👉 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/...
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