ABS-Report-Final.ipynb

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    "\n",
    "\n",
    "# Exploring Trends in Australian Census Data\n",
    "\n",
    "---"
   ]
  },
  {
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   "source": [
    "\n",
    "- Name 1: Anay Joshi\n",
    "- Name 2: Wong Jun Xiang Joseph\n",
    "- Name 3: Paransh Patel\n",
    "\n"
   ]
  },
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    "nbgrader": {
     "grade": false,
     "grade_id": "assignment",
     "locked": true,
     "points": 25,
     "schema_version": 3,
     "solution": false,
     "task": true
    }
   },
   "source": [
    "\n",
    "<hr>\n"
   ]
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   "source": [
    "# Report"
   ]
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   "source": [
    "### Aim\n",
    "\n",
    "This assignment aims to investigate the changes in marital status patterns across different age and sex groups in Australia from 1996 to 2021 through ABS Census data. In addition to the exploration of marital trends across various age groups, we will be comparing and contrasting the disparities between males and females and discuss its evolution over the 25 years. Furthermore, in other instances we may look at the data from a higher level looking at people in general rather than specific genders. Through analysing and presenting this data effectively, we are able to seek valuable insights into the evolving landscape of marital statuses within Australia. Studying marital status over different ages over time years is essential for gaining insights into the demographic, social, economic, and cultural factors that shape our societies. This information can provide meaningful insights about our population and its changes over time.\n",
    "\n",
    "### Hypothesis\n",
    "\n",
    "Our hypothesis states that Australia's distribution of registered marital statuses across various ages and genders has undergone intricate and meaningful transformations over the last 25 years. Firstly, we anticipate that the proportion of individuals in the married category has declined over time, reflecting evolving societal values around marriage. Secondly, we expect an increase in singles over time, suggesting a shift in lifestyle choices and societal norms. We also hope to see an increase in divorces over all age categories, reflecting the current societal belief that 50% of marriages end in divorce. Additionally, our hypothesis anticipates gender\\-specific variations in these trends, shedding light on the gender factors in shaping marital decisions. Looking more into gender\\-specific statistics, we expect the proportion of single males to be similar to that of females. When it comes to married proportions, we expect the proportion of married females to be more significant than males, which follows our prediction that there are more single males. Looking into age\\-specific statistics, we expect the mean age of married people to lie in the late 30s to mid\\-20s. We also hope to observe the mean age for marriage to be at older ages over time and, conversely, for divorce to be at younger ages over time (i.e., people are getting married later and divorced earlier). We predict this again due to societal values shifting and people's desire to pursue a career and other hobbies rather than a relationship. We will test these hypotheses by various means, using mean, mode, and median and, more importantly, data visualisation to observe and communicate trends we discover.\n",
    "\n"
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   "source": [
    "file_2021 = '2021Census_G05_AUS_AUS.csv'\n",
    "file_2016 = '2016Census_G05_AUS.csv'\n",
    "file_2011 = '2011Census_B05_AUST_short.csv'\n",
    "file_2006 = '2006_data.csv'\n",
    "file_2001 = '2001_data.csv'\n",
    "file_1996 = '1996_data.csv'\n",
    "DATA_LIST = [file_2021,file_2016,file_2011,file_2006,file_2001,file_1996]\n",
    "POPULATION_LIST = [] # will store the total no. people who took part in the census for each year (same order as DATA_LIST)"
   ]
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   "source": [
    "### Data\n",
    "\n",
    "##### Data Source\n",
    "\n",
    "The raw data from 2021, 2016 and 2011 takes a significantly different form to the earlier years. This because the Australian Bureau of Statistics (ABS) has discontinued DataPacks for earlier years. Moreover, the data pertains to **all of Australia** rather than just a particular city or region. The data for the 2021, 2016 and 2011 census can be found here:\n",
    "https://www.abs.gov.au/census/find-census-data/datapacks\n",
    "\n",
    "To select the year, click the drop down menu to select and download the data for all of Australia. the data pertaining to marital status must be gathered. After having done this, the metadata outlies which table contains our `csv` file. It is G05 for 2021 and 2016 and B05 for 2011.\n",
    "\n",
    "For data from 2006, 2001 and 1996, DataPacks are not available so the raw data was sourced from Basic Community Profiles, the data for these older years can be found here:\n",
    "https://www.abs.gov.au/census/find-census-data/search-by-area\n",
    "\n",
    "Once again select the drop down menu and find the relevant years and select the region as all of Australia. Download the 'Basic Community Profiles' for all the relevant years. The image below shows the layout of the data in these Basic Community Profiles (e.g is from 2006).\n",
    "\n",
    "<div style=\"max-width:50%\">\n",
    "  <img src=\".Australian_Trends.ipynb.upload/paste-0.5317331536794683\" style=\"max-width:90%\" />\n",
    "</div>\n",
    "\n",
    "##### What is the data about?\n",
    "\n",
    "The data shows the number of people from multiple age ranges ranging from age 15 to 85 or over (or 90 or over for the 1996 dataset) who have a particular marital status **in Australia**. For example, if the age range was 15-19 and the status was \"Married\" the data would tell us the number of occurrences of married people between 15-19 years of age in Australia. It does *not* tell us when people first marry, rather the number of people who *are* married. Furthermore, the data splits this into 3 categories based on sex. Male, Female and Persons. Persons does not refer to a particular sex but rather is just a summation of the data for males and females. In some cases it gives a better overview of population as a whole rather than looking at the data sex specifically. The marital statuses outlined in the dataset are: \"Married\", \"Separated\", \"Divorced\", \"Widowed\" and \"Never married\".  In this analysis the marital status of \"Widowed\" will not be considered.\n",
    "\n",
    "##### Raw data\n",
    "\n",
    "The raw data from each of the 3 files in 2021, 2016 and 2011 take the following form:\n",
    "\n",
    "```\n",
    "AUS_CODE_2021,M_15_19_yr_Married,M_15_19_yr_Separated,M_15_19_yr_Divorced,M_15_19_yr_Widowed,M_15_19_yr_Never_married,...\\n\n",
    "AUS,1030,201,166,204,747727,749330,19037,1235,591,393,785566,806829,514158,23911,25601,1340,1226219...\n",
    "```\n",
    "\n",
    "The raw data consists of 2 lines (separated by a `\\n` character). The first line of data contains the categories, i.e., the age ranges, sex and marital statuses. The second contains the frequency/number of occurrences in that category. The values in the top line all pair up one to one with the bottom line. For example, `M_15_19_yr_Married` has 1030 occurrences out of the total number of people who participated in the census. \n",
    "\n",
    "The data from 2006, 2001 and 1996 is less consistent with its format and less conducive to be used for data analysis purposes. However since it is the only available data from older years, particular emphasis has been made to format it correctly in a way that it can be consistently and repeatedly used without recurring errors. The data comes from 'Basic Community Profiles' and not 'DataPacks.' Its use is primarily to act as a summary. The data was first exported as a `csv` then cleaned using python. The raw data contains additional rows which are not needed hence they were first cropped out (done using python). The data remaining was as follows (the data shown is the 2006 data exported from Excel to csv, refer to the image above to see what the Excel spreadsheet looked like):\n",
    "\n",
    "```\n",
    ",Married(a),Separated,Divorced,Widowed,Never married,Total,,,,,,,,,,\n",
    ",MALES,,,,,,,,,,,,,,,\n",
    ",,,,,,,,,,,,,,,,\n",
    "15-19 years,\"1,772\",407,544,297,\"692,779\",\"695,799\",,,,,,,,,,\n",
    "20-24 years,\"28,385\",\"1,705\",\"1,054\",430,\"650,082\",\"681,656\",,,,,,,,,,\n",
    "25-34 years,\"483,213\",\"23,825\",\"29,556\",\"1,287\",\"783,176\",\"1,321,057\",,,,,,,,,,\n",
    "35-44 years,\"879,627\",\"55,379\",\"116,897\",\"4,008\",\"381,334\",\"1,437,245\",,,,,,,,,,\n",
    "45-54 years,\"911,200\",\"66,048\",\"178,089\",\"11,036\",\"193,708\",\"1,360,081\",,,,,,,,,,\n",
    "55-64 years,\"794,367\",\"46,136\",\"147,917\",\"22,887\",\"84,826\",\"1,096,133\",,,,,,,,,,\n",
    "65-74 years,\"501,072\",\"21,587\",\"65,566\",\"39,523\",\"40,695\",\"668,443\",,,,,,,,,,\n",
    "75-84 years,\"289,456\",\"8,946\",\"23,804\",\"67,473\",\"22,781\",\"412,460\",,,,,,,,,,\n",
    "85 years and over,\"55,290\",\"1,567\",\"3,448\",\"39,041\",\"5,699\",\"105,045\",,,,,,,,,,\n",
    ",,,,,,,,,,,,,,,,\n",
    "Total,\"3,944,382\",\"225,600\",\"566,875\",\"185,982\",\"2,855,080\",\"7,777,919\",,,,,,,,,,\n",
    "...\n",
    "```\n",
    "\n",
    "This is data for males (in 2006), however similar blocks were present after this for females and persons. Data from these blocks was extracted and stored in an identical format as the data from more recent years (this was in an effort to streamline the data analysis process). The data from 2001 and 1996 were different in format to the data from 2006. The data from 2001 and 1996 looked as follows (example of 2001, but 1996 looks similar):\n",
    "\n",
    "```\n",
    ",Married,,,Separated,,,Divorced,,,Widowed,,,Never married,,,Total,,\n",
    ",Males   ,Females,,Males,Females,,Males,Females,,Males,Females,,Males,Females,,Males,Females,Persons\n",
    ",,,,,,,,,,,,,,,,,,\n",
    "15-19 years,\"2,043\",\"4,679\",,339,442,,355,369,,259,699,,\"674,517\",\"641,562\",,\"677,513\",\"647,751\",\"1,325,264\"\n",
    "20-24 years,\"34,216\",\"71,388\",,\"2,362\",\"5,895\",,972,\"2,198\",,308,543,,\"591,461\",\"531,739\",,\"629,319\",\"611,763\",\"1,241,082\"\n",
    "25-29 years,\"186,721\",\"267,371\",,\"11,682\",\"21,795\",,\"9,228\",\"17,689\",,470,\"1,295\",,\"446,355\",\"356,351\",,\"654,456\",\"664,501\",\"1,318,957\"\n",
    "30-34 years,\"351,046\",\"420,058\",,\"23,595\",\"35,839\",,\"30,449\",\"46,753\",,898,\"2,891\",,\"282,061\",\"210,641\",,\"688,049\",\"716,182\",\"1,404,231\"\n",
    "35-39 \n",
    "...\n",
    "```\n",
    "\n",
    "Here the data was not in blocks but instead one row had all the data needed for a particular age group. Once again data was extracted and stored in an identical format as the data from more recent years in an effort to streamline the data analysis process. One point to note was that the years over which the data spanned was more specific for the older data (in increments of 5, rather than 10 for more recent years). Moreover in 1996 the data went as high as ages 90+, while all other years only went as high as 85+.\n",
    "\n",
    "It is important to note that the raw data from 1996, 2001 and 2006 contained some other information above and below which was not of importance, an example has been shown here from the 2006 dataset.\n",
    "\n",
    "```\n",
    "﻿AUSTRALIAN BUREAU OF STATISTICS  2006 Census of Population and Housing,,,,,,List of tables,,,,,,,,,,\n",
    "Australia (0) 7759538.2 sq. Kms,,,,,,Concepts and Definitions,,,,,,,,,,\n",
    ",,,,,,Census Dictionary,,,,,,,,,,\n",
    "B05 REGISTERED MARITAL STATUS BY AGE BY SEX,,,,,,Census Data Quality Statement,,,,,,,,,,\n",
    "Count of persons aged 15 years and over ,,,,,,© Commonwealth of Australia 2007,,,,,,,,,,\n",
    "Based on place of usual residence,,,,,,,,,,,,,,,,\n",
    ",,,,,,,,,,,,,,,,\n",
    "```\n",
    "\n",
    "During the data cleaning process, all this was cropped out with the aid of the `csv` import from python.\n",
    "\n"
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    "### Data Cleaning\n",
    "\n",
    "Firstly, the `read`-type functions reads these files, categorises them into age\\_category and frequency values, and extracts categories \\(demographic information\\) and their corresponding numeric data. Furthermore, additional data preparation is conducted for clarity and consistency. It converts all category names to uppercase for consistency and substitutes terms like \"NEVER\\_MARRIED\" to \"SINGLE\" for clarity and interpretability. The output comprises organized lists, 'categories' and 'numbers', housing the refined data. This meticulous refinement process is necessary for uniformity and reliability, ensuring the dataset's readiness for analytical endeavours.\n"
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    "# Read 2021, 2016 and 2011 data\n",
    "def read_data_2021_2016_2011 (file):\n",
    "    categories = []\n",
    "    numbers = []\n",
    "    with open(file,'r') as file_A:\n",
    "        splitted_file = file_A.read().split(\"\\n\")\n",
    "        list_of_cats = splitted_file[0].split(',')[1:]\n",
    "        list_of_nums = splitted_file[1].split(',')[1:]\n",
    "        # Iterate through the index of total no. of cateories (same as total no. of freq)\n",
    "        for i in range(len(list_of_cats)):\n",
    "            list_of_cats[i] = list_of_cats[i].upper()\n",
    "            if \"TOT\" in list_of_cats[i]:\n",
    "                continue # Skip past any rows about totals\n",
    "            if \"NEVER_MARRIED\" in list_of_cats[i]:\n",
    "                # Make the data more interpretable \"Never Married\" -> \"Single\"\n",
    "                list_of_cats[i] = list_of_cats[i].replace(\"NEVER_MARRIED\",\"SINGLE\")\n",
    "             \n",
    "            categories.append(list_of_cats[i])\n",
    "            numbers.append(int(list_of_nums[i]))\n",
    "    return list(zip(categories,numbers)), int(list_of_nums[-1])\n",
    "\n",
    "import csv\n",
    "\n",
    "def read_data_2006(filename):\n",
    "    needed_data = []\n",
    "    with open(filename, 'r') as file:\n",
    "        csv_reader = csv.reader(file)\n",
    "        for row in csv_reader:\n",
    "            # Keep only relevant rows of data (Note: total is kept here so that the population can be extracted later)\n",
    "            if ('years' in row[0] or 'Total' in row[0]):\n",
    "                needed_data.append(row[:7])\n",
    "    return needed_data[1:]\n",
    "\n",
    "\n",
    "def extract_needed_info_2006(data):\n",
    "    output = []\n",
    "    genders = [\"M\",\"F\",\"P\"]\n",
    "    statuses = [\"MARRIED\",\"SEPARATED\",\"DIVORCED\",\"WIDOWED\",\"SINGLE\"] # statuses appear in the order in the raw rata\n",
    "    s = 0\n",
    "    e = 10 # Difference between 's' and 'e' is 10 - as 10 is the size of each block of data\n",
    "    # Iterate through the genders M,F,P as they appear in the 2006 csv file.\n",
    "    for sex in genders:\n",
    "        for data_for_each_sex in data[s:e-1]: # 'e-1' is so the row related to totals is not included\n",
    "            if data_for_each_sex[0] == \"85 years and over\":\n",
    "                age_range = \"85OV_YR\"\n",
    "            else:\n",
    "                age_range = data_for_each_sex[0][0:2]+\"_\"+data_for_each_sex[0][3:5]+\"_YR\"\n",
    "            for i,freq in enumerate(data_for_each_sex[1:-1]):\n",
    "                output.append((sex+\"_\"+age_range+\"_\"+statuses[i],int(freq.replace(\",\",\"\")))) # Format data in the same way as recent years\n",
    "        if sex == \"P\":\n",
    "            POPULATION_LIST.append(int(data[e-1][-1].replace(\",\",\"\"))) # Extract and store the total population\n",
    "        s+=10\n",
    "        e+=10 # Move to the next block\n",
    "    return output\n",
    "\n",
    "\n",
    "def read_data_1996_2001(filename):\n",
    "    needed_data = []\n",
    "    with open(filename, 'r') as file:\n",
    "        csv_reader = csv.reader(file)\n",
    "        for row in csv_reader:\n",
    "            if ('years' in row[0] or 'Total' in row[0]):\n",
    "                needed_data.append(row)\n",
    "    return needed_data[1:]\n",
    "\n",
    "\n",
    "def extract_needed_info_1996_2001(data):\n",
    "    output = []\n",
    "    statuses = [\"MARRIED\",\"SEPARATED\",\"DIVORCED\",\"WIDOWED\",\"SINGLE\"]\n",
    "    total_row = [item for item in data[-1] if item != '']\n",
    "    POPULATION_LIST.append(int(total_row[-1].replace(\",\",\"\")))\n",
    "    for year_data in data[:-1]:\n",
    "        year_list = [item for item in year_data if item != ''] # Cleaned to remove empty strings\n",
    "        if year_list[0] == '85-89 years':\n",
    "                age_range = \"85_89_YR\"\n",
    "        elif '90' in year_list[0]:\n",
    "                age_range = \"90OV_YR\"\n",
    "        elif year_list[0] == '85 years and over':\n",
    "            age_range = \"85OV_YR\"\n",
    "        else:\n",
    "            age_range = year_list[0][0:2]+\"_\"+year_list[0][3:5]+\"_YR\"\n",
    "        c = 0\n",
    "        s = 0\n",
    "        for i,freq in enumerate(year_list[1:-3]):\n",
    "            if c == 0:\n",
    "                output.append((\"M_\"+age_range+\"_\"+statuses[s],int(freq.replace(\",\",\"\"))))\n",
    "                c+=1\n",
    "            else:\n",
    "                output.append((\"F_\"+age_range+\"_\"+statuses[s],int(freq.replace(\",\",\"\"))))\n",
    "                c = 0\n",
    "                output.append((\"P_\"+age_range+\"_\"+statuses[s],int(output[-1][1])+int(output[-2][1])))\n",
    "                s+=1\n",
    "    return output\n",
    "\n",
    "\n",
    "def read_all_files(files):\n",
    "    c = 0\n",
    "    FINAL_DATA = []\n",
    "    for file in files:\n",
    "        if c <= 2:\n",
    "            FINAL_DATA.append(read_data_2021_2016_2011(file)[0])\n",
    "            POPULATION_LIST.append(read_data_2021_2016_2011(file)[1])\n",
    "        elif c == 3:\n",
    "            FINAL_DATA.append(extract_needed_info_2006(read_data_2006(file)))\n",
    "        else:\n",
    "            FINAL_DATA.append(extract_needed_info_1996_2001(read_data_1996_2001(file)))\n",
    "        c+=1\n",
    "    return FINAL_DATA\n",
    "        "
   ]
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    "The above functions all serve a purpose to read the raw data and output it in a form that can be universally used by other functions. This is ideal as we don't want to continuously change the way we extract info from the data when passing it into different functions. The function `read_data_2021_2016_2011(file)` takes in one file \\(of which it must be 2021, 2016 or 2011\\). It then pairs up the age categories with their frequency as a tuple. This is done to all data pairs. It also ignores any data to do with totals \\(with one exception being the total number of people who took part in the census\\) to keep the data as clean as possible. Moreover, it changes the wording of \"Never Married\" to \"Single\" as it makes more sense and is easier to interpret.\n",
    "\n",
    "Cleaning the data for earlier years was not as simple, as the data was first exported from Excel spreadsheets to a CSV format, then meticulously cropped to include the actual information. Then, depending on how the data was formatted, it was iterated through and placed into lists such that the data would have the same format as the more recent years.\n",
    "\n",
    "`read_data_2006()` and `read_data_1996_2001()` serve the purpose of cropping out preliminary information from the raw data. It extracts all the information needed but does not organise it or structure it. It merely crops the data and prepares it to be passed into the two extract\\_needed\\_info functions. It is this function's job to organise and structure the data to look like 2021, 2016 and 2011's data.\n",
    "\n",
    "Since the data from 2006 was significantly different in format to 1996 and 2001, a separate function had to be made to read in that data appropriately. Once cropped, the raw data was structured in blocks, with each block pertaining to a particular gender \\(M,F,P\\). We started with the first block and iterated through, then moved onto the subsequent block until all 3 blocks were traversed.\n",
    "\n",
    "The raw data from 2001 and 1996 was similar, but there were still some subtle differences. Such as the age ranges were more specific and went for older ages. Nonetheless, we had planned to augment \\(spread\\) the data out over the age ranges. Hence, this was not a concern. The data for these 2 years was structured in one block with each row having data for a particular age. We looped through each row, extracting the ages and keeping track of the columns we were analysing. The columns were structured in an order as follows: `Married-M, Married-F, Separated-M, Separated-F, Divorced-M, Divorced-F...,` with empty columns separating them. We kept track of the columns and ignored the last 3 as they had data about totals.\n",
    "\n",
    "Overall the data was collated and organised in the following structure such that each row looked as follows:\n",
    "\n",
    "```\n",
    "({sex M/F/P}_{start age}_{end_age}_{marital status} , {freq/number of occurences})\n",
    "```\n",
    "\n",
    "Finally, the `read_all_files()` function takes in a list of every file name. This has been created and named `DATALIST` and is stored as a global variable. It simply calls all the other `read` and `extract` functions to create a list of lists where each sub\\-list contains organised and structured data for a particular year. The lists are ordered from most recent \\(at index 0\\) to oldest \\(at index 5\\).\n",
    "I.e., \\[0 = 2021, 1 = 2016, 2 = 2011, 3 = 2006, 4 = 2001, 5 = 1996\\]\n",
    "\n",
    "It is important to note while the actual data needed excluded any rows regarding the Total, we did need the total number of people who took part in the census \\(which will act as our total population to base proportions on\\). This was not stored alongside the cleaned data but rather separately in the `POPULATION_LIST` list declared at the beginning of the file. During the reading of the data, it was imperative we did not remove that information, just simply ignore it until we needed to store it. The method in which this population statistic is extracted and copied into the `POPULATION_LIST` list varies depending on the dataset. For the data from 2021, 2016 and 2011, the last row of the dataset was always the total population statistic. Hence, it returned alongside the data as a tuple i.e.,\n",
    "\n",
    "```\n",
    "return list(zip(categories,numbers)), int(list_of_nums[-1])\n",
    "```\n",
    "\n",
    "The population statistic was then copied into the  `POPULATION_LIST` list during the `read_all_files()` function. The same cannot be said for the data from the Basic Community Profiles. The population statistic was extracted during the final stages of iteration through the data. For example, in 2006, the following line of code was present.\n",
    "\n",
    "```\n",
    "if sex == \"P\":\n",
    "            POPULATION_LIST.append(int(data[e-1][-1].replace(\",\",\"\")))\n",
    "```\n",
    "\n",
    "In the final iteration of `sexes` in the 2006 data \\(which was related to persons\\), it finds the population statistic and stores it.\n",
    "\n",
    "Similarly, the following code finds the last row of data \\(outputted by the `read_data_1996_2001` function\\), which contains information about the totals and extracts the final total population value.\n",
    "\n",
    "```\n",
    "total_row = [item for item in data[-1] if item != '']\n",
    "POPULATION_LIST.append(int(total_row[-1].replace(\",\",\"\")))\n",
    "```\n",
    "\n"
   ]
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    "def spread_data_row(age_cat, num):\n",
    "    # Extract age category information.\n",
    "    status = age_cat.split('_')[-1]\n",
    "    sex = age_cat.split('_')[0]\n",
    "    \n",
    "    # Determine age range based on the category.\n",
    "    if \"85OV\" in age_cat:\n",
    "        start, end = 85, 100\n",
    "    elif \"90OV\" in age_cat:\n",
    "        start, end = 90, 100\n",
    "    else:\n",
    "        start, end = age_cat[2:4], age_cat[5:7]\n",
    "    start, end = int(start), int(end)\n",
    "    \n",
    "    # Calculate the base freq for each spreaded age\n",
    "    num_years = end - start + 1\n",
    "    base_value = num // num_years\n",
    "    remainder = num % num_years\n",
    "    \n",
    "    # Generate age categories within the range.\n",
    "    age_list = [f'{sex}_{n}_{status}' for n in range(start, end + 1)]\n",
    "    \n",
    "    # Distribute frequency across age brackets.\n",
    "    distribution = [base_value] * num_years\n",
    "    \n",
    "    for i in range(remainder):\n",
    "        distribution[i] += 1\n",
    "    \n",
    "    return list(zip(age_list, distribution))\n",
    "\n",
    "def spread_data():\n",
    "    all_yrs_data = read_all_files(DATA_LIST)\n",
    "    FINAL_SPREADED_DATA = []\n",
    "    each_yr_data = []\n",
    "    \n",
    "    for data in all_yrs_data:\n",
    "        for cat, freq in data:\n",
    "            # For each row in the DATA_LIST spread its row\n",
    "            for tup in spread_data_row(cat, freq):\n",
    "                each_yr_data.append(tup)\n",
    "        \n",
    "        FINAL_SPREADED_DATA.append(each_yr_data)\n",
    "        each_yr_data = []\n",
    "    \n",
    "    return FINAL_SPREADED_DATA\n",
    "\n",
    "                "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "The function `spread_data()` is critical in data analysis by enabling the constant spread of numerical values across designated age categories. This distribution process is necessary as it is a prerequisite for calculating accurate means and other statistics within a particular age group. \n",
    "\n",
    "Firstly, the function starts by extracting the marital status \\(e.g., \"SINGLE,\" \"MARRIED\"\\) and gender \\(e.g., \"M\", \"F\", \"P\"\\) from the given age ranges in `age_cat`. It then determines the start and end ages based on the provided age\\_cat or assigns default values of 85 and 100 if the age\\_category is over 85 \\(\"85OV\"\\) or 90 to 100 if its \"90OV\" for the 1996 dataset.\n",
    "\n",
    "Secondly, the function `spread_data()` calls the `spread_data_row()` function, which calculates the number of years within the age range and distributes the numeric value num evenly across these years. The function `spread_data()` makes repeated calls to `spread_data_row()` to ensure that num is spread evenly across the age ranges for every `(age_cat,num)` pair. \n",
    "\n",
    "This results in a meticulously structured list of age categories, each with an equal distribution, ensuring data uniformity within age cohorts. This function is essential in computing the means and conducting detailed statistical analyses within specific age groups, enhancing the comprehensiveness of data\\-driven insights.\n",
    "\n",
    "However, while this assumption simplifies the data distribution process, it introduces potential limitations. The primary cons of this assumption include the risk of oversimplification in scenarios where age distributions are not equal, potentially leading to inaccuracies in mean calculations and other statistical analyses. For example, data spread over the age range 15\\_19 will contain \\(roughly\\) the same number of occurrences per age. This, in reality, will likely not be the case as specific ages may have higher occurrences compared to other ages. Therefore, when applying this function to real\\-world data analysis tasks, the assumption of uniform age distribution \\(over an age range\\) should be considered and evaluated based on the specific dataset and research objectives.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
   ],
   "source": [
    "def get_marital_status_data_M_F_P(status, sex, spread):\n",
    "    status = status.upper()\n",
    "    sex = sex.upper()\n",
    "    married_M_F_P = []\n",
    "    # Input sanitation\n",
    "    if status not in [\"MARRIED\", \"DIVORCED\", \"SEPARATED\", \"WIDOWED\", \"SINGLE\"\n",
    "                      ] or sex not in [\"M\", \"F\", \"P\"]:\n",
    "        raise ValueError(\"That category or sex does not exist in the data!\")\n",
    "    if spread:\n",
    "        all_yrs_data = spread_data()\n",
    "    else:\n",
    "        all_yrs_data = read_all_files(DATA_LIST)\n",
    "    for data in all_yrs_data:\n",
    "        relevant_data = []\n",
    "        for cat, freq in data:\n",
    "            # Keep only the rows which have the informaton requested\n",
    "            if status in cat and sex + '_' in cat:\n",
    "                relevant_data.append((cat, freq))\n",
    "        # Append the selected rows into a new list with only the relevant data\n",
    "        married_M_F_P.append(relevant_data)\n",
    "    return married_M_F_P"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "The function `get_marital_status_data_M_F_P(status, sex, spread)` \\(shown above\\) plays a critical role in data refinement and analysis. This function facilitates the extraction and structuring of data to the predefined criteria around marital status \\(e.g., \"MARRIED,\" \"SINGLE\"\\) and sex \\(e.g., \"M\" for male, \"F\" for female, \"P\" for persons\\). Furthermore, it does not assume the existence of specific status or sex categories in the data. Instead, it verifies the validity of each provided input to safeguard data integrity. This validation mechanism is vital in data cleaning and data preparation, ensuring that only relevant and accurate categories are considered. Furthermore, this function streamlines the process of filtering insights from marital status demographics. Therefore, concentrating on precise categories simplifies the analytical process, enables researchers to focus on specific insights, and increases the precision of their findings in a professional and systematic manner.\n",
    "\n",
    "The function takes in 3 parameters, namely the `status`, `sex` and whether the outputted data should be spread or not. Depending on the inputs it will produce a subset from the total dataset specifically with the information requested for each of the 6 year's census data. It will iterate through all the 6 year's of data and find only the rows which contain the `sex` and `status` specified. It loops over each year, extracts the rows then places all the relevant rows into the `relevant_data` list. That is repeated for every year, thus producing an output which is a list of lists, where each sub-list relates to data for a particular year. The lists are ordered from most recent (at index 0) to oldest (at index 5).\n",
    "I.e., [0 = 2021, 1 = 2016, 2 = 2011, 3 = 2006, 4 = 2001, 5 = 1996]\n",
    "\n",
    "E.g. the following code snippet produces the first 10 rows of data which has been spread for the male sex for the marital status of \"Single\" in 2016.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('M_15_SINGLE', 144928),\n",
       " ('M_16_SINGLE', 144928),\n",
       " ('M_17_SINGLE', 144928),\n",
       " ('M_18_SINGLE', 144928),\n",
       " ('M_19_SINGLE', 144927),\n",
       " ('M_20_SINGLE', 153042),\n",
       " ('M_21_SINGLE', 153042),\n",
       " ('M_22_SINGLE', 153042),\n",
       " ('M_23_SINGLE', 153042),\n",
       " ('M_24_SINGLE', 153041)]"
      ]
     },
     "execution_count": 7,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The first [0] specifies the year as 2021, indexes can go up to [5] for the year 1996\n",
    "get_marital_status_data_M_F_P(\"Single\",\"M\",spread=True)[1][:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
   ],
   "source": [
    "#Mean Function\n",
    "\n",
    "def mean_of_data(data):\n",
    "    total_sum = 0\n",
    "    n = 0\n",
    "    for cat, num in data:\n",
    "        age = int(cat.split('_')[1])\n",
    "        n += num\n",
    "        # Keep track of the total sum of occurences\n",
    "        total_sum += age * num\n",
    "        # Keep track of the sum of all spread age values\n",
    "        \n",
    "    mean = total_sum / n\n",
    "    return round(mean, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "The `mean_of_data(data)` function is vital in our data analysis as it computes the mean ages within the dataset. This is an important metric for identifying trends in marital statuses. Finding the mean provides essential insights into the central age tendency, enabling the identification of age\\-related patterns and trends. \n",
    "\n",
    "\\begin{equation}\n",
    "\\text{Mean} = \\frac{\\text{Total Sum of Ages}}{\\text{Sum of total Number of Occurrences for each age}}\n",
    "\\end{equation}\n",
    "\n",
    "Through the comparison of the mean ages across different years or different sex and marital status, we can observe changes in marital behaviour, such as delayed marriages. Additionally,  analysis of the means highlights generational shifts in marriage patterns, assisting in our understanding of evolving trends. Therefore, the function's ability to provide a quantifiable metric for comparison across various demographics enhances our capability to recognise and interpret variations, ultimately facilitating trend identification in the data.\n",
    "\n",
    "The `mean_of_data(data)` function takes a single parameter of which it will calculate the mean for. The data it takes in will be directly in the form `get_marital_status_data_M_F_P()` outputs, thus allowing ease of use.\n",
    "For example, the following code calculates the mean age for married males in 2021.\n",
    "\n",
    "*Note, the data the `mean_of_data()` takes in data **which is already** spread, hence the `spread = True`*\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "54.25"
      ]
     },
     "execution_count": 9,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_of_data(get_marital_status_data_M_F_P(\"Married\",\"M\",spread = True)[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
   ],
   "source": [
    "# Mode Function\n",
    "\n",
    "def mode(clean_data):\n",
    "\n",
    "    #Splitting the age and frequency into 2 list\n",
    "    age_cat = []\n",
    "    num_list = []\n",
    "    for age, num in clean_data:\n",
    "        num_list.append(num)\n",
    "        age_range = age.split('_')\n",
    "        for i in age_range:\n",
    "            if i.isdigit():\n",
    "                age_cat.append(int(i))\n",
    "\n",
    "    #sorting the data from cleaned_data in ascending order\n",
    "    sorted_data = sorted(list(zip(age_cat, num_list)), key=lambda x: x[0])\n",
    "\n",
    "    #finding the average mode age\n",
    "    max_num = 0\n",
    "    mode_ages = []\n",
    "    for age, num in sorted_data:\n",
    "        if num > max_num:\n",
    "            max_num = num\n",
    "            mode_ages = [age]\n",
    "        elif num == max_num:\n",
    "            mode_ages.append(age)\n",
    "    average_mode_age = sum(mode_ages) / len(mode_ages)\n",
    "    return average_mode_age"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "The `mode(clean_data`\\) function is an essential component of our data analysis toolkit, contributing to our hypothesis and overarching aim analysis. This function finds the mode of a specific demographic within our meticulously cleaned dataset \\(derived from the `get_marital_status_data_M_F_P(status,sex,spread)` function\\). Firstly, `mode(clean_data)` extracts and segregates the age and frequency data into distinct lists and sorts the lists in ascending order based on age. Subsequently, it identifies the age category/ categories with the highest frequency, showing prominent demographic trends. The benefits of this function are significant as it identifies dominating age groups, which helps with our in\\-depth trend analysis and hypothesis testing. Although the function has some drawbacks, such as averaging the modes in instances with more than 1 mode, it is still a crucial tool for understanding age\\-related trends in marital statuses and increases the depth of our dataset's insights.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
   ],
   "source": [
    "# Median Function\n",
    "\n",
    "def median(clean_data):\n",
    "    #Splitting the age and frequency into 2 list\n",
    "    age_cat = []\n",
    "    num_list = []\n",
    "    for age, num in clean_data:\n",
    "        num_list.append(num)\n",
    "        age_range = age.split('_')\n",
    "        for i in age_range:\n",
    "            if i.isdigit():\n",
    "                age_cat.append(int(i))\n",
    "\n",
    "    #sorting the data from cleaned_data in ascending order\n",
    "    sorted_data = sorted(list(zip(age_cat, num_list)), key=lambda x: x[0])\n",
    "\n",
    "    #Making a full list where there is 1 age cat for each occurence in the frequency\n",
    "    full_age_cat = []\n",
    "    for index in range(len(sorted_data)):\n",
    "        full_age_cat.extend([sorted_data[index][0]] * sorted_data[index][1])\n",
    "\n",
    "    #Finding the median position\n",
    "    median_position = sum(num_list) / 2\n",
    "\n",
    "    #If the median position is between 2 numbers find both positions\n",
    "    median_position_list = []\n",
    "    if str(median_position).isdecimal():\n",
    "        median_position_list.append(median_position)\n",
    "\n",
    "    else:\n",
    "        median_position_list.append(sum(num_list) // 2)\n",
    "        median_position_list.append((sum(num_list) // 2) + 1)\n",
    "\n",
    "    #Finding the median age\n",
    "    median_age_list = []\n",
    "    for item in median_position_list:\n",
    "        median_age_list.append(full_age_cat[item])\n",
    "    median_age = sum(median_age_list) / len(median_age_list)\n",
    "    return round(median_age, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "The `median(clean_data)` function is a critical tool in our data analysis toolkit. Firstly, the function organises and sorts the age and frequency data \\(derived from the `get_marital_status_data_M_F_P(status,sex,spread)` function\\) without assuming sequential order, safeguarding and enhancing the accuracy in data handling. Secondly, the function finds the median position. The function then constructs a list with age categories repeated according to their frequencies and calculates the median age based on the calculated median position. By offering a statistical metric that is less susceptible to outliers than the mean, this function enhances the accuracy of the analysis. \n",
    "\n",
    "The `median (clean_data)` function assists in attaining research objectives stated in the hypothesis and aim. It is essential for identifying patterns in Australian marital statuses across various age groups and genders. Calculating the dataset's median age gives age categories a central point of reference and makes it possible to spot changes in marital status distributions. For example, changes in the median age can signify changes in the specific marital statuses within different years. Additionally, it could discover gender\\-specific patterns in marital choices by applying the function individually to male and female datasets, further aligning with the research goals.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### Data Analysis/Interpretation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
   ],
   "source": [
    "def convert_freq_to_proportions(DATA):\n",
    "    \n",
    "    #Creating lists to store the proportions for each year and the overall proportions\n",
    "    i = 0\n",
    "    FINAL_PROP_DATA = []\n",
    "    each_year_prop_data = []\n",
    "    \n",
    "    #Calculating the proportions and putting into a list for each year\n",
    "    for sub_list in DATA:\n",
    "        for cat, freq in sub_list:\n",
    "            freq = freq / POPULATION_LIST[i]\n",
    "            each_year_prop_data.append((cat, freq))\n",
    "            \n",
    "        #Compling each proportion list for each year into a master list    \n",
    "        FINAL_PROP_DATA.append(each_year_prop_data)\n",
    "        each_year_prop_data = []\n",
    "        i += 1\n",
    "    return FINAL_PROP_DATA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "The `convert_freq_to_proportions(DATA)` function is an aspect of the data analysis strategy that enhances the meaningfulness of the findings. It effectively standardises the data by methodically converting absolute frequencies of marital status categories into proportions relative to the total population that participated in the census that year. The function helps us to conduct more insightful comparisons between various years with different population sizes by turning frequencies into proportions. As this analysis accounts for the population variations, it allows us to draw precise conclusions on the evolution of marital patterns. Therefore, the use of the `convert_freq_to_proportions(DATA)` function increases the precision of the data analysis, making the research findings more relevant to the research objectives. \n",
    "\n",
    "\\begin{equation}\n",
    "\\text{Proportion} = \\frac{\\text{Freq of people in that category}}{\\text{Total number of people who took part in the census}}\n",
    "\\end{equation}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "def plot_line_graph_for_MS(status, sex):\n",
    "\n",
    "    DATA = get_marital_status_data_M_F_P(status, sex, spread=True)\n",
    "    DATA = convert_freq_to_proportions(DATA)\n",
    "\n",
    "    # Extract age ranges and frequencies for each year\n",
    "    age_ranges = [entry[0] for entry in DATA[0]]\n",
    "    age_ranges_labels = [age_range[2:4] for age_range in age_ranges[:-1]\n",
    "                         ]  # Extract only the age range part\n",
    "    age_ranges_labels.append(\"100\")\n",
    "    freq_1996 = [entry[1] for entry in DATA[5]]\n",
    "    freq_2001 = [entry[1] for entry in DATA[4]]\n",
    "    freq_2006 = [entry[1] for entry in DATA[3]]\n",
    "    freq_2011 = [entry[1] for entry in DATA[2]]\n",
    "    freq_2016 = [entry[1] for entry in DATA[1]]\n",
    "    freq_2021 = [entry[1] for entry in DATA[0]]\n",
    "\n",
    "    # Create a numpy array for x-axis positions \n",
    "    #(numy array is typically how pyplot documentation has achieved this)\n",
    "    x_positions = np.arange(len(age_ranges_labels))\n",
    "\n",
    "    plt.figure(figsize=(10, 6))\n",
    "\n",
    "    # Plot lines for each year\n",
    "    plt.plot(x_positions, freq_1996, label='1996', linewidth=2)\n",
    "    plt.plot(x_positions, freq_2001, label='2001', linewidth=2)\n",
    "    plt.plot(x_positions, freq_2006, label='2006', linewidth=2)\n",
    "    plt.plot(x_positions, freq_2011, label='2011', linewidth=2)\n",
    "    plt.plot(x_positions, freq_2016, label='2016', linewidth=2)\n",
    "    plt.plot(x_positions, freq_2021, label='2021', linewidth=2)\n",
    "\n",
    "    plt.xlabel('Age Ranges')\n",
    "    plt.ylabel('Proportion')\n",
    "    plt.title(f'Proportion of people who are {status} by Age Range over time')\n",
    "    # Set x-axis ticks to show every 5 age ranges\n",
    "    x_tick_positions = np.arange(0, len(age_ranges_labels), 5)\n",
    "    plt.xticks(x_tick_positions,\n",
    "               [age_ranges_labels[i] for i in x_tick_positions])\n",
    "    plt.legend()\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "The function above `plot_line_graph_for_MS()` creates lines for each of the years (based on the proportions) for a particular marital status. It then sets the x-axis increments to every 5 years (rather than every year) to make the graph more readable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "The following graphs have been plotted based on the proportion of people rather than simply the number of people. This was done so inaccurate trends were not spotted due to the increasing national population. When plotting against the proportion, it considers the overall population and gives a ratio rather than just a frequency.\n",
    "\n",
    "We can observe several trends, notably from Married, Divorced and Single proportions for overall people.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "execution_count": 14,
     "metadata": {
      "image/png": {
       "height": 423,
       "width": 712
      },
      "needs_background": "light"
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_line_graph_for_MS(\"Married\",\"P\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "We can see that the graph roughly follows a normal distribution \\(which is skewed to the right a little\\), with the mean sitting between 40\\-54 \\(which we will delve into later\\). We can also see that a more significant proportion of those ages 65 and older are married in 2021 compared to previous years, such as 2016 and 2011. Moreover, when it comes to younger ages, we can see that as the years have gone by, the proportion of people who are married at ages 64 and younger has declined. An interesting observation can be made for the years 2021 and 2016. For younger ages, both years have the lowest proportion of married individuals. However, in the later years, we can see that they have one of the highest proportion of married individuals compared to other years. This sudden change can be due to modern people focusing on other aspects of their lives rather than getting into relationships early on in life.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "execution_count": 15,
     "metadata": {
      "image/png": {
       "height": 423,
       "width": 712
      },
      "needs_background": "light"
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_line_graph_for_MS(\"Divorced\",\"P\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "After analysing the graph, we can see a notable symmetrical pattern. Within this dataset, it becomes evident that couples aged between 45 and 65 exhibit the highest divorce rates compared to other age groups. Furthermore, a noteworthy observation can be made, indicating that as the years progress, divorces within couples aged 65 and above exhibit a consistent increase, with each increase in the year category showing an increase in divorce rates. This observation underscores a notable trend. We can observe that the divorce rate is rising steadily over time. Our hypothesis states that we would expect to see an increase in divorces over all age categories, which has been proven to be true by the graph shown above.\n",
    "\n",
    "The peak divorce rates within the 45\\-54 and 55\\-64 age ranges can be attributed to multifaceted factors. Life events, such as the birth of a child or children leaving the household, can profoundly influence the stability of marriages in these age brackets. Moreover, societal trends and norms play a pivotal role, potentially normalising divorce within these age groups. The acceptance of divorce as a viable option or a desire to remarry may consistently affect these individuals, particularly if their social circles and peer groups reinforce such behaviour. These combined factors contribute to the observed patterns in divorce rates across different age ranges, shedding light on the complex dynamics underlying marital stability.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "execution_count": 16,
     "metadata": {
      "image/png": {
       "height": 423,
       "width": 712
      },
      "needs_background": "light"
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_line_graph_for_MS(\"Single\",\"P\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "The line graph presents a compelling and consistent downward trend in the proportion of single individuals across these age groups. Notably, this trend is skewed to the right, indicating that a higher proportion of singles is observed among younger age groups while steadily decreasing as age increases. A noteworthy observation emerges from the data for the year 2021, revealing a significant shift in the proportion of single individuals across age groups. While the 15\\-24 age group exhibited the lowest prevalence of single individuals, a marked transition occurred in the subsequent age group \\(24\\-34\\), where it surged to become the age category with the highest concentration of single individuals. Every age group after that shows that the 2021 dataset has the highest number of single individuals, then 2016, followed by 2011, 2006, 2001 and 1996 respectively. This directly related to our hypothesis which states that we would expect to see more single and de facto relationships as the years progress. This abrupt transformation can be attributed to many societal factors, including evolving norms and values. In contemporary society, individuals may place greater emphasis on educational pursuits and personal development, prioritising these pursuits over early commitments to marriage or partnerships.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
   ],
   "source": [
    "def plot_line_graph_for_sex_against_MS(status):\n",
    "\n",
    "    age_ranges_labels = ['1996', '2001', '2006', '2011', '2016', '2021']  # Extract only the age range part\n",
    "    fem_data = get_marital_status_data_M_F_P(status, \"F\", spread=True)\n",
    "    male_data = get_marital_status_data_M_F_P(status, \"M\", spread=True)\n",
    "    male_female_totals = [[], []]\n",
    "    for i in range(5, -1, -1):\n",
    "        sum = 0\n",
    "        for index, freq in male_data[i]:\n",
    "            sum += freq\n",
    "        male_female_totals[0].append(sum / POPULATION_LIST[i])\n",
    "    for i in range(5, -1, -1):\n",
    "        sum = 0\n",
    "        for index, freq in fem_data[i]:\n",
    "            sum += freq\n",
    "        male_female_totals[1].append(sum / POPULATION_LIST[i])\n",
    "\n",
    "    # Create a numpy array for x-axis positions\n",
    "    x_positions = np.arange(6)\n",
    "\n",
    "    plt.figure(figsize=(10, 6))\n",
    "\n",
    "    # Plot lines for each year\n",
    "    plt.plot(x_positions,\n",
    "             male_female_totals[0],\n",
    "             label='Male',\n",
    "             linewidth=2,\n",
    "             color=(41 / 255, 134 / 255, 204 / 255))\n",
    "    plt.plot(x_positions,\n",
    "             male_female_totals[1],\n",
    "             label='Female',\n",
    "             linewidth=2,\n",
    "             color=(251 / 255, 163 / 255, 196 / 255))\n",
    "\n",
    "    plt.xlabel('Years')\n",
    "    plt.ylabel('Proportion')\n",
    "    plt.title(f'Proportion of Males and Females who are {status} over time')\n",
    "    plt.xticks(x_positions, age_ranges_labels)\n",
    "    plt.legend()\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "The above function gets the male and female data for a particular marital status using `get_marital_status_data_M_F_P()` then totals it up for each year. Then the total is converted into a proportion. That is done for each year, for both males and females. Both those lines are then plotted.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "execution_count": 18,
     "metadata": {
      "image/png": {
       "height": 423,
       "width": 712
      },
      "needs_background": "light"
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_line_graph_for_sex_against_MS(\"Single\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "The following graph outlines the proportion of single males and females from each year \\(1996\\-2021\\). We can see a definitive positive trend over the years. As time progresses, the proportion of single males and females increases. This aligns with our hypothesis and cements the idea that people value careers and their goals more than relationships. However, an important point to note is that males consistently have a higher proportion of singleness compared to females. This interesting finding suggests that females value relationships more than males and thus have lower proportions of single people across all ages.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "execution_count": 19,
     "metadata": {
      "image/png": {
       "height": 423,
       "width": 712
      },
      "needs_background": "light"
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_line_graph_for_sex_against_MS(\"Married\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "This graph depicts the \\(total\\) proportion of married males and females from 1996 to 2021. The data reveals that there isn't a significant difference in the proportion of single males compared to females. Contrary to our hypothesis, there was a subtle shift in marriage proportions during 2011 and onwards, with more males being married than females. However, in line with our expectations, the graph shows a consistent downward trend in marriage rates for both genders over time. This downward trend could be due to evolving cultural attitudes towards marriage being shifted towards greater acceptance of alternative lifestyles and relationship choices. Alternatively, the high divorce rates shown in the previous analysis could lead to this outcome. Experience of family or friends going through a divorce can lead individuals to be more cautious about marriage. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### Measures of Central Tendency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
   ],
   "source": [
    "def plot_line_graph_for_medians():\n",
    "    years_labels = ['1996', '2001', '2006', '2011', '2016', '2021']\n",
    "    \n",
    "    status_colours = [\"Green\", \"Red\", \"Blue\"]\n",
    "    \n",
    "    for sex in ['M', 'F']:\n",
    "        for i,status in enumerate(['Married', 'Divorced', 'Single']):\n",
    "            median_data = []\n",
    "            for year in range(5,-1,-1):\n",
    "                median_data.append(median(get_marital_status_data_M_F_P(status,sex,spread = True)[year]))\n",
    "            if sex == 'M':\n",
    "                plt.plot(years_labels, median_data, label=f'{status}-{sex}', color = status_colours[i])\n",
    "            else:\n",
    "                plt.plot(years_labels, median_data, linestyle = \"--\", label=f'{status}-{sex}', color = status_colours[i])\n",
    "            \n",
    "    plt.xlabel('Year')\n",
    "    plt.ylabel('Median')\n",
    "    plt.title(f'Median Age by Marital Status by Sex over time')\n",
    "    plt.legend()\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "The code above generates 6 lines in the data for 3 statuses and both genders for each status. It iterates over a particular sex and generates 3 lines for that sex's marital status. For each sex and marital status pair it makes a call to our `median()` function to calculate the median and append that data into a list. The ordering of data in this list is carefully ensured to match up with the output for `get_marital_status_data_M_F_P()`. This is then done again for the other sex. Lines for the same sex are distinguished by dashed and not dashed (see legend). Same marital status lines are coloured with the same colour."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "execution_count": 21,
     "metadata": {
      "image/png": {
       "height": 495,
       "width": 856
      },
      "needs_background": "light"
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_line_graph_for_medians()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "The graph shows each category having an upward trend across the years. One noticeable feature outlined by the graph is from 2006 to 2011 for all males. We can see that the median age for the single males stayed the same. The median age for married men decreased while the divorce rates increased significantly. With exception to this unusual period, the median age for all categories seems to be increasing as time progresses. The plot outlines how, in every marital status \\(out of the ones displayed in the graph\\) females always have a lower median age: an average lower age to marry, a lower age to divorce, and a lower age to be single. The contents of this graph should not be mistaken to suggest 'when' people are marrying, as this graph does not tell us that information. An important thing to note is that the graph shows the median age as opposed to the mean age for one primary reason, which is that the mean is affected more by outliers; hence, the median is a better measure of the \"typical age\" we would expect to see someone with these marital statuses. We can compare the median and means more specifically by determining their numerical value. For example, the following code shows married males' mean and median in 2021.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean = 54.25\n",
      "Median = 53.0\n"
     ]
    }
   ],
   "source": [
    "print(\"Mean =\" , mean_of_data(get_marital_status_data_M_F_P(\"Married\",\"M\",spread = True)[0]))\n",
    "print(\"Median =\",  median(get_marital_status_data_M_F_P(\"Married\",\"M\",spread = True)[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "We can see that a difference does exist, likely due to how the data is spread out. The fact that there isn't a significant difference suggests that there are probably few outliers in the dataset \\(at least for 2021\\), but this seems reasonable, considering the data is from a large dataset and a reputable source. Aside from mean and median, another measure of central tendency is mode. This will tell us which age for a given marital status has the most occurrences.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mode = 37.0\n"
     ]
    }
   ],
   "source": [
    "print(\"Mode =\" , mode(get_marital_status_data_M_F_P(\"Married\",\"M\",spread = True)[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "This makes sense from our previous plots of proportions against age for married people. The age group with the largest proportion \\(in 2021\\) of married people was in the range of 35\\-45, which this numerical answer also suggests. The reason there would be such a significant difference between the median/mean, and the mode is likely because the graph is right\\-skewed, meaning it has many more occurrences at higher ages compared to lower ages \\(which gives a longer tail on the right side of the plot\\). Because of the higher numbers of older age groups, the median and mean are shifted to give a more central measure, but the mode remains at the age with the highest occurrence.\n",
    "\n",
    "In our hypothesis it was stated that we expect the mean for married people to be between \"mid 20s\" and \"late 30s.\" We can test our hypothesis as follows:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean = 53.01\n",
      "Mode = 37.5\n"
     ]
    }
   ],
   "source": [
    "print(\"Mean =\" , mean_of_data(get_marital_status_data_M_F_P(\"Married\",\"P\",spread = True)[0]))\n",
    "print(\"Mode =\" , mode(get_marital_status_data_M_F_P(\"Married\",\"P\",spread = True)[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "We can see that our initial prediction \\(in the hypothesis\\) for the mean age of married people was largely incorrect, as the actual mean is closer to 53. In fact, our predicted mean range \\(from the hypothesis\\) for married people is actually the mode for that dataset, which shows a clear distinction between mean and mode and how we initially mistook the mean for the mode.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## Conclusion\n",
    "\n",
    "In this analysis, we investigated how marital status patterns have changed across different age and sex groups in Australia from 1996 to 2021 through ABS Census data. We explored trends across other age groups for marital statuses, comparing and contrasting the differences in Male and Female statistics and how they have evolved over 25 years. Through analysing and presenting this data effectively, we outlined valuable insights into the evolving landscape of marital statuses within Australia, meeting the standards of professionalism.\n",
    "\n",
    "##### _Assumptions and Limitations_\n",
    "\n",
    "Although the analysis has identified unique patterns in marital statuses, it is essential to note the various assumptions made in the analysis and the limitations caused by those assumptions. Firstly, the analysis uses the function `spread(data)`, which provides a uniform distribution across the different age ranges. However, in reality, all ages are unlikely to have an equal distribution. This assumption will limit the accuracy of the mean, median and mode calculation. Furthermore, the analysis of mode and median averages the age when there is more than one mode or median. This assumption will result in inaccuracy, especially in the modes when there is a bimodal data distribution \\(as seen in the graph below\\). Overall, the contents of the data as age ranges rather than specific ages limit specificity and reduce precision.\n",
    "\n",
    "<div style=\"max-width:50%\">\n",
    "  <img src=\".Australian_Trends.ipynb.upload/paste-0.6997157559968874\" style=\"max-width:70%\"/>\n",
    "</div>\n",
    "\n",
    "##### *Hypotheses vs results*\n",
    "\n",
    "*The Proportion of individuals in the married category will decline over time*\n",
    "\n",
    "We anticipated that the proportion of individuals in the married category would decline over time, reflecting evolving societal values around marriage. The plot for the proportion of married males and females over time clearly shows a decline in proportion of BOTH males and females over time. Hence this prediction was accurate.\n",
    "\n",
    "*As time progresses, proportion of singles will increase*\n",
    "\n",
    "The results illustrated by the \"Proportion of people who are single by age range over time\" graph works to prove our hypothesis correct. As expected, the graph shows that as the years increase, the number of single individuals in Australia increases, despite the age category. We can see that 1996 had the lowest proportion of single individuals. However, as the years progressed, the proportion increased gradually. This increase could be explained by multiple factors like people placing greater emphasis on education or personal development and pushing away relationships for later stages in life. At younger ages the opposite is true, but generally for all ages above 25, our prediction is valid. This can also be seen in the plot for males and females who are single. The proportion of both males and females increases over time.\n",
    "\n",
    "_There will be an increase in divorces over all age categories_\n",
    "\n",
    "_\"People who are divorced by age range over time_\" graph shows that with each increase in the year category an increase in the divorce rates also occurs consistently. We expected that societal trends and norms will play a pivotal role in normalising divorces and hence increasing the divorce rates. Societal norms such as the acceptance of divorce or the desire to remarry will play a vital part in the trend of divorce rates over the years. The graph illustrates this perfectly amongst older individuals as we can see that after age 55, the proportion of divorces is increasing **per year** and we observe that the modern years have higher divorced individuals than earlier years. We can see this based off how the more recent years such as 2021 and 2016 are greater in proportion for those aged 55+. However, this does not match in the younger generation of people. The first half of the graph shows that the earlier the years are, the higher the proportion of divorced people which goes against out original hypothesis. Up until the age of 55, 2021 had the least proportion of divorced individuals and 1996 actually had one of the highest. In short, our hypothesis was proven false by the younger generation, however, it was valid for the older population ages roughly 55+.\n",
    "\n",
    "_Proportion of single males will be similar to females over time_ \n",
    "\n",
    "For this simple gender\\-specific analysis, we expected that the proportion of single males in Australia would be similar to the proportion of single females. The plot \"Proportion of Males and Females who are single over time\" consists of 2 lines, one for single males over the years and one for single females over the years. It is evident that the proportion of Males who are single is greater than the proportion of females who are single, thus proving our hypothesis to be incorrect.\n",
    "\n",
    "_The proportion of married females to be more significant than males_\n",
    "\n",
    "Our hypothesis was proven incorrect by _\"Proportion of males and females who are married over time_\" graph. The data reveals that there isn't a significant difference in the proportion of single males compared to females. We expected to see a higher married proportion of females than the males, however,  the contrary has happened. There was in fact, an unexpected shift in marriage proportions during 2011 and onwards. After 2011, we can see that the males actually had higher proportion married than females.\n",
    "\n",
    "## References\n",
    "\n",
    "**PyPlot (Matplotlib) documentation**: https://matplotlib.org/stable/tutorials/introductory/pyplot.html\n",
    "\n",
    "**CSV documentation**: https://docs.python.org/3/library/csv.html\n",
    "\n"
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