🚀 The Ultimate Guide to Cleaning Data with Pandas & Scikit-Learn: 20+ Must-Know Commands!

Why Data Cleaning is the Key to AI & ML Success

Did you know that 80% of a data scientist’s time is spent cleaning and preparing data? Before building machine learning models, your dataset needs to be error-free, well-structured, and normalized. That’s where pandas and scikit-learn come in!

In this guide, I’ll walk you through 20+ powerful pandas & sklearn commands to analyze, clean, and preprocess your dataset like a pro. 💡

📌 Step-by-Step Guide to Cleaning Data in Python

✅ 1. Import the Necessary Libraries

import pandas as pd
import numpy as np
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import StandardScaler, MinMaxScaler, LabelEncoder, OneHotEncoder

🔹 These are the core libraries for data manipulation and preprocessing.

✅ 2. Load the Dataset

df = pd.read_csv("data.csv")  # Replace with your dataset

🔹 Reads your dataset into a pandas DataFrame for easy processing.

✅ 3. Preview Your Data

df.head() # shows the first 5 rows…