<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>Unnamed: 0</th>
      <th>Retailer</th>
      <th>Retailer ID</th>
      <th>Invoice Date</th>
      <th>Region</th>
      <th>State</th>
      <th>City</th>
      <th>Product</th>
      <th>Price per Unit</th>
      <th>Units Sold</th>
      <th>Total Sales</th>
      <th>Operating Profit</th>
      <th>Operating Margin</th>
      <th>Sales Method</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>NaN</td>
      <td>Foot Locker</td>
      <td>1185732</td>
      <td>1/1/20</td>
      <td>Northeast</td>
      <td>New York</td>
      <td>New York</td>
      <td>Men's Street Footwear</td>
      <td>$50.00</td>
      <td>1,200</td>
      <td>$600,000</td>
      <td>$300,000</td>
      <td>50%</td>
      <td>In-store</td>
    </tr>
    <tr>
      <th>1</th>
      <td>NaN</td>
      <td>Foot Locker</td>
      <td>1185732</td>
      <td>1/2/20</td>
      <td>Northeast</td>
      <td>New York</td>
      <td>New York</td>
      <td>Men's Athletic Footwear</td>
      <td>$50.00</td>
      <td>1,000</td>
      <td>$500,000</td>
      <td>$150,000</td>
      <td>30%</td>
      <td>In-store</td>
    </tr>
    <tr>
      <th>2</th>
      <td>NaN</td>
      <td>Foot Locker</td>
      <td>1185732</td>
      <td>1/3/20</td>
      <td>Northeast</td>
      <td>New York</td>
      <td>New York</td>
      <td>Women's Street Footwear</td>
      <td>$40.00</td>
      <td>1,000</td>
      <td>$400,000</td>
      <td>$140,000</td>
      <td>35%</td>
      <td>In-store</td>
    </tr>
    <tr>
      <th>3</th>
      <td>NaN</td>
      <td>Foot Locker</td>
      <td>1185732</td>
      <td>1/4/20</td>
      <td>Northeast</td>
      <td>New York</td>
      <td>New York</td>
      <td>Women's Athletic Footwear</td>
      <td>$45.00</td>
      <td>850</td>
      <td>$382,500</td>
      <td>$133,875</td>
      <td>35%</td>
      <td>In-store</td>
    </tr>
    <tr>
      <th>4</th>
      <td>NaN</td>
      <td>Foot Locker</td>
      <td>1185732</td>
      <td>1/5/20</td>
      <td>Northeast</td>
      <td>New York</td>
      <td>New York</td>
      <td>Men's Apparel</td>
      <td>$60.00</td>
      <td>900</td>
      <td>$540,000</td>
      <td>$162,000</td>
      <td>30%</td>
      <td>In-store</td>
    </tr>
  </tbody>
</table>
</div>

   Unnamed: 0     Retailer  Retailer ID Invoice Date     Region     State  \
0         NaN  Foot Locker      1185732       1/1/20  Northeast  New York   
1         NaN  Foot Locker      1185732       1/2/20  Northeast  New York   
2         NaN  Foot Locker      1185732       1/3/20  Northeast  New York   
3         NaN  Foot Locker      1185732       1/4/20  Northeast  New York   
4         NaN  Foot Locker      1185732       1/5/20  Northeast  New York   

       City                    Product Price per Unit Units Sold Total Sales  \
0  New York      Men's Street Footwear        $50.00       1,200   $600,000    
1  New York    Men's Athletic Footwear        $50.00       1,000   $500,000    
2  New York    Women's Street Footwear        $40.00       1,000   $400,000    
3  New York  Women's Athletic Footwear        $45.00         850   $382,500    
4  New York              Men's Apparel        $60.00         900   $540,000    

  Operating Profit Operating Margin Sales Method  
0        $300,000               50%     In-store  
1        $150,000               30%     In-store  
2        $140,000               35%     In-store  
3        $133,875               35%     In-store  
4        $162,000               30%     In-store  

City           Charleston
Total Sales     9479502.0
Name: 10, dtype: object

stdout

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Bar Chart of Revenue by Product Category

Create a bar chart of revenue by product category

Can you show the revenue by region?

Which city brings in the most revenue for mens street footwear

Make a bar chart of all the citiies that purchase mens street footwear

Which years are this data spanning?

Display a revenue graph over time from 2020 through 2021

Aggregate over time

Forecast 2022 revenue using Prophet. Color the line red