Python Pandas DataFrame: Exercises, Practice, Solution
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Pandas DataFrame [81 exercises with solution]
1.Write a Pandas program to create a dataframe from a dictionary and display it.
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
Click me to see the sample solution
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 19.0Click me to see the sample solution
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: <class 'pandas.core.frame.DataFrame'> Index: 10 entries, a to j Data columns (total 4 columns): .... dtypes: float64(1), int64(1), object(2) memory usage: 400.0+ bytes NoneClick me to see the sample solution
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 16.5Click me to see the sample solution
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
Click me to see the sample solution
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 yesClick me to see the sample solution
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
Click me to see the sample solution
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: 4Click me to see the sample solution
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 NaN h 1 Laura no NaNClick me to see the sample solution
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 19.0Click me to see the sample solution
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
Click me to see the sample solution
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 19.0Click me to see the sample solution
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: 19Click me to see the sample solution
14. Write a Pandas program to calculate the mean of all students' scores. Data is stored in 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: Mean score for each different student in data frame: 13.5625Click me to see the sample solution
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:Click me to see the sample solution
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 19.0
16. 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
f Michael 20.0 3 yes
g Matthew 14.5 1 yes
h Laura NaN 1 no
i Kevin 8.0 2 no
c Katherine 16.5 2 yes
j Jonas 19.0 1 yes
d James NaN 3 no
e Emily 9.0 2 no
b Dima 9.0 3 no
a Anastasia 12.5 1 yes
Click me to see the sample solution
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 19.0Click me to see the sample solution
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 19.0Click me to see the sample solution
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
Click me to see the sample solution
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 RedClick me to see the sample solution
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 16.5Click me to see the sample solution
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']Click me to see the sample solution
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 9Click me to see the sample solution
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 0Click me to see the sample solution
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 5Click me to see the sample solution
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 12Click me to see the sample solution
27. Write a Pandas program to write a DataFrame to CSV file using tab separator.
Sample data:
Original DataFrameClick me to see the sample solution
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
Click me to see the sample solution
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
Click me to see the sample solution
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 1Click me to see the sample solution
31. 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 9Click me to see the sample solution
32. Write a Pandas program to replace all the NaN values with Zero's in a column of a dataframe.
Sample data:
Original DataFrameClick me to see the sample solution
attempts name qualify score 0 1 Anastasia yes 12.5 1 3 Dima no 9.0 2 2 Katherine yes 16.5 ..... 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 ..... 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
....
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
....
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
.....
8 2 Kevin no 8.0
9 1 Jonas yes 19.0
Click me to see the sample solution
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 ...... 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 ...... 8 2 Kevin no 10.2 9 1 Jonas yes 19.0Click me to see the sample solution
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: 2Click me to see the sample solution
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 0Click me to see the sample solution
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 ..... 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 .... 8 2 Kevin no 8.0 9 1 Jonas yes 19.0 Reset the Index:Click me to see the sample solution
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 divide 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
.....
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
.....
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
Click me to see the sample solution
39. Write a Pandas program to combining two series into a DataFrame.
Sample data:
Data Series:
0 100
1 200
2 python
3 300.12
4 400
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
Click me to see the sample solution
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 .... 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 .... 4 2 Emily no 9.0 8 2 Kevin no 8.0 2 2 Katherine yes 16.5Click me to see the sample solution
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
Click me to see the sample solution
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 9Click me to see the sample solution
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]Click me to see the sample solution
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
Click me to see the sample solution
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: 2Click me to see the sample solution
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.Click me to see the sample solution
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 4 col3 7 Name: 0, dtype: int64 Value of Row4 col1 4 col2 9 col3 1 Name: 3, dtype: int64Click me to see the sample solution
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: objectClick me to see the sample solution
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 2Click me to see the sample solution
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 20.0Click me to see the sample solution
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
Click me to see the sample solution
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 3000.0
3 -4000.0
4 NaN
5 NaN
Click me to see the sample solution
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
Click me to see the sample solution
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
Click me to see the sample solution
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
Click me to see the sample solution
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
Click me to see the sample solution
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
Click me to see the sample solution
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
Click me to see the sample solution
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
Click me to see the sample solution
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
Click me to see the sample solution
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
Click me to see the sample solution
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
Click me to see the sample solution
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
Click me to see the sample solution
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
Click me to see the sample solution
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
Click me to see the sample solution
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
Click me to see the sample solution
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)
Click me to see the sample solution
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
Click me to see the sample solution
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
Click me to see the sample solution
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]
Click me to see the sample solution
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:
<class 'pandas.core.frame.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
Click me to see the sample solution
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
Click me to see the sample solution
73. 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
Click me to see the sample solution
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
Click me to see the sample solution
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
Click me to see the sample solution
76. 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 <class 'float'>
1 <class 'float'>
2 <class 'str'>
3 <class 'str'>
4 <class 'str'>
Name: purchase, dtype: object
New Data Types:
0 <class 'float'>
1 <class 'float'>
2 <class 'float'>
3 <class 'float'>
4 <class 'float'>
Name: purchase, dtype: object
Click me to see the sample solution
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')
Click me to see the sample solution
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 0
2 27
3 0
...
10 34
11 0
12 0
13 0
14 0
Click me to see the sample solution
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
Click me to see the sample solution
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
1 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
Click me to see the sample solution
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 5
0 1 4 7
Click me to see the sample solution
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