Fill Missing Data
<pre>
DataFrame <code>products</code>
+-------------+--------+
| Column Name | Type   |
+-------------+--------+
| name        | object |
| quantity    | int    |
| price       | int    |
+-------------+--------+
</pre>

<p>Write a solution to fill in the missing value as <code><strong>0</strong></code> in the <code>quantity</code> column.</p>

<p>The result format is in the following example.</p>

<p>&nbsp;</p>
<pre>
<strong class="example">Example 1:</strong>
<strong>Input:</strong>+-----------------+----------+-------+
| name            | quantity | price |
+-----------------+----------+-------+
| Wristwatch      | None     | 135   |
| WirelessEarbuds | None     | 821   |
| GolfClubs       | 779      | 9319  |
| Printer         | 849      | 3051  |
+-----------------+----------+-------+
<strong>Output:
</strong>+-----------------+----------+-------+
| name            | quantity | price |
+-----------------+----------+-------+
| Wristwatch      | 0        | 135   |
| WirelessEarbuds | 0        | 821   |
| GolfClubs       | 779      | 9319  |
| Printer         | 849      | 3051  |
+-----------------+----------+-------+
<strong>Explanation:</strong> 
The quantity for Wristwatch and WirelessEarbuds are filled by 0.</pre>

[]
[]
[{"value": "pythondata", "text": "Pandas", "defaultCode": "import pandas as pd\n\ndef fillMissingValues(products: pd.DataFrame) -> pd.DataFrame:\n    "}]
{
  "pythondata": [
    "products = pd.DataFrame([], columns=['name', 'quantity', 'price']).astype({'name':'object', 'quantity':'Int64', 'price':'Int64'})"
  ],
  "database": true,
  "name": "fillMissingValues",
  "languages": [
    "pythondata"
  ]
}
{"pythondata": ["Pandas", "<p>Python 3.10 with Pandas 2.2.2 and NumPy 1.26.4</p>"]}
[]