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PANDAS DATA FRAME

1.Write a Pandas program to get the powers of an array values element-wise. Note: First array elements raised to powers from second array Sample data: {‘X’:[78,85,96,80,86], ‘Y’:[84,94,89,83,86],’Z’:[86,97,96,72,83]} Expected Output: X Y Z 0 78 84 86 1 85 94 97 2 96 89 96 3 80 83 72 4 86 86 83

2. Write a Pandas program to create and display a DataFrame from a specified dictionary data which has the index labels. Sample Python dictionary data and list labels: exam_data = {‘name’: [‘Anastasia’, ‘Dima’, ‘Katherine’, ‘James’, ‘Emily’, ‘Michael’, ‘Matthew’, ‘Laura’, ‘Kevin’, ‘Jonas’], ‘score’: [12.5, 9, 16.5, np.nan, 9, 20, 14.5, np.nan, 8, 19], ‘attempts’: [1, 3, 2, 3, 2, 3, 1, 1, 2, 1], ‘qualify’: [‘yes’, ‘no’, ‘yes’, ‘no’, ‘no’, ‘yes’, ‘yes’, ‘no’, ‘no’, ‘yes’]} labels = [‘a’, ‘b’, ‘c’, ‘d’, ‘e’, ‘f’, ‘g’, ‘h’, ‘i’, ‘j’] Expected Output: attempts name qualify score a 1 Anastasia yes 12.5 b 3 Dima no 9.0 …. i 2 Kevin no 8.0 j 1 Jonas yes 19.0

3. Write a Pandas program to display a summary of the basic information about a specified DataFrame and its data. Sample Python dictionary data and list labels: exam_data = {‘name’: [‘Anastasia’, ‘Dima’, ‘Katherine’, ‘James’, ‘Emily’, ‘Michael’, ‘Matthew’, ‘Laura’, ‘Kevin’, ‘Jonas’], ‘score’: [12.5, 9, 16.5, np.nan, 9, 20, 14.5, np.nan, 8, 19], ‘attempts’: [1, 3, 2, 3, 2, 3, 1, 1, 2, 1], ‘qualify’: [‘yes’, ‘no’, ‘yes’, ‘no’, ‘no’, ‘yes’, ‘yes’, ‘no’, ‘no’, ‘yes’]} labels = [‘a’, ‘b’, ‘c’, ‘d’, ‘e’, ‘f’, ‘g’, ‘h’, ‘i’, ‘j’] Expected Output: Summary of the basic information about this DataFrame and its data: Index: 10 entries, a to j Data columns (total 4 columns): …. dtypes: float64(1), int64(1), object(2) memory usage: 400.0+ bytes None

4. Write a Pandas program to get the first 3 rows of a given DataFrame. Sample Python dictionary data and list labels: exam_data = {‘name’: [‘Anastasia’, ‘Dima’, ‘Katherine’, ‘James’, ‘Emily’, ‘Michael’, ‘Matthew’, ‘Laura’, ‘Kevin’, ‘Jonas’], ‘score’: [12.5, 9, 16.5, np.nan, 9, 20, 14.5, np.nan, 8, 19], ‘attempts’: [1, 3, 2, 3, 2, 3, 1, 1, 2, 1], ‘qualify’: [‘yes’, ‘no’, ‘yes’, ‘no’, ‘no’, ‘yes’, ‘yes’, ‘no’, ‘no’, ‘yes’]} labels = [‘a’, ‘b’, ‘c’, ‘d’, ‘e’, ‘f’, ‘g’, ‘h’, ‘i’, ‘j’] Expected Output: First three rows of the data frame: attempts name qualify score a 1 Anastasia yes 12.5 b 3 Dima no 9.0 c 2 Katherine yes 16.5 5. Write a Pandas program to select the ‘name’ and ‘score’ columns from the following DataFrame. Sample Python dictionary data and list labels: exam_data = {‘name’: [‘Anastasia’, ‘Dima’, ‘Katherine’, ‘James’, ‘Emily’, ‘Michael’, ‘Matthew’, ‘Laura’, ‘Kevin’, ‘Jonas’], ‘score’: [12.5, 9, 16.5, np.nan, 9, 20, 14.5, np.nan, 8, 19], ‘attempts’: [1, 3, 2, 3, 2, 3, 1, 1, 2, 1], ‘qualify’: [‘yes’, ‘no’, ‘yes’, ‘no’, ‘no’, ‘yes’, ‘yes’, ‘no’, ‘no’, ‘yes’]} labels = [‘a’, ‘b’, ‘c’, ‘d’, ‘e’, ‘f’, ‘g’, ‘h’, ‘i’, ‘j’] Expected Output: select specific columns: name score a Anastasia 12.5 b Dima 9.0 c Katherine 16.5 … h Laura NaN i Kevin 8.0 j Jonas 19.0

6. Write a Pandas program to select the specified columns and rows from a given data frame. Sample Python dictionary data and list labels: Select ‘name’ and ‘score’ columns in rows 1, 3, 5, 6 from the following data frame. exam_data = {‘name’: [‘Anastasia’, ‘Dima’, ‘Katherine’, ‘James’, ‘Emily’, ‘Michael’, ‘Matthew’, ‘Laura’, ‘Kevin’, ‘Jonas’], ‘score’: [12.5, 9, 16.5, np.nan, 9, 20, 14.5, np.nan, 8, 19], ‘attempts’: [1, 3, 2, 3, 2, 3, 1, 1, 2, 1], ‘qualify’: [‘yes’, ‘no’, ‘yes’, ‘no’, ‘no’, ‘yes’, ‘yes’, ‘no’, ‘no’, ‘yes’]} labels = [‘a’, ‘b’, ‘c’, ‘d’, ‘e’, ‘f’, ‘g’, ‘h’, ‘i’, ‘j’] Expected Output: Select specific columns and rows: score qualify b 9.0 no d NaN no f 20.0 yes g 14.5 yes

7. Write a Pandas program to select the rows where the number of attempts in the examination is greater than 2. Sample Python dictionary data and list labels: exam_data = {‘name’: [‘Anastasia’, ‘Dima’, ‘Katherine’, ‘James’, ‘Emily’, ‘Michael’, ‘Matthew’, ‘Laura’, ‘Kevin’, ‘Jonas’], ‘score’: [12.5, 9, 16.5, np.nan, 9, 20, 14.5, np.nan, 8, 19], ‘attempts’: [1, 3, 2, 3, 2, 3, 1, 1, 2, 1], ‘qualify’: [‘yes’, ‘no’, ‘yes’, ‘no’, ‘no’, ‘yes’, ‘yes’, ‘no’, ‘no’, ‘yes’]} labels = [‘a’, ‘b’, ‘c’, ‘d’, ‘e’, ‘f’, ‘g’, ‘h’, ‘i’, ‘j’] Expected Output: Number of attempts in the examination is greater than 2: name score attempts qualify b Dima 9.0 3 no d James NaN 3 no f Michael 20.0 3 yes

8. Write a Pandas program to count the number of rows and columns of a DataFrame. Sample Python dictionary data and list labels: exam_data = {‘name’: [‘Anastasia’, ‘Dima’, ‘Katherine’, ‘James’, ‘Emily’, ‘Michael’, ‘Matthew’, ‘Laura’, ‘Kevin’, ‘Jonas’], ‘score’: [12.5, 9, 16.5, np.nan, 9, 20, 14.5, np.nan, 8, 19], ‘attempts’: [1, 3, 2, 3, 2, 3, 1, 1, 2, 1], ‘qualify’: [‘yes’, ‘no’, ‘yes’, ‘no’, ‘no’, ‘yes’, ‘yes’, ‘no’, ‘no’, ‘yes’]} labels = [‘a’, ‘b’, ‘c’, ‘d’, ‘e’, ‘f’, ‘g’, ‘h’, ‘i’, ‘j’] Expected Output: Number of Rows: 10 Number of Columns: 4

9. Write a Pandas program to select the rows where the score is missing, i.e. is NaN. Sample Python dictionary data and list labels: exam_data = {‘name’: [‘Anastasia’, ‘Dima’, ‘Katherine’, ‘James’, ‘Emily’, ‘Michael’, ‘Matthew’, ‘Laura’, ‘Kevin’, ‘Jonas’], ‘score’: [12.5, 9, 16.5, np.nan, 9, 20, 14.5, np.nan, 8, 19], ‘attempts’: [1, 3, 2, 3, 2, 3, 1, 1, 2, 1], ‘qualify’: [‘yes’, ‘no’, ‘yes’, ‘no’, ‘no’, ‘yes’, ‘yes’, ‘no’, ‘no’, ‘yes’]} labels = [‘a’, ‘b’, ‘c’, ‘d’, ‘e’, ‘f’, ‘g’, labels = [‘a’, ‘b’, ‘c’, ‘d’, ‘e’, ‘f’, ‘g’, ‘h’, ‘i’, ‘j’] Expected Output: Rows where score is missing: attempts name qualify score d 3 James no NaNh 1 Laura no NaN

10. Write a Pandas program to select the rows the score is between 15 and 20 (inclusive). Sample Python dictionary data and list labels: exam_data = {‘name’: [‘Anastasia’, ‘Dima’, ‘Katherine’, ‘James’, ‘Emily’, ‘Michael’, ‘Matthew’, ‘Laura’, ‘Kevin’, ‘Jonas’], ‘score’: [12.5, 9, 16.5, np.nan, 9, 20, 14.5, np.nan, 8, 19], ‘attempts’: [1, 3, 2, 3, 2, 3, 1, 1, 2, 1], ‘qualify’: [‘yes’, ‘no’, ‘yes’, ‘no’, ‘no’, ‘yes’, ‘yes’, ‘no’, ‘no’, ‘yes’]} labels = [‘a’, ‘b’, ‘c’, ‘d’, ‘e’, ‘f’, ‘g’, labels = [‘a’, ‘b’, ‘c’, ‘d’, ‘e’, ‘f’, ‘g’, ‘h’, ‘i’, ‘j’] Expected Output: Rows where score between 15 and 20 (inclusive): attempts name qualify score c 2 Katherine yes 16.5 f 3 Michael yes 20.0 j 1 Jonas yes 19.0

11. Write a Pandas program to select the rows where number of attempts in the examination is less than 2 and score greater than 15. Sample Python dictionary data and list labels: exam_data = {‘name’: [‘Anastasia’, ‘Dima’, ‘Katherine’, ‘James’, ‘Emily’, ‘Michael’, ‘Matthew’, ‘Laura’, ‘Kevin’, ‘Jonas’], ‘score’: [12.5, 9, 16.5, np.nan, 9, 20, 14.5, np.nan, 8, 19], ‘attempts’: [1, 3, 2, 3, 2, 3, 1, 1, 2, 1], ‘qualify’: [‘yes’, ‘no’, ‘yes’, ‘no’, ‘no’, ‘yes’, ‘yes’, ‘no’, ‘no’, ‘yes’]} labels = [‘a’, ‘b’, ‘c’, ‘d’, ‘e’, ‘f’, ‘g’, labels = [‘a’, ‘b’, ‘c’, ‘d’, ‘e’, ‘f’, ‘g’, ‘h’, ‘i’, ‘j’] Expected Output: Number of attempts in the examination is less than 2 and score greater than 15 : name score attempts qualify j Jonas 19.0 1 yes

12. Write a Pandas program to change the score in row ‘d’ to 11.5. Sample Python dictionary data and list labels: exam_data = {‘name’: [‘Anastasia’, ‘Dima’, ‘Katherine’, ‘James’, ‘Emily’, ‘Michael’, ‘Matthew’, ‘Laura’, ‘Kevin’, ‘Jonas’], ‘score’: [12.5, 9, 16.5, np.nan, 9, 20, 14.5, np.nan, 8, 19], ‘attempts’: [1, 3, 2, 3, 2, 3, 1, 1, 2, 1], ‘qualify’: [‘yes’, ‘no’, ‘yes’, ‘no’, ‘no’, ‘yes’, ‘yes’, ‘no’, ‘no’, ‘yes’]} labels = [‘a’, ‘b’, ‘c’, ‘d’, ‘e’, ‘f’, ‘g’, ‘h’, ‘i’, ‘j’] Expected Output: Change the score in row ‘d’ to 11.5: attempts name qualify score a 1 Anastasia yes 12.5 b 3 Dima no 9.0 c 2 Katherine yes 16.5 … i 2 Kevin no 8.0 j 1 Jonas yes 19.0

13. Write a Pandas program to calculate the sum of the examination attempts by the students. Sample Python dictionary data and list labels: exam_data = {‘name’: [‘Anastasia’, ‘Dima’, ‘Katherine’, ‘James’, ‘Emily’, ‘Michael’, ‘Matthew’, ‘Laura’, ‘Kevin’, ‘Jonas’], ‘score’: [12.5, 9, 16.5, np.nan, 9, 20, 14.5, np.nan, 8, 19], ‘attempts’: [1, 3, 2, 3, 2, 3, 1, 1, 2, 1], ‘qualify’: [‘yes’, ‘no’, ‘yes’, ‘no’, ‘no’, ‘yes’, ‘yes’, ‘no’, ‘no’, ‘yes’]} labels = [‘a’, ‘b’, ‘c’, ‘d’, ‘e’, ‘f’, ‘g’, ‘h’, ‘i’, ‘j’] Expected Output: Sum of the examination attempts by the students: 19

14. Write a Pandas program to calculate the mean score for each different student in DataFrame. Sample Python dictionary data and list labels: exam_data = {‘name’: [‘Anastasia’, ‘Dima’, ‘Katherine’, ‘James’, ‘Emily’, ‘Michael’, ‘Matthew’, ‘Laura’, ‘Kevin’, ‘Jonas’], ‘score’: [12.5, 9, 16.5, np.nan, 9, 20, 14.5, np.nan, 8, 19],’attempts’: [1, 3, 2, 3, 2, 3, 1, 1, 2, 1], ‘qualify’: [‘yes’, ‘no’, ‘yes’, ‘no’, ‘no’, ‘yes’, ‘yes’, ‘no’, ‘no’, ‘yes’]} labels = [‘a’, ‘b’, ‘c’, ‘d’, ‘e’, ‘f’, ‘g’, ‘h’, ‘i’, ‘j’] Expected Output: Mean score for each different student in data frame: 13.5625

15. Write a Pandas program to append a new row ‘k’ to data frame with given values for each column. Now delete the new row and return the original DataFrame. Sample Python dictionary data and list labels: exam_data = {‘name’: [‘Anastasia’, ‘Dima’, ‘Katherine’, ‘James’, ‘Emily’, ‘Michael’, ‘Matthew’, ‘Laura’, ‘Kevin’, ‘Jonas’], ‘score’: [12.5, 9, 16.5, np.nan, 9, 20, 14.5, np.nan, 8, 19], ‘attempts’: [1, 3, 2, 3, 2, 3, 1, 1, 2, 1], ‘qualify’: [‘yes’, ‘no’, ‘yes’, ‘no’, ‘no’, ‘yes’, ‘yes’, ‘no’, ‘no’, ‘yes’]} labels = [‘a’, ‘b’, ‘c’, ‘d’, ‘e’, ‘f’, ‘g’, ‘h’, ‘i’, ‘j’] Values for each column will be: name : “Suresh”, score: 15.5, attempts: 1, qualify: “yes”, label: “k” Expected Output: Append a new row: Print all records after insert a new record: attempts name qualify score a 1 Anastasia yes 12.5 b 3 Dima no 9.0 …… j 1 Jonas yes 19.0 k 1 Suresh yes 15.5 Delete the new row and display the original rows: attempts name qualify score a 1 Anastasia yes 12.5 b 3 Dima no 9.0 …….. i 2 Kevin no 8.0 j 1 Jonas yes

19.Write a Pandas program to sort the DataFrame first by ‘name’ in descending order, then by ‘score’ in ascending order. Sample Python dictionary data and list labels: exam_data = {‘name’: [‘Anastasia’, ‘Dima’, ‘Katherine’, ‘James’, ‘Emily’, ‘Michael’, ‘Matthew’, ‘Laura’, ‘Kevin’, ‘Jonas’], ‘score’: [12.5, 9, 16.5, np.nan, 9, 20, 14.5, np.nan, 8, 19], ‘attempts’: [1, 3, 2, 3, 2, 3, 1, 1, 2, 1], ‘qualify’: [‘yes’, ‘no’, ‘yes’, ‘no’, ‘no’, ‘yes’, ‘yes’, ‘no’, ‘no’, ‘yes’]} labels = [‘a’, ‘b’, ‘c’, ‘d’, ‘e’, ‘f’, ‘g’, ‘h’, ‘i’, ‘j’] Values for each column will be: name : “Suresh”, score: 15.5, attempts: 1, qualify: “yes”, label: “k” Expected Output: Orginal rows: name score attempts qualify a Anastasia 12.5 1 yes b Dima 9.0 3 no c Katherine 16.5 2 yes d James NaN 3 no e Emily 9.0 2 no f Michael 20.0 3 yes g Matthew 14.5 1 yes h Laura NaN 1 no i Kevin 8.0 2 no j Jonas 19.0 1 yes Sort the data frame first by ‘name’ in descending order, then by ‘score’ in ascending order: name score attempts qualify a Anastasia 12.5 1 yes b Dima 9.0 3 no c Katherine 16.5 2 yes d James NaN 3 no e Emily 9.0 2 no f Michael 20.0 3 yes g Matthew 14.5 1 yes h Laura NaN 1 no i Kevin 8.0 2 noj Jonas 19.0 1 yes

17. Write a Pandas program to replace the ‘qualify’ column contains the values ‘yes’ and ‘no’ with True and False. Sample Python dictionary data and list labels: exam_data = {‘name’: [‘Anastasia’, ‘Dima’, ‘Katherine’, ‘James’, ‘Emily’, ‘Michael’, ‘Matthew’, ‘Laura’, ‘Kevin’, ‘Jonas’], ‘score’: [12.5, 9, 16.5, np.nan, 9, 20, 14.5, np.nan, 8, 19], ‘attempts’: [1, 3, 2, 3, 2, 3, 1, 1, 2, 1], ‘qualify’: [‘yes’, ‘no’, ‘yes’, ‘no’, ‘no’, ‘yes’, ‘yes’, ‘no’, ‘no’, ‘yes’]} labels = [‘a’, ‘b’, ‘c’, ‘d’, ‘e’, ‘f’, ‘g’, ‘h’, ‘i’, ‘j’] Expected Output: Replace the ‘qualify’ column contains the values ‘yes’ and ‘no’ with T rue and False: attempts name qualify score a 1 Anastasia True 12.5 b 3 Dima False 9.0 …… i 2 Kevin False 8.0 j 1 Jonas True 19.0

18. Write a Pandas program to change the name ‘James’ to ‘Suresh’ in name column of the DataFrame. Sample Python dictionary data and list labels: exam_data = {‘name’: [‘Anastasia’, ‘Dima’, ‘Katherine’, ‘James’, ‘Emily’, ‘Michael’, ‘Matthew’, ‘Laura’, ‘Kevin’, ‘Jonas’], ‘score’: [12.5, 9, 16.5, np.nan, 9, 20, 14.5, np.nan, 8, 19], ‘attempts’: [1, 3, 2, 3, 2, 3, 1, 1, 2, 1], ‘qualify’: [‘yes’, ‘no’, ‘yes’, ‘no’, ‘no’, ‘yes’, ‘yes’, ‘no’, ‘no’, ‘yes’]} labels = [‘a’, ‘b’, ‘c’, ‘d’, ‘e’, ‘f’, ‘g’, ‘h’, ‘i’, ‘j’] Expected Output: Change the name ‘James’ to \?Suresh\?: attempts name qualify score a 1 Anastasia yes 12.5 b 3 Dima no 9.0……. i 2 Kevin no 8.0 j 1 Jonas yes 19.0

19. Write a Pandas program to delete the ‘attempts’ column from the DataFrame. Sample Python dictionary data and list labels: exam_data = {‘name’: [‘Anastasia’, ‘Dima’, ‘Katherine’, ‘James’, ‘Emily’, ‘Michael’, ‘Matthew’, ‘Laura’, ‘Kevin’, ‘Jonas’], ‘score’: [12.5, 9, 16.5, np.nan, 9, 20, 14.5, np.nan, 8, 19], ‘attempts’: [1, 3, 2, 3, 2, 3, 1, 1, 2, 1], ‘qualify’: [‘yes’, ‘no’, ‘yes’, ‘no’, ‘no’, ‘yes’, ‘yes’, ‘no’, ‘no’, ‘yes’]} labels = [‘a’, ‘b’, ‘c’, ‘d’, ‘e’, ‘f’, ‘g’, ‘h’, ‘i’, ‘j’] Expected Output: Delete the ‘attempts’ column from the data frame: name qualify score a Anastasia yes 12.5 b Dima no 9.0 ….. i Kevin no 8.0 j Jonas yes 19.0

20. Write a Pandas program to insert a new column in existing DataFrame. Sample Python dictionary data and list labels: exam_data = {‘name’: [‘Anastasia’, ‘Dima’, ‘Katherine’, ‘James’, ‘Emily’, ‘Michael’, ‘Matthew’, ‘Laura’, ‘Kevin’, ‘Jonas’], ‘score’: [12.5, 9, 16.5, np.nan, 9, 20, 14.5, np.nan, 8, 19], ‘attempts’: [1, 3, 2, 3, 2, 3, 1, 1, 2, 1], ‘qualify’: [‘yes’, ‘no’, ‘yes’, ‘no’, ‘no’, ‘yes’, ‘yes’, ‘no’, ‘no’, ‘yes’]} labels = [‘a’, ‘b’, ‘c’, ‘d’, ‘e’, ‘f’, ‘g’, ‘h’, ‘i’, ‘j’] Expected Output: New DataFrame after inserting the ‘color’ column attempts name qualify score color a 1 Anastasia yes 12.5 Red b 3 Dima no 9.0 Blue……. i 2 Kevin no 8.0 Green j 1 Jonas yes 19.0 Red

21. Write a Pandas program to iterate over rows in a DataFrame. Sample Python dictionary data and list labels: exam_data = [{‘name’:’Anastasia’, ‘score’:12.5}, {‘name’:’Dima’,’score’:9}, {‘name’:’Katherine’,’score’:16.5}] Expected Output: Anastasia 12.5 Dima 9.0 Katherine 16.5

22. Write a Pandas program to get list from DataFrame column headers. Sample Python dictionary data and list labels: exam_data = {‘name’: [‘Anastasia’, ‘Dima’, ‘Katherine’, ‘James’, ‘Emily’, ‘Michael’, ‘Matthew’, ‘Laura’, ‘Kevin’, ‘Jonas’], ‘score’: [12.5, 9, 16.5, np.nan, 9, 20, 14.5, np.nan, 8, 19], ‘attempts’: [1, 3, 2, 3, 2, 3, 1, 1, 2, 1], ‘qualify’: [‘yes’, ‘no’, ‘yes’, ‘no’, ‘no’, ‘yes’, ‘yes’, ‘no’, ‘no’, ‘yes’]} labels = [‘a’, ‘b’, ‘c’, ‘d’, ‘e’, ‘f’, ‘g’, ‘h’, ‘i’, ‘j’] Expected Output: [‘attempts’, ‘name’, ‘qualify’, ‘score’]

23. Write a Pandas program to rename columns of a given DataFrame Sample data: Original DataFrame col1 col2 col3 0 1 4 7 1 2 5 8 2 3 6 9 New DataFrame after renaming columns: Column1 Column2 Column3 0 1 4 7 1 2 5 8 2 3 6 9

24. Write a Pandas program to select rows from a given DataFrame based on values in some columns. Sample data: Original DataFrame col1 col2 col3 0 1 4 7 1 4 5 8 2 3 6 9 3 4 7 0 4 5 8 1 Rows for colum1 value == 4 col1 col2 col3 1 4 5 8 3 4 7 0

25. Write a Pandas program to change the order of a DataFrame columns. Sample data: Original DataFrame col1 col2 col3 0 1 4 7 1 4 5 8 2 3 6 9 3 4 7 0 4 5 8 1 After altering col1 and col3 col3 col2 col1 0 7 4 1 1 8 5 4 2 9 6 3 3 0 7 4 4 1 8 5

26.Write a Pandas program to add one row in an existing DataFrame. Sample data: Original DataFrame col1 col2 col3 0 1 4 7 1 4 5 8 2 3 6 9 3 4 7 0 4 5 8 1 After add one row: col1 col2 col3 0 1 4 7 1 4 5 8 2 3 6 9 3 4 7 0 4 5 8 1 5 10 11 12

27. Write a Pandas program to write a DataFrame to CSV file using tab separator. Sample data: Original DataFrame col1 col2 col3 0 1 4 7 1 4 5 8 2 3 6 9 3 4 7 0 4 5 8 1 Data from new_file.csv file: col1\tcol2\tcol3 0 1\t4\t7 1 4\t5\t8 2 3\t6\t9 3 4\t7\t0 4 5\t8\t1

28.Write a Pandas program to count city wise number of people from a given of data set (city, name of the person). Sample data: city Number of people 0 California 4 1 Georgia 2 2 Los Angeles 4 29. Write a Pandas program to delete DataFrame row(s) based on given column value. Sample data: Original DataFrame col1 col2 col3 0 1 4 7 1 4 5 8 2 3 6 9 3 4 7 0 4 5 8 1 New DataFrame col1 col2 col3 0 1 4 7 2 3 6 9 3 4 7 0 4 5 8 1 30. Write a Pandas program to widen output display to see more columns. Sample data: Original DataFrame col1 col2 col3 0 1 4 7 1 4 5 8 2 3 6 9 3 4 7 0 4 5 8 1

29.Write a Pandas program to select a row of series/dataframe by given integer index. Sample data: Original DataFrame col1 col2 col3 0 1 4 7 1 4 5 8 2 3 6 9 3 4 7 0 4 5 8 1 Index-2: Details col1 col2 col3 2 3 6 9

32. Write a Pandas program to replace all the NaN values with Zero’s in a column of a dataframe. Sample data: Original DataFrame attempts name qualify score 0 1 Anastasia yes 12.5 1 3 Dima no 9.0 2 2 Katherine yes 16.5 3 3 James no NaN 4 2 Emily no 9.0 5 3 Michael yes 20.0 6 1 Matthew yes 14.5 7 1 Laura no NaN 8 2 Kevin no 8.0 9 1 Jonas yes 19.0 New DataFrame replacing all NaN with 0: attempts name qualify score 0 1 Anastasia yes 12.5 1 3 Dima no 9.0 2 2 Katherine yes 16.5 3 3 James no 0.0 4 2 Emily no 9.0 5 3 Michael yes 20.0 6 1 Matthew yes 14.5 7 1 Laura no 0.0 8 2 Kevin no 8.0 9 1 Jonas yes 19.0

33. Write a Pandas program to convert index in a column of the given dataframe. Sample data: Original DataFrame attempts name qualify score 0 1 Anastasia yes 12.5 1 3 Dima no 9.0 2 2 Katherine yes 16.5 3 3 James no NaN 4 2 Emily no 9.0 5 3 Michael yes 20.0 6 1 Matthew yes 14.5 7 1 Laura no NaN 8 2 Kevin no 8.0 9 1 Jonas yes 19.0 After converting index in a column: index attempts name qualify score 0 0 1 Anastasia yes 12.5 1 1 3 Dima no 9.0 2 2 2 Katherine yes 16.5 3 3 3 James no NaN 4 4 2 Emily no 9.0 5 5 3 Michael yes 20.0 6 6 1 Matthew yes 14.5 7 7 1 Laura no NaN 8 8 2 Kevin no 8.0 9 9 1 Jonas yes 19.0 Hiding index: index attempts name qualify score 0 1 Anastasia yes 12.5 1 3 Dima no 9.0 2 2 Katherine yes 16.5 3 3 James no NaN 4 2 Emily no 9.0 5 3 Michael yes 20.0 6 1 Matthew yes 14.5 7 1 Laura no NaN 8 2 Kevin no 8.0 9 1 Jonas yes 19.0

34. Write a Pandas program to set a given value for particular cell in DataFrame using index value. Sample data: Original DataFrame attempts name qualify score 0 1 Anastasia yes 12.5 1 3 Dima no 9.0 2 2 Katherine yes 16.5 3 3 James no NaN 4 2 Emily no 9.0 5 3 Michael yes 20.0 6 1 Matthew yes 14.5 7 1 Laura no NaN 8 2 Kevin no 8.0 9 1 Jonas yes 19.0 Set a given value for particular cell in the DataFrame attempts name qualify score 0 1 Anastasia yes 12.5 1 3 Dima no 9.0 2 2 Katherine yes 16.5 3 3 James no NaN 4 2 Emily no 9.0 5 3 Michael yes 20.0 6 1 Matthew yes 14.5 7 1 Laura no NaN 8 2 Kevin no 10.29 1 Jonas yes 19.0

35. Write a Pandas program to count the NaN values in one or more columns in DataFrame. Sample data: Original DataFrame attempts name qualify score 0 1 Anastasia yes 12.5 1 3 Dima no 9.0 2 2 Katherine yes 16.5 3 3 James no NaN 4 2 Emily no 9.0 5 3 Michael yes 20.0 6 1 Matthew yes 14.5 7 1 Laura no NaN 8 2 Kevin no 8.0 9 1 Jonas yes 19.0 Number of NaN values in one or more columns: 2

36. Write a Pandas program to drop a list of rows from a specified DataFrame. Sample data: Original DataFrame col1 col2 col3 0 1 4 7 1 4 5 8 2 3 6 9 3 4 7 0 4 5 8 1 New DataFrame after removing 2nd & 4th rows: col1 col2 col3 0 1 4 7 1 4 5 8 3 4 7 0

37. Write a Pandas program to reset index in a given DataFrame. Sample data: Original DataFrame attempts name qualify score 0 1 Anastasia yes 12.5 1 3 Dima no 9.0 2 2 Katherine yes 16.5 3 3 James no NaN 4 2 Emily no 9.0 5 3 Michael yes 20.0 6 1 Matthew yes 14.5 7 1 Laura no NaN 8 2 Kevin no 8.0 9 1 Jonas yes 19.0 After removing first and second rows attempts name qualify score 2 2 Katherine yes 16.5 3 3 James no NaN 4 2 Emily no 9.0 5 3 Michael yes 20.0 6 1 Matthew yes 14.5 7 1 Laura no NaN 8 2 Kevin no 8.0 9 1 Jonas yes 19.0 Reset the Index: index attempts name qualify score 0 2 2 Katherine yes 16.5 1 3 3 James no NaN 2 4 2 Emily no 9.0 3 5 3 Michael yes 20.0 4 6 1 Matthew yes 14.5 5 7 1 Laura no NaN 6 8 2 Kevin no 8.0 7 9 1 Jonas yes 19.0

38.Write a Pandas program to devide a DataFrame in a given ratio. Sample data: Original DataFrame: 0 1 0 0.316147 -0.767359 1 -0.813410 -2.522672 2 0.869615 1.194704 3 -0.892915 -0.055133 4 -0.341126 0.518266 5 1.857342 1.361229 6 -0.044353 -1.205002 7 -0.726346 -0.535147 8 -1.350726 0.563117 9 1.051666 -0.441533 70% of the said DataFrame: 0 1 8 -1.350726 0.563117 2 0.869615 1.194704 5 1.857342 1.361229 6 -0.044353 -1.205002 3 -0.892915 -0.055133 1 -0.813410 -2.522672 0 0.316147 -0.767359 30% of the said DataFrame: 0 1 4 -0.341126 0.518266 7 -0.726346 -0.535147 9 1.051666 -0.441533 39. Write a Pandas program to combining two series into a DataFrame. Sample data: Data Series: 0 100 1 200 2 python 3 300.124 400 dtype: object 0 10 1 20 2 php 3 30.12 4 40 dtype: object New DataFrame combining two series: 0 1 0 100 10 1 200 20 2 python php 3 300.12 30.12 4 400 40

40. Write a Pandas program to shuffle a given DataFrame rows. Sample data: Original DataFrame: attempts name qualify score 0 1 Anastasia yes 12.5 1 3 Dima no 9.0 2 2 Katherine yes 16.5 3 3 James no NaN 4 2 Emily no 9.0 5 3 Michael yes 20.0 6 1 Matthew yes 14.5 7 1 Laura no NaN 8 2 Kevin no 8.0 9 1 Jonas yes 19.0 New DataFrame: attempts name qualify score 5 3 Michael yes 20.0 0 1 Anastasia yes 12.5 9 1 Jonas yes 19.0 6 1 Matthew yes 14.5 7 1 Laura no NaN 1 3 Dima no 9.0 3 3 James no NaN 4 2 Emily no 9.0 8 2 Kevin no 8.0 2 2 Katherine yes 16.5

41. Write a Pandas program to convert DataFrame column type from string to datetime. Sample data: String Date: 0 3/11/2000 1 3/12/2000 2 3/13/2000 dtype: object Original DataFrame (string to datetime): 0 0 2000-03-11 1 2000-03-12 2 2000-03-13

42. Write a Pandas program to rename a specific column name in a given DataFrame. Sample data: Original DataFrame col1 col2 col3 0 1 4 7 1 2 5 8 2 3 6 9 New DataFrame after renaming second column: col1 Column2 col3 0 1 4 7 1 2 5 8 2 3 6 9

43. Write a Pandas program to get a list of a specified column of a DataFrame. Sample data: Powered by Original DataFrame col1 col2 col3 0 1 4 7 1 2 5 8 2 3 6 9 Col2 of the DataFrame to list: [4, 5, 6]

44. Write a Pandas program to create a DataFrame from a Numpy array and specify the index column and column headers. Sample Output: Column1 Column2 Column3 Index1 0 0.0 0.0 Index2 0 0.0 0.0 Index3 0 0.0 0.0 ……… Index12 0 0.0 0.0 Index13 0 0.0 0.0 Index14 0 0.0 0.0 Index15 0 0.0 0.0

45. Write a Pandas program to find the row for where the value of a given column is maximum. Sample Output: Original DataFrame col1 col2 col3 0 1 4 7 1 2 5 8 2 3 6 12 3 4 9 1 4 7 5 11 Row where col1 has maximum value: 4 Row where col2 has maximum value: 3 Row where col3 has maximum value: 2

46. Write a Pandas program to check whether a given column is present in a DataFrame or not. Sample data: Original DataFrame col1 col2 col3 0 1 4 7 1 2 5 8 2 3 6 12 3 4 9 1 4 7 5 11 Col4 is not present in DataFrame. Col1 is present in DataFrame.

47. Write a Pandas program to get the specified row value of a given DataFrame. Sample data: Original DataFrame col1 col2 col3 0 1 4 7 1 2 5 8 2 3 6 12 3 4 9 1 4 7 5 11 Value of Row col1 1 col2 4col3 7 Name: 0, dtype: int64 Value of Row4 col1 4 col2 9 col3 1 Name: 3, dtype: int64

48. Write a Pandas program to get the datatypes of columns of a DataFrame. Sample data: Original DataFrame: attempts name qualify score 0 1 Anastasia yes 12.5 1 3 Dima no 9.0 ……. 8 2 Kevin no 8.0 9 1 Jonas yes 19.0 Data types of the columns of the said DataFrame: attempts int64 name object qualify object score float64 dtype: object

49. Write a Pandas program to append data to an empty DataFrame. Sample data: Original DataFrame: After appending some data: col1 col2 0 0 0 1 1 1 2 2 2

50.Write a Pandas program to sort a given DataFrame by two or more columns. Sample data: Original DataFrame: attempts name qualify score 0 1 Anastasia yes 12.5 1 3 Dima no 9.0 …….. 8 2 Kevin no 8.0 9 1 Jonas yes 19.0 Sort the above DataFrame on attempts, name: attempts name qualify score 0 1 Anastasia yes 12.5 9 1 Jonas yes 19.0 7 1 Laura no NaN 6 1 Matthew yes 14.5 4 2 Emily no 9.0 2 2 Katherine yes 16.5 8 2 Kevin no 8.0 1 3 Dima no 9.0 3 3 James no NaN 5 3 Michael yes 20.0

51. Write a Pandas program to convert the datatype of a given column (floats to ints). Sample data: Original DataFrame: attempts name qualify score 0 1 Anastasia yes 12.50 1 3 Dima no 9.10 …… 8 2 Kevin no 8.80 9 1 Jonas yes 19.13 Data types of the columns of the said DataFrame: attempts int64 name object qualify object score float64 dtype: object Now change the Data type of ‘score’ column from float to int: attempts name qualify score 0 1 Anastasia yes 12 1 3 Dima no 9 2 2 Katherine yes 16 3 3 James no 12 4 2 Emily no 9 5 3 Michael yes 20 6 1 Matthew yes 14 7 1 Laura no 11 8 2 Kevin no 8 9 1 Jonas yes 19 Data types of the columns of the DataFrame now: attempts int64 name object qualify object score int64 dtype: object

52. Write a Pandas program to remove infinite values from a given DataFrame. Sample data: Original DataFrame: 0 0 1000.000000 1 2000.000000 2 3000.000000 3 -4000.000000 4 inf 5 -inf Removing infinite values: 0 0 1000.0 1 2000.0 2 3000.03 -4000.0 4 NaN 5 NaN

53. Write a Pandas program to insert a given column at a specific column index in a DataFrame. Sample data: Original DataFrame col2 col3 0 4 7 1 5 8 2 6 12 3 9 1 4 5 11 New DataFrame col1 col2 col3 0 1 4 7 1 2 5 8 2 3 6 12 3 4 9 1 4 7 5 11

54. Write a Pandas program to convert a given list of lists into a Dataframe. Sample data: Original list of lists: [[2, 4], [1, 3]] New DataFrame col1 col2 0 2 4 1 1 3

55. Write a Pandas program to group by the first column and get second column as lists in rows. Sample data: original DataFrame col1 col2 0 C1 1 1 C1 2 2 C2 3 3 C2 3 4 C2 4 5 C3 6 6 C2 5 Group on the col1: col1 C1 [1, 2] C2 [3, 3, 4, 5] C3 [6] Name: col2, dtype: object 56. Write a Pandas program to get column index from column name of a given DataFrame. Sample Output: Original DataFrame col1 col2 col3 0 1 4 7 1 2 5 8 2 3 6 12 3 4 9 1 4 7 5 11 Index of ‘col2’ 1

57. Write a Pandas program to count number of columns of a DataFrame. Sample Output: Original DataFrame col1 col2 col3 0 1 4 7 1 2 5 8 2 3 6 12 3 4 9 1 4 7 5 11 Number of columns: 3

58. Write a Pandas program to select all columns, except one given column in a DataFrame. Sample Output: Original DataFrame col1 col2 col3 0 1 4 7 1 2 5 8 2 3 6 12 3 4 9 1 4 7 5 11 All columns except ‘col3’: col1 col2 0 1 4 1 2 5 2 3 6 3 4 9 4 7 5

59. Write a Pandas program to get first n records of a DataFrame. Sample Output: Original DataFrame col1 col2 col3 0 1 4 7 1 2 5 5 2 3 6 8 3 4 9 12 4 7 5 1 5 11 0 11 First 3 rows of the said DataFrame’: col1 col2 col3 0 1 4 7 1 2 5 5 2 3 6 8

60. Write a Pandas program to get last n records of a DataFrame. Sample Output: Original DataFrame col1 col2 col3 0 1 4 7 1 2 5 5 2 3 6 8 3 4 9 12 4 7 5 1 5 11 0 11 Last 3 rows of the said DataFrame’: col1 col2 col3 3 4 9 12 4 7 5 1 5 11 0 11

61. Write a Pandas program to get topmost n records within each group of a DataFrame. Sample Output: Original DataFrame col1 col2 col3 0 1 4 7 1 2 5 5 2 3 6 8 3 4 9 12 4 7 5 1 5 11 0 11 topmost n records within each group of a DataFrame: col1 col2 col3 5 11 0 11 4 7 5 1 3 4 9 12 col1 col2 col3 3 4 9 12 2 3 6 8 1 2 5 5 4 7 5 1 col1 col2 col3 3 4 9 12 5 11 0 11 2 3 6 8

62. Write a Pandas program to remove first n rows of a given DataFrame. Sample Output: Original DataFrame col1 col2 col3 0 1 4 7 1 2 5 5 2 3 6 8 3 4 9 12 4 7 5 1 5 11 0 11 After removing first 3 rows of the said DataFrame: col1 col2 col3 3 4 9 12 4 7 5 1 5 11 0 11

63. Write a Pandas program to remove last n rows of a given DataFrame. Sample Output: Original DataFrame col1 col2 col3 0 1 4 7 1 2 5 5 2 3 6 8 3 4 9 12 4 7 5 1 5 11 0 11 After removing last 3 rows of the said DataFrame: col1 col2 col3 0 1 4 7 1 2 5 5 2 3 6 8

64. Write a Pandas program to add a prefix or suffix to all columns of a given DataFrame. Sample Output: Original DataFrame W X Y Z 0 68 78 84 86 1 75 85 94 97 2 86 96 89 96 3 80 80 83 72 4 66 86 86 83 Add prefix: A_W A_X A_Y A_Z 0 68 78 84 86 1 75 85 94 97 2 86 96 89 96 3 80 80 83 72 4 66 86 86 83 Add suffix: W_1 X_1 Y_1 Z_1 0 68 78 84 86 1 75 85 94 97 2 86 96 89 96 3 80 80 83 72 4 66 86 86 83

65.Write a Pandas program to reverse order (rows, columns) of a given DataFrame. Sample Output: Original DataFrame W X Y Z 0 68 78 84 86 1 75 85 94 97 2 86 96 89 96 3 80 80 83 72 4 66 86 86 83 Reverse column order: Z Y X W 0 86 84 78 68 1 97 94 85 75 2 96 89 96 86 3 72 83 80 80 4 83 86 86 66 Reverse row order: W X Y Z 4 66 86 86 83 3 80 80 83 72 2 86 96 89 96 1 75 85 94 97 0 68 78 84 86 Reverse row order and reset index: W X Y Z 0 66 86 86 83 1 80 80 83 72 2 86 96 89 96 3 75 85 94 97 4 68 78 84 86

66. Write a Pandas program to select columns by data type of a given DataFrame. Sample Output: Original DataFrame name date_of_birth age 0 Alberto Franco 17/05/2002 18.5 1 Gino Mcneill 16/02/1999 21.2 2 Ryan Parkes 25/09/1998 22.5 3 Eesha Hinton 11/05/2002 22.0 4 Syed Wharton 15/09/1997 23.0 Select numerical columns age 0 18.5 1 21.2 2 22.5 3 22.0 4 23.0 Select string columns name date_of_birth 0 Alberto Franco 17/05/2002 1 Gino Mcneill 16/02/1999 2 Ryan Parkes 25/09/1998 3 Eesha Hinton 11/05/2002 4 Syed Wharton 15/09/1997 67.

67.Write a Pandas program to split a given DataFrame into two random subsets. Sample Output: Original Dataframe and shape: name date_of_birth age 0 Alberto Franco 17/05/2002 18 1 Gino Mcneill 16/02/1999 21 2 Ryan Parkes 25/09/1998 22 3 Eesha Hinton 11/05/2002 22 4 Syed Wharton 15/09/1997 23 (5, 3) Subset-1 and shape: name date_of_birth age 1 Gino Mcneill 16/02/1999 21 4 Syed Wharton 15/09/1997 23 2 Ryan Parkes 25/09/1998 22 (3, 3) Subset-2 and shape: name date_of_birth age 0 Alberto Franco 17/05/2002 18 3 Eesha Hinton 11/05/2002 22 (2, 3) 68. Write a Pandas program to rename all columns with the same pattern of a given DataFrame. Sample Output: Original DataFrame Name Date_Of_Birth Age 0 Alberto Franco 17/05/2002 18.5 1 Gino Mcneill 16/02/1999 21.2 2 Ryan Parkes 25/09/1998 22.5 3 Eesha Hinton 11/05/2002 22.0 4 Syed Wharton 15/09/1997 23.0 Remove trailing (at the end) whitesapce and convert to lowercase of the columns name name date_of_birth age 0 Alberto Franco 17/05/2002 18.5 1 Gino Mcneill 16/02/1999 21.2 2 Ryan Parkes 25/09/1998 22.5 3 Eesha Hinton 11/05/2002 22.0 4 Syed Wharton 15/09/1997 23.0 69. Write a Pandas program to merge datasets and check uniqueness. Sample Output: Original DataFrame: Name Date_Of_Birth Age 0 Alberto Franco 17/05/2002 18.5 1 Gino Mcneill 16/02/1999 21.2 2 Ryan Parkes 25/09/1998 22.5 3 Eesha Hinton 11/05/2002 22.0 4 Syed Wharton 15/09/1997 23.0 New DataFrames: Name Date_Of_Birth Age 2 Ryan Parkes 25/09/1998 22.5 3 Eesha Hinton 11/05/2002 22.0 4 Syed Wharton 15/09/1997 23.0 Name Date_Of_Birth Age 0 Alberto Franco 17/05/2002 18.5 1 Gino Mcneill 16/02/1999 21.2 3 Eesha Hinton 11/05/2002 22.0 4 Syed Wharton 15/09/1997 23.0 “one_to_one”: check if merge keys are unique in both left and right datasets:” Name Date_Of_Birth Age 0 Eesha Hinton 11/05/2002 22.0 1 Syed Wharton 15/09/1997 23.0 “one_to_many” or “1:m”: check if merge keys are unique in left dataset: Name Date_Of_Birth Age 0 Eesha Hinton 11/05/2002 22.0 1 Syed Wharton 15/09/1997 23.0 “any_to_one” or “m:1”: check if merge keys are unique in right dataset: Name Date_Of_Birth Age 0 Eesha Hinton 11/05/2002 22.0 1 Syed Wharton 15/09/1997 23.0

70. Write a Pandas program to convert continuous values of a column in a given DataFrame to categorical. Input: { ‘Name’: [‘Alberto Franco’,’Gino Mcneill’,’Ryan Parkes’, ‘Eesha Hinton’, ‘Syed Wharton’], ‘Age’: [18, 22, 40, 50, 80, 5] } Output: Age group: 0 kids 1 adult 2 elderly 3 adult 4 elderly 5 kids Name: age_groups, dtype: category Categories (3, object): [kids < adult < elderly] 71. Write a Pandas program to display memory usage of a given DataFrame and every column of the DataFrame. Sample Output: Original DataFrame: Name Date_Of_Birth Age 0 Alberto Franco 17/05/2002 18.5 1 Gino Mcneill 16/02/1999 21.2 2 Ryan Parkes 25/09/1998 22.5 3 Eesha Hinton 11/05/2002 22.0 4 Syed Wharton 15/09/1997 23.0 Global usage of memory of the DataFrame: RangeIndex: 5 entries, 0 to 4 Data columns (total 3 columns): Name 5 non-null object Date_Of_Birth 5 non-null object Age 5 non-null float64 dtypes: float64(1), object(2) memory usage: 801.0 bytes None The usage of memory of every column of the said DataFrame: Index 80 Name 346 Date_Of_Birth 335 Age 40 dtype: int64 72. Write a Pandas program to combine many given series to create a DataFrame. Sample Output: Original Series: 0 php 1 python 2 java 3 c# 4 c++ dtype: object 0 1 1 2 2 3 3 4 4 5 dtype: int64 Combine above series to a dataframe: index 0 0 1 python 1 2 java 2 3 c# 3 4 c++ 4 5 NaN Using pandas concat: 0 1 0 php 1 1 python 2 2 java 3 3 c# 4 4 c++ 5 Using pandas DataFrame with a dictionary, gives a specific name to the columns: col1 col2 0 php 1 1 python 2 2 java 3 3 c# 4 4 c++ 5

71.Write a Pandas program to create DataFrames that contains random values, contains missing values, contains datetime values and contains mixed values. Sample Output: DataFrame: Contains random values: A B C D Dog2w4Dv4l 0.591058 1.883454 -1.608613 -0.502516 kV7mfdFcF9 0.629642 -0.474377 0.567357 1.658445 ……. DataFrame: Contains missing values: A B C D i6i6Xn9l9c -0.299335 0.410871 -0.431840 -0.302177 OGo5KNNYNJ -0.174594 -1.366146 0.435063 -2.779446 u0mG9q1L7C 1.019094 -0.061077 -1.138138 -0.218460 RNJGqpci4o -0.380815 0.189970 -2.148521 -1.163589 vXIcxItZ1D NaN -0.079448 0.604777 0.065290 …….. DataFrame: Contains datetime values: A B C D 2000-01-03 0.665402 0.860808 -0.180986 -0.970889 2000-01-04 -1.511533 0.487539 -0.710355 -0.807816 2000-01-05 -0.773294 0.197918 -1.214035 1.049529 2000-01-06 -1.074894 1.774147 -0.620025 0.740779 ……. DataFrame: Contains mixed values: A B C D 0 0.0 0.0 foo1 2009-01-01 1 1.0 1.0 foo2 2009-01-02 2 2.0 0.0 foo3 2009-01-05 3 3.0 1.0 foo4 2009-01-06 4 4.0 0.0 foo5 2009-01-07

74. Write a Pandas program to fill missing values in time series data. From Wikipedia , in the mathematical field of numerical analysis, interpolation is a type of estimation, a method of constructing new data points within the range of a discrete set of known data points.
Sample Output: Original DataFrame: c1 c2 2000-01-03 120.0 7.0 2000-01-04 130.0 NaN 2000-01-05 140.0 10.0 2000-01-06 150.0 NaN 2000-01-07 NaN 5.5 2000-01-10 170.0 16.5 DataFrame after interpolate: c1 c2 2000-01-03 120.0 7.00 2000-01-04 130.0 8.50 2000-01-05 140.0 10.00 2000-01-06 150.0 7.75 2000-01-07 160.0 5.50 2000-01-10 170.0 16.50 75. Write a Pandas program to use a local variable within a query. Sample Output: Original DataFrame W X Y Z 0 68 78 84 86 1 75 85 94 97 2 86 96 89 96 3 80 80 83 72 4 66 86 86 83 Values which are less than maximum value of ‘W’ column W X Y Z 0 68 78 84 86 1 75 85 94 97 3 80 80 83 72 4 66 86 86 83

75.Write a Pandas program to clean object column with mixed data of a given DataFrame using regular expression. Sample Output: Original dataframe: agent purchase 0 a001 4500 1 a002 7500 2 a003 $3000.25 3 a003 $1250.35 4 a004 9000.00 Data Types: 0 1 2 3 4 Name: purchase, dtype: object New Data Types: 0 1 2 3 4 Name: purchase, dtype: object

77. Write a Pandas program to get the numeric representation of an array by identifying distinct values of a given column of a dataframe. Sample Output: Original DataFrame: Name Date_Of_Birth Age 0 Alberto Franco 17/05/2002 18.5 1 Gino Mcneill 16/02/1999 21.2 2 Ryan Parkes 25/09/1998 22.5 3 Eesha Hinton 11/05/2002 22.0 4 Gino Mcneill 15/09/1997 23.0 Numeric representation of an array by identifying distinct values:[0 1 2 3 1] Index([‘Alberto Franco’, ‘Gino Mcneill’, ‘Ryan Parkes’, ‘Eesha Hinton’], dtype=’object’)

78. Write a Pandas program to replace the current value in a dataframe column based on last largest value. If the current value is less than last largest value replaces the value with 0. Test data: rnum 0 23 1 21 2 27 3 22 … 10 34 11 19 12 31 13 32 14 19 Sample Output: Original DataFrame: rnum 0 23 1 21 2 27 3 22 … 10 34 11 19 12 31 13 32 14 19 Replace current value in a dataframe column based on last largest value: rnum 0 23 1 02 27 3 0 … 10 34 11 0 12 0 13 0 14 0

79. Write a Pandas program to create a DataFrame from the clipboard (data from an Excel spreadsheet or a Google Sheet). Sample Excel Data: Sample Output: Data from clipboard to DataFrame: 1 2 3 4 0 2 3 4 5 1 4 5 1 0 2 2 3 7 8

80. Write a Pandas program to check for inequality of two given DataFrames. Sample Output: Original DataFrames: W X Y Z 0 68.0 78.0 84 86 1 75.0 85.0 94 97 2 86.0 NaN 89 96 3 80.0 80.0 83 72 4 NaN 86.0 86 83 W X Y Z 0 78.0 78 84 86 75.0 85 84 97 2 86.0 96 89 96 3 80.0 80 83 72 4 NaN 76 86 83 Check for inequality of the said dataframes: W X Y Z 0 True False False False 1 False False True False 2 False True False False 3 False False False False 4 True True False False

81. Write a Pandas program to get lowest n records within each group of a given DataFrame. Sample Output: Original DataFrame col1 col2 col3 0 1 4 7 1 2 5 5 2 3 6 8 3 4 9 12 4 7 5 1 5 11 0 11 Lowest n records within each group of a DataFrame: col1 col2 col3 0 1 4 7 1 2 5 5 2 3 6 8 col1 col2 col3 5 11 0 11 0 1 4 7 1 2 5 5 col1 col2 col3 4 7 5 1 1 2 5 50 1 4 7

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