CAPD_G30_London_Borough_Data_Science_Project.ipynb

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      "source": [
        "\n",
        "# Project : London Housing Project\n",
        "\n",
        "Find the ***which boroughs of London(out of 32) 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. 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",
        "##Subject Code : SEZG585 - Assignment Implementation\n",
        "##Team : \n",
        "- Neetin Verma - 2020MT93012\n",
        "- Amogh Bab K A - 2020MT93252\n",
        "- Sanjib Bhadra - 2020MT93511\n",
        "\n",
        " \n",
        "# Data Science Techniques Used In Project Imp:\n",
        "    - Data ingestion and inspection\n",
        "    - Exploratory data analysis \n",
        "    - Tidying and cleaning\n",
        "    - Transforming DataFrames\n",
        "    - Subsetting DataFrames with lists\n",
        "    - Filtering DataFrames\n",
        "    - Grouping data\n",
        "    - Melting data\n",
        "    - Advanced indexing\n",
        "#Tools to be used:\n",
        "###Data Science \n",
        "    - Matplotlib\n",
        "    - Fundamental data types\n",
        "    - Dictionaries\n",
        "    - Handling dates and times\n",
        "    - Function definition\n",
        "    - Default arguments, variable length, and scope \n",
        "    - Lambda functions and error handling\n",
        "###UI Interface\n",
        "    - React Js\n",
        "    - Bootstrap\n",
        "    - Html\n",
        "    - CSS\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ipgd2nV8Jpgl"
      },
      "source": [
        "## The Data Science Pipeline\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",
        " "
      ]
    },
    {
      "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"
      ],
      "execution_count": null,
      "outputs": []
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    {
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        "id": "koUrawxsJpgq"
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      "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",
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        "outputId": "843cb4f9-733c-4f2d-a78f-2e1d065c0164"
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      "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",
        "# 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",
        "\n",
        "properties = pd.read_excel(\"https://data.london.gov.uk/download/uk-house-price-index/70ac0766-8902-4eb5-aab5-01951aaed773/UK%20House%20price%20index.xls\", sheetname='Average price', index_col= None)\n",
        "\n",
        "properties.head(2)"
      ],
      "execution_count": null,
      "outputs": [
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              "<div>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>City of London</th>\n",
              "      <th>Barking &amp; Dagenham</th>\n",
              "      <th>Barnet</th>\n",
              "      <th>Bexley</th>\n",
              "      <th>Brent</th>\n",
              "      <th>Bromley</th>\n",
              "      <th>Camden</th>\n",
              "      <th>Croydon</th>\n",
              "      <th>Ealing</th>\n",
              "      <th>Enfield</th>\n",
              "      <th>...</th>\n",
              "      <th>NORTH WEST</th>\n",
              "      <th>YORKS &amp; THE HUMBER</th>\n",
              "      <th>EAST MIDLANDS</th>\n",
              "      <th>WEST MIDLANDS</th>\n",
              "      <th>EAST OF ENGLAND</th>\n",
              "      <th>LONDON</th>\n",
              "      <th>SOUTH EAST</th>\n",
              "      <th>SOUTH WEST</th>\n",
              "      <th>Unnamed: 46</th>\n",
              "      <th>England</th>\n",
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              "  </thead>\n",
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              "      <th>NaT</th>\n",
              "      <td>E09000001</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>E92000001</td>\n",
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              "    <tr>\n",
              "      <th>1995-01-01</th>\n",
              "      <td>91449</td>\n",
              "      <td>50460.2</td>\n",
              "      <td>93284.5</td>\n",
              "      <td>64958.1</td>\n",
              "      <td>71306.6</td>\n",
              "      <td>81671.5</td>\n",
              "      <td>120933</td>\n",
              "      <td>69158.2</td>\n",
              "      <td>79885.9</td>\n",
              "      <td>72514.7</td>\n",
              "      <td>...</td>\n",
              "      <td>43958.5</td>\n",
              "      <td>44803.4</td>\n",
              "      <td>45544.5</td>\n",
              "      <td>48527.5</td>\n",
              "      <td>56701.6</td>\n",
              "      <td>74435.8</td>\n",
              "      <td>64018.9</td>\n",
              "      <td>54705.2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>53202.8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2 rows × 48 columns</p>\n",
              "</div>"
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              "           City of London Barking & Dagenham     Barnet     Bexley      Brent  \\\n",
              "NaT             E09000001          E09000002  E09000003  E09000004  E09000005   \n",
              "1995-01-01          91449            50460.2    93284.5    64958.1    71306.6   \n",
              "\n",
              "              Bromley     Camden    Croydon     Ealing    Enfield    ...      \\\n",
              "NaT         E09000006  E09000007  E09000008  E09000009  E09000010    ...       \n",
              "1995-01-01    81671.5     120933    69158.2    79885.9    72514.7    ...       \n",
              "\n",
              "           NORTH WEST YORKS & THE HUMBER EAST MIDLANDS WEST MIDLANDS  \\\n",
              "NaT         E12000002          E12000003     E12000004     E12000005   \n",
              "1995-01-01    43958.5            44803.4       45544.5       48527.5   \n",
              "\n",
              "           EAST OF ENGLAND     LONDON SOUTH EAST SOUTH WEST Unnamed: 46  \\\n",
              "NaT              E12000006  E12000007  E12000008  E12000009         NaN   \n",
              "1995-01-01         56701.6    74435.8    64018.9    54705.2         NaN   \n",
              "\n",
              "              England  \n",
              "NaT         E92000001  \n",
              "1995-01-01    53202.8  \n",
              "\n",
              "[2 rows x 48 columns]"
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      "cell_type": "markdown",
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      "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",
        "\n"
      ]
    },
    {
      "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",
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      "source": [
        "#check the number of rows & col.\n",
        "print(properties.shape)\n",
        "\n",
        "#Total records\n",
        "print(properties.size)\n",
        "\n",
        "\n",
        "properties.head(2)\n",
        "\n",
        "\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
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          "text": [
            "(304, 48)\n",
            "14592\n"
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>City of London</th>\n",
              "      <th>Barking &amp; Dagenham</th>\n",
              "      <th>Barnet</th>\n",
              "      <th>Bexley</th>\n",
              "      <th>Brent</th>\n",
              "      <th>Bromley</th>\n",
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              "      <th>SOUTH EAST</th>\n",
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              "      <th>Unnamed: 46</th>\n",
              "      <th>England</th>\n",
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              "      <td>E92000001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1995-01-01</th>\n",
              "      <td>91449</td>\n",
              "      <td>50460.2</td>\n",
              "      <td>93284.5</td>\n",
              "      <td>64958.1</td>\n",
              "      <td>71306.6</td>\n",
              "      <td>81671.5</td>\n",
              "      <td>120933</td>\n",
              "      <td>69158.2</td>\n",
              "      <td>79885.9</td>\n",
              "      <td>72514.7</td>\n",
              "      <td>...</td>\n",
              "      <td>43958.5</td>\n",
              "      <td>44803.4</td>\n",
              "      <td>45544.5</td>\n",
              "      <td>48527.5</td>\n",
              "      <td>56701.6</td>\n",
              "      <td>74435.8</td>\n",
              "      <td>64018.9</td>\n",
              "      <td>54705.2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>53202.8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2 rows × 48 columns</p>\n",
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            ],
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              "           City of London Barking & Dagenham     Barnet     Bexley      Brent  \\\n",
              "NaT             E09000001          E09000002  E09000003  E09000004  E09000005   \n",
              "1995-01-01          91449            50460.2    93284.5    64958.1    71306.6   \n",
              "\n",
              "              Bromley     Camden    Croydon     Ealing    Enfield    ...      \\\n",
              "NaT         E09000006  E09000007  E09000008  E09000009  E09000010    ...       \n",
              "1995-01-01    81671.5     120933    69158.2    79885.9    72514.7    ...       \n",
              "\n",
              "           NORTH WEST YORKS & THE HUMBER EAST MIDLANDS WEST MIDLANDS  \\\n",
              "NaT         E12000002          E12000003     E12000004     E12000005   \n",
              "1995-01-01    43958.5            44803.4       45544.5       48527.5   \n",
              "\n",
              "           EAST OF ENGLAND     LONDON SOUTH EAST SOUTH WEST Unnamed: 46  \\\n",
              "NaT              E12000006  E12000007  E12000008  E12000009         NaN   \n",
              "1995-01-01         56701.6    74435.8    64018.9    54705.2         NaN   \n",
              "\n",
              "              England  \n",
              "NaT         E92000001  \n",
              "1995-01-01    53202.8  \n",
              "\n",
              "[2 rows x 48 columns]"
            ]
          },
          "execution_count": 160,
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    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tE9Sqt9-oAta"
      },
      "source": [
        "**2.2. Cleaning the data**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cdAu1A3YoH_r",
        "outputId": "57a332ba-d837-4cb2-99d0-80ccdf4a81bb"
      },
      "source": [
        "#transposing the dataframe \n",
        "properties_transposed = properties.T\n",
        "\n",
        "#Finding Row index \n",
        "properties_transposed.columns[:]\n",
        "\n",
        "#Resetting the index\n",
        "properties_transposed.reset_index(inplace = True) \n",
        "\n",
        "\n",
        "print(properties_transposed.shape)\n",
        "\n",
        "print(properties_transposed.size)\n",
        "\n",
        "properties_transposed.head(2)\n",
        "\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(48, 305)\n",
            "14640\n"
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              "      <th>index</th>\n",
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              "      <td>53700.3</td>\n",
              "      <td>52113.1</td>\n",
              "      <td>52232.2</td>\n",
              "      <td>...</td>\n",
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            ],
            "text/plain": [
              "                index        NaN 1995-01-01 00:00:00 1995-02-01 00:00:00  \\\n",
              "0      City of London  E09000001               91449             82202.8   \n",
              "1  Barking & Dagenham  E09000002             50460.2             51085.8   \n",
              "\n",
              "  1995-03-01 00:00:00 1995-04-01 00:00:00 1995-05-01 00:00:00  \\\n",
              "0             79120.7             77101.2             84409.1   \n",
              "1               51269             53133.5             53042.2   \n",
              "\n",
              "  1995-06-01 00:00:00 1995-07-01 00:00:00 1995-08-01 00:00:00  \\\n",
              "0             94900.5              110128              112329   \n",
              "1             53700.3             52113.1             52232.2   \n",
              "\n",
              "          ...         2019-06-01 00:00:00 2019-07-01 00:00:00  \\\n",
              "0         ...                      761526              756407   \n",
              "1         ...                      293889              297426   \n",
              "\n",
              "  2019-08-01 00:00:00 2019-09-01 00:00:00 2019-10-01 00:00:00  \\\n",
              "0              813770              810455              826227   \n",
              "1              299421              304778              304579   \n",
              "\n",
              "  2019-11-01 00:00:00 2019-12-01 00:00:00 2020-01-01 00:00:00  \\\n",
              "0              776894              737275              757377   \n",
              "1              306390              301283              304187   \n",
              "\n",
              "  2020-02-01 00:00:00 2020-03-01 00:00:00  \n",
              "0              765416              792583  \n",
              "1              304719              327136  \n",
              "\n",
              "[2 rows x 305 columns]"
            ]
          },
          "execution_count": 210,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o1uLbJAsoIjK"
      },
      "source": [
        "**2.3. Cleaning the data (part 2)**\n",
        "\n",
        "we have to **rename** a couple columns. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GKkmn1AnoVZS",
        "outputId": "39d4ac11-2367-4421-ff25-85b56bc5de65"
      },
      "source": [
        "#Renaming the first 2 col\n",
        "properties_transposed = properties_transposed.rename(columns = {'index':'Area'})\n",
        "properties_transposed = properties_transposed.rename(columns = {pd.NaT:'Area_Code'})\n",
        "\n",
        "\n",
        "#Dropping empty rows,\n",
        "properties_transposed = properties_transposed.dropna(how =\"any\")\n",
        "\n",
        "print(properties_transposed.shape)\n",
        "\n",
        "\n",
        "properties_transposed.head(2)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(45, 305)\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Area</th>\n",
              "      <th>Area_Code</th>\n",
              "      <th>1995-01-01 00:00:00</th>\n",
              "      <th>1995-02-01 00:00:00</th>\n",
              "      <th>1995-03-01 00:00:00</th>\n",
              "      <th>1995-04-01 00:00:00</th>\n",
              "      <th>1995-05-01 00:00:00</th>\n",
              "      <th>1995-06-01 00:00:00</th>\n",
              "      <th>1995-07-01 00:00:00</th>\n",
              "      <th>1995-08-01 00:00:00</th>\n",
              "      <th>...</th>\n",
              "      <th>2019-06-01 00:00:00</th>\n",
              "      <th>2019-07-01 00:00:00</th>\n",
              "      <th>2019-08-01 00:00:00</th>\n",
              "      <th>2019-09-01 00:00:00</th>\n",
              "      <th>2019-10-01 00:00:00</th>\n",
              "      <th>2019-11-01 00:00:00</th>\n",
              "      <th>2019-12-01 00:00:00</th>\n",
              "      <th>2020-01-01 00:00:00</th>\n",
              "      <th>2020-02-01 00:00:00</th>\n",
              "      <th>2020-03-01 00:00:00</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>City of London</td>\n",
              "      <td>E09000001</td>\n",
              "      <td>91449</td>\n",
              "      <td>82202.8</td>\n",
              "      <td>79120.7</td>\n",
              "      <td>77101.2</td>\n",
              "      <td>84409.1</td>\n",
              "      <td>94900.5</td>\n",
              "      <td>110128</td>\n",
              "      <td>112329</td>\n",
              "      <td>...</td>\n",
              "      <td>761526</td>\n",
              "      <td>756407</td>\n",
              "      <td>813770</td>\n",
              "      <td>810455</td>\n",
              "      <td>826227</td>\n",
              "      <td>776894</td>\n",
              "      <td>737275</td>\n",
              "      <td>757377</td>\n",
              "      <td>765416</td>\n",
              "      <td>792583</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Barking &amp; Dagenham</td>\n",
              "      <td>E09000002</td>\n",
              "      <td>50460.2</td>\n",
              "      <td>51085.8</td>\n",
              "      <td>51269</td>\n",
              "      <td>53133.5</td>\n",
              "      <td>53042.2</td>\n",
              "      <td>53700.3</td>\n",
              "      <td>52113.1</td>\n",
              "      <td>52232.2</td>\n",
              "      <td>...</td>\n",
              "      <td>293889</td>\n",
              "      <td>297426</td>\n",
              "      <td>299421</td>\n",
              "      <td>304778</td>\n",
              "      <td>304579</td>\n",
              "      <td>306390</td>\n",
              "      <td>301283</td>\n",
              "      <td>304187</td>\n",
              "      <td>304719</td>\n",
              "      <td>327136</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2 rows × 305 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                 Area  Area_Code 1995-01-01 00:00:00 1995-02-01 00:00:00  \\\n",
              "0      City of London  E09000001               91449             82202.8   \n",
              "1  Barking & Dagenham  E09000002             50460.2             51085.8   \n",
              "\n",
              "  1995-03-01 00:00:00 1995-04-01 00:00:00 1995-05-01 00:00:00  \\\n",
              "0             79120.7             77101.2             84409.1   \n",
              "1               51269             53133.5             53042.2   \n",
              "\n",
              "  1995-06-01 00:00:00 1995-07-01 00:00:00 1995-08-01 00:00:00  \\\n",
              "0             94900.5              110128              112329   \n",
              "1             53700.3             52113.1             52232.2   \n",
              "\n",
              "          ...         2019-06-01 00:00:00 2019-07-01 00:00:00  \\\n",
              "0         ...                      761526              756407   \n",
              "1         ...                      293889              297426   \n",
              "\n",
              "  2019-08-01 00:00:00 2019-09-01 00:00:00 2019-10-01 00:00:00  \\\n",
              "0              813770              810455              826227   \n",
              "1              299421              304778              304579   \n",
              "\n",
              "  2019-11-01 00:00:00 2019-12-01 00:00:00 2020-01-01 00:00:00  \\\n",
              "0              776894              737275              757377   \n",
              "1              306390              301283              304187   \n",
              "\n",
              "  2020-02-01 00:00:00 2020-03-01 00:00:00  \n",
              "0              765416              792583  \n",
              "1              304719              327136  \n",
              "\n",
              "[2 rows x 305 columns]"
            ]
          },
          "execution_count": 211,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jy8BzXHmoWEw"
      },
      "source": [
        "**2.4.Transforming the data**\n",
        " "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S2wM0qLuo2Zt",
        "outputId": "0bb81949-8a88-4b1e-8853-b372e67d0c4a"
      },
      "source": [
        "#Using melt() to transpore the year-month & prices, below example trying for only 1995 \n",
        "properties_transposed = properties_transposed.reset_index(drop=True)\n",
        "\n",
        "properties_transposed = pd.melt(properties_transposed, id_vars=['Area','Area_Code'])\n",
        "\n",
        "print(properties_transposed.shape)\n",
        "#print(properties_transposed.size)\n",
        "properties_transposed.head(2)\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(13635, 4)\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Area</th>\n",
              "      <th>Area_Code</th>\n",
              "      <th>variable</th>\n",
              "      <th>value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>City of London</td>\n",
              "      <td>E09000001</td>\n",
              "      <td>1995-01-01</td>\n",
              "      <td>91449</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Barking &amp; Dagenham</td>\n",
              "      <td>E09000002</td>\n",
              "      <td>1995-01-01</td>\n",
              "      <td>50460.2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 Area  Area_Code   variable    value\n",
              "0      City of London  E09000001 1995-01-01    91449\n",
              "1  Barking & Dagenham  E09000002 1995-01-01  50460.2"
            ]
          },
          "execution_count": 212,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "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",
        "outputId": "5b6ad8a2-5e66-497b-bfb8-afdbda12971e"
      },
      "source": [
        "\n",
        "#To change the datatype of col from Object to Float\n",
        "\n",
        "properties_transposed[\"value\"] = properties_transposed[\"value\"].astype(float)\n",
        "\n",
        "#Check the datatypes\n",
        "print(properties_transposed.dtypes)\n",
        "\n",
        "\n",
        "print(properties_transposed[['Area']].count())\n",
        "print(properties_transposed[['Area_Code']].count())\n",
        "print(properties_transposed[['variable']].count())\n",
        "print(properties_transposed[['value']].count())\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Area                 object\n",
            "Area_Code            object\n",
            "variable     datetime64[ns]\n",
            "value               float64\n",
            "dtype: object\n",
            "Area    13635\n",
            "dtype: int64\n",
            "Area_Code    13635\n",
            "dtype: int64\n",
            "variable    13635\n",
            "dtype: int64\n",
            "value    13635\n",
            "dtype: int64\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. "
      ]
    },
    {
      "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?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BnvTW5a3p0fC",
        "outputId": "1596a638-3e9d-4fb1-be4b-9d7ef3e3c058"
      },
      "source": [
        "#Filtering data for 32 london boroughs\n",
        " \n",
        "nonBoroughs = ['Inner London', 'Outer London',\n",
        "               'NORTH EAST', 'NORTH WEST', 'YORKS & THE HUMBER',\n",
        "               'EAST MIDLANDS', 'WEST MIDLANDS',\n",
        "              'EAST OF ENGLAND', 'LONDON', 'SOUTH EAST',\n",
        "              'SOUTH WEST', 'England']\n",
        "\n",
        "#properties_transposed= properties_transposed.loc[properties_transposed['Area'] != 'nonBoroughs']\n",
        "\n",
        "properties_transposed[properties_transposed['Area'].isin(nonBoroughs)]\n",
        "properties_transposed[~properties_transposed['Area'].isin(nonBoroughs)]\n",
        "properties_transposed = properties_transposed[~properties_transposed['Area'].isin(nonBoroughs)]\n",
        "df = properties_transposed\n",
        "properties_transposed.head(2)\n",
        "\n",
        "#Creating line plot for one borough\n",
        "properties_trans_city = properties_transposed[properties_transposed['Area'] == 'Kingston upon Thames' ]\n",
        "df = pd.DataFrame(data=properties_trans_city);\n",
        "df.plot.line(x=\"Year\", title=\"Value of House Price Index between 1995 and 2020\");\n",
        "plt.show();\n",
        "\n",
        "\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "image/png": 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swYYbbsgJJ5zA999/X+E+GjVqxFVXXbXc8s0335zjjz++1LLZs2fTq1cv2rRp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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f531bd785c0>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nAg5pT9cqHAR"
      },
      "source": [
        "#create year col\n",
        "properties_transposed['Year'] = properties_transposed['variable'].map(lambda x: x.year)\n",
        "\n",
        "#Dropping variable col \n",
        "properties_transposed = properties_transposed.drop(['variable'], axis = 1) \n",
        "\n",
        "                                   \n",
        "#Calculating mean \n",
        "properties_transposed = properties_transposed.groupby(by=['Area','Year']).mean().reset_index()\n",
        "properties_transposed.head(2)\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "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"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2knuTxAEqoJ4"
      },
      "source": [
        "**3. Modeling**\n",
        "\n",
        "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",
        "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"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e0DF92cyqnu8",
        "outputId": "9634fd48-5f27-45ef-ffc7-34406bc8a30f"
      },
      "source": [
        "#Put Area into list\n",
        "area_list = properties_transposed['Area'].tolist()\n",
        "\n",
        "#Taking out the distinct Area by using set and again putting it back into list, could have used unique\n",
        "area_list= list(set(area_list))\n",
        "\n",
        "\n",
        "#Function to pass DF along with city and calculate ratio of 1998 / 2018 for all boroughs\n",
        "def create_price_ratio(properties_transposed, city):\n",
        "  \n",
        "    city_df =   properties_transposed[properties_transposed['Area'] == city]\n",
        "    a =     city_df[city_df['Year'] == 1998]['value']\n",
        "    b =     city_df[city_df['Year'] == 2018]['value']\n",
        "  \n",
        "\n",
        "    return [city, float(b) / float(a)]\n",
        "\n",
        "#empty list to be fillied with return city and ratio\n",
        "final_list = []\n",
        "\n",
        "for city in area_list:\n",
        "    final_list.append(create_price_ratio(properties_transposed, city))\n",
        "\n",
        "final_list\n",
        "\n",
        "#Passed ratio output into dataframe \n",
        "londonhousing = pd.DataFrame(final_list,columns =['Area_Name', 'Ratio']) \n",
        "londonhousing\n",
        "\n",
        "#Sorted dataframe in decending order\n",
        "londonhousing = londonhousing.sort('Ratio', ascending=False)\n",
        "\n",
        "londonhousing.head(5)\n",
        "\n",
        "                        "
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/home/verma_neetin/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:31: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Area_Name</th>\n",
              "      <th>Ratio</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Hackney</td>\n",
              "      <td>6.198286</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Waltham Forest</td>\n",
              "      <td>5.834756</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Southwark</td>\n",
              "      <td>5.516485</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Lewisham</td>\n",
              "      <td>5.449221</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Westminster</td>\n",
              "      <td>5.353565</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Area_Name     Ratio\n",
              "12         Hackney  6.198286\n",
              "14  Waltham Forest  5.834756\n",
              "10       Southwark  5.516485\n",
              "9         Lewisham  5.449221\n",
              "24     Westminster  5.353565"
            ]
          },
          "execution_count": 301,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cKTyr437UgDa",
        "outputId": "a6a6569f-84b3-4a33-a3fd-ee60579d795f"
      },
      "source": [
        "#Get list of top 15 areas.\n",
        "londonhousing = londonhousing.head(20)\n",
        "\n",
        "#Ploting top 15 areas in decending order, where house prices has increased \n",
        "import matplotlib.pyplot as plot\n",
        "londonhousing.plot.bar(x=\"Area_Name\", \n",
        "                       y=\"Ratio\",\n",
        "                       title=\"Housing Price Increase \",\n",
        "                       legend=True,\n",
        "                       width= 0.75,\n",
        "                       figsize=(8,5.8),\n",
        "                       colormap='Paired',\n",
        "                       edgecolor='blue')\n",
        "plot.show();\n",
        "\n",
        "#Area seen max increase in housing price.\n",
        "londonhousing.head(1)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
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35hGPeASbbbYZBx10EFBaLPt14403ArD99tuvcNmMGTMYGRlhyZIl4/g/6p9d1ZIkSTU9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\njmrrGc2oQTjDbvvYsoi4HXhuZl7SdC0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SziMSxasYqnU8x/aMmnF1lBG8NRtYe/k0SecA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\nAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f5319a33dd8>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Area_Name</th>\n",
              "      <th>Ratio</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Hackney</td>\n",
              "      <td>6.198286</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Area_Name     Ratio\n",
              "12   Hackney  6.198286"
            ]
          },
          "execution_count": 297,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NzYUI7FxJpgv"
      },
      "source": [
        "### 4. Conclusion\n",
        "**Hackney boroughs of Londonhave seen the greatest increase in housing prices, on average, over the last two decades**"
      ]
    }
  ]
}