Fixing Typos & Preparing Data for Missing Value Imputation with Python

Описание: 8_Fixing Typos & Preparing Data for Missing Value Imputation with Python

Dataset and code: www.savilagames.io

🔹 Note: This video is part of a complete step-by-step tutorial. To get the full context and follow along smoothly, please watch the playlist in order.

Summary:

Handling missing data is a crucial step in data analysis, but blindly dropping missing values can lead to loss of important information. In this lesson, we explore three ways to handle missing data using Pandas:

1. Drop missing values (not always ideal).
2. Fill missing values with zeros or averages.
3. Use smart imputation by leveraging available data.

We then apply a more advanced approach: reconstructing missing sales values using SKU-level transactions from the same year.
However, before doing this, we need to clean up SKU names, which contain typos and inconsistent formats (e.g., extra symbols like ‘@’ or ‘_FR’). We create a Python function to standardize SKUs and apply it to our dataset. This ensures accurate grouping and calculations, setting the stage for effective data imputation in the next lesson.

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Step-by-Step:

1️⃣ Check Missing Values 🕵️

Analyze the missing values in your dataset.
Understand how much data you would lose if you drop them.

2️⃣ ExploreMissing Value Strategies 💡

Option 1: Drop rows with missing values (dropna()).
Option 2: Fill missing values with zeros or averages (fillna()).
Option 3 (Best Approach): Estimate missing sales from other transactions using the SKU price and quantity.

3️⃣ Identify Typos in SKUs 🧐

Examine the unique SKU names.
Find patterns and duplicates caused by typos (e.g., SKU6001, @SKU6001, SKU6001_FR).

4️⃣ Filter for Specific SKUs 📊

Use .str.contains() to filter and inspect rows with a specific SKU pattern.
List out all versions of a particular SKU to identify errors.

5️⃣ Create a Cleaning Function 🧼

Build a Python function to fix typos by removing unwanted characters (e.g., @, _FR).
Test the function with different variations of the SKU.

6️⃣ Apply the Function to the Dataset 🚀

Use .apply() to clean the entire SKU column.
Verify that the unique SKU count decreases, indicating successful cleanup.

7️⃣ Confirm Data is Clean ✅

Rerun unique SKU checks to ensure only the correct versions remain.
Now your data is ready for accurate imputation!