \n",
+      " 9   phylum            728 non-null    object \n",
+      " 10  class             628 non-null    object \n",
+      " 11  order             726 non-null    object \n",
+      " 12  family            721 non-null    object \n",
+      " 13  genus             779 non-null    object \n",
+      " 14  species           685 non-null    object \n",
+      " 15  common            1000 non-null   object \n",
+      " 16  data_source       1000 non-null   object \n",
+      "dtypes: float64(4), object(13)\n",
+      "memory usage: 140.6+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_small = df.loc[df.data_source != 'iNat'].sample(1000)\n",
+    "df_small.info(show_counts = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 779 entries, 7986767 to 8659983\n",
+      "Data columns (total 17 columns):\n",
+      " #   Column            Non-Null Count  Dtype  \n",
+      "---  ------            --------------  -----  \n",
+      " 0   treeoflife_id     779 non-null    object \n",
+      " 1   eol_content_id    506 non-null    float64\n",
+      " 2   eol_page_id       506 non-null    float64\n",
+      " 3   bioscan_part      21 non-null     float64\n",
+      " 4   bioscan_filename  21 non-null     object \n",
+      " 5   inat21_filename   252 non-null    object \n",
+      " 6   inat21_cls_name   252 non-null    object \n",
+      " 7   inat21_cls_num    252 non-null    float64\n",
+      " 8   kingdom           647 non-null    object \n",
+      " 9   phylum            647 non-null    object \n",
+      " 10  class             549 non-null    object \n",
+      " 11  order             645 non-null    object \n",
+      " 12  family            643 non-null    object \n",
+      " 13  genus             779 non-null    object \n",
+      " 14  species           684 non-null    object \n",
+      " 15  common            779 non-null    object \n",
+      " 16  data_source       779 non-null    object \n",
+      "dtypes: float64(4), object(13)\n",
+      "memory usage: 109.5+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_small.loc[df_small.genus.notna()].info(show_counts = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We have less `species` indicated than `genus`, so let's get some samples of null `genus` with non-null `species`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 2375692 entries, 4 to 10436517\n",
+      "Data columns (total 17 columns):\n",
+      " #   Column            Non-Null Count    Dtype  \n",
+      "---  ------            --------------    -----  \n",
+      " 0   treeoflife_id     2375692 non-null  object \n",
+      " 1   eol_content_id    1501528 non-null  float64\n",
+      " 2   eol_page_id       1501528 non-null  float64\n",
+      " 3   bioscan_part      874164 non-null   float64\n",
+      " 4   bioscan_filename  874164 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           974474 non-null   object \n",
+      " 9   phylum            974438 non-null   object \n",
+      " 10  class             965636 non-null   object \n",
+      " 11  order             974301 non-null   object \n",
+      " 12  family            956107 non-null   object \n",
+      " 13  genus             0 non-null        object \n",
+      " 14  species           253 non-null      object \n",
+      " 15  common            2375692 non-null  object \n",
+      " 16  data_source       2375692 non-null  object \n",
+      "dtypes: float64(4), object(13)\n",
+      "memory usage: 326.3+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "bio_eol = df.loc[df.data_source != 'iNat']\n",
+    "null_genus = bio_eol.loc[bio_eol.genus.isna()]\n",
+    "null_genus.info(show_counts = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Add a sample of these to our test dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_test = pd.concat([df_small, null_genus.loc[null_genus.species.notna()].sample(100)])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We also want to check for scientific name in the `species` column (i.e., genus species). This can cause inaccuracies in our counts, impact `common` column, and cause inconsistencies in the text training. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10002735                    caprata\n",
+       "5642543      paradidyma malaise2555\n",
+       "5208448                  lapponicum\n",
+       "1762361                    irrorata\n",
+       "556451                    perplexus\n",
+       "3296890              (unidentified)\n",
+       "2533982           senegalia senegal\n",
+       "9869450                 punctinalis\n",
+       "4220628     coriaria welw. ex oliv.\n",
+       "7126430                   micrantha\n",
+       "Name: species, dtype: object"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_test.loc[df_test['species'].notna(), 'species'].sample(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "161\n"
+     ]
+    }
+   ],
+   "source": [
+    "count = 0\n",
+    "for species in list(df_test.loc[df_test['species'].notna(), 'species']):\n",
+    "    if len(species.split(\" \")) > 1:\n",
+    "        count += 1\n",
+    "print(count)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Good, these are represented too, so we can save this file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_test.to_csv(\"../data/tol_hierarchy_test.csv\", index = False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There is also apparently a species which is labeled as `(unidentified)`. Let's check where that is and if there's more than one (in our full dataset)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 2470 entries, 1236 to 10435982\n",
+      "Data columns (total 17 columns):\n",
+      " #   Column            Non-Null Count  Dtype  \n",
+      "---  ------            --------------  -----  \n",
+      " 0   treeoflife_id     2470 non-null   object \n",
+      " 1   eol_content_id    2470 non-null   float64\n",
+      " 2   eol_page_id       2470 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             2470 non-null   object \n",
+      " 14  species           2470 non-null   object \n",
+      " 15  common            2470 non-null   object \n",
+      " 16  data_source       2470 non-null   object \n",
+      "dtypes: float64(4), object(13)\n",
+      "memory usage: 347.3+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.loc[df.species == \"(unidentified)\"].info(show_counts = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Okay, there are a LOT of them. All in EOL. They do seem to have `genus` label, but nothing else, so let's look at a subset of these."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "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>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",
+       "      <th>inat21_filename</th>\n",
+       "      <th>inat21_cls_name</th>\n",
+       "      <th>inat21_cls_num</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>data_source</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>10369023</th>\n",
+       "      <td>b98a7e24-4848-4134-bad3-eedfce536fab</td>\n",
+       "      <td>14844649.0</td>\n",
+       "      <td>64430448.0</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>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Cis</td>\n",
+       "      <td>(unidentified)</td>\n",
+       "      <td>Cis (unidentified)</td>\n",
+       "      <td>EOL</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7268955</th>\n",
+       "      <td>228a79c2-a482-4cd4-9a56-a000c4780b68</td>\n",
+       "      <td>14842294.0</td>\n",
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+       "      <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>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Sarcophaga</td>\n",
+       "      <td>(unidentified)</td>\n",
+       "      <td>Sarcophaga (unidentified)</td>\n",
+       "      <td>EOL</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1549482</th>\n",
+       "      <td>f81c2ce7-b2dc-4d13-8991-5a20907610bb</td>\n",
+       "      <td>14842814.0</td>\n",
+       "      <td>64432966.0</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>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Chromista</td>\n",
+       "      <td>(unidentified)</td>\n",
+       "      <td>Chromista (unidentified)</td>\n",
+       "      <td>EOL</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6552233</th>\n",
+       "      <td>724ac709-31ee-41df-b1b2-a4fbcda8a867</td>\n",
+       "      <td>14845112.0</td>\n",
+       "      <td>64430050.0</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>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Myrmica</td>\n",
+       "      <td>(unidentified)</td>\n",
+       "      <td>Myrmica (unidentified)</td>\n",
+       "      <td>EOL</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7064292</th>\n",
+       "      <td>079cac60-e040-4ae0-a587-d9a73b0bff65</td>\n",
+       "      <td>14851701.0</td>\n",
+       "      <td>64429842.0</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>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Phygadeuon</td>\n",
+       "      <td>(unidentified)</td>\n",
+       "      <td>Phygadeuon (unidentified)</td>\n",
+       "      <td>EOL</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>972647</th>\n",
+       "      <td>01633bbe-44f9-4e8d-b4db-d2d9678906eb</td>\n",
+       "      <td>14845039.0</td>\n",
+       "      <td>64429476.0</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>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Cheilosia</td>\n",
+       "      <td>(unidentified)</td>\n",
+       "      <td>Cheilosia (unidentified)</td>\n",
+       "      <td>EOL</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6605868</th>\n",
+       "      <td>9a4cf29a-06d5-4c1c-94dc-6f0486347755</td>\n",
+       "      <td>14843938.0</td>\n",
+       "      <td>64427784.0</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>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Entoloma</td>\n",
+       "      <td>(unidentified)</td>\n",
+       "      <td>Entoloma (unidentified)</td>\n",
+       "      <td>EOL</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1138925</th>\n",
+       "      <td>2b4c8067-3595-4d87-897e-9d3600e3a86f</td>\n",
+       "      <td>14842566.0</td>\n",
+       "      <td>64430882.0</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>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Hydropsyche</td>\n",
+       "      <td>(unidentified)</td>\n",
+       "      <td>Hydropsyche (unidentified)</td>\n",
+       "      <td>EOL</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018314</th>\n",
+       "      <td>2726bcc0-5c18-401a-a0a0-ad4a2cb4e302</td>\n",
+       "      <td>14845986.0</td>\n",
+       "      <td>64426150.0</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>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Chlorophyta</td>\n",
+       "      <td>(unidentified)</td>\n",
+       "      <td>Chlorophyta (unidentified)</td>\n",
+       "      <td>EOL</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1675874</th>\n",
+       "      <td>578809ee-126c-468e-a39b-2878e1dafc21</td>\n",
+       "      <td>14845571.0</td>\n",
+       "      <td>64431544.0</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>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Porifera</td>\n",
+       "      <td>(unidentified)</td>\n",
+       "      <td>Porifera (unidentified)</td>\n",
+       "      <td>EOL</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                 treeoflife_id  eol_content_id  eol_page_id   \n",
+       "10369023  b98a7e24-4848-4134-bad3-eedfce536fab      14844649.0   64430448.0  \\\n",
+       "7268955   228a79c2-a482-4cd4-9a56-a000c4780b68      14842294.0   64429582.0   \n",
+       "1549482   f81c2ce7-b2dc-4d13-8991-5a20907610bb      14842814.0   64432966.0   \n",
+       "6552233   724ac709-31ee-41df-b1b2-a4fbcda8a867      14845112.0   64430050.0   \n",
+       "7064292   079cac60-e040-4ae0-a587-d9a73b0bff65      14851701.0   64429842.0   \n",
+       "972647    01633bbe-44f9-4e8d-b4db-d2d9678906eb      14845039.0   64429476.0   \n",
+       "6605868   9a4cf29a-06d5-4c1c-94dc-6f0486347755      14843938.0   64427784.0   \n",
+       "1138925   2b4c8067-3595-4d87-897e-9d3600e3a86f      14842566.0   64430882.0   \n",
+       "2018314   2726bcc0-5c18-401a-a0a0-ad4a2cb4e302      14845986.0   64426150.0   \n",
+       "1675874   578809ee-126c-468e-a39b-2878e1dafc21      14845571.0   64431544.0   \n",
+       "\n",
+       "          bioscan_part bioscan_filename inat21_filename inat21_cls_name   \n",
+       "10369023           NaN              NaN             NaN             NaN  \\\n",
+       "7268955            NaN              NaN             NaN             NaN   \n",
+       "1549482            NaN              NaN             NaN             NaN   \n",
+       "6552233            NaN              NaN             NaN             NaN   \n",
+       "7064292            NaN              NaN             NaN             NaN   \n",
+       "972647             NaN              NaN             NaN             NaN   \n",
+       "6605868            NaN              NaN             NaN             NaN   \n",
+       "1138925            NaN              NaN             NaN             NaN   \n",
+       "2018314            NaN              NaN             NaN             NaN   \n",
+       "1675874            NaN              NaN             NaN             NaN   \n",
+       "\n",
+       "          inat21_cls_num kingdom phylum class order family        genus   \n",
+       "10369023             NaN     NaN    NaN   NaN   NaN    NaN          Cis  \\\n",
+       "7268955              NaN     NaN    NaN   NaN   NaN    NaN   Sarcophaga   \n",
+       "1549482              NaN     NaN    NaN   NaN   NaN    NaN    Chromista   \n",
+       "6552233              NaN     NaN    NaN   NaN   NaN    NaN      Myrmica   \n",
+       "7064292              NaN     NaN    NaN   NaN   NaN    NaN   Phygadeuon   \n",
+       "972647               NaN     NaN    NaN   NaN   NaN    NaN    Cheilosia   \n",
+       "6605868              NaN     NaN    NaN   NaN   NaN    NaN     Entoloma   \n",
+       "1138925              NaN     NaN    NaN   NaN   NaN    NaN  Hydropsyche   \n",
+       "2018314              NaN     NaN    NaN   NaN   NaN    NaN  Chlorophyta   \n",
+       "1675874              NaN     NaN    NaN   NaN   NaN    NaN     Porifera   \n",
+       "\n",
+       "                 species                      common data_source  \n",
+       "10369023  (unidentified)          Cis (unidentified)         EOL  \n",
+       "7268955   (unidentified)   Sarcophaga (unidentified)         EOL  \n",
+       "1549482   (unidentified)    Chromista (unidentified)         EOL  \n",
+       "6552233   (unidentified)      Myrmica (unidentified)         EOL  \n",
+       "7064292   (unidentified)   Phygadeuon (unidentified)         EOL  \n",
+       "972647    (unidentified)    Cheilosia (unidentified)         EOL  \n",
+       "6605868   (unidentified)     Entoloma (unidentified)         EOL  \n",
+       "1138925   (unidentified)  Hydropsyche (unidentified)         EOL  \n",
+       "2018314   (unidentified)  Chlorophyta (unidentified)         EOL  \n",
+       "1675874   (unidentified)     Porifera (unidentified)         EOL  "
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.loc[df.species == \"(unidentified)\"].sample(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We need to check EOL for `(unidentified)` as we had removed `not_classified` from BIOSCAN. Probably something else to add to the `check_taxa` script."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's generate the stats we should get warnings for so that we can write a test function for this script as well.\n",
+    "\n",
+    "Truncate this to just taxa columns so only the pertinent info is printed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "TAXA = [\"kingdom\",\n",
+    "        \"phylum\",\n",
+    "        \"class\",\n",
+    "        \"order\",\n",
+    "        \"family\",\n",
+    "        \"genus\",\n",
+    "        \"species\"\n",
+    "        ]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_test_taxa = df_test[TAXA]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 779 entries, 7986767 to 8659983\n",
+      "Data columns (total 7 columns):\n",
+      " #   Column   Non-Null Count  Dtype \n",
+      "---  ------   --------------  ----- \n",
+      " 0   kingdom  647 non-null    object\n",
+      " 1   phylum   647 non-null    object\n",
+      " 2   class    549 non-null    object\n",
+      " 3   order    645 non-null    object\n",
+      " 4   family   643 non-null    object\n",
+      " 5   genus    779 non-null    object\n",
+      " 6   species  684 non-null    object\n",
+      "dtypes: object(7)\n",
+      "memory usage: 48.7+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_test_taxa.loc[df_test_taxa.genus.notna()].info(show_counts = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We should have\n",
+    " - 132 missing `kingdom` and `phylum`\n",
+    " - 230 missing `class`\n",
+    " - 134 missing `family`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "101"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_test_taxa.loc[df_test_taxa['genus'].isna(), 'species'].notna().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "101 entries for which `species` is non-null, but `genus` is missing."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "missing_genera = df_test_taxa.loc[df_test_taxa['genus'].isna()]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 143 entries, 4772452 to 3877527\n",
+      "Data columns (total 7 columns):\n",
+      " #   Column   Non-Null Count  Dtype \n",
+      "---  ------   --------------  ----- \n",
+      " 0   kingdom  3 non-null      object\n",
+      " 1   phylum   3 non-null      object\n",
+      " 2   class    3 non-null      object\n",
+      " 3   order    3 non-null      object\n",
+      " 4   family   0 non-null      object\n",
+      " 5   genus    0 non-null      object\n",
+      " 6   species  0 non-null      object\n",
+      "dtypes: object(7)\n",
+      "memory usage: 8.9+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "missing_genera.loc[missing_genera.family.isna()].info(show_counts = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Only 3 instances where `family` is null and higher order taxa are not."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 178 entries, 4943756 to 1467368\n",
+      "Data columns (total 7 columns):\n",
+      " #   Column   Non-Null Count  Dtype \n",
+      "---  ------   --------------  ----- \n",
+      " 0   kingdom  178 non-null    object\n",
+      " 1   phylum   178 non-null    object\n",
+      " 2   class    141 non-null    object\n",
+      " 3   order    178 non-null    object\n",
+      " 4   family   178 non-null    object\n",
+      " 5   genus    0 non-null      object\n",
+      " 6   species  101 non-null    object\n",
+      "dtypes: object(7)\n",
+      "memory usage: 11.1+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "missing_genera.loc[missing_genera.family.notna()].info(show_counts = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For `family`, there should be a warning that 37 `class` values are not indicated."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We do want to check if there are cases of null genus with labeled species and other taxa."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 101 entries, 10434700 to 1467368\n",
+      "Data columns (total 7 columns):\n",
+      " #   Column   Non-Null Count  Dtype \n",
+      "---  ------   --------------  ----- \n",
+      " 0   kingdom  101 non-null    object\n",
+      " 1   phylum   101 non-null    object\n",
+      " 2   class    65 non-null     object\n",
+      " 3   order    101 non-null    object\n",
+      " 4   family   101 non-null    object\n",
+      " 5   genus    0 non-null      object\n",
+      " 6   species  101 non-null    object\n",
+      "dtypes: object(7)\n",
+      "memory usage: 6.3+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "missing_genera.loc[missing_genera.species.notna()].info(show_counts = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We should have\n",
+    " - no instances of missing `kingdom`, `phylum`, `order`, or `family` \n",
+    " - 36 instances of missing `class`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "std",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}