{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Web Scraping and EDA in Python 3 using Requests, BeautifulSoup, Pandas, Matplotlib, Seaborn"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Source: \n",
"## Thomas Brinkhoff: City Population, http://www.citypopulation.de\n",
"\n",
"In this jupyter notebook I will try to explain how to scrape content from a website using BeautifulSoup and Requests libraries.\n",
"\n",
"Please note there might be some policies and rules for a website for using the data. So before you do the web scraping please do not forget to read the data usage policies.\n",
"\n",
"For this article's purpose I am scraping the data from www.citypopulation.de website which has population statistics for different countries.\n",
"\n",
"Data use policy: http://citypopulation.de/termsofuse.html (DATA -> Population Data)\n",
"\n",
"\n",
"\n",
"### The data that I will be extracting in this jupyter notebook is for Oceania -> NEW ZEALAND http://citypopulation.de/en/newzealand/\n",
"- I am only scraping data for North and South Islands (excluded Chatham islands)\n",
"- North island: http://citypopulation.de/en/newzealand/northisland/\n",
"- South island: http://citypopulation.de/en/newzealand/southisland/"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"## Let us scrape the website and fetch the information using requests and beautifulsoup libraries\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Scraping website"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Python environment versions (for reference)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Python 3.7.0\n"
]
}
],
"source": [
"!python --version"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"pip 19.3.1 from c:\\python37\\lib\\site-packages\\pip (python 3.7)\n",
"\n"
]
}
],
"source": [
"!pip --version"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"# Importing libraries\n",
"import requests\n",
"import bs4\n",
"from bs4 import BeautifulSoup\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Requests version: 2.19.1\n",
"BeautifulSoup version: 4.7.1\n",
"Pandas version: 0.23.4\n"
]
}
],
"source": [
"print('Requests version: {}'.format(requests.__version__))\n",
"print('BeautifulSoup version: {}'.format(bs4.__version__))\n",
"print('Pandas version: {}'.format(pd.__version__))"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"# URLs to scrape\n",
"# This is a dictionary object with URLs. \n",
"# We will use this dictionary to scrape information for each url at a time.\n",
"urls = {\n",
" 'north': 'http://citypopulation.de/en/newzealand/northisland',\n",
" 'south': 'http://citypopulation.de/en/newzealand/southisland/'\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<Response [200]>\n"
]
}
],
"source": [
"# Using requests to get the information\n",
"output = requests.get(urls['north'])\n",
"print(output)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### If you see a HTTP status code 200, meaning our request was a success\n",
"\n",
"#### Reference link here: https://en.wikipedia.org/wiki/List_of_HTTP_status_codes"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'<!DOCTYPE html>\\r\\n<html lang=\"en\">\\r\\n<head>\\r\\n<meta charset=\"utf-8\">\\r\\n<meta name=\"description\" content=\"North Island (New Zealand): Regions & Settlements with population statistics, charts and maps.\"'"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# What's in the output?\n",
"# Let's output upto 200 characters \n",
"output.text[:200]"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"233654\n",
"<class 'str'>\n"
]
}
],
"source": [
"print(len(output.text))\n",
"print(type(output.text))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### As you see the output above, output.text has the html tags and content of the webpage.\n",
"\n",
"#### Please know that you need some understanding of HTML and tags.\n",
"\n",
"#### Now let's use BeautifulSoup to parse the output.text string."
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"bs_output = BeautifulSoup(markup=output.text, features=\"html.parser\")"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"7"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(bs_output.contents)"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"list"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(bs_output.contents)"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['html', '\\n']"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bs_output.contents[:2]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Feel free to print the whole output of the contents\n",
"#### bs_output.contents displays the html tags and content of the URL\n",
"\n",
"The beauty of BeautifulSoup's parser is that you can interact with each elements and parts of html tags including classes and id values.\n",
"\n",
"You might wonder wonder what is the difference between requests' output.text and bs4's bs_output.contents?\n",
"- Well requests output is a single string vs bs4's output is a list of objects\n",
"\n",
"Example below:"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<a href=\"/\">Home</a>,\n",
" <a href=\"/Oceania.html\" itemprop=\"url\"><span itemprop=\"name\">Oceania</span></a>,\n",
" <a href=\"/en/newzealand/\" itemprop=\"url\"><span itemprop=\"name\">New Zealand</span></a>,\n",
" <a href=\"javascript:cp.changePageLang('en','de')\"><img alt=\"\" src=\"/images/icons/de.svg\" title=\"Deutsch\"/></a>,\n",
" <a href=\"javascript:openMap()\"><img alt=\"Show Map\" id=\"smap\" src=\"/images/smaps/newzealand-cities.png\" title=\"Show Map\"/></a>]"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bs_output.find_all('a')[:5]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### You see the output above? \n",
"\n",
"I've printed 5 items from the list output of BeautifulSoup's find_all function. I passed <a> tag as 'a' to find all <a> tag elements in the bs_output. Likewise you can extract and play around with all the html tags and their contents.\n",
" \n",
"#### Before we do further extraction let us try to understand which parts of our URL page we would like to extract the data from."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 2. Exploring HTML content and extraction\n",
"\n",
""
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [],
"source": [
"# Our data is in the <table> tag with id='ts'\n",
"#\n",
"table_output = bs_output.find(name='table', attrs={'id': 'ts'})"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['\\n', <thead>\n",
" <tr id=\"tsh\"><th class=\"rname\" data-coltype=\"name\" onclick=\"javascript:sort('ts',0,false)\"><a href=\"javascript:sort('ts',0,false)\">Name</a></th>\n",
" <th class=\"rstatus\" data-coltype=\"status\" onclick=\"javascript:sort('ts',1,false)\"><a href=\"javascript:sort('ts',1,false)\">Status</a></th><th class=\"radm rarea\" data-coltype=\"adm\" onclick=\"javascript:sort('ts',2,false)\"><a href=\"javascript:sort('ts',2,false)\">Region</a></th><th class=\"rpop prio5\" data-coldate=\"1996-06-30\" data-colhead=\"E 1996-06-30\" data-coltype=\"pop\" onclick=\"javascript:sort('ts',3,true)\"><a href=\"javascript:sort('ts',3,true)\">Population</a><br/><span class=\"unit\">Estimate<br/>1996-06-30</span></th><th class=\"rpop prio4\" data-coldate=\"2001-06-30\" data-colhead=\"E 2001-06-30\" data-coltype=\"pop\" onclick=\"javascript:sort('ts',4,true)\"><a href=\"javascript:sort('ts',4,true)\">Population</a><br/><span class=\"unit\">Estimate<br/>2001-06-30</span></th><th class=\"rpop prio3\" data-coldate=\"2006-06-30\" data-colhead=\"E 2006-06-30\" data-coltype=\"pop\" onclick=\"javascript:sort('ts',5,true)\"><a href=\"javascript:sort('ts',5,true)\">Population</a><br/><span class=\"unit\">Estimate<br/>2006-06-30</span></th><th class=\"rpop prio2\" data-coldate=\"2013-06-30\" data-colhead=\"E 2013-06-30\" data-coltype=\"pop\" onclick=\"javascript:sort('ts',6,true)\"><a href=\"javascript:sort('ts',6,true)\">Population</a><br/><span class=\"unit\">Estimate<br/>2013-06-30</span></th><th class=\"rpop prio1\" data-coldate=\"2018-06-30\" data-colhead=\"E 2018-06-30\" data-coltype=\"pop\" onclick=\"javascript:sort('ts',7,true)\"><a href=\"javascript:sort('ts',7,true)\">Population</a><br/><span class=\"unit\">Estimate<br/>2018-06-30</span></th><th class=\"sc\" data-coltype=\"other\"> </th></tr>\n",
" </thead>]"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"table_output.contents[:2] # prints a list of tag elements"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['Name',\n",
" 'Status',\n",
" 'Region',\n",
" 'PopulationEstimate1996-06-30',\n",
" 'PopulationEstimate2001-06-30',\n",
" 'PopulationEstimate2006-06-30',\n",
" 'PopulationEstimate2013-06-30',\n",
" 'PopulationEstimate2018-06-30',\n",
" '\\xa0']"
]
},
"execution_count": 66,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Extracting column names from <tr> tag\n",
"[x.text for x in table_output.find_all('th')] # outputs a list of values of the <th> elements in the table output"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Notice the output above, we don't need the last value"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['Name',\n",
" 'Status',\n",
" 'Region',\n",
" 'PopulationEstimate1996-06-30',\n",
" 'PopulationEstimate2001-06-30',\n",
" 'PopulationEstimate2006-06-30',\n",
" 'PopulationEstimate2013-06-30',\n",
" 'PopulationEstimate2018-06-30']"
]
},
"execution_count": 79,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Append the column names into table_columns empty list\n",
"#\n",
"table_columns = [x.get_text() for x in table_output.find_all('th')][:-1]\n",
"table_columns"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {},
"outputs": [],
"source": [
"# Extracting table output which is in <tbody> tag\n",
"#\n",
"table_body = table_output.find_all('tbody')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Feel free to print the output of table_body, it will be long output result"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {},
"outputs": [],
"source": [
"north_island_output = []\n",
"for item in table_body:\n",
" rows = item.find_all('tr') # extracts <tr> elements in <tbody>\n",
" for row in rows:\n",
" td = row.find_all('td') # extracts <td> elements in each row i.e. <tr>\n",
" td_values = [val.text for val in td] # extracts value of each <td>\n",
" north_island_output.append(td_values) # appends values to the list"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['Algies Bay',\n",
" 'Rural Settlement',\n",
" 'Auckland',\n",
" '550',\n",
" '690',\n",
" '800',\n",
" '870',\n",
" '980',\n",
" '→']"
]
},
"execution_count": 99,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"north_island_output[1]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Again, in the output above we don't need the last value, so let's fix that"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {},
"outputs": [],
"source": [
"north_island_output = []\n",
"for item in table_body:\n",
" rows = item.find_all('tr') # extracts <tr> elements in <tbody>\n",
" for row in rows:\n",
" td = row.find_all('td') # extracts <td> elements in each row i.e. <tr>\n",
" td_values = [val.text for val in td] # extracts value of each <td>\n",
" north_island_output.append(td_values[:-1]) # appends values to the list, also excludes the last value that is not needed"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['Algies Bay',\n",
" 'Rural Settlement',\n",
" 'Auckland',\n",
" '550',\n",
" '690',\n",
" '800',\n",
" '870',\n",
" '980']"
]
},
"execution_count": 101,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"north_island_output[1]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### That's it, we have our table column names and the table data append those two into a dataframe"
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['Name',\n",
" 'Status',\n",
" 'Region',\n",
" 'PopulationEstimate1996-06-30',\n",
" 'PopulationEstimate2001-06-30',\n",
" 'PopulationEstimate2006-06-30',\n",
" 'PopulationEstimate2013-06-30',\n",
" 'PopulationEstimate2018-06-30']"
]
},
"execution_count": 102,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"table_columns"
]
},
{
"cell_type": "code",
"execution_count": 103,
"metadata": {},
"outputs": [],
"source": [
"north_data = pd.DataFrame(\n",
" data=north_island_output,\n",
" columns=table_columns\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 104,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Name</th>\n",
" <th>Status</th>\n",
" <th>Region</th>\n",
" <th>PopulationEstimate1996-06-30</th>\n",
" <th>PopulationEstimate2001-06-30</th>\n",
" <th>PopulationEstimate2006-06-30</th>\n",
" <th>PopulationEstimate2013-06-30</th>\n",
" <th>PopulationEstimate2018-06-30</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Ahipara</td>\n",
" <td>Rural Settlement</td>\n",
" <td>Northland</td>\n",
" <td>930</td>\n",
" <td>1,050</td>\n",
" <td>1,120</td>\n",
" <td>1,130</td>\n",
" <td>1,180</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Algies Bay</td>\n",
" <td>Rural Settlement</td>\n",
" <td>Auckland</td>\n",
" <td>550</td>\n",
" <td>690</td>\n",
" <td>800</td>\n",
" <td>870</td>\n",
" <td>980</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Arapuni</td>\n",
" <td>Rural Settlement</td>\n",
" <td>Waikato</td>\n",
" <td>290</td>\n",
" <td>260</td>\n",
" <td>230</td>\n",
" <td>250</td>\n",
" <td>260</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Ashhurst</td>\n",
" <td>Small Urban Area</td>\n",
" <td>Manawatu-Wanganui</td>\n",
" <td>2,530</td>\n",
" <td>2,520</td>\n",
" <td>2,510</td>\n",
" <td>2,750</td>\n",
" <td>2,990</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Athenree</td>\n",
" <td>Rural Settlement</td>\n",
" <td>Bay of Plenty</td>\n",
" <td>510</td>\n",
" <td>530</td>\n",
" <td>630</td>\n",
" <td>700</td>\n",
" <td>740</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Name Status Region \\\n",
"0 Ahipara Rural Settlement Northland \n",
"1 Algies Bay Rural Settlement Auckland \n",
"2 Arapuni Rural Settlement Waikato \n",
"3 Ashhurst Small Urban Area Manawatu-Wanganui \n",
"4 Athenree Rural Settlement Bay of Plenty \n",
"\n",
" PopulationEstimate1996-06-30 PopulationEstimate2001-06-30 \\\n",
"0 930 1,050 \n",
"1 550 690 \n",
"2 290 260 \n",
"3 2,530 2,520 \n",
"4 510 530 \n",
"\n",
" PopulationEstimate2006-06-30 PopulationEstimate2013-06-30 \\\n",
"0 1,120 1,130 \n",
"1 800 870 \n",
"2 230 250 \n",
"3 2,510 2,750 \n",
"4 630 700 \n",
"\n",
" PopulationEstimate2018-06-30 \n",
"0 1,180 \n",
"1 980 \n",
"2 260 \n",
"3 2,990 \n",
"4 740 "
]
},
"execution_count": 104,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"north_data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### We can follow the same procedure above for south island data as well. \n",
"\n",
"### For a better approach let us put the whole process using a function."
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {},
"outputs": [],
"source": [
"# Importing libraries\n",
"import requests\n",
"from bs4 import BeautifulSoup\n",
"import pandas as pd\n",
"\n",
"# Variables\n",
"south_island_output = []\n",
"north_island_output = []\n",
"table_columns = []\n",
"\n",
"# URLs for North and South islands\n",
"urls = {\n",
" 'north': 'http://citypopulation.de/en/newzealand/northisland',\n",
" 'south': 'http://citypopulation.de/en/newzealand/southisland/'\n",
"}\n",
"\n",
"# Function that downloads the data\n",
"def download_data():\n",
" \"\"\"\n",
" Function extracts td values by looping each child element of the parent.\n",
" \n",
" Two empty lists south_island_output and north_island_output are initialised.\n",
" \n",
" A urls dictionary object with north island and south island urls is also initialised.\n",
" \n",
" Pseudo code:\n",
" - for each item in the dictionary\n",
" - connect to the url\n",
" - if success (response code == 200), then loop through the page data\n",
" - for each row_item in body (loop - look for <tr> element):\n",
" - for each row in the row_item (loop and look for <td> element):\n",
" - for each <td> element, extract the text value\n",
" - finally append those text values into output list\n",
" \"\"\" \n",
" for url in urls:\n",
" print(url, urls[url])\n",
" \n",
" ## response\n",
" response = requests.get(urls[url])\n",
" \n",
" if response.status_code == 200:\n",
" print('Response code is 200. Success!')\n",
" try:\n",
" ## web scraping\n",
" soup = BeautifulSoup(response.text, \"html.parser\")\n",
" table = soup.find(name='table', attrs={'id': 'ts'})\n",
" table_columns.append([x.get_text() for x in table.find_all('th')][:-1])\n",
" body = table.find_all('tbody')\n",
" for item in body:\n",
" rows = item.find_all('tr') \n",
" for row in rows:\n",
" td = row.find_all('td')\n",
" td_values = [val.text for val in td]\n",
" if url == 'north':\n",
" north_island_output.append(td_values[:-1]) # excluding last column that has an arrow as a value\n",
" else:\n",
" south_island_output.append(td_values[:-1]) # excluding last column that has an arrow as a value\n",
" except Exception as ex:\n",
" print(str(ex))\n",
" else:\n",
" print('Oops! {0}'.format(response.status_code))"
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"north http://citypopulation.de/en/newzealand/northisland\n",
"Response code is 200. Success!\n",
"south http://citypopulation.de/en/newzealand/southisland/\n",
"Response code is 200. Success!\n"
]
}
],
"source": [
"download_data()"
]
},
{
"cell_type": "code",
"execution_count": 108,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['Name',\n",
" 'Status',\n",
" 'Region',\n",
" 'PopulationEstimate1996-06-30',\n",
" 'PopulationEstimate2001-06-30',\n",
" 'PopulationEstimate2006-06-30',\n",
" 'PopulationEstimate2013-06-30',\n",
" 'PopulationEstimate2018-06-30']"
]
},
"execution_count": 108,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"table_columns[0]"
]
},
{
"cell_type": "code",
"execution_count": 110,
"metadata": {},
"outputs": [],
"source": [
"# North island dataframe\n",
"north_island_data = pd.DataFrame(data=north_island_output, columns=table_columns[0])\n",
"\n",
"# South island dataframe\n",
"south_island_data = pd.DataFrame(data=south_island_output, columns=table_columns[0])"
]
},
{
"cell_type": "code",
"execution_count": 111,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Name</th>\n",
" <th>Status</th>\n",
" <th>Region</th>\n",
" <th>PopulationEstimate1996-06-30</th>\n",
" <th>PopulationEstimate2001-06-30</th>\n",
" <th>PopulationEstimate2006-06-30</th>\n",
" <th>PopulationEstimate2013-06-30</th>\n",
" <th>PopulationEstimate2018-06-30</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Ahipara</td>\n",
" <td>Rural Settlement</td>\n",
" <td>Northland</td>\n",
" <td>930</td>\n",
" <td>1,050</td>\n",
" <td>1,120</td>\n",
" <td>1,130</td>\n",
" <td>1,180</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Algies Bay</td>\n",
" <td>Rural Settlement</td>\n",
" <td>Auckland</td>\n",
" <td>550</td>\n",
" <td>690</td>\n",
" <td>800</td>\n",
" <td>870</td>\n",
" <td>980</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Arapuni</td>\n",
" <td>Rural Settlement</td>\n",
" <td>Waikato</td>\n",
" <td>290</td>\n",
" <td>260</td>\n",
" <td>230</td>\n",
" <td>250</td>\n",
" <td>260</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Ashhurst</td>\n",
" <td>Small Urban Area</td>\n",
" <td>Manawatu-Wanganui</td>\n",
" <td>2,530</td>\n",
" <td>2,520</td>\n",
" <td>2,510</td>\n",
" <td>2,750</td>\n",
" <td>2,990</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Athenree</td>\n",
" <td>Rural Settlement</td>\n",
" <td>Bay of Plenty</td>\n",
" <td>510</td>\n",
" <td>530</td>\n",
" <td>630</td>\n",
" <td>700</td>\n",
" <td>740</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Name Status Region \\\n",
"0 Ahipara Rural Settlement Northland \n",
"1 Algies Bay Rural Settlement Auckland \n",
"2 Arapuni Rural Settlement Waikato \n",
"3 Ashhurst Small Urban Area Manawatu-Wanganui \n",
"4 Athenree Rural Settlement Bay of Plenty \n",
"\n",
" PopulationEstimate1996-06-30 PopulationEstimate2001-06-30 \\\n",
"0 930 1,050 \n",
"1 550 690 \n",
"2 290 260 \n",
"3 2,530 2,520 \n",
"4 510 530 \n",
"\n",
" PopulationEstimate2006-06-30 PopulationEstimate2013-06-30 \\\n",
"0 1,120 1,130 \n",
"1 800 870 \n",
"2 230 250 \n",
"3 2,510 2,750 \n",
"4 630 700 \n",
"\n",
" PopulationEstimate2018-06-30 \n",
"0 1,180 \n",
"1 980 \n",
"2 260 \n",
"3 2,990 \n",
"4 740 "
]
},
"execution_count": 111,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"north_island_data.head()"
]
},
{
"cell_type": "code",
"execution_count": 112,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Name</th>\n",
" <th>Status</th>\n",
" <th>Region</th>\n",
" <th>PopulationEstimate1996-06-30</th>\n",
" <th>PopulationEstimate2001-06-30</th>\n",
" <th>PopulationEstimate2006-06-30</th>\n",
" <th>PopulationEstimate2013-06-30</th>\n",
" <th>PopulationEstimate2018-06-30</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Ahaura</td>\n",
" <td>Rural Settlement</td>\n",
" <td>West Coast</td>\n",
" <td>120</td>\n",
" <td>140</td>\n",
" <td>110</td>\n",
" <td>100</td>\n",
" <td>80</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Akaroa</td>\n",
" <td>Rural Settlement</td>\n",
" <td>Canterbury</td>\n",
" <td>680</td>\n",
" <td>610</td>\n",
" <td>620</td>\n",
" <td>670</td>\n",
" <td>630</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Alexandra</td>\n",
" <td>Small Urban Area</td>\n",
" <td>Otago</td>\n",
" <td>4,690</td>\n",
" <td>4,480</td>\n",
" <td>4,940</td>\n",
" <td>4,920</td>\n",
" <td>5,510</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Allanton</td>\n",
" <td>Rural Settlement</td>\n",
" <td>Otago</td>\n",
" <td>220</td>\n",
" <td>240</td>\n",
" <td>260</td>\n",
" <td>260</td>\n",
" <td>290</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Amberley</td>\n",
" <td>Small Urban Area</td>\n",
" <td>Canterbury</td>\n",
" <td>1,050</td>\n",
" <td>1,160</td>\n",
" <td>1,340</td>\n",
" <td>1,620</td>\n",
" <td>1,800</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Name Status Region PopulationEstimate1996-06-30 \\\n",
"0 Ahaura Rural Settlement West Coast 120 \n",
"1 Akaroa Rural Settlement Canterbury 680 \n",
"2 Alexandra Small Urban Area Otago 4,690 \n",
"3 Allanton Rural Settlement Otago 220 \n",
"4 Amberley Small Urban Area Canterbury 1,050 \n",
"\n",
" PopulationEstimate2001-06-30 PopulationEstimate2006-06-30 \\\n",
"0 140 110 \n",
"1 610 620 \n",
"2 4,480 4,940 \n",
"3 240 260 \n",
"4 1,160 1,340 \n",
"\n",
" PopulationEstimate2013-06-30 PopulationEstimate2018-06-30 \n",
"0 100 80 \n",
"1 670 630 \n",
"2 4,920 5,510 \n",
"3 260 290 \n",
"4 1,620 1,800 "
]
},
"execution_count": 112,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"south_island_data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 3. EDA"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### For data analysis, let's take out unwanted columns from our dataframes"
]
},
{
"cell_type": "code",
"execution_count": 113,
"metadata": {},
"outputs": [],
"source": [
"north_island_data = north_island_data[['Name', 'Status','Region', 'PopulationEstimate2018-06-30']]\n",
"south_island_data = south_island_data[['Name', 'Status','Region', 'PopulationEstimate2018-06-30']]"
]
},
{
"cell_type": "code",
"execution_count": 114,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Name</th>\n",
" <th>Status</th>\n",
" <th>Region</th>\n",
" <th>PopulationEstimate2018-06-30</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Ahipara</td>\n",
" <td>Rural Settlement</td>\n",
" <td>Northland</td>\n",
" <td>1,180</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Algies Bay</td>\n",
" <td>Rural Settlement</td>\n",
" <td>Auckland</td>\n",
" <td>980</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Name Status Region PopulationEstimate2018-06-30\n",
"0 Ahipara Rural Settlement Northland 1,180\n",
"1 Algies Bay Rural Settlement Auckland 980"
]
},
"execution_count": 114,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"north_island_data.head(2)"
]
},
{
"cell_type": "code",
"execution_count": 115,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Name</th>\n",
" <th>Status</th>\n",
" <th>Region</th>\n",
" <th>PopulationEstimate2018-06-30</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Ahaura</td>\n",
" <td>Rural Settlement</td>\n",
" <td>West Coast</td>\n",
" <td>80</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Akaroa</td>\n",
" <td>Rural Settlement</td>\n",
" <td>Canterbury</td>\n",
" <td>630</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Name Status Region PopulationEstimate2018-06-30\n",
"0 Ahaura Rural Settlement West Coast 80\n",
"1 Akaroa Rural Settlement Canterbury 630"
]
},
"execution_count": 115,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"south_island_data.head(2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3.1 North island"
]
},
{
"cell_type": "code",
"execution_count": 116,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(350, 4)"
]
},
"execution_count": 116,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"north_island_data.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Some data cleaning\n",
"\n",
"Population column values have \",\" comma in the values. Let's replace , and convert the column from string to integer type."
]
},
{
"cell_type": "code",
"execution_count": 119,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Name object\n",
"Status object\n",
"Region object\n",
"PopulationEstimate2018-06-30 object\n",
"dtype: object"
]
},
"execution_count": 119,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"north_island_data.dtypes"
]
},
{
"cell_type": "code",
"execution_count": 123,
"metadata": {},
"outputs": [],
"source": [
"north_island_data['PopulationEstimate2018-06-30'] = north_island_data['PopulationEstimate2018-06-30'].str.replace(',', '')"
]
},
{
"cell_type": "code",
"execution_count": 124,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Name</th>\n",
" <th>Status</th>\n",
" <th>Region</th>\n",
" <th>PopulationEstimate2018-06-30</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Ahipara</td>\n",
" <td>Rural Settlement</td>\n",
" <td>Northland</td>\n",
" <td>1180</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Algies Bay</td>\n",
" <td>Rural Settlement</td>\n",
" <td>Auckland</td>\n",
" <td>980</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Arapuni</td>\n",
" <td>Rural Settlement</td>\n",
" <td>Waikato</td>\n",
" <td>260</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Ashhurst</td>\n",
" <td>Small Urban Area</td>\n",
" <td>Manawatu-Wanganui</td>\n",
" <td>2990</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Athenree</td>\n",
" <td>Rural Settlement</td>\n",
" <td>Bay of Plenty</td>\n",
" <td>740</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Name Status Region \\\n",
"0 Ahipara Rural Settlement Northland \n",
"1 Algies Bay Rural Settlement Auckland \n",
"2 Arapuni Rural Settlement Waikato \n",
"3 Ashhurst Small Urban Area Manawatu-Wanganui \n",
"4 Athenree Rural Settlement Bay of Plenty \n",
"\n",
" PopulationEstimate2018-06-30 \n",
"0 1180 \n",
"1 980 \n",
"2 260 \n",
"3 2990 \n",
"4 740 "
]
},
"execution_count": 124,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"north_island_data.head()"
]
},
{
"cell_type": "code",
"execution_count": 125,
"metadata": {},
"outputs": [],
"source": [
"north_island_data['PopulationEstimate2018-06-30'] = pd.to_numeric(north_island_data['PopulationEstimate2018-06-30'])"
]
},
{
"cell_type": "code",
"execution_count": 126,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Name object\n",
"Status object\n",
"Region object\n",
"PopulationEstimate2018-06-30 int64\n",
"dtype: object"
]
},
"execution_count": 126,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"north_island_data.dtypes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Renaming population column"
]
},
{
"cell_type": "code",
"execution_count": 142,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Name</th>\n",
" <th>Status</th>\n",
" <th>Region</th>\n",
" <th>PopulationEstimate2018</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Ahipara</td>\n",
" <td>Rural Settlement</td>\n",
" <td>Northland</td>\n",
" <td>1180</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Algies Bay</td>\n",
" <td>Rural Settlement</td>\n",
" <td>Auckland</td>\n",
" <td>980</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Name Status Region PopulationEstimate2018\n",
"0 Ahipara Rural Settlement Northland 1180\n",
"1 Algies Bay Rural Settlement Auckland 980"
]
},
"execution_count": 142,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"north_island_data = north_island_data.rename(columns={'PopulationEstimate2018-06-30': 'PopulationEstimate2018'})\n",
"north_island_data.head(2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Top 10 most populated places in north island"
]
},
{
"cell_type": "code",
"execution_count": 152,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Name</th>\n",
" <th>Status</th>\n",
" <th>Region</th>\n",
" <th>PopulationEstimate2018</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>Auckland</td>\n",
" <td>Main Urban Area</td>\n",
" <td>Auckland</td>\n",
" <td>1467800</td>\n",
" </tr>\n",
" <tr>\n",
" <th>333</th>\n",
" <td>Wellington</td>\n",
" <td>Main Urban Area</td>\n",
" <td>Wellington</td>\n",
" <td>215400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
" <td>Hamilton</td>\n",
" <td>Main Urban Area</td>\n",
" <td>Waikato</td>\n",
" <td>169300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>267</th>\n",
" <td>Tauranga</td>\n",
" <td>Main Urban Area</td>\n",
" <td>Bay of Plenty</td>\n",
" <td>135000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105</th>\n",
" <td>Lower Hutt</td>\n",
" <td>Main Urban Area</td>\n",
" <td>Wellington</td>\n",
" <td>104900</td>\n",
" </tr>\n",
" <tr>\n",
" <th>198</th>\n",
" <td>Palmerston North</td>\n",
" <td>Large Urban Area</td>\n",
" <td>Manawatu-Wanganui</td>\n",
" <td>80300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>141</th>\n",
" <td>Napier</td>\n",
" <td>Large Urban Area</td>\n",
" <td>Hawke's Bay</td>\n",
" <td>62800</td>\n",
" </tr>\n",
" <tr>\n",
" <th>219</th>\n",
" <td>Porirua</td>\n",
" <td>Large Urban Area</td>\n",
" <td>Wellington</td>\n",
" <td>55500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>143</th>\n",
" <td>New Plymouth</td>\n",
" <td>Large Urban Area</td>\n",
" <td>Taranaki</td>\n",
" <td>55300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>241</th>\n",
" <td>Rotorua</td>\n",
" <td>Large Urban Area</td>\n",
" <td>Bay of Plenty</td>\n",
" <td>54500</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Name Status Region \\\n",
"6 Auckland Main Urban Area Auckland \n",
"333 Wellington Main Urban Area Wellington \n",
"46 Hamilton Main Urban Area Waikato \n",
"267 Tauranga Main Urban Area Bay of Plenty \n",
"105 Lower Hutt Main Urban Area Wellington \n",
"198 Palmerston North Large Urban Area Manawatu-Wanganui \n",
"141 Napier Large Urban Area Hawke's Bay \n",
"219 Porirua Large Urban Area Wellington \n",
"143 New Plymouth Large Urban Area Taranaki \n",
"241 Rotorua Large Urban Area Bay of Plenty \n",
"\n",
" PopulationEstimate2018 \n",
"6 1467800 \n",
"333 215400 \n",
"46 169300 \n",
"267 135000 \n",
"105 104900 \n",
"198 80300 \n",
"141 62800 \n",
"219 55500 \n",
"143 55300 \n",
"241 54500 "
]
},
"execution_count": 152,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"north_island_data.sort_values(by='PopulationEstimate2018', ascending=False).head(10)"
]
},
{
"cell_type": "code",
"execution_count": 179,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 1224x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"top_10 = north_island_data.sort_values(by='PopulationEstimate2018', ascending=False).head(10)\n",
"\n",
"plt.figure(figsize=(17, 6))\n",
"sns.barplot(x='Name', y='PopulationEstimate2018', data=top_10)\n",
"plt.title(\"Top 10 most populated places in North Island NZ\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Top 10 least populated places in north island"
]
},
{
"cell_type": "code",
"execution_count": 151,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Name</th>\n",
" <th>Status</th>\n",
" <th>Region</th>\n",
" <th>PopulationEstimate2018</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>Castlepoint</td>\n",
" <td>Rural Settlement</td>\n",
" <td>Wellington</td>\n",
" <td>50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>328</th>\n",
" <td>Waitotara</td>\n",
" <td>Rural Settlement</td>\n",
" <td>Taranaki</td>\n",
" <td>60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>234</th>\n",
" <td>Raurimu</td>\n",
" <td>Rural Settlement</td>\n",
" <td>Manawatu-Wanganui</td>\n",
" <td>70</td>\n",
" </tr>\n",
" <tr>\n",
" <th>309</th>\n",
" <td>Waiinu Beach</td>\n",
" <td>Rural Settlement</td>\n",
" <td>Taranaki</td>\n",
" <td>70</td>\n",
" </tr>\n",
" <tr>\n",
" <th>340</th>\n",
" <td>Whangapoua</td>\n",
" <td>Rural Settlement</td>\n",
" <td>Waikato</td>\n",
" <td>70</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Atiamuri</td>\n",
" <td>Rural Settlement</td>\n",
" <td>Waikato</td>\n",
" <td>70</td>\n",
" </tr>\n",
" <tr>\n",
" <th>184</th>\n",
" <td>Ormondville</td>\n",
" <td>Rural Settlement</td>\n",
" <td>Manawatu-Wanganui</td>\n",
" <td>70</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>Baddeleys Beach - Campbells Beach</td>\n",
" <td>Rural Settlement</td>\n",
" <td>Auckland</td>\n",
" <td>70</td>\n",
" </tr>\n",
" <tr>\n",
" <th>324</th>\n",
" <td>Waitangi</td>\n",
" <td>Rural Settlement</td>\n",
" <td>Northland</td>\n",
" <td>80</td>\n",
" </tr>\n",
" <tr>\n",
" <th>230</th>\n",
" <td>Rainbows End</td>\n",
" <td>Rural Settlement</td>\n",
" <td>Auckland</td>\n",
" <td>80</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Name Status Region \\\n",
"20 Castlepoint Rural Settlement Wellington \n",
"328 Waitotara Rural Settlement Taranaki \n",
"234 Raurimu Rural Settlement Manawatu-Wanganui \n",
"309 Waiinu Beach Rural Settlement Taranaki \n",
"340 Whangapoua Rural Settlement Waikato \n",
"5 Atiamuri Rural Settlement Waikato \n",
"184 Ormondville Rural Settlement Manawatu-Wanganui \n",
"8 Baddeleys Beach - Campbells Beach Rural Settlement Auckland \n",
"324 Waitangi Rural Settlement Northland \n",
"230 Rainbows End Rural Settlement Auckland \n",
"\n",
" PopulationEstimate2018 \n",
"20 50 \n",
"328 60 \n",
"234 70 \n",
"309 70 \n",
"340 70 \n",
"5 70 \n",
"184 70 \n",
"8 70 \n",
"324 80 \n",
"230 80 "
]
},
"execution_count": 151,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"north_island_data.sort_values(by='PopulationEstimate2018', ascending=True).head(10)"
]
},
{
"cell_type": "code",
"execution_count": 178,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1944x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"bottom_10 = north_island_data.sort_values(by='PopulationEstimate2018', ascending=True).head(10)\n",
"\n",
"plt.figure(figsize=(27, 6))\n",
"sns.barplot(x='Name', y='PopulationEstimate2018', data=bottom_10)\n",
"plt.title(\"Top 10 least populated places in North Island NZ\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Population counts grouped by Region"
]
},
{
"cell_type": "code",
"execution_count": 270,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>sum</th>\n",
" <th>count</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Region</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Auckland</th>\n",
" <td>1612730</td>\n",
" <td>59</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bay of Plenty</th>\n",
" <td>259400</td>\n",
" <td>38</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Gisborne</th>\n",
" <td>39320</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Hawke's Bay</th>\n",
" <td>140620</td>\n",
" <td>21</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Manawatu-Wanganui</th>\n",
" <td>199140</td>\n",
" <td>50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Northland</th>\n",
" <td>109850</td>\n",
" <td>58</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Taranaki</th>\n",
" <td>93240</td>\n",
" <td>22</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Waikato</th>\n",
" <td>353070</td>\n",
" <td>77</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Wellington</th>\n",
" <td>498300</td>\n",
" <td>16</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" sum count\n",
"Region \n",
"Auckland 1612730 59\n",
"Bay of Plenty 259400 38\n",
"Gisborne 39320 9\n",
"Hawke's Bay 140620 21\n",
"Manawatu-Wanganui 199140 50\n",
"Northland 109850 58\n",
"Taranaki 93240 22\n",
"Waikato 353070 77\n",
"Wellington 498300 16"
]
},
"execution_count": 270,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"region_totals = north_island_data.groupby('Region')['PopulationEstimate2018'].agg(['sum', 'count'])\n",
"region_totals"
]
},
{
"cell_type": "code",
"execution_count": 271,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1224x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(17, 6))\n",
"sns.barplot(x=region_totals.index, y='sum', data=region_totals)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3.2 South island"
]
},
{
"cell_type": "code",
"execution_count": 272,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(227, 4)"
]
},
"execution_count": 272,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"south_island_data.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Some data cleaning\n",
"\n",
"Population column values have \",\" comma in the values. Let's replace , and convert the column from string to integer type."
]
},
{
"cell_type": "code",
"execution_count": 273,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Name object\n",
"Status object\n",
"Region object\n",
"PopulationEstimate2018-06-30 object\n",
"dtype: object"
]
},
"execution_count": 273,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"south_island_data.dtypes"
]
},
{
"cell_type": "code",
"execution_count": 274,
"metadata": {},
"outputs": [],
"source": [
"south_island_data['PopulationEstimate2018-06-30'] = south_island_data['PopulationEstimate2018-06-30'].str.replace(',', '')"
]
},
{
"cell_type": "code",
"execution_count": 275,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Name</th>\n",
" <th>Status</th>\n",
" <th>Region</th>\n",
" <th>PopulationEstimate2018-06-30</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Ahaura</td>\n",
" <td>Rural Settlement</td>\n",
" <td>West Coast</td>\n",
" <td>80</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Akaroa</td>\n",
" <td>Rural Settlement</td>\n",
" <td>Canterbury</td>\n",
" <td>630</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Alexandra</td>\n",
" <td>Small Urban Area</td>\n",
" <td>Otago</td>\n",
" <td>5510</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Allanton</td>\n",
" <td>Rural Settlement</td>\n",
" <td>Otago</td>\n",
" <td>290</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Amberley</td>\n",
" <td>Small Urban Area</td>\n",
" <td>Canterbury</td>\n",
" <td>1800</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Name Status Region PopulationEstimate2018-06-30\n",
"0 Ahaura Rural Settlement West Coast 80\n",
"1 Akaroa Rural Settlement Canterbury 630\n",
"2 Alexandra Small Urban Area Otago 5510\n",
"3 Allanton Rural Settlement Otago 290\n",
"4 Amberley Small Urban Area Canterbury 1800"
]
},
"execution_count": 275,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"south_island_data.head()"
]
},
{
"cell_type": "code",
"execution_count": 276,
"metadata": {},
"outputs": [],
"source": [
"south_island_data['PopulationEstimate2018-06-30'] = pd.to_numeric(south_island_data['PopulationEstimate2018-06-30'])"
]
},
{
"cell_type": "code",
"execution_count": 277,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Name object\n",
"Status object\n",
"Region object\n",
"PopulationEstimate2018-06-30 int64\n",
"dtype: object"
]
},
"execution_count": 277,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"south_island_data.dtypes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Renaming population column"
]
},
{
"cell_type": "code",
"execution_count": 278,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Name</th>\n",
" <th>Status</th>\n",
" <th>Region</th>\n",
" <th>PopulationEstimate2018</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Ahaura</td>\n",
" <td>Rural Settlement</td>\n",
" <td>West Coast</td>\n",
" <td>80</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Akaroa</td>\n",
" <td>Rural Settlement</td>\n",
" <td>Canterbury</td>\n",
" <td>630</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Name Status Region PopulationEstimate2018\n",
"0 Ahaura Rural Settlement West Coast 80\n",
"1 Akaroa Rural Settlement Canterbury 630"
]
},
"execution_count": 278,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"south_island_data = south_island_data.rename(columns={'PopulationEstimate2018-06-30': 'PopulationEstimate2018'})\n",
"south_island_data.head(2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Top 10 most populated places in south island"
]
},
{
"cell_type": "code",
"execution_count": 279,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Name</th>\n",
" <th>Status</th>\n",
" <th>Region</th>\n",
" <th>PopulationEstimate2018</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>Christchurch</td>\n",
" <td>Main Urban Area</td>\n",
" <td>Canterbury</td>\n",
" <td>377200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td>Dunedin</td>\n",
" <td>Main Urban Area</td>\n",
" <td>Otago</td>\n",
" <td>104500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>125</th>\n",
" <td>Nelson</td>\n",
" <td>Large Urban Area</td>\n",
" <td>Nelson</td>\n",
" <td>49300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>73</th>\n",
" <td>Invercargill</td>\n",
" <td>Large Urban Area</td>\n",
" <td>Southland</td>\n",
" <td>48700</td>\n",
" </tr>\n",
" <tr>\n",
" <th>195</th>\n",
" <td>Timaru</td>\n",
" <td>Medium Urban Area</td>\n",
" <td>Canterbury</td>\n",
" <td>28300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>Blenheim</td>\n",
" <td>Medium Urban Area</td>\n",
" <td>Marlborough</td>\n",
" <td>26400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>Ashburton</td>\n",
" <td>Medium Urban Area</td>\n",
" <td>Canterbury</td>\n",
" <td>19600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>158</th>\n",
" <td>Rangiora</td>\n",
" <td>Medium Urban Area</td>\n",
" <td>Canterbury</td>\n",
" <td>18400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>166</th>\n",
" <td>Rolleston</td>\n",
" <td>Medium Urban Area</td>\n",
" <td>Canterbury</td>\n",
" <td>16350</td>\n",
" </tr>\n",
" <tr>\n",
" <th>154</th>\n",
" <td>Queenstown</td>\n",
" <td>Medium Urban Area</td>\n",
" <td>Otago</td>\n",
" <td>15650</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Name Status Region PopulationEstimate2018\n",
"28 Christchurch Main Urban Area Canterbury 377200\n",
"40 Dunedin Main Urban Area Otago 104500\n",
"125 Nelson Large Urban Area Nelson 49300\n",
"73 Invercargill Large Urban Area Southland 48700\n",
"195 Timaru Medium Urban Area Canterbury 28300\n",
"19 Blenheim Medium Urban Area Marlborough 26400\n",
"11 Ashburton Medium Urban Area Canterbury 19600\n",
"158 Rangiora Medium Urban Area Canterbury 18400\n",
"166 Rolleston Medium Urban Area Canterbury 16350\n",
"154 Queenstown Medium Urban Area Otago 15650"
]
},
"execution_count": 279,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"south_island_data.sort_values(by='PopulationEstimate2018', ascending=False).head(10)"
]
},
{
"cell_type": "code",
"execution_count": 283,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 1224x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"top_10 = south_island_data.sort_values(by='PopulationEstimate2018', ascending=False).head(10)\n",
"\n",
"plt.figure(figsize=(17, 6))\n",
"sns.barplot(x='Name', y='PopulationEstimate2018', data=top_10)\n",
"plt.title(\"Top 10 most populated places in South Island NZ\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Top 10 least populated places in south island"
]
},
{
"cell_type": "code",
"execution_count": 284,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Name</th>\n",
" <th>Status</th>\n",
" <th>Region</th>\n",
" <th>PopulationEstimate2018</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>59</th>\n",
" <td>Haast</td>\n",
" <td>Rural Settlement</td>\n",
" <td>West Coast</td>\n",
" <td>50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>Arthur's Pass</td>\n",
" <td>Rural Settlement</td>\n",
" <td>Canterbury</td>\n",
" <td>60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>127</th>\n",
" <td>Ngakuta Bay</td>\n",
" <td>Rural Settlement</td>\n",
" <td>Marlborough</td>\n",
" <td>60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>150</th>\n",
" <td>Pounawea</td>\n",
" <td>Rural Settlement</td>\n",
" <td>Otago</td>\n",
" <td>60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>112</th>\n",
" <td>Milford Huts</td>\n",
" <td>Rural Settlement</td>\n",
" <td>Canterbury</td>\n",
" <td>60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>153</th>\n",
" <td>Purau</td>\n",
" <td>Rural Settlement</td>\n",
" <td>Canterbury</td>\n",
" <td>60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>116</th>\n",
" <td>Moana</td>\n",
" <td>Rural Settlement</td>\n",
" <td>West Coast</td>\n",
" <td>70</td>\n",
" </tr>\n",
" <tr>\n",
" <th>174</th>\n",
" <td>Selwyn Huts</td>\n",
" <td>Rural Settlement</td>\n",
" <td>Canterbury</td>\n",
" <td>70</td>\n",
" </tr>\n",
" <tr>\n",
" <th>103</th>\n",
" <td>Makikihi</td>\n",
" <td>Rural Settlement</td>\n",
" <td>Canterbury</td>\n",
" <td>80</td>\n",
" </tr>\n",
" <tr>\n",
" <th>209</th>\n",
" <td>Waipopo</td>\n",
" <td>Rural Settlement</td>\n",
" <td>Canterbury</td>\n",
" <td>80</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Name Status Region PopulationEstimate2018\n",
"59 Haast Rural Settlement West Coast 50\n",
"9 Arthur's Pass Rural Settlement Canterbury 60\n",
"127 Ngakuta Bay Rural Settlement Marlborough 60\n",
"150 Pounawea Rural Settlement Otago 60\n",
"112 Milford Huts Rural Settlement Canterbury 60\n",
"153 Purau Rural Settlement Canterbury 60\n",
"116 Moana Rural Settlement West Coast 70\n",
"174 Selwyn Huts Rural Settlement Canterbury 70\n",
"103 Makikihi Rural Settlement Canterbury 80\n",
"209 Waipopo Rural Settlement Canterbury 80"
]
},
"execution_count": 284,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"south_island_data.sort_values(by='PopulationEstimate2018', ascending=True).head(10)"
]
},
{
"cell_type": "code",
"execution_count": 285,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1944x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"bottom_10 = south_island_data.sort_values(by='PopulationEstimate2018', ascending=True).head(10)\n",
"\n",
"plt.figure(figsize=(27, 6))\n",
"sns.barplot(x='Name', y='PopulationEstimate2018', data=bottom_10)\n",
"plt.title(\"Top 10 least populated places in South Island NZ\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Population counts grouped by Region"
]
},
{
"cell_type": "code",
"execution_count": 286,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>sum</th>\n",
" <th>count</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Region</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Canterbury</th>\n",
" <td>544370</td>\n",
" <td>87</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Marlborough</th>\n",
" <td>37490</td>\n",
" <td>17</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Nelson</th>\n",
" <td>49300</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Otago</th>\n",
" <td>200050</td>\n",
" <td>58</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Southland</th>\n",
" <td>72450</td>\n",
" <td>23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Tasman</th>\n",
" <td>35640</td>\n",
" <td>19</td>\n",
" </tr>\n",
" <tr>\n",
" <th>West Coast</th>\n",
" <td>23000</td>\n",
" <td>22</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" sum count\n",
"Region \n",
"Canterbury 544370 87\n",
"Marlborough 37490 17\n",
"Nelson 49300 1\n",
"Otago 200050 58\n",
"Southland 72450 23\n",
"Tasman 35640 19\n",
"West Coast 23000 22"
]
},
"execution_count": 286,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"region_totals = south_island_data.groupby('Region')['PopulationEstimate2018'].agg(['sum', 'count'])\n",
"region_totals"
]
},
{
"cell_type": "code",
"execution_count": 288,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1224x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(17, 6))\n",
"sns.barplot(x=region_totals.index, y='sum', data=region_totals)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.7.0"
}
},
"nbformat": 4,
"nbformat_minor": 4
}