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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="n">housing_data</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">'data/rental_contracts_sg.csv'</span><span class="p">,</span> <span class="n">skiprows</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="n">housing_data</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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<th></th>
<th>S/N</th>
<th>Building/Project Name</th>
<th>Street Name</th>
<th>Postal District</th>
<th>Type</th>
<th>No. of Bedroom(for Non-Landed Only)</th>
<th>Monthly Gross Rent($)</th>
<th>Floor Area (sq m)</th>
<th>Lease Commencement Date</th>
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<th>0</th>
<td>1</td>
<td>THE FLORAVALE</td>
<td>WESTWOOD AVENUE</td>
<td>22.0</td>
<td>Executive Condominium</td>
<td>3.0</td>
<td>2400.0</td>
<td>120 to 130</td>
<td>Jan-2018</td>
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<th>1</th>
<td>2</td>
<td>WOODSVALE</td>
<td>WOODLANDS DRIVE 72</td>
<td>25.0</td>
<td>Executive Condominium</td>
<td>3.0</td>
<td>2300.0</td>
<td>110 to 120</td>
<td>Jan-2018</td>
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<th>2</th>
<td>3</td>
<td>NORTHOAKS</td>
<td>WOODLANDS CRESCENT</td>
<td>25.0</td>
<td>Executive Condominium</td>
<td>2.0</td>
<td>2000.0</td>
<td>90 to 100</td>
<td>Jan-2018</td>
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<th>3</th>
<td>4</td>
<td>LA CASA</td>
<td>WOODLANDS DRIVE 16</td>
<td>25.0</td>
<td>Executive Condominium</td>
<td>2.0</td>
<td>2000.0</td>
<td>90 to 100</td>
<td>Jan-2018</td>
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<th>4</th>
<td>5</td>
<td>THE FLORAVALE</td>
<td>WESTWOOD AVENUE</td>
<td>22.0</td>
<td>Executive Condominium</td>
<td>3.0</td>
<td>2500.0</td>
<td>130 to 140</td>
<td>Jan-2018</td>
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<h2 id="Cleaning-up...">Cleaning up...<a class="anchor-link" href="#Cleaning-up...">¶</a></h2><p>When getting data online, we usually have <em>messy</em> or <em>not cleaned up</em> information. Let me show you what I mean.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">housing_data</span><span class="o">.</span><span class="n">info</span><span class="p">()</span>
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<pre><class 'pandas.core.frame.DataFrame'>
RangeIndex: 1528 entries, 0 to 1527
Data columns (total 9 columns):
S/N 1527 non-null object
Building/Project Name 1524 non-null object
Street Name 1524 non-null object
Postal District 1524 non-null float64
Type 1524 non-null object
No. of Bedroom(for Non-Landed Only) 1523 non-null float64
Monthly Gross Rent($) 1523 non-null float64
Floor Area (sq m) 1523 non-null object
Lease Commencement Date 1523 non-null object
dtypes: float64(3), object(6)
memory usage: 107.5+ KB
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<ul>
<li>First, we see that we don't have consistent numbers o f rows for each column.</li>
<li>The postal district column should actually be an <em>integer</em>, rather than an <em>float64</em>.</li>
<li>Floor areas are <em>objects</em> because the data in this column is actually a range of the unit area, e.g. 120 to 130 for 1th entry.
Let's have a look at the last rows first.</li>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">housing_data</span><span class="o">.</span><span class="n">tail</span><span class="p">()</span>
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<th></th>
<th>S/N</th>
<th>Building/Project Name</th>
<th>Street Name</th>
<th>Postal District</th>
<th>Type</th>
<th>No. of Bedroom(for Non-Landed Only)</th>
<th>Monthly Gross Rent($)</th>
<th>Floor Area (sq m)</th>
<th>Lease Commencement Date</th>
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<th>1523</th>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
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<th>1524</th>
<td>1523 record(s) retrieved based on user-specifi...</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
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<th>1525</th>
<td>Search By Property Type (Executive Condominiums)</td>
<td>From Date (FEB-2015)</td>
<td>To Date (JAN-2018) & Postal Districts: 20</td>
<td>22.0</td>
<td>25</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
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<th>1526</th>
<td>Notes:</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
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<th>1527</th>
<td>To convert Sqm to Sqft, multiply by a factor 1...</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
<td>NaN</td>
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<p>Shocks! I also took the comments at the end of the csv file, we obviously need to remove this. But please take note that information or comments at the end of the file may be valuable in explaining the dataset. But let's just remove it for now.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">housing_data</span> <span class="o">=</span> <span class="n">housing_data</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:</span><span class="mi">1523</span><span class="p">]</span>
<span class="n">housing_data</span><span class="o">.</span><span class="n">tail</span><span class="p">()</span>
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<th>Building/Project Name</th>
<th>Street Name</th>
<th>Postal District</th>
<th>Type</th>
<th>No. of Bedroom(for Non-Landed Only)</th>
<th>Monthly Gross Rent($)</th>
<th>Floor Area (sq m)</th>
<th>Lease Commencement Date</th>
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<th>1518</th>
<td>1519</td>
<td>LA CASA</td>
<td>WOODLANDS DRIVE 16</td>
<td>25.0</td>
<td>Executive Condominium</td>
<td>2.0</td>
<td>2000.0</td>
<td>90 to 100</td>
<td>Feb-2015</td>
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<th>1519</th>
<td>1520</td>
<td>NORTHOAKS</td>
<td>WOODLANDS CRESCENT</td>
<td>25.0</td>
<td>Executive Condominium</td>
<td>2.0</td>
<td>2400.0</td>
<td>90 to 100</td>
<td>Feb-2015</td>
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<th>1520</th>
<td>1521</td>
<td>BISHAN LOFT</td>
<td>BISHAN STREET 11</td>
<td>20.0</td>
<td>Executive Condominium</td>
<td>3.0</td>
<td>4300.0</td>
<td>120 to 130</td>
<td>Feb-2015</td>
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<th>1521</th>
<td>1522</td>
<td>SUMMERDALE</td>
<td>BOON LAY DRIVE</td>
<td>22.0</td>
<td>Executive Condominium</td>
<td>3.0</td>
<td>2800.0</td>
<td>110 to 120</td>
<td>Feb-2015</td>
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<th>1522</th>
<td>1523</td>
<td>SUMMERDALE</td>
<td>BOON LAY DRIVE</td>
<td>22.0</td>
<td>Executive Condominium</td>
<td>3.0</td>
<td>3000.0</td>
<td>110 to 120</td>
<td>Feb-2015</td>
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<p>Looks good. Now we should fix the postal district data type.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">housing_data</span><span class="p">[</span><span class="s1">'Postal District'</span><span class="p">]</span><span class="o">=</span><span class="n">housing_data</span><span class="p">[</span><span class="s1">'Postal District'</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">'int'</span><span class="p">)</span>
<span class="n">housing_data</span><span class="o">.</span><span class="n">info</span><span class="p">()</span>
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<pre><class 'pandas.core.frame.DataFrame'>
RangeIndex: 1523 entries, 0 to 1522
Data columns (total 9 columns):
S/N 1523 non-null object
Building/Project Name 1523 non-null object
Street Name 1523 non-null object
Postal District 1523 non-null int32
Type 1523 non-null object
No. of Bedroom(for Non-Landed Only) 1523 non-null float64
Monthly Gross Rent($) 1523 non-null float64
Floor Area (sq m) 1523 non-null object
Lease Commencement Date 1523 non-null object
dtypes: float64(2), int32(1), object(6)
memory usage: 101.2+ KB
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">housing_data</span><span class="o">.</span><span class="n">describe</span><span class="p">()</span>
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<th>Postal District</th>
<th>No. of Bedroom(for Non-Landed Only)</th>
<th>Monthly Gross Rent($)</th>
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<th>count</th>
<td>1523.000000</td>
<td>1523.000000</td>
<td>1523.000000</td>
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<th>mean</th>
<td>23.137229</td>
<td>2.985555</td>
<td>2762.843729</td>
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<th>std</th>
<td>1.903783</td>
<td>0.273300</td>
<td>530.273116</td>
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<th>min</th>
<td>20.000000</td>
<td>2.000000</td>
<td>750.000000</td>
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<th>25%</th>
<td>22.000000</td>
<td>3.000000</td>
<td>2450.000000</td>
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<th>50%</th>
<td>22.000000</td>
<td>3.000000</td>
<td>2700.000000</td>
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<th>75%</th>
<td>25.000000</td>
<td>3.000000</td>
<td>3000.000000</td>
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<th>max</th>
<td>25.000000</td>
<td>4.000000</td>
<td>6380.000000</td>
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<p>The describe function displays some statistics for all of my dataset, but I want to see per district, so I can compare them. We can use the <code>groupby</code> function of the <code>pandas</code> module.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">groupedby_district</span> <span class="o">=</span> <span class="n">housing_data</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">'Postal District'</span><span class="p">)</span>
<span class="c1">#use the describe function again</span>
<span class="n">groupedby_district</span><span class="o">.</span><span class="n">describe</span><span class="p">()</span>
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<th>count</th>
<th>mean</th>
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<th>25%</th>
<th>50%</th>
<th>75%</th>
<th>max</th>
<th>count</th>
<th>mean</th>
<th>std</th>
<th>min</th>
<th>25%</th>
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<th>Postal District</th>
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<th>20</th>
<td>229.0</td>
<td>3563.078603</td>
<td>614.124875</td>
<td>1700.0</td>
<td>3000.0</td>
<td>3600.0</td>
<td>3900.0</td>
<td>6380.0</td>
<td>229.0</td>
<td>3.126638</td>
<td>0.333295</td>
<td>3.0</td>
<td>3.0</td>
<td>3.0</td>
<td>3.0</td>
<td>4.0</td>