Unit 4 Challenge - Tier 3 DONE.ipynb

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    "# 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) "
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    "## 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"
   ]
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    "### 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. "
   ]
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   "source": [
    "#### 1.1. Importing Libraries"
   ]
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    "# Let's import the pandas, numpy libraries as pd, and np respectively. \n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "# Load the pyplot collection of functions from matplotlib, as plt \n",
    "\n",
    "import matplotlib.pyplot as plt\n"
   ]
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    "#### 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. "
   ]
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    "# 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_LondonHousePrices = \"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_LondonHousePrices, sheet_name='Average price', index_col= None)"
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    "### 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"
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    "**2.1. Exploring your data** \n",
    "\n",
    "Think about your pandas functions for checking out a dataframe. "
   ]
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   "source": [
    "#look at the number of rows and columns.\n",
    "properties.shape"
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       "      <td>NaN</td>\n",
       "      <td>53096.2</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>82188.9</td>\n",
       "      <td>...</td>\n",
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       "  Unnamed: 0 City of London Barking & Dagenham     Barnet     Bexley  \\\n",
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       "4 1995-04-01        77101.2            53133.5    90762.9    64277.7   \n",
       "\n",
       "       Brent    Bromley     Camden    Croydon     Ealing  ... NORTH WEST  \\\n",
       "0  E09000005  E09000006  E09000007  E09000008  E09000009  ...  E12000002   \n",
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       "\n",
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       "0          E12000003     E12000004     E12000005       E12000006  E12000007   \n",
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   "source": [
    "#look at the data  \n",
    "properties.head()"
   ]
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    "**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."
   ]
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       "      <td>904803</td>\n",
       "      <td>869929</td>\n",
       "      <td>785001</td>\n",
       "      <td>794881</td>\n",
       "      <td>768714</td>\n",
       "      <td>721490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barking &amp; Dagenham</th>\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>51471.6</td>\n",
       "      <td>...</td>\n",
       "      <td>303054</td>\n",
       "      <td>302748</td>\n",
       "      <td>301570</td>\n",
       "      <td>293553</td>\n",
       "      <td>292478</td>\n",
       "      <td>299074</td>\n",
       "      <td>303701</td>\n",
       "      <td>304696</td>\n",
       "      <td>301170</td>\n",
       "      <td>308583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barnet</th>\n",
       "      <td>E09000003</td>\n",
       "      <td>93284.5</td>\n",
       "      <td>93190.2</td>\n",
       "      <td>92247.5</td>\n",
       "      <td>90762.9</td>\n",
       "      <td>90258</td>\n",
       "      <td>90107.2</td>\n",
       "      <td>91441.2</td>\n",
       "      <td>92361.3</td>\n",
       "      <td>93273.1</td>\n",
       "      <td>...</td>\n",
       "      <td>521612</td>\n",
       "      <td>516411</td>\n",
       "      <td>522770</td>\n",
       "      <td>529115</td>\n",
       "      <td>527494</td>\n",
       "      <td>517266</td>\n",
       "      <td>521528</td>\n",
       "      <td>525762</td>\n",
       "      <td>535663</td>\n",
       "      <td>536263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bexley</th>\n",
       "      <td>E09000004</td>\n",
       "      <td>64958.1</td>\n",
       "      <td>64787.9</td>\n",
       "      <td>64367.5</td>\n",
       "      <td>64277.7</td>\n",
       "      <td>63997.1</td>\n",
       "      <td>64252.3</td>\n",
       "      <td>63722.7</td>\n",
       "      <td>64432.6</td>\n",
       "      <td>64509.5</td>\n",
       "      <td>...</td>\n",
       "      <td>336863</td>\n",
       "      <td>338817</td>\n",
       "      <td>339095</td>\n",
       "      <td>341997</td>\n",
       "      <td>338857</td>\n",
       "      <td>340723</td>\n",
       "      <td>343385</td>\n",
       "      <td>348046</td>\n",
       "      <td>345461</td>\n",
       "      <td>344254</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 311 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          0                    1                    2    \\\n",
       "Unnamed: 0                NaT  1995-01-01 00:00:00  1995-02-01 00:00:00   \n",
       "City of London      E09000001                91449              82202.8   \n",
       "Barking & Dagenham  E09000002              50460.2              51085.8   \n",
       "Barnet              E09000003              93284.5              93190.2   \n",
       "Bexley              E09000004              64958.1              64787.9   \n",
       "\n",
       "                                    3                    4    \\\n",
       "Unnamed: 0          1995-03-01 00:00:00  1995-04-01 00:00:00   \n",
       "City of London                  79120.7              77101.2   \n",
       "Barking & Dagenham                51269              53133.5   \n",
       "Barnet                          92247.5              90762.9   \n",
       "Bexley                          64367.5              64277.7   \n",
       "\n",
       "                                    5                    6    \\\n",
       "Unnamed: 0          1995-05-01 00:00:00  1995-06-01 00:00:00   \n",
       "City of London                  84409.1              94900.5   \n",
       "Barking & Dagenham              53042.2              53700.3   \n",
       "Barnet                            90258              90107.2   \n",
       "Bexley                          63997.1              64252.3   \n",
       "\n",
       "                                    7                    8    \\\n",
       "Unnamed: 0          1995-07-01 00:00:00  1995-08-01 00:00:00   \n",
       "City of London                   110128               112329   \n",
       "Barking & Dagenham              52113.1              52232.2   \n",
       "Barnet                          91441.2              92361.3   \n",
       "Bexley                          63722.7              64432.6   \n",
       "\n",
       "                                    9    ...                  301  \\\n",
       "Unnamed: 0          1995-09-01 00:00:00  ...  2020-01-01 00:00:00   \n",
       "City of London                   104473  ...               773679   \n",
       "Barking & Dagenham              51471.6  ...               303054   \n",
       "Barnet                          93273.1  ...               521612   \n",
       "Bexley                          64509.5  ...               336863   \n",
       "\n",
       "                                    302                  303  \\\n",
       "Unnamed: 0          2020-02-01 00:00:00  2020-03-01 00:00:00   \n",
       "City of London                   792769               828658   \n",
       "Barking & Dagenham               302748               301570   \n",
       "Barnet                           516411               522770   \n",
       "Bexley                           338817               339095   \n",
       "\n",
       "                                    304                  305  \\\n",
       "Unnamed: 0          2020-04-01 00:00:00  2020-05-01 00:00:00   \n",
       "City of London                   900126               904803   \n",
       "Barking & Dagenham               293553               292478   \n",
       "Barnet                           529115               527494   \n",
       "Bexley                           341997               338857   \n",
       "\n",
       "                                    306                  307  \\\n",
       "Unnamed: 0          2020-06-01 00:00:00  2020-07-01 00:00:00   \n",
       "City of London                   869929               785001   \n",
       "Barking & Dagenham               299074               303701   \n",
       "Barnet                           517266               521528   \n",
       "Bexley                           340723               343385   \n",
       "\n",
       "                                    308                  309  \\\n",
       "Unnamed: 0          2020-08-01 00:00:00  2020-09-01 00:00:00   \n",
       "City of London                   794881               768714   \n",
       "Barking & Dagenham               304696               301170   \n",
       "Barnet                           525762               535663   \n",
       "Bexley                           348046               345461   \n",
       "\n",
       "                                    310  \n",
       "Unnamed: 0          2020-10-01 00:00:00  \n",
       "City of London                   721490  \n",
       "Barking & Dagenham               308583  \n",
       "Barnet                           536263  \n",
       "Bexley                           344254  \n",
       "\n",
       "[5 rows x 311 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#transposing\n",
    "properties = properties.transpose()\n",
    "properties.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>index</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
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       "      <th>309</th>\n",
       "      <th>310</th>\n",
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       "      <td>Unnamed: 0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>1995-01-01 00:00:00</td>\n",
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       "      <td>1995-03-01 00:00:00</td>\n",
       "      <td>1995-04-01 00:00:00</td>\n",
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       "      <td>1995-07-01 00:00:00</td>\n",
       "      <td>1995-08-01 00:00:00</td>\n",
       "      <td>...</td>\n",
       "      <td>2020-01-01 00:00:00</td>\n",
       "      <td>2020-02-01 00:00:00</td>\n",
       "      <td>2020-03-01 00:00:00</td>\n",
       "      <td>2020-04-01 00:00:00</td>\n",
       "      <td>2020-05-01 00:00:00</td>\n",
       "      <td>2020-06-01 00:00:00</td>\n",
       "      <td>2020-07-01 00:00:00</td>\n",
       "      <td>2020-08-01 00:00:00</td>\n",
       "      <td>2020-09-01 00:00:00</td>\n",
       "      <td>2020-10-01 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</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>773679</td>\n",
       "      <td>792769</td>\n",
       "      <td>828658</td>\n",
       "      <td>900126</td>\n",
       "      <td>904803</td>\n",
       "      <td>869929</td>\n",
       "      <td>785001</td>\n",
       "      <td>794881</td>\n",
       "      <td>768714</td>\n",
       "      <td>721490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</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>303054</td>\n",
       "      <td>302748</td>\n",
       "      <td>301570</td>\n",
       "      <td>293553</td>\n",
       "      <td>292478</td>\n",
       "      <td>299074</td>\n",
       "      <td>303701</td>\n",
       "      <td>304696</td>\n",
       "      <td>301170</td>\n",
       "      <td>308583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Barnet</td>\n",
       "      <td>E09000003</td>\n",
       "      <td>93284.5</td>\n",
       "      <td>93190.2</td>\n",
       "      <td>92247.5</td>\n",
       "      <td>90762.9</td>\n",
       "      <td>90258</td>\n",
       "      <td>90107.2</td>\n",
       "      <td>91441.2</td>\n",
       "      <td>92361.3</td>\n",
       "      <td>...</td>\n",
       "      <td>521612</td>\n",
       "      <td>516411</td>\n",
       "      <td>522770</td>\n",
       "      <td>529115</td>\n",
       "      <td>527494</td>\n",
       "      <td>517266</td>\n",
       "      <td>521528</td>\n",
       "      <td>525762</td>\n",
       "      <td>535663</td>\n",
       "      <td>536263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bexley</td>\n",
       "      <td>E09000004</td>\n",
       "      <td>64958.1</td>\n",
       "      <td>64787.9</td>\n",
       "      <td>64367.5</td>\n",
       "      <td>64277.7</td>\n",
       "      <td>63997.1</td>\n",
       "      <td>64252.3</td>\n",
       "      <td>63722.7</td>\n",
       "      <td>64432.6</td>\n",
       "      <td>...</td>\n",
       "      <td>336863</td>\n",
       "      <td>338817</td>\n",
       "      <td>339095</td>\n",
       "      <td>341997</td>\n",
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       "      <td>340723</td>\n",
       "      <td>343385</td>\n",
       "      <td>348046</td>\n",
       "      <td>345461</td>\n",
       "      <td>344254</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 312 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                index          0                    1                    2  \\\n",
       "0          Unnamed: 0        NaT  1995-01-01 00:00:00  1995-02-01 00:00:00   \n",
       "1      City of London  E09000001                91449              82202.8   \n",
       "2  Barking & Dagenham  E09000002              50460.2              51085.8   \n",
       "3              Barnet  E09000003              93284.5              93190.2   \n",
       "4              Bexley  E09000004              64958.1              64787.9   \n",
       "\n",
       "                     3                    4                    5  \\\n",
       "0  1995-03-01 00:00:00  1995-04-01 00:00:00  1995-05-01 00:00:00   \n",
       "1              79120.7              77101.2              84409.1   \n",
       "2                51269              53133.5              53042.2   \n",
       "3              92247.5              90762.9                90258   \n",
       "4              64367.5              64277.7              63997.1   \n",
       "\n",
       "                     6                    7                    8  ...  \\\n",
       "0  1995-06-01 00:00:00  1995-07-01 00:00:00  1995-08-01 00:00:00  ...   \n",
       "1              94900.5               110128               112329  ...   \n",
       "2              53700.3              52113.1              52232.2  ...   \n",
       "3              90107.2              91441.2              92361.3  ...   \n",
       "4              64252.3              63722.7              64432.6  ...   \n",
       "\n",
       "                   301                  302                  303  \\\n",
       "0  2020-01-01 00:00:00  2020-02-01 00:00:00  2020-03-01 00:00:00   \n",
       "1               773679               792769               828658   \n",
       "2               303054               302748               301570   \n",
       "3               521612               516411               522770   \n",
       "4               336863               338817               339095   \n",
       "\n",
       "                   304                  305                  306  \\\n",
       "0  2020-04-01 00:00:00  2020-05-01 00:00:00  2020-06-01 00:00:00   \n",
       "1               900126               904803               869929   \n",
       "2               293553               292478               299074   \n",
       "3               529115               527494               517266   \n",
       "4               341997               338857               340723   \n",
       "\n",
       "                   307                  308                  309  \\\n",
       "0  2020-07-01 00:00:00  2020-08-01 00:00:00  2020-09-01 00:00:00   \n",
       "1               785001               794881               768714   \n",
       "2               303701               304696               301170   \n",
       "3               521528               525762               535663   \n",
       "4               343385               348046               345461   \n",
       "\n",
       "                   310  \n",
       "0  2020-10-01 00:00:00  \n",
       "1               721490  \n",
       "2               308583  \n",
       "3               536263  \n",
       "4               344254  \n",
       "\n",
       "[5 rows x 312 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#reset index\n",
    "properties = properties.reset_index()\n",
    "properties.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>2020-05-01 00:00:00</td>\n",
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       "      <td>2020-09-01 00:00:00</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>City of London</td>\n",
       "      <td>E09000001</td>\n",
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       "      <td>82202.8</td>\n",
       "      <td>79120.7</td>\n",
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       "      <td>794881</td>\n",
       "      <td>768714</td>\n",
       "      <td>721490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</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>303054</td>\n",
       "      <td>302748</td>\n",
       "      <td>301570</td>\n",
       "      <td>293553</td>\n",
       "      <td>292478</td>\n",
       "      <td>299074</td>\n",
       "      <td>303701</td>\n",
       "      <td>304696</td>\n",
       "      <td>301170</td>\n",
       "      <td>308583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Barnet</td>\n",
       "      <td>E09000003</td>\n",
       "      <td>93284.5</td>\n",
       "      <td>93190.2</td>\n",
       "      <td>92247.5</td>\n",
       "      <td>90762.9</td>\n",
       "      <td>90258</td>\n",
       "      <td>90107.2</td>\n",
       "      <td>91441.2</td>\n",
       "      <td>92361.3</td>\n",
       "      <td>...</td>\n",
       "      <td>521612</td>\n",
       "      <td>516411</td>\n",
       "      <td>522770</td>\n",
       "      <td>529115</td>\n",
       "      <td>527494</td>\n",
       "      <td>517266</td>\n",
       "      <td>521528</td>\n",
       "      <td>525762</td>\n",
       "      <td>535663</td>\n",
       "      <td>536263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bexley</td>\n",
       "      <td>E09000004</td>\n",
       "      <td>64958.1</td>\n",
       "      <td>64787.9</td>\n",
       "      <td>64367.5</td>\n",
       "      <td>64277.7</td>\n",
       "      <td>63997.1</td>\n",
       "      <td>64252.3</td>\n",
       "      <td>63722.7</td>\n",
       "      <td>64432.6</td>\n",
       "      <td>...</td>\n",
       "      <td>336863</td>\n",
       "      <td>338817</td>\n",
       "      <td>339095</td>\n",
       "      <td>341997</td>\n",
       "      <td>338857</td>\n",
       "      <td>340723</td>\n",
       "      <td>343385</td>\n",
       "      <td>348046</td>\n",
       "      <td>345461</td>\n",
       "      <td>344254</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 312 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "0          Unnamed: 0        NaN  1995-01-01 00:00:00  1995-02-01 00:00:00  \\\n",
       "0          Unnamed: 0        NaT  1995-01-01 00:00:00  1995-02-01 00:00:00   \n",
       "1      City of London  E09000001                91449              82202.8   \n",
       "2  Barking & Dagenham  E09000002              50460.2              51085.8   \n",
       "3              Barnet  E09000003              93284.5              93190.2   \n",
       "4              Bexley  E09000004              64958.1              64787.9   \n",
       "\n",
       "0  1995-03-01 00:00:00  1995-04-01 00:00:00  1995-05-01 00:00:00  \\\n",
       "0  1995-03-01 00:00:00  1995-04-01 00:00:00  1995-05-01 00:00:00   \n",
       "1              79120.7              77101.2              84409.1   \n",
       "2                51269              53133.5              53042.2   \n",
       "3              92247.5              90762.9                90258   \n",
       "4              64367.5              64277.7              63997.1   \n",
       "\n",
       "0  1995-06-01 00:00:00  1995-07-01 00:00:00  1995-08-01 00:00:00  ...  \\\n",
       "0  1995-06-01 00:00:00  1995-07-01 00:00:00  1995-08-01 00:00:00  ...   \n",
       "1              94900.5               110128               112329  ...   \n",
       "2              53700.3              52113.1              52232.2  ...   \n",
       "3              90107.2              91441.2              92361.3  ...   \n",
       "4              64252.3              63722.7              64432.6  ...   \n",
       "\n",
       "0  2020-01-01 00:00:00  2020-02-01 00:00:00  2020-03-01 00:00:00  \\\n",
       "0  2020-01-01 00:00:00  2020-02-01 00:00:00  2020-03-01 00:00:00   \n",
       "1               773679               792769               828658   \n",
       "2               303054               302748               301570   \n",
       "3               521612               516411               522770   \n",
       "4               336863               338817               339095   \n",
       "\n",
       "0  2020-04-01 00:00:00  2020-05-01 00:00:00  2020-06-01 00:00:00  \\\n",
       "0  2020-04-01 00:00:00  2020-05-01 00:00:00  2020-06-01 00:00:00   \n",
       "1               900126               904803               869929   \n",
       "2               293553               292478               299074   \n",
       "3               529115               527494               517266   \n",
       "4               341997               338857               340723   \n",
       "\n",
       "0  2020-07-01 00:00:00  2020-08-01 00:00:00  2020-09-01 00:00:00  \\\n",
       "0  2020-07-01 00:00:00  2020-08-01 00:00:00  2020-09-01 00:00:00   \n",
       "1               785001               794881               768714   \n",
       "2               303701               304696               301170   \n",
       "3               521528               525762               535663   \n",
       "4               343385               348046               345461   \n",
       "\n",
       "0  2020-10-01 00:00:00  \n",
       "0  2020-10-01 00:00:00  \n",
       "1               721490  \n",
       "2               308583  \n",
       "3               536263  \n",
       "4               344254  \n",
       "\n",
       "[5 rows x 312 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#assigning 1st row as column names\n",
    "properties.columns = properties.iloc[0]\n",
    "properties.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1995-07-01 00:00:00</th>\n",
       "      <th>1995-08-01 00:00:00</th>\n",
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       "      <th>2020-02-01 00:00:00</th>\n",
       "      <th>2020-03-01 00:00:00</th>\n",
       "      <th>2020-04-01 00:00:00</th>\n",
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       "    </tr>\n",
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       "    <tr>\n",
       "      <th>1</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",
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       "      <td>721490</td>\n",
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       "    <tr>\n",
       "      <th>2</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>303054</td>\n",
       "      <td>302748</td>\n",
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       "      <td>293553</td>\n",
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       "      <td>303701</td>\n",
       "      <td>304696</td>\n",
       "      <td>301170</td>\n",
       "      <td>308583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Barnet</td>\n",
       "      <td>E09000003</td>\n",
       "      <td>93284.5</td>\n",
       "      <td>93190.2</td>\n",
       "      <td>92247.5</td>\n",
       "      <td>90762.9</td>\n",
       "      <td>90258</td>\n",
       "      <td>90107.2</td>\n",
       "      <td>91441.2</td>\n",
       "      <td>92361.3</td>\n",
       "      <td>...</td>\n",
       "      <td>521612</td>\n",
       "      <td>516411</td>\n",
       "      <td>522770</td>\n",
       "      <td>529115</td>\n",
       "      <td>527494</td>\n",
       "      <td>517266</td>\n",
       "      <td>521528</td>\n",
       "      <td>525762</td>\n",
       "      <td>535663</td>\n",
       "      <td>536263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bexley</td>\n",
       "      <td>E09000004</td>\n",
       "      <td>64958.1</td>\n",
       "      <td>64787.9</td>\n",
       "      <td>64367.5</td>\n",
       "      <td>64277.7</td>\n",
       "      <td>63997.1</td>\n",
       "      <td>64252.3</td>\n",
       "      <td>63722.7</td>\n",
       "      <td>64432.6</td>\n",
       "      <td>...</td>\n",
       "      <td>336863</td>\n",
       "      <td>338817</td>\n",
       "      <td>339095</td>\n",
       "      <td>341997</td>\n",
       "      <td>338857</td>\n",
       "      <td>340723</td>\n",
       "      <td>343385</td>\n",
       "      <td>348046</td>\n",
       "      <td>345461</td>\n",
       "      <td>344254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Brent</td>\n",
       "      <td>E09000005</td>\n",
       "      <td>71306.6</td>\n",
       "      <td>72022.3</td>\n",
       "      <td>72015.8</td>\n",
       "      <td>72965.6</td>\n",
       "      <td>73704</td>\n",
       "      <td>74310.5</td>\n",
       "      <td>74127</td>\n",
       "      <td>73547</td>\n",
       "      <td>...</td>\n",
       "      <td>428436</td>\n",
       "      <td>469962</td>\n",
       "      <td>465941</td>\n",
       "      <td>469583</td>\n",
       "      <td>481570</td>\n",
       "      <td>485388</td>\n",
       "      <td>485619</td>\n",
       "      <td>496606</td>\n",
       "      <td>522546</td>\n",
       "      <td>518636</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 312 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "0          Unnamed: 0        NaN 1995-01-01 00:00:00 1995-02-01 00:00:00  \\\n",
       "1      City of London  E09000001               91449             82202.8   \n",
       "2  Barking & Dagenham  E09000002             50460.2             51085.8   \n",
       "3              Barnet  E09000003             93284.5             93190.2   \n",
       "4              Bexley  E09000004             64958.1             64787.9   \n",
       "5               Brent  E09000005             71306.6             72022.3   \n",
       "\n",
       "0 1995-03-01 00:00:00 1995-04-01 00:00:00 1995-05-01 00:00:00  \\\n",
       "1             79120.7             77101.2             84409.1   \n",
       "2               51269             53133.5             53042.2   \n",
       "3             92247.5             90762.9               90258   \n",
       "4             64367.5             64277.7             63997.1   \n",
       "5             72015.8             72965.6               73704   \n",
       "\n",
       "0 1995-06-01 00:00:00 1995-07-01 00:00:00 1995-08-01 00:00:00  ...  \\\n",
       "1             94900.5              110128              112329  ...   \n",
       "2             53700.3             52113.1             52232.2  ...   \n",
       "3             90107.2             91441.2             92361.3  ...   \n",
       "4             64252.3             63722.7             64432.6  ...   \n",
       "5             74310.5               74127               73547  ...   \n",
       "\n",
       "0 2020-01-01 00:00:00 2020-02-01 00:00:00 2020-03-01 00:00:00  \\\n",
       "1              773679              792769              828658   \n",
       "2              303054              302748              301570   \n",
       "3              521612              516411              522770   \n",
       "4              336863              338817              339095   \n",
       "5              428436              469962              465941   \n",
       "\n",
       "0 2020-04-01 00:00:00 2020-05-01 00:00:00 2020-06-01 00:00:00  \\\n",
       "1              900126              904803              869929   \n",
       "2              293553              292478              299074   \n",
       "3              529115              527494              517266   \n",
       "4              341997              338857              340723   \n",
       "5              469583              481570              485388   \n",
       "\n",
       "0 2020-07-01 00:00:00 2020-08-01 00:00:00 2020-09-01 00:00:00  \\\n",
       "1              785001              794881              768714   \n",
       "2              303701              304696              301170   \n",
       "3              521528              525762              535663   \n",
       "4              343385              348046              345461   \n",
       "5              485619              496606              522546   \n",
       "\n",
       "0 2020-10-01 00:00:00  \n",
       "1              721490  \n",
       "2              308583  \n",
       "3              536263  \n",
       "4              344254  \n",
       "5              518636  \n",
       "\n",
       "[5 rows x 312 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dropping duplicate row\n",
    "properties = properties.drop(properties.index[0])\n",
    "properties.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "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",
   "execution_count": 9,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "GKkmn1AnoVZS"
   },
   "outputs": [
    {
     "data": {
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       "      <th>1995-01-01 00:00:00</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>City of London</td>\n",
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       "      <td>91449</td>\n",
       "      <td>82202.8</td>\n",
       "      <td>79120.7</td>\n",
       "      <td>77101.2</td>\n",
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       "      <td>768714</td>\n",
       "      <td>721490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</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",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Barnet</td>\n",
       "      <td>E09000003</td>\n",
       "      <td>93284.5</td>\n",
       "      <td>93190.2</td>\n",
       "      <td>92247.5</td>\n",
       "      <td>90762.9</td>\n",
       "      <td>90258</td>\n",
       "      <td>90107.2</td>\n",
       "      <td>91441.2</td>\n",
       "      <td>92361.3</td>\n",
       "      <td>...</td>\n",
       "      <td>521612</td>\n",
       "      <td>516411</td>\n",
       "      <td>522770</td>\n",
       "      <td>529115</td>\n",
       "      <td>527494</td>\n",
       "      <td>517266</td>\n",
       "      <td>521528</td>\n",
       "      <td>525762</td>\n",
       "      <td>535663</td>\n",
       "      <td>536263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bexley</td>\n",
       "      <td>E09000004</td>\n",
       "      <td>64958.1</td>\n",
       "      <td>64787.9</td>\n",
       "      <td>64367.5</td>\n",
       "      <td>64277.7</td>\n",
       "      <td>63997.1</td>\n",
       "      <td>64252.3</td>\n",
       "      <td>63722.7</td>\n",
       "      <td>64432.6</td>\n",
       "      <td>...</td>\n",
       "      <td>336863</td>\n",
       "      <td>338817</td>\n",
       "      <td>339095</td>\n",
       "      <td>341997</td>\n",
       "      <td>338857</td>\n",
       "      <td>340723</td>\n",
       "      <td>343385</td>\n",
       "      <td>348046</td>\n",
       "      <td>345461</td>\n",
       "      <td>344254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Brent</td>\n",
       "      <td>E09000005</td>\n",
       "      <td>71306.6</td>\n",
       "      <td>72022.3</td>\n",
       "      <td>72015.8</td>\n",
       "      <td>72965.6</td>\n",
       "      <td>73704</td>\n",
       "      <td>74310.5</td>\n",
       "      <td>74127</td>\n",
       "      <td>73547</td>\n",
       "      <td>...</td>\n",
       "      <td>428436</td>\n",
       "      <td>469962</td>\n",
       "      <td>465941</td>\n",
       "      <td>469583</td>\n",
       "      <td>481570</td>\n",
       "      <td>485388</td>\n",
       "      <td>485619</td>\n",
       "      <td>496606</td>\n",
       "      <td>522546</td>\n",
       "      <td>518636</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 312 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "0      London_Borough         ID 1995-01-01 00:00:00 1995-02-01 00:00:00  \\\n",
       "1      City of London  E09000001               91449             82202.8   \n",
       "2  Barking & Dagenham  E09000002             50460.2             51085.8   \n",
       "3              Barnet  E09000003             93284.5             93190.2   \n",
       "4              Bexley  E09000004             64958.1             64787.9   \n",
       "5               Brent  E09000005             71306.6             72022.3   \n",
       "\n",
       "0 1995-03-01 00:00:00 1995-04-01 00:00:00 1995-05-01 00:00:00  \\\n",
       "1             79120.7             77101.2             84409.1   \n",
       "2               51269             53133.5             53042.2   \n",
       "3             92247.5             90762.9               90258   \n",
       "4             64367.5             64277.7             63997.1   \n",
       "5             72015.8             72965.6               73704   \n",
       "\n",
       "0 1995-06-01 00:00:00 1995-07-01 00:00:00 1995-08-01 00:00:00  ...  \\\n",
       "1             94900.5              110128              112329  ...   \n",
       "2             53700.3             52113.1             52232.2  ...   \n",
       "3             90107.2             91441.2             92361.3  ...   \n",
       "4             64252.3             63722.7             64432.6  ...   \n",
       "5             74310.5               74127               73547  ...   \n",
       "\n",
       "0 2020-01-01 00:00:00 2020-02-01 00:00:00 2020-03-01 00:00:00  \\\n",
       "1              773679              792769              828658   \n",
       "2              303054              302748              301570   \n",
       "3              521612              516411              522770   \n",
       "4              336863              338817              339095   \n",
       "5              428436              469962              465941   \n",
       "\n",
       "0 2020-04-01 00:00:00 2020-05-01 00:00:00 2020-06-01 00:00:00  \\\n",
       "1              900126              904803              869929   \n",
       "2              293553              292478              299074   \n",
       "3              529115              527494              517266   \n",
       "4              341997              338857              340723   \n",
       "5              469583              481570              485388   \n",
       "\n",
       "0 2020-07-01 00:00:00 2020-08-01 00:00:00 2020-09-01 00:00:00  \\\n",
       "1              785001              794881              768714   \n",
       "2              303701              304696              301170   \n",
       "3              521528              525762              535663   \n",
       "4              343385              348046              345461   \n",
       "5              485619              496606              522546   \n",
       "\n",
       "0 2020-10-01 00:00:00  \n",
       "1              721490  \n",
       "2              308583  \n",
       "3              536263  \n",
       "4              344254  \n",
       "5              518636  \n",
       "\n",
       "[5 rows x 312 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#renaming columns\n",
    "properties = properties.rename(columns={ 'Unnamed: 0':'London_Borough', pd.NaT: 'ID'})\n",
    "properties.head()\n",
    "\n",
    "\n",
    "#properties.columns[0]: \"London_Borough\", properties.columns[1]: \"ID\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "jy8BzXHmoWEw"
   },
   "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",
   "execution_count": 10,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "S2wM0qLuo2Zt"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>London_Borough</th>\n",
       "      <th>ID</th>\n",
       "      <th>0</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",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Barnet</td>\n",
       "      <td>E09000003</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>93284.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bexley</td>\n",
       "      <td>E09000004</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>64958.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Brent</td>\n",
       "      <td>E09000005</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>71306.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       London_Borough         ID          0    value\n",
       "0      City of London  E09000001 1995-01-01    91449\n",
       "1  Barking & Dagenham  E09000002 1995-01-01  50460.2\n",
       "2              Barnet  E09000003 1995-01-01  93284.5\n",
       "3              Bexley  E09000004 1995-01-01  64958.1\n",
       "4               Brent  E09000005 1995-01-01  71306.6"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#melt and check\n",
    "properties = pd.melt(properties, id_vars= ['London_Borough', 'ID'])\n",
    "properties.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "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",
   "execution_count": 11,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ZcR4IHbcpOaq"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>London_Borough</th>\n",
       "      <th>ID</th>\n",
       "      <th>Month</th>\n",
       "      <th>Average_price</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",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Barnet</td>\n",
       "      <td>E09000003</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>93284.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bexley</td>\n",
       "      <td>E09000004</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>64958.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Brent</td>\n",
       "      <td>E09000005</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>71306.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       London_Borough         ID      Month Average_price\n",
       "0      City of London  E09000001 1995-01-01         91449\n",
       "1  Barking & Dagenham  E09000002 1995-01-01       50460.2\n",
       "2              Barnet  E09000003 1995-01-01       93284.5\n",
       "3              Bexley  E09000004 1995-01-01       64958.1\n",
       "4               Brent  E09000005 1995-01-01       71306.6"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#rename columns and check\n",
    "properties = properties.rename(columns = {0: 'Month', 'value': 'Average_price'})\n",
    "properties.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "London_Borough            object\n",
       "ID                        object\n",
       "Month             datetime64[ns]\n",
       "Average_price             object\n",
       "dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check data types\n",
    "properties.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "London_Borough            object\n",
       "ID                        object\n",
       "Month             datetime64[ns]\n",
       "Average_price            float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#change Average_price to float and check again\n",
    "properties['Average_price'] = pd.to_numeric(properties['Average_price'])\n",
    "properties.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "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",
   "execution_count": 14,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "BnvTW5a3p0fC"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "London_Borough    14880\n",
       "ID                13950\n",
       "Month             14880\n",
       "Average_price     13950\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check for missing data\n",
    "properties.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['City of London', 'Barking & Dagenham', 'Barnet', 'Bexley',\n",
       "       'Brent', 'Bromley', 'Camden', 'Croydon', 'Ealing', 'Enfield',\n",
       "       'Greenwich', 'Hackney', 'Hammersmith & Fulham', 'Haringey',\n",
       "       'Harrow', 'Havering', 'Hillingdon', 'Hounslow', 'Islington',\n",
       "       'Kensington & Chelsea', 'Kingston upon Thames', 'Lambeth',\n",
       "       'Lewisham', 'Merton', 'Newham', 'Redbridge',\n",
       "       'Richmond upon Thames', 'Southwark', 'Sutton', 'Tower Hamlets',\n",
       "       'Waltham Forest', 'Wandsworth', 'Westminster', 'Unnamed: 34',\n",
       "       'Inner London', 'Outer London', 'Unnamed: 37', 'NORTH EAST',\n",
       "       'NORTH WEST', 'YORKS & THE HUMBER', 'EAST MIDLANDS',\n",
       "       'WEST MIDLANDS', 'EAST OF ENGLAND', 'LONDON', 'SOUTH EAST',\n",
       "       'SOUTH WEST', 'Unnamed: 47', 'England'], dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#find non-boroughs\n",
    "properties['London_Borough'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>London_Borough</th>\n",
       "      <th>ID</th>\n",
       "      <th>Month</th>\n",
       "      <th>Average_price</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>91448.98487</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>50460.22660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Barnet</td>\n",
       "      <td>E09000003</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>93284.51832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bexley</td>\n",
       "      <td>E09000004</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>64958.09036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Brent</td>\n",
       "      <td>E09000005</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>71306.56698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Bromley</td>\n",
       "      <td>E09000006</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>81671.47692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Camden</td>\n",
       "      <td>E09000007</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>120932.88810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Croydon</td>\n",
       "      <td>E09000008</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>69158.16225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Ealing</td>\n",
       "      <td>E09000009</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>79885.89069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Enfield</td>\n",
       "      <td>E09000010</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>72514.69096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Greenwich</td>\n",
       "      <td>E09000011</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>62300.10169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Hackney</td>\n",
       "      <td>E09000012</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>61296.52637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Hammersmith &amp; Fulham</td>\n",
       "      <td>E09000013</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>124902.86020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Haringey</td>\n",
       "      <td>E09000014</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>76287.56947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Harrow</td>\n",
       "      <td>E09000015</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>84769.52599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Havering</td>\n",
       "      <td>E09000016</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>68000.13774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Hillingdon</td>\n",
       "      <td>E09000017</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>73834.82964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Hounslow</td>\n",
       "      <td>E09000018</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>72231.70537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Islington</td>\n",
       "      <td>E09000019</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>92516.48557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Kensington &amp; Chelsea</td>\n",
       "      <td>E09000020</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>182694.83260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Kingston upon Thames</td>\n",
       "      <td>E09000021</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>80875.84843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Lambeth</td>\n",
       "      <td>E09000022</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>67770.98843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Lewisham</td>\n",
       "      <td>E09000023</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>60491.26109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Merton</td>\n",
       "      <td>E09000024</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>82070.61330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Newham</td>\n",
       "      <td>E09000025</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>53539.31919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Redbridge</td>\n",
       "      <td>E09000026</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>72189.58437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Richmond upon Thames</td>\n",
       "      <td>E09000027</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>109326.12450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Southwark</td>\n",
       "      <td>E09000028</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>67885.20344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Sutton</td>\n",
       "      <td>E09000029</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>71536.97357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Tower Hamlets</td>\n",
       "      <td>E09000030</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>59865.18995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Waltham Forest</td>\n",
       "      <td>E09000031</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>61319.44913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Wandsworth</td>\n",
       "      <td>E09000032</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>88559.04381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Westminster</td>\n",
       "      <td>E09000033</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>133025.27720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Inner London</td>\n",
       "      <td>E13000001</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>78251.97650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Outer London</td>\n",
       "      <td>E13000002</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>72958.79836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>NORTH EAST</td>\n",
       "      <td>E12000001</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>42076.35411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>NORTH WEST</td>\n",
       "      <td>E12000002</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>43958.48001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>YORKS &amp; THE HUMBER</td>\n",
       "      <td>E12000003</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>44803.42878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>EAST MIDLANDS</td>\n",
       "      <td>E12000004</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>45544.52227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>WEST MIDLANDS</td>\n",
       "      <td>E12000005</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>48527.52339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>EAST OF ENGLAND</td>\n",
       "      <td>E12000006</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>56701.59610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>LONDON</td>\n",
       "      <td>E12000007</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>74435.76052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>SOUTH EAST</td>\n",
       "      <td>E12000008</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>64018.87894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>SOUTH WEST</td>\n",
       "      <td>E12000009</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>54705.15790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>England</td>\n",
       "      <td>E92000001</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>53202.77128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>City of London</td>\n",
       "      <td>E09000001</td>\n",
       "      <td>1995-02-01</td>\n",
       "      <td>82202.77314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Barking &amp; Dagenham</td>\n",
       "      <td>E09000002</td>\n",
       "      <td>1995-02-01</td>\n",
       "      <td>51085.77983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Barnet</td>\n",
       "      <td>E09000003</td>\n",
       "      <td>1995-02-01</td>\n",
       "      <td>93190.16963</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          London_Borough         ID      Month  Average_price\n",
       "0         City of London  E09000001 1995-01-01    91448.98487\n",
       "1     Barking & Dagenham  E09000002 1995-01-01    50460.22660\n",
       "2                 Barnet  E09000003 1995-01-01    93284.51832\n",
       "3                 Bexley  E09000004 1995-01-01    64958.09036\n",
       "4                  Brent  E09000005 1995-01-01    71306.56698\n",
       "5                Bromley  E09000006 1995-01-01    81671.47692\n",
       "6                 Camden  E09000007 1995-01-01   120932.88810\n",
       "7                Croydon  E09000008 1995-01-01    69158.16225\n",
       "8                 Ealing  E09000009 1995-01-01    79885.89069\n",
       "9                Enfield  E09000010 1995-01-01    72514.69096\n",
       "10             Greenwich  E09000011 1995-01-01    62300.10169\n",
       "11               Hackney  E09000012 1995-01-01    61296.52637\n",
       "12  Hammersmith & Fulham  E09000013 1995-01-01   124902.86020\n",
       "13              Haringey  E09000014 1995-01-01    76287.56947\n",
       "14                Harrow  E09000015 1995-01-01    84769.52599\n",
       "15              Havering  E09000016 1995-01-01    68000.13774\n",
       "16            Hillingdon  E09000017 1995-01-01    73834.82964\n",
       "17              Hounslow  E09000018 1995-01-01    72231.70537\n",
       "18             Islington  E09000019 1995-01-01    92516.48557\n",
       "19  Kensington & Chelsea  E09000020 1995-01-01   182694.83260\n",
       "20  Kingston upon Thames  E09000021 1995-01-01    80875.84843\n",
       "21               Lambeth  E09000022 1995-01-01    67770.98843\n",
       "22              Lewisham  E09000023 1995-01-01    60491.26109\n",
       "23                Merton  E09000024 1995-01-01    82070.61330\n",
       "24                Newham  E09000025 1995-01-01    53539.31919\n",
       "25             Redbridge  E09000026 1995-01-01    72189.58437\n",
       "26  Richmond upon Thames  E09000027 1995-01-01   109326.12450\n",
       "27             Southwark  E09000028 1995-01-01    67885.20344\n",
       "28                Sutton  E09000029 1995-01-01    71536.97357\n",
       "29         Tower Hamlets  E09000030 1995-01-01    59865.18995\n",
       "30        Waltham Forest  E09000031 1995-01-01    61319.44913\n",
       "31            Wandsworth  E09000032 1995-01-01    88559.04381\n",
       "32           Westminster  E09000033 1995-01-01   133025.27720\n",
       "34          Inner London  E13000001 1995-01-01    78251.97650\n",
       "35          Outer London  E13000002 1995-01-01    72958.79836\n",
       "37            NORTH EAST  E12000001 1995-01-01    42076.35411\n",
       "38            NORTH WEST  E12000002 1995-01-01    43958.48001\n",
       "39    YORKS & THE HUMBER  E12000003 1995-01-01    44803.42878\n",
       "40         EAST MIDLANDS  E12000004 1995-01-01    45544.52227\n",
       "41         WEST MIDLANDS  E12000005 1995-01-01    48527.52339\n",
       "42       EAST OF ENGLAND  E12000006 1995-01-01    56701.59610\n",
       "43                LONDON  E12000007 1995-01-01    74435.76052\n",
       "44            SOUTH EAST  E12000008 1995-01-01    64018.87894\n",
       "45            SOUTH WEST  E12000009 1995-01-01    54705.15790\n",
       "47               England  E92000001 1995-01-01    53202.77128\n",
       "48        City of London  E09000001 1995-02-01    82202.77314\n",
       "49    Barking & Dagenham  E09000002 1995-02-01    51085.77983\n",
       "50                Barnet  E09000003 1995-02-01    93190.16963"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#delete NaN values\n",
    "noNaN = properties.dropna()\n",
    "noNaN.head(48)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "London_Borough    13950\n",
       "ID                13950\n",
       "Month             13950\n",
       "Average_price     13950\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#confirm NaN values have been deleted\n",
    "noNaN.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['City of London', 'Barking & Dagenham', 'Barnet', 'Bexley',\n",
       "       'Brent', 'Bromley', 'Camden', 'Croydon', 'Ealing', 'Enfield',\n",
       "       'Greenwich', 'Hackney', 'Hammersmith & Fulham', 'Haringey',\n",
       "       'Harrow', 'Havering', 'Hillingdon', 'Hounslow', 'Islington',\n",
       "       'Kensington & Chelsea', 'Kingston upon Thames', 'Lambeth',\n",
       "       'Lewisham', 'Merton', 'Newham', 'Redbridge',\n",
       "       'Richmond upon Thames', 'Southwark', 'Sutton', 'Tower Hamlets',\n",
       "       'Waltham Forest', 'Wandsworth', 'Westminster', 'Inner London',\n",
       "       'Outer London', 'NORTH EAST', 'NORTH WEST', 'YORKS & THE HUMBER',\n",
       "       'EAST MIDLANDS', 'WEST MIDLANDS', 'EAST OF ENGLAND', 'LONDON',\n",
       "       'SOUTH EAST', 'SOUTH WEST', 'England'], dtype=object)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#another way to check\n",
    "noNaN['London_Borough'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# list of nonboroughs to remove \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']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>London_Borough</th>\n",
       "      <th>ID</th>\n",
       "      <th>Month</th>\n",
       "      <th>Average_price</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>91448.98487</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>50460.22660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Barnet</td>\n",
       "      <td>E09000003</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>93284.51832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bexley</td>\n",
       "      <td>E09000004</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>64958.09036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Brent</td>\n",
       "      <td>E09000005</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>71306.56698</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       London_Borough         ID      Month  Average_price\n",
       "0      City of London  E09000001 1995-01-01    91448.98487\n",
       "1  Barking & Dagenham  E09000002 1995-01-01    50460.22660\n",
       "2              Barnet  E09000003 1995-01-01    93284.51832\n",
       "3              Bexley  E09000004 1995-01-01    64958.09036\n",
       "4               Brent  E09000005 1995-01-01    71306.56698"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#remove nonboroughs and check\n",
    "noNaN = noNaN[~noNaN.London_Borough.isin(nonBoroughs)]\n",
    "noNaN.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>London_Borough</th>\n",
       "      <th>ID</th>\n",
       "      <th>Month</th>\n",
       "      <th>Average_price</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>91448.98487</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>50460.22660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Barnet</td>\n",
       "      <td>E09000003</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>93284.51832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bexley</td>\n",
       "      <td>E09000004</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>64958.09036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Brent</td>\n",
       "      <td>E09000005</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>71306.56698</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       London_Borough         ID      Month  Average_price\n",
       "0      City of London  E09000001 1995-01-01    91448.98487\n",
       "1  Barking & Dagenham  E09000002 1995-01-01    50460.22660\n",
       "2              Barnet  E09000003 1995-01-01    93284.51832\n",
       "3              Bexley  E09000004 1995-01-01    64958.09036\n",
       "4               Brent  E09000005 1995-01-01    71306.56698"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#final, cleaned DataFrame\n",
    "df = noNaN\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "London_Borough            object\n",
       "ID                        object\n",
       "Month             datetime64[ns]\n",
       "Average_price            float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes\n",
    "#same as properties.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "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",
   "execution_count": 31,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "nAg5pT9cqHAR"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Price')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#check change in price for one borough, Brent\n",
    "brent_prices = df[df['London_Borough'] == 'Brent']\n",
    "#set and display plot\n",
    "ax = brent_prices.plot(kind ='line', x = 'Month', y='Average_price')\n",
    "ax.set_ylabel('Price')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "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",
   "execution_count": 32,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "e0DF92cyqnu8"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>London_Borough</th>\n",
       "      <th>ID</th>\n",
       "      <th>Month</th>\n",
       "      <th>Average_price</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14860</th>\n",
       "      <td>Sutton</td>\n",
       "      <td>E09000029</td>\n",
       "      <td>2020-10-01</td>\n",
       "      <td>385190.4259</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14861</th>\n",
       "      <td>Tower Hamlets</td>\n",
       "      <td>E09000030</td>\n",
       "      <td>2020-10-01</td>\n",
       "      <td>470150.7691</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14862</th>\n",
       "      <td>Waltham Forest</td>\n",
       "      <td>E09000031</td>\n",
       "      <td>2020-10-01</td>\n",
       "      <td>459386.6641</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14863</th>\n",
       "      <td>Wandsworth</td>\n",
       "      <td>E09000032</td>\n",
       "      <td>2020-10-01</td>\n",
       "      <td>638185.4331</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14864</th>\n",
       "      <td>Westminster</td>\n",
       "      <td>E09000033</td>\n",
       "      <td>2020-10-01</td>\n",
       "      <td>930164.4873</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       London_Borough         ID      Month  Average_price  Year\n",
       "14860          Sutton  E09000029 2020-10-01    385190.4259  2020\n",
       "14861   Tower Hamlets  E09000030 2020-10-01    470150.7691  2020\n",
       "14862  Waltham Forest  E09000031 2020-10-01    459386.6641  2020\n",
       "14863      Wandsworth  E09000032 2020-10-01    638185.4331  2020\n",
       "14864     Westminster  E09000033 2020-10-01    930164.4873  2020"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#reduce the number of data points and check\n",
    "df['Year'] = df['Month'].apply(lambda t: t.year)\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Average_price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>London_Borough</th>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Brent</th>\n",
       "      <th>2000</th>\n",
       "      <td>140962.500458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Westminster</th>\n",
       "      <th>2011</th>\n",
       "      <td>632513.602858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barking &amp; Dagenham</th>\n",
       "      <th>2002</th>\n",
       "      <td>112221.912482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Haringey</th>\n",
       "      <th>2000</th>\n",
       "      <td>145997.085533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Havering</th>\n",
       "      <th>2004</th>\n",
       "      <td>195992.397675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Merton</th>\n",
       "      <th>2019</th>\n",
       "      <td>506193.058817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sutton</th>\n",
       "      <th>2018</th>\n",
       "      <td>379262.580582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tower Hamlets</th>\n",
       "      <th>2009</th>\n",
       "      <td>258248.290575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wandsworth</th>\n",
       "      <th>2018</th>\n",
       "      <td>596649.139044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hillingdon</th>\n",
       "      <th>2016</th>\n",
       "      <td>400791.486383</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Average_price\n",
       "London_Borough     Year               \n",
       "Brent              2000  140962.500458\n",
       "Westminster        2011  632513.602858\n",
       "Barking & Dagenham 2002  112221.912482\n",
       "Haringey           2000  145997.085533\n",
       "Havering           2004  195992.397675\n",
       "Merton             2019  506193.058817\n",
       "Sutton             2018  379262.580582\n",
       "Tower Hamlets      2009  258248.290575\n",
       "Wandsworth         2018  596649.139044\n",
       "Hillingdon         2016  400791.486383"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calculate the mean for each borough and each year\n",
    "dfgrp = df.groupby(by=['London_Borough', 'Year']).mean()\n",
    "dfgrp.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>London_Borough</th>\n",
       "      <th>Year</th>\n",
       "      <th>Average_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Barking &amp; Dagenham</td>\n",
       "      <td>1995</td>\n",
       "      <td>51817.969390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Barking &amp; Dagenham</td>\n",
       "      <td>1996</td>\n",
       "      <td>51718.192690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Barking &amp; Dagenham</td>\n",
       "      <td>1997</td>\n",
       "      <td>55974.262309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Barking &amp; Dagenham</td>\n",
       "      <td>1998</td>\n",
       "      <td>60285.821083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Barking &amp; Dagenham</td>\n",
       "      <td>1999</td>\n",
       "      <td>65320.934441</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       London_Borough  Year  Average_price\n",
       "0  Barking & Dagenham  1995   51817.969390\n",
       "1  Barking & Dagenham  1996   51718.192690\n",
       "2  Barking & Dagenham  1997   55974.262309\n",
       "3  Barking & Dagenham  1998   60285.821083\n",
       "4  Barking & Dagenham  1999   65320.934441"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#reindexin and checking\n",
    "dfg = dfg.reset_index()\n",
    "dfg.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "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",
   "execution_count": 40,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "cKTyr437UgDa"
   },
   "outputs": [],
   "source": [
    "#calculate ratio\n",
    "def create_price_ratio(d):\n",
    "    y1998 = float(d['Average_price'][d['Year']==1998])\n",
    "    y2018 = float(d['Average_price'][d['Year']==2018])\n",
    "    ratio = [y1998/y2018]\n",
    "    return ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Barking & Dagenham': [0.20422256235393685], 'Barnet': [0.22945274120785797], 'Bexley': [0.2353507654063011], 'Brent': [0.2043086864360114], 'Bromley': [0.24421308489837312], 'Camden': [0.20261973503252542], 'City of London': [0.18862157770244367], 'Croydon': [0.23803288028014047], 'Ealing': [0.23194048191708755], 'Enfield': [0.23455064269011863], 'Greenwich': [0.20995010893854218], 'Hackney': [0.16133493530705734], 'Hammersmith & Fulham': [0.24167443054605853], 'Haringey': [0.19475619095546956], 'Harrow': [0.24635417785626296], 'Havering': [0.23120155787014757], 'Hillingdon': [0.23807975835429931], 'Hounslow': [0.25148317824115635], 'Islington': [0.20643891170300285], 'Kensington & Chelsea': [0.19675491852791563], 'Kingston upon Thames': [0.23416190234282552], 'Lambeth': [0.20170435486140822], 'Lewisham': [0.1835124676472171], 'Merton': [0.21091380604361798], 'Newham': [0.18848754146121072], 'Redbridge': [0.2236545053715767], 'Richmond upon Thames': [0.24967779731157863], 'Southwark': [0.18127484171283462], 'Sutton': [0.24280551426824518], 'Tower Hamlets': [0.2161367227623553], 'Waltham Forest': [0.1713867782439487], 'Wandsworth': [0.2101851809159322], 'Westminster': [0.18679140473024677]}\n"
     ]
    }
   ],
   "source": [
    "#calculating the ratio for all boroughs\n",
    "final = {}\n",
    "\n",
    "for b in dfg['London_Borough'].unique():\n",
    "    borough = dfg[dfg['London_Borough'] == b]\n",
    "    final[b] = create_price_ratio(borough)\n",
    "\n",
    "print(final) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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>City of London</th>\n",
       "      <th>Croydon</th>\n",
       "      <th>Ealing</th>\n",
       "      <th>Enfield</th>\n",
       "      <th>...</th>\n",
       "      <th>Merton</th>\n",
       "      <th>Newham</th>\n",
       "      <th>Redbridge</th>\n",
       "      <th>Richmond upon Thames</th>\n",
       "      <th>Southwark</th>\n",
       "      <th>Sutton</th>\n",
       "      <th>Tower Hamlets</th>\n",
       "      <th>Waltham Forest</th>\n",
       "      <th>Wandsworth</th>\n",
       "      <th>Westminster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.204223</td>\n",
       "      <td>0.229453</td>\n",
       "      <td>0.235351</td>\n",
       "      <td>0.204309</td>\n",
       "      <td>0.244213</td>\n",
       "      <td>0.20262</td>\n",
       "      <td>0.188622</td>\n",
       "      <td>0.238033</td>\n",
       "      <td>0.23194</td>\n",
       "      <td>0.234551</td>\n",
       "      <td>...</td>\n",
       "      <td>0.210914</td>\n",
       "      <td>0.188488</td>\n",
       "      <td>0.223655</td>\n",
       "      <td>0.249678</td>\n",
       "      <td>0.181275</td>\n",
       "      <td>0.242806</td>\n",
       "      <td>0.216137</td>\n",
       "      <td>0.171387</td>\n",
       "      <td>0.210185</td>\n",
       "      <td>0.186791</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Barking & Dagenham    Barnet    Bexley     Brent   Bromley   Camden  \\\n",
       "0            0.204223  0.229453  0.235351  0.204309  0.244213  0.20262   \n",
       "\n",
       "   City of London   Croydon   Ealing   Enfield  ...    Merton    Newham  \\\n",
       "0        0.188622  0.238033  0.23194  0.234551  ...  0.210914  0.188488   \n",
       "\n",
       "   Redbridge  Richmond upon Thames  Southwark    Sutton  Tower Hamlets  \\\n",
       "0   0.223655              0.249678   0.181275  0.242806       0.216137   \n",
       "\n",
       "   Waltham Forest  Wandsworth  Westminster  \n",
       "0        0.171387    0.210185     0.186791  \n",
       "\n",
       "[1 rows x 33 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#converting dictionary to DataFrame\n",
    "df_ratios = pd.DataFrame(final)\n",
    "df_ratios.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Barking &amp; Dagenham</td>\n",
       "      <td>0.204223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Barnet</td>\n",
       "      <td>0.229453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bexley</td>\n",
       "      <td>0.235351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Brent</td>\n",
       "      <td>0.204309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bromley</td>\n",
       "      <td>0.244213</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                index         0\n",
       "0  Barking & Dagenham  0.204223\n",
       "1              Barnet  0.229453\n",
       "2              Bexley  0.235351\n",
       "3               Brent  0.204309\n",
       "4             Bromley  0.244213"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#transposing and reseting index\n",
    "df_ratios_T = df_ratios.T\n",
    "df_ratios = df_ratios_T.reset_index()\n",
    "df_ratios.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Borough</th>\n",
       "      <th>2018</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Barking &amp; Dagenham</td>\n",
       "      <td>0.204223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Barnet</td>\n",
       "      <td>0.229453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bexley</td>\n",
       "      <td>0.235351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Brent</td>\n",
       "      <td>0.204309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bromley</td>\n",
       "      <td>0.244213</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Borough      2018\n",
       "0  Barking & Dagenham  0.204223\n",
       "1              Barnet  0.229453\n",
       "2              Bexley  0.235351\n",
       "3               Brent  0.204309\n",
       "4             Bromley  0.244213"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#renaming index to London_Borough and 0 to 2018\n",
    "df_ratios.rename(columns={'index':'Borough', 0:'2018'}, inplace=True)\n",
    "df_ratios.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 Borough      2018\n",
      "17              Hounslow  0.251483\n",
      "26  Richmond upon Thames  0.249678\n",
      "14                Harrow  0.246354\n",
      "4                Bromley  0.244213\n",
      "28                Sutton  0.242806\n",
      "12  Hammersmith & Fulham  0.241674\n",
      "16            Hillingdon  0.238080\n",
      "7                Croydon  0.238033\n",
      "2                 Bexley  0.235351\n",
      "9                Enfield  0.234551\n",
      "20  Kingston upon Thames  0.234162\n",
      "8                 Ealing  0.231940\n",
      "15              Havering  0.231202\n",
      "1                 Barnet  0.229453\n",
      "25             Redbridge  0.223655\n"
     ]
    }
   ],
   "source": [
    "#sort in descending order on ratio and check first 15 values\n",
    "top15 = df_ratios.sort_values(by='2018',ascending=False).head(15)\n",
    "print(top15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, 'Hounslow'),\n",
       " Text(1, 0, 'Richmond upon Thames'),\n",
       " Text(2, 0, 'Harrow'),\n",
       " Text(3, 0, 'Bromley'),\n",
       " Text(4, 0, 'Sutton'),\n",
       " Text(5, 0, 'Hammersmith & Fulham'),\n",
       " Text(6, 0, 'Hillingdon'),\n",
       " Text(7, 0, 'Croydon'),\n",
       " Text(8, 0, 'Bexley'),\n",
       " Text(9, 0, 'Enfield'),\n",
       " Text(10, 0, 'Kingston upon Thames'),\n",
       " Text(11, 0, 'Ealing'),\n",
       " Text(12, 0, 'Havering'),\n",
       " Text(13, 0, 'Barnet'),\n",
       " Text(14, 0, 'Redbridge')]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot to check change in price\n",
    "ax = top15[['Borough','2018']].plot(kind='bar')\n",
    "ax.set_xticklabels(top15.Borough)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 Borough      2018\n",
      "17              Hounslow  0.251483\n",
      "26  Richmond upon Thames  0.249678\n",
      "14                Harrow  0.246354\n",
      "4                Bromley  0.244213\n",
      "28                Sutton  0.242806\n",
      "12  Hammersmith & Fulham  0.241674\n",
      "16            Hillingdon  0.238080\n",
      "7                Croydon  0.238033\n",
      "2                 Bexley  0.235351\n",
      "9                Enfield  0.234551\n",
      "20  Kingston upon Thames  0.234162\n",
      "8                 Ealing  0.231940\n",
      "15              Havering  0.231202\n",
      "1                 Barnet  0.229453\n",
      "25             Redbridge  0.223655\n",
      "29         Tower Hamlets  0.216137\n",
      "23                Merton  0.210914\n",
      "31            Wandsworth  0.210185\n",
      "10             Greenwich  0.209950\n",
      "18             Islington  0.206439\n",
      "3                  Brent  0.204309\n",
      "0     Barking & Dagenham  0.204223\n",
      "5                 Camden  0.202620\n",
      "21               Lambeth  0.201704\n",
      "19  Kensington & Chelsea  0.196755\n",
      "13              Haringey  0.194756\n",
      "6         City of London  0.188622\n",
      "24                Newham  0.188488\n",
      "32           Westminster  0.186791\n",
      "22              Lewisham  0.183512\n",
      "27             Southwark  0.181275\n",
      "30        Waltham Forest  0.171387\n",
      "11               Hackney  0.161335\n"
     ]
    }
   ],
   "source": [
    "all = df_ratios.sort_values(by='2018',ascending=False)\n",
    "print(all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, 'Hounslow'),\n",
       " Text(1, 0, 'Richmond upon Thames'),\n",
       " Text(2, 0, 'Harrow'),\n",
       " Text(3, 0, 'Bromley'),\n",
       " Text(4, 0, 'Sutton'),\n",
       " Text(5, 0, 'Hammersmith & Fulham'),\n",
       " Text(6, 0, 'Hillingdon'),\n",
       " Text(7, 0, 'Croydon'),\n",
       " Text(8, 0, 'Bexley'),\n",
       " Text(9, 0, 'Enfield'),\n",
       " Text(10, 0, 'Kingston upon Thames'),\n",
       " Text(11, 0, 'Ealing'),\n",
       " Text(12, 0, 'Havering'),\n",
       " Text(13, 0, 'Barnet'),\n",
       " Text(14, 0, 'Redbridge'),\n",
       " Text(15, 0, 'Tower Hamlets'),\n",
       " Text(16, 0, 'Merton'),\n",
       " Text(17, 0, 'Wandsworth'),\n",
       " Text(18, 0, 'Greenwich'),\n",
       " Text(19, 0, 'Islington'),\n",
       " Text(20, 0, 'Brent'),\n",
       " Text(21, 0, 'Barking & Dagenham'),\n",
       " Text(22, 0, 'Camden'),\n",
       " Text(23, 0, 'Lambeth'),\n",
       " Text(24, 0, 'Kensington & Chelsea'),\n",
       " Text(25, 0, 'Haringey'),\n",
       " Text(26, 0, 'City of London'),\n",
       " Text(27, 0, 'Newham'),\n",
       " Text(28, 0, 'Westminster'),\n",
       " Text(29, 0, 'Lewisham'),\n",
       " Text(30, 0, 'Southwark'),\n",
       " Text(31, 0, 'Waltham Forest'),\n",
       " Text(32, 0, 'Hackney')]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot to check change in price\n",
    "al = all[['Borough','2018']].plot(kind='bar')\n",
    "al.set_xticklabels(all.Borough)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "NzYUI7FxJpgv"
   },
   "source": [
    " 4. Conclusion\n",
    "    \n",
    "What can you conclude? Type out your conclusion below. \n",
    "\n",
    "\"The greatest increase in housing prices was in the Hackney\"\n",
    "\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! "
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "Springboard Data Science Career Track Unit 4 Challenge - Tier 3 Complete .ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}