Data Handling in PYTHON for AI-ML – Date, Time & Text | Day 26/90 | AI Course in English
Day 26 of your Complete AI Course in English focuses on mastering DateTime and Text/String data handling – two data types that appear in 80% of real-world datasets but trip up most beginners. After building Pandas foundations (Days 19-25), this session equips you to parse messy dates, extract time intelligence, and clean text columns for professional AI/ML workflows.
Datetime conversion starts with pd.to_datetime() handling every format imaginable: "MM/DD/YYYY", "DD-MM-YY", ISO timestamps, Excel serial dates. You'll use format='%Y-%m-%d' for speed, dayfirst=True for regional formats, and errors='coerce' for bad dates. Real examples convert web logs, transaction timestamps, customer join dates into clean datetime columns.
Time-based feature engineering uses .dt accessor: dt.year, dt.month, dt.weekday, dt.hour, dt.is_month_end, dt.days_in_month. You'll create ML-ready features like seasonality flags, weekend indicators, quarter revenue trends, recency scores – essential for churn prediction, demand forecasting, and customer behavior models.
String operations leverage .str methods: str.lower(), str.strip(), str.replace('[^A-Za-z]',''), str.contains(r'@'), str.len(). You'll clean messy customer names, product descriptions, addresses by removing whitespace, standardizing case, extracting emails/phones with regex, and handling missing text gracefully.
Combined real-world scenarios show the power: cleaning "Order Date" column while standardizing "Customer Name", extracting purchase hour from timestamps while categorizing products, creating "days_since_last_purchase" features from datetime diffs. These workflows mirror production data pipelines at companies like Amazon, Uber, and Flipkart.
Advanced techniques include str.split(), str.get() for parsing names/titles, pd.to_numeric() after text cleaning, and datetime arithmetic (df['recency'] = today - df['last_order_date']). You'll handle edge cases like leap years, timezones, and locale-specific formats.
By the end of Day 26, you'll transform messy datetime/text data into clean, feature-rich inputs for machine learning: parsed timestamps, engineered time features, standardized text columns. 🎯 Outcome: Students handle real datasets with confidence across all data types.