<div>
<style scoped>
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</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>Transaction ID</th>
      <th>Date</th>
      <th>Customer ID</th>
      <th>Gender</th>
      <th>Age</th>
      <th>Product Category</th>
      <th>Quantity</th>
      <th>Price per Unit</th>
      <th>Total Amount</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>180</td>
      <td>1/1/2023</td>
      <td>CUST180</td>
      <td>Male</td>
      <td>41</td>
      <td>Clothing</td>
      <td>3</td>
      <td>300</td>
      <td>900</td>
    </tr>
    <tr>
      <th>1</th>
      <td>522</td>
      <td>1/1/2023</td>
      <td>CUST522</td>
      <td>Male</td>
      <td>46</td>
      <td>Beauty</td>
      <td>3</td>
      <td>500</td>
      <td>1500</td>
    </tr>
    <tr>
      <th>2</th>
      <td>559</td>
      <td>1/1/2023</td>
      <td>CUST559</td>
      <td>Female</td>
      <td>40</td>
      <td>Clothing</td>
      <td>4</td>
      <td>300</td>
      <td>1200</td>
    </tr>
    <tr>
      <th>3</th>
      <td>163</td>
      <td>1/2/2023</td>
      <td>CUST163</td>
      <td>Female</td>
      <td>64</td>
      <td>Clothing</td>
      <td>3</td>
      <td>50</td>
      <td>150</td>
    </tr>
    <tr>
      <th>4</th>
      <td>303</td>
      <td>1/2/2023</td>
      <td>CUST303</td>
      <td>Male</td>
      <td>19</td>
      <td>Electronics</td>
      <td>3</td>
      <td>30</td>
      <td>90</td>
    </tr>
  </tbody>
</table>
</div>

   Transaction ID      Date Customer ID  Gender  Age Product Category  \
0             180  1/1/2023     CUST180    Male   41         Clothing   
1             522  1/1/2023     CUST522    Male   46           Beauty   
2             559  1/1/2023     CUST559  Female   40         Clothing   
3             163  1/2/2023     CUST163  Female   64         Clothing   
4             303  1/2/2023     CUST303    Male   19      Electronics   

   Quantity  Price per Unit  Total Amount  
0         3             300           900  
1         3             500          1500  
2         4             300          1200  
3         3              50           150  
4         3              30            90  

<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 Category</th>
      <th>total_sales</th>
      <th>average_sales</th>
      <th>total_quantity</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>Beauty</td>
      <td>142015</td>
      <td>464.101307</td>
      <td>768</td>
    </tr>
    <tr>
      <th>1</th>
      <td>Clothing</td>
      <td>155580</td>
      <td>443.247863</td>
      <td>894</td>
    </tr>
    <tr>
      <th>2</th>
      <td>Electronics</td>
      <td>156875</td>
      <td>460.043988</td>
      <td>848</td>
    </tr>
  </tbody>
</table>
</div>

  Product Category  total_sales  average_sales  total_quantity
0           Beauty       142015     464.101307             768
1         Clothing       155580     443.247863             894
2      Electronics       156875     460.043988             848

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<style scoped>
    .dataframe tbody tr th:only-of-type {
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    }

    .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>total_sales</th>
      <th>average_sales</th>
      <th>total_quantity</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>Female</td>
      <td>232840</td>
      <td>456.549020</td>
      <td>1298</td>
    </tr>
    <tr>
      <th>1</th>
      <td>Male</td>
      <td>221630</td>
      <td>454.159836</td>
      <td>1212</td>
    </tr>
  </tbody>
</table>
</div>

   Gender  total_sales  average_sales  total_quantity
0  Female       232840     456.549020            1298
1    Male       221630     454.159836            1212

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</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>Date</th>
      <th>total_sales</th>
      <th>average_sales</th>
      <th>total_quantity</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>2023-01-01</td>
      <td>3600</td>
      <td>1200.000000</td>
      <td>10</td>
    </tr>
    <tr>
      <th>1</th>
      <td>2023-01-02</td>
      <td>1765</td>
      <td>441.250000</td>
      <td>10</td>
    </tr>
    <tr>
      <th>2</th>
      <td>2023-01-03</td>
      <td>600</td>
      <td>600.000000</td>
      <td>2</td>
    </tr>
    <tr>
      <th>3</th>
      <td>2023-01-04</td>
      <td>1240</td>
      <td>413.333333</td>
      <td>8</td>
    </tr>
    <tr>
      <th>4</th>
      <td>2023-01-05</td>
      <td>1100</td>
      <td>366.666667</td>
      <td>5</td>
    </tr>
  </tbody>
</table>
</div>

        Date  total_sales  average_sales  total_quantity
0 2023-01-01         3600    1200.000000              10
1 2023-01-02         1765     441.250000              10
2 2023-01-03          600     600.000000               2
3 2023-01-04         1240     413.333333               8
4 2023-01-05         1100     366.666667               5

Descriptive Analysis of Retail Sales Dataset

Do descriptive analysis from this dataset through excel pivot table and gives inferences also.

Sales Summary by Product Category

Sales Summary by Gender

Can you analyze sales trends over time?