Update master files

Remove references to v1 of record 3082688 (deduplication)
Add in taxonomic information for 3 homogeneous records missing taxa info.

Jiggins_Zenodo_Img_Master.csv

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:75f4effe4a36aaf206c6047537ab75e4037bba7b56dbd376209983f52822b7c6
-size 13583175
+oid sha256:fe79fdb62909be50eff6f7016f02301c56ce09c9c0b164dce680810ec4149b2a
+size 15528648

notebooks/standardize_datasets.ipynb

@@ -0,0 +1,2635 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It turns out [record 3082688](https://zenodo.org/records/3082688) is version 2 of [record 1247307](https://zenodo.org/records/1247307), where some mislabeling was fixed. Images from record 1247307 should not be used, so we will remove them here.\n",
+    "\n",
+    "Will also add `hybrid_stat` and `file_url` columns so these can be explored with all functionality of the [data dashboard](https://huggingface.co/spaces/imageomics/dashboard-prototype)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv(\"../Jiggins_Zenodo_Img_Master.csv\", low_memory=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "CAMID               12586\n",
+       "X                   49359\n",
+       "Image_name          37821\n",
+       "View                    7\n",
+       "zenodo_name            36\n",
+       "zenodo_link            32\n",
+       "Sequence            11301\n",
+       "Taxonomic_Name        363\n",
+       "Locality              645\n",
+       "Sample_accession     1571\n",
+       "Collected_by           12\n",
+       "Other_ID             3088\n",
+       "Date                  810\n",
+       "Dataset                 8\n",
+       "Store                 142\n",
+       "Brood                 226\n",
+       "Death_Date             82\n",
+       "Cross_Type             30\n",
+       "Stage                   1\n",
+       "Sex                     3\n",
+       "Unit_Type               6\n",
+       "file_type               3\n",
+       "record_number          32\n",
+       "species               246\n",
+       "subspecies            155\n",
+       "genus                  94\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.nunique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.series.Series'>\n",
+      "RangeIndex: 49359 entries, 0 to 49358\n",
+      "Series name: record_number\n",
+      "Non-Null Count  Dtype\n",
+      "--------------  -----\n",
+      "49359 non-null  int64\n",
+      "dtypes: int64(1)\n",
+      "memory usage: 385.7 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.record_number.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "CAMID               12586\n",
+       "X                   46439\n",
+       "Image_name          37821\n",
+       "View                    7\n",
+       "zenodo_name            35\n",
+       "zenodo_link            31\n",
+       "Sequence            11301\n",
+       "Taxonomic_Name        363\n",
+       "Locality              645\n",
+       "Sample_accession     1571\n",
+       "Collected_by           12\n",
+       "Other_ID             3088\n",
+       "Date                  810\n",
+       "Dataset                 8\n",
+       "Store                 142\n",
+       "Brood                 226\n",
+       "Death_Date             82\n",
+       "Cross_Type             30\n",
+       "Stage                   1\n",
+       "Sex                     3\n",
+       "Unit_Type               6\n",
+       "file_type               3\n",
+       "record_number          31\n",
+       "species               246\n",
+       "subspecies            155\n",
+       "genus                  94\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = df.loc[df.record_number != 1247307]\n",
+    "df.nunique()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Checking all records on Zenodo for the [Butterfly Genetics Group](https://zenodo.org/communities/butterfly/records) (the overall source of the Jiggins data), [record 3477891](https://zenodo.org/records/3477891) and [record 2548678](https://zenodo.org/records/2548678) are marked as version 2 of records 3477412 & 1880783, respectively. Let's check that the first versions are not included.\n",
+    "\n",
+    "It is also noted in [record 2548678](https://zenodo.org/records/2548678):\n",
+    ">Some images overlap with 'Cambridge butterfly wing collection batch 1', taken by Eva Whiltshire. Images here differ in having a white reflectance standard for calibration. Information on duplicates can be found in 'CAM.coll.patricio.batch2.csv'.\n",
+    "\n",
+    "Note that this is a reference to [record 1247307](https://zenodo.org/records/1247307), so the overlap is in fact with Cambridge butterfly wing collection batch 1- version 2 ([record 3082688](https://zenodo.org/records/3082688)); it is unclear as yet if the duplication information will be acurate considering the mislabelings fixed between the versions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0, 26)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "v1s = [3477412, 1880783]\n",
+    "df.loc[df[\"record_number\"].isin(v1s)].shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The good news is that neither of these earlier versions seem to have been picked up, so we can move on to assessing this and adding the `hybrid_stat` and `file_url` columns."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "file_type\n",
+       "jpg    34152\n",
+       "raw    12226\n",
+       "tif       61\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.file_type.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "View\n",
+       "dorsal                22022\n",
+       "ventral               21704\n",
+       "forewing dorsal         406\n",
+       "hindwing dorsal         406\n",
+       "forewing ventral        406\n",
+       "hindwing ventral        406\n",
+       "dorsal and ventral       18\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.View.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We have 31 unique records represented in the full dataset. When we reduce down to just the Heliconius images, this will probably be less."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Add File URL Column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "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>CAMID</th>\n",
+       "      <th>X</th>\n",
+       "      <th>Image_name</th>\n",
+       "      <th>View</th>\n",
+       "      <th>zenodo_name</th>\n",
+       "      <th>zenodo_link</th>\n",
+       "      <th>Sequence</th>\n",
+       "      <th>Taxonomic_Name</th>\n",
+       "      <th>Locality</th>\n",
+       "      <th>Sample_accession</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Cross_Type</th>\n",
+       "      <th>Stage</th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>Unit_Type</th>\n",
+       "      <th>file_type</th>\n",
+       "      <th>record_number</th>\n",
+       "      <th>species</th>\n",
+       "      <th>subspecies</th>\n",
+       "      <th>genus</th>\n",
+       "      <th>file_url</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>34925</th>\n",
+       "      <td>CAM041457</td>\n",
+       "      <td>38799</td>\n",
+       "      <td>CAM041457_d.CR2</td>\n",
+       "      <td>dorsal</td>\n",
+       "      <td>0.gmk.broods.all.csv</td>\n",
+       "      <td>https://zenodo.org/record/4291095</td>\n",
+       "      <td>41,457</td>\n",
+       "      <td>Heliconius timareta</td>\n",
+       "      <td>Reventador road 2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>raw</td>\n",
+       "      <td>4291095</td>\n",
+       "      <td>Heliconius timareta</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Heliconius</td>\n",
+       "      <td>https://zenodo.org/record/4291095/files/CAM041...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28286</th>\n",
+       "      <td>CAM036145</td>\n",
+       "      <td>41369</td>\n",
+       "      <td>CAM036145_v.JPG</td>\n",
+       "      <td>ventral</td>\n",
+       "      <td>Filelist.csv</td>\n",
+       "      <td>https://zenodo.org/record/5561246</td>\n",
+       "      <td>36,145</td>\n",
+       "      <td>Methona singularis</td>\n",
+       "      <td>Guaribas - RG16</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>jpg</td>\n",
+       "      <td>5561246</td>\n",
+       "      <td>Methona singularis</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Methona</td>\n",
+       "      <td>https://zenodo.org/record/5561246/files/CAM036...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21858</th>\n",
+       "      <td>CAM017409</td>\n",
+       "      <td>19047</td>\n",
+       "      <td>CAM017409_d.CR2</td>\n",
+       "      <td>dorsal</td>\n",
+       "      <td>CAM.coll.PS.list.individuals.haplotagging.new....</td>\n",
+       "      <td>https://zenodo.org/record/4153502</td>\n",
+       "      <td>17,409</td>\n",
+       "      <td>Heliconius erato ssp. lativitta</td>\n",
+       "      <td>San Pedro de Arajuno, Río Arajuno</td>\n",
+       "      <td>ERS353373</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>wild</td>\n",
+       "      <td>raw</td>\n",
+       "      <td>4153502</td>\n",
+       "      <td>Heliconius erato</td>\n",
+       "      <td>lativitta</td>\n",
+       "      <td>Heliconius</td>\n",
+       "      <td>https://zenodo.org/record/4153502/files/CAM017...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12708</th>\n",
+       "      <td>CAM010412</td>\n",
+       "      <td>26792</td>\n",
+       "      <td>CAM010412_v.CR2</td>\n",
+       "      <td>ventral</td>\n",
+       "      <td>2001_2.broods.batch.1.csv</td>\n",
+       "      <td>https://zenodo.org/record/2549524</td>\n",
+       "      <td>10,412</td>\n",
+       "      <td>Heliconius sp.</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Unknown</td>\n",
+       "      <td>reared</td>\n",
+       "      <td>raw</td>\n",
+       "      <td>2549524</td>\n",
+       "      <td>Heliconius sp.</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Heliconius</td>\n",
+       "      <td>https://zenodo.org/record/2549524/files/CAM010...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>4 rows × 27 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           CAMID      X       Image_name     View  \\\n",
+       "34925  CAM041457  38799  CAM041457_d.CR2   dorsal   \n",
+       "28286  CAM036145  41369  CAM036145_v.JPG  ventral   \n",
+       "21858  CAM017409  19047  CAM017409_d.CR2   dorsal   \n",
+       "12708  CAM010412  26792  CAM010412_v.CR2  ventral   \n",
+       "\n",
+       "                                             zenodo_name  \\\n",
+       "34925                               0.gmk.broods.all.csv   \n",
+       "28286                                       Filelist.csv   \n",
+       "21858  CAM.coll.PS.list.individuals.haplotagging.new....   \n",
+       "12708                          2001_2.broods.batch.1.csv   \n",
+       "\n",
+       "                             zenodo_link Sequence  \\\n",
+       "34925  https://zenodo.org/record/4291095   41,457   \n",
+       "28286  https://zenodo.org/record/5561246   36,145   \n",
+       "21858  https://zenodo.org/record/4153502   17,409   \n",
+       "12708  https://zenodo.org/record/2549524   10,412   \n",
+       "\n",
+       "                        Taxonomic_Name                           Locality  \\\n",
+       "34925              Heliconius timareta                  Reventador road 2   \n",
+       "28286               Methona singularis                    Guaribas - RG16   \n",
+       "21858  Heliconius erato ssp. lativitta  San Pedro de Arajuno, Río Arajuno   \n",
+       "12708                   Heliconius sp.                                NaN   \n",
+       "\n",
+       "      Sample_accession  ... Cross_Type Stage      Sex Unit_Type file_type  \\\n",
+       "34925              NaN  ...        NaN   NaN     Male       NaN       raw   \n",
+       "28286              NaN  ...        NaN   NaN     Male       NaN       jpg   \n",
+       "21858        ERS353373  ...        NaN   NaN   Female      wild       raw   \n",
+       "12708              NaN  ...        NaN   NaN  Unknown    reared       raw   \n",
+       "\n",
+       "      record_number              species subspecies       genus  \\\n",
+       "34925       4291095  Heliconius timareta        NaN  Heliconius   \n",
+       "28286       5561246   Methona singularis        NaN     Methona   \n",
+       "21858       4153502     Heliconius erato  lativitta  Heliconius   \n",
+       "12708       2549524       Heliconius sp.        NaN  Heliconius   \n",
+       "\n",
+       "                                                file_url  \n",
+       "34925  https://zenodo.org/record/4291095/files/CAM041...  \n",
+       "28286  https://zenodo.org/record/5561246/files/CAM036...  \n",
+       "21858  https://zenodo.org/record/4153502/files/CAM017...  \n",
+       "12708  https://zenodo.org/record/2549524/files/CAM010...  \n",
+       "\n",
+       "[4 rows x 27 columns]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"file_url\"] = df[\"zenodo_link\"] + \"/files/\" + df[\"Image_name\"]\n",
+    "df.sample(4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Add Proper Taxonomic Name for Crosstypes\n",
+    "\n",
+    "We want the cross types to also have full taxonomic names (`Heliconius <species> <subspecies>`) so this can be used in downloading to appropriate branches and also for easier diversity counts. Cross Types will still be easily filtered using the `Cross_Type` column."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "subspecies\n",
+       "(malleti x plesseni) x malleti                 1007\n",
+       "plesseni x (malleti x plesseni)                 462\n",
+       "(plesseni x malleti) x (malleti x plesseni)     348\n",
+       "(plesseni x malleti) x plesseni                 345\n",
+       "malleti x (plesseni x malleti)                  324\n",
+       "(plesseni x malleti) x (plesseni x malleti)     228\n",
+       "(malleti x plesseni) x plesseni                 216\n",
+       "plesseni x malleti                              212\n",
+       "malleti x plesseni                              156\n",
+       "lativitta x notabilis                           111\n",
+       "plesseni x (plesseni x malleti)                 106\n",
+       "(lativitta x notabilis) x notabilis              90\n",
+       "(lativitta x notabilis) x lativitta              90\n",
+       "(malleti x plesseni) x (malleti x plesseni)      81\n",
+       "(plesseni x malleti) x malleti                   80\n",
+       "(malleti x plesseni) x (plesseni x malleti)      42\n",
+       "malleti                                          28\n",
+       "plesseni                                         28\n",
+       "(latRo x notabilis) x notabilis                  12\n",
+       "latRo x notabilis                                 4\n",
+       "lativitta                                         4\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.loc[df[\"Cross_Type\"].notna(), \"subspecies\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Cross_Type\n",
+       "Test cross (4 spots x 4 spots)                           150\n",
+       "Test cross (N heterozygocity - NBNN x malleti - thin)    114\n",
+       "Test cross (N heterozygozity)                             78\n",
+       "Test cross (short HW bar)                                 54\n",
+       "Test cross (4 spots x 2 banded)                           48\n",
+       "2 banded                                                  16\n",
+       "hybrid                                                    10\n",
+       "Ac heterozygote                                            4\n",
+       "Test cross (2 banded F2 x 2 banded F2)                     4\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.loc[(df[\"Cross_Type\"].notna()) & (df[\"subspecies\"].isna()), \"Cross_Type\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice that we have hybrids here which should be indicated as such though they don't all have an ` x ` in their name. We'll remember this for the `hybrid_stat` column and generate using the Cross Type indicator. Our only non-hybrid Cross Types are `malleti`, `plesseni`, and `lativitta`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['hybrid', 'Ac heterozygote', '2 banded',\n",
+       "       'Test cross (2 banded F2 x 2 banded F2)',\n",
+       "       'Test cross (4 spots x 2 banded)', 'Test cross (N heterozygozity)',\n",
+       "       'Test cross (short HW bar)', 'Test cross (4 spots x 4 spots)',\n",
+       "       'Test cross (N heterozygocity - NBNN x malleti - thin)'],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "non_specific_cross_hybrids = df.loc[(df[\"Cross_Type\"].notna()) & (df[\"subspecies\"].isna()), \"Cross_Type\"].unique()\n",
+    "non_specific_cross_hybrids"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['cythera', 'cyrbia', 'venus', 'venus x chestertonii', 'vulcanus',\n",
+       "       'chestertonii', 'vulcanus x melpomene', 'lativitta', 'malleti',\n",
+       "       'erato', 'ssp.nov.P', 'melpomene', 'willmotti',\n",
+       "       'chestertonii x venus', 'eleuchia', 'cydnides', 'weymeri',\n",
+       "       'chioneus', 'demophoon', 'hydara', 'sapho', 'numata', 'iulia',\n",
+       "       'melpomene x thelxiope', 'wallacei', 'rosina', 'formosus',\n",
+       "       'melpomene x rosina', 'menapis', 'hydara x petiverana', 'decumana',\n",
+       "       'relata', 'agna', 'isthmia', 'bicoloratus', 'euphrasius',\n",
+       "       'salapia', 'matronalis', 'ethica', 'agnosia', 'andromica',\n",
+       "       'hippocrenis', 'saturata', 'evanides', 'lyra', 'pagasa',\n",
+       "       'staudingeri', 'valora', 'cassotis', 'chiriquensis', 'alithea',\n",
+       "       'timareta', 'plesseni', 'notabilis', 'amaryllis', 'thelxinoe',\n",
+       "       'doris', 'amaryllis x aglaope', 'aglaope', 'carbo', 'lycaste',\n",
+       "       'idae', 'macrinus', 'abida', 'notilla', 'neustetteri', 'giulia',\n",
+       "       'panamensis', 'eleusinus', 'agalla', 'xanthina', 'ecuadorensis',\n",
+       "       'congener', 'etylus', 'derasa', 'lenaeus', 'petiverana',\n",
+       "       'melicerta', 'thelxiopeia', 'meriana', 'plesseni x malleti',\n",
+       "       'notabilis x lativitta', 'sara', 'silvana', 'amalfreda',\n",
+       "       'hydara x amalfreda', 'flavescens', 'hydara x erato',\n",
+       "       'meriana x melpomene', 'nanna', 'daetina', 'nesaea', 'phyllis',\n",
+       "       'laphria', 'paraiya', 'lysimnia', 'daeta', 'yanetta', 'pyrrha',\n",
+       "       'casabranca', 'tristero', 'dignus', 'bellula',\n",
+       "       'dignus x lativitta', 'malleti x bellula', 'napoensis',\n",
+       "       'clysonymus', 'sotericus', 'primularis', 'sprucei', 'bassleri',\n",
+       "       'eximius', 'manabiana', 'nigrippus', 'hygiana', 'neildi',\n",
+       "       'clysonomus', 'hierax', 'magdalena', 'calathus', 'corena',\n",
+       "       'zelinde', 'florencia', 'weymeri f. gustavi', 'weymeri f. weymeri',\n",
+       "       'wanningeri', 'cydno', 'cordula', 'linaresi', 'lisethae',\n",
+       "       'hermogenes', 'martinae', 'vicina', 'malleti x vicina',\n",
+       "       'reductimacula', 'sergestus', 'felix', 'aerotome'], dtype=object)"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.loc[df[\"Cross_Type\"].isna(), \"subspecies\"].dropna().unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_cross_taxa_name(species, cross_type):\n",
+    "    # label unspecified hybrids as such\n",
+    "    if cross_type in non_specific_cross_hybrids:\n",
+    "        return species + \" \" + \"cross hybrid\"\n",
+    "    \n",
+    "    # separate out hybrids from non-hybrids\n",
+    "    subsp_cross_list = cross_type.split(\"(\")\n",
+    "    if len(subsp_cross_list) > 1:\n",
+    "        subsp_cross = subsp_cross_list[1].split(\")\")[0]\n",
+    "    else:\n",
+    "        subsp_cross = cross_type\n",
+    "    \n",
+    "    # Ensure order of hybrid names is consistent so they get placed in same folders (using scripts/download_jiggins.py)\n",
+    "    # And so counts by taxonomic name aren't needlessly skewed\n",
+    "    if subsp_cross == \"malleti x plesseni\":\n",
+    "        subsp_cross = \"plesseni x malleti\"\n",
+    "    elif subsp_cross == \"lativitta x notabilis\":\n",
+    "        subsp_cross = \"notabilis x lativitta\"\n",
+    "    \n",
+    "    # Set taxa_name as with non cross types labeled to subspecies\n",
+    "    taxa_name = species + \" ssp. \" + subsp_cross\n",
+    "    return taxa_name"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "H.sp. ssp. malleti\n",
+      "H.sp. ssp. plesseni x malleti\n",
+      "H.sp. ssp. plesseni\n",
+      "H.sp. ssp. plesseni x malleti\n",
+      "H.sp. ssp. latRo x notabilis\n",
+      "H.sp. ssp. latRo x notabilis\n",
+      "H.sp. ssp. plesseni x malleti\n",
+      "H.sp. ssp. plesseni x malleti\n",
+      "H.sp. ssp. plesseni x malleti\n",
+      "H.sp. ssp. plesseni x malleti\n",
+      "H.sp. ssp. notabilis x lativitta\n",
+      "H.sp. ssp. plesseni x malleti\n",
+      "H.sp. ssp. plesseni x malleti\n",
+      "H.sp. ssp. plesseni x malleti\n",
+      "H.sp. ssp. plesseni x malleti\n",
+      "H.sp. ssp. plesseni x malleti\n",
+      "H.sp. cross hybrid\n",
+      "H.sp. ssp. plesseni x malleti\n",
+      "H.sp. ssp. notabilis x lativitta\n",
+      "H.sp. ssp. notabilis x lativitta\n",
+      "H.sp. cross hybrid\n",
+      "H.sp. ssp. plesseni x malleti\n",
+      "H.sp. cross hybrid\n",
+      "H.sp. ssp. lativitta\n",
+      "H.sp. cross hybrid\n",
+      "H.sp. cross hybrid\n",
+      "H.sp. cross hybrid\n",
+      "H.sp. cross hybrid\n",
+      "H.sp. cross hybrid\n",
+      "H.sp. cross hybrid\n"
+     ]
+    }
+   ],
+   "source": [
+    "for cross_type in list(df[\"Cross_Type\"].dropna().unique()):\n",
+    "    print(get_cross_taxa_name(\"H.sp.\", cross_type))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Heliconius melpomene', 'Heliconius erato'], dtype=object)"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.loc[df[\"Cross_Type\"].notna(), \"species\"].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Heliconius melpomene ssp. malleti',\n",
+       "       'Heliconius melpomene ssp. plesseni x malleti',\n",
+       "       'Heliconius melpomene ssp. plesseni',\n",
+       "       'Heliconius erato ssp. latRo x notabilis',\n",
+       "       'Heliconius erato ssp. notabilis x lativitta',\n",
+       "       'Heliconius erato cross hybrid',\n",
+       "       'Heliconius melpomene cross hybrid',\n",
+       "       'Heliconius erato ssp. lativitta'], dtype=object)"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# https://stackoverflow.com/a/52854800\n",
+    "df.loc[df[\"Cross_Type\"].notna(), \"Taxonomic_Name\"] = df.loc[df[\"Cross_Type\"].notna()].apply(lambda x: get_cross_taxa_name(x[\"species\"], x[\"Cross_Type\"]), axis = 1)\n",
+    "df.loc[df[\"Cross_Type\"].notna(), \"Taxonomic_Name\"].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "366"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.Taxonomic_Name.nunique()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So we've added 3 more unique values of `Taxonomic_Name`, that checks out: `Heliconius erato ssp. latRo x notabilis`, `Heliconius erato cross hybrid`, and `Heliconius melpomene cross hybrid`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Add Hybrid Status Column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "20"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hybrids = [taxa_name for taxa_name in list(df[\"Taxonomic_Name\"].dropna().unique()) if (\" x \" in taxa_name) or (\"hybrid\" in taxa_name)]\n",
+    "len(hybrids)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Heliconius erato ssp. venus x chestertonii',\n",
+       " 'Heliconius melpomene ssp. vulcanus x melpomene',\n",
+       " 'Heliconius erato ssp. chestertonii x venus',\n",
+       " 'Heliconius melpomene ssp. melpomene x thelxiope',\n",
+       " 'Heliconius melpomene ssp. melpomene x rosina',\n",
+       " 'Heliconius hybrid',\n",
+       " 'Heliconius erato ssp. hydara x petiverana',\n",
+       " 'Heliconius melpomene ssp. amaryllis x aglaope',\n",
+       " 'Anartia fatima x amathea',\n",
+       " 'Heliconius melpomene ssp. plesseni x malleti',\n",
+       " 'Heliconius erato ssp. notabilis x lativitta',\n",
+       " 'Heliconius erato ssp. latRo x notabilis',\n",
+       " 'Heliconius erato cross hybrid',\n",
+       " 'Heliconius melpomene cross hybrid',\n",
+       " 'Heliconius erato ssp. hydara x amalfreda',\n",
+       " 'Heliconius erato ssp. hydara x erato',\n",
+       " 'Heliconius melpomene ssp. meriana x melpomene',\n",
+       " 'Heliconius melpomene ssp. dignus x lativitta',\n",
+       " 'Heliconius melpomene ssp. malleti x bellula',\n",
+       " 'Heliconius melpomene ssp. malleti x vicina']"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hybrids"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Observe that we have one species-level hybrid: `Anartia fatima x amathea`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sp_hybrid_parents = [\"Anartia fatima\", \"Anartia amathea\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df[\"hybrid_stat\"] = None\n",
+    "df.loc[df[\"subspecies\"].notna(), \"hybrid_stat\"] = \"non-hybrid\"\n",
+    "df.loc[df[\"species\"].isin(sp_hybrid_parents), \"hybrid_stat\"] = \"non-hybrid\"\n",
+    "\n",
+    "df.loc[df[\"Taxonomic_Name\"].isin(hybrids), \"hybrid_stat\"] = \"hybrid\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "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>CAMID</th>\n",
+       "      <th>X</th>\n",
+       "      <th>Image_name</th>\n",
+       "      <th>View</th>\n",
+       "      <th>zenodo_name</th>\n",
+       "      <th>zenodo_link</th>\n",
+       "      <th>Sequence</th>\n",
+       "      <th>Taxonomic_Name</th>\n",
+       "      <th>Locality</th>\n",
+       "      <th>Sample_accession</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Stage</th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>Unit_Type</th>\n",
+       "      <th>file_type</th>\n",
+       "      <th>record_number</th>\n",
+       "      <th>species</th>\n",
+       "      <th>subspecies</th>\n",
+       "      <th>genus</th>\n",
+       "      <th>file_url</th>\n",
+       "      <th>hybrid_stat</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>7326</th>\n",
+       "      <td>CAM002890</td>\n",
+       "      <td>8482</td>\n",
+       "      <td>CAM002890_v.JPG</td>\n",
+       "      <td>ventral</td>\n",
+       "      <td>CAM.coll.images.batch5.csv</td>\n",
+       "      <td>https://zenodo.org/record/2684906</td>\n",
+       "      <td>2,890</td>\n",
+       "      <td>Anartia fatima</td>\n",
+       "      <td>El Tirao,</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>wild</td>\n",
+       "      <td>jpg</td>\n",
+       "      <td>2684906</td>\n",
+       "      <td>Anartia fatima</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Anartia</td>\n",
+       "      <td>https://zenodo.org/record/2684906/files/CAM002...</td>\n",
+       "      <td>non-hybrid</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7327</th>\n",
+       "      <td>CAM002890</td>\n",
+       "      <td>45376</td>\n",
+       "      <td>CAM002890_v.JPG</td>\n",
+       "      <td>ventral</td>\n",
+       "      <td>occurences_and_multimedia.csv</td>\n",
+       "      <td>https://zenodo.org/record/3477891</td>\n",
+       "      <td>2,890</td>\n",
+       "      <td>Anartia fatima</td>\n",
+       "      <td>El Tirao,</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>wild</td>\n",
+       "      <td>jpg</td>\n",
+       "      <td>3477891</td>\n",
+       "      <td>Anartia fatima</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Anartia</td>\n",
+       "      <td>https://zenodo.org/record/3477891/files/CAM002...</td>\n",
+       "      <td>non-hybrid</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7328</th>\n",
+       "      <td>CAM002890</td>\n",
+       "      <td>45375</td>\n",
+       "      <td>CAM002890_d.JPG</td>\n",
+       "      <td>dorsal</td>\n",
+       "      <td>occurences_and_multimedia.csv</td>\n",
+       "      <td>https://zenodo.org/record/3477891</td>\n",
+       "      <td>2,890</td>\n",
+       "      <td>Anartia fatima</td>\n",
+       "      <td>El Tirao,</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>wild</td>\n",
+       "      <td>jpg</td>\n",
+       "      <td>3477891</td>\n",
+       "      <td>Anartia fatima</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Anartia</td>\n",
+       "      <td>https://zenodo.org/record/3477891/files/CAM002...</td>\n",
+       "      <td>non-hybrid</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7329</th>\n",
+       "      <td>CAM002890</td>\n",
+       "      <td>8481</td>\n",
+       "      <td>CAM002890_d.JPG</td>\n",
+       "      <td>dorsal</td>\n",
+       "      <td>CAM.coll.images.batch5.csv</td>\n",
+       "      <td>https://zenodo.org/record/2684906</td>\n",
+       "      <td>2,890</td>\n",
+       "      <td>Anartia fatima</td>\n",
+       "      <td>El Tirao,</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>wild</td>\n",
+       "      <td>jpg</td>\n",
+       "      <td>2684906</td>\n",
+       "      <td>Anartia fatima</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Anartia</td>\n",
+       "      <td>https://zenodo.org/record/2684906/files/CAM002...</td>\n",
+       "      <td>non-hybrid</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>4 rows × 28 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          CAMID      X       Image_name     View  \\\n",
+       "7326  CAM002890   8482  CAM002890_v.JPG  ventral   \n",
+       "7327  CAM002890  45376  CAM002890_v.JPG  ventral   \n",
+       "7328  CAM002890  45375  CAM002890_d.JPG   dorsal   \n",
+       "7329  CAM002890   8481  CAM002890_d.JPG   dorsal   \n",
+       "\n",
+       "                        zenodo_name                        zenodo_link  \\\n",
+       "7326     CAM.coll.images.batch5.csv  https://zenodo.org/record/2684906   \n",
+       "7327  occurences_and_multimedia.csv  https://zenodo.org/record/3477891   \n",
+       "7328  occurences_and_multimedia.csv  https://zenodo.org/record/3477891   \n",
+       "7329     CAM.coll.images.batch5.csv  https://zenodo.org/record/2684906   \n",
+       "\n",
+       "     Sequence  Taxonomic_Name   Locality Sample_accession  ... Stage     Sex  \\\n",
+       "7326    2,890  Anartia fatima  El Tirao,              NaN  ...   NaN  Female   \n",
+       "7327    2,890  Anartia fatima  El Tirao,              NaN  ...   NaN  Female   \n",
+       "7328    2,890  Anartia fatima  El Tirao,              NaN  ...   NaN  Female   \n",
+       "7329    2,890  Anartia fatima  El Tirao,              NaN  ...   NaN  Female   \n",
+       "\n",
+       "     Unit_Type file_type record_number         species subspecies    genus  \\\n",
+       "7326      wild       jpg       2684906  Anartia fatima        NaN  Anartia   \n",
+       "7327      wild       jpg       3477891  Anartia fatima        NaN  Anartia   \n",
+       "7328      wild       jpg       3477891  Anartia fatima        NaN  Anartia   \n",
+       "7329      wild       jpg       2684906  Anartia fatima        NaN  Anartia   \n",
+       "\n",
+       "                                               file_url hybrid_stat  \n",
+       "7326  https://zenodo.org/record/2684906/files/CAM002...  non-hybrid  \n",
+       "7327  https://zenodo.org/record/3477891/files/CAM002...  non-hybrid  \n",
+       "7328  https://zenodo.org/record/3477891/files/CAM002...  non-hybrid  \n",
+       "7329  https://zenodo.org/record/2684906/files/CAM002...  non-hybrid  \n",
+       "\n",
+       "[4 rows x 28 columns]"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.loc[df[\"species\"].isin(sp_hybrid_parents)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "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>CAMID</th>\n",
+       "      <th>X</th>\n",
+       "      <th>Image_name</th>\n",
+       "      <th>View</th>\n",
+       "      <th>zenodo_name</th>\n",
+       "      <th>zenodo_link</th>\n",
+       "      <th>Sequence</th>\n",
+       "      <th>Taxonomic_Name</th>\n",
+       "      <th>Locality</th>\n",
+       "      <th>Sample_accession</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Stage</th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>Unit_Type</th>\n",
+       "      <th>file_type</th>\n",
+       "      <th>record_number</th>\n",
+       "      <th>species</th>\n",
+       "      <th>subspecies</th>\n",
+       "      <th>genus</th>\n",
+       "      <th>file_url</th>\n",
+       "      <th>hybrid_stat</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>0 rows × 28 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Empty DataFrame\n",
+       "Columns: [CAMID, X, Image_name, View, zenodo_name, zenodo_link, Sequence, Taxonomic_Name, Locality, Sample_accession, Collected_by, Other_ID, Date, Dataset, Store, Brood, Death_Date, Cross_Type, Stage, Sex, Unit_Type, file_type, record_number, species, subspecies, genus, file_url, hybrid_stat]\n",
+       "Index: []\n",
+       "\n",
+       "[0 rows x 28 columns]"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.loc[df[\"species\"].str.lower() == \"amathea\"] #not represented in the dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Taxonomic_Name\n",
+       "Anartia fatima x amathea    60\n",
+       "Anartia fatima               4\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.loc[df[\"genus\"] == \"Anartia\", \"Taxonomic_Name\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "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>CAMID</th>\n",
+       "      <th>X</th>\n",
+       "      <th>Image_name</th>\n",
+       "      <th>View</th>\n",
+       "      <th>zenodo_name</th>\n",
+       "      <th>zenodo_link</th>\n",
+       "      <th>Sequence</th>\n",
+       "      <th>Taxonomic_Name</th>\n",
+       "      <th>Locality</th>\n",
+       "      <th>Sample_accession</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Stage</th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>Unit_Type</th>\n",
+       "      <th>file_type</th>\n",
+       "      <th>record_number</th>\n",
+       "      <th>species</th>\n",
+       "      <th>subspecies</th>\n",
+       "      <th>genus</th>\n",
+       "      <th>file_url</th>\n",
+       "      <th>hybrid_stat</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>40498</th>\n",
+       "      <td>CAM043842</td>\n",
+       "      <td>32098</td>\n",
+       "      <td>CAM043842_hwv.JPG</td>\n",
+       "      <td>hindwing ventral</td>\n",
+       "      <td>batch2.Peru.image.names.Zenodo.csv</td>\n",
+       "      <td>https://zenodo.org/record/4287444</td>\n",
+       "      <td>43,842</td>\n",
+       "      <td>Catoblepia berecynthia</td>\n",
+       "      <td>B1prim3</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>jpg</td>\n",
+       "      <td>4287444</td>\n",
+       "      <td>Catoblepia berecynthia</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Catoblepia</td>\n",
+       "      <td>https://zenodo.org/record/4287444/files/CAM043...</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9192</th>\n",
+       "      <td>CAM008567</td>\n",
+       "      <td>46249</td>\n",
+       "      <td>CAM008567_v.JPG</td>\n",
+       "      <td>ventral</td>\n",
+       "      <td>occurences_and_multimedia.csv</td>\n",
+       "      <td>https://zenodo.org/record/3477891</td>\n",
+       "      <td>8,567</td>\n",
+       "      <td>Heliconius demeter</td>\n",
+       "      <td>Tarapoto-Yurimaguas (Km15), Tunel Cumbre, Fond...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>wild</td>\n",
+       "      <td>jpg</td>\n",
+       "      <td>3477891</td>\n",
+       "      <td>Heliconius demeter</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Heliconius</td>\n",
+       "      <td>https://zenodo.org/record/3477891/files/CAM008...</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14127</th>\n",
+       "      <td>CAM010971</td>\n",
+       "      <td>27577</td>\n",
+       "      <td>CAM010971_d.CR2</td>\n",
+       "      <td>dorsal</td>\n",
+       "      <td>2001_2.broods.batch.1.csv</td>\n",
+       "      <td>https://zenodo.org/record/2549524</td>\n",
+       "      <td>10,971</td>\n",
+       "      <td>Heliconius sp.</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>reared</td>\n",
+       "      <td>raw</td>\n",
+       "      <td>2549524</td>\n",
+       "      <td>Heliconius sp.</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Heliconius</td>\n",
+       "      <td>https://zenodo.org/record/2549524/files/CAM010...</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5315</th>\n",
+       "      <td>CAM000537</td>\n",
+       "      <td>6676</td>\n",
+       "      <td>CAM000537_d.JPG</td>\n",
+       "      <td>dorsal</td>\n",
+       "      <td>CAM.coll.images.batch3.csv</td>\n",
+       "      <td>https://zenodo.org/record/2682458</td>\n",
+       "      <td>537</td>\n",
+       "      <td>Heliconius melpomene ssp. rosina</td>\n",
+       "      <td>Pipeline road 4km,</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>wild</td>\n",
+       "      <td>jpg</td>\n",
+       "      <td>2682458</td>\n",
+       "      <td>Heliconius melpomene</td>\n",
+       "      <td>rosina</td>\n",
+       "      <td>Heliconius</td>\n",
+       "      <td>https://zenodo.org/record/2682458/files/CAM000...</td>\n",
+       "      <td>non-hybrid</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>585</th>\n",
+       "      <td>19N0204</td>\n",
+       "      <td>19553</td>\n",
+       "      <td>19N0204_d.JPG</td>\n",
+       "      <td>dorsal</td>\n",
+       "      <td>0.sheffield.ps.nn.ikiam.batch1.csv</td>\n",
+       "      <td>https://zenodo.org/record/4288311</td>\n",
+       "      <td>204</td>\n",
+       "      <td>Heliconius erato ssp. lativitta</td>\n",
+       "      <td>Ikiam Mariposario</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>reared</td>\n",
+       "      <td>jpg</td>\n",
+       "      <td>4288311</td>\n",
+       "      <td>Heliconius erato</td>\n",
+       "      <td>lativitta</td>\n",
+       "      <td>Heliconius</td>\n",
+       "      <td>https://zenodo.org/record/4288311/files/19N020...</td>\n",
+       "      <td>non-hybrid</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38875</th>\n",
+       "      <td>CAM043366</td>\n",
+       "      <td>30469</td>\n",
+       "      <td>CAM043366_v.CR2</td>\n",
+       "      <td>ventral</td>\n",
+       "      <td>batch1.Peru.image.names.Zenodo.csv</td>\n",
+       "      <td>https://zenodo.org/record/3569598</td>\n",
+       "      <td>43,366</td>\n",
+       "      <td>Eunica pusilla</td>\n",
+       "      <td>B6rec3</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>raw</td>\n",
+       "      <td>3569598</td>\n",
+       "      <td>Eunica pusilla</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Eunica</td>\n",
+       "      <td>https://zenodo.org/record/3569598/files/CAM043...</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19226</th>\n",
+       "      <td>CAM016687</td>\n",
+       "      <td>18917</td>\n",
+       "      <td>CAM016687_d.CR2</td>\n",
+       "      <td>dorsal</td>\n",
+       "      <td>CAM.coll.PS.list.individuals.haplotagging.new....</td>\n",
+       "      <td>https://zenodo.org/record/4153502</td>\n",
+       "      <td>16,687</td>\n",
+       "      <td>Heliconius melpomene ssp. plesseni</td>\n",
+       "      <td>El Topo, Baños - Puyo road,</td>\n",
+       "      <td>SRS7540355</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>wild</td>\n",
+       "      <td>raw</td>\n",
+       "      <td>4153502</td>\n",
+       "      <td>Heliconius melpomene</td>\n",
+       "      <td>plesseni</td>\n",
+       "      <td>Heliconius</td>\n",
+       "      <td>https://zenodo.org/record/4153502/files/CAM016...</td>\n",
+       "      <td>non-hybrid</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>7 rows × 28 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           CAMID      X         Image_name              View  \\\n",
+       "40498  CAM043842  32098  CAM043842_hwv.JPG  hindwing ventral   \n",
+       "9192   CAM008567  46249    CAM008567_v.JPG           ventral   \n",
+       "14127  CAM010971  27577    CAM010971_d.CR2            dorsal   \n",
+       "5315   CAM000537   6676    CAM000537_d.JPG            dorsal   \n",
+       "585      19N0204  19553      19N0204_d.JPG            dorsal   \n",
+       "38875  CAM043366  30469    CAM043366_v.CR2           ventral   \n",
+       "19226  CAM016687  18917    CAM016687_d.CR2            dorsal   \n",
+       "\n",
+       "                                             zenodo_name  \\\n",
+       "40498                 batch2.Peru.image.names.Zenodo.csv   \n",
+       "9192                       occurences_and_multimedia.csv   \n",
+       "14127                          2001_2.broods.batch.1.csv   \n",
+       "5315                          CAM.coll.images.batch3.csv   \n",
+       "585                   0.sheffield.ps.nn.ikiam.batch1.csv   \n",
+       "38875                 batch1.Peru.image.names.Zenodo.csv   \n",
+       "19226  CAM.coll.PS.list.individuals.haplotagging.new....   \n",
+       "\n",
+       "                             zenodo_link Sequence  \\\n",
+       "40498  https://zenodo.org/record/4287444   43,842   \n",
+       "9192   https://zenodo.org/record/3477891    8,567   \n",
+       "14127  https://zenodo.org/record/2549524   10,971   \n",
+       "5315   https://zenodo.org/record/2682458      537   \n",
+       "585    https://zenodo.org/record/4288311      204   \n",
+       "38875  https://zenodo.org/record/3569598   43,366   \n",
+       "19226  https://zenodo.org/record/4153502   16,687   \n",
+       "\n",
+       "                           Taxonomic_Name  \\\n",
+       "40498              Catoblepia berecynthia   \n",
+       "9192                   Heliconius demeter   \n",
+       "14127                      Heliconius sp.   \n",
+       "5315     Heliconius melpomene ssp. rosina   \n",
+       "585       Heliconius erato ssp. lativitta   \n",
+       "38875                      Eunica pusilla   \n",
+       "19226  Heliconius melpomene ssp. plesseni   \n",
+       "\n",
+       "                                                Locality Sample_accession  \\\n",
+       "40498                                            B1prim3              NaN   \n",
+       "9192   Tarapoto-Yurimaguas (Km15), Tunel Cumbre, Fond...              NaN   \n",
+       "14127                                                NaN              NaN   \n",
+       "5315                                  Pipeline road 4km,              NaN   \n",
+       "585                                    Ikiam Mariposario              NaN   \n",
+       "38875                                             B6rec3              NaN   \n",
+       "19226                        El Topo, Baños - Puyo road,       SRS7540355   \n",
+       "\n",
+       "       ... Stage   Sex Unit_Type file_type record_number  \\\n",
+       "40498  ...   NaN   NaN       NaN       jpg       4287444   \n",
+       "9192   ...   NaN  Male      wild       jpg       3477891   \n",
+       "14127  ...   NaN  Male    reared       raw       2549524   \n",
+       "5315   ...   NaN  Male      wild       jpg       2682458   \n",
+       "585    ...   NaN  Male    reared       jpg       4288311   \n",
+       "38875  ...   NaN   NaN       NaN       raw       3569598   \n",
+       "19226  ...   NaN  Male      wild       raw       4153502   \n",
+       "\n",
+       "                      species subspecies       genus  \\\n",
+       "40498  Catoblepia berecynthia        NaN  Catoblepia   \n",
+       "9192       Heliconius demeter        NaN  Heliconius   \n",
+       "14127          Heliconius sp.        NaN  Heliconius   \n",
+       "5315     Heliconius melpomene     rosina  Heliconius   \n",
+       "585          Heliconius erato  lativitta  Heliconius   \n",
+       "38875          Eunica pusilla        NaN      Eunica   \n",
+       "19226    Heliconius melpomene   plesseni  Heliconius   \n",
+       "\n",
+       "                                                file_url hybrid_stat  \n",
+       "40498  https://zenodo.org/record/4287444/files/CAM043...        None  \n",
+       "9192   https://zenodo.org/record/3477891/files/CAM008...        None  \n",
+       "14127  https://zenodo.org/record/2549524/files/CAM010...        None  \n",
+       "5315   https://zenodo.org/record/2682458/files/CAM000...  non-hybrid  \n",
+       "585    https://zenodo.org/record/4288311/files/19N020...  non-hybrid  \n",
+       "38875  https://zenodo.org/record/3569598/files/CAM043...        None  \n",
+       "19226  https://zenodo.org/record/4153502/files/CAM016...  non-hybrid  \n",
+       "\n",
+       "[7 rows x 28 columns]"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.sample(7)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Investigate entries with no Taxonomic Information"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 3886 entries, 0 to 49350\n",
+      "Data columns (total 28 columns):\n",
+      " #   Column            Non-Null Count  Dtype \n",
+      "---  ------            --------------  ----- \n",
+      " 0   CAMID             3886 non-null   object\n",
+      " 1   X                 3886 non-null   int64 \n",
+      " 2   Image_name        3886 non-null   object\n",
+      " 3   View              3594 non-null   object\n",
+      " 4   zenodo_name       3886 non-null   object\n",
+      " 5   zenodo_link       3886 non-null   object\n",
+      " 6   Sequence          2951 non-null   object\n",
+      " 7   Taxonomic_Name    0 non-null      object\n",
+      " 8   Locality          70 non-null     object\n",
+      " 9   Sample_accession  24 non-null     object\n",
+      " 10  Collected_by      0 non-null      object\n",
+      " 11  Other_ID          30 non-null     object\n",
+      " 12  Date              70 non-null     object\n",
+      " 13  Dataset           461 non-null    object\n",
+      " 14  Store             345 non-null    object\n",
+      " 15  Brood             5 non-null      object\n",
+      " 16  Death_Date        2 non-null      object\n",
+      " 17  Cross_Type        0 non-null      object\n",
+      " 18  Stage             9 non-null      object\n",
+      " 19  Sex               35 non-null     object\n",
+      " 20  Unit_Type         47 non-null     object\n",
+      " 21  file_type         3886 non-null   object\n",
+      " 22  record_number     3886 non-null   int64 \n",
+      " 23  species           0 non-null      object\n",
+      " 24  subspecies        0 non-null      object\n",
+      " 25  genus             0 non-null      object\n",
+      " 26  file_url          3886 non-null   object\n",
+      " 27  hybrid_stat       0 non-null      object\n",
+      "dtypes: int64(2), object(26)\n",
+      "memory usage: 880.4+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.loc[df.Taxonomic_Name.isna()].info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Wow, there are a surprising number of images with no taxonomic information."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "no_taxa_cams = list(df.loc[df[\"Taxonomic_Name\"].isna(), \"CAMID\"].unique())\n",
+    "no_taxa_entries = list(df.loc[df[\"Taxonomic_Name\"].isna(), \"X\"])\n",
+    "\n",
+    "no_taxa_cam_match = [camid for camid in list(df.loc[~df[\"X\"].isin(no_taxa_entries), \"CAMID\"]) if camid in no_taxa_cams]\n",
+    "len(no_taxa_cam_match)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are no CAMIDs that these can match to for taxonomic info recovery. Last option is to check the records they come from, then we'll remove them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "21\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "zenodo_link\n",
+       "https://zenodo.org/record/5526257    1324\n",
+       "https://zenodo.org/record/2554218     492\n",
+       "https://zenodo.org/record/4288311     486\n",
+       "https://zenodo.org/record/2555086     440\n",
+       "https://zenodo.org/record/5731587     388\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print(df.loc[df[\"X\"].isin(no_taxa_entries), \"record_number\"].nunique())\n",
+    "df.loc[df[\"X\"].isin(no_taxa_entries), \"zenodo_link\"].value_counts()[:5]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These are across most of the records. We can check the one with the most, but otherwise they should likely be dropped (can save them in a separate CSV in case they are to be assessed at a later point).\n",
+    "\n",
+    "The first appear to be all _Heliconius erato ssp. cyrbia_, so those could be realigned.\n",
+    "\n",
+    "The second indicates all _Heliconius erato_.\n",
+    "\n",
+    "The third is a mix, so not resolvable without an expert.\n",
+    "\n",
+    "It seems record 2555086 is all bred specimens of _Heliconius erato demophoon_.\n",
+    "\n",
+    "The fifth record is a mix, and not all are labeled in the excel file (in fact, there's a lot of red which is probably why they were excluded)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.series.Series'>\n",
+      "Index: 1324 entries, 45868 to 47191\n",
+      "Series name: Taxonomic_Name\n",
+      "Non-Null Count  Dtype \n",
+      "--------------  ----- \n",
+      "0 non-null      object\n",
+      "dtypes: object(1)\n",
+      "memory usage: 20.7+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.loc[df[\"record_number\"] == 5526257, \"Taxonomic_Name\"].info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Ah, so these are all null, but all are of the same subspecies, so they can be labeled."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pot_single_taxa_records = []\n",
+    "for record_num in (df.loc[df[\"X\"].isin(no_taxa_entries), \"record_number\"].unique()):\n",
+    "    temp = df.loc[df[\"record_number\"] == record_num]\n",
+    "    if temp.loc[temp[\"Taxonomic_Name\"].notna()].shape[0] == 0:\n",
+    "        pot_single_taxa_records.append(record_num)\n",
+    "\n",
+    "len(pot_single_taxa_records)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[5731587, 5526257, 2554218, 2555086]"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pot_single_taxa_records"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "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>CAMID</th>\n",
+       "      <th>X</th>\n",
+       "      <th>Image_name</th>\n",
+       "      <th>View</th>\n",
+       "      <th>zenodo_name</th>\n",
+       "      <th>zenodo_link</th>\n",
+       "      <th>Sequence</th>\n",
+       "      <th>Taxonomic_Name</th>\n",
+       "      <th>Locality</th>\n",
+       "      <th>Sample_accession</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Stage</th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>Unit_Type</th>\n",
+       "      <th>file_type</th>\n",
+       "      <th>record_number</th>\n",
+       "      <th>species</th>\n",
+       "      <th>subspecies</th>\n",
+       "      <th>genus</th>\n",
+       "      <th>file_url</th>\n",
+       "      <th>hybrid_stat</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>46747</th>\n",
+       "      <td>CAM045219</td>\n",
+       "      <td>43439</td>\n",
+       "      <td>CAM045219_d.JPG</td>\n",
+       "      <td>dorsal</td>\n",
+       "      <td>image.names.cook.island.erato.csv</td>\n",
+       "      <td>https://zenodo.org/record/5526257</td>\n",
+       "      <td>45,219</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>jpg</td>\n",
+       "      <td>5526257</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>https://zenodo.org/record/5526257/files/CAM045...</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>47062</th>\n",
+       "      <td>CAM045298</td>\n",
+       "      <td>43756</td>\n",
+       "      <td>CAM045298_v.CR2</td>\n",
+       "      <td>ventral</td>\n",
+       "      <td>image.names.cook.island.erato.csv</td>\n",
+       "      <td>https://zenodo.org/record/5526257</td>\n",
+       "      <td>45,298</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>raw</td>\n",
+       "      <td>5526257</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>https://zenodo.org/record/5526257/files/CAM045...</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>37255</th>\n",
+       "      <td>CAM042050</td>\n",
+       "      <td>43982</td>\n",
+       "      <td>CAM042050_d.JPG</td>\n",
+       "      <td>dorsal</td>\n",
+       "      <td>Collection_August2019.csv</td>\n",
+       "      <td>https://zenodo.org/record/5731587</td>\n",
+       "      <td>42,050</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>jpg</td>\n",
+       "      <td>5731587</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>https://zenodo.org/record/5731587/files/CAM042...</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46992</th>\n",
+       "      <td>CAM045281</td>\n",
+       "      <td>43687</td>\n",
+       "      <td>CAM045281_d.JPG</td>\n",
+       "      <td>dorsal</td>\n",
+       "      <td>image.names.cook.island.erato.csv</td>\n",
+       "      <td>https://zenodo.org/record/5526257</td>\n",
+       "      <td>45,281</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>jpg</td>\n",
+       "      <td>5526257</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>https://zenodo.org/record/5526257/files/CAM045...</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49106</th>\n",
+       "      <td>F901</td>\n",
+       "      <td>38167</td>\n",
+       "      <td>F901_d.CR2</td>\n",
+       "      <td>dorsal</td>\n",
+       "      <td>Anniina.Mattila.Bred.F.csv</td>\n",
+       "      <td>https://zenodo.org/record/2555086</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>raw</td>\n",
+       "      <td>2555086</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>https://zenodo.org/record/2555086/files/F901_d...</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 28 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           CAMID      X       Image_name     View  \\\n",
+       "46747  CAM045219  43439  CAM045219_d.JPG   dorsal   \n",
+       "47062  CAM045298  43756  CAM045298_v.CR2  ventral   \n",
+       "37255  CAM042050  43982  CAM042050_d.JPG   dorsal   \n",
+       "46992  CAM045281  43687  CAM045281_d.JPG   dorsal   \n",
+       "49106       F901  38167       F901_d.CR2   dorsal   \n",
+       "\n",
+       "                             zenodo_name                        zenodo_link  \\\n",
+       "46747  image.names.cook.island.erato.csv  https://zenodo.org/record/5526257   \n",
+       "47062  image.names.cook.island.erato.csv  https://zenodo.org/record/5526257   \n",
+       "37255          Collection_August2019.csv  https://zenodo.org/record/5731587   \n",
+       "46992  image.names.cook.island.erato.csv  https://zenodo.org/record/5526257   \n",
+       "49106         Anniina.Mattila.Bred.F.csv  https://zenodo.org/record/2555086   \n",
+       "\n",
+       "      Sequence Taxonomic_Name Locality Sample_accession  ... Stage  Sex  \\\n",
+       "46747   45,219            NaN      NaN              NaN  ...   NaN  NaN   \n",
+       "47062   45,298            NaN      NaN              NaN  ...   NaN  NaN   \n",
+       "37255   42,050            NaN      NaN              NaN  ...   NaN  NaN   \n",
+       "46992   45,281            NaN      NaN              NaN  ...   NaN  NaN   \n",
+       "49106      NaN            NaN      NaN              NaN  ...   NaN  NaN   \n",
+       "\n",
+       "      Unit_Type file_type record_number species subspecies genus  \\\n",
+       "46747       NaN       jpg       5526257     NaN        NaN   NaN   \n",
+       "47062       NaN       raw       5526257     NaN        NaN   NaN   \n",
+       "37255       NaN       jpg       5731587     NaN        NaN   NaN   \n",
+       "46992       NaN       jpg       5526257     NaN        NaN   NaN   \n",
+       "49106       NaN       raw       2555086     NaN        NaN   NaN   \n",
+       "\n",
+       "                                                file_url hybrid_stat  \n",
+       "46747  https://zenodo.org/record/5526257/files/CAM045...        None  \n",
+       "47062  https://zenodo.org/record/5526257/files/CAM045...        None  \n",
+       "37255  https://zenodo.org/record/5731587/files/CAM042...        None  \n",
+       "46992  https://zenodo.org/record/5526257/files/CAM045...        None  \n",
+       "49106  https://zenodo.org/record/2555086/files/F901_d...        None  \n",
+       "\n",
+       "[5 rows x 28 columns]"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.loc[df.record_number.isin(pot_single_taxa_records)].sample(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Christopher agrees that the following is sufficient to label images from the following three records:\n",
+    ">3 records seem to have all of just one indicated species/subspecies: https://zenodo.org/record/5526257, https://zenodo.org/record/2554218, and https://zenodo.org/record/2555086. According to their Zenodo pages, the first appear to be all _Heliconius erato ssp. cyrbia_, the second indicates all _Heliconius erato_, and the third it seems is all bred specimens of _Heliconius erato demophoon_. \n",
+    "\n",
+    "`Unit_Type` and some other details could potentially be realigned at a later date, but we'll stick with taxonomic information for now."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.loc[df[\"record_number\"] == 5526257, \"Taxonomic_Name\"] = \"Heliconius erato ssp. cyrbia\"\n",
+    "df.loc[df[\"record_number\"] == 5526257, \"genus\"] = \"Heliconius\"\n",
+    "df.loc[df[\"record_number\"] == 5526257, \"species\"] = \"Heliconius erato\"\n",
+    "df.loc[df[\"record_number\"] == 5526257, \"subspecies\"] = \"cyrbia\"\n",
+    "df.loc[df[\"record_number\"] == 5526257, \"hybrid_stat\"] = \"non-hybrid\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.loc[df[\"record_number\"] == 2554218, \"Taxonomic_Name\"] = \"Heliconius erato\"\n",
+    "df.loc[df[\"record_number\"] == 2554218, \"genus\"] = \"Heliconius\"\n",
+    "df.loc[df[\"record_number\"] == 2554218, \"species\"] = \"Heliconius erato\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.loc[df[\"record_number\"] == 2555086, \"Taxonomic_Name\"] = \"Heliconius erato ssp. demophoon\"\n",
+    "df.loc[df[\"record_number\"] == 2555086, \"genus\"] = \"Heliconius\"\n",
+    "df.loc[df[\"record_number\"] == 2555086, \"species\"] = \"Heliconius erato\"\n",
+    "df.loc[df[\"record_number\"] == 2555086, \"subspecies\"] = \"demophoon\"\n",
+    "df.loc[df[\"record_number\"] == 2555086, \"hybrid_stat\"] = \"non-hybrid\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Save record of entries with no Taxonomic Info"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1630, 28)"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "missing_taxa_df = df.loc[df.Taxonomic_Name.isna()]\n",
+    "missing_taxa_df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "missing_taxa_df.to_csv(\"../metadata/Missing_taxa_Jiggins_Zenodo_Master.csv\", index = False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Drop Entries with no Taxonomic Information"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "master_df = df.loc[df.Taxonomic_Name.notna()]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Final stats for all data in master file summarized here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "CAMID               11991\n",
+       "X                   44809\n",
+       "Image_name          36281\n",
+       "View                    7\n",
+       "zenodo_name            33\n",
+       "zenodo_link            30\n",
+       "Sequence            10905\n",
+       "Taxonomic_Name        366\n",
+       "Locality              645\n",
+       "Sample_accession     1559\n",
+       "Collected_by           12\n",
+       "Other_ID             3081\n",
+       "Date                  807\n",
+       "Dataset                 8\n",
+       "Store                 137\n",
+       "Brood                 224\n",
+       "Death_Date             81\n",
+       "Cross_Type             30\n",
+       "Stage                   1\n",
+       "Sex                     3\n",
+       "Unit_Type               4\n",
+       "file_type               3\n",
+       "record_number          30\n",
+       "species               246\n",
+       "subspecies            155\n",
+       "genus                  94\n",
+       "file_url            44794\n",
+       "hybrid_stat             2\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "master_df.nunique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 44809 entries, 6 to 49358\n",
+      "Data columns (total 28 columns):\n",
+      " #   Column            Non-Null Count  Dtype \n",
+      "---  ------            --------------  ----- \n",
+      " 0   CAMID             44809 non-null  object\n",
+      " 1   X                 44809 non-null  int64 \n",
+      " 2   Image_name        44809 non-null  object\n",
+      " 3   View              44030 non-null  object\n",
+      " 4   zenodo_name       44809 non-null  object\n",
+      " 5   zenodo_link       44809 non-null  object\n",
+      " 6   Sequence          43877 non-null  object\n",
+      " 7   Taxonomic_Name    44809 non-null  object\n",
+      " 8   Locality          31708 non-null  object\n",
+      " 9   Sample_accession  4572 non-null   object\n",
+      " 10  Collected_by      3043 non-null   object\n",
+      " 11  Other_ID          14352 non-null  object\n",
+      " 12  Date              30730 non-null  object\n",
+      " 13  Dataset           37024 non-null  object\n",
+      " 14  Store             36220 non-null  object\n",
+      " 15  Brood             14258 non-null  object\n",
+      " 16  Death_Date        316 non-null    object\n",
+      " 17  Cross_Type        4452 non-null   object\n",
+      " 18  Stage             6 non-null      object\n",
+      " 19  Sex               33312 non-null  object\n",
+      " 20  Unit_Type         30923 non-null  object\n",
+      " 21  file_type         44809 non-null  object\n",
+      " 22  record_number     44809 non-null  int64 \n",
+      " 23  species           44809 non-null  object\n",
+      " 24  subspecies        24559 non-null  object\n",
+      " 25  genus             44809 non-null  object\n",
+      " 26  file_url          44809 non-null  object\n",
+      " 27  hybrid_stat       25139 non-null  object\n",
+      "dtypes: int64(2), object(26)\n",
+      "memory usage: 9.9+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "master_df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Update Master File with Hybrid Status and URL Columns (& unique records)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "master_df.to_csv(\"../Jiggins_Zenodo_Img_Master.csv\", index = False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Make Heliconius Subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 34265 entries, 6 to 49358\n",
+      "Data columns (total 28 columns):\n",
+      " #   Column            Non-Null Count  Dtype \n",
+      "---  ------            --------------  ----- \n",
+      " 0   CAMID             34265 non-null  object\n",
+      " 1   X                 34265 non-null  int64 \n",
+      " 2   Image_name        34265 non-null  object\n",
+      " 3   View              33486 non-null  object\n",
+      " 4   zenodo_name       34265 non-null  object\n",
+      " 5   zenodo_link       34265 non-null  object\n",
+      " 6   Sequence          33333 non-null  object\n",
+      " 7   Taxonomic_Name    34265 non-null  object\n",
+      " 8   Locality          21180 non-null  object\n",
+      " 9   Sample_accession  4572 non-null   object\n",
+      " 10  Collected_by      3043 non-null   object\n",
+      " 11  Other_ID          6404 non-null   object\n",
+      " 12  Date              20244 non-null  object\n",
+      " 13  Dataset           29926 non-null  object\n",
+      " 14  Store             26526 non-null  object\n",
+      " 15  Brood             14242 non-null  object\n",
+      " 16  Death_Date        316 non-null    object\n",
+      " 17  Cross_Type        4452 non-null   object\n",
+      " 18  Stage             6 non-null      object\n",
+      " 19  Sex               30984 non-null  object\n",
+      " 20  Unit_Type         29055 non-null  object\n",
+      " 21  file_type         34265 non-null  object\n",
+      " 22  record_number     34265 non-null  int64 \n",
+      " 23  species           34265 non-null  object\n",
+      " 24  subspecies        23801 non-null  object\n",
+      " 25  genus             34265 non-null  object\n",
+      " 26  file_url          34265 non-null  object\n",
+      " 27  hybrid_stat       24317 non-null  object\n",
+      "dtypes: int64(2), object(26)\n",
+      "memory usage: 7.6+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "heliconius_subset = master_df.loc[master_df.genus.str.lower() == \"heliconius\"]\n",
+    "\n",
+    "heliconius_subset.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "CAMID               10109\n",
+       "X                   34265\n",
+       "Image_name          29192\n",
+       "View                    3\n",
+       "zenodo_name            33\n",
+       "zenodo_link            30\n",
+       "Sequence             9031\n",
+       "Taxonomic_Name        132\n",
+       "Locality              472\n",
+       "Sample_accession     1559\n",
+       "Collected_by           12\n",
+       "Other_ID             1865\n",
+       "Date                  776\n",
+       "Dataset                 8\n",
+       "Store                 121\n",
+       "Brood                 224\n",
+       "Death_Date             81\n",
+       "Cross_Type             30\n",
+       "Stage                   1\n",
+       "Sex                     3\n",
+       "Unit_Type               4\n",
+       "file_type               3\n",
+       "record_number          30\n",
+       "species                37\n",
+       "subspecies            110\n",
+       "genus                   1\n",
+       "file_url            34250\n",
+       "hybrid_stat             2\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "heliconius_subset.nunique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "View\n",
+       "dorsal                16882\n",
+       "ventral               16586\n",
+       "dorsal and ventral       18\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "heliconius_subset.View.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that this subset is distributed across 30 Zenodo records from the [Butterfly Genetics Group](https://zenodo.org/communities/butterfly?q=&l=list&p=1&s=10&sort=newest)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Save the Heliconius Subset to CSV\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "heliconius_subset.to_csv(\"../Jiggins_Heliconius_Master.csv\", index = False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Make Dorsal Subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 22175 entries, 7 to 49357\n",
+      "Data columns (total 28 columns):\n",
+      " #   Column            Non-Null Count  Dtype \n",
+      "---  ------            --------------  ----- \n",
+      " 0   CAMID             22175 non-null  object\n",
+      " 1   X                 22175 non-null  int64 \n",
+      " 2   Image_name        22175 non-null  object\n",
+      " 3   View              22175 non-null  object\n",
+      " 4   zenodo_name       22175 non-null  object\n",
+      " 5   zenodo_link       22175 non-null  object\n",
+      " 6   Sequence          21709 non-null  object\n",
+      " 7   Taxonomic_Name    22175 non-null  object\n",
+      " 8   Locality          15615 non-null  object\n",
+      " 9   Sample_accession  2294 non-null   object\n",
+      " 10  Collected_by      1533 non-null   object\n",
+      " 11  Other_ID          6916 non-null   object\n",
+      " 12  Date              15347 non-null  object\n",
+      " 13  Dataset           18250 non-null  object\n",
+      " 14  Store             18254 non-null  object\n",
+      " 15  Brood             6920 non-null   object\n",
+      " 16  Death_Date        106 non-null    object\n",
+      " 17  Cross_Type        2230 non-null   object\n",
+      " 18  Stage             3 non-null      object\n",
+      " 19  Sex               16403 non-null  object\n",
+      " 20  Unit_Type         15203 non-null  object\n",
+      " 21  file_type         22175 non-null  object\n",
+      " 22  record_number     22175 non-null  int64 \n",
+      " 23  species           22175 non-null  object\n",
+      " 24  subspecies        12040 non-null  object\n",
+      " 25  genus             22175 non-null  object\n",
+      " 26  file_url          22175 non-null  object\n",
+      " 27  hybrid_stat       12330 non-null  object\n",
+      "dtypes: int64(2), object(26)\n",
+      "memory usage: 4.9+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "dorsal_views = [view for view in list(master_df.View.dropna().unique()) if \"dorsal\" in view]\n",
+    "\n",
+    "dorsal_subset = master_df.loc[master_df[\"View\"].isin(dorsal_views)]\n",
+    "dorsal_subset.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "CAMID               11776\n",
+       "X                   22175\n",
+       "Image_name          17907\n",
+       "View                    4\n",
+       "zenodo_name            33\n",
+       "zenodo_link            30\n",
+       "Sequence            10713\n",
+       "Taxonomic_Name        362\n",
+       "Locality              642\n",
+       "Sample_accession     1552\n",
+       "Collected_by           12\n",
+       "Other_ID             2890\n",
+       "Date                  791\n",
+       "Dataset                 8\n",
+       "Store                 137\n",
+       "Brood                 215\n",
+       "Death_Date             63\n",
+       "Cross_Type             30\n",
+       "Stage                   1\n",
+       "Sex                     3\n",
+       "Unit_Type               4\n",
+       "file_type               3\n",
+       "record_number          30\n",
+       "species               245\n",
+       "subspecies            152\n",
+       "genus                  94\n",
+       "file_url            22168\n",
+       "hybrid_stat             2\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dorsal_subset.nunique()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Observe that we still have duplicate samples (duplicated `CAMID`), so we'll add a column indicating this (`CAM_Dupe`). We will not leave the first instance as a non-duplicate to have a clear assessment of all duplication (eg., is it just across a couple records).\n",
+    "\n",
+    "Note that they will be duplicated for the images that are of a dorsal forewing or hindwing, so we will label those as `single_wing`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/nv/f0fq1p1n1_3b11x579py_0q80000gq/T/ipykernel_21745/2272441064.py:1: 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",
+      "  dorsal_subset[\"CAM_Dupe\"] = dorsal_subset.duplicated(subset = \"CAMID\", keep = False)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "CAM_Dupe\n",
+       "True           17213\n",
+       "False           4170\n",
+       "single-wing      792\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dorsal_subset[\"CAM_Dupe\"] = dorsal_subset.duplicated(subset = \"CAMID\", keep = False)\n",
+    "dorsal_subset.loc[dorsal_subset[\"View\"].isin([\"forewing dorsal\", \"hindwing dorsal\"]), \"CAM_Dupe\"] = \"single-wing\"\n",
+    "dorsal_subset[\"CAM_Dupe\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "29"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dorsal_subset.loc[dorsal_subset[\"CAM_Dupe\"] == True, \"record_number\"].nunique()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Okay, nearly all records have duplication even in the dorsal subset. That does make sense when we have just over half as many unique `CAMID`s as number of images."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "record_number\n",
+       "4287444    396\n",
+       "4288250    284\n",
+       "3569598    112\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dorsal_subset.loc[dorsal_subset[\"CAM_Dupe\"] == \"single-wing\", \"record_number\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Single-wing images are constrained to 3 records."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "file_type\n",
+       "jpg    11752\n",
+       "raw     5440\n",
+       "tif       21\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dorsal_subset.loc[dorsal_subset[\"CAM_Dupe\"] == True, \"file_type\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Some of this duplication is by file type."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True     8415\n",
+       "False    3337\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dorsal_subset.loc[(dorsal_subset[\"CAM_Dupe\"] == True) & (dorsal_subset[\"file_type\"] == \"jpg\")].duplicated(subset = \"CAMID\", keep = False).value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False    4106\n",
+       "True     1334\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dorsal_subset.loc[(dorsal_subset[\"CAM_Dupe\"] == True) & (dorsal_subset[\"file_type\"] == \"raw\")].duplicated(subset = \"CAMID\", keep = False).value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We have multiple jpg images & multiple raw images of the same specimen. Note that this does not necessarily mean these are duplicates of the same images. There are also jpg copies provided alongside raw images."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Save Dorsal Subset to CSV"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dorsal_subset.to_csv(\"../Jiggins_Zenodo_dorsal_Img_Master.csv\", index = False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "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
+}