{
"cells": [
{
"cell_type": "markdown",
"id": "98e5b4d2-70fa-405d-a0c0-d8072400378e",
"metadata": {
"tags": []
},
"source": [
"# <div align='center'><font color='#F39C12'><strong>Kerala Bird Atlas (KBA) -EDA</strong></font></div>\n",
"<div style=\"text-align:center;\">\n",
"<a href = 'https://colab.research.google.com/github/jishnukoliyadan/Kerala_Bird_Atlas//blob/master/Kerala_Bird_Atlas.ipynb'><img src = 'https://raw.githubusercontent.com/jishnukoliyadan/usefull_items/master/svgs/Colab_Run_In.svg' width = 10%></a>\n",
"<a href = 'https://github.com/jishnukoliyadan/Kerala_Bird_Atlas'><img src = 'https://raw.githubusercontent.com/jishnukoliyadan/usefull_items/master/svgs/GitHub_View_Source.svg' width = 10%></a>\n",
"<a href = 'https://www.kaggle.com/code/jishnukoliyadan/kerala-bird-atlas-eda'><img src = 'https://raw.githubusercontent.com/jishnukoliyadan/usefull_items/master/svgs/Kaggle_View_On.svg' width = 10%></a>\n",
"<a href = 'https://nbviewer.org/github/jishnukoliyadan/Kerala_Bird_Atlas/blob/master/Kerala_Bird_Atlas.ipynb'><img src = 'https://raw.githubusercontent.com/jishnukoliyadan/usefull_items/master/svgs/NbViwer_View_In.svg' width = 10%></a>\n",
"</div>\n",
"<hr>\n",
"\n",
"<img src='images/Kerala-Bird-Atlas-Cover-1.jpeg'>\n",
"\n",
"The <font color='#F39C12'>Kerala Bird Atlas (KBA)</font>, the first-of-its-kind state-level bird atlas in India, has created solid baseline data about the distribution and abundance of various bird species across all major habitats giving an impetus for futuristic studies.\n",
"\n",
"The entire state of Kerala was systematically surveyed twice a year during **2015–20**. It is arguably **Asia’s largest bird atlas** in terms of geographical extent, sampling effort and species coverage derived from the aggregation of 25,000 checklists.\n",
"\n",
"\n",
"<font color='#F39C12'>KBA</font> accounted for nearly three lakh records of `361` species, including `94` very rare species, `103` rare species, `110` common species, `44` very common species, and `10` most abundant species. \n",
"\n",
"## <font color='#D22CD3'>Objective</font>\n",
"\n",
"Citizen-science driven exercises (e.g. bird surveys) and online platforms (e.g. [eBird](http://ebird.org/)) provide voluminous data on bird occurrence. However, the semi-structured nature of their data collection makes it difficult to compare bird distribution across space and time.\n",
"\n",
"Data on the distribution of species and the factors governing the same are prerequisites for effective and efficient conservation efforts. Such information is necessary to inform the selection of protected areas, to assess habitat associations and to predict the likely effects of future en-vironmental changes.\n",
"\n",
"\n",
"## <font color='#D22CD3'>Methodology</font>\n",
"### <font color='#2DD32C'>Spatial extent</font>\n",
"Kerala lies between `8°18'`N and `12°48'`N lat. and `74°52'`E and `77°22'`E long. in southwestern India. Wedged between the Arabian Sea and the windward side of the Western Ghats, it receives abundant rainfall (`180–360` cm) and experiences a tropical climate. Elevation in the region varies from `–2.2` m (Kuttanad) to `2695` m (Anamudi peak). It is spread across an area of `38,863` sq. km, of which `27%` is under forest cover, `66%` is under cultivation and `7%` constitutes built-up areas/wetlands/uncultivated land. Onefourth of the Western Ghats range falls within Kerala. Surveys for <font color='#F39C12'>KBA</font> were conducted in all `14` administrative units (districts) of Kerala.\n",
"### <font color='#2DD32C'>Sampling protocol</font>\n",
"Kerala was divided into cells of size `3.75 min x 3.75 min` (equivalent to `6.6 km x 6.6 km`) aligned to Survey of India maps. A total of 915 cells were laid out covering the entire state. Each cell was further divided into four quadrants of size `3.3 km x 3.3 km`. Each quadrant was then sub-divided into `9` sub-cells of size `1.1 x 1.1 km`. A single, randomly selected sub-cell in every quadrant was chosen for the survey. Grids were laid and the randomly selected sub-cells were marked on the map prior to the survey. A total of `63` sub-cells were found to be located in inaccessible cliffs or valleys, and these were replaced by adjacent accessible sub-cells with the same habitat type from the same quadrants.\n",
"## <font color='#D22CD3'>Dataset</font>\n",
"\n",
" \n",
"This observation dataset contains 3 files :\n",
"* kba_data.rds\n",
"* kba_names.rds\n",
"* kba_species.details.rds\n",
"\n",
"\n",
"<font color='blue'>Reference : </font>[Bird Count India](https://birdcount.in/kerala-bird-atlas/), [The Hindu](https://www.thehindu.com/news/national/kerala/kerala-gets-its-first-ever-scientific-bird-atlas/article38307794.ece), [Times of India](https://timesofindia.indiatimes.com/city/kochi/mammoth-survey-for-kerala-bird-atlas-ends/articleshow/78134787.cms), [Mysore Bird Atlas](https://birdcount.in/mysore-bird-atlas/)"
]
},
{
"cell_type": "markdown",
"id": "a9e535ad",
"metadata": {},
"source": [
" # <font color='#9115EA'>How to deal RDS files inside python 🐍 ?</font>\n",
"\n",
"RDS Format is a file format for storing R objects. Our data can be saved in RDS format. The **<font color='red'>.rds</font>** file extension is most often used in R.\n",
"\n",
"Saving data into R data formats can <font color='blue'>reduce the size</font> of large files by considerably.\n",
"\n",
"<div class=\"alert alert-success\" role=\"alert\">\n",
"💡 We can process <font color='red'>.rds</font> files using <font color='red'>pyreadr</font> package.\n",
"</div>\n",
"\n",
"We can install it easily with pip: `pip install pyreadr`\n",
"<br><br>\n",
"\n",
"```python\n",
"!pip install pyreadr\n",
"\n",
"import pyreadr\n",
"\n",
"result = pyreadr.read_r('../path_to_file.rds') # also works for RData\n",
"print(f\"Class type of 'result' \\t:: {type(result)}\")\n",
"\n",
"df = result[None] # extract the pandas data frame\n",
"print(f\"Class type of 'df' \\t:: {type(df)}\")\n",
"\n",
">>> Class type of 'result' \t:: <class 'collections.OrderedDict'>\n",
">>> Class type of 'df' \t :: <class 'pandas.core.frame.DataFrame'>\n",
"```\n",
"\n",
"Reference : [StackoverFlow](https://stackoverflow.com/a/53956614), [GitHub](https://github.com/ofajardo/pyreadr)"
]
},
{
"cell_type": "markdown",
"id": "3994c9e1",
"metadata": {},
"source": [
"# <font color='#D22CD3'>More about Dataset</font>\n",
"\n",
"<!-- ### Species of conservation concern -->\n",
"\n",
"- **<font color='#D97026'>Date</font>** : The field surveys were conducted from `2015 to 2020`.\n",
"\n",
"- **<font color='#D97026'>Season</font>** : The field surveys were conducted twice a year ie, during\n",
" - Dry (mid-January to mid-March) season\n",
" - This season coincides with the peak activity of migratory species.\n",
" - Wet (mid-July to mid-September) season\n",
" - This season (monsoon) coincides with the breeding period of many resident species.\n",
"\n",
"\n",
"- **<font color='#D97026'>n.observers</font>** aka **Number of observers**\n",
" - The number of observers or volunteers for atlas surveys.\n",
" - Though the protocol mentioned `2–5` volunteers for each atlas surveys groups, but there are occations more volunteers involved in the survey.\n",
" - So the number of observers ranges from `2-13`.\n",
"\n",
"\n",
"- We calculated the **<font color='#D97026'>endemic score</font>** of every cell based on the number of endemic species reported from it.\n",
" - The **<font color='#D97026'>endemic score</font>** is **the sum of scores/total species count per unit checklist**\n",
" - 1 : The species restricted to Western Ghats–Sri Lanka biodiversity hotspot\n",
" - 0 : The rest (non-endemic species).\n",
"\n",
"\n",
"- We calculated the threat and **<font color='#D97026'>SoIB (State of India’s Bird)</font>** score for every cell. SoIB utilized the eBird data to estimate indices of population trends and range size for 867 of India’s 1333 bird species.\n",
" - 2 : High\n",
" - 1 : Moderate\n",
" - 0 : Low\n",
"\n",
"\n",
"- Threat categories were based on the **<font color='#D97026'>IUCN Red List 32</font>** and were scored as follows:\n",
" - 4 : Critically endangered\n",
" - 3 : Endangered\n",
" - 2 : Vulnerable\n",
" - 1 : Near-threatened\n",
" - 0 : Least-concern\n",
"\n",
"\n",
"**<font color='red'>NOTE</font>** :\n",
"1. There was the possibility of passage migrants (e.g. Eurasian Cuckoo, Amur Falcon) crossing through Kerala for a very short duration (a couple of weeks) during the intervening un-surveyed months. The atlas survey did not focus on such passage migrants.\n",
"1. The threat score and SoIB score were mapped separately for wet and dry seasons. \n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "306d321f",
"metadata": {},
"outputs": [],
"source": [
"# !pip install pyreadr # https://github.com/ofajardo/pyreadr#installation\n",
"import pyreadr\n",
"\n",
"import re\n",
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"from os import listdir\n",
"\n",
"from operator import itemgetter\n",
"\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"\n",
"from tqdm import tqdm # For progress bar\n",
"\n",
"# Suppress warnings \n",
"import warnings\n",
"warnings.filterwarnings('ignore')\n",
"\n",
"# Settings for pretty nice plots\n",
"# https://matplotlib.org/3.5.1/gallery/style_sheets/style_sheets_reference.html\n",
"plt.style.use('fivethirtyeight')\n",
"plt.show()\n",
"\n",
"\n",
"# For highlighting text on 'print' command\n",
"class text_co:\n",
" reset = '\\u001b[0m'\n",
" blue = '\\u001b[34;1m'\n",
" red = '\\u001b[31;1m'\n",
" light_blue = '\\u001b[34m'"
]
},
{
"cell_type": "markdown",
"id": "6868e5c2",
"metadata": {},
"source": [
"# Reading the Datasets"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "048501e9",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['kba_species.details.rds', 'kba_names.rds', 'kba_data.rds']\n"
]
}
],
"source": [
"# List of files available\n",
"\n",
"print(listdir('../Kerala_Birds/data_files'))"
]
},
{
"cell_type": "markdown",
"id": "de677da4",
"metadata": {},
"source": [
"<div class=\"alert alert-info\" role=\"alert\">\n",
" 📌 Note : Here we are only considering <font color='red'>.rds</font> files.\n",
"</div>"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "c96a644d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Class type of 'kba_data_df' is : <class 'pandas.core.frame.DataFrame'>\n",
"Class type of 'kba_names_df' is : <class 'pandas.core.frame.DataFrame'>\n",
"Class type of 'kba_species_df' is : <class 'pandas.core.frame.DataFrame'>\n"
]
}
],
"source": [
"# Like mentioned above we are using \"pyreadr\" for reading \".rds\" files.\n",
"PATH = '../Kerala_Birds/data_files/'\n",
"\n",
"kba_data = pyreadr.read_r(PATH + 'kba_data.rds')\n",
"kba_names = pyreadr.read_r(PATH + 'kba_names.rds')\n",
"kba_species = pyreadr.read_r(PATH + 'kba_species.details.rds')\n",
"\n",
"kba_data_df = kba_data[None]\n",
"kba_names_df = kba_names[None]\n",
"kba_species_df = kba_species[None]\n",
"\n",
"# Let's check and confirm all are Pandas dataframes\n",
"\n",
"print(f\"Class type of 'kba_data_df' is : {type(kba_data_df)}\")\n",
"print(f\"Class type of 'kba_names_df' is : {type(kba_names_df)}\")\n",
"print(f\"Class type of 'kba_species_df' is : {type(kba_species_df)}\")"
]
},
{
"cell_type": "markdown",
"id": "492acb13",
"metadata": {},
"source": [
"<div class=\"alert alert-success\" role=\"alert\">\n",
" 🥳 All are Pandas dataframes.\n",
"</div>"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "44d664c1",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Size of 'kba_data_df' : \u001b[34;1m(300882, 10)\n",
"\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Common.Name</th>\n",
" <th>Date</th>\n",
" <th>Time</th>\n",
" <th>n.observers</th>\n",
" <th>County</th>\n",
" <th>Sub.cell</th>\n",
" <th>Season</th>\n",
" <th>DEM</th>\n",
" <th>Cell.ID</th>\n",
" <th>List.ID</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Asian Koel</td>\n",
" <td>7/16/2015</td>\n",
" <td>16:30</td>\n",
" <td>2.0</td>\n",
" <td>Alappuzha</td>\n",
" <td>[51,2,2]</td>\n",
" <td>Wet</td>\n",
" <td>5.0</td>\n",
" <td>[76.28,9.84]</td>\n",
" <td>List.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Black-rumped Flameback</td>\n",
" <td>7/16/2015</td>\n",
" <td>16:30</td>\n",
" <td>2.0</td>\n",
" <td>Alappuzha</td>\n",
" <td>[51,2,2]</td>\n",
" <td>Wet</td>\n",
" <td>5.0</td>\n",
" <td>[76.28,9.84]</td>\n",
" <td>List.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Black Drongo</td>\n",
" <td>7/16/2015</td>\n",
" <td>16:30</td>\n",
" <td>2.0</td>\n",
" <td>Alappuzha</td>\n",
" <td>[51,2,2]</td>\n",
" <td>Wet</td>\n",
" <td>5.0</td>\n",
" <td>[76.28,9.84]</td>\n",
" <td>List.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Brahminy Kite</td>\n",
" <td>7/16/2015</td>\n",
" <td>16:30</td>\n",
" <td>2.0</td>\n",
" <td>Alappuzha</td>\n",
" <td>[51,2,2]</td>\n",
" <td>Wet</td>\n",
" <td>5.0</td>\n",
" <td>[76.28,9.84]</td>\n",
" <td>List.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Common Myna</td>\n",
" <td>7/16/2015</td>\n",
" <td>16:30</td>\n",
" <td>2.0</td>\n",
" <td>Alappuzha</td>\n",
" <td>[51,2,2]</td>\n",
" <td>Wet</td>\n",
" <td>5.0</td>\n",
" <td>[76.28,9.84]</td>\n",
" <td>List.1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Common.Name Date Time n.observers County Sub.cell \\\n",
"0 Asian Koel 7/16/2015 16:30 2.0 Alappuzha [51,2,2] \n",
"1 Black-rumped Flameback 7/16/2015 16:30 2.0 Alappuzha [51,2,2] \n",
"2 Black Drongo 7/16/2015 16:30 2.0 Alappuzha [51,2,2] \n",
"3 Brahminy Kite 7/16/2015 16:30 2.0 Alappuzha [51,2,2] \n",
"4 Common Myna 7/16/2015 16:30 2.0 Alappuzha [51,2,2] \n",
"\n",
" Season DEM Cell.ID List.ID \n",
"0 Wet 5.0 [76.28,9.84] List.1 \n",
"1 Wet 5.0 [76.28,9.84] List.1 \n",
"2 Wet 5.0 [76.28,9.84] List.1 \n",
"3 Wet 5.0 [76.28,9.84] List.1 \n",
"4 Wet 5.0 [76.28,9.84] List.1 "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Let's see aand check the size of our all 3 datasets before going to exploration\n",
"\n",
"print(f\"Size of 'kba_data_df' : {text_co.blue}{kba_data_df.shape}\\n\")\n",
"kba_data_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "779740ca",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Size of 'kba_names_df' : \u001b[34;1m(492, 4)\n",
"\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Common.Name</th>\n",
" <th>Scientific.Name</th>\n",
" <th>Action</th>\n",
" <th>Assigned.Name.for.Atlas</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Asian Koel</td>\n",
" <td>Eudynamys scolopaceus</td>\n",
" <td></td>\n",
" <td>Asian Koel</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Black-rumped Flameback</td>\n",
" <td>Dinopium benghalense</td>\n",
" <td></td>\n",
" <td>Black-rumped Flameback</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Black Drongo</td>\n",
" <td>Dicrurus macrocercus</td>\n",
" <td></td>\n",
" <td>Black Drongo</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Brahminy Kite</td>\n",
" <td>Haliastur indus</td>\n",
" <td></td>\n",
" <td>Brahminy Kite</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Common Myna</td>\n",
" <td>Acridotheres tristis</td>\n",
" <td></td>\n",
" <td>Common Myna</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Common.Name Scientific.Name Action \\\n",
"0 Asian Koel Eudynamys scolopaceus \n",
"1 Black-rumped Flameback Dinopium benghalense \n",
"2 Black Drongo Dicrurus macrocercus \n",
"3 Brahminy Kite Haliastur indus \n",
"4 Common Myna Acridotheres tristis \n",
"\n",
" Assigned.Name.for.Atlas \n",
"0 Asian Koel \n",
"1 Black-rumped Flameback \n",
"2 Black Drongo \n",
"3 Brahminy Kite \n",
"4 Common Myna "
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print(f\"Size of 'kba_names_df' : {text_co.blue}{kba_names_df.shape}\\n\")\n",
"kba_names_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "4ef64bef",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Size of 'kba_species_df' : \u001b[34;1m(361, 9)\n",
"\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Common.Name</th>\n",
" <th>Scientific.Name</th>\n",
" <th>Resident.status</th>\n",
" <th>IUCN.Redlist.Status</th>\n",
" <th>Distribution.Status</th>\n",
" <th>SoIB.status</th>\n",
" <th>Order</th>\n",
" <th>Family</th>\n",
" <th>Endemicity</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Zitting Cisticola</td>\n",
" <td>Cisticola juncidis</td>\n",
" <td>Resident</td>\n",
" <td>Least Concern</td>\n",
" <td>Very Large</td>\n",
" <td>Low</td>\n",
" <td>Passeriformes</td>\n",
" <td>Cisticolidae</td>\n",
" <td>Not endemic</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Ashy Prinia</td>\n",
" <td>Prinia socialis</td>\n",
" <td>Resident</td>\n",
" <td>Least Concern</td>\n",
" <td>Very Large</td>\n",
" <td>Low</td>\n",
" <td>Passeriformes</td>\n",
" <td>Cisticolidae</td>\n",
" <td>Not endemic</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Yellow-throated Bulbul</td>\n",
" <td>Pycnonotus xantholaemus</td>\n",
" <td>Resident</td>\n",
" <td>Vulnerable</td>\n",
" <td>Moderate</td>\n",
" <td>Moderate</td>\n",
" <td>Passeriformes</td>\n",
" <td>Pycnonotidae</td>\n",
" <td>Not endemic</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Yellow-footed Green-Pigeon</td>\n",
" <td>Treron phoenicopterus</td>\n",
" <td>Resident</td>\n",
" <td>Least Concern</td>\n",
" <td>Very Large</td>\n",
" <td>Low</td>\n",
" <td>Columbiformes</td>\n",
" <td>Columbidae</td>\n",
" <td>Not endemic</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Yellow-eyed Babbler</td>\n",
" <td>Chrysomma sinense</td>\n",
" <td>Resident</td>\n",
" <td>Least Concern</td>\n",
" <td>Very Large</td>\n",
" <td>Low</td>\n",
" <td>Passeriformes</td>\n",
" <td>Sylviidae</td>\n",
" <td>Not endemic</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Common.Name Scientific.Name Resident.status \\\n",
"0 Zitting Cisticola Cisticola juncidis Resident \n",
"1 Ashy Prinia Prinia socialis Resident \n",
"2 Yellow-throated Bulbul Pycnonotus xantholaemus Resident \n",
"3 Yellow-footed Green-Pigeon Treron phoenicopterus Resident \n",
"4 Yellow-eyed Babbler Chrysomma sinense Resident \n",
"\n",
" IUCN.Redlist.Status Distribution.Status SoIB.status Order \\\n",
"0 Least Concern Very Large Low Passeriformes \n",
"1 Least Concern Very Large Low Passeriformes \n",
"2 Vulnerable Moderate Moderate Passeriformes \n",
"3 Least Concern Very Large Low Columbiformes \n",
"4 Least Concern Very Large Low Passeriformes \n",
"\n",
" Family Endemicity \n",
"0 Cisticolidae Not endemic \n",
"1 Cisticolidae Not endemic \n",
"2 Pycnonotidae Not endemic \n",
"3 Columbidae Not endemic \n",
"4 Sylviidae Not endemic "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print(f\"Size of 'kba_species_df' : {text_co.blue}{kba_species_df.shape}\\n\")\n",
"kba_species_df.head()"
]
},
{
"cell_type": "markdown",
"id": "d5b2b8e1",
"metadata": {},
"source": [
"# Missing Values"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "c0e42564",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"kba_data dataframe\n",
"------------------\n",
"Common_Name 0\n",
"Date 0\n",
"Time 0\n",
"n_observers 0\n",
"County 0\n",
"Sub_cell 127\n",
"Season 0\n",
"DEM 0\n",
"Cell_ID 127\n",
"List_ID 127\n",
"dtype: int64 \n",
"\n",
"kba_names dataframe\n",
"-------------------\n",
"Common_Name 0\n",
"Scientific_Name 0\n",
"Action 0\n",
"Assigned_Name_for_Atlas 0\n",
"dtype: int64 \n",
"\n",
"kba_species dataframe\n",
"---------------------\n",
"Common_Name 0\n",
"Scientific_Name 0\n",
"Resident_status 0\n",
"IUCN_Redlist_Status 0\n",
"Distribution_Status 0\n",
"SoIB_status 0\n",
"Order 0\n",
"Family 0\n",
"Endemicity 0\n",
"dtype: int64\n"
]
}
],
"source": [
"# Renaming column names for easiness. Replacing '.' with '_'\n",
"\n",
"kba_data_df.columns = ['_'.join(column.split('.')) for column in kba_data_df.columns]\n",
"kba_names_df.columns = ['_'.join(column.split('.')) for column in kba_names_df.columns]\n",
"kba_species_df.columns = ['_'.join(column.split('.')) for column in kba_species_df.columns]\n",
"\n",
"# Checking for missing Values\n",
"print('kba_data dataframe')\n",
"print('-' * 18)\n",
"print(kba_data_df.isnull().sum(), '\\n')\n",
"\n",
"print('kba_names dataframe')\n",
"print('-' * 19)\n",
"print(kba_names_df.isnull().sum(), '\\n')\n",
"\n",
"print('kba_species dataframe')\n",
"print('-' * 21)\n",
"print(kba_species_df.isnull().sum())"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "a26a8c66",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[34;1mSub_cell\u001b[0m is having \u001b[31;1m127\u001b[0m missing values ie, \u001b[31;1m0.0422%\u001b[0m of total data\n",
"\u001b[34;1mCell_ID\u001b[0m is having \u001b[31;1m127\u001b[0m missing values ie, \u001b[31;1m0.0422%\u001b[0m of total data\n",
"\u001b[34;1mList_ID\u001b[0m is having \u001b[31;1m127\u001b[0m missing values ie, \u001b[31;1m0.0422%\u001b[0m of total data\n",
"\n",
"Shape of '\u001b[34;1mkba_data_df\u001b[0m' after dropping columns \u001b[34;1m(300882, 7)\n"
]
}
],
"source": [
"missing_val_columns = ['Sub_cell', 'Cell_ID', 'List_ID']\n",
"\n",
"for column in missing_val_columns:\n",
" missing_count = kba_data_df[column].isnull().sum()\n",
" per_value = missing_count * 100 / kba_data_df.shape[0]\n",
" print(f'{text_co.blue}{column}{text_co.reset} is having {text_co.red}{missing_count}{text_co.reset} missing values ie, {text_co.red}{per_value:.4f}%{text_co.reset} of total data')\n",
"\n",
"kba_data_df.drop(missing_val_columns, 1, inplace = True)\n",
"\n",
"print(f\"\\nShape of '{text_co.blue}kba_data_df{text_co.reset}' after dropping columns {text_co.blue}{kba_data_df.shape}\")"
]
},
{
"cell_type": "markdown",
"id": "59cbc609",
"metadata": {},
"source": [
"# Generating New Features"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "a59c2276-16ae-4fde-b130-7633a8f03fab",
"metadata": {},
"outputs": [],
"source": [
"# Some names in 'kba_data_df' and 'kba_species_df' are different, so correcting it\n",
"\n",
"kba_data_df['Common_Name'] = kba_data_df.Common_Name.apply(lambda x : re.sub(r'Gray Heron', 'Grey Heron', x))\n",
"kba_data_df['Common_Name'] = kba_data_df.Common_Name.apply(lambda x : re.sub(r'Gray Wagtail', 'Grey Wagtail', x))\n",
"kba_data_df['Common_Name'] = kba_data_df.Common_Name.apply(lambda x : re.sub(r'Gray Francolin', 'Grey Francolin', x))\n",
"kba_data_df['Common_Name'] = kba_data_df.Common_Name.apply(lambda x : re.sub(r'Dollarbird', 'Oriental Dollarbird', x))\n",
"kba_data_df['Common_Name'] = kba_data_df.Common_Name.apply(lambda x : re.sub(r'Gray Junglefowl', 'Grey Junglefowl', x))\n",
"kba_data_df['Common_Name'] = kba_data_df.Common_Name.apply(lambda x : re.sub(r'Rock Pigeon', 'Rock Pigeon (Feral)', x))\n",
"kba_data_df['Common_Name'] = kba_data_df.Common_Name.apply(lambda x : re.sub(r'Gray-headed Bulbul', 'Grey-headed Bulbul', x))\n",
"kba_data_df['Common_Name'] = kba_data_df.Common_Name.apply(lambda x : re.sub(r'Large Gray Babbler', 'Large Grey Babbler', x))\n",
"kba_data_df['Common_Name'] = kba_data_df.Common_Name.apply(lambda x : re.sub(r'Gray-bellied Cuckoo', 'Grey-bellied Cuckoo', x))\n",
"kba_data_df['Common_Name'] = kba_data_df.Common_Name.apply(lambda x : re.sub(r'Gray-headed Lapwing', 'Grey-headed Lapwing', x))\n",
"kba_data_df['Common_Name'] = kba_data_df.Common_Name.apply(lambda x : re.sub(r'Gray-headed Swamphen', 'Grey-headed Swamphen', x))\n",
"kba_data_df['Common_Name'] = kba_data_df.Common_Name.apply(lambda x : re.sub(r'Gray-breasted Prinia', 'Grey-breasted Prinia', x))\n",
"kba_data_df['Common_Name'] = kba_data_df.Common_Name.apply(lambda x : re.sub(r'Indian Gray Hornbill', 'Indian Grey Hornbill', x))\n",
"kba_data_df['Common_Name'] = kba_data_df.Common_Name.apply(lambda x : re.sub(r'Malabar Gray Hornbill', 'Malabar Grey Hornbill', x))\n",
"kba_data_df['Common_Name'] = kba_data_df.Common_Name.apply(lambda x : re.sub(r'Gray-fronted Green-Pigeon', 'Grey-fronted Green-Pigeon', x))\n",
"kba_data_df['Common_Name'] = kba_data_df.Common_Name.apply(lambda x : re.sub(r'Gray-headed Canary-Flycatcher', 'Grey-headed Canary-Flycatcher', x))"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "9c766760",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<style type=\"text/css\">\n",
"#T_7da17 caption {\n",
" text-align: center;\n",
" font-size: 160%;\n",
" color: #135EA9;\n",
" background-color: #F6E7DB;\n",
"}\n",
"</style>\n",
"<table id=\"T_7da17\">\n",
" <caption>Top 5 rows of 'kba_data_df' DataFrame</caption>\n",
" <thead>\n",
" <tr>\n",
" <th class=\"blank level0\" > </th>\n",
" <th id=\"T_7da17_level0_col0\" class=\"col_heading level0 col0\" >Common_Name</th>\n",
" <th id=\"T_7da17_level0_col1\" class=\"col_heading level0 col1\" >Date</th>\n",
" <th id=\"T_7da17_level0_col2\" class=\"col_heading level0 col2\" >Time</th>\n",
" <th id=\"T_7da17_level0_col3\" class=\"col_heading level0 col3\" >n_observers</th>\n",
" <th id=\"T_7da17_level0_col4\" class=\"col_heading level0 col4\" >County</th>\n",
" <th id=\"T_7da17_level0_col5\" class=\"col_heading level0 col5\" >Season</th>\n",
" <th id=\"T_7da17_level0_col6\" class=\"col_heading level0 col6\" >Time_Hour</th>\n",
" <th id=\"T_7da17_level0_col7\" class=\"col_heading level0 col7\" >AM/PM</th>\n",
" <th id=\"T_7da17_level0_col8\" class=\"col_heading level0 col8\" >DEM</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th id=\"T_7da17_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
" <td id=\"T_7da17_row0_col0\" class=\"data row0 col0\" >Asian Koel</td>\n",
" <td id=\"T_7da17_row0_col1\" class=\"data row0 col1\" >2015-07-16 00:00:00</td>\n",
" <td id=\"T_7da17_row0_col2\" class=\"data row0 col2\" >16:30</td>\n",
" <td id=\"T_7da17_row0_col3\" class=\"data row0 col3\" >2</td>\n",
" <td id=\"T_7da17_row0_col4\" class=\"data row0 col4\" >Alappuzha</td>\n",
" <td id=\"T_7da17_row0_col5\" class=\"data row0 col5\" >Wet</td>\n",
" <td id=\"T_7da17_row0_col6\" class=\"data row0 col6\" >16</td>\n",
" <td id=\"T_7da17_row0_col7\" class=\"data row0 col7\" >PM</td>\n",
" <td id=\"T_7da17_row0_col8\" class=\"data row0 col8\" >5.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_7da17_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
" <td id=\"T_7da17_row1_col0\" class=\"data row1 col0\" >Black-rumped Flameback</td>\n",
" <td id=\"T_7da17_row1_col1\" class=\"data row1 col1\" >2015-07-16 00:00:00</td>\n",
" <td id=\"T_7da17_row1_col2\" class=\"data row1 col2\" >16:30</td>\n",
" <td id=\"T_7da17_row1_col3\" class=\"data row1 col3\" >2</td>\n",
" <td id=\"T_7da17_row1_col4\" class=\"data row1 col4\" >Alappuzha</td>\n",
" <td id=\"T_7da17_row1_col5\" class=\"data row1 col5\" >Wet</td>\n",
" <td id=\"T_7da17_row1_col6\" class=\"data row1 col6\" >16</td>\n",
" <td id=\"T_7da17_row1_col7\" class=\"data row1 col7\" >PM</td>\n",
" <td id=\"T_7da17_row1_col8\" class=\"data row1 col8\" >5.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_7da17_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
" <td id=\"T_7da17_row2_col0\" class=\"data row2 col0\" >Black Drongo</td>\n",
" <td id=\"T_7da17_row2_col1\" class=\"data row2 col1\" >2015-07-16 00:00:00</td>\n",
" <td id=\"T_7da17_row2_col2\" class=\"data row2 col2\" >16:30</td>\n",
" <td id=\"T_7da17_row2_col3\" class=\"data row2 col3\" >2</td>\n",
" <td id=\"T_7da17_row2_col4\" class=\"data row2 col4\" >Alappuzha</td>\n",
" <td id=\"T_7da17_row2_col5\" class=\"data row2 col5\" >Wet</td>\n",
" <td id=\"T_7da17_row2_col6\" class=\"data row2 col6\" >16</td>\n",
" <td id=\"T_7da17_row2_col7\" class=\"data row2 col7\" >PM</td>\n",
" <td id=\"T_7da17_row2_col8\" class=\"data row2 col8\" >5.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_7da17_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
" <td id=\"T_7da17_row3_col0\" class=\"data row3 col0\" >Brahminy Kite</td>\n",
" <td id=\"T_7da17_row3_col1\" class=\"data row3 col1\" >2015-07-16 00:00:00</td>\n",
" <td id=\"T_7da17_row3_col2\" class=\"data row3 col2\" >16:30</td>\n",
" <td id=\"T_7da17_row3_col3\" class=\"data row3 col3\" >2</td>\n",
" <td id=\"T_7da17_row3_col4\" class=\"data row3 col4\" >Alappuzha</td>\n",
" <td id=\"T_7da17_row3_col5\" class=\"data row3 col5\" >Wet</td>\n",
" <td id=\"T_7da17_row3_col6\" class=\"data row3 col6\" >16</td>\n",
" <td id=\"T_7da17_row3_col7\" class=\"data row3 col7\" >PM</td>\n",
" <td id=\"T_7da17_row3_col8\" class=\"data row3 col8\" >5.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_7da17_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
" <td id=\"T_7da17_row4_col0\" class=\"data row4 col0\" >Common Myna</td>\n",
" <td id=\"T_7da17_row4_col1\" class=\"data row4 col1\" >2015-07-16 00:00:00</td>\n",
" <td id=\"T_7da17_row4_col2\" class=\"data row4 col2\" >16:30</td>\n",
" <td id=\"T_7da17_row4_col3\" class=\"data row4 col3\" >2</td>\n",
" <td id=\"T_7da17_row4_col4\" class=\"data row4 col4\" >Alappuzha</td>\n",
" <td id=\"T_7da17_row4_col5\" class=\"data row4 col5\" >Wet</td>\n",
" <td id=\"T_7da17_row4_col6\" class=\"data row4 col6\" >16</td>\n",
" <td id=\"T_7da17_row4_col7\" class=\"data row4 col7\" >PM</td>\n",
" <td id=\"T_7da17_row4_col8\" class=\"data row4 col8\" >5.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n"
],
"text/plain": [
"<pandas.io.formats.style.Styler at 0x7fe128533100>"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Convert 'Date' column from 'category' to 'datetime64[ns]'\n",
"\n",
"kba_data_df['Date'] = pd.to_datetime(kba_data_df['Date']).astype('datetime64')\n",
"\n",
"# Converting 'Time' column to integer for creating new feature\n",
"\n",
"# https://www.geeksforgeeks.org/python-get-first-element-of-each-sublist/\n",
"# https://www.geeksforgeeks.org/python-converting-all-strings-in-list-to-integers/\n",
"\n",
"# kba_data_df['Time_Hour'] = list(map(int, list(map(itemgetter(0), kba_data_df.Time.str.split(':')))))\n",
"kba_data_df.insert(6, 'Time_Hour', list(map(int, list(map(itemgetter(0), kba_data_df.Time.str.split(':'))))))\n",
"'''\n",
"map()'s making complicated ??? Instead of this we can use the bellow also. Dose the same job.\n",
"\n",
"kba_data_df['Time2'] = list(map(itemgetter(0), kba_data_df.Time.str.split(':')))\n",
"kba_data_df['Time2'] = kba_data_df['Time2'].astype('int64')\n",
"'''\n",
"# kba_data_df['AM/PM'] = ['PM' if kba_data_df['Time2'][i]>12 else 'AM' for i in range(kba_data_df.shape[0])]\n",
"\n",
"kba_data_df.insert(7, 'AM/PM', ['PM' if kba_data_df['Time_Hour'][i]>12 else 'AM' for i in range(kba_data_df.shape[0])]\n",
")\n",
"\n",
"# kba_data_df.drop('Time_Hour', 1, inplace = True)\n",
"\n",
"kba_data_df['n_observers'] = pd.to_datetime(kba_data_df['n_observers']).astype('int64') # Human count is always int, so converting into int.\n",
"\n",
"styles = [dict(selector='caption', props=[('text-align', 'center'), ('font-size', '160%'), ('color', '#135EA9'), ('background-color' , '#F6E7DB')])]\n",
"\n",
"kba_data_df.head(5).style.set_caption(\"Top 5 rows of 'kba_data_df' DataFrame\").set_table_styles(styles)"
]
},
{
"cell_type": "markdown",
"id": "c3e6b845",
"metadata": {},
"source": [
"# Number of Distinct Birds Observed"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "a9ef339b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of distinct birds observed : \u001b[34;1m492\n",
"\n"
]
},
{
"data": {
"text/html": [
"<style type=\"text/css\">\n",
"#T_ba98c caption {\n",
" text-align: center;\n",
" font-size: 160%;\n",
" color: #135EA9;\n",
" background-color: #F6E7DB;\n",
"}\n",
"#T_ba98c_row0_col1 {\n",
" background-color: #08306b;\n",
" color: #f1f1f1;\n",
"}\n",
"#T_ba98c_row1_col1 {\n",
" background-color: #0e59a2;\n",
" color: #f1f1f1;\n",
"}\n",
"#T_ba98c_row2_col1 {\n",
" background-color: #5ca4d0;\n",
" color: #f1f1f1;\n",
"}\n",
"#T_ba98c_row3_col1 {\n",
" background-color: #6dafd7;\n",
" color: #f1f1f1;\n",
"}\n",
"#T_ba98c_row4_col1 {\n",
" background-color: #84bcdb;\n",
" color: #000000;\n",
"}\n",
"#T_ba98c_row5_col1 {\n",
" background-color: #94c4df;\n",
" color: #000000;\n",
"}\n",
"#T_ba98c_row6_col1 {\n",
" background-color: #9fcae1;\n",
" color: #000000;\n",
"}\n",
"#T_ba98c_row7_col1 {\n",
" background-color: #a0cbe2;\n",
" color: #000000;\n",
"}\n",
"#T_ba98c_row8_col1 {\n",
" background-color: #add0e6;\n",
" color: #000000;\n",
"}\n",
"#T_ba98c_row9_col1 {\n",
" background-color: #afd1e7;\n",
" color: #000000;\n",
"}\n",
"#T_ba98c_row10_col1 {\n",
" background-color: #cfe1f2;\n",
" color: #000000;\n",
"}\n",
"#T_ba98c_row11_col1 {\n",
" background-color: #d9e8f5;\n",
" color: #000000;\n",
"}\n",
"#T_ba98c_row12_col1 {\n",
" background-color: #dbe9f6;\n",
" color: #000000;\n",
"}\n",
"#T_ba98c_row13_col1 {\n",
" background-color: #e3eef8;\n",
" color: #000000;\n",
"}\n",
"#T_ba98c_row14_col1 {\n",
" background-color: #e7f0fa;\n",
" color: #000000;\n",
"}\n",
"#T_ba98c_row15_col1, #T_ba98c_row16_col1 {\n",
" background-color: #eef5fc;\n",
" color: #000000;\n",
"}\n",
"#T_ba98c_row17_col1 {\n",
" background-color: #f2f7fd;\n",
" color: #000000;\n",
"}\n",
"#T_ba98c_row18_col1 {\n",
" background-color: #f4f9fe;\n",
" color: #000000;\n",
"}\n",
"#T_ba98c_row19_col1 {\n",
" background-color: #f7fbff;\n",
" color: #000000;\n",
"}\n",
"</style>\n",
"<table id=\"T_ba98c\">\n",
" <caption>Most visited 20 birds with visit count</caption>\n",
" <thead>\n",
" <tr>\n",
" <th class=\"blank level0\" > </th>\n",
" <th id=\"T_ba98c_level0_col0\" class=\"col_heading level0 col0\" >Bird_Name</th>\n",
" <th id=\"T_ba98c_level0_col1\" class=\"col_heading level0 col1\" >Count</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th id=\"T_ba98c_level0_row0\" class=\"row_heading level0 row0\" >1</th>\n",
" <td id=\"T_ba98c_row0_col0\" class=\"data row0 col0\" >White-cheeked Barbet</td>\n",
" <td id=\"T_ba98c_row0_col1\" class=\"data row0 col1\" >13860</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_ba98c_level0_row1\" class=\"row_heading level0 row1\" >2</th>\n",
" <td id=\"T_ba98c_row1_col0\" class=\"data row1 col0\" >House Crow</td>\n",
" <td id=\"T_ba98c_row1_col1\" class=\"data row1 col1\" >12381</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_ba98c_level0_row2\" class=\"row_heading level0 row2\" >3</th>\n",
" <td id=\"T_ba98c_row2_col0\" class=\"data row2 col0\" >Large-billed Crow</td>\n",
" <td id=\"T_ba98c_row2_col1\" class=\"data row2 col1\" >9641</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_ba98c_level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
" <td id=\"T_ba98c_row3_col0\" class=\"data row3 col0\" >Common Myna</td>\n",
" <td id=\"T_ba98c_row3_col1\" class=\"data row3 col1\" >9147</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_ba98c_level0_row4\" class=\"row_heading level0 row4\" >5</th>\n",
" <td id=\"T_ba98c_row4_col0\" class=\"data row4 col0\" >Rufous Treepie</td>\n",
" <td id=\"T_ba98c_row4_col1\" class=\"data row4 col1\" >8655</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_ba98c_level0_row5\" class=\"row_heading level0 row5\" >6</th>\n",
" <td id=\"T_ba98c_row5_col0\" class=\"data row5 col0\" >Greater Coucal</td>\n",
" <td id=\"T_ba98c_row5_col1\" class=\"data row5 col1\" >8265</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_ba98c_level0_row6\" class=\"row_heading level0 row6\" >7</th>\n",
" <td id=\"T_ba98c_row6_col0\" class=\"data row6 col0\" >White-throated Kingfisher</td>\n",
" <td id=\"T_ba98c_row6_col1\" class=\"data row6 col1\" >8017</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_ba98c_level0_row7\" class=\"row_heading level0 row7\" >8</th>\n",
" <td id=\"T_ba98c_row7_col0\" class=\"data row7 col0\" >Purple-rumped Sunbird</td>\n",
" <td id=\"T_ba98c_row7_col1\" class=\"data row7 col1\" >8003</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_ba98c_level0_row8\" class=\"row_heading level0 row8\" >9</th>\n",
" <td id=\"T_ba98c_row8_col0\" class=\"data row8 col0\" >Common Tailorbird</td>\n",
" <td id=\"T_ba98c_row8_col1\" class=\"data row8 col1\" >7612</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_ba98c_level0_row9\" class=\"row_heading level0 row9\" >10</th>\n",
" <td id=\"T_ba98c_row9_col0\" class=\"data row9 col0\" >Red-whiskered Bulbul</td>\n",
" <td id=\"T_ba98c_row9_col1\" class=\"data row9 col1\" >7567</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_ba98c_level0_row10\" class=\"row_heading level0 row10\" >11</th>\n",
" <td id=\"T_ba98c_row10_col0\" class=\"data row10 col0\" >Greater Racket-tailed Drongo</td>\n",
" <td id=\"T_ba98c_row10_col1\" class=\"data row10 col1\" >6456</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_ba98c_level0_row11\" class=\"row_heading level0 row11\" >12</th>\n",
" <td id=\"T_ba98c_row11_col0\" class=\"data row11 col0\" >Indian Pond-Heron</td>\n",
" <td id=\"T_ba98c_row11_col1\" class=\"data row11 col1\" >5962</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_ba98c_level0_row12\" class=\"row_heading level0 row12\" >13</th>\n",
" <td id=\"T_ba98c_row12_col0\" class=\"data row12 col0\" >Oriental Magpie-Robin</td>\n",
" <td id=\"T_ba98c_row12_col1\" class=\"data row12 col1\" >5872</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_ba98c_level0_row13\" class=\"row_heading level0 row13\" >14</th>\n",
" <td id=\"T_ba98c_row13_col0\" class=\"data row13 col0\" >Black Drongo</td>\n",
" <td id=\"T_ba98c_row13_col1\" class=\"data row13 col1\" >5526</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_ba98c_level0_row14\" class=\"row_heading level0 row14\" >15</th>\n",
" <td id=\"T_ba98c_row14_col0\" class=\"data row14 col0\" >Jungle Babbler</td>\n",
" <td id=\"T_ba98c_row14_col1\" class=\"data row14 col1\" >5330</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_ba98c_level0_row15\" class=\"row_heading level0 row15\" >16</th>\n",
" <td id=\"T_ba98c_row15_col0\" class=\"data row15 col0\" >Black-rumped Flameback</td>\n",
" <td id=\"T_ba98c_row15_col1\" class=\"data row15 col1\" >5026</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_ba98c_level0_row16\" class=\"row_heading level0 row16\" >17</th>\n",
" <td id=\"T_ba98c_row16_col0\" class=\"data row16 col0\" >Pale-billed Flowerpecker</td>\n",
" <td id=\"T_ba98c_row16_col1\" class=\"data row16 col1\" >4969</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_ba98c_level0_row17\" class=\"row_heading level0 row17\" >18</th>\n",
" <td id=\"T_ba98c_row17_col0\" class=\"data row17 col0\" >Black-hooded Oriole</td>\n",
" <td id=\"T_ba98c_row17_col1\" class=\"data row17 col1\" >4813</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_ba98c_level0_row18\" class=\"row_heading level0 row18\" >19</th>\n",
" <td id=\"T_ba98c_row18_col0\" class=\"data row18 col0\" >Asian Koel</td>\n",
" <td id=\"T_ba98c_row18_col1\" class=\"data row18 col1\" >4708</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_ba98c_level0_row19\" class=\"row_heading level0 row19\" >20</th>\n",
" <td id=\"T_ba98c_row19_col0\" class=\"data row19 col0\" >Rock Pigeon (Feral)</td>\n",
" <td id=\"T_ba98c_row19_col1\" class=\"data row19 col1\" >4554</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n"
],
"text/plain": [
"<pandas.io.formats.style.Styler at 0x7fe0df8bf730>"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print(f'Number of distinct birds observed : {text_co.blue}{kba_data_df.Common_Name.nunique()}\\n')\n",
"\n",
"temp_df = pd.DataFrame(kba_data_df.Common_Name.value_counts())\n",
"temp_df.insert(0, 'Bird_Name', temp_df.index)\n",
"temp_df.index = np.arange(len(temp_df.index)) + 1\n",
"temp_df.rename(columns={'Common_Name': 'Count'}, inplace=True)\n",
"\n",
"temp_df.head(20).style.background_gradient(cmap = 'Blues').set_caption('Most visited 20 birds with visit count').set_table_styles(styles)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "fc2681ea",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of birds that only visited \u001b[34;1m1\u001b[0m time during the entire survey \u001b[31;1m:\u001b[0m \u001b[34;1m31\u001b[0m\n",
"Number of birds that only visited \u001b[34;1m2\u001b[0m time during the entire survey \u001b[31;1m:\u001b[0m \u001b[34;1m23\u001b[0m\n",
"Number of birds that only visited \u001b[34;1m4\u001b[0m time during the entire survey \u001b[31;1m:\u001b[0m \u001b[34;1m21\u001b[0m\n",
"Number of birds that only visited \u001b[34;1m3\u001b[0m time during the entire survey \u001b[31;1m:\u001b[0m \u001b[34;1m20\u001b[0m\n",
"Number of birds that only visited \u001b[34;1m5\u001b[0m time during the entire survey \u001b[31;1m:\u001b[0m \u001b[34;1m14\u001b[0m\n"
]
}
],
"source": [
"bird_visit_df = pd.DataFrame(temp_df.Count.value_counts(ascending=False)).reset_index().rename(columns = {'index' : 'No_of_visit', 'Count' : 'Bird_Count'}).sort_values('No_of_visit')\n",
"\n",
"for i in range(5):\n",
" print(f'Number of birds that only visited {text_co.blue}{bird_visit_df.loc[i][0]}{text_co.reset} time during the entire survey {text_co.red}:{text_co.reset} {text_co.blue}{bird_visit_df.loc[i][1]}{text_co.reset}')"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "31f46633",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 1440x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(20,8))\n",
"sns.lineplot(x = bird_visit_df[bird_visit_df.No_of_visit < 200].No_of_visit, y = bird_visit_df[bird_visit_df.No_of_visit < 200].Bird_Count)\n",
"plt.suptitle('Number of Birds vs Visit chart', fontsize = 30, c = '#1D32E2')\n",
"plt.title('Where no. of visit by a single bird is < 200', fontsize = 15, c = '#C79438')\n",
"plt.xlabel('Number of visits')\n",
"plt.ylabel('Number of birds')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "f71553b0",
"metadata": {},
"source": [
"<div class=\"alert alert-warning\" role=\"alert\">\n",
"<p><strong><font color = 'red'>Q</font>.</strong> Why we are exactly limited the graph to <code>< 200</code> <font color = 'red'>??</font></p>\n",
"<p>The curve is became almost flat after 200. This indicates that <em>if a bird visited more than 200 times, then the bird count is 1 or 2 and for higher visits it becomes simply 1</em>\n",
"</div>"
]
},
{
"cell_type": "markdown",
"id": "22388f41-b547-4d36-8546-8e33edf6c642",
"metadata": {
"tags": []
},
"source": [
"<div class=\"alert alert-success\" role=\"alert\">\n",
" <h3><font color ='red'>Observation</font></h3>\n",
" <ul>\n",
" <li><strong>White-cheeked Barbet</strong> (which is a <em>Resident</em> one) visited for <strong>13,860</strong> times during the survey period and this is the most recorded bird.</li>\n",
" <li><em>White-cheeked Barbet</em> and <em>House Crow</em> had over <strong>10,000</strong> records.</li>\n",
" <li>There are <strong>31</strong> birds, those had only <em>single occurance</em> in the database, means only one visit during the entire survey.</li>\n",
" </ul>\n",
" </div>"
]
},
{
"cell_type": "markdown",
"id": "da239c44",
"metadata": {},
"source": [
"# Categorizing Birds Based on the Occurance of Records (Both Dry and Wet Season Combined)\n",
"\n",
"<strong>Categorization Criteria</strong>\n",
"<ul>\n",
"<li>Very Rare    ::: 0.1% of max records</li>\n",
"<li>Rare      ::: 0.1 - 1% of max records</li>\n",
"<li>Common    ::: 1 - 10% of max records</li>\n",
"<li>Very Common  ::: 10 - 50% of max records</li>\n",
"<li>Most Abundant  ::: > 50% of max records</li>\n",
"</ul>"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "a59efebc",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Maximum recorded count for \u001b[34;1m1\u001b[0m bird \u001b[31;1m:\u001b[0m \u001b[34m13860\u001b[0m\n",
"\n"
]
},
{
"data": {
"text/html": [
"<style type=\"text/css\">\n",
"#T_e99a0 caption {\n",
" text-align: center;\n",
" font-size: 160%;\n",
" color: #135EA9;\n",
" background-color: #F6E7DB;\n",
"}\n",
"#T_e99a0_row0_col2, #T_e99a0_row3_col3, #T_e99a0_row3_col4 {\n",
" background-color: #DBEAF6;\n",
"}\n",
"</style>\n",
"<table id=\"T_e99a0\">\n",
" <caption>Categorizing birds based on the occurance in record data</caption>\n",
" <thead>\n",
" <tr>\n",
" <th class=\"blank level0\" > </th>\n",
" <th id=\"T_e99a0_level0_col0\" class=\"col_heading level0 col0\" >Category</th>\n",
" <th id=\"T_e99a0_level0_col1\" class=\"col_heading level0 col1\" >Classification Criterion</th>\n",
" <th id=\"T_e99a0_level0_col2\" class=\"col_heading level0 col2\" >Birds Count</th>\n",
" <th id=\"T_e99a0_level0_col3\" class=\"col_heading level0 col3\" >Total Records</th>\n",
" <th id=\"T_e99a0_level0_col4\" class=\"col_heading level0 col4\" >% Contribution to KBA Dataset</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th id=\"T_e99a0_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
" <td id=\"T_e99a0_row0_col0\" class=\"data row0 col0\" >Very Rare</td>\n",
" <td id=\"T_e99a0_row0_col1\" class=\"data row0 col1\" >0.1% of max records</td>\n",
" <td id=\"T_e99a0_row0_col2\" class=\"data row0 col2\" >167</td>\n",
" <td id=\"T_e99a0_row0_col3\" class=\"data row0 col3\" >796</td>\n",
" <td id=\"T_e99a0_row0_col4\" class=\"data row0 col4\" >0.260000</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_e99a0_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
" <td id=\"T_e99a0_row1_col0\" class=\"data row1 col0\" >Rare</td>\n",
" <td id=\"T_e99a0_row1_col1\" class=\"data row1 col1\" >0.1-1% of max records</td>\n",
" <td id=\"T_e99a0_row1_col2\" class=\"data row1 col2\" >148</td>\n",
" <td id=\"T_e99a0_row1_col3\" class=\"data row1 col3\" >8056</td>\n",
" <td id=\"T_e99a0_row1_col4\" class=\"data row1 col4\" >2.680000</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_e99a0_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
" <td id=\"T_e99a0_row2_col0\" class=\"data row2 col0\" >Common</td>\n",
" <td id=\"T_e99a0_row2_col1\" class=\"data row2 col1\" >1-10% of max records</td>\n",
" <td id=\"T_e99a0_row2_col2\" class=\"data row2 col2\" >123</td>\n",
" <td id=\"T_e99a0_row2_col3\" class=\"data row2 col3\" >62642</td>\n",
" <td id=\"T_e99a0_row2_col4\" class=\"data row2 col4\" >20.820000</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_e99a0_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
" <td id=\"T_e99a0_row3_col0\" class=\"data row3 col0\" >Very Common</td>\n",
" <td id=\"T_e99a0_row3_col1\" class=\"data row3 col1\" >10-50% of max records</td>\n",
" <td id=\"T_e99a0_row3_col2\" class=\"data row3 col2\" >44</td>\n",
" <td id=\"T_e99a0_row3_col3\" class=\"data row3 col3\" >136240</td>\n",
" <td id=\"T_e99a0_row3_col4\" class=\"data row3 col4\" >45.280000</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_e99a0_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
" <td id=\"T_e99a0_row4_col0\" class=\"data row4 col0\" >Most Abundant</td>\n",
" <td id=\"T_e99a0_row4_col1\" class=\"data row4 col1\" >> 50% of max records</td>\n",
" <td id=\"T_e99a0_row4_col2\" class=\"data row4 col2\" >10</td>\n",
" <td id=\"T_e99a0_row4_col3\" class=\"data row4 col3\" >93148</td>\n",
" <td id=\"T_e99a0_row4_col4\" class=\"data row4 col4\" >30.960000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n"
],
"text/plain": [
"<pandas.io.formats.style.Styler at 0x7fe0df7fc2b0>"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"max_records = temp_df.Count.values[0]\n",
"very_rare = int(0.1 / 100 * max_records)\n",
"rare = int(1 / 100 * max_records)\n",
"common = int(10 / 100 * max_records)\n",
"very_common = int(50 / 100 * max_records)\n",
"\n",
"print(f' Maximum recorded count for {text_co.blue}1{text_co.reset} bird {text_co.red}:{text_co.reset} {text_co.light_blue}{max_records}{text_co.reset}\\n')\n",
"\n",
"category = ['Very Rare', 'Rare', 'Common', 'Very Common', 'Most Abundant']\n",
"criterion_text = ['0.1% of max records', '0.1-1% of max records', '1-10% of max records', '10-50% of max records', '> 50% of max records']\n",
"\n",
"cr_very_rare = temp_df.Count.values <= very_rare\n",
"cr_rare = (temp_df.Count.values > very_rare) & (temp_df.Count.values <= rare)\n",
"cr_common = (temp_df.Count.values > rare) & (temp_df.Count.values <= common)\n",
"cr_very_common = (temp_df.Count.values > common) & (temp_df.Count.values <= very_common)\n",
"cr_most_abundant = temp_df.Count.values > very_common\n",
"\n",
"criterion_list = [cr_very_rare, cr_rare, cr_common, cr_very_common, cr_most_abundant]\n",
"\n",
"species_count, bird_name_cr, cont_percentage, total_records = [], [], [], []\n",
"\n",
"for criterion in criterion_list:\n",
" species_count.append(np.count_nonzero(criterion))\n",
" bird_name_cr.append(temp_df[criterion].Bird_Name.values)\n",
"\n",
"for count, name in zip(species_count, bird_name_cr):\n",
" sum_ = 0\n",
" for i in range(count):\n",
" count = len(kba_data_df[kba_data_df.Common_Name == name[i]])\n",
" sum_ += count\n",
" total_records.append(sum_)\n",
" cont_percentage.append(round(sum_ * 100 / len(kba_data_df), 2))\n",
" \n",
"birds_distribution = pd.DataFrame({ 'Category' : category, 'Classification Criterion' : criterion_text,\n",
" 'Birds Count' : species_count, 'Total Records' : total_records,\n",
" '% Contribution to KBA Dataset' : cont_percentage})\n",
"\n",
"# https://datascientyst.com/set-caption-customize-font-size-color-in-pandas-dataframe/\n",
"\n",
"df_caption = 'Categorizing birds based on the occurance in record data'\n",
"\n",
"styles = [dict(selector='caption', props=[('text-align', 'center'), ('font-size', '160%'), ('color', '#135EA9'), ('background-color' , '#F6E7DB')])]\n",
"\n",
"birds_distribution.style.set_caption(df_caption).set_table_styles(styles).highlight_max(subset = ['Birds Count', 'Total Records', '% Contribution to KBA Dataset'], color = '#DBEAF6')"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "b90163a4",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"All the '\u001b[34;1mkba_data_set\u001b[0m' birds present in '\u001b[34;1mkba_species_set\u001b[0m' \u001b[31;1m? \u001b[34;1mFalse\u001b[0m\n",
"\n",
"What is the diffence in bird count on these 2 data sets \u001b[31;1m? \u001b[31;1m:\u001b[0m \u001b[34;1m131\u001b[0m\n",
"\n",
"Shape of new joined data frame \u001b[31;1m:\u001b[0m \u001b[34;1m(291543, 17)\u001b[0m\n",
"\n",
"Current number of unique birds in joined dataframe \u001b[31;1m:\u001b[0m \u001b[34;1m361\n"
]
}
],
"source": [
"# Let's join 'kba_data_df' and 'kba_species_df' on 'Common_Name' for further analysis\n",
"\n",
"kba_species_set = set(kba_species_df.Common_Name.unique())\n",
"kba_data_set = set(kba_data_df.Common_Name.unique())\n",
"\n",
"print(f\"All the '{text_co.blue}kba_data_set{text_co.reset}' birds present in '{text_co.blue}kba_species_set{text_co.reset}' {text_co.red}? {text_co.blue}{len(kba_data_set) == len(kba_species_set)}{text_co.reset}\\n\")\n",
"print(f'What is the diffence in bird count on these 2 data sets {text_co.red}? {text_co.red}:{text_co.reset} {text_co.blue}{len(kba_data_set) - len(kba_species_set)}{text_co.reset}\\n')\n",
"\n",
"kba_species_data = kba_data_df.merge(kba_species_df, on = 'Common_Name')\n",
"\n",
"print(f'Shape of new joined data frame {text_co.red}:{text_co.reset} {text_co.blue}{kba_species_data.shape}{text_co.reset}\\n')\n",
"\n",
"print(f'Current number of unique birds in joined dataframe {text_co.red}:{text_co.reset} {text_co.blue}{kba_species_data.Common_Name.nunique()}')"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "7eb633a1",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 1440x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# https://stackoverflow.com/a/41924823\n",
"\n",
"district_wise_count = pd.DataFrame(kba_species_data.County.value_counts()).reset_index().rename(columns = {'index' : 'District_Name', 'County' : 'Bird_Count'}).sort_values('Bird_Count', ascending = True).reset_index(drop=True)\n",
"median_count = int(district_wise_count.Bird_Count.median())\n",
"\n",
"plt.figure(figsize=(20,8))\n",
"sns.lineplot(x = district_wise_count.District_Name, y= district_wise_count.Bird_Count)\n",
"plt.suptitle('Bird Count vs Districts', fontsize = 30, c = '#1D32E2')\n",
"plt.title('Total number of birds counted from each district of Kerala', fontsize = 15, c = '#C79438')\n",
"plt.xlabel('District Names of Kerala')\n",
"plt.ylabel('Number of birds counted')\n",
"plt.xticks(rotation = 20)\n",
"plt.axhline(median_count, c = 'g', linestyle = '-.', linewidth = 1.5)\n",
"plt.text('Kottayam', median_count , f'Median count :: {median_count}', fontsize = 14, va = 'center', ha = 'center', backgroundcolor = 'w', c = 'r')\n",
"\n",
"plt.savefig('images/bird_vs_district.svg', bbox_inches = 'tight', pad_inches = 0.3)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "e3ed4110",
"metadata": {},
"source": [
"<div class=\"alert alert-success\" role=\"alert\">\n",
" <h3><font color ='red'>Observation</font></h3>\n",
" <ul>\n",
" <li>We have about <strong>0.26%</strong> (<em>167 Birds</em>) who are classified as <strong>very rare</strong> dring the course of survey.</li>\n",
" <li><strong>44</strong> birds are classified as <strong>very common</strong> by showing <em>1,36,240</em> times during the survey period.</li>\n",
" <li>With over <strong>35,000</strong> bird watch <strong>Palakkad</strong> became one of the most bird watched district in Kerala</li>\n",
" </ul>\n",
"</div>"
]
},
{
"cell_type": "markdown",
"id": "322dce30-0aa9-473e-bfd2-d1cce465ad34",
"metadata": {},
"source": [
"# Resident Status of Birds"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "4f964668",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<style type=\"text/css\">\n",
"#T_11dd6 caption {\n",
" text-align: center;\n",
" font-size: 160%;\n",
" color: #135EA9;\n",
" background-color: #F6E7DB;\n",
"}\n",
"#T_11dd6_row0_col1, #T_11dd6_row0_col2 {\n",
" background-color: #DBEAF6;\n",
"}\n",
"</style>\n",
"<table id=\"T_11dd6\">\n",
" <caption>Categorizing birds based on their Resident Status</caption>\n",
" <thead>\n",
" <tr>\n",
" <th class=\"blank level0\" > </th>\n",
" <th id=\"T_11dd6_level0_col0\" class=\"col_heading level0 col0\" >Resident Status of Birds</th>\n",
" <th id=\"T_11dd6_level0_col1\" class=\"col_heading level0 col1\" >Number of Birds</th>\n",
" <th id=\"T_11dd6_level0_col2\" class=\"col_heading level0 col2\" >% Contribution to KBA Dataset</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th id=\"T_11dd6_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
" <td id=\"T_11dd6_row0_col0\" class=\"data row0 col0\" >Resident</td>\n",
" <td id=\"T_11dd6_row0_col1\" class=\"data row0 col1\" >249</td>\n",
" <td id=\"T_11dd6_row0_col2\" class=\"data row0 col2\" >68.975069</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_11dd6_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
" <td id=\"T_11dd6_row1_col0\" class=\"data row1 col0\" >WinterMigrant</td>\n",
" <td id=\"T_11dd6_row1_col1\" class=\"data row1 col1\" >111</td>\n",
" <td id=\"T_11dd6_row1_col2\" class=\"data row1 col2\" >30.747922</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_11dd6_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
" <td id=\"T_11dd6_row2_col0\" class=\"data row2 col0\" >SummerMigrant</td>\n",
" <td id=\"T_11dd6_row2_col1\" class=\"data row2 col1\" >1</td>\n",
" <td id=\"T_11dd6_row2_col2\" class=\"data row2 col2\" >0.277008</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n"
],
"text/plain": [
"<pandas.io.formats.style.Styler at 0x7fe0de2505b0>"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"number_birds = []\n",
"resident_list = kba_species_df.Resident_status.unique()\n",
"for resident in resident_list:\n",
" number_birds.append(len(kba_species_df[kba_species_df.Resident_status == resident]))\n",
"\n",
"pd.DataFrame({'Resident Status of Birds' : resident_list, 'Number of Birds' : number_birds, '% Contribution to KBA Dataset' : (number_birds / np.sum(number_birds) * 100)}).style.set_caption('Categorizing birds based on their Resident Status').set_table_styles(styles).highlight_max(subset = ['Number of Birds', '% Contribution to KBA Dataset'], color = '#DBEAF6')"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "f703ec18",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<style type=\"text/css\">\n",
"#T_40c10 caption {\n",
" text-align: center;\n",
" font-size: 160%;\n",
" color: #135EA9;\n",
" background-color: #F6E7DB;\n",
"}\n",
"</style>\n",
"<table id=\"T_40c10\">\n",
" <caption>The only 1 'SummerMigrant' in this record</caption>\n",
" <thead>\n",
" <tr>\n",
" <th class=\"blank level0\" > </th>\n",
" <th id=\"T_40c10_level0_col0\" class=\"col_heading level0 col0\" >Common_Name</th>\n",
" <th id=\"T_40c10_level0_col1\" class=\"col_heading level0 col1\" >Scientific_Name</th>\n",
" <th id=\"T_40c10_level0_col2\" class=\"col_heading level0 col2\" >Resident_status</th>\n",
" <th id=\"T_40c10_level0_col3\" class=\"col_heading level0 col3\" >IUCN_Redlist_Status</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th id=\"T_40c10_level0_row0\" class=\"row_heading level0 row0\" >311</th>\n",
" <td id=\"T_40c10_row0_col0\" class=\"data row0 col0\" >Blue-cheeked Bee-eater</td>\n",
" <td id=\"T_40c10_row0_col1\" class=\"data row0 col1\" >Merops persicus</td>\n",
" <td id=\"T_40c10_row0_col2\" class=\"data row0 col2\" >SummerMigrant</td>\n",
" <td id=\"T_40c10_row0_col3\" class=\"data row0 col3\" >Least Concern</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n"
],
"text/plain": [
"<pandas.io.formats.style.Styler at 0x7fe0de205ac0>"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\n",
"\n",
"kba_species_df[kba_species_df.Resident_status == 'SummerMigrant'][['Common_Name', 'Scientific_Name', 'Resident_status', 'IUCN_Redlist_Status']].style.set_caption(\"The only 1 'SummerMigrant' in this record\").set_table_styles(styles)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "a5a5eb4e",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<style type=\"text/css\">\n",
"#T_4462f caption {\n",
" text-align: center;\n",
" font-size: 160%;\n",
" color: #135EA9;\n",
" background-color: #F6E7DB;\n",
"}\n",
"#T_4462f_row0_col0, #T_4462f_row0_col1, #T_4462f_row0_col2, #T_4462f_row1_col0, #T_4462f_row1_col1, #T_4462f_row1_col2, #T_4462f_row2_col0, #T_4462f_row2_col1, #T_4462f_row2_col2, #T_4462f_row3_col0, #T_4462f_row3_col1, #T_4462f_row3_col2, #T_4462f_row4_col0, #T_4462f_row4_col1, #T_4462f_row4_col2 {\n",
" background-color: #E5F3FA;\n",
"}\n",
"#T_4462f_row5_col0, #T_4462f_row5_col1, #T_4462f_row5_col2, #T_4462f_row6_col0, #T_4462f_row6_col1, #T_4462f_row6_col2, #T_4462f_row7_col0, #T_4462f_row7_col1, #T_4462f_row7_col2, #T_4462f_row8_col0, #T_4462f_row8_col1, #T_4462f_row8_col2, #T_4462f_row9_col0, #T_4462f_row9_col1, #T_4462f_row9_col2 {\n",
" background-color: #FEEDFF;\n",
"}\n",
"#T_4462f_row10_col0, #T_4462f_row10_col1, #T_4462f_row10_col2 {\n",
" background-color: #E0ECE4;\n",
"}\n",
"</style>\n",
"<table id=\"T_4462f\">\n",
" <caption>Top 5 Birds from each Resident category</caption>\n",
" <thead>\n",
" <tr>\n",
" <th class=\"blank level0\" > </th>\n",
" <th id=\"T_4462f_level0_col0\" class=\"col_heading level0 col0\" >Common_Name</th>\n",
" <th id=\"T_4462f_level0_col1\" class=\"col_heading level0 col1\" >Resident_status</th>\n",
" <th id=\"T_4462f_level0_col2\" class=\"col_heading level0 col2\" >Resident_count</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th id=\"T_4462f_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
" <td id=\"T_4462f_row0_col0\" class=\"data row0 col0\" >White-cheeked Barbet</td>\n",
" <td id=\"T_4462f_row0_col1\" class=\"data row0 col1\" >Resident</td>\n",
" <td id=\"T_4462f_row0_col2\" class=\"data row0 col2\" >13860</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_4462f_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
" <td id=\"T_4462f_row1_col0\" class=\"data row1 col0\" >House Crow</td>\n",
" <td id=\"T_4462f_row1_col1\" class=\"data row1 col1\" >Resident</td>\n",
" <td id=\"T_4462f_row1_col2\" class=\"data row1 col2\" >12381</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_4462f_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
" <td id=\"T_4462f_row2_col0\" class=\"data row2 col0\" >Large-billed Crow</td>\n",
" <td id=\"T_4462f_row2_col1\" class=\"data row2 col1\" >Resident</td>\n",
" <td id=\"T_4462f_row2_col2\" class=\"data row2 col2\" >9641</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_4462f_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
" <td id=\"T_4462f_row3_col0\" class=\"data row3 col0\" >Common Myna</td>\n",
" <td id=\"T_4462f_row3_col1\" class=\"data row3 col1\" >Resident</td>\n",
" <td id=\"T_4462f_row3_col2\" class=\"data row3 col2\" >9147</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_4462f_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
" <td id=\"T_4462f_row4_col0\" class=\"data row4 col0\" >Rufous Treepie</td>\n",
" <td id=\"T_4462f_row4_col1\" class=\"data row4 col1\" >Resident</td>\n",
" <td id=\"T_4462f_row4_col2\" class=\"data row4 col2\" >8655</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_4462f_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
" <td id=\"T_4462f_row5_col0\" class=\"data row5 col0\" >Blyth's Reed Warbler</td>\n",
" <td id=\"T_4462f_row5_col1\" class=\"data row5 col1\" >WinterMigrant</td>\n",
" <td id=\"T_4462f_row5_col2\" class=\"data row5 col2\" >2753</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_4462f_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
" <td id=\"T_4462f_row6_col0\" class=\"data row6 col0\" >Indian Golden Oriole</td>\n",
" <td id=\"T_4462f_row6_col1\" class=\"data row6 col1\" >WinterMigrant</td>\n",
" <td id=\"T_4462f_row6_col2\" class=\"data row6 col2\" >2387</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_4462f_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
" <td id=\"T_4462f_row7_col0\" class=\"data row7 col0\" >Blue-tailed Bee-eater</td>\n",
" <td id=\"T_4462f_row7_col1\" class=\"data row7 col1\" >WinterMigrant</td>\n",
" <td id=\"T_4462f_row7_col2\" class=\"data row7 col2\" >1172</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_4462f_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
" <td id=\"T_4462f_row8_col0\" class=\"data row8 col0\" >Barn Swallow</td>\n",
" <td id=\"T_4462f_row8_col1\" class=\"data row8 col1\" >WinterMigrant</td>\n",
" <td id=\"T_4462f_row8_col2\" class=\"data row8 col2\" >1108</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_4462f_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
" <td id=\"T_4462f_row9_col0\" class=\"data row9 col0\" >Ashy Drongo</td>\n",
" <td id=\"T_4462f_row9_col1\" class=\"data row9 col1\" >WinterMigrant</td>\n",
" <td id=\"T_4462f_row9_col2\" class=\"data row9 col2\" >885</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_4462f_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
" <td id=\"T_4462f_row10_col0\" class=\"data row10 col0\" >Blue-cheeked Bee-eater</td>\n",
" <td id=\"T_4462f_row10_col1\" class=\"data row10 col1\" >SummerMigrant</td>\n",
" <td id=\"T_4462f_row10_col2\" class=\"data row10 col2\" >5</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n"
],
"text/plain": [
"<pandas.io.formats.style.Styler at 0x7fe0db0c79d0>"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"kba_species_data['Resident_count'] = np.ones(len(kba_species_data))\n",
"\n",
"Name_Resident_Count = pd.DataFrame(kba_species_data.groupby(['Common_Name', 'Resident_status'])['Resident_count'].sum()).reset_index().sort_values('Resident_count', ascending = False).reset_index(drop=True)\n",
"\n",
"temp_df = pd.DataFrame(columns = ['Common_Name', 'Resident_status', 'Resident_count'])\n",
"\n",
"for resident in resident_list:\n",
" temp_df = pd.concat([temp_df, Name_Resident_Count[Name_Resident_Count.Resident_status == resident].head()])\n",
" \n",
"temp_df.Resident_count = temp_df.Resident_count.astype('int')\n",
"\n",
"temp_df = temp_df.reset_index(drop = True)\n",
"\n",
"idx = pd.IndexSlice\n",
"temp_df.style.set_caption('Top 5 Birds from each Resident category').set_table_styles(styles).set_properties(**{'background-color': '#E5F3FA'}, subset = idx[idx[:4]]).set_properties(**{'background-color': '#FEEDFF'}, subset = idx[idx[5:9]]).set_properties(**{'background-color': '#E0ECE4'}, subset = idx[idx[10]])"
]
},
{
"cell_type": "markdown",
"id": "127aa673-d7f4-4d91-93e7-ca9fa43f3870",
"metadata": {},
"source": [
"<div class=\"alert alert-success\" role=\"alert\">\n",
" <h3><font color ='red'>Observation</font></h3>\n",
" <ul>\n",
" <li>About <strong>68.9%</strong> (<em>249</em>) of the recorded birds are <strong>Resident</strong> one and <strong>30.7%</strong> (<em>111</em>) were <strong>Winter Migrants</strong></li>\n",
" <li><strong>White-cheeked Barbet</strong>(<em>13,860</em>) and <strong>House Crow</strong>(<em>12,381</em>)are the most visited birds during the entire survey with morethan 10,000 times and both are <strong>Resident</strong> birds.</li>\n",
" <li><strong>Blue-cheeked Bee-eater</strong> was the <em>only one</em> <strong>Summer Migrant</strong> bird and was visited for <strong>5</strong> time.</li>\n",
" </ul>\n",
"</div>"
]
},
{
"cell_type": "markdown",
"id": "ecf0afd4-e1f7-4541-afea-5e76cc727778",
"metadata": {},
"source": [
"# Wet & Dry Season birds counts"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "ca2d58e4",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of unique bird families present : \u001b[34;1m76\u001b[0m\n",
"===========================================\n",
"Number of total Birds observed during \u001b[34;1mDry season\u001b[0m is \u001b[34;1m:\u001b[0m \u001b[34;1m169704\u001b[0m\n",
"Number of total Birds observed during \u001b[34;1mWet season\u001b[0m is \u001b[34;1m:\u001b[0m \u001b[34;1m131178\u001b[0m\n",
"============================================================\n",
"Number of unique Birds visited during the \u001b[34;1mWet season\u001b[0m is \u001b[34;1m:\u001b[0m \u001b[34;1m404\u001b[0m\n",
"Number of unique Birds visited during the \u001b[34;1mDry season\u001b[0m is \u001b[34;1m:\u001b[0m \u001b[34;1m475\u001b[0m\n"
]
}
],
"source": [
"print(f'Number of unique bird families present : {text_co.blue}{kba_species_df.Family.nunique()}{text_co.reset}')\n",
"print('=' * 43)\n",
"\n",
"# Total Birds visit\n",
"season_count = kba_data_df.Season.value_counts()\n",
"for seas, count in zip(season_count.index, season_count.values):\n",
" print(f'Number of total Birds observed during {text_co.blue}{seas} season{text_co.reset} is {text_co.blue}:{text_co.reset} {text_co.blue}{count}{text_co.reset}')\n",
"\n",
"print('=' * 60)\n",
"\n",
"# Unique Birds visit\n",
"for season in kba_data_df.Season.unique():\n",
" bird_group_season = kba_data_df.groupby('Season').get_group(season).Common_Name\n",
" unique_count = bird_group_season.nunique()\n",
" print(f'Number of unique Birds visited during the {text_co.blue}{season} season{text_co.reset} is {text_co.blue}:{text_co.reset} {text_co.blue}{unique_count}{text_co.reset}')"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "ca81b415",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of Birds who visited in \u001b[31;1mboth\u001b[0m \u001b[34;1mDry\u001b[0m and \u001b[34;1mWet Season\u001b[34;1m is \u001b[31;1m:\u001b[0m \u001b[34;1m387\u001b[0m\n",
"Number of Birds who visited \u001b[31;1monly\u001b[0m in \u001b[34;1mDry Season\u001b[0m is \u001b[31;1m:\u001b[34;1m \u001b[34;1m88\u001b[0m\n",
"Number of Birds who visited \u001b[31;1monly\u001b[0m in \u001b[34;1mWet Season\u001b[0m is \u001b[31;1m:\u001b[34;1m \u001b[34;1m17\u001b[0m\n"
]
}
],
"source": [
"# Wet Seaason DataFrame\n",
"wet_season_bird_df = pd.DataFrame(kba_data_df.groupby('Season').get_group('Wet').Common_Name.value_counts()).reset_index().rename(columns = {'index' : 'Bird_Name', 'Common_Name' : 'Wet_Visit_Count_Count'})\n",
"wet_season_bird_df = wet_season_bird_df[wet_season_bird_df.Wet_Visit_Count_Count != 0].sort_values('Wet_Visit_Count_Count', ascending = False)\n",
"\n",
"# Dry Seaason DataFrame\n",
"dry_season_bird_df = pd.DataFrame(kba_data_df.groupby('Season').get_group('Dry').Common_Name.value_counts()).reset_index().rename(columns = {'index' : 'Bird_Name', 'Common_Name' : 'Dry_Visit_Count_Count'})\n",
"dry_season_bird_df = dry_season_bird_df[dry_season_bird_df.Dry_Visit_Count_Count != 0].sort_values('Dry_Visit_Count_Count', ascending = False)\n",
"\n",
"wet_season_bird_count = set(wet_season_bird_df.Bird_Name.values)\n",
"dry_season_bird_count = set(dry_season_bird_df.Bird_Name.values)\n",
"\n",
"print(f'Number of Birds who visited in {text_co.red}both{text_co.reset} {text_co.blue}Dry{text_co.reset} and {text_co.blue}Wet Season{text_co.blue} is {text_co.red}:{text_co.reset} {text_co.blue}{len(wet_season_bird_count.intersection(dry_season_bird_count))}{text_co.reset}')\n",
"print(f'Number of Birds who visited {text_co.red}only{text_co.reset} in {text_co.blue}Dry Season{text_co.reset} is {text_co.red}:{text_co.blue} {text_co.blue}{len(dry_season_bird_count.difference(wet_season_bird_count))}{text_co.reset}')\n",
"print(f'Number of Birds who visited {text_co.red}only{text_co.reset} in {text_co.blue}Wet Season{text_co.reset} is {text_co.red}:{text_co.blue} {text_co.blue}{len(wet_season_bird_count.difference(dry_season_bird_count))}{text_co.reset}')"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "3df511e5",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<style type=\"text/css\">\n",
"#T_c0aea caption {\n",
" text-align: center;\n",
" font-size: 160%;\n",
" color: #135EA9;\n",
" background-color: #F6E7DB;\n",
"}\n",
"</style>\n",
"<table id=\"T_c0aea\">\n",
" <caption>Top 7 Birds visited during Wet Season</caption>\n",
" <thead>\n",
" <tr>\n",
" <th class=\"blank level0\" > </th>\n",
" <th id=\"T_c0aea_level0_col0\" class=\"col_heading level0 col0\" >Bird_Name</th>\n",
" <th id=\"T_c0aea_level0_col1\" class=\"col_heading level0 col1\" >Wet_Visit_Count_Count</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th id=\"T_c0aea_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
" <td id=\"T_c0aea_row0_col0\" class=\"data row0 col0\" >House Crow</td>\n",
" <td id=\"T_c0aea_row0_col1\" class=\"data row0 col1\" >6297</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_c0aea_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
" <td id=\"T_c0aea_row1_col0\" class=\"data row1 col0\" >White-cheeked Barbet</td>\n",
" <td id=\"T_c0aea_row1_col1\" class=\"data row1 col1\" >5281</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_c0aea_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
" <td id=\"T_c0aea_row2_col0\" class=\"data row2 col0\" >Large-billed Crow</td>\n",
" <td id=\"T_c0aea_row2_col1\" class=\"data row2 col1\" >4656</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_c0aea_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
" <td id=\"T_c0aea_row3_col0\" class=\"data row3 col0\" >Common Myna</td>\n",
" <td id=\"T_c0aea_row3_col1\" class=\"data row3 col1\" >4569</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_c0aea_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
" <td id=\"T_c0aea_row4_col0\" class=\"data row4 col0\" >Rufous Treepie</td>\n",
" <td id=\"T_c0aea_row4_col1\" class=\"data row4 col1\" >4480</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_c0aea_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
" <td id=\"T_c0aea_row5_col0\" class=\"data row5 col0\" >Greater Coucal</td>\n",
" <td id=\"T_c0aea_row5_col1\" class=\"data row5 col1\" >4344</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_c0aea_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
" <td id=\"T_c0aea_row6_col0\" class=\"data row6 col0\" >Purple-rumped Sunbird</td>\n",
" <td id=\"T_c0aea_row6_col1\" class=\"data row6 col1\" >4220</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n"
],
"text/plain": [
"<pandas.io.formats.style.Styler at 0x7fe0de17c250>"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"wet_season_bird_df.head(7).style.set_caption('Top 7 Birds visited during Wet Season').set_table_styles(styles)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "e55f29c8",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<style type=\"text/css\">\n",
"#T_b735e caption {\n",
" text-align: center;\n",
" font-size: 160%;\n",
" color: #135EA9;\n",
" background-color: #F6E7DB;\n",
"}\n",
"</style>\n",
"<table id=\"T_b735e\">\n",
" <caption>Top 7 Birds visited during Dry Season</caption>\n",
" <thead>\n",
" <tr>\n",
" <th class=\"blank level0\" > </th>\n",
" <th id=\"T_b735e_level0_col0\" class=\"col_heading level0 col0\" >Bird_Name</th>\n",
" <th id=\"T_b735e_level0_col1\" class=\"col_heading level0 col1\" >Dry_Visit_Count_Count</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th id=\"T_b735e_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
" <td id=\"T_b735e_row0_col0\" class=\"data row0 col0\" >White-cheeked Barbet</td>\n",
" <td id=\"T_b735e_row0_col1\" class=\"data row0 col1\" >8579</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_b735e_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
" <td id=\"T_b735e_row1_col0\" class=\"data row1 col0\" >House Crow</td>\n",
" <td id=\"T_b735e_row1_col1\" class=\"data row1 col1\" >6084</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_b735e_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
" <td id=\"T_b735e_row2_col0\" class=\"data row2 col0\" >Large-billed Crow</td>\n",
" <td id=\"T_b735e_row2_col1\" class=\"data row2 col1\" >4985</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_b735e_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
" <td id=\"T_b735e_row3_col0\" class=\"data row3 col0\" >Common Myna</td>\n",
" <td id=\"T_b735e_row3_col1\" class=\"data row3 col1\" >4578</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_b735e_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
" <td id=\"T_b735e_row4_col0\" class=\"data row4 col0\" >Red-whiskered Bulbul</td>\n",
" <td id=\"T_b735e_row4_col1\" class=\"data row4 col1\" >4236</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_b735e_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
" <td id=\"T_b735e_row5_col0\" class=\"data row5 col0\" >Rufous Treepie</td>\n",
" <td id=\"T_b735e_row5_col1\" class=\"data row5 col1\" >4175</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_b735e_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
" <td id=\"T_b735e_row6_col0\" class=\"data row6 col0\" >Greater Coucal</td>\n",
" <td id=\"T_b735e_row6_col1\" class=\"data row6 col1\" >3921</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n"
],
"text/plain": [
"<pandas.io.formats.style.Styler at 0x7fe0de17c7f0>"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dry_season_bird_df.head(7).style.set_caption('Top 7 Birds visited during Dry Season').set_table_styles(styles)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "0f5e6dd4",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 1080x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Extract Year and Month for further analysis\n",
"# https://datagy.io/pandas-extract-date-from-datetime/\n",
"kba_data_df['Year'] = kba_data_df.Date.dt.year\n",
"kba_data_df['Month'] = kba_data_df.Date.dt.month_name()\n",
"\n",
"\n",
"#Lets plot some figure to get intutions\n",
"plt.figure(figsize=(15,8))\n",
"plt.suptitle('Number of Birds visited per Year chart', fontsize = 30, c = '#1D32E2')\n",
"plt.title('On each season', fontsize = 15, c = '#C79438')\n",
"plt.xlabel('Year of observation')\n",
"plt.ylabel('Number of birds visited')\n",
"sns.histplot(data = kba_data_df, x = 'Year', hue = 'Season', multiple = 'stack', palette = ['red', 'green'])\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "f2b20905",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 1440x1440 with 6 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"grouped = kba_data_df.groupby('Year')\n",
"\n",
"img_index = 1\n",
"plt.figure(figsize=(20,20))\n",
"plt.suptitle('Monthly season wise Birds count distribution', fontsize = 30, c = '#1D32E2')\n",
"for name, group in grouped:\n",
" plt.subplot(2, 3, img_index)\n",
" plt.title(f' Year : {name}')\n",
" sns.histplot(data = group, x = 'Month', hue = 'Season', multiple = 'stack')\n",
" plt.xticks(rotation = 15)\n",
" img_index += 1\n",
"\n",
"plt.show() "
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "1bd5a9c9-2020-4679-ace6-0243411a642f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Around \u001b[31;1m55%\u001b[0m of whole the survey were done during week-ends.\n"
]
}
],
"source": [
"# https://pandas.pydata.org/docs/reference/api/pandas.DatetimeIndex.weekday.html\n",
"# The day of the week with Monday=0, Sunday=6.\n",
"\n",
"bird_watch_weekEnd = (kba_data_df.Date.dt.weekday >=5).value_counts()[0]\n",
"percent_weekend = int(bird_watch_weekEnd*100/len(kba_data_df))\n",
"print(f'Around {text_co.red}{percent_weekend}%{text_co.reset} of whole the survey were done during week-ends.')"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "1128e437",
"metadata": {},
"outputs": [],
"source": [
"percnt_over_year = pd.DataFrame(columns = ['Year', 'Wet_Bird_Count', 'Dry_Bird_Count'], index=range(6))\n",
"\n",
"for se, co in zip(['Dry', 'Wet'], ['Dry_Bird_Count', 'Wet_Bird_Count']):\n",
" if se == 'Dry': a = 1\n",
" else : a = 0\n",
" for na, gr in kba_data_df[kba_data_df.Season == se].groupby('Year'):\n",
" percnt_over_year.loc[a].Year = na\n",
" percnt_over_year.loc[a][co] = gr.shape[0]\n",
" a += 1\n",
"\n",
"percnt_over_year.fillna(0, inplace = True)\n",
"percnt_over_year.insert(2, 'Wet_Cum_Percentage', percnt_over_year.Wet_Bird_Count.cumsum() / percnt_over_year.Wet_Bird_Count.sum())\n",
"percnt_over_year.insert(4, 'Dry_Cum_Percentage', percnt_over_year.Dry_Bird_Count.cumsum() / percnt_over_year.Dry_Bird_Count.sum())"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "d28274b7-1db9-42ec-8ccb-4a747f6216e5",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 1008x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# https://stackoverflow.com/a/41924823\n",
"\n",
"plt.figure(figsize=(14, 8))\n",
"\n",
"plt.suptitle('Percentage Bird watch completed in each year', fontsize = 30, c = '#1D32E2')\n",
"plt.title('(Cumulative Percentage)', fontsize = 15, c = '#C79438')\n",
"plt.xlabel('Year of observation')\n",
"plt.ylabel('Percentage completed')\n",
"\n",
"sns.lineplot(data = percnt_over_year, y = 'Wet_Cum_Percentage', x = 'Year', color = 'red', label = 'Wet Season')\n",
"sns.lineplot(data = percnt_over_year, y = 'Dry_Cum_Percentage', x = 'Year', color = 'blue', label = 'Dry Season')\n",
"plt.axhline(0.92, linewidth=1, linestyle = '-.', c = 'g')\n",
"plt.axhline(0.73, linewidth=1, linestyle = '-.', c = 'g')\n",
"\n",
"plt.text(2016, 0.92, '92% Cumulative', fontsize = 10, va = 'center', ha = 'center', backgroundcolor = 'w', c = 'g')\n",
"plt.text(2017, 0.73, '73% Cumulative', fontsize = 10, va = 'center', ha = 'center', backgroundcolor = 'w', c = 'g')\n",
"\n",
"plt.legend(loc = 5)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "846cb15e-02ca-4e75-a723-6351300dbdc2",
"metadata": {
"jp-MarkdownHeadingCollapsed": true,
"tags": []
},
"source": [
"<div class=\"alert alert-success\" role=\"alert\">\n",
" <h3><font color ='red'>Observation</font></h3>\n",
" <ul>\n",
" <li>There are <strong>404</strong> and <strong>475</strong> unique birds visited during the <strong>wet</strong> and <strong>dry</strong> seasons respectively.</li>\n",
" <li>We have counted <strong>88</strong> birds those and those only came during <strong>dry</strong> season, <strong>17</strong> birds only during <strong>wet</strong> season.</li>\n",
" <li><em>White-cheeked Barbet</em>, <em>House Crow</em>, <em>Large-billed Crow</em> and <em>Common Myna</em> are the top <strong>4</strong> birds by visit count on during both <strong>wet</strong> and <strong>dry</strong> seasons.</li>\n",
" <li><strong>Dry season</strong> survey beagn in <em>2016</em> and it concluded in <em>2020</em>.</li>\n",
" <li>Most of the <strong>Wet season</strong> survey happened before <em>2018</em>.</li>\n",
" <li>In <em>2017</em> alone observers watched morethan <strong>1,00,000</strong> birds.</li>\n",
" <li>About <strong>92%</strong> of the <strong>wet</strong> season survey were done during <em>2015</em> and <em>2017</em>.</li>\n",
" <li>Nearly <strong>73%</strong> of the <strong>dry</strong> season survey completed in between <em>2016</em> and <em>2018</em>.</li>\n",
" <li>Around <strong>55%</strong> of whole the survey were done during week-ends</li>\n",
" </ul>\n",
"</div>"
]
},
{
"cell_type": "markdown",
"id": "284f6578-a9d8-4db8-a114-fb8d9351e5bf",
"metadata": {},
"source": [
"# Bird Watching Timings\n",
"\n",
"We do have a time data in-hand. Let's check what is the time the data collectors used mostly."
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "701bee97-e2bc-4bc4-8807-3374818eb5ec",
"metadata": {},
"outputs": [],
"source": [
"def df_to_hour(df):\n",
" # return pd.to_datetime(df.Time).dt.hour\n",
" return df.Time_Hour"
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "27dc811f-8b36-4a2c-94b9-6ae6a7eddd71",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 1440x576 with 3 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"all_season = df_to_hour(kba_data_df)\n",
"wet_season_hr= df_to_hour(kba_data_df[kba_data_df['Season'] == 'Wet'])\n",
"dry_season_hr= df_to_hour(kba_data_df[kba_data_df['Season'] == 'Dry'])\n",
"\n",
"season_time = [(all_season, 'All Season'), (wet_season_hr, 'Wet Season'), (dry_season_hr, 'Dry Season')]\n",
"\n",
"plt.figure(figsize=(20, 8))\n",
"img_index = 1\n",
"for data, sea in season_time:\n",
" plt.suptitle('Bird Watch Timings', fontsize = 30, c = '#1D32E2')\n",
" plt.subplot(1, 3, img_index)\n",
" plt.title(sea)\n",
" sns.histplot(data = data, bins = 24)\n",
" img_index += 1\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "056828db-1eb6-4ab2-92c6-cee3e39d1239",
"metadata": {},
"source": [
"<div class=\"alert alert-success\" role=\"alert\">\n",
" <h3><font color ='red'>Observation</font></h3>\n",
" <ul>\n",
" <li>Majority of the data is collected during <strong>morning</strong> and that too between <strong>6-10am</strong>.</li>\n",
" <li>The next most data collected time is between <strong>3-6pm</strong>.</li>\n",
" <li>Eventough there are few data collections are done in noon time.</li>\n",
" <li>This shows the <strong>best</strong> time for <strong>bird watching</strong> is on <strong>morning</strong></li>\n",
" </ul>\n",
"</div>"
]
},
{
"cell_type": "markdown",
"id": "809dfe79-b71c-4d1b-b71f-cfdfcc112229",
"metadata": {},
"source": [
"# Endemic and Threatened Species\n",
"<ul>\n",
"<li><font color = 'red'><strong>Endemic Birds</strong></font> are species which are found only in one geographic region and nowhere else in the world.</li>\n",
"<li>The \n",
"<a href = 'https://en.wikipedia.org/wiki/Western_Ghats'>Western Ghats</a> is one such area and is one of the world's ten \"hottest <a href = 'https://en.wikipedia.org/wiki/Biodiversity_hotspot'>biodiversity hotsports</a>\".</li>\n",
"</ul>\n"
]
},
{
"cell_type": "markdown",
"id": "478afd28-ddae-442b-b254-7e559ef29ca2",
"metadata": {},
"source": [
"### Species from Western Ghats"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "2d20015e-b2c5-4f2c-93f2-711270d1910e",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"endemicity_ = pd.DataFrame(kba_species_df.Endemicity.value_counts()).reset_index().rename(columns = {'Endemicity' : 'Count', 'index' : 'label'})\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.add_axes([0,0,1.25,1.25])\n",
"ax.axis('equal')\n",
"plt.pie(endemicity_.Count, labels = endemicity_.label, autopct = '%1.2f%%', colors = ['#2ca12d', '#f77e12'])\n",
"plt.title(\"Bird's Endemicity\", fontsize = 30, c = '#1D32E2')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "482c042d-c07e-413e-925a-6540e12f1bea",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"def processDF(df, endimic, count_name):\n",
" \n",
" columns_ = ['Common_Name', 'Resident_status', 'IUCN_Redlist_Status', 'Family']\n",
" df = df[df.Endemicity == endimic][columns_].reset_index(drop = True)\n",
" iucn_df = pd.DataFrame(df.IUCN_Redlist_Status.value_counts()).reset_index().rename(columns = {'index' : 'IUCN_Status', 'IUCN_Redlist_Status' : count_name})\n",
" \n",
" return df, iucn_df"
]
},
{
"cell_type": "code",
"execution_count": 33,
"id": "13ee2634-fae3-4bd6-b8dd-a3f89143d2f0",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Number of different species families in \u001b[34;1mWestern Ghats \u001b[0m : \u001b[31;1m21\u001b[0m\n",
"\n"
]
},
{
"data": {
"text/html": [
"<style type=\"text/css\">\n",
"#T_f2912 caption {\n",
" text-align: center;\n",
" font-size: 160%;\n",
" color: #135EA9;\n",
" background-color: #F6E7DB;\n",
"}\n",
"#T_f2912_row0_col0, #T_f2912_row0_col1, #T_f2912_row0_col2, #T_f2912_row0_col3, #T_f2912_row1_col0, #T_f2912_row1_col1, #T_f2912_row1_col2, #T_f2912_row1_col3 {\n",
" background-color: #FEEDFF;\n",
"}\n",
"#T_f2912_row2_col0, #T_f2912_row2_col1, #T_f2912_row2_col2, #T_f2912_row2_col3, #T_f2912_row3_col0, #T_f2912_row3_col1, #T_f2912_row3_col2, #T_f2912_row3_col3, #T_f2912_row4_col0, #T_f2912_row4_col1, #T_f2912_row4_col2, #T_f2912_row4_col3, #T_f2912_row5_col0, #T_f2912_row5_col1, #T_f2912_row5_col2, #T_f2912_row5_col3, #T_f2912_row6_col0, #T_f2912_row6_col1, #T_f2912_row6_col2, #T_f2912_row6_col3, #T_f2912_row7_col0, #T_f2912_row7_col1, #T_f2912_row7_col2, #T_f2912_row7_col3, #T_f2912_row8_col0, #T_f2912_row8_col1, #T_f2912_row8_col2, #T_f2912_row8_col3, #T_f2912_row9_col0, #T_f2912_row9_col1, #T_f2912_row9_col2, #T_f2912_row9_col3, #T_f2912_row10_col0, #T_f2912_row10_col1, #T_f2912_row10_col2, #T_f2912_row10_col3, #T_f2912_row11_col0, #T_f2912_row11_col1, #T_f2912_row11_col2, #T_f2912_row11_col3, #T_f2912_row12_col0, #T_f2912_row12_col1, #T_f2912_row12_col2, #T_f2912_row12_col3, #T_f2912_row13_col0, #T_f2912_row13_col1, #T_f2912_row13_col2, #T_f2912_row13_col3, #T_f2912_row14_col0, #T_f2912_row14_col1, #T_f2912_row14_col2, #T_f2912_row14_col3, #T_f2912_row15_col0, #T_f2912_row15_col1, #T_f2912_row15_col2, #T_f2912_row15_col3, #T_f2912_row16_col0, #T_f2912_row16_col1, #T_f2912_row16_col2, #T_f2912_row16_col3, #T_f2912_row17_col0, #T_f2912_row17_col1, #T_f2912_row17_col2, #T_f2912_row17_col3, #T_f2912_row18_col0, #T_f2912_row18_col1, #T_f2912_row18_col2, #T_f2912_row18_col3, #T_f2912_row19_col0, #T_f2912_row19_col1, #T_f2912_row19_col2, #T_f2912_row19_col3, #T_f2912_row20_col0, #T_f2912_row20_col1, #T_f2912_row20_col2, #T_f2912_row20_col3, #T_f2912_row21_col0, #T_f2912_row21_col1, #T_f2912_row21_col2, #T_f2912_row21_col3, #T_f2912_row22_col0, #T_f2912_row22_col1, #T_f2912_row22_col2, #T_f2912_row22_col3, #T_f2912_row23_col0, #T_f2912_row23_col1, #T_f2912_row23_col2, #T_f2912_row23_col3, #T_f2912_row24_col0, #T_f2912_row24_col1, #T_f2912_row24_col2, #T_f2912_row24_col3, #T_f2912_row25_col0, #T_f2912_row25_col1, #T_f2912_row25_col2, #T_f2912_row25_col3 {\n",
" background-color: #E5F3FA;\n",
"}\n",
"#T_f2912_row26_col0, #T_f2912_row26_col1, #T_f2912_row26_col2, #T_f2912_row26_col3, #T_f2912_row27_col0, #T_f2912_row27_col1, #T_f2912_row27_col2, #T_f2912_row27_col3 {\n",
" background-color: #FBFDEE;\n",
"}\n",
"#T_f2912_row28_col0, #T_f2912_row28_col1, #T_f2912_row28_col2, #T_f2912_row28_col3, #T_f2912_row29_col0, #T_f2912_row29_col1, #T_f2912_row29_col2, #T_f2912_row29_col3, #T_f2912_row30_col0, #T_f2912_row30_col1, #T_f2912_row30_col2, #T_f2912_row30_col3, #T_f2912_row31_col0, #T_f2912_row31_col1, #T_f2912_row31_col2, #T_f2912_row31_col3, #T_f2912_row32_col0, #T_f2912_row32_col1, #T_f2912_row32_col2, #T_f2912_row32_col3 {\n",
" background-color: #E0ECE4;\n",
"}\n",
"</style>\n",
"<table id=\"T_f2912\">\n",
" <caption>All Birds from Western Ghats</caption>\n",
" <thead>\n",
" <tr>\n",
" <th class=\"blank level0\" > </th>\n",
" <th id=\"T_f2912_level0_col0\" class=\"col_heading level0 col0\" >Common_Name</th>\n",
" <th id=\"T_f2912_level0_col1\" class=\"col_heading level0 col1\" >Resident_status</th>\n",
" <th id=\"T_f2912_level0_col2\" class=\"col_heading level0 col2\" >IUCN_Redlist_Status</th>\n",
" <th id=\"T_f2912_level0_col3\" class=\"col_heading level0 col3\" >Family</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th id=\"T_f2912_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
" <td id=\"T_f2912_row0_col0\" class=\"data row0 col0\" >Banasura Laughingthrush</td>\n",
" <td id=\"T_f2912_row0_col1\" class=\"data row0 col1\" >Resident</td>\n",
" <td id=\"T_f2912_row0_col2\" class=\"data row0 col2\" >Endangered</td>\n",
" <td id=\"T_f2912_row0_col3\" class=\"data row0 col3\" >Leiothrichidae</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_f2912_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
" <td id=\"T_f2912_row1_col0\" class=\"data row1 col0\" >Nilgiri Laughingthrush</td>\n",
" <td id=\"T_f2912_row1_col1\" class=\"data row1 col1\" >Resident</td>\n",
" <td id=\"T_f2912_row1_col2\" class=\"data row1 col2\" >Endangered</td>\n",
" <td id=\"T_f2912_row1_col3\" class=\"data row1 col3\" >Leiothrichidae</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_f2912_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
" <td id=\"T_f2912_row2_col0\" class=\"data row2 col0\" >White-bellied Treepie</td>\n",
" <td id=\"T_f2912_row2_col1\" class=\"data row2 col1\" >Resident</td>\n",
" <td id=\"T_f2912_row2_col2\" class=\"data row2 col2\" >Least Concern</td>\n",
" <td id=\"T_f2912_row2_col3\" class=\"data row2 col3\" >Corvidae</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_f2912_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
" <td id=\"T_f2912_row3_col0\" class=\"data row3 col0\" >Black-and-orange Flycatcher</td>\n",
" <td id=\"T_f2912_row3_col1\" class=\"data row3 col1\" >Resident</td>\n",
" <td id=\"T_f2912_row3_col2\" class=\"data row3 col2\" >Least Concern</td>\n",
" <td id=\"T_f2912_row3_col3\" class=\"data row3 col3\" >Muscicapidae</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_f2912_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
" <td id=\"T_f2912_row4_col0\" class=\"data row4 col0\" >Square-tailed Bulbul</td>\n",
" <td id=\"T_f2912_row4_col1\" class=\"data row4 col1\" >Resident</td>\n",
" <td id=\"T_f2912_row4_col2\" class=\"data row4 col2\" >Least Concern</td>\n",
" <td id=\"T_f2912_row4_col3\" class=\"data row4 col3\" >Pycnonotidae</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_f2912_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
" <td id=\"T_f2912_row5_col0\" class=\"data row5 col0\" >Crimson-backed Sunbird</td>\n",
" <td id=\"T_f2912_row5_col1\" class=\"data row5 col1\" >Resident</td>\n",
" <td id=\"T_f2912_row5_col2\" class=\"data row5 col2\" >Least Concern</td>\n",
" <td id=\"T_f2912_row5_col3\" class=\"data row5 col3\" >Nectariniidae</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_f2912_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
" <td id=\"T_f2912_row6_col0\" class=\"data row6 col0\" >Wayanad Laughingthrush</td>\n",
" <td id=\"T_f2912_row6_col1\" class=\"data row6 col1\" >Resident</td>\n",
" <td id=\"T_f2912_row6_col2\" class=\"data row6 col2\" >Least Concern</td>\n",
" <td id=\"T_f2912_row6_col3\" class=\"data row6 col3\" >Leiothrichidae</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_f2912_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
" <td id=\"T_f2912_row7_col0\" class=\"data row7 col0\" >Grey-fronted Green-Pigeon</td>\n",
" <td id=\"T_f2912_row7_col1\" class=\"data row7 col1\" >Resident</td>\n",
" <td id=\"T_f2912_row7_col2\" class=\"data row7 col2\" >Least Concern</td>\n",
" <td id=\"T_f2912_row7_col3\" class=\"data row7 col3\" >Columbidae</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_f2912_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
" <td id=\"T_f2912_row8_col0\" class=\"data row8 col0\" >Orange Minivet</td>\n",
" <td id=\"T_f2912_row8_col1\" class=\"data row8 col1\" >Resident</td>\n",
" <td id=\"T_f2912_row8_col2\" class=\"data row8 col2\" >Least Concern</td>\n",
" <td id=\"T_f2912_row8_col3\" class=\"data row8 col3\" >Campephagidae</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_f2912_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
" <td id=\"T_f2912_row9_col0\" class=\"data row9 col0\" >Legge's Hawk-Eagle</td>\n",
" <td id=\"T_f2912_row9_col1\" class=\"data row9 col1\" >Resident</td>\n",
" <td id=\"T_f2912_row9_col2\" class=\"data row9 col2\" >Least Concern</td>\n",
" <td id=\"T_f2912_row9_col3\" class=\"data row9 col3\" >Accipitridae</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_f2912_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
" <td id=\"T_f2912_row10_col0\" class=\"data row10 col0\" >Indian Swiftlet</td>\n",
" <td id=\"T_f2912_row10_col1\" class=\"data row10 col1\" >Resident</td>\n",
" <td id=\"T_f2912_row10_col2\" class=\"data row10 col2\" >Least Concern</td>\n",
" <td id=\"T_f2912_row10_col3\" class=\"data row10 col3\" >Apodidae</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_f2912_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
" <td id=\"T_f2912_row11_col0\" class=\"data row11 col0\" >Malabar Barbet</td>\n",
" <td id=\"T_f2912_row11_col1\" class=\"data row11 col1\" >Resident</td>\n",
" <td id=\"T_f2912_row11_col2\" class=\"data row11 col2\" >Least Concern</td>\n",
" <td id=\"T_f2912_row11_col3\" class=\"data row11 col3\" >Megalaimidae</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_f2912_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
" <td id=\"T_f2912_row12_col0\" class=\"data row12 col0\" >Malabar Grey Hornbill</td>\n",
" <td id=\"T_f2912_row12_col1\" class=\"data row12 col1\" >Resident</td>\n",
" <td id=\"T_f2912_row12_col2\" class=\"data row12 col2\" >Least Concern</td>\n",
" <td id=\"T_f2912_row12_col3\" class=\"data row12 col3\" >Bucerotidae</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_f2912_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
" <td id=\"T_f2912_row13_col0\" class=\"data row13 col0\" >Rufous Babbler</td>\n",
" <td id=\"T_f2912_row13_col1\" class=\"data row13 col1\" >Resident</td>\n",
" <td id=\"T_f2912_row13_col2\" class=\"data row13 col2\" >Least Concern</td>\n",
" <td id=\"T_f2912_row13_col3\" class=\"data row13 col3\" >Leiothrichidae</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_f2912_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
" <td id=\"T_f2912_row14_col0\" class=\"data row14 col0\" >Malabar Parakeet</td>\n",
" <td id=\"T_f2912_row14_col1\" class=\"data row14 col1\" >Resident</td>\n",
" <td id=\"T_f2912_row14_col2\" class=\"data row14 col2\" >Least Concern</td>\n",
" <td id=\"T_f2912_row14_col3\" class=\"data row14 col3\" >Psittaculidae</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_f2912_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
" <td id=\"T_f2912_row15_col0\" class=\"data row15 col0\" >Malabar Lark</td>\n",
" <td id=\"T_f2912_row15_col1\" class=\"data row15 col1\" >Resident</td>\n",
" <td id=\"T_f2912_row15_col2\" class=\"data row15 col2\" >Least Concern</td>\n",
" <td id=\"T_f2912_row15_col3\" class=\"data row15 col3\" >Alaudidae</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_f2912_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
" <td id=\"T_f2912_row16_col0\" class=\"data row16 col0\" >Malabar Woodshrike</td>\n",
" <td id=\"T_f2912_row16_col1\" class=\"data row16 col1\" >Resident</td>\n",
" <td id=\"T_f2912_row16_col2\" class=\"data row16 col2\" >Least Concern</td>\n",
" <td id=\"T_f2912_row16_col3\" class=\"data row16 col3\" >Vangidae</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_f2912_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
" <td id=\"T_f2912_row17_col0\" class=\"data row17 col0\" >White-bellied Blue Flycatcher</td>\n",
" <td id=\"T_f2912_row17_col1\" class=\"data row17 col1\" >Resident</td>\n",
" <td id=\"T_f2912_row17_col2\" class=\"data row17 col2\" >Least Concern</td>\n",
" <td id=\"T_f2912_row17_col3\" class=\"data row17 col3\" >Muscicapidae</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_f2912_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
" <td id=\"T_f2912_row18_col0\" class=\"data row18 col0\" >Dark-fronted Babbler</td>\n",
" <td id=\"T_f2912_row18_col1\" class=\"data row18 col1\" >Resident</td>\n",
" <td id=\"T_f2912_row18_col2\" class=\"data row18 col2\" >Least Concern</td>\n",
" <td id=\"T_f2912_row18_col3\" class=\"data row18 col3\" >Timaliidae</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_f2912_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
" <td id=\"T_f2912_row19_col0\" class=\"data row19 col0\" >Hill Swallow</td>\n",
" <td id=\"T_f2912_row19_col1\" class=\"data row19 col1\" >Resident</td>\n",
" <td id=\"T_f2912_row19_col2\" class=\"data row19 col2\" >Least Concern</td>\n",
" <td id=\"T_f2912_row19_col3\" class=\"data row19 col3\" >Hirundinidae</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_f2912_level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
" <td id=\"T_f2912_row20_col0\" class=\"data row20 col0\" >Malabar Starling</td>\n",
" <td id=\"T_f2912_row20_col1\" class=\"data row20 col1\" >Resident</td>\n",
" <td id=\"T_f2912_row20_col2\" class=\"data row20 col2\" >Least Concern</td>\n",
" <td id=\"T_f2912_row20_col3\" class=\"data row20 col3\" >Sturnidae</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_f2912_level0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
" <td id=\"T_f2912_row21_col0\" class=\"data row21 col0\" >Flame-throated Bulbul</td>\n",
" <td id=\"T_f2912_row21_col1\" class=\"data row21 col1\" >Resident</td>\n",
" <td id=\"T_f2912_row21_col2\" class=\"data row21 col2\" >Least Concern</td>\n",
" <td id=\"T_f2912_row21_col3\" class=\"data row21 col3\" >Pycnonotidae</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_f2912_level0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
" <td id=\"T_f2912_row22_col0\" class=\"data row22 col0\" >Nilgiri Thrush</td>\n",
" <td id=\"T_f2912_row22_col1\" class=\"data row22 col1\" >Resident</td>\n",
" <td id=\"T_f2912_row22_col2\" class=\"data row22 col2\" >Least Concern</td>\n",
" <td id=\"T_f2912_row22_col3\" class=\"data row22 col3\" >Turdidae</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_f2912_level0_row23\" class=\"row_heading level0 row23\" >23</th>\n",
" <td id=\"T_f2912_row23_col0\" class=\"data row23 col0\" >Nilgiri Flycatcher</td>\n",
" <td id=\"T_f2912_row23_col1\" class=\"data row23 col1\" >Resident</td>\n",
" <td id=\"T_f2912_row23_col2\" class=\"data row23 col2\" >Least Concern</td>\n",
" <td id=\"T_f2912_row23_col3\" class=\"data row23 col3\" >Muscicapidae</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_f2912_level0_row24\" class=\"row_heading level0 row24\" >24</th>\n",
" <td id=\"T_f2912_row24_col0\" class=\"data row24 col0\" >Nilgiri Flowerpecker</td>\n",
" <td id=\"T_f2912_row24_col1\" class=\"data row24 col1\" >Resident</td>\n",
" <td id=\"T_f2912_row24_col2\" class=\"data row24 col2\" >Least Concern</td>\n",
" <td id=\"T_f2912_row24_col3\" class=\"data row24 col3\" >Dicaeidae</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_f2912_level0_row25\" class=\"row_heading level0 row25\" >25</th>\n",
" <td id=\"T_f2912_row25_col0\" class=\"data row25 col0\" >Yellow-browed Bulbul</td>\n",
" <td id=\"T_f2912_row25_col1\" class=\"data row25 col1\" >Resident</td>\n",
" <td id=\"T_f2912_row25_col2\" class=\"data row25 col2\" >Least Concern</td>\n",
" <td id=\"T_f2912_row25_col3\" class=\"data row25 col3\" >Pycnonotidae</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_f2912_level0_row26\" class=\"row_heading level0 row26\" >26</th>\n",
" <td id=\"T_f2912_row26_col0\" class=\"data row26 col0\" >Grey-headed Bulbul</td>\n",
" <td id=\"T_f2912_row26_col1\" class=\"data row26 col1\" >Resident</td>\n",
" <td id=\"T_f2912_row26_col2\" class=\"data row26 col2\" >Near Threatened</td>\n",
" <td id=\"T_f2912_row26_col3\" class=\"data row26 col3\" >Pycnonotidae</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_f2912_level0_row27\" class=\"row_heading level0 row27\" >27</th>\n",
" <td id=\"T_f2912_row27_col0\" class=\"data row27 col0\" >Palani Laughingthrush</td>\n",
" <td id=\"T_f2912_row27_col1\" class=\"data row27 col1\" >Resident</td>\n",
" <td id=\"T_f2912_row27_col2\" class=\"data row27 col2\" >Near Threatened</td>\n",
" <td id=\"T_f2912_row27_col3\" class=\"data row27 col3\" >Leiothrichidae</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_f2912_level0_row28\" class=\"row_heading level0 row28\" >28</th>\n",
" <td id=\"T_f2912_row28_col0\" class=\"data row28 col0\" >Nilgiri Pipit</td>\n",
" <td id=\"T_f2912_row28_col1\" class=\"data row28 col1\" >Resident</td>\n",
" <td id=\"T_f2912_row28_col2\" class=\"data row28 col2\" >Vulnerable</td>\n",
" <td id=\"T_f2912_row28_col3\" class=\"data row28 col3\" >Motacillidae</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_f2912_level0_row29\" class=\"row_heading level0 row29\" >29</th>\n",
" <td id=\"T_f2912_row29_col0\" class=\"data row29 col0\" >Broad-tailed Grassbird</td>\n",
" <td id=\"T_f2912_row29_col1\" class=\"data row29 col1\" >Resident</td>\n",
" <td id=\"T_f2912_row29_col2\" class=\"data row29 col2\" >Vulnerable</td>\n",
" <td id=\"T_f2912_row29_col3\" class=\"data row29 col3\" >Locustellidae</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_f2912_level0_row30\" class=\"row_heading level0 row30\" >30</th>\n",
" <td id=\"T_f2912_row30_col0\" class=\"data row30 col0\" >White-bellied Sholakili</td>\n",
" <td id=\"T_f2912_row30_col1\" class=\"data row30 col1\" >Resident</td>\n",
" <td id=\"T_f2912_row30_col2\" class=\"data row30 col2\" >Vulnerable</td>\n",
" <td id=\"T_f2912_row30_col3\" class=\"data row30 col3\" >Muscicapidae</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_f2912_level0_row31\" class=\"row_heading level0 row31\" >31</th>\n",
" <td id=\"T_f2912_row31_col0\" class=\"data row31 col0\" >Ashambu Laughingthrush</td>\n",
" <td id=\"T_f2912_row31_col1\" class=\"data row31 col1\" >Resident</td>\n",
" <td id=\"T_f2912_row31_col2\" class=\"data row31 col2\" >Vulnerable</td>\n",
" <td id=\"T_f2912_row31_col3\" class=\"data row31 col3\" >Leiothrichidae</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_f2912_level0_row32\" class=\"row_heading level0 row32\" >32</th>\n",
" <td id=\"T_f2912_row32_col0\" class=\"data row32 col0\" >Nilgiri Wood-Pigeon</td>\n",
" <td id=\"T_f2912_row32_col1\" class=\"data row32 col1\" >Resident</td>\n",
" <td id=\"T_f2912_row32_col2\" class=\"data row32 col2\" >Vulnerable</td>\n",
" <td id=\"T_f2912_row32_col3\" class=\"data row32 col3\" >Columbidae</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n"
],
"text/plain": [
"<pandas.io.formats.style.Styler at 0x7fe0d9775c40>"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"western_birds, western_iucn = processDF(kba_species_df, 'Western Ghats', 'Western_G')\n",
"_, non_western_iucn = processDF(kba_species_df, 'Not endemic', 'non_Western_G')\n",
"\n",
"print(f'\\nNumber of different species families in {text_co.blue}Western Ghats {text_co.reset} : {text_co.red}{western_birds.Family.nunique()}{text_co.reset}\\n')\n",
"\n",
"western_birds.sort_values('IUCN_Redlist_Status').reset_index(drop = True).style.set_caption('All Birds from Western Ghats').set_table_styles(styles).set_properties(**{'background-color': '#FEEDFF'}, subset = idx[idx[:1]]).set_properties(**{'background-color': '#E5F3FA'}, subset = idx[idx[2:25]]).set_properties(**{'background-color': '#FBFDEE'}, subset = idx[idx[26:27]]).set_properties(**{'background-color': '#E0ECE4'}, subset = idx[idx[28:]])\n"
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "73e52b16-dbc6-45f4-ae6f-0b80bbbcc64d",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<style type=\"text/css\">\n",
"#T_c2816 caption {\n",
" text-align: center;\n",
" font-size: 160%;\n",
" color: #135EA9;\n",
" background-color: #F6E7DB;\n",
"}\n",
"</style>\n",
"<table id=\"T_c2816\">\n",
" <caption>Endemic Species with 1000+ Visit</caption>\n",
" <thead>\n",
" <tr>\n",
" <th class=\"blank level0\" > </th>\n",
" <th id=\"T_c2816_level0_col0\" class=\"col_heading level0 col0\" >Common_Name</th>\n",
" <th id=\"T_c2816_level0_col1\" class=\"col_heading level0 col1\" >Visit_Count_During_Survey</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th id=\"T_c2816_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
" <td id=\"T_c2816_row0_col0\" class=\"data row0 col0\" >Yellow-browed Bulbul</td>\n",
" <td id=\"T_c2816_row0_col1\" class=\"data row0 col1\" >2687</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_c2816_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
" <td id=\"T_c2816_row1_col0\" class=\"data row1 col0\" >Nilgiri Flowerpecker</td>\n",
" <td id=\"T_c2816_row1_col1\" class=\"data row1 col1\" >2288</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_c2816_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
" <td id=\"T_c2816_row2_col0\" class=\"data row2 col0\" >Orange Minivet</td>\n",
" <td id=\"T_c2816_row2_col1\" class=\"data row2 col1\" >2286</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_c2816_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
" <td id=\"T_c2816_row3_col0\" class=\"data row3 col0\" >Crimson-backed Sunbird</td>\n",
" <td id=\"T_c2816_row3_col1\" class=\"data row3 col1\" >1781</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_c2816_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
" <td id=\"T_c2816_row4_col0\" class=\"data row4 col0\" >Malabar Parakeet</td>\n",
" <td id=\"T_c2816_row4_col1\" class=\"data row4 col1\" >1443</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_c2816_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
" <td id=\"T_c2816_row5_col0\" class=\"data row5 col0\" >Malabar Grey Hornbill</td>\n",
" <td id=\"T_c2816_row5_col1\" class=\"data row5 col1\" >1346</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n"
],
"text/plain": [
"<pandas.io.formats.style.Styler at 0x7fe0d67607f0>"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"western_birds_only = pd.merge(kba_data_df[['Common_Name', 'Season', 'County']], western_birds, on = 'Common_Name', how = 'inner')\n",
"pd.DataFrame(western_birds_only.Common_Name.value_counts()).reset_index().rename(columns = {'Common_Name' : 'Visit_Count_During_Survey', 'index' : 'Common_Name'}).head(6).style.set_caption('Endemic Species with 1000+ Visit').set_table_styles(styles)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "42187c1e-1236-4679-b906-131dca838bc7",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<style type=\"text/css\">\n",
"#T_b1970 caption {\n",
" text-align: center;\n",
" font-size: 160%;\n",
" color: #135EA9;\n",
" background-color: #F6E7DB;\n",
"}\n",
"#T_b1970_row0_col1, #T_b1970_row0_col2, #T_b1970_row0_col3, #T_b1970_row0_col4 {\n",
" background-color: #DBEAF6;\n",
"}\n",
"</style>\n",
"<table id=\"T_b1970\">\n",
" <caption>Endemic Species IUCN Status Count</caption>\n",
" <thead>\n",
" <tr>\n",
" <th class=\"blank level0\" > </th>\n",
" <th id=\"T_b1970_level0_col0\" class=\"col_heading level0 col0\" >IUCN_Status</th>\n",
" <th id=\"T_b1970_level0_col1\" class=\"col_heading level0 col1\" >Western_G</th>\n",
" <th id=\"T_b1970_level0_col2\" class=\"col_heading level0 col2\" >% Western_G</th>\n",
" <th id=\"T_b1970_level0_col3\" class=\"col_heading level0 col3\" >non_Western_G</th>\n",
" <th id=\"T_b1970_level0_col4\" class=\"col_heading level0 col4\" >% Non_Western_G</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th id=\"T_b1970_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
" <td id=\"T_b1970_row0_col0\" class=\"data row0 col0\" >Least Concern</td>\n",
" <td id=\"T_b1970_row0_col1\" class=\"data row0 col1\" >24</td>\n",
" <td id=\"T_b1970_row0_col2\" class=\"data row0 col2\" >72.730000</td>\n",
" <td id=\"T_b1970_row0_col3\" class=\"data row0 col3\" >303</td>\n",
" <td id=\"T_b1970_row0_col4\" class=\"data row0 col4\" >92.380000</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_b1970_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
" <td id=\"T_b1970_row1_col0\" class=\"data row1 col0\" >Vulnerable</td>\n",
" <td id=\"T_b1970_row1_col1\" class=\"data row1 col1\" >5</td>\n",
" <td id=\"T_b1970_row1_col2\" class=\"data row1 col2\" >15.150000</td>\n",
" <td id=\"T_b1970_row1_col3\" class=\"data row1 col3\" >6</td>\n",
" <td id=\"T_b1970_row1_col4\" class=\"data row1 col4\" >1.830000</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_b1970_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
" <td id=\"T_b1970_row2_col0\" class=\"data row2 col0\" >Near Threatened</td>\n",
" <td id=\"T_b1970_row2_col1\" class=\"data row2 col1\" >2</td>\n",
" <td id=\"T_b1970_row2_col2\" class=\"data row2 col2\" >6.060000</td>\n",
" <td id=\"T_b1970_row2_col3\" class=\"data row2 col3\" >16</td>\n",
" <td id=\"T_b1970_row2_col4\" class=\"data row2 col4\" >4.880000</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_b1970_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
" <td id=\"T_b1970_row3_col0\" class=\"data row3 col0\" >Endangered</td>\n",
" <td id=\"T_b1970_row3_col1\" class=\"data row3 col1\" >2</td>\n",
" <td id=\"T_b1970_row3_col2\" class=\"data row3 col2\" >6.060000</td>\n",
" <td id=\"T_b1970_row3_col3\" class=\"data row3 col3\" >1</td>\n",
" <td id=\"T_b1970_row3_col4\" class=\"data row3 col4\" >0.300000</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_b1970_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
" <td id=\"T_b1970_row4_col0\" class=\"data row4 col0\" >Critically Endangered</td>\n",
" <td id=\"T_b1970_row4_col1\" class=\"data row4 col1\" >0</td>\n",
" <td id=\"T_b1970_row4_col2\" class=\"data row4 col2\" >0.000000</td>\n",
" <td id=\"T_b1970_row4_col3\" class=\"data row4 col3\" >2</td>\n",
" <td id=\"T_b1970_row4_col4\" class=\"data row4 col4\" >0.610000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n"
],
"text/plain": [
"<pandas.io.formats.style.Styler at 0x7fe0d670d190>"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"iucn_status = pd.merge(western_iucn, non_western_iucn, how = 'outer').fillna(0)\n",
"\n",
"iucn_status['Western_G'] = iucn_status['Western_G'].apply(np.int64)\n",
"\n",
"iucn_status.insert(2, '% Western_G', iucn_status['Western_G'].apply(lambda x : round(x *100 / sum(iucn_status.Western_G), 2)))\n",
"iucn_status.insert(4, '% Non_Western_G', iucn_status['non_Western_G'].apply(lambda x : round(x *100 / sum(iucn_status.non_Western_G), 2)))\n",
"\n",
"iucn_status.style.set_caption('Endemic Species IUCN Status Count').set_table_styles(styles).set_table_styles(styles).highlight_max(subset = ['Western_G', '% Western_G', 'non_Western_G', '% Non_Western_G'], color = '#DBEAF6')"
]
},
{
"cell_type": "code",
"execution_count": 36,
"id": "7057f34c-8c17-49a2-be24-4597ee06a6f7",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 1440x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"district_wise_count = pd.DataFrame(western_birds_only.County.value_counts()).reset_index().rename(columns = {'index' : 'District_Name', 'County' : 'Bird_Count'}).sort_values('Bird_Count', ascending = True).reset_index(drop=True)\n",
"median_count = int(district_wise_count.Bird_Count.median())\n",
"\n",
"plt.figure(figsize=(20,8))\n",
"sns.lineplot(x = district_wise_count.District_Name, y= district_wise_count.Bird_Count)\n",
"plt.suptitle('Western Ghats Bird Count vs Districts', fontsize = 30, c = '#1D32E2')\n",
"plt.title('Total number of birds counted from each district of Kerala', fontsize = 15, c = '#C79438')\n",
"plt.xlabel('District Names of Kerala')\n",
"plt.ylabel('Number of birds counted')\n",
"plt.xticks(rotation = 20)\n",
"plt.axhline(median_count, c = 'g', linestyle = '-.', linewidth = 1.5)\n",
"plt.text('Kottayam', median_count , f'Median count :: {median_count}', fontsize = 14, va = 'center', ha = 'center', backgroundcolor = 'w', c = 'r')\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "b7df894b-b0db-4050-ab22-365e65022066",
"metadata": {},
"source": [
"<div class=\"alert alert-success\" role=\"alert\">\n",
" <h3><font color ='red'>Observation</font></h3>\n",
" <ul>\n",
" <li>The data shows around <strong>9%</strong> of the birds regularly found in <strong>Western Ghats</strong> region.</li>\n",
" <li>Out of <strong>76</strong> bird families <strong>21</strong> were regularly found in Western Ghats.</li>\n",
" <li>In total <strong>33</strong> species from <strong>21</strong> families were endimic to Western Ghats.</li>\n",
" <li><strong>6</strong> endimic species have over <strong>1000+</strong> occurances in the dataset</li>\n",
" <li>The <em>Vulnerable</em> and <em>Endangered</em> species birds are more attracted towards the <strong>Western Ghats</strong>.</li>\n",
" </ul>\n",
"</div>"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.9.12"
}
},
"nbformat": 4,
"nbformat_minor": 5
}