Pandas Handling Missing Values: Exercises, Practice, Solution
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Pandas Handling Missing Values [ 20 exercises with solution]
1. Write a Pandas program to detect missing values of a given DataFrame. Display True or False. Go to the editor
Test Data:
ord_no purch_amt ord_date customer_id salesman_id 0 70001.0 150.50 2012-10-05 3002 5002.0 1 NaN 270.65 2012-09-10 3001 5003.0 2 70002.0 65.26 NaN 3001 5001.0 3 70004.0 110.50 2012-08-17 3003 NaN 4 NaN 948.50 2012-09-10 3002 5002.0 5 70005.0 2400.60 2012-07-27 3001 5001.0 6 NaN 5760.00 2012-09-10 3001 5001.0 7 70010.0 1983.43 2012-10-10 3004 NaN 8 70003.0 2480.40 2012-10-10 3003 5003.0 9 70012.0 250.45 2012-06-27 3002 5002.0 10 NaN 75.29 2012-08-17 3001 5003.0 11 70013.0 3045.60 2012-04-25 3001 NaNClick me to see the sample solution
2. Write a Pandas program to identify the column(s) of a given DataFrame which have at least one missing value. Go to the editor
Test Data:
ord_no purch_amt ord_date customer_id salesman_id 0 70001.0 150.50 2012-10-05 3002 5002.0 1 NaN 270.65 2012-09-10 3001 5003.0 2 70002.0 65.26 NaN 3001 5001.0 3 70004.0 110.50 2012-08-17 3003 NaN 4 NaN 948.50 2012-09-10 3002 5002.0 5 70005.0 2400.60 2012-07-27 3001 5001.0 6 NaN 5760.00 2012-09-10 3001 5001.0 7 70010.0 1983.43 2012-10-10 3004 NaN 8 70003.0 2480.40 2012-10-10 3003 5003.0 9 70012.0 250.45 2012-06-27 3002 5002.0 10 NaN 75.29 2012-08-17 3001 5003.0 11 70013.0 3045.60 2012-04-25 3001 NaNClick me to see the sample solution
3. Write a Pandas program to count the number of missing values in each column of a given DataFrame. Go to the editor
Test Data:
ord_no purch_amt ord_date customer_id salesman_id 0 70001.0 150.50 2012-10-05 3002 5002.0 1 NaN 270.65 2012-09-10 3001 5003.0 2 70002.0 65.26 NaN 3001 5001.0 3 70004.0 110.50 2012-08-17 3003 NaN 4 NaN 948.50 2012-09-10 3002 5002.0 5 70005.0 2400.60 2012-07-27 3001 5001.0 6 NaN 5760.00 2012-09-10 3001 5001.0 7 70010.0 1983.43 2012-10-10 3004 NaN 8 70003.0 2480.40 2012-10-10 3003 5003.0 9 70012.0 250.45 2012-06-27 3002 5002.0 10 NaN 75.29 2012-08-17 3001 5003.0 11 70013.0 3045.60 2012-04-25 3001 NaNClick me to see the sample solution
4. Write a Pandas program to find and replace the missing values in a given DataFrame which do not have any valuable information. Go to the editor
Test Data:
ord_no purch_amt ord_date customer_id salesman_id 0 70001 150.5 ? 3002 5002 1 NaN 270.65 2012-09-10 3001 5003 2 70002 65.26 NaN 3001 ? 3 70004 110.5 2012-08-17 3003 5001 4 NaN 948.5 2012-09-10 3002 NaN 5 70005 2400.6 2012-07-27 3001 5002 6 -- 5760 2012-09-10 3001 5001 7 70010 ? 2012-10-10 3004 ? 8 70003 12.43 2012-10-10 -- 5003 9 70012 2480.4 2012-06-27 3002 5002 10 NaN 250.45 2012-08-17 3001 5003 11 70013 3045.6 2012-04-25 3001 --Click me to see the sample solution
5. Write a Pandas program to drop the rows where at least one element is missing in a given DataFrame. Go to the editor
Test Data:
ord_no purch_amt ord_date customer_id salesman_id 0 70001.0 150.50 2012-10-05 3002 5002.0 1 NaN 270.65 2012-09-10 3001 5003.0 2 70002.0 65.26 NaN 3001 5001.0 3 70004.0 110.50 2012-08-17 3003 NaN 4 NaN 948.50 2012-09-10 3002 5002.0 5 70005.0 2400.60 2012-07-27 3001 5001.0 6 NaN 5760.00 2012-09-10 3001 5001.0 7 70010.0 1983.43 2012-10-10 3004 NaN 8 70003.0 2480.40 2012-10-10 3003 5003.0 9 70012.0 250.45 2012-06-27 3002 5002.0 10 NaN 75.29 2012-08-17 3001 5003.0 11 70013.0 3045.60 2012-04-25 3001 NaNClick me to see the sample solution
6. Write a Pandas program to drop the columns where at least one element is missing in a given DataFrame. Go to the editor
Test Data:
ord_no purch_amt ord_date customer_id salesman_id 0 70001.0 150.50 2012-10-05 3002 5002.0 1 NaN 270.65 2012-09-10 3001 5003.0 2 70002.0 65.26 NaN 3001 5001.0 3 70004.0 110.50 2012-08-17 3003 NaN 4 NaN 948.50 2012-09-10 3002 5002.0 5 70005.0 2400.60 2012-07-27 3001 5001.0 6 NaN 5760.00 2012-09-10 3001 5001.0 7 70010.0 1983.43 2012-10-10 3004 NaN 8 70003.0 2480.40 2012-10-10 3003 5003.0 9 70012.0 250.45 2012-06-27 3002 5002.0 10 NaN 75.29 2012-08-17 3001 5003.0 11 70013.0 3045.60 2012-04-25 3001 NaNClick me to see the sample solution
7. Write a Pandas program to drop the rows where all elements are missing in a given DataFrame. Go to the editor
Test Data:
ord_no purch_amt ord_date customer_id 0 NaN NaN NaN NaN 1 NaN 270.65 2012-09-10 3001.0 2 70002.0 65.26 NaN 3001.0 3 70004.0 110.50 2012-08-17 3003.0 4 NaN 948.50 2012-09-10 3002.0 5 70005.0 2400.60 2012-07-27 3001.0 6 NaN 5760.00 2012-09-10 3001.0 7 70010.0 1983.43 2012-10-10 3004.0 8 70003.0 2480.40 2012-10-10 3003.0 9 70012.0 250.45 2012-06-27 3002.0 10 NaN 75.29 2012-08-17 3001.0 11 70013.0 3045.60 2012-04-25 3001.0Click me to see the sample solution
8. Write a Pandas program to keep the rows with at least 2 NaN values in a given DataFrame. Go to the editor
Test Data:
ord_no purch_amt ord_date customer_id 0 NaN NaN NaN NaN 1 NaN 270.65 2012-09-10 3001.0 2 70002.0 65.26 NaN 3001.0 3 NaN NaN NaN NaN 4 NaN 948.50 2012-09-10 3002.0 5 70005.0 2400.60 2012-07-27 3001.0 6 NaN 5760.00 2012-09-10 3001.0 7 70010.0 1983.43 2012-10-10 3004.0 8 70003.0 2480.40 2012-10-10 3003.0 9 70012.0 250.45 2012-06-27 3002.0 10 NaN 75.29 2012-08-17 3001.0 11 NaN NaN NaN NaNClick me to see the sample solution
9. Write a Pandas program to drop those rows from a given DataFrame in which specific columns have missing values. Go to the editor
Test Data:
ord_no purch_amt ord_date customer_id 0 NaN NaN NaN NaN 1 NaN 270.65 2012-09-10 3001.0 2 70002.0 65.26 NaN 3001.0 3 NaN NaN NaN NaN 4 NaN 948.50 2012-09-10 3002.0 5 70005.0 2400.60 2012-07-27 3001.0 6 NaN 5760.00 2012-09-10 3001.0 7 70010.0 1983.43 2012-10-10 3004.0 8 70003.0 2480.40 2012-10-10 3003.0 9 70012.0 250.45 2012-06-27 3002.0 10 NaN 75.29 2012-08-17 3001.0 11 NaN NaN NaN NaNClick me to see the sample solution
10. Write a Pandas program to keep the valid entries of a given DataFrame. Go to the editor
Test Data:
ord_no purch_amt ord_date customer_id 0 NaN NaN NaN NaN 1 NaN 270.65 2012-09-10 3001.0 2 70002.0 65.26 NaN 3001.0 3 NaN NaN NaN NaN 4 NaN 948.50 2012-09-10 3002.0 5 70005.0 2400.60 2012-07-27 3001.0 6 NaN 5760.00 2012-09-10 3001.0 7 70010.0 1983.43 2012-10-10 3004.0 8 70003.0 2480.40 2012-10-10 3003.0 9 70012.0 250.45 2012-06-27 3002.0 10 NaN 75.29 2012-08-17 3001.0 11 NaN NaN NaN NaNClick me to see the sample solution
11. Write a Pandas program to calculate the total number of missing values in a DataFrame. Go to the editor
Test Data:
ord_no purch_amt ord_date customer_id 0 NaN NaN NaN NaN 1 NaN 270.65 2012-09-10 3001.0 2 70002.0 65.26 NaN 3001.0 3 NaN NaN NaN NaN 4 NaN 948.50 2012-09-10 3002.0 5 70005.0 2400.60 2012-07-27 3001.0 6 NaN 5760.00 2012-09-10 3001.0 7 70010.0 1983.43 2012-10-10 3004.0 8 70003.0 2480.40 2012-10-10 3003.0 9 70012.0 250.45 2012-06-27 3002.0 10 NaN 75.29 2012-08-17 3001.0 11 NaN NaN NaN NaNClick me to see the sample solution
12. Write a Pandas program to replace NaNs with a single constant value in specified columns in a DataFrame. Go to the editor
Test Data:
ord_no purch_amt ord_date customer_id 0 NaN NaN NaN NaN 1 NaN 270.65 2012-09-10 3001.0 2 70002.0 65.26 NaN 3001.0 3 NaN NaN NaN NaN 4 NaN 948.50 2012-09-10 3002.0 5 70005.0 2400.60 2012-07-27 3001.0 6 NaN 5760.00 2012-09-10 3001.0 7 70010.0 1983.43 2012-10-10 3004.0 8 70003.0 2480.40 2012-10-10 3003.0 9 70012.0 250.45 2012-06-27 3002.0 10 NaN 75.29 2012-08-17 3001.0 11 NaN NaN NaN NaNClick me to see the sample solution
13. Write a Pandas program to replace NaNs with the value from the previous row or the next row in a given DataFrame. Go to the editor
Test Data:
ord_no purch_amt sale_amt ord_date customer_id salesman_id 0 70001.0 150.50 10.50 2012-10-05 3002 5002.0 1 NaN NaN 20.65 2012-09-10 3001 5003.0 2 70002.0 65.26 NaN NaN 3001 5001.0 3 70004.0 110.50 11.50 2012-08-17 3003 NaN 4 NaN 948.50 98.50 2012-09-10 3002 5002.0 5 70005.0 NaN NaN 2012-07-27 3001 5001.0 6 NaN 5760.00 57.00 2012-09-10 3001 5001.0 7 70010.0 1983.43 19.43 2012-10-10 3004 NaN 8 70003.0 NaN NaN 2012-10-10 3003 5003.0 9 70012.0 250.45 25.45 2012-06-27 3002 5002.0 10 NaN 75.29 75.29 2012-08-17 3001 5003.0 11 70013.0 3045.60 35.60 2012-04-25 3001 NaNClick me to see the sample solution
14. Write a Pandas program to replace NaNs with median or mean of the specified columns in a given DataFrame. Go to the editor
Test Data:
ord_no purch_amt sale_amt ord_date customer_id salesman_id 0 70001.0 150.50 10.50 2012-10-05 3002 5002.0 1 NaN NaN 20.65 2012-09-10 3001 5003.0 2 70002.0 65.26 NaN NaN 3001 5001.0 3 70004.0 110.50 11.50 2012-08-17 3003 NaN 4 NaN 948.50 98.50 2012-09-10 3002 5002.0 5 70005.0 NaN NaN 2012-07-27 3001 5001.0 6 NaN 5760.00 57.00 2012-09-10 3001 5001.0 7 70010.0 1983.43 19.43 2012-10-10 3004 NaN 8 70003.0 NaN NaN 2012-10-10 3003 5003.0 9 70012.0 250.45 25.45 2012-06-27 3002 5002.0 10 NaN 75.29 75.29 2012-08-17 3001 5003.0 11 70013.0 3045.60 35.60 2012-04-25 3001 NaNClick me to see the sample solution
15. Write a Pandas program to interpolate the missing values using the Linear Interpolation method in a given DataFrame. Go to the editor
From Wikipedia, in mathematics, linear interpolation is a method of curve fitting using linear polynomials to construct new data points within the range of a discrete set of known data points.
Test Data:
ord_no purch_amt sale_amt ord_date customer_id salesman_id 0 70001.0 150.50 10.50 2012-10-05 3002 5002.0 1 NaN NaN 20.65 2012-09-10 3001 5003.0 2 70002.0 65.26 NaN NaN 3001 5001.0 3 70004.0 110.50 11.50 2012-08-17 3003 NaN 4 NaN 948.50 98.50 2012-09-10 3002 5002.0 5 70005.0 NaN NaN 2012-07-27 3001 5001.0 6 NaN 5760.00 57.00 2012-09-10 3001 5001.0 7 70010.0 1983.43 19.43 2012-10-10 3004 NaN 8 70003.0 NaN NaN 2012-10-10 3003 5003.0 9 70012.0 250.45 25.45 2012-06-27 3002 5002.0 10 NaN 75.29 75.29 2012-08-17 3001 5003.0 11 70013.0 3045.60 35.60 2012-04-25 3001 NaNClick me to see the sample solution
16. Write a Pandas program to count the number of missing values of a specified column in a given DataFrame. Go to the editor
Test Data:
ord_no purch_amt sale_amt ord_date customer_id salesman_id 0 70001.0 150.50 10.50 2012-10-05 3002 5002.0 1 NaN NaN 20.65 2012-09-10 3001 5003.0 2 70002.0 65.26 NaN NaN 3001 5001.0 3 70004.0 110.50 11.50 2012-08-17 3003 NaN 4 NaN 948.50 98.50 2012-09-10 3002 5002.0 5 70005.0 NaN NaN 2012-07-27 3001 5001.0 6 NaN 5760.00 57.00 2012-09-10 3001 5001.0 7 70010.0 1983.43 19.43 2012-10-10 3004 NaN 8 70003.0 NaN NaN 2012-10-10 3003 5003.0 9 70012.0 250.45 25.45 2012-06-27 3002 5002.0 10 NaN 75.29 75.29 2012-08-17 3001 5003.0 11 70013.0 3045.60 35.60 2012-04-25 3001 NaNClick me to see the sample solution
17. Write a Pandas program to count the missing values in a given DataFrame. Go to the editor
Test Data:
ord_no purch_amt sale_amt ord_date customer_id salesman_id 0 70001.0 150.50 10.50 2012-10-05 3002 5002.0 1 NaN NaN 20.65 2012-09-10 3001 5003.0 2 70002.0 65.26 NaN NaN 3001 5001.0 3 70004.0 110.50 11.50 2012-08-17 3003 NaN 4 NaN 948.50 98.50 2012-09-10 3002 5002.0 5 70005.0 NaN NaN 2012-07-27 3001 5001.0 6 NaN 5760.00 57.00 2012-09-10 3001 5001.0 7 70010.0 1983.43 19.43 2012-10-10 3004 NaN 8 70003.0 NaN NaN 2012-10-10 3003 5003.0 9 70012.0 250.45 25.45 2012-06-27 3002 5002.0 10 NaN 75.29 75.29 2012-08-17 3001 5003.0 11 70013.0 3045.60 35.60 2012-04-25 3001 NaNClick me to see the sample solution
18. Write a Pandas program to find the Indexes of missing values in a given DataFrame. Go to the editor
Test Data:
ord_no purch_amt sale_amt ord_date customer_id salesman_id 0 70001.0 150.50 10.50 2012-10-05 3002 5002.0 1 NaN NaN 20.65 2012-09-10 3001 5003.0 2 70002.0 65.26 NaN NaN 3001 5001.0 3 70004.0 110.50 11.50 2012-08-17 3003 NaN 4 NaN 948.50 98.50 2012-09-10 3002 5002.0 5 70005.0 NaN NaN 2012-07-27 3001 5001.0 6 NaN 5760.00 57.00 2012-09-10 3001 5001.0 7 70010.0 1983.43 19.43 2012-10-10 3004 NaN 8 70003.0 NaN NaN 2012-10-10 3003 5003.0 9 70012.0 250.45 25.45 2012-06-27 3002 5002.0 10 NaN 75.29 75.29 2012-08-17 3001 5003.0 11 70013.0 3045.60 35.60 2012-04-25 3001 NaNClick me to see the sample solution
19. Write a Pandas program to replace the missing values with the most frequent values present in each column of a given DataFrame. Go to the editor
Test Data:
ord_no purch_amt sale_amt ord_date customer_id salesman_id 0 70001.0 150.50 10.50 2012-10-05 3002 5002.0 1 NaN NaN 20.65 2012-09-10 3001 5003.0 2 70002.0 65.26 NaN NaN 3001 5001.0 3 70004.0 110.50 11.50 2012-08-17 3003 NaN 4 NaN 948.50 98.50 2012-09-10 3002 5002.0 5 70005.0 NaN NaN 2012-07-27 3001 5001.0 6 NaN 5760.00 57.00 2012-09-10 3001 5001.0 7 70010.0 1983.43 19.43 2012-10-10 3004 NaN 8 70003.0 NaN NaN 2012-10-10 3003 5003.0 9 70012.0 250.45 25.45 2012-06-27 3002 5002.0 10 NaN 75.29 75.29 2012-08-17 3001 5003.0 11 70013.0 3045.60 35.60 2012-04-25 3001 NaNClick me to see the sample solution
20. Write a Pandas program to create a hitmap for more information about the distribution of missing values in a given DataFrame. Go to the editor
Test Data:
ord_no purch_amt sale_amt ord_date customer_id salesman_id 0 70001.0 150.50 10.50 2012-10-05 3002 5002.0 1 NaN NaN 20.65 2012-09-10 3001 5003.0 2 70002.0 65.26 NaN NaN 3001 5001.0 3 70004.0 110.50 11.50 2012-08-17 3003 NaN 4 NaN 948.50 98.50 2012-09-10 3002 5002.0 5 70005.0 NaN NaN 2012-07-27 3001 5001.0 6 NaN 5760.00 57.00 2012-09-10 3001 5001.0 7 70010.0 1983.43 19.43 2012-10-10 3004 NaN 8 70003.0 NaN NaN 2012-10-10 3003 5003.0 9 70012.0 250.45 25.45 2012-06-27 3002 5002.0 10 NaN 75.29 75.29 2012-08-17 3001 5003.0 11 70013.0 3045.60 35.60 2012-04-25 3001 NaNClick me to see the sample solution
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