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<h1>Exploring Starbucks Offers Dataset</h1>
<h3 class="subheading">Trying to find interesting questions for further analysis</h3>
<span class="meta">Posted by
<a href="https://katkasian.github.io/author/kateryna-kasianenko.html">Kateryna Kasianenko</a>
on Sun 19 April 2020
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<article>
<p>An attempt to explore a Starbucks offers dataset, which has been analyzed by some Medium users such as <a href="https://towardsdatascience.com/using-starbucks-app-user-data-to-predict-effective-offers-20b799f3a6d5">Syuen Loh</a> or <a href="https://towardsdatascience.com/starbucks-analyze-a-coffee-b4eef811aa4a">Saleh Alitr</a>. The aim for this stage of my work on this project, illustrated by this notebook, is to find some interesting questions that could be answered by the data at hand that have not been explored in the work of the aforementioned authors. In the later stages, I will aim to answer these questions.</p>
<div class="highlight"><pre><span></span><span class="c1">#importing necessary libraries</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="kn">as</span> <span class="nn">pd</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">seaborn</span> <span class="kn">as</span> <span class="nn">sns</span>
<span class="kn">import</span> <span class="nn">random</span>
</pre></div>
<div class="highlight"><pre><span></span><span class="c1">#setting up the style and the color schema to be used</span>
<span class="n">plt</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">use</span><span class="p">(</span><span class="s1">'seaborn'</span><span class="p">)</span>
<span class="n">my_colors</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"tab:blue"</span><span class="p">,</span> <span class="s2">"tab:orange"</span><span class="p">,</span> <span class="s2">"tab:olive"</span><span class="p">,</span> <span class="s2">"tab:pink"</span><span class="p">,</span>
<span class="s2">"tab:cyan"</span><span class="p">,</span> <span class="s2">"tab:grey"</span><span class="p">,</span> <span class="s2">"tab:purple"</span><span class="p">,</span> <span class="s2">"tab:brown"</span><span class="p">,</span>
<span class="s2">"gold"</span><span class="p">,</span> <span class="s2">"teal"</span><span class="p">]</span>
</pre></div>
<h1><strong>Dataset Exploration 1. Exploring existing tables</strong></h1>
<h2><strong>Exploring Users</strong></h2>
<div class="highlight"><pre><span></span><span class="c1">##reading the profile table</span>
<span class="n">path</span> <span class="o">=</span> <span class="s2">"https://raw.githubusercontent.com/salitr/starbucks_offers_analysis/master/profile.json"</span>
</pre></div>
<div class="highlight"><pre><span></span><span class="n">df_users</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_json</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">lines</span> <span class="o">=</span> <span class="bp">True</span><span class="p">)</span>
</pre></div>
<div class="highlight"><pre><span></span><span class="n">df_users</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
</pre></div>
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>gender</th>
<th>age</th>
<th>id</th>
<th>became_member_on</th>
<th>income</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>None</td>
<td>118</td>
<td>68be06ca386d4c31939f3a4f0e3dd783</td>
<td>20170212</td>
<td>NaN</td>
</tr>
<tr>
<th>1</th>
<td>F</td>
<td>55</td>
<td>0610b486422d4921ae7d2bf64640c50b</td>
<td>20170715</td>
<td>112000.0</td>
</tr>
<tr>
<th>2</th>
<td>None</td>
<td>118</td>
<td>38fe809add3b4fcf9315a9694bb96ff5</td>
<td>20180712</td>
<td>NaN</td>
</tr>
<tr>
<th>3</th>
<td>F</td>
<td>75</td>
<td>78afa995795e4d85b5d9ceeca43f5fef</td>
<td>20170509</td>
<td>100000.0</td>
</tr>
<tr>
<th>4</th>
<td>None</td>
<td>118</td>
<td>a03223e636434f42ac4c3df47e8bac43</td>
<td>20170804</td>
<td>NaN</td>
</tr>
</tbody>
</table>
</div>
<div class="highlight"><pre><span></span><span class="c1">#as some rows contain bad data (no gender and age 118), we need to drop rows with NaN</span>
<span class="n">df_users</span> <span class="o">=</span> <span class="n">df_users</span><span class="o">.</span><span class="n">dropna</span><span class="p">(</span><span class="n">how</span> <span class="o">=</span> <span class="s2">"any"</span><span class="p">)</span>
</pre></div>
<div class="highlight"><pre><span></span><span class="n">df_users</span>
</pre></div>
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>gender</th>
<th>age</th>
<th>id</th>
<th>became_member_on</th>
<th>income</th>
</tr>
</thead>
<tbody>
<tr>
<th>1</th>
<td>F</td>
<td>55</td>
<td>0610b486422d4921ae7d2bf64640c50b</td>
<td>20170715</td>
<td>112000.0</td>
</tr>
<tr>
<th>3</th>
<td>F</td>
<td>75</td>
<td>78afa995795e4d85b5d9ceeca43f5fef</td>
<td>20170509</td>
<td>100000.0</td>
</tr>
<tr>
<th>5</th>
<td>M</td>
<td>68</td>
<td>e2127556f4f64592b11af22de27a7932</td>
<td>20180426</td>
<td>70000.0</td>
</tr>
<tr>
<th>8</th>
<td>M</td>
<td>65</td>
<td>389bc3fa690240e798340f5a15918d5c</td>
<td>20180209</td>
<td>53000.0</td>
</tr>
<tr>
<th>12</th>
<td>M</td>
<td>58</td>
<td>2eeac8d8feae4a8cad5a6af0499a211d</td>
<td>20171111</td>
<td>51000.0</td>
</tr>
<tr>
<th>...</th>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
</tr>
<tr>
<th>16995</th>
<td>F</td>
<td>45</td>
<td>6d5f3a774f3d4714ab0c092238f3a1d7</td>
<td>20180604</td>
<td>54000.0</td>
</tr>
<tr>
<th>16996</th>
<td>M</td>
<td>61</td>
<td>2cb4f97358b841b9a9773a7aa05a9d77</td>
<td>20180713</td>
<td>72000.0</td>
</tr>
<tr>
<th>16997</th>
<td>M</td>
<td>49</td>
<td>01d26f638c274aa0b965d24cefe3183f</td>
<td>20170126</td>
<td>73000.0</td>
</tr>
<tr>
<th>16998</th>
<td>F</td>
<td>83</td>
<td>9dc1421481194dcd9400aec7c9ae6366</td>
<td>20160307</td>
<td>50000.0</td>
</tr>
<tr>
<th>16999</th>
<td>F</td>
<td>62</td>
<td>e4052622e5ba45a8b96b59aba68cf068</td>
<td>20170722</td>
<td>82000.0</td>
</tr>
</tbody>
</table>
<p>14825 rows × 5 columns</p>
</div>
<div class="highlight"><pre><span></span><span class="c1">#exploring the gender distribution of users: grouping by gender and vizualizing </span>
<span class="c1">#the number of users identifying as certain gender, along with the proportion</span>
<span class="c1">#of a certain group</span>
<span class="n">genders</span> <span class="o">=</span> <span class="p">(</span><span class="n">df_users</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s2">"gender"</span><span class="p">)</span><span class="o">.</span><span class="n">size</span><span class="p">()</span> <span class="o">/</span> <span class="n">df_users</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span><span class="o">.</span><span class="n">values</span>
</pre></div>
<div class="highlight"><pre><span></span><span class="n">ax</span> <span class="o">=</span> <span class="n">df_users</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s2">"gender"</span><span class="p">)</span><span class="o">.</span><span class="n">size</span><span class="p">()</span><span class="o">.</span><span class="n">plot</span><span class="o">.</span><span class="n">barh</span><span class="p">(</span><span class="n">color</span> <span class="o">=</span> <span class="n">my_colors</span><span class="p">[:</span><span class="nb">len</span><span class="p">(</span><span class="n">genders</span><span class="p">)])</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">10000</span><span class="p">)</span>
<span class="k">for</span> <span class="n">n</span><span class="p">,</span> <span class="n">patch</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">ax</span><span class="o">.</span><span class="n">patches</span><span class="p">):</span>
<span class="n">ax</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="n">patch</span><span class="o">.</span><span class="n">get_width</span><span class="p">()</span> <span class="o">+</span> <span class="mi">30</span><span class="p">,</span> <span class="n">patch</span><span class="o">.</span><span class="n">get_y</span><span class="p">()</span> <span class="o">+</span> <span class="mf">0.21</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">genders</span><span class="p">[</span><span class="n">n</span><span class="p">]</span><span class="o">*</span><span class="mi">100</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span><span class="o">+</span><span class="s2">"%"</span><span class="p">,</span>
<span class="n">fontsize</span> <span class="o">=</span> <span class="mi">10</span><span class="p">,</span> <span class="n">color</span> <span class="o">=</span> <span class="s1">'dimgrey'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"Gender of users"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
</pre></div>
<p><img alt="png" src="starbucks_dataset_exploration_files/starbucks_dataset_exploration_12_0.png"></p>
<div class="highlight"><pre><span></span><span class="c1">#exploring the age distribution of the </span>
<span class="n">df_users</span><span class="o">.</span><span class="n">age</span><span class="o">.</span><span class="n">describe</span><span class="p">()</span>
</pre></div>
<div class="highlight"><pre><span></span>count 14825.000000
mean 54.393524
std 17.383705
min 18.000000
25% 42.000000
50% 55.000000
75% 66.000000
max 101.000000
Name: age, dtype: float64
</pre></div>
<div class="highlight"><pre><span></span><span class="c1">#exploring the age groups: dividing the users in 7 age groups </span>
<span class="n">df_users</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="s2">"agegroup"</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">cut</span><span class="p">(</span><span class="n">df_users</span><span class="o">.</span><span class="n">age</span><span class="p">,</span> <span class="p">[</span><span class="mi">18</span><span class="p">,</span> <span class="mi">30</span><span class="p">,</span> <span class="mi">40</span><span class="p">,</span> <span class="mi">50</span><span class="p">,</span> <span class="mi">60</span><span class="p">,</span> <span class="mi">70</span><span class="p">,</span> <span class="mi">80</span><span class="p">,</span> <span class="mi">90</span><span class="p">])</span>
</pre></div>
<div class="highlight"><pre><span></span><span class="n">agegroup_proportion</span> <span class="o">=</span> <span class="p">(</span><span class="n">df_users</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s2">"agegroup"</span><span class="p">)</span><span class="o">.</span><span class="n">size</span><span class="p">()</span> <span class="o">/</span> <span class="n">df_users</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span><span class="o">.</span><span class="n">values</span>
</pre></div>
<div class="highlight"><pre><span></span><span class="n">ax</span> <span class="o">=</span> <span class="n">df_users</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s2">"agegroup"</span><span class="p">)</span><span class="o">.</span><span class="n">size</span><span class="p">()</span><span class="o">.</span><span class="n">plot</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">color</span> <span class="o">=</span> <span class="n">my_colors</span><span class="p">[:</span><span class="nb">len</span><span class="p">(</span><span class="n">agegroup_proportion</span><span class="p">)])</span>
<span class="k">for</span> <span class="n">n</span><span class="p">,</span> <span class="n">patch</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">ax</span><span class="o">.</span><span class="n">patches</span><span class="p">):</span>
<span class="n">ax</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="n">patch</span><span class="o">.</span><span class="n">get_x</span><span class="p">(),</span> <span class="n">patch</span><span class="o">.</span><span class="n">get_height</span><span class="p">()</span> <span class="o">+</span> <span class="mi">60</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">agegroup_proportion</span><span class="p">[</span><span class="n">n</span><span class="p">]</span><span class="o">*</span><span class="mi">100</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span><span class="o">+</span><span class="s2">"%"</span><span class="p">,</span>
<span class="n">fontsize</span> <span class="o">=</span> <span class="mi">10</span><span class="p">,</span> <span class="n">color</span> <span class="o">=</span> <span class="s1">'dimgrey'</span><span class="p">)</span>
<span class="n">labs</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">get_xticklabels</span><span class="p">()</span>
<span class="n">texts</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">lab</span> <span class="ow">in</span> <span class="n">labs</span><span class="p">:</span>
<span class="n">str_to_modify</span> <span class="o">=</span> <span class="n">lab</span><span class="o">.</span><span class="n">get_text</span><span class="p">()</span>
<span class="n">text</span> <span class="o">=</span> <span class="n">str_to_modify</span><span class="p">[</span><span class="mi">1</span><span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
<span class="n">text</span> <span class="o">=</span> <span class="s2">"-"</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">text</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">", "</span><span class="p">))</span>
<span class="n">texts</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">text</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xticklabels</span><span class="p">(</span><span class="n">texts</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"Users by age"</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">""</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
</pre></div>
<p><img alt="png" src="starbucks_dataset_exploration_files/starbucks_dataset_exploration_16_0.png"></p>
<div class="highlight"><pre><span></span><span class="c1">#converting the time of join column to datetime and exploring the timeframe of joining by year</span>
<span class="n">df_users</span><span class="p">[</span><span class="s2">"became_member_dt"</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_datetime</span><span class="p">(</span><span class="n">df_users</span><span class="o">.</span><span class="n">became_member_on</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="nb">str</span><span class="p">(</span><span class="n">x</span><span class="p">)[:</span><span class="mi">4</span><span class="p">]</span> <span class="o">+</span> <span class="s2">"-"</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">x</span><span class="p">)[</span><span class="mi">4</span><span class="p">:</span><span class="mi">6</span><span class="p">]</span> <span class="o">+</span> <span class="s2">"-"</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">x</span><span class="p">)[</span><span class="mi">6</span><span class="p">:]))</span>
</pre></div>
<div class="highlight"><pre><span></span><span class="n">year_propotion</span> <span class="o">=</span> <span class="p">(</span><span class="n">df_users</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="n">df_users</span><span class="o">.</span><span class="n">became_member_dt</span><span class="o">.</span><span class="n">dt</span><span class="o">.</span><span class="n">year</span><span class="p">)</span><span class="o">.</span><span class="n">size</span><span class="p">()</span> <span class="o">/</span> <span class="n">df_users</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span><span class="o">.</span><span class="n">values</span>
<span class="n">year_propotion</span>
</pre></div>
<div class="highlight"><pre><span></span>array([0.01848229, 0.0446543 , 0.10772344, 0.20397976, 0.37767285,
0.24748735])
</pre></div>
<div class="highlight"><pre><span></span><span class="n">ax</span> <span class="o">=</span> <span class="n">df_users</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="n">df_users</span><span class="o">.</span><span class="n">became_member_dt</span><span class="o">.</span><span class="n">dt</span><span class="o">.</span><span class="n">year</span><span class="p">)</span><span class="o">.</span><span class="n">size</span><span class="p">()</span><span class="o">.</span><span class="n">plot</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">color</span> <span class="o">=</span> <span class="n">my_colors</span><span class="p">[:</span><span class="nb">len</span><span class="p">(</span><span class="n">year_propotion</span><span class="p">)])</span>
<span class="k">for</span> <span class="n">n</span><span class="p">,</span> <span class="n">patch</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">ax</span><span class="o">.</span><span class="n">patches</span><span class="p">):</span>
<span class="n">ax</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="n">patch</span><span class="o">.</span><span class="n">get_x</span><span class="p">()</span><span class="o">+</span><span class="mf">0.05</span><span class="p">,</span> <span class="n">patch</span><span class="o">.</span><span class="n">get_height</span><span class="p">()</span> <span class="o">+</span> <span class="mi">60</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">year_propotion</span><span class="p">[</span><span class="n">n</span><span class="p">]</span><span class="o">*</span><span class="mi">100</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span><span class="o">+</span><span class="s2">"%"</span><span class="p">,</span>
<span class="n">fontsize</span> <span class="o">=</span> <span class="mi">10</span><span class="p">,</span> <span class="n">color</span> <span class="o">=</span> <span class="s1">'dimgrey'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">""</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"Users by year of join"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
<p><img alt="png" src="starbucks_dataset_exploration_files/starbucks_dataset_exploration_19_0.png"></p>
<div class="highlight"><pre><span></span><span class="c1">#exploring the income of users</span>
<span class="n">sns</span><span class="o">.</span><span class="n">distplot</span><span class="p">(</span><span class="n">df_users</span><span class="o">.</span><span class="n">income</span><span class="p">,</span> <span class="n">bins</span> <span class="o">=</span> <span class="mi">10</span><span class="p">)</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">"Income of users"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">axvline</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">df_users</span><span class="o">.</span><span class="n">income</span><span class="p">),</span> <span class="n">color</span> <span class="o">=</span> <span class="s2">"tab:orange"</span><span class="p">);</span>
</pre></div>
<p><img alt="png" src="starbucks_dataset_exploration_files/starbucks_dataset_exploration_20_0.png"></p>
<p><em>Observations</em>: The dataset contains 15% more users identified as men than women, around 1% of users specified their gender as "other". Average income is around 60k a year. More than 80% of users have joined the platform from 2016 to 2018. Around 60% of users are in their 40s to 60s.</p>
<h2><strong>Exploring Events</strong></h2>
<div class="highlight"><pre><span></span><span class="c1">#reading the events table and checking the data</span>
<span class="n">path_events</span> <span class="o">=</span> <span class="s2">"https://raw.githubusercontent.com/salitr/starbucks_offers_analysis/master/transcript.json"</span>
<span class="n">df_events</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_json</span><span class="p">(</span><span class="n">path_events</span><span class="p">,</span> <span class="n">lines</span> <span class="o">=</span> <span class="bp">True</span><span class="p">)</span>
</pre></div>
<div class="highlight"><pre><span></span><span class="n">df_events</span><span class="o">.</span><span class="n">describe</span><span class="p">()</span>
</pre></div>
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>time</th>
</tr>
</thead>
<tbody>
<tr>
<th>count</th>
<td>306534.000000</td>
</tr>
<tr>
<th>mean</th>
<td>366.382940</td>
</tr>
<tr>
<th>std</th>
<td>200.326314</td>
</tr>
<tr>
<th>min</th>
<td>0.000000</td>
</tr>
<tr>
<th>25%</th>
<td>186.000000</td>
</tr>
<tr>
<th>50%</th>
<td>408.000000</td>
</tr>
<tr>
<th>75%</th>
<td>528.000000</td>
</tr>
<tr>
<th>max</th>
<td>714.000000</td>
</tr>
</tbody>
</table>
</div>
<div class="highlight"><pre><span></span><span class="n">df_events</span><span class="o">.</span><span class="n">info</span><span class="p">()</span>
</pre></div>
<div class="highlight"><pre><span></span><class 'pandas.core.frame.DataFrame'>
RangeIndex: 306534 entries, 0 to 306533
Data columns (total 4 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 person 306534 non-null object
1 event 306534 non-null object
2 value 306534 non-null object
3 time 306534 non-null int64
dtypes: int64(1), object(3)
memory usage: 9.4+ MB
</pre></div>
<div class="highlight"><pre><span></span><span class="c1">#exploring types of events</span>
<span class="n">df_events</span><span class="o">.</span><span class="n">event</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()</span>
</pre></div>
<div class="highlight"><pre><span></span>transaction 138953
offer received 76277
offer viewed 57725
offer completed 33579
Name: event, dtype: int64
</pre></div>
<div class="highlight"><pre><span></span><span class="c1">#there are two types of events: transactions and offers, each appears to have </span>
<span class="c1">#a "time" value, which, according to the explanation of the dataset, is the </span>
<span class="c1">#number of hours since the start of the offer </span>
<span class="n">df_events</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">df_events</span><span class="o">.</span><span class="n">event</span> <span class="o">==</span> <span class="s1">'transaction'</span><span class="p">]</span>
</pre></div>
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>person</th>
<th>event</th>
<th>value</th>
<th>time</th>
</tr>
</thead>
<tbody>
<tr>
<th>12654</th>
<td>02c083884c7d45b39cc68e1314fec56c</td>
<td>transaction</td>
<td>{'amount': 0.8300000000000001}</td>
<td>0</td>
</tr>
<tr>
<th>12657</th>
<td>9fa9ae8f57894cc9a3b8a9bbe0fc1b2f</td>
<td>transaction</td>
<td>{'amount': 34.56}</td>
<td>0</td>
</tr>
<tr>
<th>12659</th>
<td>54890f68699049c2a04d415abc25e717</td>
<td>transaction</td>
<td>{'amount': 13.23}</td>
<td>0</td>
</tr>
<tr>
<th>12670</th>
<td>b2f1cd155b864803ad8334cdf13c4bd2</td>
<td>transaction</td>
<td>{'amount': 19.51}</td>
<td>0</td>
</tr>
<tr>
<th>12671</th>
<td>fe97aa22dd3e48c8b143116a8403dd52</td>
<td>transaction</td>
<td>{'amount': 18.97}</td>
<td>0</td>
</tr>
<tr>
<th>...</th>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
</tr>
<tr>
<th>306529</th>
<td>b3a1272bc9904337b331bf348c3e8c17</td>
<td>transaction</td>
<td>{'amount': 1.5899999999999999}</td>
<td>714</td>
</tr>
<tr>
<th>306530</th>
<td>68213b08d99a4ae1b0dcb72aebd9aa35</td>
<td>transaction</td>
<td>{'amount': 9.53}</td>
<td>714</td>
</tr>
<tr>
<th>306531</th>
<td>a00058cf10334a308c68e7631c529907</td>
<td>transaction</td>
<td>{'amount': 3.61}</td>
<td>714</td>
</tr>
<tr>
<th>306532</th>
<td>76ddbd6576844afe811f1a3c0fbb5bec</td>
<td>transaction</td>
<td>{'amount': 3.5300000000000002}</td>
<td>714</td>
</tr>
<tr>
<th>306533</th>
<td>c02b10e8752c4d8e9b73f918558531f7</td>
<td>transaction</td>
<td>{'amount': 4.05}</td>
<td>714</td>
</tr>
</tbody>
</table>
<p>138953 rows × 4 columns</p>
</div>
<div class="highlight"><pre><span></span><span class="c1">#plotting the average transaction time since the start of the offer</span>
<span class="n">ax</span> <span class="o">=</span> <span class="n">df_events</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">df_events</span><span class="o">.</span><span class="n">event</span> <span class="o">==</span> <span class="s1">'transaction'</span><span class="p">,</span> <span class="s1">'time'</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">color</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="n">my_colors</span><span class="p">))</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">"Number of transactions"</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">"Hours"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"Transactions by hours since the start of the offer"</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">18000</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
<p><img alt="png" src="starbucks_dataset_exploration_files/starbucks_dataset_exploration_28_0.png"></p>
<div class="highlight"><pre><span></span><span class="c1">#plotting the duration of time since the beginning of the offer and an offer-related event</span>
<span class="n">offer_events</span> <span class="o">=</span> <span class="n">df_events</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">df_events</span><span class="o">.</span><span class="n">event</span> <span class="o">!=</span> <span class="s1">'transaction'</span><span class="p">]</span>
<span class="n">ax</span> <span class="o">=</span> <span class="n">sns</span><span class="o">.</span><span class="n">boxplot</span><span class="p">(</span><span class="n">offer_events</span><span class="o">.</span><span class="n">time</span><span class="p">,</span> <span class="n">offer_events</span><span class="o">.</span><span class="n">event</span><span class="p">,</span> <span class="n">palette</span> <span class="o">=</span> <span class="n">sns</span><span class="o">.</span><span class="n">color_palette</span><span class="p">(</span><span class="n">my_colors</span><span class="p">[</span><span class="mi">3</span><span class="p">:</span><span class="mi">6</span><span class="p">]))</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">"Offer event"</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">"Time, hours"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"Offer-related events by time since the start of the offer"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
<p><img alt="png" src="starbucks_dataset_exploration_files/starbucks_dataset_exploration_29_0.png"></p>
<p><em>Observations</em>: Clients appear to be receiving offers at different times - not only when the offer starts. Therefore, even though more transactions appear to be made in the later stage since the offer started, but there is a need to look at the time between an offer was received/viewed and the time a transaction was made.</p>
<h2><strong>Exploring Offers</strong></h2>
<div class="highlight"><pre><span></span><span class="c1">#reading the offers table</span>
<span class="n">path_offers</span> <span class="o">=</span> <span class="s2">"https://raw.githubusercontent.com/salitr/starbucks_offers_analysis/master/portfolio.json"</span>
<span class="n">df_offers</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_json</span><span class="p">(</span><span class="n">path_offers</span><span class="p">,</span> <span class="n">lines</span> <span class="o">=</span> <span class="bp">True</span><span class="p">)</span>
</pre></div>
<div class="highlight"><pre><span></span><span class="n">df_offers</span>
</pre></div>
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>reward</th>
<th>channels</th>
<th>difficulty</th>
<th>duration</th>
<th>offer_type</th>
<th>id</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>10</td>
<td>[email, mobile, social]</td>
<td>10</td>
<td>7</td>
<td>bogo</td>
<td>ae264e3637204a6fb9bb56bc8210ddfd</td>
</tr>
<tr>
<th>1</th>
<td>10</td>
<td>[web, email, mobile, social]</td>
<td>10</td>
<td>5</td>
<td>bogo</td>
<td>4d5c57ea9a6940dd891ad53e9dbe8da0</td>
</tr>
<tr>
<th>2</th>
<td>0</td>
<td>[web, email, mobile]</td>
<td>0</td>
<td>4</td>
<td>informational</td>
<td>3f207df678b143eea3cee63160fa8bed</td>
</tr>
<tr>
<th>3</th>
<td>5</td>
<td>[web, email, mobile]</td>
<td>5</td>
<td>7</td>
<td>bogo</td>
<td>9b98b8c7a33c4b65b9aebfe6a799e6d9</td>
</tr>
<tr>
<th>4</th>
<td>5</td>
<td>[web, email]</td>
<td>20</td>
<td>10</td>
<td>discount</td>
<td>0b1e1539f2cc45b7b9fa7c272da2e1d7</td>
</tr>
<tr>
<th>5</th>
<td>3</td>
<td>[web, email, mobile, social]</td>
<td>7</td>
<td>7</td>
<td>discount</td>
<td>2298d6c36e964ae4a3e7e9706d1fb8c2</td>
</tr>
<tr>
<th>6</th>
<td>2</td>
<td>[web, email, mobile, social]</td>
<td>10</td>
<td>10</td>
<td>discount</td>
<td>fafdcd668e3743c1bb461111dcafc2a4</td>
</tr>
<tr>
<th>7</th>
<td>0</td>
<td>[email, mobile, social]</td>
<td>0</td>
<td>3</td>
<td>informational</td>
<td>5a8bc65990b245e5a138643cd4eb9837</td>
</tr>
<tr>
<th>8</th>
<td>5</td>
<td>[web, email, mobile, social]</td>
<td>5</td>
<td>5</td>
<td>bogo</td>
<td>f19421c1d4aa40978ebb69ca19b0e20d</td>
</tr>
<tr>
<th>9</th>
<td>2</td>
<td>[web, email, mobile]</td>
<td>10</td>
<td>7</td>
<td>discount</td>
<td>2906b810c7d4411798c6938adc9daaa5</td>
</tr>
</tbody>
</table>
</div>
<div class="highlight"><pre><span></span><span class="c1">#according to the description of the dataset, duration refers to days since the beginning of the offer.</span>
<span class="c1">#it might be helpful to convert it to hours as the events table records events in hours</span>
<span class="c1">#moreover, it might be interesting to know if a platform of distribtution affects the success of a campaign</span>
<span class="c1">#we will need to engineer separate columns for each platform</span>
</pre></div>
<div class="highlight"><pre><span></span><span class="c1">#converting days to hours</span>
<span class="n">df_offers</span><span class="p">[</span><span class="s2">"duration_hours"</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_offers</span><span class="o">.</span><span class="n">duration</span> <span class="o">*</span> <span class="mi">24</span>
</pre></div>
<div class="highlight"><pre><span></span><span class="n">df_offers</span> <span class="o">=</span> <span class="n">df_offers</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="s2">"duration"</span><span class="p">,</span> <span class="n">axis</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
</pre></div>
<div class="highlight"><pre><span></span><span class="c1">#creating separate columns for offers</span>
<span class="n">df_offers</span><span class="p">[</span><span class="s1">'email'</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_offers</span><span class="o">.</span><span class="n">channels</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span><span class="mi">1</span> <span class="k">if</span> <span class="s1">'email'</span> <span class="ow">in</span> <span class="n">x</span> <span class="k">else</span> <span class="mi">0</span><span class="p">)</span>
<span class="n">df_offers</span><span class="p">[</span><span class="s1">'web'</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_offers</span><span class="o">.</span><span class="n">channels</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span><span class="mi">1</span> <span class="k">if</span> <span class="s1">'web'</span> <span class="ow">in</span> <span class="n">x</span> <span class="k">else</span> <span class="mi">0</span><span class="p">)</span>
<span class="n">df_offers</span><span class="p">[</span><span class="s1">'mobile'</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_offers</span><span class="o">.</span><span class="n">channels</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span><span class="mi">1</span> <span class="k">if</span> <span class="s1">'mobile'</span> <span class="ow">in</span> <span class="n">x</span> <span class="k">else</span> <span class="mi">0</span><span class="p">)</span>
<span class="n">df_offers</span><span class="p">[</span><span class="s1">'social'</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_offers</span><span class="o">.</span><span class="n">channels</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span><span class="mi">1</span> <span class="k">if</span> <span class="s1">'social'</span> <span class="ow">in</span> <span class="n">x</span> <span class="k">else</span> <span class="mi">0</span><span class="p">)</span>
</pre></div>
<div class="highlight"><pre><span></span><span class="n">df_offers</span> <span class="o">=</span> <span class="n">df_offers</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="s2">"channels"</span><span class="p">,</span> <span class="n">axis</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
</pre></div>
<div class="highlight"><pre><span></span><span class="c1">#cleaned offers table</span>
<span class="n">df_offers</span>
</pre></div>
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>reward</th>
<th>difficulty</th>
<th>offer_type</th>
<th>id</th>
<th>duration_hours</th>
<th>email</th>
<th>web</th>
<th>mobile</th>
<th>social</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>10</td>
<td>10</td>
<td>bogo</td>
<td>ae264e3637204a6fb9bb56bc8210ddfd</td>
<td>168</td>
<td>1</td>
<td>0</td>
<td>1</td>
<td>1</td>
</tr>
<tr>
<th>1</th>
<td>10</td>
<td>10</td>
<td>bogo</td>
<td>4d5c57ea9a6940dd891ad53e9dbe8da0</td>
<td>120</td>
<td>1</td>
<td>1</td>
<td>1</td>
<td>1</td>
</tr>
<tr>
<th>2</th>
<td>0</td>
<td>0</td>
<td>informational</td>
<td>3f207df678b143eea3cee63160fa8bed</td>
<td>96</td>
<td>1</td>
<td>1</td>
<td>1</td>
<td>0</td>
</tr>
<tr>
<th>3</th>
<td>5</td>
<td>5</td>
<td>bogo</td>
<td>9b98b8c7a33c4b65b9aebfe6a799e6d9</td>
<td>168</td>
<td>1</td>
<td>1</td>
<td>1</td>
<td>0</td>
</tr>
<tr>
<th>4</th>
<td>5</td>
<td>20</td>
<td>discount</td>
<td>0b1e1539f2cc45b7b9fa7c272da2e1d7</td>
<td>240</td>
<td>1</td>
<td>1</td>
<td>0</td>
<td>0</td>
</tr>
<tr>
<th>5</th>
<td>3</td>
<td>7</td>
<td>discount</td>
<td>2298d6c36e964ae4a3e7e9706d1fb8c2</td>
<td>168</td>
<td>1</td>
<td>1</td>
<td>1</td>
<td>1</td>
</tr>
<tr>
<th>6</th>
<td>2</td>
<td>10</td>
<td>discount</td>
<td>fafdcd668e3743c1bb461111dcafc2a4</td>
<td>240</td>
<td>1</td>
<td>1</td>
<td>1</td>
<td>1</td>
</tr>
<tr>
<th>7</th>
<td>0</td>
<td>0</td>
<td>informational</td>
<td>5a8bc65990b245e5a138643cd4eb9837</td>
<td>72</td>
<td>1</td>
<td>0</td>
<td>1</td>
<td>1</td>
</tr>
<tr>
<th>8</th>
<td>5</td>
<td>5</td>
<td>bogo</td>
<td>f19421c1d4aa40978ebb69ca19b0e20d</td>
<td>120</td>
<td>1</td>
<td>1</td>
<td>1</td>
<td>1</td>
</tr>
<tr>
<th>9</th>
<td>2</td>
<td>10</td>
<td>discount</td>
<td>2906b810c7d4411798c6938adc9daaa5</td>
<td>168</td>
<td>1</td>
<td>1</td>
<td>1</td>
<td>0</td>
</tr>
</tbody>
</table>
</div>
<div class="highlight"><pre><span></span><span class="c1">#exploring duration by hours</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
<span class="n">ax</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">df_offers</span><span class="o">.</span><span class="n">index</span><span class="p">,</span> <span class="n">df_offers</span><span class="o">.</span><span class="n">duration_hours</span><span class="p">,</span> <span class="n">color</span> <span class="o">=</span> <span class="n">my_colors</span><span class="p">[:</span><span class="n">df_offers</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]])</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">([</span><span class="n">x</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">df_offers</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])])</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">"Offers by duration (hours)"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>