Today, let's move to the next topic in the Data Science Learning Series: 🔹 *Data Type Conversions* Every column in a DataFrame has a data type like: int64 → whole numbers float64 → decimal numbers object → text or mixed data bool → True/False datetime64 → dates & times *Using the right data type:* - Saves memory - Improves performance - Helps with accurate analysis & modeling 🔍 *How to Check Data Types* df.dtypes 🔁 *Convert Data Types in Pandas* ✅ 1. String to Integer df['column'] = df['column'].astype(int) ✅ 2. String to Float df['column'] = df['column'].astype(float) ✅ 3. String to DateTime df['Date'] = pd.to_datetime(df['Date']) ✅ 4. Object to Category df['Category'] = df['Category'].astype('category') *Useful when column has limited repeating values — reduces memory usage.* ⚠️ *Common Errors* Converting strings like "abc" to int will cause a crash. Use pd.to_numeric(df['column'], errors='coerce') to handle this by converting bad values to NaN. 📊 *Real-Life Example:* In an e-commerce dataset: - Convert OrderDate to datetime for sorting and time series - Convert ProductCategory to category to save memory - Convert Price from string to float before calculating totals *React with ❤️ once you're ready for the quiz* Data Science Learning Series: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D/998 Python Cheatsheet: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L/1660