Datetime Format & Timezone Conversion | Python Interview Question For Data Analyst | Tuning Data
Python Interview Question For Data Analyst | Python Datetime Format & Timezone Conversion
Master Python Datetime formatting and Timezone conversion in this comprehensive Pandas tutorial! Learn how to handle messy date data from multiple sources and convert everything to a standard timezone - perfect for aspiring data analysts preparing for interviews.
In this video, I solve a real-world e-commerce problem for "ShopKaro" handling orders from Website (IST), Mobile App (multiple timezones), and API (UTC) with inconsistent date formats.
🎯 What You'll Learn:
Parse multiple date formats using Python datetime
Convert timezones (UTC, PST, EST, SGT) to IST using pytz
Handle ISO 8601 formats with timezone offsets
Deal with missing and invalid date values
Create custom date parsing functions
Apply lambda functions for timezone conversion
Clean messy real-world datetime data
💼 Business Use Cases Covered:
✅ Accurate hourly/daily sales analysis
✅ Peak hours identification for marketing campaigns
✅ SLA tracking - order to delivery time calculation
✅ Revenue reporting with correct date-wise calculations
✅ Fraud detection - identifying suspicious multi-timezone orders
🐍 Python Concepts Covered:
pandas DataFrame operations
datetime.strptime() for parsing
pytz for timezone handling
lambda functions with apply()
Error handling with try-except
Working with pd.isna() and pd.NaT
ISO 8601 datetime format handling
Multiple date format patterns
📊 Libraries Used:
pandas
numpy
pytz
datetime
🔗 Connect With Me:
📸 Instagram: / tuning.data
💼 LinkedIn: / reactwithrajeev
🐙 GitHub:https://github.com/reactwithrajeev/Py...
📥 Download the complete Jupyter Notebook and dataset from my GitHub repository!
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