Unit 4 Challenge - Tier 3.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",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# Load the pyplot collection of functions from matplotlib, as plt\n",
    "import matplotlib.pyplot as plt\n"
   ]
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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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    "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>798499.1646</td>\n",
       "      <td>836807.0518</td>\n",
       "      <td>769391.7847</td>\n",
       "      <td>801999.0466</td>\n",
       "      <td>760599.2322</td>\n",
       "      <td>729315.7243</td>\n",
       "      <td>717528.2633</td>\n",
       "      <td>713094.5391</td>\n",
       "      <td>765822.2726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Barking &amp; Dagenham</td>\n",
       "      <td>E09000002</td>\n",
       "      <td>50460.2266</td>\n",
       "      <td>51085.77983</td>\n",
       "      <td>51268.96956</td>\n",
       "      <td>53133.50526</td>\n",
       "      <td>53042.24852</td>\n",
       "      <td>53700.34831</td>\n",
       "      <td>52113.12157</td>\n",
       "      <td>52232.19868</td>\n",
       "      <td>...</td>\n",
       "      <td>304923.5027</td>\n",
       "      <td>301702.4656</td>\n",
       "      <td>304852.1338</td>\n",
       "      <td>305012.3291</td>\n",
       "      <td>309560.1023</td>\n",
       "      <td>311007.1517</td>\n",
       "      <td>313835.8023</td>\n",
       "      <td>314187.4036</td>\n",
       "      <td>309680.5549</td>\n",
       "      <td>308760.1446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Barnet</td>\n",
       "      <td>E09000003</td>\n",
       "      <td>93284.51832</td>\n",
       "      <td>93190.16963</td>\n",
       "      <td>92247.52435</td>\n",
       "      <td>90762.87492</td>\n",
       "      <td>90258.00033</td>\n",
       "      <td>90107.23471</td>\n",
       "      <td>91441.24768</td>\n",
       "      <td>92361.31512</td>\n",
       "      <td>...</td>\n",
       "      <td>529660.1524</td>\n",
       "      <td>533619.23</td>\n",
       "      <td>530353.5205</td>\n",
       "      <td>528816.4928</td>\n",
       "      <td>532528.0926</td>\n",
       "      <td>538842.4015</td>\n",
       "      <td>538215.8867</td>\n",
       "      <td>542872.3895</td>\n",
       "      <td>545746.3543</td>\n",
       "      <td>546082.2634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bexley</td>\n",
       "      <td>E09000004</td>\n",
       "      <td>64958.09036</td>\n",
       "      <td>64787.92069</td>\n",
       "      <td>64367.49344</td>\n",
       "      <td>64277.66881</td>\n",
       "      <td>63997.13588</td>\n",
       "      <td>64252.32335</td>\n",
       "      <td>63722.70055</td>\n",
       "      <td>64432.60005</td>\n",
       "      <td>...</td>\n",
       "      <td>346679.9244</td>\n",
       "      <td>345061.4945</td>\n",
       "      <td>345404.9836</td>\n",
       "      <td>346252.9145</td>\n",
       "      <td>352260.8385</td>\n",
       "      <td>356664.073</td>\n",
       "      <td>360116.1134</td>\n",
       "      <td>364480.9055</td>\n",
       "      <td>365519.4195</td>\n",
       "      <td>368554.8059</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 319 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          91448.98487          82202.77314   \n",
       "2  Barking & Dagenham  E09000002           50460.2266          51085.77983   \n",
       "3              Barnet  E09000003          93284.51832          93190.16963   \n",
       "4              Bexley  E09000004          64958.09036          64787.92069   \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          79120.70256          77101.20804          84409.14932   \n",
       "2          51268.96956          53133.50526          53042.24852   \n",
       "3          92247.52435          90762.87492          90258.00033   \n",
       "4          64367.49344          64277.66881          63997.13588   \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          94900.51244          110128.0423          112329.4376  ...   \n",
       "2          53700.34831          52113.12157          52232.19868  ...   \n",
       "3          90107.23471          91441.24768          92361.31512  ...   \n",
       "4          64252.32335          63722.70055          64432.60005  ...   \n",
       "\n",
       "                   308                  309                  310  \\\n",
       "0  2020-08-01 00:00:00  2020-09-01 00:00:00  2020-10-01 00:00:00   \n",
       "1          827658.5167          798499.1646          836807.0518   \n",
       "2          304923.5027          301702.4656          304852.1338   \n",
       "3          529660.1524            533619.23          530353.5205   \n",
       "4          346679.9244          345061.4945          345404.9836   \n",
       "\n",
       "                   311                  312                  313  \\\n",
       "0  2020-11-01 00:00:00  2020-12-01 00:00:00  2021-01-01 00:00:00   \n",
       "1          769391.7847          801999.0466          760599.2322   \n",
       "2          305012.3291          309560.1023          311007.1517   \n",
       "3          528816.4928          532528.0926          538842.4015   \n",
       "4          346252.9145          352260.8385           356664.073   \n",
       "\n",
       "                   314                  315                  316  \\\n",
       "0  2021-02-01 00:00:00  2021-03-01 00:00:00  2021-04-01 00:00:00   \n",
       "1          729315.7243          717528.2633          713094.5391   \n",
       "2          313835.8023          314187.4036          309680.5549   \n",
       "3          538215.8867          542872.3895          545746.3543   \n",
       "4          360116.1134          364480.9055          365519.4195   \n",
       "\n",
       "                   317  \n",
       "0  2021-05-01 00:00:00  \n",
       "1          765822.2726  \n",
       "2          308760.1446  \n",
       "3          546082.2634  \n",
       "4          368554.8059  \n",
       "\n",
       "[5 rows x 319 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "properties_tp = properties.transpose().reset_index()\n",
    "properties_tp.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": 5,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "GKkmn1AnoVZS"
   },
   "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>Boroughs</th>\n",
       "      <th>ID</th>\n",
       "      <th>1995-01-01 00:00:00</th>\n",
       "      <th>1995-02-01 00:00:00</th>\n",
       "      <th>1995-03-01 00:00:00</th>\n",
       "      <th>1995-04-01 00:00:00</th>\n",
       "      <th>1995-05-01 00:00:00</th>\n",
       "      <th>1995-06-01 00:00:00</th>\n",
       "      <th>1995-07-01 00:00:00</th>\n",
       "      <th>1995-08-01 00:00:00</th>\n",
       "      <th>...</th>\n",
       "      <th>2020-08-01 00:00:00</th>\n",
       "      <th>2020-09-01 00:00:00</th>\n",
       "      <th>2020-10-01 00:00:00</th>\n",
       "      <th>2020-11-01 00:00:00</th>\n",
       "      <th>2020-12-01 00:00:00</th>\n",
       "      <th>2021-01-01 00:00:00</th>\n",
       "      <th>2021-02-01 00:00:00</th>\n",
       "      <th>2021-03-01 00:00:00</th>\n",
       "      <th>2021-04-01 00:00:00</th>\n",
       "      <th>2021-05-01 00:00:00</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>City of London</td>\n",
       "      <td>E09000001</td>\n",
       "      <td>91448.98487</td>\n",
       "      <td>82202.77314</td>\n",
       "      <td>79120.70256</td>\n",
       "      <td>77101.20804</td>\n",
       "      <td>84409.14932</td>\n",
       "      <td>94900.51244</td>\n",
       "      <td>110128.0423</td>\n",
       "      <td>112329.4376</td>\n",
       "      <td>...</td>\n",
       "      <td>827658.5167</td>\n",
       "      <td>798499.1646</td>\n",
       "      <td>836807.0518</td>\n",
       "      <td>769391.7847</td>\n",
       "      <td>801999.0466</td>\n",
       "      <td>760599.2322</td>\n",
       "      <td>729315.7243</td>\n",
       "      <td>717528.2633</td>\n",
       "      <td>713094.5391</td>\n",
       "      <td>765822.2726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Barking &amp; Dagenham</td>\n",
       "      <td>E09000002</td>\n",
       "      <td>50460.2266</td>\n",
       "      <td>51085.77983</td>\n",
       "      <td>51268.96956</td>\n",
       "      <td>53133.50526</td>\n",
       "      <td>53042.24852</td>\n",
       "      <td>53700.34831</td>\n",
       "      <td>52113.12157</td>\n",
       "      <td>52232.19868</td>\n",
       "      <td>...</td>\n",
       "      <td>304923.5027</td>\n",
       "      <td>301702.4656</td>\n",
       "      <td>304852.1338</td>\n",
       "      <td>305012.3291</td>\n",
       "      <td>309560.1023</td>\n",
       "      <td>311007.1517</td>\n",
       "      <td>313835.8023</td>\n",
       "      <td>314187.4036</td>\n",
       "      <td>309680.5549</td>\n",
       "      <td>308760.1446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Barnet</td>\n",
       "      <td>E09000003</td>\n",
       "      <td>93284.51832</td>\n",
       "      <td>93190.16963</td>\n",
       "      <td>92247.52435</td>\n",
       "      <td>90762.87492</td>\n",
       "      <td>90258.00033</td>\n",
       "      <td>90107.23471</td>\n",
       "      <td>91441.24768</td>\n",
       "      <td>92361.31512</td>\n",
       "      <td>...</td>\n",
       "      <td>529660.1524</td>\n",
       "      <td>533619.23</td>\n",
       "      <td>530353.5205</td>\n",
       "      <td>528816.4928</td>\n",
       "      <td>532528.0926</td>\n",
       "      <td>538842.4015</td>\n",
       "      <td>538215.8867</td>\n",
       "      <td>542872.3895</td>\n",
       "      <td>545746.3543</td>\n",
       "      <td>546082.2634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bexley</td>\n",
       "      <td>E09000004</td>\n",
       "      <td>64958.09036</td>\n",
       "      <td>64787.92069</td>\n",
       "      <td>64367.49344</td>\n",
       "      <td>64277.66881</td>\n",
       "      <td>63997.13588</td>\n",
       "      <td>64252.32335</td>\n",
       "      <td>63722.70055</td>\n",
       "      <td>64432.60005</td>\n",
       "      <td>...</td>\n",
       "      <td>346679.9244</td>\n",
       "      <td>345061.4945</td>\n",
       "      <td>345404.9836</td>\n",
       "      <td>346252.9145</td>\n",
       "      <td>352260.8385</td>\n",
       "      <td>356664.073</td>\n",
       "      <td>360116.1134</td>\n",
       "      <td>364480.9055</td>\n",
       "      <td>365519.4195</td>\n",
       "      <td>368554.8059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Brent</td>\n",
       "      <td>E09000005</td>\n",
       "      <td>71306.56698</td>\n",
       "      <td>72022.26197</td>\n",
       "      <td>72015.76274</td>\n",
       "      <td>72965.63094</td>\n",
       "      <td>73704.04743</td>\n",
       "      <td>74310.48167</td>\n",
       "      <td>74127.03788</td>\n",
       "      <td>73547.0411</td>\n",
       "      <td>...</td>\n",
       "      <td>497729.0327</td>\n",
       "      <td>519561.8277</td>\n",
       "      <td>522239.3216</td>\n",
       "      <td>513100.814</td>\n",
       "      <td>495066.6459</td>\n",
       "      <td>511347.8856</td>\n",
       "      <td>514388.7387</td>\n",
       "      <td>519212.9441</td>\n",
       "      <td>498083.9417</td>\n",
       "      <td>508570.7598</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 319 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "0            Boroughs         ID 1995-01-01 00:00:00 1995-02-01 00:00:00  \\\n",
       "1      City of London  E09000001         91448.98487         82202.77314   \n",
       "2  Barking & Dagenham  E09000002          50460.2266         51085.77983   \n",
       "3              Barnet  E09000003         93284.51832         93190.16963   \n",
       "4              Bexley  E09000004         64958.09036         64787.92069   \n",
       "5               Brent  E09000005         71306.56698         72022.26197   \n",
       "\n",
       "0 1995-03-01 00:00:00 1995-04-01 00:00:00 1995-05-01 00:00:00  \\\n",
       "1         79120.70256         77101.20804         84409.14932   \n",
       "2         51268.96956         53133.50526         53042.24852   \n",
       "3         92247.52435         90762.87492         90258.00033   \n",
       "4         64367.49344         64277.66881         63997.13588   \n",
       "5         72015.76274         72965.63094         73704.04743   \n",
       "\n",
       "0 1995-06-01 00:00:00 1995-07-01 00:00:00 1995-08-01 00:00:00  ...  \\\n",
       "1         94900.51244         110128.0423         112329.4376  ...   \n",
       "2         53700.34831         52113.12157         52232.19868  ...   \n",
       "3         90107.23471         91441.24768         92361.31512  ...   \n",
       "4         64252.32335         63722.70055         64432.60005  ...   \n",
       "5         74310.48167         74127.03788          73547.0411  ...   \n",
       "\n",
       "0 2020-08-01 00:00:00 2020-09-01 00:00:00 2020-10-01 00:00:00  \\\n",
       "1         827658.5167         798499.1646         836807.0518   \n",
       "2         304923.5027         301702.4656         304852.1338   \n",
       "3         529660.1524           533619.23         530353.5205   \n",
       "4         346679.9244         345061.4945         345404.9836   \n",
       "5         497729.0327         519561.8277         522239.3216   \n",
       "\n",
       "0 2020-11-01 00:00:00 2020-12-01 00:00:00 2021-01-01 00:00:00  \\\n",
       "1         769391.7847         801999.0466         760599.2322   \n",
       "2         305012.3291         309560.1023         311007.1517   \n",
       "3         528816.4928         532528.0926         538842.4015   \n",
       "4         346252.9145         352260.8385          356664.073   \n",
       "5          513100.814         495066.6459         511347.8856   \n",
       "\n",
       "0 2021-02-01 00:00:00 2021-03-01 00:00:00 2021-04-01 00:00:00  \\\n",
       "1         729315.7243         717528.2633         713094.5391   \n",
       "2         313835.8023         314187.4036         309680.5549   \n",
       "3         538215.8867         542872.3895         545746.3543   \n",
       "4         360116.1134         364480.9055         365519.4195   \n",
       "5         514388.7387         519212.9441         498083.9417   \n",
       "\n",
       "0 2021-05-01 00:00:00  \n",
       "1         765822.2726  \n",
       "2         308760.1446  \n",
       "3         546082.2634  \n",
       "4         368554.8059  \n",
       "5         508570.7598  \n",
       "\n",
       "[5 rows x 319 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Converts row 0 to be the column names\n",
    "properties_tp.columns = properties_tp.iloc[0]\n",
    "\n",
    "# Drops row 0 since its not needed anymore\n",
    "properties_tp = properties_tp.drop(0)\n",
    "\n",
    "# Renames first 2 columns to appropriate names (Boroughs and ID)\n",
    "properties_tp = properties_tp.rename(columns = {properties_tp.columns[0] : 'Boroughs', properties_tp.columns[1] : 'ID'})\n",
    "\n",
    "properties_tp.head()"
   ]
  },
  {
   "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": 6,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "S2wM0qLuo2Zt"
   },
   "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>Boroughs</th>\n",
       "      <th>ID</th>\n",
       "      <th>Date</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>50460.2266</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": [
       "             Boroughs         ID       Date Average Price\n",
       "0      City of London  E09000001 1995-01-01   91448.98487\n",
       "1  Barking & Dagenham  E09000002 1995-01-01    50460.2266\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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "properties_melt = properties_tp.melt(id_vars = [\"Boroughs\", \"ID\"], var_name=\"Date\", value_name=\"Average Price\")\n",
    "properties_melt.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": 7,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ZcR4IHbcpOaq"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Boroughs                 object\n",
       "ID                       object\n",
       "Date             datetime64[ns]\n",
       "Average Price           float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checkes the type for each column and changes to appropriate type\n",
    "properties_melt.dtypes\n",
    "\n",
    "properties_melt[\"Average Price\"] = pd.to_numeric(properties_melt[\"Average Price\"])\n",
    "properties_melt.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": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "BnvTW5a3p0fC"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checks if there are NAs in each column\n",
    "properties_melt.isna().sum()\n",
    "\n",
    "# Drops the NAs\n",
    "properties_melt = properties_melt.dropna()\n",
    "\n",
    "# Rechecks if thare are any NAs\n",
    "properties_melt.isna().sum()\n",
    "\n",
    "# Checks list of boroughs in datafram\n",
    "np.unique(properties_melt[\"Boroughs\"])\n",
    "\n",
    "# Checks the number of boroughs\n",
    "len(np.unique(properties_melt[\"Boroughs\"]))\n",
    "\n",
    "# Create list of London Boroughs\n",
    "list_London_Boroughs = ['Westminster', 'Kensington & Chelsea', 'Hammersmith & Fulham', \n",
    "                        'Wandsworth', 'Lambeth', 'Southwark', 'Tower Hamlets',\n",
    "                        'Hackney', 'Islington', 'Camden', 'Brent', 'Ealing', 'Hounslow',\n",
    "                        'Richmond upon Thames', 'Kingston upon Thames', 'Merton',\n",
    "                        'Sutton', 'Croydon', 'Bromley', 'Lewisham', 'Greenwich', 'Bexley',\n",
    "                        'Havering', 'Barking & Dagenham', 'Redbridge', 'Newham', 'Waltham Forest',\n",
    "                        'Haringey', 'Enfield', 'Barnet', 'Harrow', 'Hillingdon']\n",
    "\n",
    "# Checks properties_melt boroughs against list_London_Boroughs and keeps actual london boroughs\n",
    "properties_melt = properties_melt[properties_melt[\"Boroughs\"].isin(list_London_Boroughs)]\n",
    "\n",
    "# Confirms number of boroughs is 32\n",
    "len(np.unique(properties_melt[\"Boroughs\"]))\n"
   ]
  },
  {
   "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": 9,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "nAg5pT9cqHAR"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Subets properties to \"City of London\" borough\n",
    "properties_London = properties_melt.query('Boroughs == \"Westminster\"')\n",
    "\n",
    "# Plits a lien graph of the borough\n",
    "properties_London.plot(x=\"Date\", y=\"Average Price\", kind=\"line\")\n",
    "plt.show()"
   ]
  },
  {
   "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": 10,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "e0DF92cyqnu8"
   },
   "outputs": [],
   "source": [
    "# Converts the Date to Year as well as Renames\n",
    "properties_melt[\"Date\"] = properties_melt[\"Date\"].dt.year\n",
    "properties_melt = properties_melt.rename(columns = {'Date' : 'Year'})\n",
    "\n",
    "def create_price_ratio(borough):\n",
    "    # Subets based on borough input\n",
    "    temp_df = properties_melt.query('Boroughs == \"%s\"' %borough)\n",
    "    \n",
    "    # Takes the average of the average price for the year 1998 and 2018\n",
    "    avg_price_1998 = temp_df[temp_df[\"Year\"] == 1998][\"Average Price\"].mean()\n",
    "    avg_price_2018 = temp_df[temp_df[\"Year\"] == 2018][\"Average Price\"].mean()\n",
    "    \n",
    "    # Calculates the price ratio\n",
    "    price_ratio = avg_price_1998 / avg_price_2018\n",
    "    \n",
    "    return price_ratio"
   ]
  },
  {
   "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": 11,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "cKTyr437UgDa"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Boroughs  Price Ratio\n",
      "16  Hounslow     0.251483\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Empty dict\n",
    "house_price_ratio_1998_2018 = {}\n",
    "\n",
    "# Fills in the empty dict with borough and price ratio pairs\n",
    "for borough in np.unique(properties_melt[\"Boroughs\"]):\n",
    "    price_ratio = create_price_ratio(borough)\n",
    "    \n",
    "    house_price_ratio_1998_2018[borough] = price_ratio\n",
    "\n",
    "# Converts dict to dataframe\n",
    "house_price_ratio_1998_2018_df = pd.DataFrame(house_price_ratio_1998_2018.items())\n",
    "\n",
    "# Renames columns\n",
    "house_price_ratio_1998_2018_df = house_price_ratio_1998_2018_df.rename(columns = {house_price_ratio_1998_2018_df.columns[0] : 'Boroughs', house_price_ratio_1998_2018_df.columns[1] : 'Price Ratio'})\n",
    "\n",
    "# Sorts and prints the borough with the highest price ratio\n",
    "house_price_ratio_1998_2018_df_sorted = house_price_ratio_1998_2018_df.sort_values(\"Price Ratio\", ascending=False)\n",
    "print(house_price_ratio_1998_2018_df_sorted.head(1))\n",
    "\n",
    "# Bar graphs the sorted price ratio for the boroughs\n",
    "house_price_ratio_1998_2018_df_sorted.plot(x=\"Boroughs\", y=\"Price Ratio\", kind=\"bar\", rot=90)\n",
    "plt.show()"
   ]
  },
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    "##### 4. Conclusion\n",
    "What can you conclude? Type out your conclusion below. \n",
    "\n",
    "Look back at your notebook. Think about how you might summarize what you have done, and prepare a quick presentation on it to your mentor at your next meeting. \n",
    "\n",
    "We hope you enjoyed this practical project. It should have consolidated your data hygiene and pandas skills by looking at a real-world problem involving just the kind of dataset you might encounter as a budding data scientist. Congratulations, and looking forward to seeing you at the next step in the course! "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Question to Answer:\n",
    "Which borough of London has seen the greatest average increase in housing prices over\n",
    "the (approximately) two decades covered by the dataset?\n",
    "\n",
    "Based on the data analysis above, Hounslow borough has the greatest average increase in housing prices over two decades from 1998 to 2018 with a price ratio of 0.25."
   ]
  }
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