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| { | |
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| "display_name": "Python 3", | |
| "language": "python", | |
| "name": "python3" | |
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| } | |
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| { | |
| "cell_type": "markdown", | |
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| "id": "view-in-github", | |
| "colab_type": "text" | |
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| "source": [ | |
| "<a href=\"https://colab.research.google.com/gist/KaroAntonio/387cf03974e92a47ea1f60c95fd1f3cb/karo_antonio_unit_4_tier_3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" | |
| ] | |
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| { | |
| "cell_type": "markdown", | |
| "source": [], | |
| "metadata": { | |
| "id": "SKC0OKu0-pzD" | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "Tia3MP1SJpgj" | |
| }, | |
| "source": [ | |
| "# Springboard Data Science Career Track Unit 4 Challenge - Tier 3 Complete\n", | |
| "\n", | |
| "## Objectives\n", | |
| "Hey! Great job getting through those challenging DataCamp courses. You're learning a lot in a short span of time.\n", | |
| "\n", | |
| "In this notebook, you're going to apply the skills you've been learning, bridging the gap between the controlled environment of DataCamp and the *slightly* messier work that data scientists do with actual datasets!\n", | |
| "\n", | |
| "Here’s the mystery we’re going to solve: ***which boroughs of London have seen the greatest increase in housing prices, on average, over the last two decades?***\n", | |
| "\n", | |
| "\n", | |
| "A borough is just a fancy word for district. You may be familiar with the five boroughs of New York… well, there are 32 boroughs within Greater London [(here's some info for the curious)](https://en.wikipedia.org/wiki/London_boroughs). Some of them are more desirable areas to live in, and the data will reflect that with a greater rise in housing prices.\n", | |
| "\n", | |
| "***This is the Tier 3 notebook, which means it's not filled in at all: we'll just give you the skeleton of a project, the brief and the data. It's up to you to play around with it and see what you can find out! Good luck! If you struggle, feel free to look at easier tiers for help; but try to dip in and out of them, as the more independent work you do, the better it is for your learning!***\n", | |
| "\n", | |
| "This challenge will make use of only what you learned in the following DataCamp courses:\n", | |
| "- Prework courses (Introduction to Python for Data Science, Intermediate Python for Data Science)\n", | |
| "- Data Types for Data Science\n", | |
| "- Python Data Science Toolbox (Part One)\n", | |
| "- pandas Foundations\n", | |
| "- Manipulating DataFrames with pandas\n", | |
| "- Merging DataFrames with pandas\n", | |
| "\n", | |
| "Of the tools, techniques and concepts in the above DataCamp courses, this challenge should require the application of the following:\n", | |
| "- **pandas**\n", | |
| " - **data ingestion and inspection** (pandas Foundations, Module One)\n", | |
| " - **exploratory data analysis** (pandas Foundations, Module Two)\n", | |
| " - **tidying and cleaning** (Manipulating DataFrames with pandas, Module Three)\n", | |
| " - **transforming DataFrames** (Manipulating DataFrames with pandas, Module One)\n", | |
| " - **subsetting DataFrames with lists** (Manipulating DataFrames with pandas, Module One)\n", | |
| " - **filtering DataFrames** (Manipulating DataFrames with pandas, Module One)\n", | |
| " - **grouping data** (Manipulating DataFrames with pandas, Module Four)\n", | |
| " - **melting data** (Manipulating DataFrames with pandas, Module Three)\n", | |
| " - **advanced indexing** (Manipulating DataFrames with pandas, Module Four)\n", | |
| "- **matplotlib** (Intermediate Python for Data Science, Module One)\n", | |
| "- **fundamental data types** (Data Types for Data Science, Module One)\n", | |
| "- **dictionaries** (Intermediate Python for Data Science, Module Two)\n", | |
| "- **handling dates and times** (Data Types for Data Science, Module Four)\n", | |
| "- **function definition** (Python Data Science Toolbox - Part One, Module One)\n", | |
| "- **default arguments, variable length, and scope** (Python Data Science Toolbox - Part One, Module Two)\n", | |
| "- **lambda functions and error handling** (Python Data Science Toolbox - Part One, Module Four)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "Ipgd2nV8Jpgl" | |
| }, | |
| "source": [ | |
| "## The Data Science Pipeline\n", | |
| "\n", | |
| "This is Tier Three, so we'll get you started. But after that, it's all in your hands! When you feel done with your investigations, look back over what you've accomplished, and prepare a quick presentation of your findings for the next mentor meeting.\n", | |
| "\n", | |
| "Data Science is magical. In this case study, you'll get to apply some complex machine learning algorithms. But as [David Spiegelhalter](https://www.youtube.com/watch?v=oUs1uvsz0Ok) reminds us, there is no substitute for simply **taking a really, really good look at the data.** Sometimes, this is all we need to answer our question.\n", | |
| "\n", | |
| "Data Science projects generally adhere to the four stages of Data Science Pipeline:\n", | |
| "1. Sourcing and loading\n", | |
| "2. Cleaning, transforming, and visualizing\n", | |
| "3. Modeling\n", | |
| "4. Evaluating and concluding\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "zswDqbefJpgm" | |
| }, | |
| "source": [ | |
| "### 1. Sourcing and Loading\n", | |
| "\n", | |
| "Any Data Science project kicks off by importing ***pandas***. The documentation of this wonderful library can be found [here](https://pandas.pydata.org/). As you've seen, pandas is conveniently connected to the [Numpy](http://www.numpy.org/) and [Matplotlib](https://matplotlib.org/) libraries.\n", | |
| "\n", | |
| "***Hint:*** This part of the data science pipeline will test those skills you acquired in the pandas Foundations course, Module One." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "aEau5nEvJpgm" | |
| }, | |
| "source": [ | |
| "#### 1.1. Importing Libraries" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "7Bt_Q_oPJpgn" | |
| }, | |
| "source": [ | |
| "# Let's import the pandas, numpy libraries as pd, and np respectively.\n", | |
| "import pandas as pd\n", | |
| "import numpy as np\n", | |
| "\n", | |
| "# Load the pyplot collection of functions from matplotlib, as plt\n", | |
| "import matplotlib.pyplot as plt\n", | |
| "from matplotlib.ticker import ScalarFormatter\n" | |
| ], | |
| "execution_count": 130, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "koUrawxsJpgq" | |
| }, | |
| "source": [ | |
| "#### 1.2. Loading the data\n", | |
| "Your data comes from the [London Datastore](https://data.london.gov.uk/): a free, open-source data-sharing portal for London-oriented datasets." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "AiLiD4v3Jpgr" | |
| }, | |
| "source": [ | |
| "# First, make a variable called url_LondonHousePrices, and assign it the following link, enclosed in quotation-marks as a string:\n", | |
| "# https://data.london.gov.uk/download/uk-house-price-index/70ac0766-8902-4eb5-aab5-01951aaed773/UK%20House%20price%20index.xls\n", | |
| "\n", | |
| "URL_LONDON_HOUSE_PRICES = \"https://data.london.gov.uk/download/uk-house-price-index/70ac0766-8902-4eb5-aab5-01951aaed773/UK%20House%20price%20index.xls\"\n", | |
| "\n", | |
| "# The dataset we're interested in contains the Average prices of the houses, and is actually on a particular sheet of the Excel file.\n", | |
| "# As a result, we need to specify the sheet name in the read_excel() method.\n", | |
| "# Put this data into a variable called properties.\n", | |
| "properties = pd.read_excel(URL_LONDON_HOUSE_PRICES, sheet_name='Average price', index_col= None)" | |
| ], | |
| "execution_count": 98, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "POukEJXgJpgu" | |
| }, | |
| "source": [ | |
| "### 2. Cleaning, transforming, and visualizing\n", | |
| "This second stage is arguably the most important part of any Data Science project. The first thing to do is take a proper look at the data. Cleaning forms the majority of this stage, and can be done both before or after Transformation.\n", | |
| "\n", | |
| "The end goal of data cleaning is to have tidy data. When data is tidy:\n", | |
| "\n", | |
| "1. Each variable has a column.\n", | |
| "2. Each observation forms a row.\n", | |
| "\n", | |
| "Keep the end goal in mind as you move through this process, every step will take you closer.\n", | |
| "\n", | |
| "\n", | |
| "\n", | |
| "***Hint:*** This part of the data science pipeline should test those skills you acquired in:\n", | |
| "- Intermediate Python for data science, all modules.\n", | |
| "- pandas Foundations, all modules.\n", | |
| "- Manipulating DataFrames with pandas, all modules.\n", | |
| "- Data Types for Data Science, Module Four.\n", | |
| "- Python Data Science Toolbox - Part One, all modules" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "Te0Q548tnzZa" | |
| }, | |
| "source": [ | |
| "**2.1. Exploring your data**\n", | |
| "\n", | |
| "Think about your pandas functions for checking out a dataframe." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "Rxirxw_qoAJa", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "2c18cc8e-e888-45d0-9bb1-ef287dcb3673" | |
| }, | |
| "source": [ | |
| "# N Rows, N Cols\n", | |
| "print(f'Rows: properties.shape[0], Cols: properties.shape[1]')\n", | |
| "\n", | |
| "# The datatypes of each col\n", | |
| "#print(properties.info())\n", | |
| "\n", | |
| "# The first row seems to be some sort of second header row\n", | |
| "# print(properties.iloc[0].T)\n", | |
| "\n", | |
| "# This row has numbers that look like property prices\n", | |
| "# print(properties.iloc[1].T)\n", | |
| "\n", | |
| "# This Col seems to be datetimes on a month-to-month basis\n", | |
| "# print(properties['Unnamed: 0'].unique())\n", | |
| "\n", | |
| "# These Cols are Null, can be removed\n", | |
| "NULL_COLS = ['Unnamed: 34', 'Unnamed: 37', 'Unnamed: 47']\n", | |
| "for col in NULL_COLS:\n", | |
| " print(col, properties[col].isnull().sum())\n" | |
| ], | |
| "execution_count": 99, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Rows: properties.shape[0], Cols: properties.shape[1]\n", | |
| "Unnamed: 34 348\n", | |
| "Unnamed: 37 348\n", | |
| "Unnamed: 47 348\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "tE9Sqt9-oAta" | |
| }, | |
| "source": [ | |
| "**2.2. Cleaning the data**\n", | |
| "\n", | |
| "You might find you need to transpose your dataframe, check out what its row indexes are, and reset the index. You also might find you need to assign the values of the first row to your column headings . (Hint: recall the .columns feature of DataFrames, as well as the iloc[] method).\n", | |
| "\n", | |
| "Don't be afraid to use StackOverflow for help with this." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "cdAu1A3YoH_r", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 1000 | |
| }, | |
| "outputId": "2d02bedd-68b5-4b4f-8ad6-3cb6f9be9c1b" | |
| }, | |
| "source": [ | |
| "cleaned = properties.copy()\n", | |
| "\n", | |
| "# Remove Null Cols\n", | |
| "cleaned = cleaned.drop(NULL_COLS, axis=1)\n", | |
| "\n", | |
| "# Use Borough Names as Indices, Dates as cols\n", | |
| "cleaned = cleaned.T\n", | |
| "\n", | |
| "# Use Date row as col headings\n", | |
| "cleaned.columns = cleaned.iloc[0]\n", | |
| "cleaned = cleaned.iloc[1:]\n", | |
| "\n", | |
| "cleaned.reset_index()\n", | |
| "\n", | |
| "# Remove Alt Borough Names col\n", | |
| "cleaned = cleaned.drop('NaT', axis=1)\n", | |
| "\n", | |
| "cleaned" | |
| ], | |
| "execution_count": 104, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "Unnamed: 0 1995-01-01 1995-02-01 1995-03-01 1995-04-01 \\\n", | |
| "City of London 91448.98487 82202.77314 79120.70256 77101.20804 \n", | |
| "Barking & Dagenham 50460.2266 51085.77983 51268.96956 53133.50526 \n", | |
| "Barnet 93284.51832 93190.16963 92247.52435 90762.87492 \n", | |
| "Bexley 64958.09036 64787.92069 64367.49344 64277.66881 \n", | |
| "Brent 71306.56698 72022.26197 72015.76274 72965.63094 \n", | |
| "Bromley 81671.47692 81657.55944 81449.31143 81124.41227 \n", | |
| "Camden 120932.8881 119508.8622 120282.2131 120097.899 \n", | |
| "Croydon 69158.16225 68951.09542 68712.44341 68610.04641 \n", | |
| "Ealing 79885.89069 80897.06551 81379.86288 82188.90498 \n", | |
| "Enfield 72514.69096 73155.19746 72190.44144 71442.92235 \n", | |
| "Greenwich 62300.10169 60993.26863 61377.83464 61927.7246 \n", | |
| "Hackney 61296.52637 63187.08332 63593.29935 65139.64403 \n", | |
| "Hammersmith & Fulham 124902.8602 122087.718 120635.9467 121424.6241 \n", | |
| "Haringey 76287.56947 78901.21036 78521.94855 79545.57477 \n", | |
| "Harrow 84769.52599 83396.10525 83416.23759 83567.88439 \n", | |
| "Havering 68000.13774 69393.51294 69368.02407 69444.26215 \n", | |
| "Hillingdon 73834.82964 75031.0696 74188.66949 73911.40591 \n", | |
| "Hounslow 72231.70537 71051.55852 72097.99411 71890.28339 \n", | |
| "Islington 92516.48557 94342.37334 93465.86407 93344.49305 \n", | |
| "Kensington & Chelsea 182694.8326 182345.2463 182878.8231 184176.9168 \n", | |
| "Kingston upon Thames 80875.84843 81230.13524 81111.48848 81672.80476 \n", | |
| "Lambeth 67770.98843 65381.51908 66336.51868 66388.7716 \n", | |
| "Lewisham 60491.26109 60869.27091 60288.03002 59471.03136 \n", | |
| "Merton 82070.6133 79982.74872 80661.68279 79990.54333 \n", | |
| "Newham 53539.31919 53153.88306 53458.26393 54479.75395 \n", | |
| "Redbridge 72189.58437 72141.6261 72501.35502 72228.60295 \n", | |
| "Richmond upon Thames 109326.1245 111103.0394 107325.4742 106875 \n", | |
| "Southwark 67885.20344 64799.0648 65763.29719 63073.62117 \n", | |
| "Sutton 71536.97357 70893.20851 70306.83844 69411.9439 \n", | |
| "Tower Hamlets 59865.18995 62318.53353 63938.67686 66233.19383 \n", | |
| "Waltham Forest 61319.44913 60252.12246 60871.08493 60971.39722 \n", | |
| "Wandsworth 88559.04381 88641.01678 87124.81523 87026.00225 \n", | |
| "Westminster 133025.2772 131468.3096 132260.3417 133370.2036 \n", | |
| "Inner London 78251.9765 75885.70201 76591.59947 76851.56697 \n", | |
| "Outer London 72958.79836 72937.88262 72714.53478 72591.92469 \n", | |
| "NORTH EAST 42076.35411 42571.98949 42369.72984 42095.8436 \n", | |
| "NORTH WEST 43958.48001 43925.42289 44434.8681 44267.7796 \n", | |
| "YORKS & THE HUMBER 44803.42878 44528.80721 45200.46775 45614.34341 \n", | |
| "EAST MIDLANDS 45544.52227 46051.57066 45383.82395 46124.23045 \n", | |
| "WEST MIDLANDS 48527.52339 49341.29029 49442.17973 49455.93299 \n", | |
| "EAST OF ENGLAND 56701.5961 56593.59475 56171.18278 56567.89582 \n", | |
| "LONDON 74435.76052 72777.93709 73896.84204 74455.28754 \n", | |
| "SOUTH EAST 64018.87894 63715.02399 64113.60858 64623.22395 \n", | |
| "SOUTH WEST 54705.1579 54356.14843 53583.07667 54786.01938 \n", | |
| "England 53202.77128 53096.1549 53201.2843 53590.8548 \n", | |
| "\n", | |
| "Unnamed: 0 1995-05-01 1995-06-01 1995-07-01 1995-08-01 \\\n", | |
| "City of London 84409.14932 94900.51244 110128.0423 112329.4376 \n", | |
| "Barking & Dagenham 53042.24852 53700.34831 52113.12157 52232.19868 \n", | |
| "Barnet 90258.00033 90107.23471 91441.24768 92361.31512 \n", | |
| "Bexley 63997.13588 64252.32335 63722.70055 64432.60005 \n", | |
| "Brent 73704.04743 74310.48167 74127.03788 73547.0411 \n", | |
| "Bromley 81542.61561 82382.83435 82898.52264 82054.37156 \n", | |
| "Camden 119929.2782 121887.4625 124027.5768 125529.8039 \n", | |
| "Croydon 68844.9169 69052.51103 69142.48112 68993.42545 \n", | |
| "Ealing 82077.05525 81630.66181 82352.2226 82706.65927 \n", | |
| "Enfield 70630.77955 71348.31147 71837.54011 72237.94562 \n", | |
| "Greenwich 63512.99103 64751.56404 65486.34112 65076.43195 \n", | |
| "Hackney 66193.99212 66921.17101 68390.753 68096.79385 \n", | |
| "Hammersmith & Fulham 124433.539 126175.1513 124381.5134 123625.3196 \n", | |
| "Haringey 79374.0349 79956.3621 80746.34881 81217.69074 \n", | |
| "Harrow 83853.65615 84173.24689 84226.69844 84430.61796 \n", | |
| "Havering 68534.52248 68464.60664 68680.83996 69023.36482 \n", | |
| "Hillingdon 73117.12416 74005.00585 74671.13263 74967.86534 \n", | |
| "Hounslow 72877.47219 72331.08116 73717.78844 74479.94802 \n", | |
| "Islington 94346.39917 97428.94311 98976.14077 98951.20791 \n", | |
| "Kensington & Chelsea 191474.1141 197265.7602 197963.3169 198037.4218 \n", | |
| "Kingston upon Thames 82123.51084 82205.66822 82525.793 83342.84552 \n", | |
| "Lambeth 69035.11076 68881.15764 69608.72242 68840.02827 \n", | |
| "Lewisham 58551.38387 58041.43543 58126.37811 58151.3154 \n", | |
| "Merton 80873.98643 80704.92667 81055.90335 80781.09186 \n", | |
| "Newham 55803.95958 56067.76986 55458.31693 54709.35467 \n", | |
| "Redbridge 72366.64122 72279.4325 72880.83974 73275.16891 \n", | |
| "Richmond upon Thames 107707.6799 112865.0542 114656.6011 112320.4096 \n", | |
| "Southwark 64420.49933 64155.81449 67024.74767 65525.94434 \n", | |
| "Sutton 69759.21989 70125.24728 70789.57284 69958.41918 \n", | |
| "Tower Hamlets 66432.85846 66232.16372 64692.22672 63472.27558 \n", | |
| "Waltham Forest 61494.16938 61547.79643 61933.52738 61916.4222 \n", | |
| "Wandsworth 86518.05945 88114.3351 89830.58934 90560.68078 \n", | |
| "Westminster 133911.1117 134562.1941 133450.2162 136581.5082 \n", | |
| "Inner London 79129.19443 79969.1525 80550.47935 80597.64563 \n", | |
| "Outer London 72752.99414 73189.39978 73665.90517 73691.12888 \n", | |
| "NORTH EAST 43266.45165 42315.34372 43287.74323 41899.05494 \n", | |
| "NORTH WEST 44223.61973 44112.96432 44109.58764 44193.66583 \n", | |
| "YORKS & THE HUMBER 44830.98563 45392.63981 45534.99864 45111.45939 \n", | |
| "EAST MIDLANDS 45878.00396 45679.99539 46037.67312 45922.53585 \n", | |
| "WEST MIDLANDS 50369.66188 50100.43023 49860.00809 49598.45969 \n", | |
| "EAST OF ENGLAND 56479.80183 56288.94557 57242.30186 56732.40547 \n", | |
| "LONDON 75432.02786 75606.24501 75984.24079 75529.34488 \n", | |
| "SOUTH EAST 64530.36358 65511.008 65224.88465 64851.60429 \n", | |
| "SOUTH WEST 54698.83831 54420.15939 54265.86368 54365.71495 \n", | |
| "England 53678.24041 53735.15475 53900.60633 53600.31975 \n", | |
| "\n", | |
| "Unnamed: 0 1995-09-01 1995-10-01 ... 2023-02-01 2023-03-01 \\\n", | |
| "City of London 104473.1096 108038.1181 ... 863617 925703 \n", | |
| "Barking & Dagenham 51471.61353 51513.7576 ... 345817 344085 \n", | |
| "Barnet 93273.12245 92567.38498 ... 602916 596553 \n", | |
| "Bexley 64509.54767 64529.93725 ... 400260 395674 \n", | |
| "Brent 73789.54287 73264.05385 ... 533533 527468 \n", | |
| "Bromley 81440.43008 81862.16087 ... 501979 495776 \n", | |
| "Camden 120596.8511 117458.4922 ... 869261 855575 \n", | |
| "Croydon 69393.50023 68934.61234 ... 428153 419307 \n", | |
| "Ealing 82011.08271 80522.66054 ... 526189 510453 \n", | |
| "Enfield 71725.22104 72349.13744 ... 442552 435243 \n", | |
| "Greenwich 63996.81525 63405.26599 ... 440327 435979 \n", | |
| "Hackney 68752.50284 67619.65052 ... 608855 623033 \n", | |
| "Hammersmith & Fulham 123094.0484 123442.56 ... 750259 752937 \n", | |
| "Haringey 82142.89052 81791.48237 ... 598216 593629 \n", | |
| "Harrow 83606.97863 82492.72882 ... 520809 513799 \n", | |
| "Havering 68108.186 67425.63891 ... 431159 424284 \n", | |
| "Hillingdon 73843.55239 72992.50159 ... 457002 448807 \n", | |
| "Hounslow 74426.6609 73643.14027 ... 462105 459303 \n", | |
| "Islington 99582.63778 97580.38673 ... 684858 686044 \n", | |
| "Kensington & Chelsea 197047.8333 200005.8308 ... 1332575 1357089 \n", | |
| "Kingston upon Thames 85110.96835 85726.97473 ... 548904 551255 \n", | |
| "Lambeth 70155.81997 69616.18689 ... 550359 554644 \n", | |
| "Lewisham 58742.99034 58073.14016 ... 461775 453865 \n", | |
| "Merton 80824.70075 79569.22895 ... 567147 564548 \n", | |
| "Newham 54585.50364 54789.55504 ... 410915 399979 \n", | |
| "Redbridge 73204.02373 72917.47147 ... 475465 470293 \n", | |
| "Richmond upon Thames 110424.5703 108406.7006 ... 754074 748980 \n", | |
| "Southwark 63467.00948 60885.98483 ... 541136 528426 \n", | |
| "Sutton 69937.40214 69963.76558 ... 438157 436834 \n", | |
| "Tower Hamlets 62177.59435 62066.45135 ... 480416 469344 \n", | |
| "Waltham Forest 61548.15206 60572.57036 ... 510747 503499 \n", | |
| "Wandsworth 91373.6984 90246.40944 ... 630342 622194 \n", | |
| "Westminster 135993.0705 134513.3863 ... 1088694 1045019 \n", | |
| "Inner London 80402.85 79681.52637 ... 619319 613085 \n", | |
| "Outer London 73454.44292 72971.6484 ... 486874 480879 \n", | |
| "NORTH EAST 41850.85646 41688.16474 ... 154641 151843 \n", | |
| "NORTH WEST 44088.07696 43109.99448 ... 210320 208021 \n", | |
| "YORKS & THE HUMBER 44837.86023 44309.9991 ... 204585 200170 \n", | |
| "EAST MIDLANDS 45771.66321 45154.71999 ... 245575 242862 \n", | |
| "WEST MIDLANDS 49319.69715 49597.93617 ... 252002 242888 \n", | |
| "EAST OF ENGLAND 56259.28635 55240.28898 ... 353744 348782 \n", | |
| "LONDON 74940.80872 74302.0803 ... 525386 515488 \n", | |
| "SOUTH EAST 64352.47119 64125.80591 ... 388105 386287 \n", | |
| "SOUTH WEST 54243.98694 54264.6064 ... 323640 321469 \n", | |
| "England 53309.2331 52844.6207 ... 303801 299377 \n", | |
| "\n", | |
| "Unnamed: 0 2023-04-01 2023-05-01 2023-06-01 2023-07-01 2023-08-01 \\\n", | |
| "City of London 899500 959254 994766 973133 901625 \n", | |
| "Barking & Dagenham 346762 347252 343807 341446 341710 \n", | |
| "Barnet 586098 570759 569322 575129 585896 \n", | |
| "Bexley 393908 387134 385075 387566 386939 \n", | |
| "Brent 528340 525984 526845 521706 546948 \n", | |
| "Bromley 496840 493675 494931 496618 499236 \n", | |
| "Camden 874842 857887 876394 846398 856281 \n", | |
| "Croydon 410502 407035 405209 407452 411798 \n", | |
| "Ealing 505709 502922 505917 506973 510934 \n", | |
| "Enfield 433548 434278 433616 438213 443720 \n", | |
| "Greenwich 421336 404307 406541 417320 434178 \n", | |
| "Hackney 616212 630802 629436 635033 616254 \n", | |
| "Hammersmith & Fulham 727806 750043 751120 757769 740185 \n", | |
| "Haringey 580683 588819 601554 610968 611140 \n", | |
| "Harrow 502708 498781 496799 502448 508636 \n", | |
| "Havering 422575 417581 417079 417876 416807 \n", | |
| "Hillingdon 443118 438446 438508 435001 440586 \n", | |
| "Hounslow 454758 443224 449212 445696 459027 \n", | |
| "Islington 697915 707117 704473 705500 708738 \n", | |
| "Kensington & Chelsea 1314584 1339940 1378251 1352366 1337421 \n", | |
| "Kingston upon Thames 545662 545773 542381 537078 541299 \n", | |
| "Lambeth 554141 550174 538674 547572 554526 \n", | |
| "Lewisham 457271 447970 450131 457222 465565 \n", | |
| "Merton 566337 562190 565311 570844 574966 \n", | |
| "Newham 400487 403980 411638 407848 408593 \n", | |
| "Redbridge 463588 452810 444665 449639 473665 \n", | |
| "Richmond upon Thames 744017 743047 749565 754761 756006 \n", | |
| "Southwark 518032 522408 530033 530318 531347 \n", | |
| "Sutton 431964 428882 425987 426814 426909 \n", | |
| "Tower Hamlets 459696 450126 444210 437273 442950 \n", | |
| "Waltham Forest 504687 497425 498259 493504 501593 \n", | |
| "Wandsworth 612172 613901 613681 616438 619973 \n", | |
| "Westminster 993484 997457 1026639 1009532 972906 \n", | |
| "Inner London 605626 607015 610122 609902 609562 \n", | |
| "Outer London 476480 470900 470641 472409 478840 \n", | |
| "NORTH EAST 154893 154162 157606 158508 164078 \n", | |
| "NORTH WEST 207762 209517 211627 213178 214925 \n", | |
| "YORKS & THE HUMBER 202232 200632 206339 209027 212427 \n", | |
| "EAST MIDLANDS 243103 242381 247237 247247 250772 \n", | |
| "WEST MIDLANDS 244444 246484 248306 250666 253279 \n", | |
| "EAST OF ENGLAND 343952 343016 346381 349368 351276 \n", | |
| "LONDON 519701 515576 518073 523011 528140 \n", | |
| "SOUTH EAST 385529 384311 386117 389154 391114 \n", | |
| "SOUTH WEST 319511 319494 320714 323088 327551 \n", | |
| "England 299628 299291 302561 304943 308325 \n", | |
| "\n", | |
| "Unnamed: 0 2023-09-01 2023-10-01 2023-11-01 \n", | |
| "City of London 875029 831708 727023 \n", | |
| "Barking & Dagenham 339983 341370 353585 \n", | |
| "Barnet 586665 579777 581265 \n", | |
| "Bexley 388778 385477 381785 \n", | |
| "Brent 550526 552639 535808 \n", | |
| "Bromley 502192 505164 500485 \n", | |
| "Camden 832767 839617 830349 \n", | |
| "Croydon 417526 408220 395865 \n", | |
| "Ealing 511126 505769 482601 \n", | |
| "Enfield 451634 452392 455363 \n", | |
| "Greenwich 428789 424932 414055 \n", | |
| "Hackney 609909 615288 617169 \n", | |
| "Hammersmith & Fulham 739759 713999 709075 \n", | |
| "Haringey 612523 595423 599888 \n", | |
| "Harrow 511645 517757 502314 \n", | |
| "Havering 421107 423298 426530 \n", | |
| "Hillingdon 442656 447717 459552 \n", | |
| "Hounslow 471909 467980 468360 \n", | |
| "Islington 719651 720439 703413 \n", | |
| "Kensington & Chelsea 1272242 1269883 1127021 \n", | |
| "Kingston upon Thames 543989 552659 542817 \n", | |
| "Lambeth 575578 563661 548093 \n", | |
| "Lewisham 477529 469141 458033 \n", | |
| "Merton 576474 570383 569635 \n", | |
| "Newham 420945 420281 425493 \n", | |
| "Redbridge 492627 495002 503086 \n", | |
| "Richmond upon Thames 760831 780584 778347 \n", | |
| "Southwark 545022 537011 535001 \n", | |
| "Sutton 434295 434706 423148 \n", | |
| "Tower Hamlets 447515 447209 438709 \n", | |
| "Waltham Forest 506034 513334 502114 \n", | |
| "Wandsworth 639699 631396 625786 \n", | |
| "Westminster 929560 934961 870963 \n", | |
| "Inner London 614822 608651 595172 \n", | |
| "Outer London 482818 482883 478294 \n", | |
| "NORTH EAST 160997 160136 159871 \n", | |
| "NORTH WEST 215632 215276 213333 \n", | |
| "YORKS & THE HUMBER 208233 209545 209526 \n", | |
| "EAST MIDLANDS 249003 245580 243577 \n", | |
| "WEST MIDLANDS 253848 251361 243655 \n", | |
| "EAST OF ENGLAND 352286 347320 346659 \n", | |
| "LONDON 531306 516852 505283 \n", | |
| "SOUTH EAST 391341 387374 385844 \n", | |
| "SOUTH WEST 326490 327993 319221 \n", | |
| "England 307635 305148 301613 \n", | |
| "\n", | |
| "[45 rows x 347 columns]" | |
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| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th>Unnamed: 0</th>\n", | |
| " <th>1995-01-01</th>\n", | |
| " <th>1995-02-01</th>\n", | |
| " <th>1995-03-01</th>\n", | |
| " <th>1995-04-01</th>\n", | |
| " <th>1995-05-01</th>\n", | |
| " <th>1995-06-01</th>\n", | |
| " <th>1995-07-01</th>\n", | |
| " <th>1995-08-01</th>\n", | |
| " <th>1995-09-01</th>\n", | |
| " <th>1995-10-01</th>\n", | |
| " <th>...</th>\n", | |
| " <th>2023-02-01</th>\n", | |
| " <th>2023-03-01</th>\n", | |
| " <th>2023-04-01</th>\n", | |
| " <th>2023-05-01</th>\n", | |
| " <th>2023-06-01</th>\n", | |
| " <th>2023-07-01</th>\n", | |
| " <th>2023-08-01</th>\n", | |
| " <th>2023-09-01</th>\n", | |
| " <th>2023-10-01</th>\n", | |
| " <th>2023-11-01</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>City of London</th>\n", | |
| " <td>91448.98487</td>\n", | |
| " <td>82202.77314</td>\n", | |
| " <td>79120.70256</td>\n", | |
| " <td>77101.20804</td>\n", | |
| " <td>84409.14932</td>\n", | |
| " <td>94900.51244</td>\n", | |
| " <td>110128.0423</td>\n", | |
| " <td>112329.4376</td>\n", | |
| " <td>104473.1096</td>\n", | |
| " <td>108038.1181</td>\n", | |
| " <td>...</td>\n", | |
| " <td>863617</td>\n", | |
| " <td>925703</td>\n", | |
| " <td>899500</td>\n", | |
| " <td>959254</td>\n", | |
| " <td>994766</td>\n", | |
| " <td>973133</td>\n", | |
| " <td>901625</td>\n", | |
| " <td>875029</td>\n", | |
| " <td>831708</td>\n", | |
| " <td>727023</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Barking & Dagenham</th>\n", | |
| " <td>50460.2266</td>\n", | |
| " <td>51085.77983</td>\n", | |
| " <td>51268.96956</td>\n", | |
| " <td>53133.50526</td>\n", | |
| " <td>53042.24852</td>\n", | |
| " <td>53700.34831</td>\n", | |
| " <td>52113.12157</td>\n", | |
| " <td>52232.19868</td>\n", | |
| " <td>51471.61353</td>\n", | |
| " <td>51513.7576</td>\n", | |
| " <td>...</td>\n", | |
| " <td>345817</td>\n", | |
| " <td>344085</td>\n", | |
| " <td>346762</td>\n", | |
| " <td>347252</td>\n", | |
| " <td>343807</td>\n", | |
| " <td>341446</td>\n", | |
| " <td>341710</td>\n", | |
| " <td>339983</td>\n", | |
| " <td>341370</td>\n", | |
| " <td>353585</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Barnet</th>\n", | |
| " <td>93284.51832</td>\n", | |
| " <td>93190.16963</td>\n", | |
| " <td>92247.52435</td>\n", | |
| " <td>90762.87492</td>\n", | |
| " <td>90258.00033</td>\n", | |
| " <td>90107.23471</td>\n", | |
| " <td>91441.24768</td>\n", | |
| " <td>92361.31512</td>\n", | |
| " <td>93273.12245</td>\n", | |
| " <td>92567.38498</td>\n", | |
| " <td>...</td>\n", | |
| " <td>602916</td>\n", | |
| " <td>596553</td>\n", | |
| " <td>586098</td>\n", | |
| " <td>570759</td>\n", | |
| " <td>569322</td>\n", | |
| " <td>575129</td>\n", | |
| " <td>585896</td>\n", | |
| " <td>586665</td>\n", | |
| " <td>579777</td>\n", | |
| " <td>581265</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Bexley</th>\n", | |
| " <td>64958.09036</td>\n", | |
| " <td>64787.92069</td>\n", | |
| " <td>64367.49344</td>\n", | |
| " <td>64277.66881</td>\n", | |
| " <td>63997.13588</td>\n", | |
| " <td>64252.32335</td>\n", | |
| " <td>63722.70055</td>\n", | |
| " <td>64432.60005</td>\n", | |
| " <td>64509.54767</td>\n", | |
| " <td>64529.93725</td>\n", | |
| " <td>...</td>\n", | |
| " <td>400260</td>\n", | |
| " <td>395674</td>\n", | |
| " <td>393908</td>\n", | |
| " <td>387134</td>\n", | |
| " <td>385075</td>\n", | |
| " <td>387566</td>\n", | |
| " <td>386939</td>\n", | |
| " <td>388778</td>\n", | |
| " <td>385477</td>\n", | |
| " <td>381785</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Brent</th>\n", | |
| " <td>71306.56698</td>\n", | |
| " <td>72022.26197</td>\n", | |
| " <td>72015.76274</td>\n", | |
| " <td>72965.63094</td>\n", | |
| " <td>73704.04743</td>\n", | |
| " <td>74310.48167</td>\n", | |
| " <td>74127.03788</td>\n", | |
| " <td>73547.0411</td>\n", | |
| " <td>73789.54287</td>\n", | |
| " <td>73264.05385</td>\n", | |
| " <td>...</td>\n", | |
| " <td>533533</td>\n", | |
| " <td>527468</td>\n", | |
| " <td>528340</td>\n", | |
| " <td>525984</td>\n", | |
| " <td>526845</td>\n", | |
| " <td>521706</td>\n", | |
| " <td>546948</td>\n", | |
| " <td>550526</td>\n", | |
| " <td>552639</td>\n", | |
| " <td>535808</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Bromley</th>\n", | |
| " <td>81671.47692</td>\n", | |
| " <td>81657.55944</td>\n", | |
| " <td>81449.31143</td>\n", | |
| " <td>81124.41227</td>\n", | |
| " <td>81542.61561</td>\n", | |
| " <td>82382.83435</td>\n", | |
| " <td>82898.52264</td>\n", | |
| " <td>82054.37156</td>\n", | |
| " <td>81440.43008</td>\n", | |
| " <td>81862.16087</td>\n", | |
| " <td>...</td>\n", | |
| " <td>501979</td>\n", | |
| " <td>495776</td>\n", | |
| " <td>496840</td>\n", | |
| " <td>493675</td>\n", | |
| " <td>494931</td>\n", | |
| " <td>496618</td>\n", | |
| " <td>499236</td>\n", | |
| " <td>502192</td>\n", | |
| " <td>505164</td>\n", | |
| " <td>500485</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Camden</th>\n", | |
| " <td>120932.8881</td>\n", | |
| " <td>119508.8622</td>\n", | |
| " <td>120282.2131</td>\n", | |
| " <td>120097.899</td>\n", | |
| " <td>119929.2782</td>\n", | |
| " <td>121887.4625</td>\n", | |
| " <td>124027.5768</td>\n", | |
| " <td>125529.8039</td>\n", | |
| " <td>120596.8511</td>\n", | |
| " <td>117458.4922</td>\n", | |
| " <td>...</td>\n", | |
| " <td>869261</td>\n", | |
| " <td>855575</td>\n", | |
| " <td>874842</td>\n", | |
| " <td>857887</td>\n", | |
| " <td>876394</td>\n", | |
| " <td>846398</td>\n", | |
| " <td>856281</td>\n", | |
| " <td>832767</td>\n", | |
| " <td>839617</td>\n", | |
| " <td>830349</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Croydon</th>\n", | |
| " <td>69158.16225</td>\n", | |
| " <td>68951.09542</td>\n", | |
| " <td>68712.44341</td>\n", | |
| " <td>68610.04641</td>\n", | |
| " <td>68844.9169</td>\n", | |
| " <td>69052.51103</td>\n", | |
| " <td>69142.48112</td>\n", | |
| " <td>68993.42545</td>\n", | |
| " <td>69393.50023</td>\n", | |
| " <td>68934.61234</td>\n", | |
| " <td>...</td>\n", | |
| " <td>428153</td>\n", | |
| " <td>419307</td>\n", | |
| " <td>410502</td>\n", | |
| " <td>407035</td>\n", | |
| " <td>405209</td>\n", | |
| " <td>407452</td>\n", | |
| " <td>411798</td>\n", | |
| " <td>417526</td>\n", | |
| " <td>408220</td>\n", | |
| " <td>395865</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Ealing</th>\n", | |
| " <td>79885.89069</td>\n", | |
| " <td>80897.06551</td>\n", | |
| " <td>81379.86288</td>\n", | |
| " <td>82188.90498</td>\n", | |
| " <td>82077.05525</td>\n", | |
| " <td>81630.66181</td>\n", | |
| " <td>82352.2226</td>\n", | |
| " <td>82706.65927</td>\n", | |
| " <td>82011.08271</td>\n", | |
| " <td>80522.66054</td>\n", | |
| " <td>...</td>\n", | |
| " <td>526189</td>\n", | |
| " <td>510453</td>\n", | |
| " <td>505709</td>\n", | |
| " <td>502922</td>\n", | |
| " <td>505917</td>\n", | |
| " <td>506973</td>\n", | |
| " <td>510934</td>\n", | |
| " <td>511126</td>\n", | |
| " <td>505769</td>\n", | |
| " <td>482601</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Enfield</th>\n", | |
| " <td>72514.69096</td>\n", | |
| " <td>73155.19746</td>\n", | |
| " <td>72190.44144</td>\n", | |
| " <td>71442.92235</td>\n", | |
| " <td>70630.77955</td>\n", | |
| " <td>71348.31147</td>\n", | |
| " <td>71837.54011</td>\n", | |
| " <td>72237.94562</td>\n", | |
| " <td>71725.22104</td>\n", | |
| " <td>72349.13744</td>\n", | |
| " <td>...</td>\n", | |
| " <td>442552</td>\n", | |
| " <td>435243</td>\n", | |
| " <td>433548</td>\n", | |
| " <td>434278</td>\n", | |
| " <td>433616</td>\n", | |
| " <td>438213</td>\n", | |
| " <td>443720</td>\n", | |
| " <td>451634</td>\n", | |
| " <td>452392</td>\n", | |
| " <td>455363</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Greenwich</th>\n", | |
| " <td>62300.10169</td>\n", | |
| " <td>60993.26863</td>\n", | |
| " <td>61377.83464</td>\n", | |
| " <td>61927.7246</td>\n", | |
| " <td>63512.99103</td>\n", | |
| " <td>64751.56404</td>\n", | |
| " <td>65486.34112</td>\n", | |
| " <td>65076.43195</td>\n", | |
| " <td>63996.81525</td>\n", | |
| " <td>63405.26599</td>\n", | |
| " <td>...</td>\n", | |
| " <td>440327</td>\n", | |
| " <td>435979</td>\n", | |
| " <td>421336</td>\n", | |
| " <td>404307</td>\n", | |
| " <td>406541</td>\n", | |
| " <td>417320</td>\n", | |
| " <td>434178</td>\n", | |
| " <td>428789</td>\n", | |
| " <td>424932</td>\n", | |
| " <td>414055</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Hackney</th>\n", | |
| " <td>61296.52637</td>\n", | |
| " <td>63187.08332</td>\n", | |
| " <td>63593.29935</td>\n", | |
| " <td>65139.64403</td>\n", | |
| " <td>66193.99212</td>\n", | |
| " <td>66921.17101</td>\n", | |
| " <td>68390.753</td>\n", | |
| " <td>68096.79385</td>\n", | |
| " <td>68752.50284</td>\n", | |
| " <td>67619.65052</td>\n", | |
| " <td>...</td>\n", | |
| " <td>608855</td>\n", | |
| " <td>623033</td>\n", | |
| " <td>616212</td>\n", | |
| " <td>630802</td>\n", | |
| " <td>629436</td>\n", | |
| " <td>635033</td>\n", | |
| " <td>616254</td>\n", | |
| " <td>609909</td>\n", | |
| " <td>615288</td>\n", | |
| " <td>617169</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Hammersmith & Fulham</th>\n", | |
| " <td>124902.8602</td>\n", | |
| " <td>122087.718</td>\n", | |
| " <td>120635.9467</td>\n", | |
| " <td>121424.6241</td>\n", | |
| " <td>124433.539</td>\n", | |
| " <td>126175.1513</td>\n", | |
| " <td>124381.5134</td>\n", | |
| " <td>123625.3196</td>\n", | |
| " <td>123094.0484</td>\n", | |
| " <td>123442.56</td>\n", | |
| " <td>...</td>\n", | |
| " <td>750259</td>\n", | |
| " <td>752937</td>\n", | |
| " <td>727806</td>\n", | |
| " <td>750043</td>\n", | |
| " <td>751120</td>\n", | |
| " <td>757769</td>\n", | |
| " <td>740185</td>\n", | |
| " <td>739759</td>\n", | |
| " <td>713999</td>\n", | |
| " <td>709075</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Haringey</th>\n", | |
| " <td>76287.56947</td>\n", | |
| " <td>78901.21036</td>\n", | |
| " <td>78521.94855</td>\n", | |
| " <td>79545.57477</td>\n", | |
| " <td>79374.0349</td>\n", | |
| " <td>79956.3621</td>\n", | |
| " <td>80746.34881</td>\n", | |
| " <td>81217.69074</td>\n", | |
| " <td>82142.89052</td>\n", | |
| " <td>81791.48237</td>\n", | |
| " <td>...</td>\n", | |
| " <td>598216</td>\n", | |
| " <td>593629</td>\n", | |
| " <td>580683</td>\n", | |
| " <td>588819</td>\n", | |
| " <td>601554</td>\n", | |
| " <td>610968</td>\n", | |
| " <td>611140</td>\n", | |
| " <td>612523</td>\n", | |
| " <td>595423</td>\n", | |
| " <td>599888</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Harrow</th>\n", | |
| " <td>84769.52599</td>\n", | |
| " <td>83396.10525</td>\n", | |
| " <td>83416.23759</td>\n", | |
| " <td>83567.88439</td>\n", | |
| " <td>83853.65615</td>\n", | |
| " <td>84173.24689</td>\n", | |
| " <td>84226.69844</td>\n", | |
| " <td>84430.61796</td>\n", | |
| " <td>83606.97863</td>\n", | |
| " <td>82492.72882</td>\n", | |
| " <td>...</td>\n", | |
| " <td>520809</td>\n", | |
| " <td>513799</td>\n", | |
| " <td>502708</td>\n", | |
| " <td>498781</td>\n", | |
| " <td>496799</td>\n", | |
| " <td>502448</td>\n", | |
| " <td>508636</td>\n", | |
| " <td>511645</td>\n", | |
| " <td>517757</td>\n", | |
| " <td>502314</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Havering</th>\n", | |
| " <td>68000.13774</td>\n", | |
| " <td>69393.51294</td>\n", | |
| " <td>69368.02407</td>\n", | |
| " <td>69444.26215</td>\n", | |
| " <td>68534.52248</td>\n", | |
| " <td>68464.60664</td>\n", | |
| " <td>68680.83996</td>\n", | |
| " <td>69023.36482</td>\n", | |
| " <td>68108.186</td>\n", | |
| " <td>67425.63891</td>\n", | |
| " <td>...</td>\n", | |
| " <td>431159</td>\n", | |
| " <td>424284</td>\n", | |
| " <td>422575</td>\n", | |
| " <td>417581</td>\n", | |
| " <td>417079</td>\n", | |
| " <td>417876</td>\n", | |
| " <td>416807</td>\n", | |
| " <td>421107</td>\n", | |
| " <td>423298</td>\n", | |
| " <td>426530</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Hillingdon</th>\n", | |
| " <td>73834.82964</td>\n", | |
| " <td>75031.0696</td>\n", | |
| " <td>74188.66949</td>\n", | |
| " <td>73911.40591</td>\n", | |
| " <td>73117.12416</td>\n", | |
| " <td>74005.00585</td>\n", | |
| " <td>74671.13263</td>\n", | |
| " <td>74967.86534</td>\n", | |
| " <td>73843.55239</td>\n", | |
| " <td>72992.50159</td>\n", | |
| " <td>...</td>\n", | |
| " <td>457002</td>\n", | |
| " <td>448807</td>\n", | |
| " <td>443118</td>\n", | |
| " <td>438446</td>\n", | |
| " <td>438508</td>\n", | |
| " <td>435001</td>\n", | |
| " <td>440586</td>\n", | |
| " <td>442656</td>\n", | |
| " <td>447717</td>\n", | |
| " <td>459552</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Hounslow</th>\n", | |
| " <td>72231.70537</td>\n", | |
| " <td>71051.55852</td>\n", | |
| " <td>72097.99411</td>\n", | |
| " <td>71890.28339</td>\n", | |
| " <td>72877.47219</td>\n", | |
| " <td>72331.08116</td>\n", | |
| " <td>73717.78844</td>\n", | |
| " <td>74479.94802</td>\n", | |
| " <td>74426.6609</td>\n", | |
| " <td>73643.14027</td>\n", | |
| " <td>...</td>\n", | |
| " <td>462105</td>\n", | |
| " <td>459303</td>\n", | |
| " <td>454758</td>\n", | |
| " <td>443224</td>\n", | |
| " <td>449212</td>\n", | |
| " <td>445696</td>\n", | |
| " <td>459027</td>\n", | |
| " <td>471909</td>\n", | |
| " <td>467980</td>\n", | |
| " <td>468360</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Islington</th>\n", | |
| " <td>92516.48557</td>\n", | |
| " <td>94342.37334</td>\n", | |
| " <td>93465.86407</td>\n", | |
| " <td>93344.49305</td>\n", | |
| " <td>94346.39917</td>\n", | |
| " <td>97428.94311</td>\n", | |
| " <td>98976.14077</td>\n", | |
| " <td>98951.20791</td>\n", | |
| " <td>99582.63778</td>\n", | |
| " <td>97580.38673</td>\n", | |
| " <td>...</td>\n", | |
| " <td>684858</td>\n", | |
| " <td>686044</td>\n", | |
| " <td>697915</td>\n", | |
| " <td>707117</td>\n", | |
| " <td>704473</td>\n", | |
| " <td>705500</td>\n", | |
| " <td>708738</td>\n", | |
| " <td>719651</td>\n", | |
| " <td>720439</td>\n", | |
| " <td>703413</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Kensington & Chelsea</th>\n", | |
| " <td>182694.8326</td>\n", | |
| " <td>182345.2463</td>\n", | |
| " <td>182878.8231</td>\n", | |
| " <td>184176.9168</td>\n", | |
| " <td>191474.1141</td>\n", | |
| " <td>197265.7602</td>\n", | |
| " <td>197963.3169</td>\n", | |
| " <td>198037.4218</td>\n", | |
| " <td>197047.8333</td>\n", | |
| " <td>200005.8308</td>\n", | |
| " <td>...</td>\n", | |
| " <td>1332575</td>\n", | |
| " <td>1357089</td>\n", | |
| " <td>1314584</td>\n", | |
| " <td>1339940</td>\n", | |
| " <td>1378251</td>\n", | |
| " <td>1352366</td>\n", | |
| " <td>1337421</td>\n", | |
| " <td>1272242</td>\n", | |
| " <td>1269883</td>\n", | |
| " <td>1127021</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Kingston upon Thames</th>\n", | |
| " <td>80875.84843</td>\n", | |
| " <td>81230.13524</td>\n", | |
| " <td>81111.48848</td>\n", | |
| " <td>81672.80476</td>\n", | |
| " <td>82123.51084</td>\n", | |
| " <td>82205.66822</td>\n", | |
| " <td>82525.793</td>\n", | |
| " <td>83342.84552</td>\n", | |
| " <td>85110.96835</td>\n", | |
| " <td>85726.97473</td>\n", | |
| " <td>...</td>\n", | |
| " <td>548904</td>\n", | |
| " <td>551255</td>\n", | |
| " <td>545662</td>\n", | |
| " <td>545773</td>\n", | |
| " <td>542381</td>\n", | |
| " <td>537078</td>\n", | |
| " <td>541299</td>\n", | |
| " <td>543989</td>\n", | |
| " <td>552659</td>\n", | |
| " <td>542817</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Lambeth</th>\n", | |
| " <td>67770.98843</td>\n", | |
| " <td>65381.51908</td>\n", | |
| " <td>66336.51868</td>\n", | |
| " <td>66388.7716</td>\n", | |
| " <td>69035.11076</td>\n", | |
| " <td>68881.15764</td>\n", | |
| " <td>69608.72242</td>\n", | |
| " <td>68840.02827</td>\n", | |
| " <td>70155.81997</td>\n", | |
| " <td>69616.18689</td>\n", | |
| " <td>...</td>\n", | |
| " <td>550359</td>\n", | |
| " <td>554644</td>\n", | |
| " <td>554141</td>\n", | |
| " <td>550174</td>\n", | |
| " <td>538674</td>\n", | |
| " <td>547572</td>\n", | |
| " <td>554526</td>\n", | |
| " <td>575578</td>\n", | |
| " <td>563661</td>\n", | |
| " <td>548093</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Lewisham</th>\n", | |
| " <td>60491.26109</td>\n", | |
| " <td>60869.27091</td>\n", | |
| " <td>60288.03002</td>\n", | |
| " <td>59471.03136</td>\n", | |
| " <td>58551.38387</td>\n", | |
| " <td>58041.43543</td>\n", | |
| " <td>58126.37811</td>\n", | |
| " <td>58151.3154</td>\n", | |
| " <td>58742.99034</td>\n", | |
| " <td>58073.14016</td>\n", | |
| " <td>...</td>\n", | |
| " <td>461775</td>\n", | |
| " <td>453865</td>\n", | |
| " <td>457271</td>\n", | |
| " <td>447970</td>\n", | |
| " <td>450131</td>\n", | |
| " <td>457222</td>\n", | |
| " <td>465565</td>\n", | |
| " <td>477529</td>\n", | |
| " <td>469141</td>\n", | |
| " <td>458033</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Merton</th>\n", | |
| " <td>82070.6133</td>\n", | |
| " <td>79982.74872</td>\n", | |
| " <td>80661.68279</td>\n", | |
| " <td>79990.54333</td>\n", | |
| " <td>80873.98643</td>\n", | |
| " <td>80704.92667</td>\n", | |
| " <td>81055.90335</td>\n", | |
| " <td>80781.09186</td>\n", | |
| " <td>80824.70075</td>\n", | |
| " <td>79569.22895</td>\n", | |
| " <td>...</td>\n", | |
| " <td>567147</td>\n", | |
| " <td>564548</td>\n", | |
| " <td>566337</td>\n", | |
| " <td>562190</td>\n", | |
| " <td>565311</td>\n", | |
| " <td>570844</td>\n", | |
| " <td>574966</td>\n", | |
| " <td>576474</td>\n", | |
| " <td>570383</td>\n", | |
| " <td>569635</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Newham</th>\n", | |
| " <td>53539.31919</td>\n", | |
| " <td>53153.88306</td>\n", | |
| " <td>53458.26393</td>\n", | |
| " <td>54479.75395</td>\n", | |
| " <td>55803.95958</td>\n", | |
| " <td>56067.76986</td>\n", | |
| " <td>55458.31693</td>\n", | |
| " <td>54709.35467</td>\n", | |
| " <td>54585.50364</td>\n", | |
| " <td>54789.55504</td>\n", | |
| " <td>...</td>\n", | |
| " <td>410915</td>\n", | |
| " <td>399979</td>\n", | |
| " <td>400487</td>\n", | |
| " <td>403980</td>\n", | |
| " <td>411638</td>\n", | |
| " <td>407848</td>\n", | |
| " <td>408593</td>\n", | |
| " <td>420945</td>\n", | |
| " <td>420281</td>\n", | |
| " <td>425493</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Redbridge</th>\n", | |
| " <td>72189.58437</td>\n", | |
| " <td>72141.6261</td>\n", | |
| " <td>72501.35502</td>\n", | |
| " <td>72228.60295</td>\n", | |
| " <td>72366.64122</td>\n", | |
| " <td>72279.4325</td>\n", | |
| " <td>72880.83974</td>\n", | |
| " <td>73275.16891</td>\n", | |
| " <td>73204.02373</td>\n", | |
| " <td>72917.47147</td>\n", | |
| " <td>...</td>\n", | |
| " <td>475465</td>\n", | |
| " <td>470293</td>\n", | |
| " <td>463588</td>\n", | |
| " <td>452810</td>\n", | |
| " <td>444665</td>\n", | |
| " <td>449639</td>\n", | |
| " <td>473665</td>\n", | |
| " <td>492627</td>\n", | |
| " <td>495002</td>\n", | |
| " <td>503086</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Richmond upon Thames</th>\n", | |
| " <td>109326.1245</td>\n", | |
| " <td>111103.0394</td>\n", | |
| " <td>107325.4742</td>\n", | |
| " <td>106875</td>\n", | |
| " <td>107707.6799</td>\n", | |
| " <td>112865.0542</td>\n", | |
| " <td>114656.6011</td>\n", | |
| " <td>112320.4096</td>\n", | |
| " <td>110424.5703</td>\n", | |
| " <td>108406.7006</td>\n", | |
| " <td>...</td>\n", | |
| " <td>754074</td>\n", | |
| " <td>748980</td>\n", | |
| " <td>744017</td>\n", | |
| " <td>743047</td>\n", | |
| " <td>749565</td>\n", | |
| " <td>754761</td>\n", | |
| " <td>756006</td>\n", | |
| " <td>760831</td>\n", | |
| " <td>780584</td>\n", | |
| " <td>778347</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Southwark</th>\n", | |
| " <td>67885.20344</td>\n", | |
| " <td>64799.0648</td>\n", | |
| " <td>65763.29719</td>\n", | |
| " <td>63073.62117</td>\n", | |
| " <td>64420.49933</td>\n", | |
| " <td>64155.81449</td>\n", | |
| " <td>67024.74767</td>\n", | |
| " <td>65525.94434</td>\n", | |
| " <td>63467.00948</td>\n", | |
| " <td>60885.98483</td>\n", | |
| " <td>...</td>\n", | |
| " <td>541136</td>\n", | |
| " <td>528426</td>\n", | |
| " <td>518032</td>\n", | |
| " <td>522408</td>\n", | |
| " <td>530033</td>\n", | |
| " <td>530318</td>\n", | |
| " <td>531347</td>\n", | |
| " <td>545022</td>\n", | |
| " <td>537011</td>\n", | |
| " <td>535001</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Sutton</th>\n", | |
| " <td>71536.97357</td>\n", | |
| " <td>70893.20851</td>\n", | |
| " <td>70306.83844</td>\n", | |
| " <td>69411.9439</td>\n", | |
| " <td>69759.21989</td>\n", | |
| " <td>70125.24728</td>\n", | |
| " <td>70789.57284</td>\n", | |
| " <td>69958.41918</td>\n", | |
| " <td>69937.40214</td>\n", | |
| " <td>69963.76558</td>\n", | |
| " <td>...</td>\n", | |
| " <td>438157</td>\n", | |
| " <td>436834</td>\n", | |
| " <td>431964</td>\n", | |
| " <td>428882</td>\n", | |
| " <td>425987</td>\n", | |
| " <td>426814</td>\n", | |
| " <td>426909</td>\n", | |
| " <td>434295</td>\n", | |
| " <td>434706</td>\n", | |
| " <td>423148</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Tower Hamlets</th>\n", | |
| " <td>59865.18995</td>\n", | |
| " <td>62318.53353</td>\n", | |
| " <td>63938.67686</td>\n", | |
| " <td>66233.19383</td>\n", | |
| " <td>66432.85846</td>\n", | |
| " <td>66232.16372</td>\n", | |
| " <td>64692.22672</td>\n", | |
| " <td>63472.27558</td>\n", | |
| " <td>62177.59435</td>\n", | |
| " <td>62066.45135</td>\n", | |
| " <td>...</td>\n", | |
| " <td>480416</td>\n", | |
| " <td>469344</td>\n", | |
| " <td>459696</td>\n", | |
| " <td>450126</td>\n", | |
| " <td>444210</td>\n", | |
| " <td>437273</td>\n", | |
| " <td>442950</td>\n", | |
| " <td>447515</td>\n", | |
| " <td>447209</td>\n", | |
| " <td>438709</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Waltham Forest</th>\n", | |
| " <td>61319.44913</td>\n", | |
| " <td>60252.12246</td>\n", | |
| " <td>60871.08493</td>\n", | |
| " <td>60971.39722</td>\n", | |
| " <td>61494.16938</td>\n", | |
| " <td>61547.79643</td>\n", | |
| " <td>61933.52738</td>\n", | |
| " <td>61916.4222</td>\n", | |
| " <td>61548.15206</td>\n", | |
| " <td>60572.57036</td>\n", | |
| " <td>...</td>\n", | |
| " <td>510747</td>\n", | |
| " <td>503499</td>\n", | |
| " <td>504687</td>\n", | |
| " <td>497425</td>\n", | |
| " <td>498259</td>\n", | |
| " <td>493504</td>\n", | |
| " <td>501593</td>\n", | |
| " <td>506034</td>\n", | |
| " <td>513334</td>\n", | |
| " <td>502114</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Wandsworth</th>\n", | |
| " <td>88559.04381</td>\n", | |
| " <td>88641.01678</td>\n", | |
| " <td>87124.81523</td>\n", | |
| " <td>87026.00225</td>\n", | |
| " <td>86518.05945</td>\n", | |
| " <td>88114.3351</td>\n", | |
| " <td>89830.58934</td>\n", | |
| " <td>90560.68078</td>\n", | |
| " <td>91373.6984</td>\n", | |
| " <td>90246.40944</td>\n", | |
| " <td>...</td>\n", | |
| " <td>630342</td>\n", | |
| " <td>622194</td>\n", | |
| " <td>612172</td>\n", | |
| " <td>613901</td>\n", | |
| " <td>613681</td>\n", | |
| " <td>616438</td>\n", | |
| " <td>619973</td>\n", | |
| " <td>639699</td>\n", | |
| " <td>631396</td>\n", | |
| " <td>625786</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Westminster</th>\n", | |
| " <td>133025.2772</td>\n", | |
| " <td>131468.3096</td>\n", | |
| " <td>132260.3417</td>\n", | |
| " <td>133370.2036</td>\n", | |
| " <td>133911.1117</td>\n", | |
| " <td>134562.1941</td>\n", | |
| " <td>133450.2162</td>\n", | |
| " <td>136581.5082</td>\n", | |
| " <td>135993.0705</td>\n", | |
| " <td>134513.3863</td>\n", | |
| " <td>...</td>\n", | |
| " <td>1088694</td>\n", | |
| " <td>1045019</td>\n", | |
| " <td>993484</td>\n", | |
| " <td>997457</td>\n", | |
| " <td>1026639</td>\n", | |
| " <td>1009532</td>\n", | |
| " <td>972906</td>\n", | |
| " <td>929560</td>\n", | |
| " <td>934961</td>\n", | |
| " <td>870963</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Inner London</th>\n", | |
| " <td>78251.9765</td>\n", | |
| " <td>75885.70201</td>\n", | |
| " <td>76591.59947</td>\n", | |
| " <td>76851.56697</td>\n", | |
| " <td>79129.19443</td>\n", | |
| " <td>79969.1525</td>\n", | |
| " <td>80550.47935</td>\n", | |
| " <td>80597.64563</td>\n", | |
| " <td>80402.85</td>\n", | |
| " <td>79681.52637</td>\n", | |
| " <td>...</td>\n", | |
| " <td>619319</td>\n", | |
| " <td>613085</td>\n", | |
| " <td>605626</td>\n", | |
| " <td>607015</td>\n", | |
| " <td>610122</td>\n", | |
| " <td>609902</td>\n", | |
| " <td>609562</td>\n", | |
| " <td>614822</td>\n", | |
| " <td>608651</td>\n", | |
| " <td>595172</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Outer London</th>\n", | |
| " <td>72958.79836</td>\n", | |
| " <td>72937.88262</td>\n", | |
| " <td>72714.53478</td>\n", | |
| " <td>72591.92469</td>\n", | |
| " <td>72752.99414</td>\n", | |
| " <td>73189.39978</td>\n", | |
| " <td>73665.90517</td>\n", | |
| " <td>73691.12888</td>\n", | |
| " <td>73454.44292</td>\n", | |
| " <td>72971.6484</td>\n", | |
| " <td>...</td>\n", | |
| " <td>486874</td>\n", | |
| " <td>480879</td>\n", | |
| " <td>476480</td>\n", | |
| " <td>470900</td>\n", | |
| " <td>470641</td>\n", | |
| " <td>472409</td>\n", | |
| " <td>478840</td>\n", | |
| " <td>482818</td>\n", | |
| " <td>482883</td>\n", | |
| " <td>478294</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>NORTH EAST</th>\n", | |
| " <td>42076.35411</td>\n", | |
| " <td>42571.98949</td>\n", | |
| " <td>42369.72984</td>\n", | |
| " <td>42095.8436</td>\n", | |
| " <td>43266.45165</td>\n", | |
| " <td>42315.34372</td>\n", | |
| " <td>43287.74323</td>\n", | |
| " <td>41899.05494</td>\n", | |
| " <td>41850.85646</td>\n", | |
| " <td>41688.16474</td>\n", | |
| " <td>...</td>\n", | |
| " <td>154641</td>\n", | |
| " <td>151843</td>\n", | |
| " <td>154893</td>\n", | |
| " <td>154162</td>\n", | |
| " <td>157606</td>\n", | |
| " <td>158508</td>\n", | |
| " <td>164078</td>\n", | |
| " <td>160997</td>\n", | |
| " <td>160136</td>\n", | |
| " <td>159871</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>NORTH WEST</th>\n", | |
| " <td>43958.48001</td>\n", | |
| " <td>43925.42289</td>\n", | |
| " <td>44434.8681</td>\n", | |
| " <td>44267.7796</td>\n", | |
| " <td>44223.61973</td>\n", | |
| " <td>44112.96432</td>\n", | |
| " <td>44109.58764</td>\n", | |
| " <td>44193.66583</td>\n", | |
| " <td>44088.07696</td>\n", | |
| " <td>43109.99448</td>\n", | |
| " <td>...</td>\n", | |
| " <td>210320</td>\n", | |
| " <td>208021</td>\n", | |
| " <td>207762</td>\n", | |
| " <td>209517</td>\n", | |
| " <td>211627</td>\n", | |
| " <td>213178</td>\n", | |
| " <td>214925</td>\n", | |
| " <td>215632</td>\n", | |
| " <td>215276</td>\n", | |
| " <td>213333</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>YORKS & THE HUMBER</th>\n", | |
| " <td>44803.42878</td>\n", | |
| " <td>44528.80721</td>\n", | |
| " <td>45200.46775</td>\n", | |
| " <td>45614.34341</td>\n", | |
| " <td>44830.98563</td>\n", | |
| " <td>45392.63981</td>\n", | |
| " <td>45534.99864</td>\n", | |
| " <td>45111.45939</td>\n", | |
| " <td>44837.86023</td>\n", | |
| " <td>44309.9991</td>\n", | |
| " <td>...</td>\n", | |
| " <td>204585</td>\n", | |
| " <td>200170</td>\n", | |
| " <td>202232</td>\n", | |
| " <td>200632</td>\n", | |
| " <td>206339</td>\n", | |
| " <td>209027</td>\n", | |
| " <td>212427</td>\n", | |
| " <td>208233</td>\n", | |
| " <td>209545</td>\n", | |
| " <td>209526</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>EAST MIDLANDS</th>\n", | |
| " <td>45544.52227</td>\n", | |
| " <td>46051.57066</td>\n", | |
| " <td>45383.82395</td>\n", | |
| " <td>46124.23045</td>\n", | |
| " <td>45878.00396</td>\n", | |
| " <td>45679.99539</td>\n", | |
| " <td>46037.67312</td>\n", | |
| " <td>45922.53585</td>\n", | |
| " <td>45771.66321</td>\n", | |
| " <td>45154.71999</td>\n", | |
| " <td>...</td>\n", | |
| " <td>245575</td>\n", | |
| " <td>242862</td>\n", | |
| " <td>243103</td>\n", | |
| " <td>242381</td>\n", | |
| " <td>247237</td>\n", | |
| " <td>247247</td>\n", | |
| " <td>250772</td>\n", | |
| " <td>249003</td>\n", | |
| " <td>245580</td>\n", | |
| " <td>243577</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>WEST MIDLANDS</th>\n", | |
| " <td>48527.52339</td>\n", | |
| " <td>49341.29029</td>\n", | |
| " <td>49442.17973</td>\n", | |
| " <td>49455.93299</td>\n", | |
| " <td>50369.66188</td>\n", | |
| " <td>50100.43023</td>\n", | |
| " <td>49860.00809</td>\n", | |
| " <td>49598.45969</td>\n", | |
| " <td>49319.69715</td>\n", | |
| " <td>49597.93617</td>\n", | |
| " <td>...</td>\n", | |
| " <td>252002</td>\n", | |
| " <td>242888</td>\n", | |
| " <td>244444</td>\n", | |
| " <td>246484</td>\n", | |
| " <td>248306</td>\n", | |
| " <td>250666</td>\n", | |
| " <td>253279</td>\n", | |
| " <td>253848</td>\n", | |
| " <td>251361</td>\n", | |
| " <td>243655</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>EAST OF ENGLAND</th>\n", | |
| " <td>56701.5961</td>\n", | |
| " <td>56593.59475</td>\n", | |
| " <td>56171.18278</td>\n", | |
| " <td>56567.89582</td>\n", | |
| " <td>56479.80183</td>\n", | |
| " <td>56288.94557</td>\n", | |
| " <td>57242.30186</td>\n", | |
| " <td>56732.40547</td>\n", | |
| " <td>56259.28635</td>\n", | |
| " <td>55240.28898</td>\n", | |
| " <td>...</td>\n", | |
| " <td>353744</td>\n", | |
| " <td>348782</td>\n", | |
| " <td>343952</td>\n", | |
| " <td>343016</td>\n", | |
| " <td>346381</td>\n", | |
| " <td>349368</td>\n", | |
| " <td>351276</td>\n", | |
| " <td>352286</td>\n", | |
| " <td>347320</td>\n", | |
| " <td>346659</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>LONDON</th>\n", | |
| " <td>74435.76052</td>\n", | |
| " <td>72777.93709</td>\n", | |
| " <td>73896.84204</td>\n", | |
| " <td>74455.28754</td>\n", | |
| " <td>75432.02786</td>\n", | |
| " <td>75606.24501</td>\n", | |
| " <td>75984.24079</td>\n", | |
| " <td>75529.34488</td>\n", | |
| " <td>74940.80872</td>\n", | |
| " <td>74302.0803</td>\n", | |
| " <td>...</td>\n", | |
| " <td>525386</td>\n", | |
| " <td>515488</td>\n", | |
| " <td>519701</td>\n", | |
| " <td>515576</td>\n", | |
| " <td>518073</td>\n", | |
| " <td>523011</td>\n", | |
| " <td>528140</td>\n", | |
| " <td>531306</td>\n", | |
| " <td>516852</td>\n", | |
| " <td>505283</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>SOUTH EAST</th>\n", | |
| " <td>64018.87894</td>\n", | |
| " <td>63715.02399</td>\n", | |
| " <td>64113.60858</td>\n", | |
| " <td>64623.22395</td>\n", | |
| " <td>64530.36358</td>\n", | |
| " <td>65511.008</td>\n", | |
| " <td>65224.88465</td>\n", | |
| " <td>64851.60429</td>\n", | |
| " <td>64352.47119</td>\n", | |
| " <td>64125.80591</td>\n", | |
| " <td>...</td>\n", | |
| " <td>388105</td>\n", | |
| " <td>386287</td>\n", | |
| " <td>385529</td>\n", | |
| " <td>384311</td>\n", | |
| " <td>386117</td>\n", | |
| " <td>389154</td>\n", | |
| " <td>391114</td>\n", | |
| " <td>391341</td>\n", | |
| " <td>387374</td>\n", | |
| " <td>385844</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>SOUTH WEST</th>\n", | |
| " <td>54705.1579</td>\n", | |
| " <td>54356.14843</td>\n", | |
| " <td>53583.07667</td>\n", | |
| " <td>54786.01938</td>\n", | |
| " <td>54698.83831</td>\n", | |
| " <td>54420.15939</td>\n", | |
| " <td>54265.86368</td>\n", | |
| " <td>54365.71495</td>\n", | |
| " <td>54243.98694</td>\n", | |
| " <td>54264.6064</td>\n", | |
| " <td>...</td>\n", | |
| " <td>323640</td>\n", | |
| " <td>321469</td>\n", | |
| " <td>319511</td>\n", | |
| " <td>319494</td>\n", | |
| " <td>320714</td>\n", | |
| " <td>323088</td>\n", | |
| " <td>327551</td>\n", | |
| " <td>326490</td>\n", | |
| " <td>327993</td>\n", | |
| " <td>319221</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>England</th>\n", | |
| " <td>53202.77128</td>\n", | |
| " <td>53096.1549</td>\n", | |
| " <td>53201.2843</td>\n", | |
| " <td>53590.8548</td>\n", | |
| " <td>53678.24041</td>\n", | |
| " <td>53735.15475</td>\n", | |
| " <td>53900.60633</td>\n", | |
| " <td>53600.31975</td>\n", | |
| " <td>53309.2331</td>\n", | |
| " <td>52844.6207</td>\n", | |
| " <td>...</td>\n", | |
| " <td>303801</td>\n", | |
| " <td>299377</td>\n", | |
| " <td>299628</td>\n", | |
| " <td>299291</td>\n", | |
| " <td>302561</td>\n", | |
| " <td>304943</td>\n", | |
| " <td>308325</td>\n", | |
| " <td>307635</td>\n", | |
| " <td>305148</td>\n", | |
| " <td>301613</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "<p>45 rows × 347 columns</p>\n", | |
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| " style=\"display:none;\">\n", | |
| "\n", | |
| "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n", | |
| " width=\"24px\">\n", | |
| " <g>\n", | |
| " <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n", | |
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| "\n", | |
| "<style>\n", | |
| " .colab-df-quickchart {\n", | |
| " --bg-color: #E8F0FE;\n", | |
| " --fill-color: #1967D2;\n", | |
| " --hover-bg-color: #E2EBFA;\n", | |
| " --hover-fill-color: #174EA6;\n", | |
| " --disabled-fill-color: #AAA;\n", | |
| " --disabled-bg-color: #DDD;\n", | |
| " }\n", | |
| "\n", | |
| " [theme=dark] .colab-df-quickchart {\n", | |
| " --bg-color: #3B4455;\n", | |
| " --fill-color: #D2E3FC;\n", | |
| " --hover-bg-color: #434B5C;\n", | |
| " --hover-fill-color: #FFFFFF;\n", | |
| " --disabled-bg-color: #3B4455;\n", | |
| " --disabled-fill-color: #666;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-quickchart {\n", | |
| " background-color: var(--bg-color);\n", | |
| " border: none;\n", | |
| " border-radius: 50%;\n", | |
| " cursor: pointer;\n", | |
| " display: none;\n", | |
| " fill: var(--fill-color);\n", | |
| " height: 32px;\n", | |
| " padding: 0;\n", | |
| " width: 32px;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-quickchart:hover {\n", | |
| " background-color: var(--hover-bg-color);\n", | |
| " box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n", | |
| " fill: var(--button-hover-fill-color);\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-quickchart-complete:disabled,\n", | |
| " .colab-df-quickchart-complete:disabled:hover {\n", | |
| " background-color: var(--disabled-bg-color);\n", | |
| " fill: var(--disabled-fill-color);\n", | |
| " box-shadow: none;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-spinner {\n", | |
| " border: 2px solid var(--fill-color);\n", | |
| " border-color: transparent;\n", | |
| " border-bottom-color: var(--fill-color);\n", | |
| " animation:\n", | |
| " spin 1s steps(1) infinite;\n", | |
| " }\n", | |
| "\n", | |
| " @keyframes spin {\n", | |
| " 0% {\n", | |
| " border-color: transparent;\n", | |
| " border-bottom-color: var(--fill-color);\n", | |
| " border-left-color: var(--fill-color);\n", | |
| " }\n", | |
| " 20% {\n", | |
| " border-color: transparent;\n", | |
| " border-left-color: var(--fill-color);\n", | |
| " border-top-color: var(--fill-color);\n", | |
| " }\n", | |
| " 30% {\n", | |
| " border-color: transparent;\n", | |
| " border-left-color: var(--fill-color);\n", | |
| " border-top-color: var(--fill-color);\n", | |
| " border-right-color: var(--fill-color);\n", | |
| " }\n", | |
| " 40% {\n", | |
| " border-color: transparent;\n", | |
| " border-right-color: var(--fill-color);\n", | |
| " border-top-color: var(--fill-color);\n", | |
| " }\n", | |
| " 60% {\n", | |
| " border-color: transparent;\n", | |
| " border-right-color: var(--fill-color);\n", | |
| " }\n", | |
| " 80% {\n", | |
| " border-color: transparent;\n", | |
| " border-right-color: var(--fill-color);\n", | |
| " border-bottom-color: var(--fill-color);\n", | |
| " }\n", | |
| " 90% {\n", | |
| " border-color: transparent;\n", | |
| " border-bottom-color: var(--fill-color);\n", | |
| " }\n", | |
| " }\n", | |
| "</style>\n", | |
| "\n", | |
| " <script>\n", | |
| " async function quickchart(key) {\n", | |
| " const quickchartButtonEl =\n", | |
| " document.querySelector('#' + key + ' button');\n", | |
| " quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n", | |
| " quickchartButtonEl.classList.add('colab-df-spinner');\n", | |
| " try {\n", | |
| " const charts = await google.colab.kernel.invokeFunction(\n", | |
| " 'suggestCharts', [key], {});\n", | |
| " } catch (error) {\n", | |
| " console.error('Error during call to suggestCharts:', error);\n", | |
| " }\n", | |
| " quickchartButtonEl.classList.remove('colab-df-spinner');\n", | |
| " quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n", | |
| " }\n", | |
| " (() => {\n", | |
| " let quickchartButtonEl =\n", | |
| " document.querySelector('#df-1eeedc90-0f5c-4eef-a04e-0c645f768e15 button');\n", | |
| " quickchartButtonEl.style.display =\n", | |
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| " <div id=\"id_a87c5d1f-0a15-4de6-b499-d9862b0ff8e0\">\n", | |
| " <style>\n", | |
| " .colab-df-generate {\n", | |
| " background-color: #E8F0FE;\n", | |
| " border: none;\n", | |
| " border-radius: 50%;\n", | |
| " cursor: pointer;\n", | |
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| " height: 32px;\n", | |
| " padding: 0 0 0 0;\n", | |
| " width: 32px;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-generate:hover {\n", | |
| " background-color: #E2EBFA;\n", | |
| " box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n", | |
| " fill: #174EA6;\n", | |
| " }\n", | |
| "\n", | |
| " [theme=dark] .colab-df-generate {\n", | |
| " background-color: #3B4455;\n", | |
| " fill: #D2E3FC;\n", | |
| " }\n", | |
| "\n", | |
| " [theme=dark] .colab-df-generate:hover {\n", | |
| " background-color: #434B5C;\n", | |
| " box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n", | |
| " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n", | |
| " fill: #FFFFFF;\n", | |
| " }\n", | |
| " </style>\n", | |
| " <button class=\"colab-df-generate\" onclick=\"generateWithVariable('cleaned')\"\n", | |
| " title=\"Generate code using this dataframe.\"\n", | |
| " style=\"display:none;\">\n", | |
| "\n", | |
| " <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n", | |
| " width=\"24px\">\n", | |
| " <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n", | |
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| " google.colab.kernel.accessAllowed ? 'block' : 'none';\n", | |
| "\n", | |
| " buttonEl.onclick = () => {\n", | |
| " google.colab.notebook.generateWithVariable('cleaned');\n", | |
| " }\n", | |
| " })();\n", | |
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| "\n", | |
| " </div>\n", | |
| " </div>\n" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 104 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "o1uLbJAsoIjK" | |
| }, | |
| "source": [ | |
| "**2.3. Cleaning the data (part 2)**\n", | |
| "\n", | |
| "You might we have to **rename** a couple columns. How do you do this? The clue's pretty bold..." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "GKkmn1AnoVZS", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 1000 | |
| }, | |
| "outputId": "4fb5bd65-4717-4193-9a80-68261be1ffbc" | |
| }, | |
| "source": [ | |
| "# Rename Date Col (after index reassignment)\n", | |
| "\n", | |
| "cleaned.columns.set_names('Months', inplace=True)\n", | |
| "\n", | |
| "cleaned\n", | |
| "\n" | |
| ], | |
| "execution_count": 118, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "Months 1995-01-01 1995-02-01 1995-03-01 1995-04-01 \\\n", | |
| "City of London 91448.98487 82202.77314 79120.70256 77101.20804 \n", | |
| "Barking & Dagenham 50460.2266 51085.77983 51268.96956 53133.50526 \n", | |
| "Barnet 93284.51832 93190.16963 92247.52435 90762.87492 \n", | |
| "Bexley 64958.09036 64787.92069 64367.49344 64277.66881 \n", | |
| "Brent 71306.56698 72022.26197 72015.76274 72965.63094 \n", | |
| "Bromley 81671.47692 81657.55944 81449.31143 81124.41227 \n", | |
| "Camden 120932.8881 119508.8622 120282.2131 120097.899 \n", | |
| "Croydon 69158.16225 68951.09542 68712.44341 68610.04641 \n", | |
| "Ealing 79885.89069 80897.06551 81379.86288 82188.90498 \n", | |
| "Enfield 72514.69096 73155.19746 72190.44144 71442.92235 \n", | |
| "Greenwich 62300.10169 60993.26863 61377.83464 61927.7246 \n", | |
| "Hackney 61296.52637 63187.08332 63593.29935 65139.64403 \n", | |
| "Hammersmith & Fulham 124902.8602 122087.718 120635.9467 121424.6241 \n", | |
| "Haringey 76287.56947 78901.21036 78521.94855 79545.57477 \n", | |
| "Harrow 84769.52599 83396.10525 83416.23759 83567.88439 \n", | |
| "Havering 68000.13774 69393.51294 69368.02407 69444.26215 \n", | |
| "Hillingdon 73834.82964 75031.0696 74188.66949 73911.40591 \n", | |
| "Hounslow 72231.70537 71051.55852 72097.99411 71890.28339 \n", | |
| "Islington 92516.48557 94342.37334 93465.86407 93344.49305 \n", | |
| "Kensington & Chelsea 182694.8326 182345.2463 182878.8231 184176.9168 \n", | |
| "Kingston upon Thames 80875.84843 81230.13524 81111.48848 81672.80476 \n", | |
| "Lambeth 67770.98843 65381.51908 66336.51868 66388.7716 \n", | |
| "Lewisham 60491.26109 60869.27091 60288.03002 59471.03136 \n", | |
| "Merton 82070.6133 79982.74872 80661.68279 79990.54333 \n", | |
| "Newham 53539.31919 53153.88306 53458.26393 54479.75395 \n", | |
| "Redbridge 72189.58437 72141.6261 72501.35502 72228.60295 \n", | |
| "Richmond upon Thames 109326.1245 111103.0394 107325.4742 106875 \n", | |
| "Southwark 67885.20344 64799.0648 65763.29719 63073.62117 \n", | |
| "Sutton 71536.97357 70893.20851 70306.83844 69411.9439 \n", | |
| "Tower Hamlets 59865.18995 62318.53353 63938.67686 66233.19383 \n", | |
| "Waltham Forest 61319.44913 60252.12246 60871.08493 60971.39722 \n", | |
| "Wandsworth 88559.04381 88641.01678 87124.81523 87026.00225 \n", | |
| "Westminster 133025.2772 131468.3096 132260.3417 133370.2036 \n", | |
| "Inner London 78251.9765 75885.70201 76591.59947 76851.56697 \n", | |
| "Outer London 72958.79836 72937.88262 72714.53478 72591.92469 \n", | |
| "NORTH EAST 42076.35411 42571.98949 42369.72984 42095.8436 \n", | |
| "NORTH WEST 43958.48001 43925.42289 44434.8681 44267.7796 \n", | |
| "YORKS & THE HUMBER 44803.42878 44528.80721 45200.46775 45614.34341 \n", | |
| "EAST MIDLANDS 45544.52227 46051.57066 45383.82395 46124.23045 \n", | |
| "WEST MIDLANDS 48527.52339 49341.29029 49442.17973 49455.93299 \n", | |
| "EAST OF ENGLAND 56701.5961 56593.59475 56171.18278 56567.89582 \n", | |
| "LONDON 74435.76052 72777.93709 73896.84204 74455.28754 \n", | |
| "SOUTH EAST 64018.87894 63715.02399 64113.60858 64623.22395 \n", | |
| "SOUTH WEST 54705.1579 54356.14843 53583.07667 54786.01938 \n", | |
| "England 53202.77128 53096.1549 53201.2843 53590.8548 \n", | |
| "\n", | |
| "Months 1995-05-01 1995-06-01 1995-07-01 1995-08-01 \\\n", | |
| "City of London 84409.14932 94900.51244 110128.0423 112329.4376 \n", | |
| "Barking & Dagenham 53042.24852 53700.34831 52113.12157 52232.19868 \n", | |
| "Barnet 90258.00033 90107.23471 91441.24768 92361.31512 \n", | |
| "Bexley 63997.13588 64252.32335 63722.70055 64432.60005 \n", | |
| "Brent 73704.04743 74310.48167 74127.03788 73547.0411 \n", | |
| "Bromley 81542.61561 82382.83435 82898.52264 82054.37156 \n", | |
| "Camden 119929.2782 121887.4625 124027.5768 125529.8039 \n", | |
| "Croydon 68844.9169 69052.51103 69142.48112 68993.42545 \n", | |
| "Ealing 82077.05525 81630.66181 82352.2226 82706.65927 \n", | |
| "Enfield 70630.77955 71348.31147 71837.54011 72237.94562 \n", | |
| "Greenwich 63512.99103 64751.56404 65486.34112 65076.43195 \n", | |
| "Hackney 66193.99212 66921.17101 68390.753 68096.79385 \n", | |
| "Hammersmith & Fulham 124433.539 126175.1513 124381.5134 123625.3196 \n", | |
| "Haringey 79374.0349 79956.3621 80746.34881 81217.69074 \n", | |
| "Harrow 83853.65615 84173.24689 84226.69844 84430.61796 \n", | |
| "Havering 68534.52248 68464.60664 68680.83996 69023.36482 \n", | |
| "Hillingdon 73117.12416 74005.00585 74671.13263 74967.86534 \n", | |
| "Hounslow 72877.47219 72331.08116 73717.78844 74479.94802 \n", | |
| "Islington 94346.39917 97428.94311 98976.14077 98951.20791 \n", | |
| "Kensington & Chelsea 191474.1141 197265.7602 197963.3169 198037.4218 \n", | |
| "Kingston upon Thames 82123.51084 82205.66822 82525.793 83342.84552 \n", | |
| "Lambeth 69035.11076 68881.15764 69608.72242 68840.02827 \n", | |
| "Lewisham 58551.38387 58041.43543 58126.37811 58151.3154 \n", | |
| "Merton 80873.98643 80704.92667 81055.90335 80781.09186 \n", | |
| "Newham 55803.95958 56067.76986 55458.31693 54709.35467 \n", | |
| "Redbridge 72366.64122 72279.4325 72880.83974 73275.16891 \n", | |
| "Richmond upon Thames 107707.6799 112865.0542 114656.6011 112320.4096 \n", | |
| "Southwark 64420.49933 64155.81449 67024.74767 65525.94434 \n", | |
| "Sutton 69759.21989 70125.24728 70789.57284 69958.41918 \n", | |
| "Tower Hamlets 66432.85846 66232.16372 64692.22672 63472.27558 \n", | |
| "Waltham Forest 61494.16938 61547.79643 61933.52738 61916.4222 \n", | |
| "Wandsworth 86518.05945 88114.3351 89830.58934 90560.68078 \n", | |
| "Westminster 133911.1117 134562.1941 133450.2162 136581.5082 \n", | |
| "Inner London 79129.19443 79969.1525 80550.47935 80597.64563 \n", | |
| "Outer London 72752.99414 73189.39978 73665.90517 73691.12888 \n", | |
| "NORTH EAST 43266.45165 42315.34372 43287.74323 41899.05494 \n", | |
| "NORTH WEST 44223.61973 44112.96432 44109.58764 44193.66583 \n", | |
| "YORKS & THE HUMBER 44830.98563 45392.63981 45534.99864 45111.45939 \n", | |
| "EAST MIDLANDS 45878.00396 45679.99539 46037.67312 45922.53585 \n", | |
| "WEST MIDLANDS 50369.66188 50100.43023 49860.00809 49598.45969 \n", | |
| "EAST OF ENGLAND 56479.80183 56288.94557 57242.30186 56732.40547 \n", | |
| "LONDON 75432.02786 75606.24501 75984.24079 75529.34488 \n", | |
| "SOUTH EAST 64530.36358 65511.008 65224.88465 64851.60429 \n", | |
| "SOUTH WEST 54698.83831 54420.15939 54265.86368 54365.71495 \n", | |
| "England 53678.24041 53735.15475 53900.60633 53600.31975 \n", | |
| "\n", | |
| "Months 1995-09-01 1995-10-01 ... 2023-02-01 2023-03-01 \\\n", | |
| "City of London 104473.1096 108038.1181 ... 863617 925703 \n", | |
| "Barking & Dagenham 51471.61353 51513.7576 ... 345817 344085 \n", | |
| "Barnet 93273.12245 92567.38498 ... 602916 596553 \n", | |
| "Bexley 64509.54767 64529.93725 ... 400260 395674 \n", | |
| "Brent 73789.54287 73264.05385 ... 533533 527468 \n", | |
| "Bromley 81440.43008 81862.16087 ... 501979 495776 \n", | |
| "Camden 120596.8511 117458.4922 ... 869261 855575 \n", | |
| "Croydon 69393.50023 68934.61234 ... 428153 419307 \n", | |
| "Ealing 82011.08271 80522.66054 ... 526189 510453 \n", | |
| "Enfield 71725.22104 72349.13744 ... 442552 435243 \n", | |
| "Greenwich 63996.81525 63405.26599 ... 440327 435979 \n", | |
| "Hackney 68752.50284 67619.65052 ... 608855 623033 \n", | |
| "Hammersmith & Fulham 123094.0484 123442.56 ... 750259 752937 \n", | |
| "Haringey 82142.89052 81791.48237 ... 598216 593629 \n", | |
| "Harrow 83606.97863 82492.72882 ... 520809 513799 \n", | |
| "Havering 68108.186 67425.63891 ... 431159 424284 \n", | |
| "Hillingdon 73843.55239 72992.50159 ... 457002 448807 \n", | |
| "Hounslow 74426.6609 73643.14027 ... 462105 459303 \n", | |
| "Islington 99582.63778 97580.38673 ... 684858 686044 \n", | |
| "Kensington & Chelsea 197047.8333 200005.8308 ... 1332575 1357089 \n", | |
| "Kingston upon Thames 85110.96835 85726.97473 ... 548904 551255 \n", | |
| "Lambeth 70155.81997 69616.18689 ... 550359 554644 \n", | |
| "Lewisham 58742.99034 58073.14016 ... 461775 453865 \n", | |
| "Merton 80824.70075 79569.22895 ... 567147 564548 \n", | |
| "Newham 54585.50364 54789.55504 ... 410915 399979 \n", | |
| "Redbridge 73204.02373 72917.47147 ... 475465 470293 \n", | |
| "Richmond upon Thames 110424.5703 108406.7006 ... 754074 748980 \n", | |
| "Southwark 63467.00948 60885.98483 ... 541136 528426 \n", | |
| "Sutton 69937.40214 69963.76558 ... 438157 436834 \n", | |
| "Tower Hamlets 62177.59435 62066.45135 ... 480416 469344 \n", | |
| "Waltham Forest 61548.15206 60572.57036 ... 510747 503499 \n", | |
| "Wandsworth 91373.6984 90246.40944 ... 630342 622194 \n", | |
| "Westminster 135993.0705 134513.3863 ... 1088694 1045019 \n", | |
| "Inner London 80402.85 79681.52637 ... 619319 613085 \n", | |
| "Outer London 73454.44292 72971.6484 ... 486874 480879 \n", | |
| "NORTH EAST 41850.85646 41688.16474 ... 154641 151843 \n", | |
| "NORTH WEST 44088.07696 43109.99448 ... 210320 208021 \n", | |
| "YORKS & THE HUMBER 44837.86023 44309.9991 ... 204585 200170 \n", | |
| "EAST MIDLANDS 45771.66321 45154.71999 ... 245575 242862 \n", | |
| "WEST MIDLANDS 49319.69715 49597.93617 ... 252002 242888 \n", | |
| "EAST OF ENGLAND 56259.28635 55240.28898 ... 353744 348782 \n", | |
| "LONDON 74940.80872 74302.0803 ... 525386 515488 \n", | |
| "SOUTH EAST 64352.47119 64125.80591 ... 388105 386287 \n", | |
| "SOUTH WEST 54243.98694 54264.6064 ... 323640 321469 \n", | |
| "England 53309.2331 52844.6207 ... 303801 299377 \n", | |
| "\n", | |
| "Months 2023-04-01 2023-05-01 2023-06-01 2023-07-01 2023-08-01 \\\n", | |
| "City of London 899500 959254 994766 973133 901625 \n", | |
| "Barking & Dagenham 346762 347252 343807 341446 341710 \n", | |
| "Barnet 586098 570759 569322 575129 585896 \n", | |
| "Bexley 393908 387134 385075 387566 386939 \n", | |
| "Brent 528340 525984 526845 521706 546948 \n", | |
| "Bromley 496840 493675 494931 496618 499236 \n", | |
| "Camden 874842 857887 876394 846398 856281 \n", | |
| "Croydon 410502 407035 405209 407452 411798 \n", | |
| "Ealing 505709 502922 505917 506973 510934 \n", | |
| "Enfield 433548 434278 433616 438213 443720 \n", | |
| "Greenwich 421336 404307 406541 417320 434178 \n", | |
| "Hackney 616212 630802 629436 635033 616254 \n", | |
| "Hammersmith & Fulham 727806 750043 751120 757769 740185 \n", | |
| "Haringey 580683 588819 601554 610968 611140 \n", | |
| "Harrow 502708 498781 496799 502448 508636 \n", | |
| "Havering 422575 417581 417079 417876 416807 \n", | |
| "Hillingdon 443118 438446 438508 435001 440586 \n", | |
| "Hounslow 454758 443224 449212 445696 459027 \n", | |
| "Islington 697915 707117 704473 705500 708738 \n", | |
| "Kensington & Chelsea 1314584 1339940 1378251 1352366 1337421 \n", | |
| "Kingston upon Thames 545662 545773 542381 537078 541299 \n", | |
| "Lambeth 554141 550174 538674 547572 554526 \n", | |
| "Lewisham 457271 447970 450131 457222 465565 \n", | |
| "Merton 566337 562190 565311 570844 574966 \n", | |
| "Newham 400487 403980 411638 407848 408593 \n", | |
| "Redbridge 463588 452810 444665 449639 473665 \n", | |
| "Richmond upon Thames 744017 743047 749565 754761 756006 \n", | |
| "Southwark 518032 522408 530033 530318 531347 \n", | |
| "Sutton 431964 428882 425987 426814 426909 \n", | |
| "Tower Hamlets 459696 450126 444210 437273 442950 \n", | |
| "Waltham Forest 504687 497425 498259 493504 501593 \n", | |
| "Wandsworth 612172 613901 613681 616438 619973 \n", | |
| "Westminster 993484 997457 1026639 1009532 972906 \n", | |
| "Inner London 605626 607015 610122 609902 609562 \n", | |
| "Outer London 476480 470900 470641 472409 478840 \n", | |
| "NORTH EAST 154893 154162 157606 158508 164078 \n", | |
| "NORTH WEST 207762 209517 211627 213178 214925 \n", | |
| "YORKS & THE HUMBER 202232 200632 206339 209027 212427 \n", | |
| "EAST MIDLANDS 243103 242381 247237 247247 250772 \n", | |
| "WEST MIDLANDS 244444 246484 248306 250666 253279 \n", | |
| "EAST OF ENGLAND 343952 343016 346381 349368 351276 \n", | |
| "LONDON 519701 515576 518073 523011 528140 \n", | |
| "SOUTH EAST 385529 384311 386117 389154 391114 \n", | |
| "SOUTH WEST 319511 319494 320714 323088 327551 \n", | |
| "England 299628 299291 302561 304943 308325 \n", | |
| "\n", | |
| "Months 2023-09-01 2023-10-01 2023-11-01 \n", | |
| "City of London 875029 831708 727023 \n", | |
| "Barking & Dagenham 339983 341370 353585 \n", | |
| "Barnet 586665 579777 581265 \n", | |
| "Bexley 388778 385477 381785 \n", | |
| "Brent 550526 552639 535808 \n", | |
| "Bromley 502192 505164 500485 \n", | |
| "Camden 832767 839617 830349 \n", | |
| "Croydon 417526 408220 395865 \n", | |
| "Ealing 511126 505769 482601 \n", | |
| "Enfield 451634 452392 455363 \n", | |
| "Greenwich 428789 424932 414055 \n", | |
| "Hackney 609909 615288 617169 \n", | |
| "Hammersmith & Fulham 739759 713999 709075 \n", | |
| "Haringey 612523 595423 599888 \n", | |
| "Harrow 511645 517757 502314 \n", | |
| "Havering 421107 423298 426530 \n", | |
| "Hillingdon 442656 447717 459552 \n", | |
| "Hounslow 471909 467980 468360 \n", | |
| "Islington 719651 720439 703413 \n", | |
| "Kensington & Chelsea 1272242 1269883 1127021 \n", | |
| "Kingston upon Thames 543989 552659 542817 \n", | |
| "Lambeth 575578 563661 548093 \n", | |
| "Lewisham 477529 469141 458033 \n", | |
| "Merton 576474 570383 569635 \n", | |
| "Newham 420945 420281 425493 \n", | |
| "Redbridge 492627 495002 503086 \n", | |
| "Richmond upon Thames 760831 780584 778347 \n", | |
| "Southwark 545022 537011 535001 \n", | |
| "Sutton 434295 434706 423148 \n", | |
| "Tower Hamlets 447515 447209 438709 \n", | |
| "Waltham Forest 506034 513334 502114 \n", | |
| "Wandsworth 639699 631396 625786 \n", | |
| "Westminster 929560 934961 870963 \n", | |
| "Inner London 614822 608651 595172 \n", | |
| "Outer London 482818 482883 478294 \n", | |
| "NORTH EAST 160997 160136 159871 \n", | |
| "NORTH WEST 215632 215276 213333 \n", | |
| "YORKS & THE HUMBER 208233 209545 209526 \n", | |
| "EAST MIDLANDS 249003 245580 243577 \n", | |
| "WEST MIDLANDS 253848 251361 243655 \n", | |
| "EAST OF ENGLAND 352286 347320 346659 \n", | |
| "LONDON 531306 516852 505283 \n", | |
| "SOUTH EAST 391341 387374 385844 \n", | |
| "SOUTH WEST 326490 327993 319221 \n", | |
| "England 307635 305148 301613 \n", | |
| "\n", | |
| "[45 rows x 347 columns]" | |
| ], | |
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| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th>Months</th>\n", | |
| " <th>1995-01-01</th>\n", | |
| " <th>1995-02-01</th>\n", | |
| " <th>1995-03-01</th>\n", | |
| " <th>1995-04-01</th>\n", | |
| " <th>1995-05-01</th>\n", | |
| " <th>1995-06-01</th>\n", | |
| " <th>1995-07-01</th>\n", | |
| " <th>1995-08-01</th>\n", | |
| " <th>1995-09-01</th>\n", | |
| " <th>1995-10-01</th>\n", | |
| " <th>...</th>\n", | |
| " <th>2023-02-01</th>\n", | |
| " <th>2023-03-01</th>\n", | |
| " <th>2023-04-01</th>\n", | |
| " <th>2023-05-01</th>\n", | |
| " <th>2023-06-01</th>\n", | |
| " <th>2023-07-01</th>\n", | |
| " <th>2023-08-01</th>\n", | |
| " <th>2023-09-01</th>\n", | |
| " <th>2023-10-01</th>\n", | |
| " <th>2023-11-01</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>City of London</th>\n", | |
| " <td>91448.98487</td>\n", | |
| " <td>82202.77314</td>\n", | |
| " <td>79120.70256</td>\n", | |
| " <td>77101.20804</td>\n", | |
| " <td>84409.14932</td>\n", | |
| " <td>94900.51244</td>\n", | |
| " <td>110128.0423</td>\n", | |
| " <td>112329.4376</td>\n", | |
| " <td>104473.1096</td>\n", | |
| " <td>108038.1181</td>\n", | |
| " <td>...</td>\n", | |
| " <td>863617</td>\n", | |
| " <td>925703</td>\n", | |
| " <td>899500</td>\n", | |
| " <td>959254</td>\n", | |
| " <td>994766</td>\n", | |
| " <td>973133</td>\n", | |
| " <td>901625</td>\n", | |
| " <td>875029</td>\n", | |
| " <td>831708</td>\n", | |
| " <td>727023</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Barking & Dagenham</th>\n", | |
| " <td>50460.2266</td>\n", | |
| " <td>51085.77983</td>\n", | |
| " <td>51268.96956</td>\n", | |
| " <td>53133.50526</td>\n", | |
| " <td>53042.24852</td>\n", | |
| " <td>53700.34831</td>\n", | |
| " <td>52113.12157</td>\n", | |
| " <td>52232.19868</td>\n", | |
| " <td>51471.61353</td>\n", | |
| " <td>51513.7576</td>\n", | |
| " <td>...</td>\n", | |
| " <td>345817</td>\n", | |
| " <td>344085</td>\n", | |
| " <td>346762</td>\n", | |
| " <td>347252</td>\n", | |
| " <td>343807</td>\n", | |
| " <td>341446</td>\n", | |
| " <td>341710</td>\n", | |
| " <td>339983</td>\n", | |
| " <td>341370</td>\n", | |
| " <td>353585</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Barnet</th>\n", | |
| " <td>93284.51832</td>\n", | |
| " <td>93190.16963</td>\n", | |
| " <td>92247.52435</td>\n", | |
| " <td>90762.87492</td>\n", | |
| " <td>90258.00033</td>\n", | |
| " <td>90107.23471</td>\n", | |
| " <td>91441.24768</td>\n", | |
| " <td>92361.31512</td>\n", | |
| " <td>93273.12245</td>\n", | |
| " <td>92567.38498</td>\n", | |
| " <td>...</td>\n", | |
| " <td>602916</td>\n", | |
| " <td>596553</td>\n", | |
| " <td>586098</td>\n", | |
| " <td>570759</td>\n", | |
| " <td>569322</td>\n", | |
| " <td>575129</td>\n", | |
| " <td>585896</td>\n", | |
| " <td>586665</td>\n", | |
| " <td>579777</td>\n", | |
| " <td>581265</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Bexley</th>\n", | |
| " <td>64958.09036</td>\n", | |
| " <td>64787.92069</td>\n", | |
| " <td>64367.49344</td>\n", | |
| " <td>64277.66881</td>\n", | |
| " <td>63997.13588</td>\n", | |
| " <td>64252.32335</td>\n", | |
| " <td>63722.70055</td>\n", | |
| " <td>64432.60005</td>\n", | |
| " <td>64509.54767</td>\n", | |
| " <td>64529.93725</td>\n", | |
| " <td>...</td>\n", | |
| " <td>400260</td>\n", | |
| " <td>395674</td>\n", | |
| " <td>393908</td>\n", | |
| " <td>387134</td>\n", | |
| " <td>385075</td>\n", | |
| " <td>387566</td>\n", | |
| " <td>386939</td>\n", | |
| " <td>388778</td>\n", | |
| " <td>385477</td>\n", | |
| " <td>381785</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Brent</th>\n", | |
| " <td>71306.56698</td>\n", | |
| " <td>72022.26197</td>\n", | |
| " <td>72015.76274</td>\n", | |
| " <td>72965.63094</td>\n", | |
| " <td>73704.04743</td>\n", | |
| " <td>74310.48167</td>\n", | |
| " <td>74127.03788</td>\n", | |
| " <td>73547.0411</td>\n", | |
| " <td>73789.54287</td>\n", | |
| " <td>73264.05385</td>\n", | |
| " <td>...</td>\n", | |
| " <td>533533</td>\n", | |
| " <td>527468</td>\n", | |
| " <td>528340</td>\n", | |
| " <td>525984</td>\n", | |
| " <td>526845</td>\n", | |
| " <td>521706</td>\n", | |
| " <td>546948</td>\n", | |
| " <td>550526</td>\n", | |
| " <td>552639</td>\n", | |
| " <td>535808</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Bromley</th>\n", | |
| " <td>81671.47692</td>\n", | |
| " <td>81657.55944</td>\n", | |
| " <td>81449.31143</td>\n", | |
| " <td>81124.41227</td>\n", | |
| " <td>81542.61561</td>\n", | |
| " <td>82382.83435</td>\n", | |
| " <td>82898.52264</td>\n", | |
| " <td>82054.37156</td>\n", | |
| " <td>81440.43008</td>\n", | |
| " <td>81862.16087</td>\n", | |
| " <td>...</td>\n", | |
| " <td>501979</td>\n", | |
| " <td>495776</td>\n", | |
| " <td>496840</td>\n", | |
| " <td>493675</td>\n", | |
| " <td>494931</td>\n", | |
| " <td>496618</td>\n", | |
| " <td>499236</td>\n", | |
| " <td>502192</td>\n", | |
| " <td>505164</td>\n", | |
| " <td>500485</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Camden</th>\n", | |
| " <td>120932.8881</td>\n", | |
| " <td>119508.8622</td>\n", | |
| " <td>120282.2131</td>\n", | |
| " <td>120097.899</td>\n", | |
| " <td>119929.2782</td>\n", | |
| " <td>121887.4625</td>\n", | |
| " <td>124027.5768</td>\n", | |
| " <td>125529.8039</td>\n", | |
| " <td>120596.8511</td>\n", | |
| " <td>117458.4922</td>\n", | |
| " <td>...</td>\n", | |
| " <td>869261</td>\n", | |
| " <td>855575</td>\n", | |
| " <td>874842</td>\n", | |
| " <td>857887</td>\n", | |
| " <td>876394</td>\n", | |
| " <td>846398</td>\n", | |
| " <td>856281</td>\n", | |
| " <td>832767</td>\n", | |
| " <td>839617</td>\n", | |
| " <td>830349</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Croydon</th>\n", | |
| " <td>69158.16225</td>\n", | |
| " <td>68951.09542</td>\n", | |
| " <td>68712.44341</td>\n", | |
| " <td>68610.04641</td>\n", | |
| " <td>68844.9169</td>\n", | |
| " <td>69052.51103</td>\n", | |
| " <td>69142.48112</td>\n", | |
| " <td>68993.42545</td>\n", | |
| " <td>69393.50023</td>\n", | |
| " <td>68934.61234</td>\n", | |
| " <td>...</td>\n", | |
| " <td>428153</td>\n", | |
| " <td>419307</td>\n", | |
| " <td>410502</td>\n", | |
| " <td>407035</td>\n", | |
| " <td>405209</td>\n", | |
| " <td>407452</td>\n", | |
| " <td>411798</td>\n", | |
| " <td>417526</td>\n", | |
| " <td>408220</td>\n", | |
| " <td>395865</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Ealing</th>\n", | |
| " <td>79885.89069</td>\n", | |
| " <td>80897.06551</td>\n", | |
| " <td>81379.86288</td>\n", | |
| " <td>82188.90498</td>\n", | |
| " <td>82077.05525</td>\n", | |
| " <td>81630.66181</td>\n", | |
| " <td>82352.2226</td>\n", | |
| " <td>82706.65927</td>\n", | |
| " <td>82011.08271</td>\n", | |
| " <td>80522.66054</td>\n", | |
| " <td>...</td>\n", | |
| " <td>526189</td>\n", | |
| " <td>510453</td>\n", | |
| " <td>505709</td>\n", | |
| " <td>502922</td>\n", | |
| " <td>505917</td>\n", | |
| " <td>506973</td>\n", | |
| " <td>510934</td>\n", | |
| " <td>511126</td>\n", | |
| " <td>505769</td>\n", | |
| " <td>482601</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Enfield</th>\n", | |
| " <td>72514.69096</td>\n", | |
| " <td>73155.19746</td>\n", | |
| " <td>72190.44144</td>\n", | |
| " <td>71442.92235</td>\n", | |
| " <td>70630.77955</td>\n", | |
| " <td>71348.31147</td>\n", | |
| " <td>71837.54011</td>\n", | |
| " <td>72237.94562</td>\n", | |
| " <td>71725.22104</td>\n", | |
| " <td>72349.13744</td>\n", | |
| " <td>...</td>\n", | |
| " <td>442552</td>\n", | |
| " <td>435243</td>\n", | |
| " <td>433548</td>\n", | |
| " <td>434278</td>\n", | |
| " <td>433616</td>\n", | |
| " <td>438213</td>\n", | |
| " <td>443720</td>\n", | |
| " <td>451634</td>\n", | |
| " <td>452392</td>\n", | |
| " <td>455363</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Greenwich</th>\n", | |
| " <td>62300.10169</td>\n", | |
| " <td>60993.26863</td>\n", | |
| " <td>61377.83464</td>\n", | |
| " <td>61927.7246</td>\n", | |
| " <td>63512.99103</td>\n", | |
| " <td>64751.56404</td>\n", | |
| " <td>65486.34112</td>\n", | |
| " <td>65076.43195</td>\n", | |
| " <td>63996.81525</td>\n", | |
| " <td>63405.26599</td>\n", | |
| " <td>...</td>\n", | |
| " <td>440327</td>\n", | |
| " <td>435979</td>\n", | |
| " <td>421336</td>\n", | |
| " <td>404307</td>\n", | |
| " <td>406541</td>\n", | |
| " <td>417320</td>\n", | |
| " <td>434178</td>\n", | |
| " <td>428789</td>\n", | |
| " <td>424932</td>\n", | |
| " <td>414055</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Hackney</th>\n", | |
| " <td>61296.52637</td>\n", | |
| " <td>63187.08332</td>\n", | |
| " <td>63593.29935</td>\n", | |
| " <td>65139.64403</td>\n", | |
| " <td>66193.99212</td>\n", | |
| " <td>66921.17101</td>\n", | |
| " <td>68390.753</td>\n", | |
| " <td>68096.79385</td>\n", | |
| " <td>68752.50284</td>\n", | |
| " <td>67619.65052</td>\n", | |
| " <td>...</td>\n", | |
| " <td>608855</td>\n", | |
| " <td>623033</td>\n", | |
| " <td>616212</td>\n", | |
| " <td>630802</td>\n", | |
| " <td>629436</td>\n", | |
| " <td>635033</td>\n", | |
| " <td>616254</td>\n", | |
| " <td>609909</td>\n", | |
| " <td>615288</td>\n", | |
| " <td>617169</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Hammersmith & Fulham</th>\n", | |
| " <td>124902.8602</td>\n", | |
| " <td>122087.718</td>\n", | |
| " <td>120635.9467</td>\n", | |
| " <td>121424.6241</td>\n", | |
| " <td>124433.539</td>\n", | |
| " <td>126175.1513</td>\n", | |
| " <td>124381.5134</td>\n", | |
| " <td>123625.3196</td>\n", | |
| " <td>123094.0484</td>\n", | |
| " <td>123442.56</td>\n", | |
| " <td>...</td>\n", | |
| " <td>750259</td>\n", | |
| " <td>752937</td>\n", | |
| " <td>727806</td>\n", | |
| " <td>750043</td>\n", | |
| " <td>751120</td>\n", | |
| " <td>757769</td>\n", | |
| " <td>740185</td>\n", | |
| " <td>739759</td>\n", | |
| " <td>713999</td>\n", | |
| " <td>709075</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Haringey</th>\n", | |
| " <td>76287.56947</td>\n", | |
| " <td>78901.21036</td>\n", | |
| " <td>78521.94855</td>\n", | |
| " <td>79545.57477</td>\n", | |
| " <td>79374.0349</td>\n", | |
| " <td>79956.3621</td>\n", | |
| " <td>80746.34881</td>\n", | |
| " <td>81217.69074</td>\n", | |
| " <td>82142.89052</td>\n", | |
| " <td>81791.48237</td>\n", | |
| " <td>...</td>\n", | |
| " <td>598216</td>\n", | |
| " <td>593629</td>\n", | |
| " <td>580683</td>\n", | |
| " <td>588819</td>\n", | |
| " <td>601554</td>\n", | |
| " <td>610968</td>\n", | |
| " <td>611140</td>\n", | |
| " <td>612523</td>\n", | |
| " <td>595423</td>\n", | |
| " <td>599888</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Harrow</th>\n", | |
| " <td>84769.52599</td>\n", | |
| " <td>83396.10525</td>\n", | |
| " <td>83416.23759</td>\n", | |
| " <td>83567.88439</td>\n", | |
| " <td>83853.65615</td>\n", | |
| " <td>84173.24689</td>\n", | |
| " <td>84226.69844</td>\n", | |
| " <td>84430.61796</td>\n", | |
| " <td>83606.97863</td>\n", | |
| " <td>82492.72882</td>\n", | |
| " <td>...</td>\n", | |
| " <td>520809</td>\n", | |
| " <td>513799</td>\n", | |
| " <td>502708</td>\n", | |
| " <td>498781</td>\n", | |
| " <td>496799</td>\n", | |
| " <td>502448</td>\n", | |
| " <td>508636</td>\n", | |
| " <td>511645</td>\n", | |
| " <td>517757</td>\n", | |
| " <td>502314</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Havering</th>\n", | |
| " <td>68000.13774</td>\n", | |
| " <td>69393.51294</td>\n", | |
| " <td>69368.02407</td>\n", | |
| " <td>69444.26215</td>\n", | |
| " <td>68534.52248</td>\n", | |
| " <td>68464.60664</td>\n", | |
| " <td>68680.83996</td>\n", | |
| " <td>69023.36482</td>\n", | |
| " <td>68108.186</td>\n", | |
| " <td>67425.63891</td>\n", | |
| " <td>...</td>\n", | |
| " <td>431159</td>\n", | |
| " <td>424284</td>\n", | |
| " <td>422575</td>\n", | |
| " <td>417581</td>\n", | |
| " <td>417079</td>\n", | |
| " <td>417876</td>\n", | |
| " <td>416807</td>\n", | |
| " <td>421107</td>\n", | |
| " <td>423298</td>\n", | |
| " <td>426530</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Hillingdon</th>\n", | |
| " <td>73834.82964</td>\n", | |
| " <td>75031.0696</td>\n", | |
| " <td>74188.66949</td>\n", | |
| " <td>73911.40591</td>\n", | |
| " <td>73117.12416</td>\n", | |
| " <td>74005.00585</td>\n", | |
| " <td>74671.13263</td>\n", | |
| " <td>74967.86534</td>\n", | |
| " <td>73843.55239</td>\n", | |
| " <td>72992.50159</td>\n", | |
| " <td>...</td>\n", | |
| " <td>457002</td>\n", | |
| " <td>448807</td>\n", | |
| " <td>443118</td>\n", | |
| " <td>438446</td>\n", | |
| " <td>438508</td>\n", | |
| " <td>435001</td>\n", | |
| " <td>440586</td>\n", | |
| " <td>442656</td>\n", | |
| " <td>447717</td>\n", | |
| " <td>459552</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Hounslow</th>\n", | |
| " <td>72231.70537</td>\n", | |
| " <td>71051.55852</td>\n", | |
| " <td>72097.99411</td>\n", | |
| " <td>71890.28339</td>\n", | |
| " <td>72877.47219</td>\n", | |
| " <td>72331.08116</td>\n", | |
| " <td>73717.78844</td>\n", | |
| " <td>74479.94802</td>\n", | |
| " <td>74426.6609</td>\n", | |
| " <td>73643.14027</td>\n", | |
| " <td>...</td>\n", | |
| " <td>462105</td>\n", | |
| " <td>459303</td>\n", | |
| " <td>454758</td>\n", | |
| " <td>443224</td>\n", | |
| " <td>449212</td>\n", | |
| " <td>445696</td>\n", | |
| " <td>459027</td>\n", | |
| " <td>471909</td>\n", | |
| " <td>467980</td>\n", | |
| " <td>468360</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Islington</th>\n", | |
| " <td>92516.48557</td>\n", | |
| " <td>94342.37334</td>\n", | |
| " <td>93465.86407</td>\n", | |
| " <td>93344.49305</td>\n", | |
| " <td>94346.39917</td>\n", | |
| " <td>97428.94311</td>\n", | |
| " <td>98976.14077</td>\n", | |
| " <td>98951.20791</td>\n", | |
| " <td>99582.63778</td>\n", | |
| " <td>97580.38673</td>\n", | |
| " <td>...</td>\n", | |
| " <td>684858</td>\n", | |
| " <td>686044</td>\n", | |
| " <td>697915</td>\n", | |
| " <td>707117</td>\n", | |
| " <td>704473</td>\n", | |
| " <td>705500</td>\n", | |
| " <td>708738</td>\n", | |
| " <td>719651</td>\n", | |
| " <td>720439</td>\n", | |
| " <td>703413</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Kensington & Chelsea</th>\n", | |
| " <td>182694.8326</td>\n", | |
| " <td>182345.2463</td>\n", | |
| " <td>182878.8231</td>\n", | |
| " <td>184176.9168</td>\n", | |
| " <td>191474.1141</td>\n", | |
| " <td>197265.7602</td>\n", | |
| " <td>197963.3169</td>\n", | |
| " <td>198037.4218</td>\n", | |
| " <td>197047.8333</td>\n", | |
| " <td>200005.8308</td>\n", | |
| " <td>...</td>\n", | |
| " <td>1332575</td>\n", | |
| " <td>1357089</td>\n", | |
| " <td>1314584</td>\n", | |
| " <td>1339940</td>\n", | |
| " <td>1378251</td>\n", | |
| " <td>1352366</td>\n", | |
| " <td>1337421</td>\n", | |
| " <td>1272242</td>\n", | |
| " <td>1269883</td>\n", | |
| " <td>1127021</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Kingston upon Thames</th>\n", | |
| " <td>80875.84843</td>\n", | |
| " <td>81230.13524</td>\n", | |
| " <td>81111.48848</td>\n", | |
| " <td>81672.80476</td>\n", | |
| " <td>82123.51084</td>\n", | |
| " <td>82205.66822</td>\n", | |
| " <td>82525.793</td>\n", | |
| " <td>83342.84552</td>\n", | |
| " <td>85110.96835</td>\n", | |
| " <td>85726.97473</td>\n", | |
| " <td>...</td>\n", | |
| " <td>548904</td>\n", | |
| " <td>551255</td>\n", | |
| " <td>545662</td>\n", | |
| " <td>545773</td>\n", | |
| " <td>542381</td>\n", | |
| " <td>537078</td>\n", | |
| " <td>541299</td>\n", | |
| " <td>543989</td>\n", | |
| " <td>552659</td>\n", | |
| " <td>542817</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Lambeth</th>\n", | |
| " <td>67770.98843</td>\n", | |
| " <td>65381.51908</td>\n", | |
| " <td>66336.51868</td>\n", | |
| " <td>66388.7716</td>\n", | |
| " <td>69035.11076</td>\n", | |
| " <td>68881.15764</td>\n", | |
| " <td>69608.72242</td>\n", | |
| " <td>68840.02827</td>\n", | |
| " <td>70155.81997</td>\n", | |
| " <td>69616.18689</td>\n", | |
| " <td>...</td>\n", | |
| " <td>550359</td>\n", | |
| " <td>554644</td>\n", | |
| " <td>554141</td>\n", | |
| " <td>550174</td>\n", | |
| " <td>538674</td>\n", | |
| " <td>547572</td>\n", | |
| " <td>554526</td>\n", | |
| " <td>575578</td>\n", | |
| " <td>563661</td>\n", | |
| " <td>548093</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Lewisham</th>\n", | |
| " <td>60491.26109</td>\n", | |
| " <td>60869.27091</td>\n", | |
| " <td>60288.03002</td>\n", | |
| " <td>59471.03136</td>\n", | |
| " <td>58551.38387</td>\n", | |
| " <td>58041.43543</td>\n", | |
| " <td>58126.37811</td>\n", | |
| " <td>58151.3154</td>\n", | |
| " <td>58742.99034</td>\n", | |
| " <td>58073.14016</td>\n", | |
| " <td>...</td>\n", | |
| " <td>461775</td>\n", | |
| " <td>453865</td>\n", | |
| " <td>457271</td>\n", | |
| " <td>447970</td>\n", | |
| " <td>450131</td>\n", | |
| " <td>457222</td>\n", | |
| " <td>465565</td>\n", | |
| " <td>477529</td>\n", | |
| " <td>469141</td>\n", | |
| " <td>458033</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Merton</th>\n", | |
| " <td>82070.6133</td>\n", | |
| " <td>79982.74872</td>\n", | |
| " <td>80661.68279</td>\n", | |
| " <td>79990.54333</td>\n", | |
| " <td>80873.98643</td>\n", | |
| " <td>80704.92667</td>\n", | |
| " <td>81055.90335</td>\n", | |
| " <td>80781.09186</td>\n", | |
| " <td>80824.70075</td>\n", | |
| " <td>79569.22895</td>\n", | |
| " <td>...</td>\n", | |
| " <td>567147</td>\n", | |
| " <td>564548</td>\n", | |
| " <td>566337</td>\n", | |
| " <td>562190</td>\n", | |
| " <td>565311</td>\n", | |
| " <td>570844</td>\n", | |
| " <td>574966</td>\n", | |
| " <td>576474</td>\n", | |
| " <td>570383</td>\n", | |
| " <td>569635</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Newham</th>\n", | |
| " <td>53539.31919</td>\n", | |
| " <td>53153.88306</td>\n", | |
| " <td>53458.26393</td>\n", | |
| " <td>54479.75395</td>\n", | |
| " <td>55803.95958</td>\n", | |
| " <td>56067.76986</td>\n", | |
| " <td>55458.31693</td>\n", | |
| " <td>54709.35467</td>\n", | |
| " <td>54585.50364</td>\n", | |
| " <td>54789.55504</td>\n", | |
| " <td>...</td>\n", | |
| " <td>410915</td>\n", | |
| " <td>399979</td>\n", | |
| " <td>400487</td>\n", | |
| " <td>403980</td>\n", | |
| " <td>411638</td>\n", | |
| " <td>407848</td>\n", | |
| " <td>408593</td>\n", | |
| " <td>420945</td>\n", | |
| " <td>420281</td>\n", | |
| " <td>425493</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Redbridge</th>\n", | |
| " <td>72189.58437</td>\n", | |
| " <td>72141.6261</td>\n", | |
| " <td>72501.35502</td>\n", | |
| " <td>72228.60295</td>\n", | |
| " <td>72366.64122</td>\n", | |
| " <td>72279.4325</td>\n", | |
| " <td>72880.83974</td>\n", | |
| " <td>73275.16891</td>\n", | |
| " <td>73204.02373</td>\n", | |
| " <td>72917.47147</td>\n", | |
| " <td>...</td>\n", | |
| " <td>475465</td>\n", | |
| " <td>470293</td>\n", | |
| " <td>463588</td>\n", | |
| " <td>452810</td>\n", | |
| " <td>444665</td>\n", | |
| " <td>449639</td>\n", | |
| " <td>473665</td>\n", | |
| " <td>492627</td>\n", | |
| " <td>495002</td>\n", | |
| " <td>503086</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Richmond upon Thames</th>\n", | |
| " <td>109326.1245</td>\n", | |
| " <td>111103.0394</td>\n", | |
| " <td>107325.4742</td>\n", | |
| " <td>106875</td>\n", | |
| " <td>107707.6799</td>\n", | |
| " <td>112865.0542</td>\n", | |
| " <td>114656.6011</td>\n", | |
| " <td>112320.4096</td>\n", | |
| " <td>110424.5703</td>\n", | |
| " <td>108406.7006</td>\n", | |
| " <td>...</td>\n", | |
| " <td>754074</td>\n", | |
| " <td>748980</td>\n", | |
| " <td>744017</td>\n", | |
| " <td>743047</td>\n", | |
| " <td>749565</td>\n", | |
| " <td>754761</td>\n", | |
| " <td>756006</td>\n", | |
| " <td>760831</td>\n", | |
| " <td>780584</td>\n", | |
| " <td>778347</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Southwark</th>\n", | |
| " <td>67885.20344</td>\n", | |
| " <td>64799.0648</td>\n", | |
| " <td>65763.29719</td>\n", | |
| " <td>63073.62117</td>\n", | |
| " <td>64420.49933</td>\n", | |
| " <td>64155.81449</td>\n", | |
| " <td>67024.74767</td>\n", | |
| " <td>65525.94434</td>\n", | |
| " <td>63467.00948</td>\n", | |
| " <td>60885.98483</td>\n", | |
| " <td>...</td>\n", | |
| " <td>541136</td>\n", | |
| " <td>528426</td>\n", | |
| " <td>518032</td>\n", | |
| " <td>522408</td>\n", | |
| " <td>530033</td>\n", | |
| " <td>530318</td>\n", | |
| " <td>531347</td>\n", | |
| " <td>545022</td>\n", | |
| " <td>537011</td>\n", | |
| " <td>535001</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Sutton</th>\n", | |
| " <td>71536.97357</td>\n", | |
| " <td>70893.20851</td>\n", | |
| " <td>70306.83844</td>\n", | |
| " <td>69411.9439</td>\n", | |
| " <td>69759.21989</td>\n", | |
| " <td>70125.24728</td>\n", | |
| " <td>70789.57284</td>\n", | |
| " <td>69958.41918</td>\n", | |
| " <td>69937.40214</td>\n", | |
| " <td>69963.76558</td>\n", | |
| " <td>...</td>\n", | |
| " <td>438157</td>\n", | |
| " <td>436834</td>\n", | |
| " <td>431964</td>\n", | |
| " <td>428882</td>\n", | |
| " <td>425987</td>\n", | |
| " <td>426814</td>\n", | |
| " <td>426909</td>\n", | |
| " <td>434295</td>\n", | |
| " <td>434706</td>\n", | |
| " <td>423148</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Tower Hamlets</th>\n", | |
| " <td>59865.18995</td>\n", | |
| " <td>62318.53353</td>\n", | |
| " <td>63938.67686</td>\n", | |
| " <td>66233.19383</td>\n", | |
| " <td>66432.85846</td>\n", | |
| " <td>66232.16372</td>\n", | |
| " <td>64692.22672</td>\n", | |
| " <td>63472.27558</td>\n", | |
| " <td>62177.59435</td>\n", | |
| " <td>62066.45135</td>\n", | |
| " <td>...</td>\n", | |
| " <td>480416</td>\n", | |
| " <td>469344</td>\n", | |
| " <td>459696</td>\n", | |
| " <td>450126</td>\n", | |
| " <td>444210</td>\n", | |
| " <td>437273</td>\n", | |
| " <td>442950</td>\n", | |
| " <td>447515</td>\n", | |
| " <td>447209</td>\n", | |
| " <td>438709</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Waltham Forest</th>\n", | |
| " <td>61319.44913</td>\n", | |
| " <td>60252.12246</td>\n", | |
| " <td>60871.08493</td>\n", | |
| " <td>60971.39722</td>\n", | |
| " <td>61494.16938</td>\n", | |
| " <td>61547.79643</td>\n", | |
| " <td>61933.52738</td>\n", | |
| " <td>61916.4222</td>\n", | |
| " <td>61548.15206</td>\n", | |
| " <td>60572.57036</td>\n", | |
| " <td>...</td>\n", | |
| " <td>510747</td>\n", | |
| " <td>503499</td>\n", | |
| " <td>504687</td>\n", | |
| " <td>497425</td>\n", | |
| " <td>498259</td>\n", | |
| " <td>493504</td>\n", | |
| " <td>501593</td>\n", | |
| " <td>506034</td>\n", | |
| " <td>513334</td>\n", | |
| " <td>502114</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Wandsworth</th>\n", | |
| " <td>88559.04381</td>\n", | |
| " <td>88641.01678</td>\n", | |
| " <td>87124.81523</td>\n", | |
| " <td>87026.00225</td>\n", | |
| " <td>86518.05945</td>\n", | |
| " <td>88114.3351</td>\n", | |
| " <td>89830.58934</td>\n", | |
| " <td>90560.68078</td>\n", | |
| " <td>91373.6984</td>\n", | |
| " <td>90246.40944</td>\n", | |
| " <td>...</td>\n", | |
| " <td>630342</td>\n", | |
| " <td>622194</td>\n", | |
| " <td>612172</td>\n", | |
| " <td>613901</td>\n", | |
| " <td>613681</td>\n", | |
| " <td>616438</td>\n", | |
| " <td>619973</td>\n", | |
| " <td>639699</td>\n", | |
| " <td>631396</td>\n", | |
| " <td>625786</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Westminster</th>\n", | |
| " <td>133025.2772</td>\n", | |
| " <td>131468.3096</td>\n", | |
| " <td>132260.3417</td>\n", | |
| " <td>133370.2036</td>\n", | |
| " <td>133911.1117</td>\n", | |
| " <td>134562.1941</td>\n", | |
| " <td>133450.2162</td>\n", | |
| " <td>136581.5082</td>\n", | |
| " <td>135993.0705</td>\n", | |
| " <td>134513.3863</td>\n", | |
| " <td>...</td>\n", | |
| " <td>1088694</td>\n", | |
| " <td>1045019</td>\n", | |
| " <td>993484</td>\n", | |
| " <td>997457</td>\n", | |
| " <td>1026639</td>\n", | |
| " <td>1009532</td>\n", | |
| " <td>972906</td>\n", | |
| " <td>929560</td>\n", | |
| " <td>934961</td>\n", | |
| " <td>870963</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Inner London</th>\n", | |
| " <td>78251.9765</td>\n", | |
| " <td>75885.70201</td>\n", | |
| " <td>76591.59947</td>\n", | |
| " <td>76851.56697</td>\n", | |
| " <td>79129.19443</td>\n", | |
| " <td>79969.1525</td>\n", | |
| " <td>80550.47935</td>\n", | |
| " <td>80597.64563</td>\n", | |
| " <td>80402.85</td>\n", | |
| " <td>79681.52637</td>\n", | |
| " <td>...</td>\n", | |
| " <td>619319</td>\n", | |
| " <td>613085</td>\n", | |
| " <td>605626</td>\n", | |
| " <td>607015</td>\n", | |
| " <td>610122</td>\n", | |
| " <td>609902</td>\n", | |
| " <td>609562</td>\n", | |
| " <td>614822</td>\n", | |
| " <td>608651</td>\n", | |
| " <td>595172</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Outer London</th>\n", | |
| " <td>72958.79836</td>\n", | |
| " <td>72937.88262</td>\n", | |
| " <td>72714.53478</td>\n", | |
| " <td>72591.92469</td>\n", | |
| " <td>72752.99414</td>\n", | |
| " <td>73189.39978</td>\n", | |
| " <td>73665.90517</td>\n", | |
| " <td>73691.12888</td>\n", | |
| " <td>73454.44292</td>\n", | |
| " <td>72971.6484</td>\n", | |
| " <td>...</td>\n", | |
| " <td>486874</td>\n", | |
| " <td>480879</td>\n", | |
| " <td>476480</td>\n", | |
| " <td>470900</td>\n", | |
| " <td>470641</td>\n", | |
| " <td>472409</td>\n", | |
| " <td>478840</td>\n", | |
| " <td>482818</td>\n", | |
| " <td>482883</td>\n", | |
| " <td>478294</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>NORTH EAST</th>\n", | |
| " <td>42076.35411</td>\n", | |
| " <td>42571.98949</td>\n", | |
| " <td>42369.72984</td>\n", | |
| " <td>42095.8436</td>\n", | |
| " <td>43266.45165</td>\n", | |
| " <td>42315.34372</td>\n", | |
| " <td>43287.74323</td>\n", | |
| " <td>41899.05494</td>\n", | |
| " <td>41850.85646</td>\n", | |
| " <td>41688.16474</td>\n", | |
| " <td>...</td>\n", | |
| " <td>154641</td>\n", | |
| " <td>151843</td>\n", | |
| " <td>154893</td>\n", | |
| " <td>154162</td>\n", | |
| " <td>157606</td>\n", | |
| " <td>158508</td>\n", | |
| " <td>164078</td>\n", | |
| " <td>160997</td>\n", | |
| " <td>160136</td>\n", | |
| " <td>159871</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>NORTH WEST</th>\n", | |
| " <td>43958.48001</td>\n", | |
| " <td>43925.42289</td>\n", | |
| " <td>44434.8681</td>\n", | |
| " <td>44267.7796</td>\n", | |
| " <td>44223.61973</td>\n", | |
| " <td>44112.96432</td>\n", | |
| " <td>44109.58764</td>\n", | |
| " <td>44193.66583</td>\n", | |
| " <td>44088.07696</td>\n", | |
| " <td>43109.99448</td>\n", | |
| " <td>...</td>\n", | |
| " <td>210320</td>\n", | |
| " <td>208021</td>\n", | |
| " <td>207762</td>\n", | |
| " <td>209517</td>\n", | |
| " <td>211627</td>\n", | |
| " <td>213178</td>\n", | |
| " <td>214925</td>\n", | |
| " <td>215632</td>\n", | |
| " <td>215276</td>\n", | |
| " <td>213333</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>YORKS & THE HUMBER</th>\n", | |
| " <td>44803.42878</td>\n", | |
| " <td>44528.80721</td>\n", | |
| " <td>45200.46775</td>\n", | |
| " <td>45614.34341</td>\n", | |
| " <td>44830.98563</td>\n", | |
| " <td>45392.63981</td>\n", | |
| " <td>45534.99864</td>\n", | |
| " <td>45111.45939</td>\n", | |
| " <td>44837.86023</td>\n", | |
| " <td>44309.9991</td>\n", | |
| " <td>...</td>\n", | |
| " <td>204585</td>\n", | |
| " <td>200170</td>\n", | |
| " <td>202232</td>\n", | |
| " <td>200632</td>\n", | |
| " <td>206339</td>\n", | |
| " <td>209027</td>\n", | |
| " <td>212427</td>\n", | |
| " <td>208233</td>\n", | |
| " <td>209545</td>\n", | |
| " <td>209526</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>EAST MIDLANDS</th>\n", | |
| " <td>45544.52227</td>\n", | |
| " <td>46051.57066</td>\n", | |
| " <td>45383.82395</td>\n", | |
| " <td>46124.23045</td>\n", | |
| " <td>45878.00396</td>\n", | |
| " <td>45679.99539</td>\n", | |
| " <td>46037.67312</td>\n", | |
| " <td>45922.53585</td>\n", | |
| " <td>45771.66321</td>\n", | |
| " <td>45154.71999</td>\n", | |
| " <td>...</td>\n", | |
| " <td>245575</td>\n", | |
| " <td>242862</td>\n", | |
| " <td>243103</td>\n", | |
| " <td>242381</td>\n", | |
| " <td>247237</td>\n", | |
| " <td>247247</td>\n", | |
| " <td>250772</td>\n", | |
| " <td>249003</td>\n", | |
| " <td>245580</td>\n", | |
| " <td>243577</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>WEST MIDLANDS</th>\n", | |
| " <td>48527.52339</td>\n", | |
| " <td>49341.29029</td>\n", | |
| " <td>49442.17973</td>\n", | |
| " <td>49455.93299</td>\n", | |
| " <td>50369.66188</td>\n", | |
| " <td>50100.43023</td>\n", | |
| " <td>49860.00809</td>\n", | |
| " <td>49598.45969</td>\n", | |
| " <td>49319.69715</td>\n", | |
| " <td>49597.93617</td>\n", | |
| " <td>...</td>\n", | |
| " <td>252002</td>\n", | |
| " <td>242888</td>\n", | |
| " <td>244444</td>\n", | |
| " <td>246484</td>\n", | |
| " <td>248306</td>\n", | |
| " <td>250666</td>\n", | |
| " <td>253279</td>\n", | |
| " <td>253848</td>\n", | |
| " <td>251361</td>\n", | |
| " <td>243655</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>EAST OF ENGLAND</th>\n", | |
| " <td>56701.5961</td>\n", | |
| " <td>56593.59475</td>\n", | |
| " <td>56171.18278</td>\n", | |
| " <td>56567.89582</td>\n", | |
| " <td>56479.80183</td>\n", | |
| " <td>56288.94557</td>\n", | |
| " <td>57242.30186</td>\n", | |
| " <td>56732.40547</td>\n", | |
| " <td>56259.28635</td>\n", | |
| " <td>55240.28898</td>\n", | |
| " <td>...</td>\n", | |
| " <td>353744</td>\n", | |
| " <td>348782</td>\n", | |
| " <td>343952</td>\n", | |
| " <td>343016</td>\n", | |
| " <td>346381</td>\n", | |
| " <td>349368</td>\n", | |
| " <td>351276</td>\n", | |
| " <td>352286</td>\n", | |
| " <td>347320</td>\n", | |
| " <td>346659</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>LONDON</th>\n", | |
| " <td>74435.76052</td>\n", | |
| " <td>72777.93709</td>\n", | |
| " <td>73896.84204</td>\n", | |
| " <td>74455.28754</td>\n", | |
| " <td>75432.02786</td>\n", | |
| " <td>75606.24501</td>\n", | |
| " <td>75984.24079</td>\n", | |
| " <td>75529.34488</td>\n", | |
| " <td>74940.80872</td>\n", | |
| " <td>74302.0803</td>\n", | |
| " <td>...</td>\n", | |
| " <td>525386</td>\n", | |
| " <td>515488</td>\n", | |
| " <td>519701</td>\n", | |
| " <td>515576</td>\n", | |
| " <td>518073</td>\n", | |
| " <td>523011</td>\n", | |
| " <td>528140</td>\n", | |
| " <td>531306</td>\n", | |
| " <td>516852</td>\n", | |
| " <td>505283</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>SOUTH EAST</th>\n", | |
| " <td>64018.87894</td>\n", | |
| " <td>63715.02399</td>\n", | |
| " <td>64113.60858</td>\n", | |
| " <td>64623.22395</td>\n", | |
| " <td>64530.36358</td>\n", | |
| " <td>65511.008</td>\n", | |
| " <td>65224.88465</td>\n", | |
| " <td>64851.60429</td>\n", | |
| " <td>64352.47119</td>\n", | |
| " <td>64125.80591</td>\n", | |
| " <td>...</td>\n", | |
| " <td>388105</td>\n", | |
| " <td>386287</td>\n", | |
| " <td>385529</td>\n", | |
| " <td>384311</td>\n", | |
| " <td>386117</td>\n", | |
| " <td>389154</td>\n", | |
| " <td>391114</td>\n", | |
| " <td>391341</td>\n", | |
| " <td>387374</td>\n", | |
| " <td>385844</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>SOUTH WEST</th>\n", | |
| " <td>54705.1579</td>\n", | |
| " <td>54356.14843</td>\n", | |
| " <td>53583.07667</td>\n", | |
| " <td>54786.01938</td>\n", | |
| " <td>54698.83831</td>\n", | |
| " <td>54420.15939</td>\n", | |
| " <td>54265.86368</td>\n", | |
| " <td>54365.71495</td>\n", | |
| " <td>54243.98694</td>\n", | |
| " <td>54264.6064</td>\n", | |
| " <td>...</td>\n", | |
| " <td>323640</td>\n", | |
| " <td>321469</td>\n", | |
| " <td>319511</td>\n", | |
| " <td>319494</td>\n", | |
| " <td>320714</td>\n", | |
| " <td>323088</td>\n", | |
| " <td>327551</td>\n", | |
| " <td>326490</td>\n", | |
| " <td>327993</td>\n", | |
| " <td>319221</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>England</th>\n", | |
| " <td>53202.77128</td>\n", | |
| " <td>53096.1549</td>\n", | |
| " <td>53201.2843</td>\n", | |
| " <td>53590.8548</td>\n", | |
| " <td>53678.24041</td>\n", | |
| " <td>53735.15475</td>\n", | |
| " <td>53900.60633</td>\n", | |
| " <td>53600.31975</td>\n", | |
| " <td>53309.2331</td>\n", | |
| " <td>52844.6207</td>\n", | |
| " <td>...</td>\n", | |
| " <td>303801</td>\n", | |
| " <td>299377</td>\n", | |
| " <td>299628</td>\n", | |
| " <td>299291</td>\n", | |
| " <td>302561</td>\n", | |
| " <td>304943</td>\n", | |
| " <td>308325</td>\n", | |
| " <td>307635</td>\n", | |
| " <td>305148</td>\n", | |
| " <td>301613</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "<p>45 rows × 347 columns</p>\n", | |
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| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "jy8BzXHmoWEw" | |
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| "source": [ | |
| "**2.4.Transforming the data**\n", | |
| "\n", | |
| "Remember what Wes McKinney said about tidy data?\n", | |
| "\n", | |
| "You might need to **melt** your DataFrame here." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "S2wM0qLuo2Zt", | |
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| "outputId": "09e853c0-9ea9-493d-97e6-42acb565074f" | |
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| "source": [ | |
| "# Why do we need to melt?\n", | |
| "# The DF has Boroughs for rows and each col is a month\n", | |
| "# What simplification is necessary?\n", | |
| "\n", | |
| "#melted_df = cleaned.melt(cleaned, id_vars=['Date'], var_name='E09000001', value_name='Score')\n", | |
| "\n", | |
| "cleaned.head(1)\n" | |
| ], | |
| "execution_count": 119, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "Months 1995-01-01 1995-02-01 1995-03-01 1995-04-01 \\\n", | |
| "City of London 91448.98487 82202.77314 79120.70256 77101.20804 \n", | |
| "\n", | |
| "Months 1995-05-01 1995-06-01 1995-07-01 1995-08-01 \\\n", | |
| "City of London 84409.14932 94900.51244 110128.0423 112329.4376 \n", | |
| "\n", | |
| "Months 1995-09-01 1995-10-01 ... 2023-02-01 2023-03-01 \\\n", | |
| "City of London 104473.1096 108038.1181 ... 863617 925703 \n", | |
| "\n", | |
| "Months 2023-04-01 2023-05-01 2023-06-01 2023-07-01 2023-08-01 \\\n", | |
| "City of London 899500 959254 994766 973133 901625 \n", | |
| "\n", | |
| "Months 2023-09-01 2023-10-01 2023-11-01 \n", | |
| "City of London 875029 831708 727023 \n", | |
| "\n", | |
| "[1 rows x 347 columns]" | |
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| " <thead>\n", | |
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| " <th>Months</th>\n", | |
| " <th>1995-01-01</th>\n", | |
| " <th>1995-02-01</th>\n", | |
| " <th>1995-03-01</th>\n", | |
| " <th>1995-04-01</th>\n", | |
| " <th>1995-05-01</th>\n", | |
| " <th>1995-06-01</th>\n", | |
| " <th>1995-07-01</th>\n", | |
| " <th>1995-08-01</th>\n", | |
| " <th>1995-09-01</th>\n", | |
| " <th>1995-10-01</th>\n", | |
| " <th>...</th>\n", | |
| " <th>2023-02-01</th>\n", | |
| " <th>2023-03-01</th>\n", | |
| " <th>2023-04-01</th>\n", | |
| " <th>2023-05-01</th>\n", | |
| " <th>2023-06-01</th>\n", | |
| " <th>2023-07-01</th>\n", | |
| " <th>2023-08-01</th>\n", | |
| " <th>2023-09-01</th>\n", | |
| " <th>2023-10-01</th>\n", | |
| " <th>2023-11-01</th>\n", | |
| " </tr>\n", | |
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| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>City of London</th>\n", | |
| " <td>91448.98487</td>\n", | |
| " <td>82202.77314</td>\n", | |
| " <td>79120.70256</td>\n", | |
| " <td>77101.20804</td>\n", | |
| " <td>84409.14932</td>\n", | |
| " <td>94900.51244</td>\n", | |
| " <td>110128.0423</td>\n", | |
| " <td>112329.4376</td>\n", | |
| " <td>104473.1096</td>\n", | |
| " <td>108038.1181</td>\n", | |
| " <td>...</td>\n", | |
| " <td>863617</td>\n", | |
| " <td>925703</td>\n", | |
| " <td>899500</td>\n", | |
| " <td>959254</td>\n", | |
| " <td>994766</td>\n", | |
| " <td>973133</td>\n", | |
| " <td>901625</td>\n", | |
| " <td>875029</td>\n", | |
| " <td>831708</td>\n", | |
| " <td>727023</td>\n", | |
| " </tr>\n", | |
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| "<p>1 rows × 347 columns</p>\n", | |
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| " const docLinkHtml = 'Like what you see? Visit the ' +\n", | |
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| " + ' to learn more about interactive tables.';\n", | |
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| "metadata": {}, | |
| "execution_count": 119 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "7kIsgAo7o3mf" | |
| }, | |
| "source": [ | |
| "Remember to make sure your column data types are all correct. Average prices, for example, should be floating point numbers..." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "ZcR4IHbcpOaq", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "e6aebdc1-e7f4-49df-d053-3b6df64e779d" | |
| }, | |
| "source": [ | |
| "cleaned = cleaned.astype(float)\n", | |
| "print(cleaned.info())" | |
| ], | |
| "execution_count": 120, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "<class 'pandas.core.frame.DataFrame'>\n", | |
| "Index: 45 entries, City of London to England\n", | |
| "Columns: 347 entries, 1995-01-01 to 2023-11-01\n", | |
| "dtypes: float64(347)\n", | |
| "memory usage: 123.4+ KB\n", | |
| "None\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "knLUXHLypOtw" | |
| }, | |
| "source": [ | |
| "**2.5. Cleaning the data (part 3)**\n", | |
| "\n", | |
| "Do we have an equal number of observations in the ID, Average Price, Month, and London Borough columns? Remember that there are only 32 London Boroughs. How many entries do you have in that column?\n", | |
| "\n", | |
| "Check out the contents of the London Borough column, and if you find null values, get rid of them however you see fit." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "BnvTW5a3p0fC", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "c94a05c7-a800-475b-d229-99761493c924" | |
| }, | |
| "source": [ | |
| "print(cleaned.index)" | |
| ], | |
| "execution_count": 121, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Index(['City of London', 'Barking & Dagenham', 'Barnet', 'Bexley', 'Brent',\n", | |
| " 'Bromley', 'Camden', 'Croydon', 'Ealing', 'Enfield', 'Greenwich',\n", | |
| " 'Hackney', 'Hammersmith & Fulham', 'Haringey', 'Harrow', 'Havering',\n", | |
| " 'Hillingdon', 'Hounslow', 'Islington', 'Kensington & Chelsea',\n", | |
| " 'Kingston upon Thames', 'Lambeth', 'Lewisham', 'Merton', 'Newham',\n", | |
| " 'Redbridge', 'Richmond upon Thames', 'Southwark', 'Sutton',\n", | |
| " 'Tower Hamlets', 'Waltham Forest', 'Wandsworth', 'Westminster',\n", | |
| " 'Inner London', 'Outer London', 'NORTH EAST', 'NORTH WEST',\n", | |
| " 'YORKS & THE HUMBER', 'EAST MIDLANDS', 'WEST MIDLANDS',\n", | |
| " 'EAST OF ENGLAND', 'LONDON', 'SOUTH EAST', 'SOUTH WEST', 'England'],\n", | |
| " dtype='object')\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "PGEx6mJsp6dG" | |
| }, | |
| "source": [ | |
| "**2.6. Visualizing the data**\n", | |
| "\n", | |
| "To visualize the data, why not subset on a particular London Borough? Maybe do a line plot of Month against Average Price?" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "nAg5pT9cqHAR", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 472 | |
| }, | |
| "outputId": "94507723-09f3-41b4-c6ca-84535d862883" | |
| }, | |
| "source": [ | |
| "BOROUGH = 'City of London'\n", | |
| "\n", | |
| "# Plot the Months x Avg Monthly Price using matplotlib\n", | |
| "plt.plot(cleaned.columns, cleaned.loc[BOROUGH], marker='o', linestyle='-')\n", | |
| "\n", | |
| "# Set labels and title\n", | |
| "plt.xlabel('Month')\n", | |
| "plt.ylabel('Average Monthly Price')\n", | |
| "plt.title('City of London Avg Monthly Price')\n", | |
| "\n", | |
| "# Set custom y-axis ticks\n", | |
| "plt.yticks(range(100000, 1100000, 100000), ['100K', '200K', '300K', '400K', '500K', '600K', '700K', '800K', '900K', '1M'])\n", | |
| "\n", | |
| "\n", | |
| "# Show the plot\n", | |
| "plt.grid(True)\n", | |
| "plt.show()" | |
| ], | |
| "execution_count": 138, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 640x480 with 1 Axes>" | |
| ], | |
| "image/png": 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\n" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "aWTPqSJeqHnC" | |
| }, | |
| "source": [ | |
| "To limit the number of data points you have, you might want to extract the year from every month value your *Month* column.\n", | |
| "\n", | |
| "To this end, you *could* apply a ***lambda function***. Your logic could work as follows:\n", | |
| "1. look through the `Month` column\n", | |
| "2. extract the year from each individual value in that column\n", | |
| "3. store that corresponding year as separate column.\n", | |
| "\n", | |
| "Whether you go ahead with this is up to you. Just so long as you answer our initial brief: which boroughs of London have seen the greatest house price increase, on average, over the past two decades?" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "e0DF92cyqnu8", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "a6cb5f09-2bb2-4030-9bbc-f54820bfe31e" | |
| }, | |
| "source": [ | |
| "# Rank Absolute Price Increase\n", | |
| "\n", | |
| "INIT_MONTH = '1998-01-01'\n", | |
| "LAST_MONTH = '2018-01-01'\n", | |
| "\n", | |
| "# Calculate the Difference between the latest house price and the initial house price\n", | |
| "price_increase = cleaned[LAST_MONTH] - cleaned[INIT_MONTH]\n", | |
| "\n", | |
| "# Sort the price increases so that the highest is at the top\n", | |
| "price_increase.sort_values(ascending=False)" | |
| ], | |
| "execution_count": 152, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "Kensington & Chelsea 944326.16740\n", | |
| "Westminster 737937.72280\n", | |
| "Camden 709416.11190\n", | |
| "Richmond upon Thames 669020.87550\n", | |
| "City of London 635574.01513\n", | |
| "Islington 610896.51443\n", | |
| "Hammersmith & Fulham 584172.13980\n", | |
| "Hackney 555872.47363\n", | |
| "Wandsworth 537226.95619\n", | |
| "Haringey 523600.43053\n", | |
| "Inner London 516920.02350\n", | |
| "Barnet 487980.48168\n", | |
| "Merton 487564.38670\n", | |
| "Lambeth 480322.01157\n", | |
| "Southwark 467115.79656\n", | |
| "Brent 464501.43302\n", | |
| "Kingston upon Thames 461941.15157\n", | |
| "Waltham Forest 440794.55087\n", | |
| "Redbridge 430896.41563\n", | |
| "LONDON 430847.23948\n", | |
| "Bromley 418813.52308\n", | |
| "Harrow 417544.47401\n", | |
| "Outer London 405335.20164\n", | |
| "Ealing 402715.10931\n", | |
| "Lewisham 397541.73891\n", | |
| "Hounslow 396128.29463\n", | |
| "Hillingdon 385717.17036\n", | |
| "Enfield 382848.30904\n", | |
| "Tower Hamlets 378843.81005\n", | |
| "Newham 371953.68081\n", | |
| "Havering 358529.86226\n", | |
| "Greenwich 351754.89831\n", | |
| "Sutton 351611.02643\n", | |
| "Croydon 326706.83775\n", | |
| "SOUTH EAST 321825.12106\n", | |
| "Bexley 316826.90964\n", | |
| "Barking & Dagenham 303124.77340\n", | |
| "EAST OF ENGLAND 289957.40390\n", | |
| "SOUTH WEST 264515.84210\n", | |
| "England 248410.22872\n", | |
| "EAST MIDLANDS 198032.47773\n", | |
| "WEST MIDLANDS 195127.47661\n", | |
| "NORTH WEST 169374.51999\n", | |
| "YORKS & THE HUMBER 164722.57122\n", | |
| "NORTH EAST 117794.64589\n", | |
| "dtype: float64" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 152 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "# Plot all boroughs against each other to compare them visually for validation\n", | |
| "\n", | |
| "# Clear the current plot\n", | |
| "plt.clf()\n", | |
| "\n", | |
| "# Show a selection of Boroughs to inspect increase,\n", | |
| "# Kensington & Chelsea should be the largest\n", | |
| "BOROUGHS_TO_SHOW = ['City of London','NORTH EAST', 'Outer London', 'Kensington & Chelsea']\n", | |
| "\n", | |
| "# Create a plot for each row\n", | |
| "for index, row in cleaned.iterrows():\n", | |
| " if index in BOROUGHS_TO_SHOW:\n", | |
| " plt.plot(cleaned.columns, row, linestyle='-', label=f'{index}')\n", | |
| "\n", | |
| "# Set labels and title\n", | |
| "plt.xlabel('Month')\n", | |
| "plt.ylabel('Average Monthly Price')\n", | |
| "plt.title('London Boroughs Avg Monthly Price')\n", | |
| "\n", | |
| "# Add a legend to distinguish rows\n", | |
| "plt.legend()\n", | |
| "\n", | |
| "# Show the plot\n", | |
| "plt.grid(True)\n", | |
| "plt.show()" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 472 | |
| }, | |
| "id": "hicR1MSj44EU", | |
| "outputId": "bc4d32e9-8ef5-4a8c-a4e8-7df008537ba9" | |
| }, | |
| "execution_count": 147, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 640x480 with 1 Axes>" | |
| ], | |
| "image/png": 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\n" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "2knuTxAEqoJ4" | |
| }, | |
| "source": [ | |
| "**3. Modeling**\n", | |
| "\n", | |
| "Consider creating a function that will calculate a ratio of house prices, comparing the price of a house in 2018 to the price in 1998.\n", | |
| "\n", | |
| "Consider calling this function create_price_ratio.\n", | |
| "\n", | |
| "You'd want this function to:\n", | |
| "1. Take a filter of dfg, specifically where this filter constrains the London_Borough, as an argument. For example, one admissible argument should be: dfg[dfg['London_Borough']=='Camden'].\n", | |
| "2. Get the Average Price for that Borough, for the years 1998 and 2018.\n", | |
| "4. Calculate the ratio of the Average Price for 1998 divided by the Average Price for 2018.\n", | |
| "5. Return that ratio.\n", | |
| "\n", | |
| "Once you've written this function, you ultimately want to use it to iterate through all the unique London_Boroughs and work out the ratio capturing the difference of house prices between 1998 and 2018.\n", | |
| "\n", | |
| "Bear in mind: you don't have to write a function like this if you don't want to. If you can solve the brief otherwise, then great!\n", | |
| "\n", | |
| "***Hint***: This section should test the skills you acquired in:\n", | |
| "- Python Data Science Toolbox - Part One, all modules" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "cKTyr437UgDa", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "661ddf69-abdd-41e5-964f-00d73dd29192" | |
| }, | |
| "source": [ | |
| "# Rank Price Increase Ratio (% price increase)\n", | |
| "\n", | |
| "# Does this approach yield a different ranking?\n", | |
| "\n", | |
| "#INIT_MONTH = '1995-01-01'\n", | |
| "#LAST_MONTH = '2023-11-01'\n", | |
| "\n", | |
| "INIT_MONTH = '1998-01-01'\n", | |
| "LAST_MONTH = '2018-01-01'\n", | |
| "\n", | |
| "# Calculate the Difference between the latest house price and the initial house price\n", | |
| "price_ratio = cleaned[LAST_MONTH] / cleaned[INIT_MONTH]\n", | |
| "\n", | |
| "# Sort the price increases so that the highest is at the top\n", | |
| "price_ratio.sort_values(ascending=False)" | |
| ], | |
| "execution_count": 154, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "Hackney 6.606181\n", | |
| "City of London 6.448934\n", | |
| "Westminster 6.216936\n", | |
| "Waltham Forest 6.078388\n", | |
| "Southwark 5.970622\n", | |
| "Lewisham 5.626383\n", | |
| "Inner London 5.479219\n", | |
| "Kensington & Chelsea 5.435536\n", | |
| "Haringey 5.429422\n", | |
| "Lambeth 5.343118\n", | |
| "Islington 5.313813\n", | |
| "Newham 5.292328\n", | |
| "Brent 5.268305\n", | |
| "Camden 5.085970\n", | |
| "Barking & Dagenham 5.065201\n", | |
| "Greenwich 5.029098\n", | |
| "Wandsworth 4.979788\n", | |
| "Tower Hamlets 4.964402\n", | |
| "LONDON 4.899943\n", | |
| "Merton 4.753034\n", | |
| "Redbridge 4.603885\n", | |
| "Outer London 4.603754\n", | |
| "Barnet 4.588621\n", | |
| "Havering 4.473965\n", | |
| "Bexley 4.450694\n", | |
| "Ealing 4.402776\n", | |
| "Croydon 4.398608\n", | |
| "Enfield 4.365265\n", | |
| "Bromley 4.365142\n", | |
| "Harrow 4.352343\n", | |
| "Kingston upon Thames 4.336294\n", | |
| "Hillingdon 4.320409\n", | |
| "EAST OF ENGLAND 4.319148\n", | |
| "Hounslow 4.312729\n", | |
| "Hammersmith & Fulham 4.306812\n", | |
| "Richmond upon Thames 4.197799\n", | |
| "Sutton 4.185078\n", | |
| "SOUTH EAST 4.013414\n", | |
| "SOUTH WEST 3.936714\n", | |
| "England 3.894225\n", | |
| "EAST MIDLANDS 3.635393\n", | |
| "WEST MIDLANDS 3.405265\n", | |
| "NORTH WEST 3.359968\n", | |
| "YORKS & THE HUMBER 3.333574\n", | |
| "NORTH EAST 2.806360\n", | |
| "dtype: float64" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 154 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "# Compare the Ranking of the Boroughs Price increase calculated via Absolute vs Ratio\n", | |
| "\n", | |
| "price_ratio_df = (price_ratio.reset_index()\n", | |
| " .rename(columns={0: 'Ratio', 'index': 'Borough'})\n", | |
| " .sort_values(by='Ratio', ascending=False)\n", | |
| " .reset_index()\n", | |
| " .drop(columns='index')\n", | |
| " )\n", | |
| "\n", | |
| "price_ratio_df['Ratio Ranking'] = price_ratio_df.index\n", | |
| "\n", | |
| "price_increase_df = (price_increase.reset_index()\n", | |
| " .rename(columns={0: 'Increase', 'index': 'Borough'})\n", | |
| " .sort_values(by='Increase', ascending=False)\n", | |
| " .reset_index()\n", | |
| " .drop(columns='index')\n", | |
| " )\n", | |
| "price_increase_df['Increase Ranking'] = price_increase_df.index\n", | |
| "\n", | |
| "rankings = price_increase_df.merge(price_ratio_df, on='Borough', how='inner')\n", | |
| "rankings['Ranking Diff'] = rankings['Ratio Ranking'] - rankings['Increase Ranking']\n", | |
| "\n", | |
| "rankings[['Borough', 'Ranking Diff']]\n" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 1000 | |
| }, | |
| "id": "-GDwItRr_rca", | |
| "outputId": "15f2816e-8fbc-4889-95c6-6ebce3be8d8d" | |
| }, | |
| "execution_count": 182, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| " Borough Ranking Diff\n", | |
| "0 Kensington & Chelsea 7\n", | |
| "1 Westminster 1\n", | |
| "2 Camden 11\n", | |
| "3 Richmond upon Thames 32\n", | |
| "4 City of London -3\n", | |
| "5 Islington 5\n", | |
| "6 Hammersmith & Fulham 28\n", | |
| "7 Hackney -7\n", | |
| "8 Wandsworth 8\n", | |
| "9 Haringey -1\n", | |
| "10 Inner London -4\n", | |
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| "metadata": {}, | |
| "execution_count": 182 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "NzYUI7FxJpgv" | |
| }, | |
| "source": [ | |
| "### 4. Conclusion\n", | |
| "What can you conclude? Type out your conclusion below.\n", | |
| "\n", | |
| "Look back at your notebook. Think about how you might summarize what you have done, and prepare a quick presentation on it to your mentor at your next meeting.\n", | |
| "\n", | |
| "We hope you enjoyed this practical project. It should have consolidated your data hygiene and pandas skills by looking at a real-world problem involving just the kind of dataset you might encounter as a budding data scientist. Congratulations, and looking forward to seeing you at the next step in the course!\n", | |
| "\n", | |
| "### 4. Karo's Conclusions\n", | |
| "\n", | |
| "In order to determine the borough with the greatest price increase, we loaded, explored and cleaned monthly price average data for all boroughs.\n", | |
| "\n", | |
| "Based on defining price increase as the difference of the price(2023) and price(1995), we get a different ranking of the Boroughs than if we use the ratio method of comparing price increase. The two approaches represent the difference between comparing % change in price vs absolute difference in price. Both have their benefits and drawbacks. We can see from the above comparison of the two ranking approaches that the Boroughs at the top of the price increase rankings change significantly between the two lists, but not at the bottom.\n", | |
| "\n", | |
| "According to the ratio approach the Borough the greatest % increase in price is 'Hackney'.\n", | |
| "\n", | |
| "According to the absolute price difference approach, the Borough with the greatest increase is 'Kensington & Chelsea'." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [], | |
| "metadata": { | |
| "id": "HScdte719Oxw" | |
| }, | |
| "execution_count": 149, | |
| "outputs": [] | |
| } | |
| ] | |
| } |
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