Fill Missing Data Solution in Python

C++
Java

Problem Description

Ah, the classic “Fill Missing Data” problem! It’s like trying to find your socks in the laundry—some are always missing, and you’re left with a mismatched pair. In the world of data, missing values can be as annoying as that one friend who always forgets their wallet. The task here is to fill in those pesky missing values in a DataFrame, specifically in the ‘quantity’ column.

Imagine you’re running a grocery store, and you have a list of products with their quantities. But wait! Some quantities are mysteriously absent. What do you do? You can’t just leave them blank like that one awkward silence in a conversation. Instead, you fill them with zero, because, let’s face it, if you have no apples, you have zero apples!

Code Solution

import pandas as pd

def fillMissingValues(products: pd.DataFrame) -> pd.DataFrame:
    products['quantity'].fillna(0, inplace=True)
    return products

Approach

The approach here is straightforward: we use the fillna() method from Pandas to replace any missing values in the ‘quantity’ column with zero. This is done in place, meaning the original DataFrame is modified directly. It’s like putting a band-aid on a wound—quick and effective!

Time and Space Complexity

Complexity Type	Complexity
Time Complexity	O(n), where n is the number of rows in the DataFrame.
Space Complexity	O(1), as we are not using any additional data structures that grow with the input size.

Real-World Example

Let’s say you’re managing an inventory system for a bakery. You have a list of items like bread, cakes, and pastries, but some quantities are missing because, well, life happens! By filling those missing values with zero, you can easily identify what you need to restock. No more guessing games—just pure, delicious data!