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This method involves iterating over each row of the DataFrame. We check if the 'quantity' value is null (or equivalent) and set it to zero if it is. This is a straightforward approach that uses conditional checks within loops appropriate for each programming language.
Time Complexity: O(n), where n is the number of products.
Space Complexity: O(1), as we do not use additional space proportional to the input size.
1#include <vector>
struct Product {
std::string name;
int quantity;
int price;
};
void fillMissingQuantities(std::vector<Product>& products) {
for (auto& product : products) {
if (product.quantity == -1) { // assuming -1 stands in for 'None'
product.quantity = 0;
}
}
}
int main() {
std::vector<Product> products = {
{"Wristwatch", -1, 135},
{"WirelessEarbuds", -1, 821},
{"GolfClubs", 779, 9319},
{"Printer", 849, 3051}
};
fillMissingQuantities(products);
for (const auto& product : products) {
std::cout << product.name << ": " << product.quantity << " " << product.price << std::endl;
}
return 0;
}In this C++ solution, we use a struct to hold product data and std::vector to manage a list of products. The function fillMissingQuantities iterates through the products vector to replace a placeholder (-1) with 0 for missing quantities.
This approach involves leveraging built-in functions or libraries available in programming languages, like pandas in Python or LINQ in C#, to perform the task of filling in missing values effectively and efficiently.
Time Complexity: O(n), where n is the number of products.
Space Complexity: O(1), without additional space for the task.
1import pandas as pd
2
3data = {
4 'name': ['Wristwatch', 'WirelessEarbuds', 'GolfClubs', 'Printer'],
5 'quantity': [None, None, 779, 849],
6 'price': [135, 821, 9319, 3051]
7}
8
9products = pd.DataFrame(data)
10
11products['quantity'].fillna(0, inplace=True)
12
13print(products)By employing Python's pandas library, we can use the fillna function to efficiently handle missing values in DataFrames, specifying inplace=True to maintain modifications.