<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>Product type</th>
      <th>SKU</th>
      <th>Price</th>
      <th>Availability</th>
      <th>Number of products sold</th>
      <th>Revenue generated</th>
      <th>Customer demographics</th>
      <th>Stock levels</th>
      <th>Lead times</th>
      <th>Order quantities</th>
      <th>...</th>
      <th>Location</th>
      <th>Lead time</th>
      <th>Production volumes</th>
      <th>Manufacturing lead time</th>
      <th>Manufacturing costs</th>
      <th>Inspection results</th>
      <th>Defect rates</th>
      <th>Transportation modes</th>
      <th>Routes</th>
      <th>Costs</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>haircare</td>
      <td>SKU0</td>
      <td>69.808006</td>
      <td>55</td>
      <td>802</td>
      <td>8661.996792</td>
      <td>Non-binary</td>
      <td>58</td>
      <td>7</td>
      <td>96</td>
      <td>...</td>
      <td>Mumbai</td>
      <td>29</td>
      <td>215</td>
      <td>29</td>
      <td>46.279879</td>
      <td>Pending</td>
      <td>0.226410</td>
      <td>Road</td>
      <td>Route B</td>
      <td>187.752075</td>
    </tr>
    <tr>
      <th>1</th>
      <td>skincare</td>
      <td>SKU1</td>
      <td>14.843523</td>
      <td>95</td>
      <td>736</td>
      <td>7460.900065</td>
      <td>Female</td>
      <td>53</td>
      <td>30</td>
      <td>37</td>
      <td>...</td>
      <td>Mumbai</td>
      <td>23</td>
      <td>517</td>
      <td>30</td>
      <td>33.616769</td>
      <td>Pending</td>
      <td>4.854068</td>
      <td>Road</td>
      <td>Route B</td>
      <td>503.065579</td>
    </tr>
    <tr>
      <th>2</th>
      <td>haircare</td>
      <td>SKU2</td>
      <td>11.319683</td>
      <td>34</td>
      <td>8</td>
      <td>9577.749626</td>
      <td>Unknown</td>
      <td>1</td>
      <td>10</td>
      <td>88</td>
      <td>...</td>
      <td>Mumbai</td>
      <td>12</td>
      <td>971</td>
      <td>27</td>
      <td>30.688019</td>
      <td>Pending</td>
      <td>4.580593</td>
      <td>Air</td>
      <td>Route C</td>
      <td>141.920282</td>
    </tr>
    <tr>
      <th>3</th>
      <td>skincare</td>
      <td>SKU3</td>
      <td>61.163343</td>
      <td>68</td>
      <td>83</td>
      <td>7766.836426</td>
      <td>Non-binary</td>
      <td>23</td>
      <td>13</td>
      <td>59</td>
      <td>...</td>
      <td>Kolkata</td>
      <td>24</td>
      <td>937</td>
      <td>18</td>
      <td>35.624741</td>
      <td>Fail</td>
      <td>4.746649</td>
      <td>Rail</td>
      <td>Route A</td>
      <td>254.776159</td>
    </tr>
    <tr>
      <th>4</th>
      <td>skincare</td>
      <td>SKU4</td>
      <td>4.805496</td>
      <td>26</td>
      <td>871</td>
      <td>2686.505152</td>
      <td>Non-binary</td>
      <td>5</td>
      <td>3</td>
      <td>56</td>
      <td>...</td>
      <td>Delhi</td>
      <td>5</td>
      <td>414</td>
      <td>3</td>
      <td>92.065161</td>
      <td>Fail</td>
      <td>3.145580</td>
      <td>Air</td>
      <td>Route A</td>
      <td>923.440632</td>
    </tr>
  </tbody>
</table>
<p>5 rows × 24 columns</p>
</div>

  Product type   SKU      Price  Availability  Number of products sold  \
0     haircare  SKU0  69.808006            55                      802   
1     skincare  SKU1  14.843523            95                      736   
2     haircare  SKU2  11.319683            34                        8   
3     skincare  SKU3  61.163343            68                       83   
4     skincare  SKU4   4.805496            26                      871   

   Revenue generated Customer demographics  Stock levels  Lead times  \
0        8661.996792            Non-binary            58           7   
1        7460.900065                Female            53          30   
2        9577.749626               Unknown             1          10   
3        7766.836426            Non-binary            23          13   
4        2686.505152            Non-binary             5           3   

   Order quantities  ...  Location Lead time  Production volumes  \
0                96  ...    Mumbai        29                 215   
1                37  ...    Mumbai        23                 517   
2                88  ...    Mumbai        12                 971   
3                59  ...   Kolkata        24                 937   
4                56  ...     Delhi         5                 414   

  Manufacturing lead time Manufacturing costs  Inspection results  \
0                      29           46.279879             Pending   
1                      30           33.616769             Pending   
2                      27           30.688019             Pending   
3                      18           35.624741                Fail   
4                       3           92.065161                Fail   

   Defect rates  Transportation modes   Routes       Costs  
0      0.226410                  Road  Route B  187.752075  
1      4.854068                  Road  Route B  503.065579  
2      4.580593                   Air  Route C  141.920282  
3      4.746649                  Rail  Route A  254.776159  
4      3.145580                   Air  Route A  923.440632  

[5 rows x 24 columns]

Visualizing Revenue Trends Over Time

Visualize revenue trends over time.

Can you visualize the relationship between price and revenue generated?