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.
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
import pandas as pd
def fillMissingValues(products: pd.DataFrame) -> pd.DataFrame:
products['quantity'].fillna(0, inplace=True)
return products
if __name__ == '__main__':
data = [['Wristwatch', None, 135],
['WirelessEarbuds', None, 821],
['GolfClubs', 779, 9319],
['Printer', 849, 3051]]
products = pd.DataFrame(data, columns=['name', 'quantity', 'price'])
print(fillMissingValues(products))