This content originally appeared on DEV Community and was authored by ABHISHEK N M
Data Cleaning Challenge with Pandas (Google Colab)
Data cleaning is one of the most crucial steps in any data science or analytics project. In this challenge, I worked on a real-world dataset from Kaggle with over 100,000 rows, performing various Pandas operations to clean, preprocess, and prepare it for further analysis.
Dataset Details
For this challenge, I selected the E-commerce Sales Dataset from Kaggle containing around 120,000 rows and 12 columns.
It includes data such as:
Order ID
Customer Name
Product & Quantity
Sales & Discount
Region
Order Date
Before Cleaning:
Rows → 120,000
Columns → 12
File format → .csv
Tools & Environment
Python 3
Google Colab
Libraries: Pandas, NumPy, Matplotlib
python
from google.colab import files
uploaded = files.upload()
import pandas as pd
df = pd.read_csv('ecommerce_sales.csv')
This content originally appeared on DEV Community and was authored by ABHISHEK N M