{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import seaborn as sns\n", "import plotly.express as px\n", "\n", "sns.set_style(\"whitegrid\")\n", "sns.set(rc = {'figure.figsize': (10,10)})" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_222484/3694103411.py:1: DtypeWarning: Columns (4,5,6) have mixed types. Specify dtype option on import or set low_memory=False.\n", " df = pd.read_csv(\"../data/v1-dev-names.csv\")\n" ] } ], "source": [ "df = pd.read_csv(\"../data/v1-dev-names.csv\")" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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treeoflife_ideol_content_ideol_page_idbioscan_partbioscan_filenameinat21_filenameinat21_cls_nameinat21_cls_numkingdomphylumclassorderfamilygenusspeciescommon
00824741f-cc1c-4881-b292-15fd3f7964cd29538374.065414274.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNManfredaNaNtuberose
15ca08f6b-9396-4cb9-9283-8dee158aac1827793900.0888015.0NaNNaNNaNNaNNaNMetazoaArthropodaPancrustaceaLepidopteraOenosandridaeDiscophlebialipaugesDiscophlebia lipauges
2f8c0f271-d8e5-4299-92d3-920508f74bf029121641.05618956.0NaNNaNNaNNaNNaNArchaeplastidaTracheophytaNaNSapindalesRutaceaeMelicopedenhamiiMelicope denhamii
31f53e9d1-527f-42fd-b813-9f62fa2c237227596176.0607817.0NaNNaNNaNNaNNaNMetazoaArthropodaPancrustaceaTrichopteraLimnephilidaeLimnephiluslithusLimnephilus lithus
4a05bc2a8-5453-4683-903e-ed44f0fe724520300703.0267922.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNAnatolian Black-eyed Blue
\n", "
" ], "text/plain": [ " treeoflife_id eol_content_id eol_page_id \\\n", "0 0824741f-cc1c-4881-b292-15fd3f7964cd 29538374.0 65414274.0 \n", "1 5ca08f6b-9396-4cb9-9283-8dee158aac18 27793900.0 888015.0 \n", "2 f8c0f271-d8e5-4299-92d3-920508f74bf0 29121641.0 5618956.0 \n", "3 1f53e9d1-527f-42fd-b813-9f62fa2c2372 27596176.0 607817.0 \n", "4 a05bc2a8-5453-4683-903e-ed44f0fe7245 20300703.0 267922.0 \n", "\n", " bioscan_part bioscan_filename inat21_filename inat21_cls_name \\\n", "0 NaN NaN NaN NaN \n", "1 NaN NaN NaN NaN \n", "2 NaN NaN NaN NaN \n", "3 NaN NaN NaN NaN \n", "4 NaN NaN NaN NaN \n", "\n", " inat21_cls_num kingdom phylum class order \\\n", "0 NaN NaN NaN NaN NaN \n", "1 NaN Metazoa Arthropoda Pancrustacea Lepidoptera \n", "2 NaN Archaeplastida Tracheophyta NaN Sapindales \n", "3 NaN Metazoa Arthropoda Pancrustacea Trichoptera \n", "4 NaN NaN NaN NaN NaN \n", "\n", " family genus species common \n", "0 NaN Manfreda NaN tuberose \n", "1 Oenosandridae Discophlebia lipauges Discophlebia lipauges \n", "2 Rutaceae Melicope denhamii Melicope denhamii \n", "3 Limnephilidae Limnephilus lithus Limnephilus lithus \n", "4 NaN NaN NaN Anatolian Black-eyed Blue " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 10436521 entries, 0 to 10436520\n", "Data columns (total 16 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 treeoflife_id 10436521 non-null object \n", " 1 eol_content_id 6621365 non-null float64\n", " 2 eol_page_id 6621365 non-null float64\n", " 3 bioscan_part 1128313 non-null float64\n", " 4 bioscan_filename 1128313 non-null object \n", " 5 inat21_filename 2686843 non-null object \n", " 6 inat21_cls_name 2686843 non-null object \n", " 7 inat21_cls_num 2686843 non-null float64\n", " 8 kingdom 7734559 non-null object \n", " 9 phylum 7732689 non-null object \n", " 10 class 6657484 non-null object \n", " 11 order 7690349 non-null object \n", " 12 family 7706759 non-null object \n", " 13 genus 8060829 non-null object \n", " 14 species 7179863 non-null object \n", " 15 common 10436521 non-null object \n", "dtypes: float64(4), object(12)\n", "memory usage: 1.2+ GB\n" ] } ], "source": [ "df.info(show_counts = True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Do we truly have common name labeled for all images?\n", "\n", "Sometimes it is the scientific name." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "treeoflife_id 10436521\n", "eol_content_id 6621365\n", "eol_page_id 570515\n", "bioscan_part 113\n", "bioscan_filename 1128313\n", "inat21_filename 2686843\n", "inat21_cls_name 10000\n", "inat21_cls_num 10000\n", "kingdom 5\n", "phylum 49\n", "class 136\n", "order 766\n", "family 5665\n", "genus 89914\n", "species 189846\n", "common 527316\n", "dtype: int64" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.nunique()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['kingdom', 'phylum', 'class', 'order', 'family', 'genus', 'species']" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "taxa = list(df.columns[8:15])\n", "taxa" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Check the number of images with all 7 taxonomic labels." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Index: 6657484 entries, 1 to 10436518\n", "Data columns (total 7 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 kingdom 6657484 non-null object\n", " 1 phylum 6657484 non-null object\n", " 2 class 6657484 non-null object\n", " 3 order 6615972 non-null object\n", " 4 family 6630424 non-null object\n", " 5 genus 5691848 non-null object\n", " 6 species 5267629 non-null object\n", "dtypes: object(7)\n", "memory usage: 406.3+ MB\n" ] } ], "source": [ "# Class has least non-null entries, so we'll start by filtering it out\n", "df_all_taxa = df.loc[df['class'].notna()]\n", "df_all_taxa[taxa].info(show_counts = True)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Index: 5267629 entries, 1 to 10436518\n", "Data columns (total 7 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 kingdom 5267629 non-null object\n", " 1 phylum 5267629 non-null object\n", " 2 class 5267629 non-null object\n", " 3 order 5233623 non-null object\n", " 4 family 5261785 non-null object\n", " 5 genus 5267456 non-null object\n", " 6 species 5267629 non-null object\n", "dtypes: object(7)\n", "memory usage: 321.5+ MB\n" ] } ], "source": [ "# Now species has least non-null values\n", "df_all_taxa = df_all_taxa.loc[df_all_taxa['species'].notna()]\n", "df_all_taxa[taxa].info(show_counts = True)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Index: 5233623 entries, 1 to 10436518\n", "Data columns (total 7 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 kingdom 5233623 non-null object\n", " 1 phylum 5233623 non-null object\n", " 2 class 5233623 non-null object\n", " 3 order 5233623 non-null object\n", " 4 family 5228644 non-null object\n", " 5 genus 5233450 non-null object\n", " 6 species 5233623 non-null object\n", "dtypes: object(7)\n", "memory usage: 319.4+ MB\n" ] } ], "source": [ "# Now order has least non-null values \n", "df_all_taxa = df_all_taxa.loc[df_all_taxa['order'].notna()]\n", "df_all_taxa[taxa].info(show_counts = True)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Index: 5228644 entries, 1 to 10436518\n", "Data columns (total 7 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 kingdom 5228644 non-null object\n", " 1 phylum 5228644 non-null object\n", " 2 class 5228644 non-null object\n", " 3 order 5228644 non-null object\n", " 4 family 5228644 non-null object\n", " 5 genus 5228471 non-null object\n", " 6 species 5228644 non-null object\n", "dtypes: object(7)\n", "memory usage: 319.1+ MB\n" ] } ], "source": [ "# Now family has least non-null values\n", "df_all_taxa = df_all_taxa.loc[df_all_taxa['family'].notna()]\n", "df_all_taxa[taxa].info(show_counts = True)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Index: 5228471 entries, 1 to 10436518\n", "Data columns (total 7 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 kingdom 5228471 non-null object\n", " 1 phylum 5228471 non-null object\n", " 2 class 5228471 non-null object\n", " 3 order 5228471 non-null object\n", " 4 family 5228471 non-null object\n", " 5 genus 5228471 non-null object\n", " 6 species 5228471 non-null object\n", "dtypes: object(7)\n", "memory usage: 319.1+ MB\n" ] } ], "source": [ "# Finally, genus is the only one with null values\n", "df_all_taxa = df_all_taxa.loc[df_all_taxa['genus'].notna()]\n", "df_all_taxa[taxa].info(show_counts = True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We have 5,228,471 images with full taxonomic labels." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Distributions\n", "\n", "Work through larger number of unique taxa here, on server. Also, make `df_taxa` with just taxa columns so it's smaller to process faster.\n", "\n", "All in format `num_lowerTaxon_higherTaxon`." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "df_taxa = df[taxa]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Species per higher order taxa will take longest." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's start with the taxa that have more unique values under kingdom to see where the server's at on speed too." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "num_families_kingdom = {}\n", "num_genera_kingdom = {}\n", "num_species_kingdom = {}\n", "for kingdom in df_taxa.kingdom.unique():\n", " temp = df_taxa.loc[df_taxa.kingdom == kingdom]\n", " num_families_kingdom[kingdom] = temp.family.nunique()\n", " num_genera_kingdom[kingdom] = temp['genus'].nunique()\n", " num_species_kingdom[kingdom] = temp['species'].nunique()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{nan: 0,\n", " 'Metazoa': 4272,\n", " 'Archaeplastida': 719,\n", " 'Fungi': 468,\n", " 'Animalia': 1077,\n", " 'Plantae': 286}" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "num_families_kingdom" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Seems reasonable, let's get the larger of the higher class ones going, so check genus and species counts within families." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "num_genera_family = {}\n", "num_species_family = {}\n", "for family in df_taxa.family.unique():\n", " temp = df_taxa.loc[df_taxa.family == family]\n", " num_genera_family[family] = temp['genus'].nunique()\n", " num_species_family[family] = temp['species'].nunique()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Get counts for everything below order." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "num_families_order = {}\n", "num_genera_order = {}\n", "num_species_order = {}\n", "for order in df_taxa.order.unique():\n", " temp = df_taxa.loc[df_taxa.order == order]\n", " num_families_order[order] = temp['family'].nunique()\n", " num_genera_order[order] = temp['genus'].nunique()\n", " num_species_order[order] = temp['species'].nunique()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{nan: 0,\n", " 'Lepidoptera': 128,\n", " 'Sapindales': 9,\n", " 'Trichoptera': 38,\n", " 'Hemiptera': 135,\n", " 'Coleoptera': 150,\n", " 'Squamata': 54,\n", " 'Tetraodontiformes': 10,\n", " 'Mantodea': 31,\n", " 'Heterobranchia': 43,\n", " 'Lamiales': 24,\n", " 'Fabales': 4,\n", " 'Asterales': 10,\n", " 'Carangiformes': 5,\n", " 'Piperales': 3,\n", " 'Caryophyllales': 39,\n", " 'Boletales': 18,\n", " 'Characiformes': 23,\n", " 'Poales': 14,\n", " 'Arecales': 2,\n", " 'Malpighiales': 32,\n", " 'Anura': 61,\n", " 'Agaricales': 33,\n", " 'Magnoliales': 5,\n", " 'Hymenoptera': 101,\n", " 'Littorinimorpha': 64,\n", " 'Sessilia': 11,\n", " 'Labriformes': 1,\n", " 'Ricinulei': 1,\n", " 'Helotiales': 18,\n", " 'Asparagales': 13,\n", " 'Proteales': 4,\n", " 'Araneae': 111,\n", " 'Polypodiales': 25,\n", " 'Saurischia': 265,\n", " 'Ulotrichales': 1,\n", " 'Solanales': 5,\n", " 'Diptera': 141,\n", " 'Caudata': 9,\n", " 'Saxifragales': 14,\n", " 'Therapsida': 178,\n", " 'Polyporales': 13,\n", " 'Myrtales': 7,\n", " 'Rosales': 8,\n", " 'Decapoda': 78,\n", " 'Phasmida': 13,\n", " 'Imparidentia': 17,\n", " 'Gobiiformes': 9,\n", " 'Neogastropoda': 46,\n", " 'Brassicales': 17,\n", " 'Rhabdocoela': 32,\n", " 'Cypriniformes': 275,\n", " 'Orectolobiformes': 7,\n", " 'Spariformes': 3,\n", " 'Gentianales': 5,\n", " 'Alismatales': 13,\n", " 'Malvales': 11,\n", " 'Pinales': 8,\n", " 'Bryales': 13,\n", " 'Nudipleura': 8,\n", " 'Clupeiformes': 5,\n", " 'Ericales': 21,\n", " 'Chitonida': 12,\n", " 'Apiales': 7,\n", " 'Sebacinales': 1,\n", " 'Lecanorales': 16,\n", " 'Pertusariales': 4,\n", " 'Commelinales': 5,\n", " 'Orthoptera': 36,\n", " 'Fagales': 7,\n", " 'Dipsacales': 3,\n", " 'Comatulida': 21,\n", " 'Liliales': 11,\n", " 'Monostilifera': 10,\n", " 'Perciformes': 88,\n", " 'Erysiphales': 1,\n", " 'Ephippiformes': 2,\n", " 'Carybdeida': 5,\n", " 'Eurotiales': 3,\n", " 'Odonata': 37,\n", " 'Teloschistales': 3,\n", " 'Russulales': 11,\n", " 'Heteroscleromorpha': 17,\n", " 'Polyzoniida': 3,\n", " 'Stolidobranchia': 3,\n", " 'Geraniales': 2,\n", " 'Cyprinodontiformes': 10,\n", " 'Chromadorea': 12,\n", " 'Lophiiformes': 15,\n", " 'Aquifoliales': 5,\n", " 'Valvatacea': 3,\n", " 'Gloeophyllales': 1,\n", " 'Pezizales': 14,\n", " 'Rhodymeniales': 6,\n", " 'Neuroptera': 14,\n", " 'Pteriomorphia': 12,\n", " 'Trochida': 13,\n", " 'Lutjanidae': 21,\n", " 'Ranunculales': 7,\n", " 'Salmoniformes': 1,\n", " 'Megaloptera': 2,\n", " 'Desmidiales': 4,\n", " 'Octocorallia': 6,\n", " 'Aulopiformes': 19,\n", " 'Semaeostomeae': 5,\n", " 'Ophiurida': 3,\n", " 'Ixodida': 2,\n", " 'Carcharhiniformes': 8,\n", " 'Blattodea': 16,\n", " 'Pandanales': 5,\n", " 'Xylariales': 16,\n", " 'Synallactida': 3,\n", " 'Opiliones': 52,\n", " 'Blenniiformes': 6,\n", " 'Corticiales': 1,\n", " 'Forcipulatacea': 2,\n", " 'Kurtiformes': 2,\n", " 'Syngnathiformes': 10,\n", " 'Ephemeroptera': 25,\n", " 'Bdelloidea': 4,\n", " 'Orthotrichales': 2,\n", " 'Hydroidolina': 9,\n", " 'Santalales': 8,\n", " 'Boraginales': 1,\n", " 'Lycopodiales': 1,\n", " 'Scleractinia': 26,\n", " 'Siluriformes': 36,\n", " 'Lepisosteiformes': 1,\n", " 'Atheriniformes': 38,\n", " 'Scombriformes': 16,\n", " 'Gomphales': 3,\n", " 'Testudines': 12,\n", " 'Acanthuriformes': 3,\n", " 'Carinacea': 4,\n", " 'Gobiesociformes': 1,\n", " 'Zingiberales': 8,\n", " 'Pleuronectiformes': 13,\n", " 'Microbotryales': 1,\n", " 'Dermaptera': 8,\n", " 'Capnodiales': 6,\n", " 'Trentepohliales': 1,\n", " 'Dicranales': 8,\n", " 'Ulvales': 3,\n", " 'Cycloneritida': 6,\n", " 'Dilleniales': 1,\n", " 'Hymenophyllales': 1,\n", " 'Zygentoma': 2,\n", " 'Nymphaeales': 3,\n", " 'Phlebobranchia': 6,\n", " 'Anguilliformes': 17,\n", " 'Entomophaga': 0,\n", " 'Pucciniales': 14,\n", " 'Ceramiales': 7,\n", " 'Isopoda': 49,\n", " 'Holothuriida': 2,\n", " 'Lichinales': 3,\n", " 'Lepadiformes': 4,\n", " 'Hypnales': 19,\n", " 'Verongimorpha': 3,\n", " 'Scorpiones': 15,\n", " 'Actiniaria': 9,\n", " 'Cichliformes': 2,\n", " 'Marchantiidae': 3,\n", " 'Laurales': 7,\n", " 'Carditida': 4,\n", " 'Cantharellales': 7,\n", " 'Funariales': 6,\n", " 'Tetraphidales': 2,\n", " 'Raphidioptera': 2,\n", " 'Palaeoheterodonta': 3,\n", " 'Ploima': 20,\n", " 'Plecoptera': 12,\n", " 'Amphipoda': 51,\n", " 'Marattiales': 1,\n", " 'Cucurbitales': 8,\n", " 'Haemulidae': 16,\n", " 'Rhizostomeae': 3,\n", " 'Embioptera': 8,\n", " 'Harpacticoida': 32,\n", " 'Rhynchobdellida': 2,\n", " 'Chaetodontiformes': 2,\n", " 'Oxalidales': 7,\n", " 'Collothecacea': 2,\n", " 'Salviniales': 2,\n", " 'Tubeufiales': 1,\n", " 'Seguenziida': 6,\n", " 'Psilotales': 1,\n", " 'Stauromedusae': 4,\n", " 'Polydesmida': 16,\n", " 'Halymeniales': 1,\n", " 'Mecoptera': 7,\n", " 'Laboulbeniales': 1,\n", " 'Anabantiformes': 7,\n", " 'Escalloniales': 1,\n", " 'Rhizopodaceae': 1,\n", " 'Hypocreales': 9,\n", " 'Ophioglossales': 1,\n", " 'Siphonaptera': 8,\n", " 'Metzgeriidae': 2,\n", " 'Beryciformes': 16,\n", " 'Centrarchiformes': 24,\n", " 'Selaginellales': 1,\n", " 'Zygophyllales': 2,\n", " 'Notostraca': 1,\n", " 'Acoela': 16,\n", " 'Squaliformes': 7,\n", " 'Amphilepidida': 7,\n", " 'Exobasidiales': 4,\n", " 'Gigartinales': 26,\n", " 'Celastrales': 2,\n", " 'Acipenseriformes': 2,\n", " 'Dasycladales': 2,\n", " 'Gymnotiformes': 5,\n", " 'Rajiformes': 3,\n", " 'Diaporthales': 7,\n", " 'Myliobatiformes': 9,\n", " 'Geastrales': 1,\n", " 'Phallales': 2,\n", " 'Psocodea': 42,\n", " 'Ephedrales': 1,\n", " 'Gymnophiona': 9,\n", " 'Spinulosacea': 1,\n", " 'Jungermanniidae': 1,\n", " 'Cheilostomatida': 60,\n", " 'Lepetellida': 9,\n", " 'Dioscoreales': 3,\n", " 'Bruniales': 2,\n", " 'Protobranchia': 5,\n", " 'Rhytismatales': 4,\n", " 'Candelariales': 1,\n", " 'Cornales': 7,\n", " 'Platydesmida': 1,\n", " 'Peltigerales': 9,\n", " 'Squatiniformes': 1,\n", " 'Hymenochaetales': 4,\n", " 'Cyatheales': 7,\n", " 'Dictyoceratida': 4,\n", " 'Irregularia': 4,\n", " 'Seligeriaceae': 3,\n", " 'Phyllodocida': 19,\n", " 'Lamniformes': 6,\n", " 'Auriculariales': 2,\n", " 'Atelostomata': 7,\n", " 'Calcinea': 1,\n", " 'Calcaronea': 2,\n", " 'Glomerellales': 2,\n", " 'Leucodontaceae': 6,\n", " 'Platyctenida': 2,\n", " 'Crocodylia': 2,\n", " 'Priacanthidae': 4,\n", " 'Apodida': 3,\n", " 'Architaenioglossa': 9,\n", " 'Trombidiformes': 16,\n", " 'Sordariales': 4,\n", " 'Zoantharia': 7,\n", " 'Icacinales': 1,\n", " 'Cyclopoida': 35,\n", " 'Gadiformes': 16,\n", " 'Trachylinae': 3,\n", " 'Tremellales': 6,\n", " 'Enoplea': 6,\n", " 'Cycadales': 2,\n", " 'Myxiniformes': 1,\n", " 'Stomatopoda': 3,\n", " 'Charales': 1,\n", " 'Furia': 0,\n", " 'Beloniformes': 5,\n", " 'Symphypleona': 6,\n", " 'Uranoscopiformes': 4,\n", " 'Octopoda': 13,\n", " 'Amblypygi': 3,\n", " 'Mycosphaerellaceae': 20,\n", " 'Verrucariales': 1,\n", " 'Sphaeropleales': 7,\n", " 'Cidaroida': 4,\n", " 'Arthoniales': 4,\n", " 'Sphagnales': 1,\n", " 'Entomobryomorpha': 4,\n", " 'Petrosaviales': 1,\n", " 'Osmundales': 1,\n", " 'Thelephorales': 2,\n", " 'Pleosporales': 24,\n", " 'Bryopsidales': 6,\n", " 'Plocamiales': 1,\n", " 'Synbranchiformes': 4,\n", " 'Mesostigmata': 6,\n", " 'Julida': 6,\n", " 'Chaetophorales': 2,\n", " 'Pelliidae': 3,\n", " 'Schizomida': 1,\n", " 'Umbilicariales': 1,\n", " 'Petromyzontiformes': 3,\n", " 'Mycocaliciales': 2,\n", " 'Dendrochirotida': 6,\n", " 'Vitales': 1,\n", " 'Monogenea': 5,\n", " 'Gracilariales': 1,\n", " 'Isoetales': 1,\n", " 'Neolectales': 1,\n", " 'Polypteriformes': 1,\n", " 'Mugiliformes': 1,\n", " 'Taphrinales': 1,\n", " 'Equisetales': 1,\n", " 'Patellariales': 1,\n", " 'Tricladida': 9,\n", " 'Botryosphaeriales': 3,\n", " 'Pleurostigmophora': 4,\n", " 'Ptilidiales': 2,\n", " 'Austrobaileyales': 3,\n", " 'Euryalida': 3,\n", " 'Polycladida': 30,\n", " 'Brachypoda': 1,\n", " 'Grimmiales': 2,\n", " 'Calanoida': 29,\n", " 'Dolichomicrostomida': 2,\n", " 'Ophiostomatales': 1,\n", " 'Dacrymycetales': 1,\n", " 'Holocentriformes': 1,\n", " 'Xiphosurida': 1,\n", " 'Gunnerales': 2,\n", " 'Amphinomida': 2,\n", " 'Pottiales': 3,\n", " 'Phyllachorales': 2,\n", " 'Gerreidae': 8,\n", " 'Pantopoda': 10,\n", " 'Argentiniformes': 4,\n", " 'Prolecithophora': 5,\n", " 'Pempheriformes': 16,\n", " 'Polytrichales': 1,\n", " 'Geoglossales': 1,\n", " 'Mysida': 2,\n", " 'Orbiliales': 1,\n", " 'Trechisporales': 1,\n", " 'Compsopogonales': 1,\n", " 'Flosculariaceae': 5,\n", " 'Chimaeriformes': 3,\n", " 'Proseriata': 11,\n", " 'Bangiophyceae': 3,\n", " 'Onygenales': 4,\n", " 'Amiiformes': 1,\n", " 'Ustilaginales': 5,\n", " 'Aplousobranchia': 10,\n", " 'Porellales': 4,\n", " 'Erythropeltidales': 2,\n", " 'Hapalidiales': 1,\n", " 'Tanaidacea': 4,\n", " 'Salpida': 1,\n", " 'Cestida': 1,\n", " 'Climaciaceae': 1,\n", " 'Alepocephaliformes': 2,\n", " 'Gelidiales': 3,\n", " 'Acarosporales': 1,\n", " 'Hildenbrandiales': 1,\n", " 'Ginkgoales': 1,\n", " 'Ophiacanthida': 7,\n", " 'Crassiclitellata': 10,\n", " 'Phycomycetaceae': 2,\n", " 'Ophioscolecida': 1,\n", " 'Grylloblattodea': 1,\n", " 'Pristiophoriformes': 1,\n", " 'Crossosomatales': 4,\n", " 'Heterodontiformes': 1,\n", " 'Gigantorhynchida': 1,\n", " 'Chlorellales': 2,\n", " 'Baeomycetales': 3,\n", " 'Phymatocerotales': 1,\n", " 'Hedwigiaceae': 4,\n", " 'Buxales': 1,\n", " 'Trematoda': 9,\n", " 'Sphaerotheriida': 4,\n", " 'Ophidiiformes': 13,\n", " 'Schizaeales': 3,\n", " 'Antipatharia': 6,\n", " 'Stomiiformes': 4,\n", " 'Acroechinoidea': 3,\n", " 'Molpadida': 3,\n", " 'Sarcoptiformes': 43,\n", " 'Gnetales': 1,\n", " 'Pterobryaceae': 5,\n", " 'Caproidae': 2,\n", " 'Lampriformes': 6,\n", " 'Scalpelliformes': 4,\n", " 'Notostigmophora': 1,\n", " 'Corallinales': 1,\n", " 'Fissidentaceae': 1,\n", " 'Halocyprida': 3,\n", " 'Meteoriaceae': 5,\n", " 'Osmeriformes': 4,\n", " 'Galaxiiformes': 1,\n", " 'Lingulata': 2,\n", " 'Cestoda': 9,\n", " 'Anostraca': 7,\n", " 'Gleicheniales': 3,\n", " 'Diplostraca': 14,\n", " 'Cladophorales': 5,\n", " 'Corallimorpharia': 3,\n", " 'Trochodendrales': 1,\n", " 'Poduromorpha': 5,\n", " 'Ctenostomatida': 5,\n", " 'Lobata': 4,\n", " 'Acorales': 1,\n", " 'Chordeumatida': 14,\n", " 'Syzygites': 0,\n", " 'Asterinales': 2,\n", " 'Albuliformes': 1,\n", " 'Hysteriales': 1,\n", " 'Lophogastrida': 1,\n", " 'Arhynchobdellida': 11,\n", " 'Rhinopristiformes': 5,\n", " 'Venturiales': 1,\n", " 'Pleurotomariida': 1,\n", " 'Torpediniformes': 5,\n", " 'Amphisphaeriales': 5,\n", " 'Canellales': 2,\n", " 'Andreaeales': 1,\n", " 'Palmariales': 4,\n", " 'Chlamydomonadales': 8,\n", " 'Entomophthora': 0,\n", " 'Cyttariales': 1,\n", " 'Elopiformes': 2,\n", " 'Neckeraceae': 7,\n", " 'Paracryphiales': 1,\n", " 'Pseudoscorpiones': 14,\n", " 'Urocystidales': 4,\n", " 'Nanaloricida': 1,\n", " 'Spirobolida': 10,\n", " 'Coronatae': 6,\n", " 'Myodocopida': 3,\n", " 'Percopsiformes': 3,\n", " 'Uropygi': 1,\n", " 'Solifugae': 9,\n", " 'Thysanoptera': 5,\n", " 'Doassansiales': 2,\n", " 'Garryales': 2,\n", " 'Blasiidae': 1,\n", " 'Eunicida': 5,\n", " 'Hiodontiformes': 1,\n", " 'Hysterangiales': 5,\n", " 'Hookeriales': 2,\n", " 'Beroida': 1,\n", " 'Lyssacinosida': 3,\n", " 'Notacanthiformes': 2,\n", " 'Huerteales': 3,\n", " 'Chaetosphaeriales': 1,\n", " 'Cryphaeaceae': 1,\n", " 'Lepidopleurida': 1,\n", " 'Microascales': 3,\n", " 'Fecampiida': 5,\n", " 'Hyocrinida': 1,\n", " 'Osteoglossiformes': 5,\n", " 'Chaetothyriales': 2,\n", " 'Ostropales': 7,\n", " 'Helicobasidiales': 1,\n", " 'Cepolidae': 3,\n", " 'Entylomatales': 1,\n", " 'Siphonostomatoida': 14,\n", " 'Coniochaetales': 1,\n", " 'Cystofilobasidiales': 1,\n", " 'Istiophoriformes': 2,\n", " 'Nautilida': 1,\n", " 'Kickxellaceae': 3,\n", " 'Neelipleona': 1,\n", " 'Batrachoidiformes': 1,\n", " 'Bonnemaisoniales': 2,\n", " 'Dendroceratida': 2,\n", " 'Ceriantharia': 2,\n", " 'Cyclorhagida': 2,\n", " 'Esociformes': 2,\n", " 'Atractiellales': 1,\n", " 'Myctophiformes': 2,\n", " 'Pyrenulales': 2,\n", " 'Atheliales': 1,\n", " 'Mytilinidiales': 2,\n", " 'Elasipodida': 4,\n", " 'Notothyladales': 1,\n", " 'Polymorphida': 1,\n", " 'Nemastomatales': 2,\n", " 'Palmophyllales': 1,\n", " 'Velatida': 3,\n", " 'Mucoraceae': 7,\n", " 'Coronophorales': 3,\n", " 'Nemaliales': 3,\n", " 'Spirostreptida': 6,\n", " 'Euphausiacea': 2,\n", " 'Phomatospora': 0,\n", " 'Rhynchonellata': 4,\n", " 'Zeiformes': 14,\n", " 'Copelata': 2,\n", " 'Trachichthyiformes': 5,\n", " 'Strigulales': 1,\n", " 'Podocopida': 5,\n", " 'Boliniales': 1,\n", " 'Zygnematales': 2,\n", " 'Archaeognatha': 2,\n", " 'Saccharomycetales': 5,\n", " 'Phragmophora': 1,\n", " 'Ornithischia': 9,\n", " 'Tremellodendropsidaceae': 1,\n", " 'Hexanchiformes': 2,\n", " 'Massospora': 0,\n", " 'Cyclostomatida': 6,\n", " 'Triblidiales': 1,\n", " 'Callipodida': 4,\n", " 'Strepsiptera': 4,\n", " 'Chytridiales': 1,\n", " 'Gonorynchiformes': 3,\n", " 'Chloranthales': 1,\n", " 'Ceratodontiformes': 3,\n", " 'Tetrasporales': 2,\n", " 'Pilobolaceae': 1,\n", " 'Anthocerotales': 1,\n", " 'Porocephalida': 2,\n", " 'Macrodasyida': 7,\n", " 'Lobotiformes': 3,\n", " 'Gadilida': 2,\n", " 'Buxbaumiales': 1,\n", " 'Isocrinida': 3,\n", " 'Isobryales': 4,\n", " 'Persiculida': 2,\n", " 'Dentaliida': 7,\n", " 'Ceratophyllales': 1,\n", " 'Myriangiales': 2,\n", " 'Pyrosomatida': 1,\n", " 'Sceptrulophora': 4,\n", " 'Euechinoidea': 1,\n", " 'Gordioidea': 2,\n", " 'Parachela': 4,\n", " 'Priapulomorpha': 2,\n", " 'Aphragmophora': 1,\n", " 'Leotiales': 2,\n", " 'Branchiobdellida': 2,\n", " 'Amborellales': 1,\n", " 'Thelebolales': 1,\n", " 'Cydippida': 6,\n", " 'Chaetonotida': 2,\n", " 'Oligacanthorhynchida': 1,\n", " 'Homosclerophorida': 2,\n", " 'Abrothallus': 0,\n", " 'Stereopsis': 0,\n", " 'Amylocorticiales': 1,\n", " 'Picramniales': 1,\n", " 'Enchytraeida': 1,\n", " 'Polymixiiformes': 1,\n", " 'Nemertodermatida': 2,\n", " 'Coryneliales': 1,\n", " 'Rhizophydiales': 1,\n", " 'Erynia': 0,\n", " 'Schistostegaceae': 1,\n", " 'Cumacea': 6,\n", " 'Berberidopsidales': 2,\n", " 'Dendrocerotales': 1,\n", " 'Dothideales': 3,\n", " 'Geminibasidiaceae': 2,\n", " 'Leptodontaceae': 1,\n", " 'Echinorhynchida': 3,\n", " 'Batrachospermales': 2,\n", " 'Glomerida': 2,\n", " 'Amphioxiformes': 1,\n", " 'Septobasidiales': 1,\n", " 'Monstrilloida': 1,\n", " 'Thalassocalycida': 1,\n", " 'Bivalvulida': 2,\n", " 'Ateleopodiformes': 1,\n", " 'Mantophasmatodea': 1,\n", " 'Stylonematales': 1,\n", " 'Mormonilloida': 1,\n", " 'Chirodropida': 2,\n", " 'Vampyromorpha': 1,\n", " 'Prasiolales': 1,\n", " 'Platygloeales': 2,\n", " 'Moniliformida': 1,\n", " 'Canuelloida': 2,\n", " 'Amphidiscosida': 2,\n", " 'Asaphida': 1,\n", " 'Glaucocystales': 1,\n", " 'Blastocladiales': 2,\n", " 'Lichenoconiales': 1,\n", " 'Normandina': 0,\n", " 'Zoophthora': 0,\n", " 'Polyxenida': 1,\n", " 'Trypetheliales': 2,\n", " 'Syncephalastraceae': 1,\n", " 'Acrochaetiales': 1,\n", " 'Treubiidae': 1,\n", " 'Microthamniales': 0,\n", " 'Palpigradi': 1,\n", " 'Acrospermales': 1,\n", " 'Magnaporthales': 2,\n", " 'Bothrioplanida': 1,\n", " 'Ahnfeltiales': 1,\n", " 'Limnognathida': 1,\n", " 'Endogonaceae': 1,\n", " 'Archidiales': 1,\n", " 'Tetramerocerata': 2,\n", " 'Opilioacarida': 1,\n", " 'Mortierellaceae': 1,\n", " 'Wallemiales': 1,\n", " 'Arguloida': 1,\n", " 'Seisonacea': 1,\n", " 'Echiniscoidea': 1,\n", " 'Melanosporales': 1,\n", " 'Microthyriales': 1,\n", " 'Siphonophorida': 2,\n", " 'Eryniopsis': 0,\n", " 'Malasseziales': 1,\n", " 'Schizosaccharomycetales': 1,\n", " 'Microstromatales': 0,\n", " 'Pyramimonadophyceae': 0,\n", " 'Glomus': 0,\n", " 'Doliolida': 1,\n", " 'Acrosymphytales': 2,\n", " 'Xanthopyreniaceae': 1,\n", " 'Halicryptomorpha': 1,\n", " 'Agyriales': 1,\n", " 'Jobellisiaceae': 1,\n", " 'Heterogastridiales': 1,\n", " 'Rhodogorgonales': 1,\n", " 'Platycopina': 1,\n", " 'Coelacanthiformes': 1,\n", " 'Polystilifera': 3,\n", " 'Ophioleucida': 1,\n", " 'Nectiopoda': 2,\n", " 'Dendrogastrida': 2,\n", " 'Leptostraca': 1,\n", " 'Cunninghamellaceae': 3,\n", " 'Lichida': 1,\n", " 'Balliales': 1,\n", " 'Trichosphaeriales': 1,\n", " 'Entwisleiales': 1,\n", " 'Meliolales': 1,\n", " 'Prasinococcales': 1,\n", " 'Klebsormidiales': 1,\n", " 'Pandora': 0,\n", " 'Myzostomida': 1,\n", " 'Glaucosphaerales': 1,\n", " 'Diversispora': 0,\n", " 'Ibliformes': 1,\n", " 'Platycopioida': 1,\n", " 'Rhodochaetales': 1,\n", " 'Redlichiida': 2,\n", " 'Phacopida': 3,\n", " 'Rhizophagus': 0,\n", " 'Tritirachiales': 1,\n", " 'Sebdeniales': 1,\n", " 'Multivalvulida': 1,\n", " 'Stylephoriformes': 1,\n", " 'Zoraptera': 1,\n", " 'Symbiida': 1,\n", " 'Coleochaetales': 1,\n", " 'Arthrotardigrada': 1,\n", " 'Sporidiobolales': 0,\n", " 'Tilletiales': 1,\n", " 'Bursovaginoidea': 1,\n", " 'Chaetodermatida': 2,\n", " 'Mycotyphaceae': 1,\n", " 'Tryblidiida': 1,\n", " 'Cryptophialida': 1,\n", " 'Chlorodendrales': 1,\n", " 'Mesostigmatales': 1,\n", " 'Geosiphon': 0,\n", " 'Misophrioida': 1,\n", " 'Scutellospora': 0,\n", " 'Sporodiniella': 0,\n", " 'Gnosonesimidae': 1,\n", " 'Corynexochida': 1,\n", " 'Hypnobryales': 1,\n", " 'Redeckera': 0,\n", " 'Oedogoniales': 0,\n", " 'Lepidogalaxiiformes': 1,\n", " 'Peripodida': 1,\n", " 'Thoreales': 1,\n", " 'Passeriformes': 72,\n", " 'Columbiformes': 1,\n", " 'Anseriformes': 3,\n", " 'Accipitriformes': 3,\n", " 'Nudibranchia': 16,\n", " 'Adapedonta': 1,\n", " 'Charadriiformes': 11,\n", " 'Pelecaniformes': 4,\n", " 'Piciformes': 4,\n", " 'Sphenisciformes': 1,\n", " 'Mytilida': 1,\n", " 'Suliformes': 4,\n", " 'Gruiformes': 3,\n", " 'Scolopendromorpha': 1,\n", " 'Rhodellales': 1,\n", " 'Carnivora': 10,\n", " 'Primates': 3,\n", " 'Phoenicopteriformes': 1,\n", " 'Strigiformes': 2,\n", " 'Chiroptera': 4,\n", " 'Artiodactyla': 12,\n", " 'Ciconiiformes': 1,\n", " 'Galliformes': 5,\n", " 'Lagomorpha': 2,\n", " 'Cuculiformes': 1,\n", " 'Coraciiformes': 4,\n", " 'Stylommatophora': 20,\n", " 'Colaconematales': 1,\n", " 'Venerida': 3,\n", " 'Pectinida': 2,\n", " 'Psittaciformes': 4,\n", " 'Rodentia': 11,\n", " 'Camarodonta': 2,\n", " 'Procellariiformes': 2,\n", " 'Forcipulatida': 1,\n", " 'Caprimulgiformes': 5,\n", " 'Marchantiales': 3,\n", " 'Galbuliformes': 1,\n", " 'Eulipotyphla': 3,\n", " 'Myida': 3,\n", " 'Falconiformes': 1,\n", " 'Perissodactyla': 3,\n", " 'Pilosa': 3,\n", " 'Lecideales': 1,\n", " 'Podicipediformes': 1,\n", " 'Phaeotrichales': 1,\n", " 'Scorpaeniformes': 3,\n", " 'Ostreida': 1,\n", " 'Sepiida': 1,\n", " 'Caliciales': 1,\n", " 'Trogoniformes': 1,\n", " 'Cyrtocrinida': 1,\n", " 'Dimargaritaceae': 1,\n", " 'Cingulata': 1,\n", " 'Casuariiformes': 1,\n", " 'Diprotodontia': 5,\n", " 'Rhynchocephalia': 1,\n", " 'Endeostigmata': 1,\n", " 'Jahnulales': 1,\n", " 'Filospermoidea': 1,\n", " 'Otidiformes': 1,\n", " 'Cathartiformes': 1,\n", " 'Spirulida': 1,\n", " 'Bucerotiformes': 4,\n", " 'Opisthocomiformes': 1,\n", " 'Gaviiformes': 1,\n", " 'Agaricostilbales': 0,\n", " 'Monotremata': 2,\n", " 'Gasterosteiformes': 1,\n", " 'Sabellida': 2,\n", " 'Coliiformes': 1,\n", " 'Phacidiales': 1,\n", " 'Jungermanniales': 1,\n", " 'Sirenia': 1,\n", " 'Aplysiida': 1,\n", " 'Spinulosida': 1,\n", " 'Musophagiformes': 1,\n", " 'Cardiida': 2,\n", " 'Didelphimorphia': 1,\n", " 'Systellommatophora': 1,\n", " 'Unionida': 1,\n", " 'Scutigeromorpha': 1,\n", " 'Clypeasteroida': 2,\n", " 'Anthoathecata': 1,\n", " 'Siphonophorae': 1,\n", " 'Cephalaspidea': 1,\n", " 'Haplotaxida': 1,\n", " 'Lunulariales': 1,\n", " 'Valvatida': 3,\n", " 'Hyracoidea': 1,\n", " 'Proboscidea': 1,\n", " 'Dermoptera': 1,\n", " 'Struthioniformes': 1,\n", " 'Craniata': 1,\n", " 'Rhizocarpales': 1,\n", " 'Hedwigiales': 1}" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "num_families_order" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "num_orders_class = {}\n", "num_families_class = {}\n", "num_genera_class = {}\n", "num_species_class = {}\n", "for clss in df_taxa['class'].unique():\n", " temp = df_taxa.loc[df_taxa['class'] == clss]\n", " num_orders_class[clss] = temp['order'].nunique()\n", " num_families_class[clss] = temp['family'].nunique()\n", " num_genera_class[clss] = temp['genus'].nunique()\n", " num_species_class[clss] = temp['species'].nunique()" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "num_classes_phylum = {}\n", "num_orders_phylum = {}\n", "num_families_phylum = {}\n", "num_genera_phylum = {}\n", "num_species_phylum = {}\n", "for phylum in df_taxa.phylum.unique():\n", " temp = df_taxa.loc[df_taxa.phylum == phylum]\n", " num_classes_phylum[phylum] = temp['class'].nunique()\n", " num_orders_phylum[phylum] = temp['order'].nunique()\n", " num_families_phylum[phylum] = temp['family'].nunique()\n", " num_genera_phylum[phylum] = temp['genus'].nunique()\n", " num_species_phylum[phylum] = temp['species'].nunique()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "num_phyla_kingdom = {}\n", "num_classes_kingdom = {}\n", "num_orders_kingdom = {}\n", "for kingdom in df_taxa.kingdom.unique():\n", " temp = df_taxa.loc[df_taxa.kingdom == kingdom]\n", " num_phyla_kingdom[kingdom] = temp.phylum.nunique()\n", " num_classes_kingdom[kingdom] = temp['class'].nunique()\n", " num_orders_kingdom[kingdom] = temp['order'].nunique()" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [], "source": [ "num_species_genus = {}\n", "for genus in df_taxa.genus.unique():\n", " temp = df_taxa.loc[df_taxa.genus == genus]\n", " num_species_genus[genus] = temp.species.nunique()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Quick pause to save all progress to csv before starting final long calculation, then we can just add the number of species in each genus at the end." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Compile list of dictionaries to make DataFrame with all data\n", "\n", "Format:\n", "| top_taxon | rank | num_phyla | num_classes | num_orders | num_families | num_genera | num_species |\n", "| ------ | ------ | ---- | ---- | ---- | ---- | ---- | ---- |\n", "| Animalia | Kingdom | # phyla | # classes | # orders | # families | # genera | # species |\n", "..." ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "kingdom_counts = [num_phyla_kingdom, num_classes_kingdom, num_orders_kingdom, num_families_kingdom, \n", " num_genera_kingdom, num_species_kingdom]\n", "phylum_counts = [num_classes_phylum, num_orders_phylum, num_families_phylum, num_genera_phylum, num_species_phylum]\n", "class_counts = [num_orders_class, num_families_class, num_genera_class, num_species_class]\n", "order_counts = [num_families_order, num_genera_order, num_species_order]\n", "family_counts = [num_genera_family, num_species_family]\n", "genus_counts = [num_species_genus]" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "dict_list = [{\"kingdom\": kingdom_counts},\n", " {\"phylum\": phylum_counts},\n", " {\"class\": class_counts},\n", " {\"order\": order_counts},\n", " {\"family\": family_counts},\n", " {\"genus\": genus_counts}]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Generate the first two columns of our desired DataFrame. Note that all taxa but `genus` start with `nan` so we will ignore that value --not in first iteration" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "top_taxon = []\n", "ranks = []\n", "#i = 1\n", "for taxon in taxa[:6]:\n", " vals = list(df_taxa[taxon].unique())\n", " #if taxon == 'genus': # since genus is last in list\n", " # i = 0\n", " for val in vals: #[i:]:\n", " top_taxon.append(val)\n", " ranks.append(taxon)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "taxa_cols = ['num_phyla', 'num_classes', 'num_orders', 'num_families', 'num_genera', 'num_species']" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "taxa_counts_df = pd.DataFrame({\"top_taxon\": top_taxon, \"rank\": ranks})" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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top_taxonranknum_phylanum_classesnum_ordersnum_familiesnum_generanum_species
0NaNkingdom111111
1Metazoakingdom111111
2Archaeplastidakingdom111111
3Fungikingdom111111
4Animaliakingdom111111
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96536Kribiagenus111111
96537Chilodontagenus111111
96538Epihippusgenus111111
96539Ancistriagenus111111
96540Exneriagenus111111
\n", "

96541 rows × 8 columns

\n", "
" ], "text/plain": [ " top_taxon rank num_phyla num_classes num_orders \\\n", "0 NaN kingdom 1 1 1 \n", "1 Metazoa kingdom 1 1 1 \n", "2 Archaeplastida kingdom 1 1 1 \n", "3 Fungi kingdom 1 1 1 \n", "4 Animalia kingdom 1 1 1 \n", "... ... ... ... ... ... \n", "96536 Kribia genus 1 1 1 \n", "96537 Chilodonta genus 1 1 1 \n", "96538 Epihippus genus 1 1 1 \n", "96539 Ancistria genus 1 1 1 \n", "96540 Exneria genus 1 1 1 \n", "\n", " num_families num_genera num_species \n", "0 1 1 1 \n", "1 1 1 1 \n", "2 1 1 1 \n", "3 1 1 1 \n", "4 1 1 1 \n", "... ... ... ... \n", "96536 1 1 1 \n", "96537 1 1 1 \n", "96538 1 1 1 \n", "96539 1 1 1 \n", "96540 1 1 1 \n", "\n", "[96541 rows x 8 columns]" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Set all other columns to default value of 1 since lower level taxa will only belong to the one higher level taxon\n", "taxa_counts_df[taxa_cols] = 1\n", "taxa_counts_df" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "nan 0\n", "0\n", "Metazoa 31\n", "0\n", "Archaeplastida 7\n", "0\n", "Fungi 11\n", "0\n", "Animalia 6\n", "0\n", "Plantae 5\n", "0\n", "nan 0\n", "1\n", "Metazoa 61\n", "1\n", "Archaeplastida 22\n", "1\n", "Fungi 36\n", "1\n", "Animalia 27\n", "1\n", "Plantae 14\n", "1\n", "nan 0\n", "2\n", "Metazoa 355\n", "2\n", "Archaeplastida 176\n", "2\n", "Fungi 152\n", "2\n", "Animalia 160\n", "2\n", "Plantae 85\n", "2\n", "nan 0\n", "3\n", "Metazoa 4272\n", "3\n", "Archaeplastida 719\n", "3\n", "Fungi 468\n", "3\n", "Animalia 1077\n", "3\n", "Plantae 286\n", "3\n", "nan 0\n", "4\n", "Metazoa 39686\n", "4\n", "Archaeplastida 10182\n", "4\n", "Fungi 2019\n", "4\n", "Animalia 6225\n", "4\n", "Plantae 1702\n", "4\n", "nan 0\n", "5\n", "Metazoa 74323\n", "5\n", "Archaeplastida 33054\n", "5\n", "Fungi 7713\n", "5\n", "Animalia 12476\n", "5\n", "Plantae 2499\n", "5\n" ] } ], "source": [ "marker = 0\n", "for king_dict in kingdom_counts:\n", " for key, value in king_dict.items():\n", " # key = higher taxon, value = num of lower taxa in it\n", " print(key, value)\n", " print(marker)\n", " taxa_counts_df.loc[(taxa_counts_df['top_taxon'] == key) & (taxa_counts_df['rank'] == 'kingdom'), taxa_cols[marker]] = value\n", " marker += 1" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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top_taxonranknum_phylanum_classesnum_ordersnum_familiesnum_generanum_species
0NaNkingdom111111
1Metazoakingdom316135542723968674323
2Archaeplastidakingdom7221767191018233054
3Fungikingdom113615246820197713
4Animaliakingdom6271601077622512476
...........................
96536Kribiagenus111111
96537Chilodontagenus111111
96538Epihippusgenus111111
96539Ancistriagenus111111
96540Exneriagenus111111
\n", "

96541 rows × 8 columns

\n", "
" ], "text/plain": [ " top_taxon rank num_phyla num_classes num_orders \\\n", "0 NaN kingdom 1 1 1 \n", "1 Metazoa kingdom 31 61 355 \n", "2 Archaeplastida kingdom 7 22 176 \n", "3 Fungi kingdom 11 36 152 \n", "4 Animalia kingdom 6 27 160 \n", "... ... ... ... ... ... \n", "96536 Kribia genus 1 1 1 \n", "96537 Chilodonta genus 1 1 1 \n", "96538 Epihippus genus 1 1 1 \n", "96539 Ancistria genus 1 1 1 \n", "96540 Exneria genus 1 1 1 \n", "\n", " num_families num_genera num_species \n", "0 1 1 1 \n", "1 4272 39686 74323 \n", "2 719 10182 33054 \n", "3 468 2019 7713 \n", "4 1077 6225 12476 \n", "... ... ... ... \n", "96536 1 1 1 \n", "96537 1 1 1 \n", "96538 1 1 1 \n", "96539 1 1 1 \n", "96540 1 1 1 \n", "\n", "[96541 rows x 8 columns]" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "taxa_counts_df" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [], "source": [ "marker = 1\n", "for phylum_dict in phylum_counts:\n", " for key, value in phylum_dict.items():\n", " # key = higher taxon, value = num of lower taxa in it\n", " taxa_counts_df.loc[(taxa_counts_df['top_taxon'] == key) & (taxa_counts_df['rank'] == 'phylum'), taxa_cols[marker]] = value\n", " marker += 1" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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top_taxonranknum_phylanum_classesnum_ordersnum_familiesnum_generanum_species
0NaNkingdom111111
1Metazoakingdom316135542723968674323
2Archaeplastidakingdom7221767191018233054
3Fungikingdom113615246820197713
4Animaliakingdom6271601077622512476
5Plantaekingdom5148528617022499
6NaNphylum111111
7Arthropodaphylum11310817472851560992
8Tracheophytaphylum1981463941832622
9Chordataphylum1131581551776220969
\n", "
" ], "text/plain": [ " top_taxon rank num_phyla num_classes num_orders num_families \\\n", "0 NaN kingdom 1 1 1 1 \n", "1 Metazoa kingdom 31 61 355 4272 \n", "2 Archaeplastida kingdom 7 22 176 719 \n", "3 Fungi kingdom 11 36 152 468 \n", "4 Animalia kingdom 6 27 160 1077 \n", "5 Plantae kingdom 5 14 85 286 \n", "6 NaN phylum 1 1 1 1 \n", "7 Arthropoda phylum 1 13 108 1747 \n", "8 Tracheophyta phylum 1 9 81 463 \n", "9 Chordata phylum 1 13 158 1551 \n", "\n", " num_genera num_species \n", "0 1 1 \n", "1 39686 74323 \n", "2 10182 33054 \n", "3 2019 7713 \n", "4 6225 12476 \n", "5 1702 2499 \n", "6 1 1 \n", "7 28515 60992 \n", "8 9418 32622 \n", "9 7762 20969 " ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "taxa_counts_df.head(10)" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [], "source": [ "marker = 2\n", "for class_dict in class_counts:\n", " for key, value in class_dict.items():\n", " # key = higher taxon, value = num of lower taxa in it\n", " taxa_counts_df.loc[(taxa_counts_df['top_taxon'] == key) & (taxa_counts_df['rank'] == 'class'), taxa_cols[marker]] = value\n", " marker += 1" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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top_taxonranknum_phylanum_classesnum_ordersnum_familiesnum_generanum_species
56NaNclass111111
57Pancrustaceaclass116812762487849513
58Gnathostomataclass11961504752120133
59Gastropodaclass111530122147699
60Agaricomycetesclass11201087764900
...........................
188Gnetopsidaclass111112
189Polychaetaclass111233
190Cycadopsidaclass111111
191Clitellataclass111111
192Craniiformeaclass111110
\n", "

137 rows × 8 columns

\n", "
" ], "text/plain": [ " top_taxon rank num_phyla num_classes num_orders num_families \\\n", "56 NaN class 1 1 1 1 \n", "57 Pancrustacea class 1 1 68 1276 \n", "58 Gnathostomata class 1 1 96 1504 \n", "59 Gastropoda class 1 1 15 301 \n", "60 Agaricomycetes class 1 1 20 108 \n", ".. ... ... ... ... ... ... \n", "188 Gnetopsida class 1 1 1 1 \n", "189 Polychaeta class 1 1 1 2 \n", "190 Cycadopsida class 1 1 1 1 \n", "191 Clitellata class 1 1 1 1 \n", "192 Craniiformea class 1 1 1 1 \n", "\n", " num_genera num_species \n", "56 1 1 \n", "57 24878 49513 \n", "58 7521 20133 \n", "59 2214 7699 \n", "60 776 4900 \n", ".. ... ... \n", "188 1 2 \n", "189 3 3 \n", "190 1 1 \n", "191 1 1 \n", "192 1 0 \n", "\n", "[137 rows x 8 columns]" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "taxa_counts_df.loc[taxa_counts_df['rank'] == 'class']" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [], "source": [ "marker = 3\n", "for order_dict in order_counts:\n", " for key, value in order_dict.items():\n", " # key = higher taxon, value = num of lower taxa in it\n", " taxa_counts_df.loc[(taxa_counts_df['top_taxon'] == key) & (taxa_counts_df['rank'] == 'order'), taxa_cols[marker]] = value\n", " marker += 1" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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top_taxonranknum_phylanum_classesnum_ordersnum_familiesnum_generanum_species
193NaNorder111111
194Lepidopteraorder111128759925071
195Sapindalesorder11193101362
196Trichopteraorder111382531056
197Hemipteraorder11113532906144
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955Dermopteraorder111111
956Struthioniformesorder111111
957Craniataorder111110
958Rhizocarpalesorder111111
959Hedwigialesorder111111
\n", "

767 rows × 8 columns

\n", "
" ], "text/plain": [ " top_taxon rank num_phyla num_classes num_orders \\\n", "193 NaN order 1 1 1 \n", "194 Lepidoptera order 1 1 1 \n", "195 Sapindales order 1 1 1 \n", "196 Trichoptera order 1 1 1 \n", "197 Hemiptera order 1 1 1 \n", ".. ... ... ... ... ... \n", "955 Dermoptera order 1 1 1 \n", "956 Struthioniformes order 1 1 1 \n", "957 Craniata order 1 1 1 \n", "958 Rhizocarpales order 1 1 1 \n", "959 Hedwigiales order 1 1 1 \n", "\n", " num_families num_genera num_species \n", "193 1 1 1 \n", "194 128 7599 25071 \n", "195 9 310 1362 \n", "196 38 253 1056 \n", "197 135 3290 6144 \n", ".. ... ... ... \n", "955 1 1 1 \n", "956 1 1 1 \n", "957 1 1 0 \n", "958 1 1 1 \n", "959 1 1 1 \n", "\n", "[767 rows x 8 columns]" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "taxa_counts_df.loc[taxa_counts_df['rank'] == 'order']" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [], "source": [ "marker = 4\n", "for family_dict in family_counts:\n", " for key, value in family_dict.items():\n", " # key = higher taxon, value = num of lower taxa in it\n", " taxa_counts_df.loc[(taxa_counts_df['top_taxon'] == key) & (taxa_counts_df['rank'] == 'family'), taxa_cols[marker]] = value\n", " marker += 1" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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top_taxonranknum_phylanum_classesnum_ordersnum_familiesnum_generanum_species
960NaNfamily111111
961Oenosandridaefamily111114
962Rutaceaefamily1111104618
963Limnephilidaefamily111158223
964Reduviidaefamily11113473
...........................
6621Hedwigiaceaefamily111111
6622Jasmineiricolidaefamily111111
6623Cunninghamellafamily111100
6624Eremobelbidaefamily111111
6625Rhopalomeniidaefamily111111
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5666 rows × 8 columns

\n", "
" ], "text/plain": [ " top_taxon rank num_phyla num_classes num_orders \\\n", "960 NaN family 1 1 1 \n", "961 Oenosandridae family 1 1 1 \n", "962 Rutaceae family 1 1 1 \n", "963 Limnephilidae family 1 1 1 \n", "964 Reduviidae family 1 1 1 \n", "... ... ... ... ... ... \n", "6621 Hedwigiaceae family 1 1 1 \n", "6622 Jasmineiricolidae family 1 1 1 \n", "6623 Cunninghamella family 1 1 1 \n", "6624 Eremobelbidae family 1 1 1 \n", "6625 Rhopalomeniidae family 1 1 1 \n", "\n", " num_families num_genera num_species \n", "960 1 1 1 \n", "961 1 1 4 \n", "962 1 104 618 \n", "963 1 58 223 \n", "964 1 34 73 \n", "... ... ... ... \n", "6621 1 1 1 \n", "6622 1 1 1 \n", "6623 1 0 0 \n", "6624 1 1 1 \n", "6625 1 1 1 \n", "\n", "[5666 rows x 8 columns]" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "taxa_counts_df.loc[taxa_counts_df['rank'] == 'family']" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [], "source": [ "marker = 5\n", "for genus_dict in genus_counts:\n", " for key, value in genus_dict.items():\n", " # key = higher taxon, value = num of lower taxa in it\n", " taxa_counts_df.loc[(taxa_counts_df['top_taxon'] == key) & (taxa_counts_df['rank'] == 'genus'), taxa_cols[marker]] = value\n", " marker += 1" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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top_taxonranknum_phylanum_classesnum_ordersnum_familiesnum_generanum_species
6626Manfredagenus111113
6627Discophlebiagenus111115
6628Melicopegenus1111139
6629Limnephilusgenus1111181
6630NaNgenus111111
...........................
96536Kribiagenus111111
96537Chilodontagenus111111
96538Epihippusgenus111111
96539Ancistriagenus111111
96540Exneriagenus111111
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89915 rows × 8 columns

\n", "
" ], "text/plain": [ " top_taxon rank num_phyla num_classes num_orders num_families \\\n", "6626 Manfreda genus 1 1 1 1 \n", "6627 Discophlebia genus 1 1 1 1 \n", "6628 Melicope genus 1 1 1 1 \n", "6629 Limnephilus genus 1 1 1 1 \n", "6630 NaN genus 1 1 1 1 \n", "... ... ... ... ... ... ... \n", "96536 Kribia genus 1 1 1 1 \n", "96537 Chilodonta genus 1 1 1 1 \n", "96538 Epihippus genus 1 1 1 1 \n", "96539 Ancistria genus 1 1 1 1 \n", "96540 Exneria genus 1 1 1 1 \n", "\n", " num_genera num_species \n", "6626 1 3 \n", "6627 1 5 \n", "6628 1 39 \n", "6629 1 81 \n", "6630 1 1 \n", "... ... ... \n", "96536 1 1 \n", "96537 1 1 \n", "96538 1 1 \n", "96539 1 1 \n", "96540 1 1 \n", "\n", "[89915 rows x 8 columns]" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "taxa_counts_df.loc[taxa_counts_df['rank'] == 'genus']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Save resulting DataFrame to CSV. Note that the number of species per genus did not finish." ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [], "source": [ "taxa_counts_df.to_csv(\"../data/taxa_counts.csv\", index = False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Returning to our original DataFrame, we want to get some counts by source dataset." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# Add data_source column for easier slicing\n", "df.loc[df['inat21_filename'].notna(), 'data_source'] = 'iNat21'\n", "df.loc[df['bioscan_filename'].notna(), 'data_source'] = 'BIOSCAN'\n", "df.loc[df['eol_content_id'].notna(), 'data_source'] = 'EOL'" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "518118" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.loc[df['data_source'] == 'EOL', 'common'].nunique()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "9941" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.loc[df['data_source'] == 'iNat21', 'common'].nunique()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "10635" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.loc[df['data_source'] == 'BIOSCAN', 'common'].nunique()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "viz", "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": "3.8.10" }, "orig_nbformat": 4 }, "nbformat": 4, "nbformat_minor": 2 }