Fill Missing Data

Problem

DataFrame products +-------------+--------+ | Column Name | Type | +-------------+--------+ | name | object | | quantity | int | | price | int | +-------------+--------+

Write a solution to fill in the missing value as **0** in the quantity column.

The result format is in the following example.

Examples

Example 1

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

{{< code_tabs >}}
##### MySQL
```sql
-- Not applicable for this problem as it is a pandas/DataFrame problem.

PostgreSQL

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

Python (pandas)

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).