Programming Project>PDS_UberDrive_Questions_Final - Jupyter Notebook.

Import the dataset and Load the dataset. Load the necessary libraries. Q1. Show the last 10 records of the dataset. (2 point) Q2. Show the first 10 records of the dataset. (2 points) Q3. Show the dimension of the data ...

Import the dataset and Load the dataset. Load the necessary libraries. Q1. Show the last 10 records of the dataset. (2 point) Q2. Show the first 10 records of the dataset. (2 points) Q3. Show the dimension of the dataset. (2 points) Q5. Print the information about all the variables of the data set. (2 points) Q6. Check for missing values. (2 points) Q7. How many missing values are present? (2 points) Q8. Get the initial data (name it 'df') with dropping the NA values. (2 points) Q9. Get the summary of the original data (before dropping the 'na' values). (2 points) Q10. Check the information of the new dataframe. (2 points) Q11. Get the unique start destinations. (2 points) Note: This question is based on the dataframe with no 'na' values in the 'START' variable. Q12. What is the total number of unique start destinations? (2 points) Note: This question is based on the dataframe with no 'na' values in the 'START' variable. Q13. Print the total number of unique stop destinations. (2 points) Note: This question is based on the dataframe with no 'na' values in the 'STOP' variable. Q14. Print all the Uber trips that has the starting point of San Francisco. (2 points) Note: Use the original dataframe without dropping the 'na' values. Q15. What is the most popular starting point for the Uber Out[18]: START_DATE* END_DATE* CATEGORY* START* STOP* MILES* PURPOSE* 362 05-09-2016 14:39 05-09-2016 15:06 Business Francisco San Palo Alto 20.5 Between Offices 440 6/14/2016 16:09 6/14/2016 16:39 Business San Francisco Emeryville 11.6 Meeting 836 10/19/2016 14:02 10/19/2016 14:31 Business Francisco San Berkeley 10.8 NaN 917 11-07-2016 19:17 11-07-2016 19:57 Business Francisco San Berkeley 13.2 Between Offices 919 11-08-2016 12:16 11-08-2016 12:49 Business Francisco San Berkeley 11.3 Meeting 927 11-09-2016 18:40 11-09-2016 19:17 Business Francisco San Oakland 12.7 Customer Visit 933 11-10-2016 15:17 11-10-2016 15:22 Business Francisco San Oakland 9.9 Temporary Site 966 11/15/2016 20:44 11/15/2016 21:00 Business Francisco San Berkeley 11.8 Temporary Site START_DATE* END_DATE* CATEGORY* START* STOP* \ 362 05-09-2016 14:39 05-09-2016 15:06 Business San Francisco Palo Alto 440 6/14/2016 16:09 6/14/2016 16:39 Business San Francisco Emeryville 836 10/19/2016 14:02 10/19/2016 14:31 Business San Francisco Berkeley MILES* PURPOSE* 362 20.5 Between Offices 440 11.6 Meeting 836 10.8 NaN 917 13.2 Between Offices 919 11.3 Meeting 927 12.7 Customer Visit 933 9.9 Temporary Site 966 11.8 Temporary Site dataset[dataset['START*']=='San Francisco'] print(dataset[dataset['START*']=='San Francisco']) 1 1 1 16/2/2020 PDS_UberDrive_Questions_Final - Jupyter Notebook localhost:8888/notebooks/~zGL/Project/Uber_Driver/PDS_UberDrive_Questions_Final.ipynb 12/21 Q15. What is the most popular starting point for the Uber drivers? (2 points) Note: This question is based on the dataframe with no 'na' values in the 'START' variable. Q16. What is the most popular dropping point for the Uber drivers? (2 points) Note: This question is based on the dataframe with no 'na' values in the 'STOP' variable. Q17. List the most frequent route taken by Uber drivers. (3 points) Note: This question is based on the dataframe with no 'na' values. Q18. Print all types of purposes for the trip in an array. (3 points) Note: This question is based on the dataframe with no 'na' values in the 'PURPOSE' variable. Q19. Plot a bar graph of Purposes vs Distance. (3 points) Q20. Print a dataframe of Purposes and the distance travelled for that particular Purpose. (3 points) Q21. Plot number of trips vs Category of trips. (3 points) Q22. What is proportion of trips that is Business and what is the proportion of trips that is Personal? (3 points)