Add notebook that performs taxa and view standardization.

notebooks/standardize_taxa.ipynb

@@ -0,0 +1,1464 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "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                   10\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",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.nunique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "file_type\n",
+       "jpg    37072\n",
+       "raw    12226\n",
+       "tif       61\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.file_type.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "View\n",
+       "dorsal                15128\n",
+       "ventral               13424\n",
+       "Dorsal                 8360\n",
+       "Ventral                8090\n",
+       "ventral                1644\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": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.View.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Not great that `ventral` gets listed twice as lowercase and _again_ as `Ventral`.\n",
+    "\n",
+    "### Standardize `View` Column\n",
+    "Let's standardize `View` so that there isn't a discrepancy based on case."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "View\n",
+       "dorsal                23488\n",
+       "ventral               21514\n",
+       "ventral                1644\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": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"View\"] = df.View.str.lower()\n",
+    "df.View.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['dorsal' 'ventral' nan 'dorsal and ventral' 'ventral ' 'forewing dorsal'\n",
+      " 'hindwing dorsal' 'forewing ventral' 'hindwing ventral']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df.View.unique())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Yes, one has a space after it, so we'll replace that."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "View\n",
+       "dorsal                23488\n",
+       "ventral               23158\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.loc[df[\"View\"] == \"ventral \", \"View\"] = \"ventral\"\n",
+    "df.View.value_counts() "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Add Record Number Column\n",
+    "We'll add a `record_number` column for easier matching to the license/citation file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_record_number(url):\n",
+    "    num = url.split(sep = \"/\")[-1]\n",
+    "    return num"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "32"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"record_number\"] = df.zenodo_link.apply(get_record_number)\n",
+    "df.record_number.nunique()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We have 32 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 `species` and `subspecies` Columns\n",
+    "This will make some analysis easier and allow for easy viewing on the [Data Dashboard](https://huggingface.co/spaces/imageomics/dashboard-prototype)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_species(taxa_name):\n",
+    "    if type(taxa_name) != float: #taxa name not null\n",
+    "        species = taxa_name.split(sep = \" ssp\")[0]\n",
+    "        return species\n",
+    "    else:\n",
+    "        return taxa_name"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_subspecies(taxa_name):\n",
+    "    if type(taxa_name) != float:\n",
+    "        if \"ssp.\" in taxa_name:\n",
+    "            subspecies = taxa_name.split(sep = \"ssp. \")[1]\n",
+    "        elif \"ssp \" in taxa_name:\n",
+    "            subspecies = taxa_name.split(sep = \"ssp \")[1]\n",
+    "        else:\n",
+    "            subspecies = None\n",
+    "    else:\n",
+    "        subspecies = None\n",
+    "    return subspecies"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "246"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"species\"] = df.Taxonomic_Name.apply(get_species)\n",
+    "df.species.nunique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "139"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"subspecies\"] = df.Taxonomic_Name.apply(get_subspecies)\n",
+    "df.subspecies.nunique()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Cross Types are labeled differently:\n",
+    "They are all abbreviations, we have `malleti (mal), plesseni (ple), notabilis (not), lativitta (lat)`, and Neil would guess that `latRo` refers to lativitta with a rounded apical band (e.g., a phenotypic variant of lativitta), but he couldn't say for sure without some more digging, so that will have to stay as-is. We will leave the `Test cross...` ones, but there is not much more to do with them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['mal', 'mal x ple', 'ple', 'ple x mal', 'latRo x not',\n",
+       "       '(latRo x not) x not', '(mal x ple) x mal', '(mal x ple) x ple',\n",
+       "       'ple x (mal x ple)', '(ple x mal) x (mal x ple)', 'lat x not',\n",
+       "       '(ple x mal) x ple', '(mal x ple) x (mal x ple)',\n",
+       "       '(ple x mal) x mal', '(ple x mal) x (ple x mal)',\n",
+       "       '(mal x ple) x (ple x mal)', 'hybrid', 'mal x (ple x mal)',\n",
+       "       '(lat x not) x lat', '(lat x not) x not', 'Ac heterozygote',\n",
+       "       'ple x (ple x mal)', '2 banded', 'lat',\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 mal - thin)'], dtype=object)"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.Cross_Type.dropna().unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def clean_cross_types(cross_type):\n",
+    "    if type(cross_type) != float:\n",
+    "        cross_type = cross_type.replace(\"mal\", \"malleti\")\n",
+    "        cross_type = cross_type.replace(\"ple\", \"plesseni\")\n",
+    "        cross_type = cross_type.replace(\"not\", \"notabilis\")\n",
+    "        if \"latRo\" not in cross_type:\n",
+    "            #latRo does not cross with lativitta, so only apply when latRo isn't present\n",
+    "            cross_type = cross_type.replace(\"lat\", \"lativitta\")\n",
+    "    return cross_type"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df[\"Cross_Type\"] = df[\"Cross_Type\"].apply(clean_cross_types)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can fill these cross types in for the `subspecies` column (all Cross Types are just labeled to the spceies level in `Taxonomic_Name`, so they did not get processed previously)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "156"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cross_type_subspecies = [ct for ct in list(df.Cross_Type.dropna().unique()) if \"Test\" not in ct and \"banded\" not in ct]\n",
+    "cross_type_subspecies.remove(\"hybrid\")\n",
+    "cross_type_subspecies.remove(\"Ac heterozygote\")\n",
+    "\n",
+    "for ct in cross_type_subspecies:\n",
+    "    df.loc[df[\"Cross_Type\"] == ct, \"subspecies\"] = ct\n",
+    "\n",
+    "df.subspecies.nunique()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "21"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(cross_type_subspecies)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "subspecies\n",
+       "(malleti x plesseni) x malleti                 1204\n",
+       "plesseni x (malleti x plesseni)                 600\n",
+       "malleti x (plesseni x malleti)                  370\n",
+       "(plesseni x malleti) x plesseni                 363\n",
+       "(plesseni x malleti) x (malleti x plesseni)     354\n",
+       "(plesseni x malleti) x (plesseni x malleti)     286\n",
+       "(malleti x plesseni) x plesseni                 278\n",
+       "plesseni x malleti                              234\n",
+       "malleti x plesseni                              192\n",
+       "lativitta x notabilis                           136\n",
+       "(lativitta x notabilis) x lativitta             110\n",
+       "plesseni x (plesseni x malleti)                 106\n",
+       "(lativitta x notabilis) x notabilis             106\n",
+       "(malleti x plesseni) x (malleti x plesseni)      98\n",
+       "(plesseni x malleti) x malleti                   80\n",
+       "(malleti x plesseni) x (plesseni x malleti)      56\n",
+       "malleti                                          28\n",
+       "plesseni                                         28\n",
+       "(latRo x notabilis) x notabilis                  16\n",
+       "latRo x notabilis                                 4\n",
+       "lativitta                                         4\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.loc[df[\"Cross_Type\"].notna(), \"subspecies\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "['malleti', 'plesseni', 'plesseni x malleti', 'lativitta']"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "already_present_subspecies = []\n",
+    "\n",
+    "for subspecies in list(df.loc[df[\"Cross_Type\"].notna(), \"subspecies\"].dropna().unique()):\n",
+    "    if subspecies in list(df.loc[~df[\"Cross_Type\"].notna(), \"subspecies\"].dropna().unique()):\n",
+    "        already_present_subspecies.append(subspecies)\n",
+    "\n",
+    "print(len(already_present_subspecies))\n",
+    "already_present_subspecies"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Perfect, this adds 17 more subspecies (`lativitta`, `plessani`, `maletti`, and `plesseni x malleti` were already represented). Note, this is based on _exact_ duplicates. `notabilis x lativitta` is also already in the dataset, but the order (where the cross types are concerned) general goes `maternal x paternal`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "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>Brood</th>\n",
+       "      <th>Death_Date</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",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1986</th>\n",
+       "      <td>19N1989</td>\n",
+       "      <td>21369</td>\n",
+       "      <td>19N1989_v.JPG</td>\n",
+       "      <td>ventral</td>\n",
+       "      <td>0.sheffield.ps.nn.ikiam.batch2.csv</td>\n",
+       "      <td>https://zenodo.org/record/4288311</td>\n",
+       "      <td>1,989</td>\n",
+       "      <td>Heliconius melpomene ssp. malleti</td>\n",
+       "      <td>Ikiam Mariposario</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>IKIAM.P44</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>reared</td>\n",
+       "      <td>jpg</td>\n",
+       "      <td>4288311</td>\n",
+       "      <td>Heliconius melpomene</td>\n",
+       "      <td>malleti</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45062</th>\n",
+       "      <td>CAM044423</td>\n",
+       "      <td>34391</td>\n",
+       "      <td>CAM044423_d.CR2</td>\n",
+       "      <td>dorsal</td>\n",
+       "      <td>batch2.Peru.image.names.Zenodo.csv</td>\n",
+       "      <td>https://zenodo.org/record/4287444</td>\n",
+       "      <td>44,423</td>\n",
+       "      <td>Taygetis cleopatra</td>\n",
+       "      <td>B6old6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>raw</td>\n",
+       "      <td>4287444</td>\n",
+       "      <td>Taygetis cleopatra</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48534</th>\n",
+       "      <td>E23</td>\n",
+       "      <td>37555</td>\n",
+       "      <td>E23_d.CR2</td>\n",
+       "      <td>dorsal</td>\n",
+       "      <td>Anniina.Matilla.Field.Caught.E.csv</td>\n",
+       "      <td>https://zenodo.org/record/2554218</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>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>raw</td>\n",
+       "      <td>2554218</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45206</th>\n",
+       "      <td>CAM044445</td>\n",
+       "      <td>37132</td>\n",
+       "      <td>CAM044445_d.JPG</td>\n",
+       "      <td>dorsal</td>\n",
+       "      <td>batch3.Peru.image.names.Zenodo.csv</td>\n",
+       "      <td>https://zenodo.org/record/4288250</td>\n",
+       "      <td>44,445</td>\n",
+       "      <td>Taygetis cleopatra</td>\n",
+       "      <td>B4old2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>jpg</td>\n",
+       "      <td>4288250</td>\n",
+       "      <td>Taygetis cleopatra</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12212</th>\n",
+       "      <td>CAM010238</td>\n",
+       "      <td>23307</td>\n",
+       "      <td>10238v.jpg</td>\n",
+       "      <td>ventral</td>\n",
+       "      <td>Heliconius_wing_old_photos_2001_2019_part1.csv</td>\n",
+       "      <td>https://zenodo.org/record/2552371</td>\n",
+       "      <td>10,238</td>\n",
+       "      <td>Heliconius sp.</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>B043</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>reared</td>\n",
+       "      <td>jpg</td>\n",
+       "      <td>2552371</td>\n",
+       "      <td>Heliconius sp.</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39059</th>\n",
+       "      <td>CAM043418</td>\n",
+       "      <td>30654</td>\n",
+       "      <td>CAM043418_v.JPG</td>\n",
+       "      <td>ventral</td>\n",
+       "      <td>batch1.Peru.image.names.Zenodo.csv</td>\n",
+       "      <td>https://zenodo.org/record/3569598</td>\n",
+       "      <td>43,418</td>\n",
+       "      <td>Archaeoprepona licomedes</td>\n",
+       "      <td>B6rec6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>jpg</td>\n",
+       "      <td>3569598</td>\n",
+       "      <td>Archaeoprepona licomedes</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38163</th>\n",
+       "      <td>CAM043170</td>\n",
+       "      <td>29755</td>\n",
+       "      <td>CAM043170_d.CR2</td>\n",
+       "      <td>dorsal</td>\n",
+       "      <td>batch1.Peru.image.names.Zenodo.csv</td>\n",
+       "      <td>https://zenodo.org/record/3569598</td>\n",
+       "      <td>43,170</td>\n",
+       "      <td>Adelpha mesentina</td>\n",
+       "      <td>F3rec2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>raw</td>\n",
+       "      <td>3569598</td>\n",
+       "      <td>Adelpha mesentina</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>7 rows × 25 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           CAMID      X       Image_name     View  \\\n",
+       "1986     19N1989  21369    19N1989_v.JPG  ventral   \n",
+       "45062  CAM044423  34391  CAM044423_d.CR2   dorsal   \n",
+       "48534        E23  37555        E23_d.CR2   dorsal   \n",
+       "45206  CAM044445  37132  CAM044445_d.JPG   dorsal   \n",
+       "12212  CAM010238  23307       10238v.jpg  ventral   \n",
+       "39059  CAM043418  30654  CAM043418_v.JPG  ventral   \n",
+       "38163  CAM043170  29755  CAM043170_d.CR2   dorsal   \n",
+       "\n",
+       "                                          zenodo_name  \\\n",
+       "1986               0.sheffield.ps.nn.ikiam.batch2.csv   \n",
+       "45062              batch2.Peru.image.names.Zenodo.csv   \n",
+       "48534              Anniina.Matilla.Field.Caught.E.csv   \n",
+       "45206              batch3.Peru.image.names.Zenodo.csv   \n",
+       "12212  Heliconius_wing_old_photos_2001_2019_part1.csv   \n",
+       "39059              batch1.Peru.image.names.Zenodo.csv   \n",
+       "38163              batch1.Peru.image.names.Zenodo.csv   \n",
+       "\n",
+       "                             zenodo_link Sequence  \\\n",
+       "1986   https://zenodo.org/record/4288311    1,989   \n",
+       "45062  https://zenodo.org/record/4287444   44,423   \n",
+       "48534  https://zenodo.org/record/2554218      NaN   \n",
+       "45206  https://zenodo.org/record/4288250   44,445   \n",
+       "12212  https://zenodo.org/record/2552371   10,238   \n",
+       "39059  https://zenodo.org/record/3569598   43,418   \n",
+       "38163  https://zenodo.org/record/3569598   43,170   \n",
+       "\n",
+       "                          Taxonomic_Name           Locality Sample_accession  \\\n",
+       "1986   Heliconius melpomene ssp. malleti  Ikiam Mariposario              NaN   \n",
+       "45062                 Taygetis cleopatra             B6old6              NaN   \n",
+       "48534                                NaN                NaN              NaN   \n",
+       "45206                 Taygetis cleopatra             B4old2              NaN   \n",
+       "12212                     Heliconius sp.                NaN              NaN   \n",
+       "39059           Archaeoprepona licomedes             B6rec6              NaN   \n",
+       "38163                  Adelpha mesentina             F3rec2              NaN   \n",
+       "\n",
+       "       ...      Brood Death_Date Cross_Type Stage     Sex Unit_Type file_type  \\\n",
+       "1986   ...  IKIAM.P44        NaN        NaN   NaN    Male    reared       jpg   \n",
+       "45062  ...        NaN        NaN        NaN   NaN     NaN       NaN       raw   \n",
+       "48534  ...        NaN        NaN        NaN   NaN     NaN       NaN       raw   \n",
+       "45206  ...        NaN        NaN        NaN   NaN     NaN       NaN       jpg   \n",
+       "12212  ...       B043        NaN        NaN   NaN  Female    reared       jpg   \n",
+       "39059  ...        NaN        NaN        NaN   NaN     NaN       NaN       jpg   \n",
+       "38163  ...        NaN        NaN        NaN   NaN     NaN       NaN       raw   \n",
+       "\n",
+       "      record_number                   species subspecies  \n",
+       "1986        4288311      Heliconius melpomene    malleti  \n",
+       "45062       4287444        Taygetis cleopatra       None  \n",
+       "48534       2554218                       NaN       None  \n",
+       "45206       4288250        Taygetis cleopatra       None  \n",
+       "12212       2552371            Heliconius sp.       None  \n",
+       "39059       3569598  Archaeoprepona licomedes       None  \n",
+       "38163       3569598         Adelpha mesentina       None  \n",
+       "\n",
+       "[7 rows x 25 columns]"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.sample(7)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Add Genus Column\n",
+    "\n",
+    "This willl allow us to easily remove all non Heliconius samples, and make some image stats easier to see."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_genus(species):\n",
+    "    if type(species) != float: #taxa name not null\n",
+    "        return species.split(sep = \" \")[0]\n",
+    "    return species"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "94"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"genus\"] = df[\"species\"].apply(get_genus)\n",
+    "df.genus.nunique()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Final stats for all data summarized here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "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            156\n",
+       "genus                  94\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.nunique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 49359 entries, 0 to 49358\n",
+      "Data columns (total 26 columns):\n",
+      " #   Column            Non-Null Count  Dtype \n",
+      "---  ------            --------------  ----- \n",
+      " 0   CAMID             49359 non-null  object\n",
+      " 1   X                 49359 non-null  int64 \n",
+      " 2   Image_name        49359 non-null  object\n",
+      " 3   View              48288 non-null  object\n",
+      " 4   zenodo_name       49359 non-null  object\n",
+      " 5   zenodo_link       49359 non-null  object\n",
+      " 6   Sequence          48424 non-null  object\n",
+      " 7   Taxonomic_Name    45473 non-null  object\n",
+      " 8   Locality          34015 non-null  object\n",
+      " 9   Sample_accession  5884 non-null   object\n",
+      " 10  Collected_by      5280 non-null   object\n",
+      " 11  Other_ID          14382 non-null  object\n",
+      " 12  Date              33718 non-null  object\n",
+      " 13  Dataset           40405 non-null  object\n",
+      " 14  Store             39485 non-null  object\n",
+      " 15  Brood             14942 non-null  object\n",
+      " 16  Death_Date        318 non-null    object\n",
+      " 17  Cross_Type        5133 non-null   object\n",
+      " 18  Stage             15 non-null     object\n",
+      " 19  Sex               36243 non-null  object\n",
+      " 20  Unit_Type         33890 non-null  object\n",
+      " 21  file_type         49359 non-null  object\n",
+      " 22  record_number     49359 non-null  object\n",
+      " 23  species           45473 non-null  object\n",
+      " 24  subspecies        25715 non-null  object\n",
+      " 25  genus             45473 non-null  object\n",
+      "dtypes: int64(1), object(25)\n",
+      "memory usage: 9.8+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Observe that not all images have a species label."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "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>Death_Date</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",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>48538</th>\n",
+       "      <td>E24</td>\n",
+       "      <td>37559</td>\n",
+       "      <td>E24_d.CR2</td>\n",
+       "      <td>dorsal</td>\n",
+       "      <td>Anniina.Matilla.Field.Caught.E.csv</td>\n",
+       "      <td>https://zenodo.org/record/2554218</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>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>raw</td>\n",
+       "      <td>2554218</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>37246</th>\n",
+       "      <td>CAM042045</td>\n",
+       "      <td>43973</td>\n",
+       "      <td>CAM042045_v.JPG</td>\n",
+       "      <td>ventral</td>\n",
+       "      <td>Collection_August2019.csv</td>\n",
+       "      <td>https://zenodo.org/record/5731587</td>\n",
+       "      <td>42,045</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>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>jpg</td>\n",
+       "      <td>5731587</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>37484</th>\n",
+       "      <td>CAM042166</td>\n",
+       "      <td>44211</td>\n",
+       "      <td>CAM042166_v.JPG</td>\n",
+       "      <td>ventral</td>\n",
+       "      <td>Collection_August2019.csv</td>\n",
+       "      <td>https://zenodo.org/record/5731587</td>\n",
+       "      <td>42,166</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>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>jpg</td>\n",
+       "      <td>5731587</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48780</th>\n",
+       "      <td>E83</td>\n",
+       "      <td>37777</td>\n",
+       "      <td>E83_v.CR2</td>\n",
+       "      <td>ventral</td>\n",
+       "      <td>Anniina.Matilla.Field.Caught.E.csv</td>\n",
+       "      <td>https://zenodo.org/record/2554218</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>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>raw</td>\n",
+       "      <td>2554218</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3118</th>\n",
+       "      <td>19N2627</td>\n",
+       "      <td>22498</td>\n",
+       "      <td>19N2627_v.CR2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.sheffield.ps.nn.ikiam.batch2.csv</td>\n",
+       "      <td>https://zenodo.org/record/4288311</td>\n",
+       "      <td>0</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>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>raw</td>\n",
+       "      <td>4288311</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46111</th>\n",
+       "      <td>CAM045060</td>\n",
+       "      <td>42806</td>\n",
+       "      <td>CAM045060_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>45,060</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>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>raw</td>\n",
+       "      <td>5526257</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39502</th>\n",
+       "      <td>CAM043576</td>\n",
+       "      <td>31097</td>\n",
+       "      <td>CAM043576_v.CR2</td>\n",
+       "      <td>ventral</td>\n",
+       "      <td>batch2.Peru.image.names.Zenodo.csv</td>\n",
+       "      <td>https://zenodo.org/record/4287444</td>\n",
+       "      <td>43,576</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>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>raw</td>\n",
+       "      <td>4287444</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>7 rows × 26 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           CAMID      X       Image_name     View  \\\n",
+       "48538        E24  37559        E24_d.CR2   dorsal   \n",
+       "37246  CAM042045  43973  CAM042045_v.JPG  ventral   \n",
+       "37484  CAM042166  44211  CAM042166_v.JPG  ventral   \n",
+       "48780        E83  37777        E83_v.CR2  ventral   \n",
+       "3118     19N2627  22498    19N2627_v.CR2      NaN   \n",
+       "46111  CAM045060  42806  CAM045060_v.CR2  ventral   \n",
+       "39502  CAM043576  31097  CAM043576_v.CR2  ventral   \n",
+       "\n",
+       "                              zenodo_name                        zenodo_link  \\\n",
+       "48538  Anniina.Matilla.Field.Caught.E.csv  https://zenodo.org/record/2554218   \n",
+       "37246           Collection_August2019.csv  https://zenodo.org/record/5731587   \n",
+       "37484           Collection_August2019.csv  https://zenodo.org/record/5731587   \n",
+       "48780  Anniina.Matilla.Field.Caught.E.csv  https://zenodo.org/record/2554218   \n",
+       "3118   0.sheffield.ps.nn.ikiam.batch2.csv  https://zenodo.org/record/4288311   \n",
+       "46111   image.names.cook.island.erato.csv  https://zenodo.org/record/5526257   \n",
+       "39502  batch2.Peru.image.names.Zenodo.csv  https://zenodo.org/record/4287444   \n",
+       "\n",
+       "      Sequence Taxonomic_Name Locality Sample_accession  ... Death_Date  \\\n",
+       "48538      NaN            NaN      NaN              NaN  ...        NaN   \n",
+       "37246   42,045            NaN      NaN              NaN  ...        NaN   \n",
+       "37484   42,166            NaN      NaN              NaN  ...        NaN   \n",
+       "48780      NaN            NaN      NaN              NaN  ...        NaN   \n",
+       "3118         0            NaN      NaN              NaN  ...        NaN   \n",
+       "46111   45,060            NaN      NaN              NaN  ...        NaN   \n",
+       "39502   43,576            NaN      NaN              NaN  ...        NaN   \n",
+       "\n",
+       "      Cross_Type Stage  Sex Unit_Type file_type record_number species  \\\n",
+       "48538        NaN   NaN  NaN       NaN       raw       2554218     NaN   \n",
+       "37246        NaN   NaN  NaN       NaN       jpg       5731587     NaN   \n",
+       "37484        NaN   NaN  NaN       NaN       jpg       5731587     NaN   \n",
+       "48780        NaN   NaN  NaN       NaN       raw       2554218     NaN   \n",
+       "3118         NaN   NaN  NaN       NaN       raw       4288311     NaN   \n",
+       "46111        NaN   NaN  NaN       NaN       raw       5526257     NaN   \n",
+       "39502        NaN   NaN  NaN       NaN       raw       4287444     NaN   \n",
+       "\n",
+       "      subspecies genus  \n",
+       "48538       None   NaN  \n",
+       "37246       None   NaN  \n",
+       "37484       None   NaN  \n",
+       "48780       None   NaN  \n",
+       "3118        None   NaN  \n",
+       "46111       None   NaN  \n",
+       "39502       None   NaN  \n",
+       "\n",
+       "[7 rows x 26 columns]"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.loc[df.species.isna()].sample(7)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Update Master File with Genus through Subspecies Columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.to_csv(\"../Jiggins_Zenodo_Img_Master.csv\", index = False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Make Heliconius Subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 34929 entries, 6 to 49358\n",
+      "Data columns (total 26 columns):\n",
+      " #   Column            Non-Null Count  Dtype \n",
+      "---  ------            --------------  ----- \n",
+      " 0   CAMID             34929 non-null  object\n",
+      " 1   X                 34929 non-null  int64 \n",
+      " 2   Image_name        34929 non-null  object\n",
+      " 3   View              34150 non-null  object\n",
+      " 4   zenodo_name       34929 non-null  object\n",
+      " 5   zenodo_link       34929 non-null  object\n",
+      " 6   Sequence          34929 non-null  object\n",
+      " 7   Taxonomic_Name    34929 non-null  object\n",
+      " 8   Locality          23417 non-null  object\n",
+      " 9   Sample_accession  5860 non-null   object\n",
+      " 10  Collected_by      5280 non-null   object\n",
+      " 11  Other_ID          6404 non-null   object\n",
+      " 12  Date              23162 non-null  object\n",
+      " 13  Dataset           32846 non-null  object\n",
+      " 14  Store             29446 non-null  object\n",
+      " 15  Brood             14921 non-null  object\n",
+      " 16  Death_Date        316 non-null    object\n",
+      " 17  Cross_Type        5133 non-null   object\n",
+      " 18  Stage             6 non-null      object\n",
+      " 19  Sex               33880 non-null  object\n",
+      " 20  Unit_Type         31975 non-null  object\n",
+      " 21  file_type         34929 non-null  object\n",
+      " 22  record_number     34929 non-null  object\n",
+      " 23  species           34929 non-null  object\n",
+      " 24  subspecies        24953 non-null  object\n",
+      " 25  genus             34929 non-null  object\n",
+      "dtypes: int64(1), object(25)\n",
+      "memory usage: 7.2+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "heliconius_subset = df.loc[df.genus.str.lower() == \"heliconius\"]\n",
+    "\n",
+    "heliconius_subset.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "CAMID                9546\n",
+       "X                   34929\n",
+       "Image_name          26946\n",
+       "View                    3\n",
+       "zenodo_name            31\n",
+       "zenodo_link            28\n",
+       "Sequence             8701\n",
+       "Taxonomic_Name        129\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          28\n",
+       "species                37\n",
+       "subspecies            110\n",
+       "genus                   1\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "heliconius_subset.nunique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "View\n",
+       "dorsal                17218\n",
+       "ventral               16914\n",
+       "dorsal and ventral       18\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "heliconius_subset.View.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that this subset is distributed across 28 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",
+    "We'll drop the `genus` column, since they're all `Heliconius`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "heliconius_subset[list(heliconius_subset.columns)[:-1]].to_csv(\"../Jiggins_Heliconius_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
+}