DataFrame in Python Pandas based on reduced syllabus.

1. PANDAS
DATAFRAME

2. Data Handling using Pandas – 1
Data Frames:
creation - from dictionary of Series, list of dictionaries, Text/CSV
files; display; iteration;
Operations on rows and columns: add, select, delete, rename;
Head and Tail functions;
Indexing using Labels, Boolean Indexing;
Importing/Exporting Data between CSV files and Data Frames.
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Sangita Panchal

3. Pandas DataFrame
 A pandas DataFrame is a two-dimensional array.
 Data is aligned in rows and columns.
The general format:
pandas. DataFrame (data, index, columns, dtype)
data: It is like ndarray, series, list, dict, constants or another
DataFrame.
index: It is for row label, by default 0..n-1 (np.arange(n))
columns: It is for column label, by default 0…n-1 (np.arange(n))
dtype: data type of each column
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Key Features:
• Heterogeneous data
• Size Mutable
• Data Mutable

4. Creating DataFrame using list 3
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import pandas as pd
D = pd.DataFrame([[10,20],[30,40]])
print(D)
0 1
0 10 20
1 30 40
Default columns
Default rows

5. Creating DataFrame with row index and column label 4
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import pandas as pd
data = [[10,20],[30,40]]
D = pd.DataFrame(data,columns = ['col1','col1'],index = ['row1','row2'])
print(D)
col1 col1
row1 10 20
row2 30 40
Column Label
Row Label

6. Creating DataFrame using dictionary 5
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import pandas as pd
data = {'Name':['Anu','Sia'],'Marks':[19,25]}
D = pd.DataFrame(data,index = [1,2])
The column names
are the keys in a
dictionary.
Rows using
index

7. Creating DataFrame from dictionary of Series 6
Sangita Panchal
import pandas as pd
d = {'one' : pd.Series([10, 20, 30, 40], index =['a', 'b', 'c', 'd']),
'two' : pd.Series([10, 20, 30, 40], index =['a', 'b', 'c', 'd'])}
df = pd.DataFrame(d)
print(df )

8. Creating DataFrame from list of dictionary 7
Sangita Panchal
import pandas as pd
data = [{'b': 2, 'c':3}, {'a': 10, 'b': 20, 'c': 30}]
df = pd.DataFrame(data, index =['first', 'second'])
df = pd.DataFrame(d)
print(df )

9. Writing DataFrame to csv file 8
Sangita Panchal
import pandas as pd
data = {'Name':['Anu','Sia'],'Marks':[19,25]}
D = pd.DataFrame(data,index = [1,2])
print(D)
D.to_csv("E:stu.csv")
Writing DataFrame
data to Excel file

10. Creating DataFrame using text/csv file 9
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import pandas as pd
>>> data = pd.read_csv("C:/Users/admin/Desktop/stock.csv")
>>> print(data)
No Name Qty
0 1 Pencil 12
1 2 Pen 20
2 3 Eraser 17
3 4 Sharpner 10
Create .csv file using
 Notepad
 Excel
Read data in Excel
file to DataFrame

11. Iteration in DataFrame 10
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import pandas as pd
Data = [[1006,1008],['A1','B1'],[23000,19000]]
D = pd.DataFrame(Data)
print(D)
print(D.shape, D.size)
for row in range(D.shape[0]):
print("Row ",row,"Columns",end = " ")
for col in range(D.shape[1]):
print(D[col][row], end = " ")
print()
0 1
0 1006 1008
1 A1 B1
2 23000 19000
(3, 2) 6
Row 0 Columns 1006 1008
Row 1 Columns A1 B1
Row 2 Columns 23000 1900

12. Iteration in DataFrame using iterrows() 11
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import pandas as pd
Data = {'Name': ['Amy','Anu','Jia’],
'Total':[45,32,76]}
D = pd.DataFrame(Data)
print(D)
for i in D.iterrows():
print(i)
Name Total
0 Amy 45
1 Anu 32
2 Jia 76
(0, Name Amy
Total 45
Name: 0, dtype: object)
(1, Name Anu
Total 32
Name: 1, dtype: object)
(2, Name Jia
Total 76
Name: 2, dtype: object)
iterrows() - used for iterating over
the rows as (index, series) pairs.

13. Add column, Insert column 12
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import pandas as pd
data = {'Name':['Anu','Sia'],'M1':[19,25]}
D = pd.DataFrame(data,index = [1,2])
print(D)
D['M2'] = [13,34]
D['Total'] = D['M1']+D['M2']
print("Added columns ", D, sep = "n")
print(“Inserted columns”)
D.insert(1,"Remarks",[‘A',’B'])
print(D)
Name M1
1 Anu 19
2 Sia 25
Added columns
Name M1 M2 Total
1 Anu 19 13 32
2 Sia 25 34 59
Inserted column
Name Remarks M1 M2 Total
1 Anu A 19 13 32
2 Sia B 25 34 59

14. Add Row at the end 13
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import pandas as pd
data = {'Name':['Anu','Sia'],'M1':[19,25], 'M2': [13,34],
'Total':[32, 59]}
D = pd.DataFrame(data,index = [1,2])
print(D)
D.loc[3] = ['Jia',23,24,47]
print("Added Row using loc",D, sep = "n")
Name M1 M2 Total
1 Anu 19 13 32
2 Sia 25 34 59
Added Row
Name M1 M2 Total
1 Anu 19 13 32
2 Sia 25 34 59
3 Jia 23 24 47
D = D.append({'Name':'Somy','M1':21,'M2':19,'Total':40},ignore_index = True)
print("Added Row using append",D, sep = "n")
Added Row using append
Name M1 M2 Total
0 Anu 19 13 32
1 Sia 25 34 59
2 Jia 23 24 47
3 Somy 21 19 40

15. Select Column 14
Sangita Panchal
print(D['Total'])
print(D[['M1', 'M2']])
print(D.Name)
Name M1 M2 Total
0 Anu 19 13 32
1 Sia 25 34 59
2 Jia 23 24 47
0 32
1 59
2 47
Name: Total, dtype: int64
M1 M2
0 19 13
1 25 34
2 23 24
0 Anu
1 Sia
2 Jia
Name: Name, dtype: object
You can select a column using dot notation or
using square brackets.

16. Delete column, row 15
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D.drop(columns = ['Name’], inplace = True)
print(D)
D.drop(columns = [‘M1’,’M2’], inplace = True)
print(D)
D.drop(index = [0,1],inplace = True)
print(D)
Name M1 M2 Total
0 Anu 19 13 32
1 Sia 25 34 59
2 Jia 23 24 47
M1 M2 Total
0 19 13 32
1 25 34 59
2 23 24 47
Total
0 32
1 59
2 47
Total
2 47
To delete single column - del, pop(), drop()
To delete multiple columns - drop()
To delete single / multiple rows - drop()

17. Rename column 16
Sangita Panchal
D.rename(columns = {'Name':'StuName'},inplace = True)
print(D)
D.rename(index = {0:'zero',1:'one’},columns = {"Total":"T"},
inplace = True)
print(D)
Name M1 M2 Total
0 Anu 19 13 32
1 Sia 25 34 59
2 Jia 23 24 47
StuName M1 M2 Total
0 Anu 19 13 32
1 Sia 25 34 59
2 Jia 23 24 47
StuName M1 M2 T
zero Anu 19 13 32
one Sia 25 34 59
2 Jia 23 24 47
Change column name
Change row index

18. Rename column label/row index 16a
Sangita Panchal
print(D.columns)
print(D.index)
D.columns = [‘a’,’b’,’c’,’d’]
D.index = [1,2,3]
StuName M1 M2 T
zero Anu 19 13 32
one Sia 25 34 59
2 Jia 23 24 47
Index(['StuName', 'M1', 'M2', 'T'], dtype='object')
Index(['zero', 'one', 2], dtype='object’)
a b c d
zero Anu 19 13 32
one Sia 25 34 59
2 Jia 23 24 47
a b c d
1 Anu 19 13 32
2 Sia 25 34 59
3 Jia 23 24 47

19. Head and Tail operation 17
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print("Top 2 rows",D.head(2),sep = "n")
print("Bottom 2 rows",D.tail(2), sep = "n")
Name M1 M2 Total
0 Anu 19 13 32
1 Sia 25 34 59
2 Jia 23 24 47 Top 2 rows
Name M1 M2 Total
0 Anu 19 13 32
1 Sia 25 34 59
Bottom 2 rows
Name M1 M2 Total
1 Sia 25 34 59
2 Jia 23 24 47
Note: The default of head() and tail() is 5.
print(D.head() )displays top 5 rows.
print(D.tail()) displays bottom 5 rows.

20. Indexing using Label /Position 18
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There are 3 ways of indexing:
1. DF[“col1”]
DF[[“col1”,”col2”]]
2. DF.loc[“col1”]
DF.loc[[“col1”,”col2”]]
DF.loc[[“row1”,”row2”],[“col1”,”col2”]]
DF.loc[ : :, : :]
3. DF.iloc[3]
DF.iloc[[1,3,4]]
DF.iloc[[3,4],[1,2]]
DF.iloc[ : : , : :]
For column only
For position
(Default index ]
Used for Labels

21. Indexing using Label / Position 19
Sangita Panchal
print("Select Name column ", D['Name'],sep = "n")
print("Select Row and column ",D.iloc[::2,1:3],sep = "n")
print("Select second row",D.iloc[1,:],sep = "n")
Name M1 M2 Total
0 Anu 19 13 32
1 Sia 25 34 59
2 Jia 23 24 47
Select Name column
0 Anu
1 Sia
2 Jia
Name: Name, dtype: object
Select Row and column
M1 M2
0 19 13
2 23 24
Select second row
Name Sia
M1 25
M2 34
Total 59
Name: 1, dtype: object47

22. Indexing using Label / Position 20
Sangita Panchal
print(D["Total"])
print(D.iloc[[1,2],[0,1]])
print(D.loc[[1,2],["Name","M1"]])
print(D.iloc[0:3:2,::2])
print(D.loc[0:3:2,"Name":"Total":2])

23. Indexing using Label / Position 21
Sangita Panchal
print(D.Total > 50)
print((D.M1 > 20) & (D.M2 > 20))
print((D.M1 > 20) | (D.M2 > 20))
print(~(D.Total> 40))
~ NOT
& AND
| OR

24. Indexing using Label / Position 22
Sangita Panchal
print(D[(D.M1 > 20) & (D.M2 > 20)])
print(D[(D.M1 > 20) | (D.M2 > 20)])
print(D[~(D.Total> 40)])

25. Append two DataFrame and sort 23
Sangita Panchal
import pandas as pd
data = {'Name':['A','B'],'Total':[89,78]}
D = pd.DataFrame(data)
print(D)
data1 = {'Name':['C','D'],'Total':[78,91]}
D1 = pd.DataFrame(data1)
print(D1)
D2 = D.append(D1)
print(D2)
print(D2.sort_values('Total', ascending = False))

26. max, min, sum, mean, count 24
Sangita Panchal
print(D.max())
print(D.count(axis = 1))
print(D['Total'].min())
print(D[['M1','M2']].sum())
print(D[['M1','M2','Total']].mean())
M1 42.0
M2 71.0
dtype: float64
M1 21.000000
M2 23.666667
Total 46.000000
dtype: float64
Name M1 M2 Total
0 Anu 19.0 13 32
1 Sia NaN 34 59
2 Sid 23.0 24 47
Name Sid
M1 23
M2 34
Total 59
dtype: object
0 4
1 3
2 4
dtype: int64
32
By default, these functions are applied to rows (axis =0).
For columns, axis = 1 is mandatory.
Note: D.max() cann be written as D.max(0)
D.count(axis = 1) can be written as D.count(1)

27. CBSE QUESTIONS 25
Sangita Panchal
1. In a DataFrame, Axis= 1 represents the_____________ elements.
2. In Pandas the function used to check for null values in a DataFrame is ________
3. Consider the following DataFrame df and answer any four questions from (i)-(v)
rollno name UT1 UT2 UT3 UT4
1 Prerna Singh 24 24 20 22
2 Manish Arora 18 17 19 22
3 Tanish Goel 20 22 18 24
4 Falguni Jain 22 20 24 20
5 Kanika Bhatnagar 15 20 18 22
6 Ramandeep Kaur 20 15 22 24

28. CBSE QUESTIONS 26
Sangita Panchal
1. In a DataFrame, Axis= 1 represents the_____________ elements.
2. In Pandas the function used to check for null values in a DataFrame is ________
3. Consider the following DataFrame df and answer any four questions from (i)-(v)
rollno name UT1 UT2 UT3 UT4
1 Prerna Singh 24 24 20 22
2 Manish Arora 18 17 19 22
3 Tanish Goel 20 22 18 24
4 Falguni Jain 22 20 24 20
5 Kanika Bhatnagar 15 20 18 22
6 Ramandeep Kaur 20 15 22 24
column
isnull()

29. CBSE QUESTIONS 27
Sangita Panchal
(I) Write down the command that will give the following output:
a. print(df.max)
b. print(df.max())
c. print(df.max(axis=1))
d. print(df.max, axis=1)

30. CBSE QUESTIONS 28
Sangita Panchal
(I) Write down the command that will give the following output:
a. print(df.max)
b. print(df.max())
c. print(df.max(axis=1))
d. print(df.max, axis=1)
print(df.max())

31. CBSE QUESTIONS 29
Sangita Panchal
The teacher needs to know the marks scored by the student with roll number 4. Help her
to identify the correct set of statement/s from the given options :
a. df1=df[df[‘rollno’]==4] ; print(df1)
b. df1=df[rollno==4] ; print(df1)
c. df1=df[df.rollno=4] ; print(df1)
d. df1=df[df.rollno==4] ; print(df1)
v. Ms. Sharma, the class teacher wants to add a new column, the scores of Grade with the
values, ‘ A’, ‘B’, ‘A’, ‘A’, ‘B’, ‘A’ ,to the DataFrame. Help her choose the command to do so:
a. df.column=[’A’,’B’,’A’,’A’,’B’,’A’]
b. df [‘Grade’]=[’A’,’B’,’A’,’A’,’B’,’A’]
c. df.loc[‘Grade’]= [’A’,’B’,’A’,’A’,’B’,’A’]
d. Both (b) and (c) are correct

32. CBSE QUESTIONS 30
Sangita Panchal
The teacher needs to know the marks scored by the student with roll number 4. Help her
to identify the correct set of statement/s from the given options :
a. df1=df[df[‘rollno’]==4] ; print(df1)
b. df1=df[rollno==4] ; print(df1)
c. df1=df[df.rollno=4] ; print(df1)
d. df1=df[df.rollno==4] ; print(df1)
v. Ms. Sharma, the class teacher wants to add a new column, the scores of Grade with the
values, ‘ A’, ‘B’, ‘A’, ‘A’, ‘B’, ‘A’ ,to the DataFrame. Help her choose the command to do so:
a. df.column=[’A’,’B’,’A’,’A’,’B’,’A’]
b. df [‘Grade’]=[’A’,’B’,’A’,’A’,’B’,’A’]
c. df.loc[‘Grade’]= [’A’,’B’,’A’,’A’,’B’,’A’]
d. Both (b) and (c) are correct
a and d
b

33. CBSE QUESTIONS 31
Sangita Panchal
(iii) Which of the following statement/s will give the exact number of values in each column of the
dataframe?
i. print(df.count())
ii. print(df.count(0))
iii. print(df.count)
iv. print(df.count(axis=’index’))
Choose the correct option:
a. both (i) and (ii)
b. only (ii)
c. (i), (ii) and (iii)
d. (i), (ii) and (iv)
iv. Which of the following command will display the column labels of the DataFrame?
a. print(df.columns())
b. print(df.column())
c. print(df.column)
d. print(df.columns)

34. CBSE QUESTIONS 32
Sangita Panchal
(iii) Which of the following statement/s will give the exact number of values in each column of the
dataframe?
i. print(df.count())
ii. print(df.count(0))
iii. print(df.count)
iv. print(df.count(axis=’index’))
Choose the correct option:
a. both (i) and (ii)
b. only (ii)
c. (i), (ii) and (iii)
d. (i), (ii) and (iv)
iv. Which of the following command will display the column labels of the DataFrame?
a. print(df.columns())
b. print(df.column())
c. print(df.column)
d. print(df.columns)
a
d

35. CBSE QUESTIONS 33
Sangita Panchal
Consider the following DataFrame, classframe
Write commands to :
i. Add a new column ‘Activity’ to the Dataframe
ii. Add a new row with values ( 5 , Mridula ,X, F , 9.8, Science)

36. CBSE QUESTIONS 34
Sangita Panchal
Consider the following DataFrame, classframe
Write commands to :
i. Add a new column ‘Activity’ to the Dataframe
ii. Add a new row with values ( 5 , Mridula ,X, F , 9.8, Science)
i. classframe[‘Activity’] = [‘a’,’b’,’c’,’d’]
ii. classframe.loc[‘St5’] = [5, ‘Mridula’,’X’,’F’,9.8,’Science’]

37. CBSE QUESTIONS 35
Sangita Panchal
Write a program in Python Pandas to create the following DataFrame batsman from a
Dictionary:
Perform the following operations on the DataFrame :
1)Add both the scores of a batsman and assign to column “Total”
2)Display the highest score in both Score1 and Score2 of the DataFrame.
3. Display the DataFrame

38. CBSE QUESTIONS 36
Sangita Panchal
Write a program in Python Pandas to create the following DataFrame batsman from a
Dictionary:
Perform the following operations on the DataFrame :
1)Add both the scores of a batsman and assign to column “Total”
2)Display the highest score in both Score1 and Score2 of the DataFrame.
3. Display the DataFrame
import pandas as pd]
data = {‘B_NO’: [1,2,3,4],
‘Name’:[‘Sunil Pillai’,’Gaurav Sharma’, ‘Piyush
Goel’,’Kartik Thakur’], ‘Score1’:[90,65,70,80],
’Score2’:[80,45,90,76]}
batsman = pd.DataFrame(data)
i. batsman[‘Total’] = batsman[‘Score1’] +
batsman[‘Score2’]
ii. print(batsman[[‘Score1’,’Score2’]].max())
iii. print(batsman)

39. CBSE QUESTIONS 37
Sangita Panchal
Consider the dataframe df, write the output of the following:
Eng Math Sci
2017 75 95 75
2018 99 99 NaN
2019 98 95 95
a. print(df[‘Eng’].min())
b. print(df[[‘Eng’,’Sci’]].count())
c. print(df.loc[2018].max())
d. print(df.loc[2018 : 2020,’Maths’:’Sci’].max())

40. CBSE QUESTIONS 38
Sangita Panchal
Consider the dataframe df, write the output of the following:
Eng Math Sci
2017 75 95 75
2018 99 99 NaN
2019 98 95 95
a. print(df[‘Eng’].min())
b. print(df[[‘Eng’,’Sci’]].count())
c. print(df.loc[2018].max())
d. print(df.loc[2018 : 2019,’Maths’:’Sci’].max())
a. 75
b. Eng 3
Sci 2
c. 99.0
d. Math 99.0
Sci 95.0

41. CBSE QUESTIONS 39
Sangita Panchal
(a) The ______________ method in Pandas can be used to delete rows or columns.
(b) The code to display the last three rows of the dataframe df is ___________.
(c) Consider a dataframe DF
Ecode Ename
1 Sarika
2 Monica
3 Mehak
Write the command to add a new column named “Salary” from the list Sal = [15000,
18000, 16000]
Ecode Salary Ename
1 15000 Sarika
2 18000 Monica
3 16000 Mehak

42. CBSE QUESTIONS 40
Sangita Panchal
(a) The ______________ method in Pandas can be used to delete rows or columns.
(b) The code to display the last three rows of the dataframe df is ___________.
(c) Consider a dataframe DF
Ecode Ename
1 Sarika
2 Monica
3 Mehak
Write the command to add a new column named “Salary” from the list Sal = [15000,
18000, 16000]
Ecode Salary Ename
1 15000 Sarika
2 18000 Monica
3 16000 Mehak
drop()
df.tail(3)
DF.insert(1,”salary”, Sal)
or
DF.insert(loc = 1, column = “Salary”, value = Sal)

43. CBSE QUESTIONS 41
Sangita Panchal
(d) Write a Python code to create a dataframe from the list given below:
[‘E001’, ‘Govind’, 35000],[‘E002’, ‘Jatin’, 43000], [‘E003’, ‘Deepak’, 38000]
Give the column heading as EmpCode, EmpName and EmpSalary. Also give the index as
1, 2 and 3.
import pandas as pd
data = [[‘E001’, ‘Govind’, 35000],[‘E002’, ‘Jatin’, 43000], [‘E003’, ‘Deepak’, 38000]]
df = pd.DataFrame(data, columns = [‘EmpCode’, ‘EmpName’, ‘EmpSalary’], index =
[1, 2, 3])
print(df)

44. CBSE QUESTIONS 42
Sangita Panchal
(e) Consider te following dataframe, and answer the questions given below:
import pandas as pd
df = pd.DataFrame({“Sub1”:[56, 78, 67, 98, 65], “Sub2”: [87, 92, 76, 56, 69],
“Sub3”:[65, 64, 74, 72, 81], “Sub4”:[90, 87, 56, 66, 86]})
(i) Write the code to find the mean value row wise and column wise.
(ii) Write the code to find the sum value row wise.
(i) print(df.mean(axis = 1))
print(df.mean(axis = 0))
(ii) print(df.sum(axis = 1))

45. CBSE QUESTIONS 43
Sangita Panchal
(f) Given a dataframe DF of marks obtained by 100 students as shown below:
Highest Lowest Average
English 95 56 67.8
Maths 100 65 59.8
Physics 97 68 69.8
Chemistry 99 54 79.8
CompSc 100 82 90.8
(i) Write command to compute sum of every column of the dataframe.
(ii) Write command to compute mean of Average column.
(iii) Write command to compute average of Highest and Lowest columns

46. CBSE QUESTIONS 44
Sangita Panchal
(f) Given a dataframe DF of marks obtained by 100 students as shown below:
SubjectHighest LowestAverage
English 95 56 67.8
Maths 100 65 59.8
Physics 97 68 69.8
Chemistry 99 54 79.8
CompSc 100 82 90.8
(i) Write command to compute sum of every column of the dataframe.
(ii) Write command to compute mean of Average column.
(iii) Write command to compute average of Highest and Lowest columns
DF.sum()
DF[‘Average’].mean()
DF[[‘Highest’,’Lowest’]].mean()

47. CBSE QUESTIONS 45
Sangita Panchal
(g) Find the output of the following code:
import pandas as pd
data = [{'a': 10, 'b': 20},{'a': 6, 'b1': 32, 'c': 22}]
df1 = pd.DataFrame(data, index=['first', 'second'], columns=['a', 'b', ‘c’])
df2 = pd.DataFrame(data, index=['first', 'second'], columns=['a', 'b1'])
print(df1)
print(df2)

48. CBSE QUESTIONS 46
Sangita Panchal
(g) Find the output of the following code:
import pandas as pd
data = [{'a': 10, 'b': 20},{'a': 6, 'b1': 32, 'c': 22}]
df1 = pd.DataFrame(data, index=['first', 'second'], columns=['a', 'b', ‘c’])
df2 = pd.DataFrame(data, index=['first', 'second'], columns=['a', 'b1'])
print(df1)
print(df2) a b c
first 10 20 NaN
second 6 NaN 22
a b 1
first 10 NaN
second 6 32

49. CBSE QUESTIONS 47
Sangita Panchal
(h) Write the code in pandas to create the following dataframes:
df1 df2
a. Write a command to add dataframe DF1 and DF2.
b. Write a command to subtract datafram DF2 from DF1.
c. Write a command to rename DF1 column Marks1 to M1.
d. Write a command to rename DF1 row index 0 to zero, 1 to one.

50. CBSE QUESTIONS 48
Sangita Panchal
(h) Write the code in pandas to create the following dataframes:
df1 df2
a. Write a command to add dataframe DF1 and DF2.
b. Write a command to subtract datafram DF2 from DF1.
c. Write a command to rename DF1 column Marks1 to M1.
d. Write a command to rename DF1 row index 0 to zeor, 1 to one.
import pandas as pd
DF1 = pd.DataFrame({'Mark1':[49,35,41, 45],
'Mark2':[39, 26, 43, 35]})
DF2 = pd.DataFrame({'Mark1':[43,42,39,40],’
Mark2':[30, 46,33,29]})
print(DF1)
print(DF2)
(i) print(DF1.add(DF2))
(ii) print(DF1.subtract(DF2))
(iii) DF1.rename(columns={‘Mark1’:'M1'}, inplace=True)
(iv) DF1.rename(index = {0: "zero", 1:"one"}, inplace = True)

51. CBSE QUESTIONS 49
Sangita Panchal
Consider the following dataframe df:
name marks grade
0 Amit 10 C
1 Ravit 23 A
2 Ria 21 A
3 Arnav 11 C
a) Write the command to add a new row to df with the values in data.
data = {'name':'Manan','marks':19,'grade':'c'}
b) Write the command to change the column label marks to score.
c) Write the command to arrange the dataframe in descending order of marks.
d) What will be the output of following:
df [ (df['grade'] == 'A') & (df['marks'] >= 20) ]

52. CBSE QUESTIONS 50
Sangita Panchal
Consider the following dataframe df:
name marks grade
0 Amit 10 C
1 Ravit 23 A
2 Ria 21 A
3 Arnav 11 C
a) Write the command to add a new row to df with the values in data.
data = {'name':'Manan','marks':19,'grade':'c'}
b) Write the command to change the column label marks to score.
c) Write the command to arrange the dataframe in descending order of marks.
d) What will be the output of following:
df [ (df['grade'] == 'A') & (df['marks'] >= 20) ]
df.append(data,ignore_index=True)
df.rename({'marks':'score'},axis=1)
df.sort_values('marks',ascending=False)
name marks grade
1 Ravit 23 A
2 Ria 21 A

53. CBSE QUESTIONS 51
Sangita Panchal
Consider the following dataframe:
import pandas as pd
df_sales = pd.DataFrame( {"Qtr1":[20, 40, 50, 44, 10],
"Qtr2":[58, 25, 54, 30, 29],
"Qtr3":[20, 16, 70, 36, 82],
"Qtr4":[14, 37, 17, 20, 60]} )
a) Write the command to display the top three rows from above data frame.
b) Write the command to display all the rows of the columns ‘Qtr1’ and ‘Qtr2’.
c) What will be the output of the following?
df_sales.loc[ :2 , ‘Qtr3': ]

54. CBSE QUESTIONS 52
Sangita Panchal
Consider the following dataframe:
import pandas as pd
df_sales = pd.DataFrame( {"Qtr1":[20, 40, 50, 44, 10],
"Qtr2":[58, 25, 54, 30, 29],
"Qtr3":[20, 16, 70, 36, 82],
"Qtr4":[14, 37, 17, 20, 60]} )
a) Write the command to display the top three rows from above dataframe.
b) Write the command to display all the rows of the columns ‘Qtr1’ and ‘Qtr2’.
c) What will be the output of the following?
df_sales.loc[ :2 , ‘Qtr3': ]
df_sales.head(3)
df_sales[ [‘Qtr1',’Qtr2'] ]
Qtr3 Qtr4
0 20 14
1 16 37
2 70 17