Fill Missing Data

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Input: +-----------------+----------+-------+
| name            | quantity | price |
+-----------------+----------+-------+
| Wristwatch      | None     | 135   |
| WirelessEarbuds | None     | 821   |
| GolfClubs       | 779      | 9319  |
| Printer         | 849      | 3051  |
+-----------------+----------+-------+
Output: +-----------------+----------+-------+
| name            | quantity | price |
+-----------------+----------+-------+
| Wristwatch      | 0        | 135   |
| WirelessEarbuds | 0        | 821   |
| GolfClubs       | 779      | 9319  |
| Printer         | 849      | 3051  |
+-----------------+----------+-------+
Explanation: 
The quantity for Wristwatch and WirelessEarbuds are filled by 0.

## Solution

### Method 1 – Pandas fillna

#### Intuition

We can use the `fillna` method in pandas to replace missing values (NaN) in the `quantity` column with 0. This is a direct and efficient way to handle missing data in a DataFrame.

#### Approach

1. Use the `fillna` method on the `quantity` column to replace NaN with 0.
2. Return the updated DataFrame.

#### Code


    

         
         
         
        
            
            
        
            
            
                 
                 
                
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-- Not applicable for this problem as it is a pandas/DataFrame problem.


        
        
        
         
        
        
         
         
        

             
            
             
            
                
            
                 
                     
                
            
            
            
            


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-- Not applicable for this problem as it is a pandas/DataFrame problem.


        
        
        
         
        
        
         
         
        

             
            
             
            
                
            
                 
                     
                
            
                 
                     
                
            
                 
                     
                
            
            
            
            


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def fill_missing_data(products: 'pd.DataFrame') -> 'pd.DataFrame':
    products['quantity'] = products['quantity'].fillna(0)
    return products


        
        
        
         
        
        
    



#### Complexity

- ⏰ Time complexity: `O(n)`, where n is the number of rows in the DataFrame, as each value in the column is checked once.
- 🧺 Space complexity: `O(1)`, as the operation is done in-place (except for the returned DataFrame reference).