👉 Python Project : Pandas Live Case Study on Uber Cab's Data скачать в хорошем качестве

👉 Python Project : Pandas Live Case Study on Uber Cab's Data

In this session, we'll solve a live case study involving Uber Cab's data using Python and its accompanying packages. This lecture aims to provide a comprehensive understanding of data cleaning and data transformation at an advanced level. The topics covered in this lecture include: 1. Reading the Data 2. View First n rows of Data 3. View the last 5 rows of Data 4. Get the total number of rows and column in the dataset 5. Get the total number of elements in the dataset 6. Get the total number of NULL values across every column in the dataset 7. Get the total number of Non-Null values of every column 8. Get the entries having NULL values in the 'Purpose' Column 9. Get the entries having Non-Null values in the 'Purpose' column 10. Remove the * in every column name using the rename function 11. Remove the * in every column name using the str.replace() function 12. Remove the * in every column using the lambda function 13. Get the entries in the data where the START location is 'Fort Pierce' 14. Get the entries in the data where the STOP location is 'Fort Pierce' 15. Sort the entries in the data in descending order of the 'MILES' column 16. Drop all the rows where there are NULL values in the STOP column 17. Use describe() function to get the statistical properties about the numerical columns in the data 18. Create a report in an html file using Pandas Profiling 19. Understanding the START and STOP points. 20. Use value_counts() function to demonstrate the proportion of different categorical values in the data 21. Get the number of rides where START and STOP locations are the same 22. Find the favorite starting point according to the total number of MILES covered 23. Find the starting point for the ride where maximum miles are covered 24. Check the data types of all the columns in the dataset 25. Drop the 'unknown location' value from START and STOP column 26. Find the most popular START-STOP pair according to the total number of rides covered 27. Convert the datatypes of START_DATE and END_DATE columns to datetime 28. Extract the month from START_DATE and try to get the proportion of rides of different months 29. Find the average distance covered each month 30. Extract the day from the START_DATE column 31. Find the total miles covered per category per purpose Bonus!!! 📑 𝗥𝗘𝗦𝗢𝗨𝗥𝗖𝗘𝗦: Raw Data: https://github.com/AbhisheakSaraswat/... Last Video:    • Python Adidas Sales Dashboard using Stream...   Python Excel Automation:    • Excel Automation Using Python   Python Teaser:    • A Beautiful Python Programming Teaser | In...   Python Pandas Tutorial:    • Python Pandas Tutorial | What is Pandas | ...   Python Playlist:    • Python Tutorial for Beginners   Python Data Structure Playlist:    • Python Data Structure   Python OOPs Playlist:    • Object Oriented Programming Tutorials Usin...   𝗖𝗢𝗡𝗡𝗘𝗖𝗧 𝗪𝗜𝗧𝗛 𝗠𝗘: 📝 GitHub: https://github.com/AbhisheakSaraswat Linkedin►   / abhisheak-saraswat-0b1b4a105   Telegram: https://t.me/+32-TodtiOvo2Njk9