Question 1

Question 16To calculative cumulative sum of a column of a DataFrame, you may use ............... function.sum()sum(cumulative = True)cumsum()none of these

Accepted Answer

cumsum()Reason — The cumsum() function in pandas is used to calculate the cumulative sum of a column in a DataFrame i.e., in the output of this function, the value of each row is replaced by the sum of all prior rows, including this row.

Question 2

Question 18To get top 5 rows of a DataFrame, you may use ............... function.head()head(5)top()top(5)

Accepted Answer

head(), head(5)Reason — The head() function in pandas is used to get the top n rows of a DataFrame, where n is an optional argument. If n is not provided, it defaults to 5. Hence, both head() and head(5) are correct and will return the top 5 rows of the DataFrame.

Question 3

Question 19To get bottom 3 rows of a DataFrame, you may use ............... function.tail()tail(3)bottom()bottom(3)

Accepted Answer

tail(3)Reason — The tail() function in pandas is used to get the bottom n rows of a DataFrame, where n is an optional argument. Hence, tail(3) is correct and will return the bottom 3 rows of the DataFrame.

Question 4

Question 20Function ............... can be used to drop missing values.fillna()isnull()dropna()delna()

Accepted Answer

dropna()Reason — The dropna() function in pandas is used to drop rows with missing values.

Source: This question is from Python Pandas — II, Informatics Practices &mdash; Class 12, CBSE Board.
Key Concepts CoveredThis question tests your understanding of the following concepts from the chapter Python Pandas — II: Question, Drop, Missing, Values, Fillna, Isnull. These are fundamental topics in Informatics Practices that students are expected to master as part of the CBSE Class 12 curriculum.A thorough understanding of these concepts will help you answer similar questions confidently in your CBSE examinations. These topics are frequently tested in both objective and subjective sections of Informatics Practices papers. We recommend revising the relevant section of your textbook alongside practising these solved examples to build a strong foundation.
How to Approach MCQsMultiple choice questions test your ability to quickly recall facts and apply concepts. Read all options carefully before selecting your answer. Use the process of elimination to rule out clearly incorrect options first. If two options seem similar, look for the subtle difference that makes one more accurate. For numerical MCQs, verify your answer by substituting it back into the problem.
Key Points to RememberRead the question carefully and identify all parts before answering.
Use the terminology specific to this subject and chapter.
Review the textbook content for this chapter before attempting questions.
Practice writing concise, well-structured answers within time limits.
Related Questions from Python Pandas — IIQ1: Question 1 Write a program to print a DataFrame one column at a time and print only first three...
Q10: Question 10 Write code that just produces single True/False as a result for the presence of missing...
Q11: Question 11 Functions sum() and cumsum() produce the same result.
Q12: Question 12 Assume that required libraries (Pandas and Numpy) are imported and DataFrame ndf has...
Q13: Question 13 Given the two DataFrames as : >>> dfc1 - - 0 1 0 2 a 1 3 b 2 4 c >>> dfc2 - - 0 1 2 0 2...
Practice more questions from Python Pandas — II &mdash; Informatics Practices, Class 12 CBSE

Question 5

Question 21Which of the following methods of combining two DataFrames is a patching method ?concat()merge()join()none of these

Accepted Answer

join()Reason — The join() function in pandas is a patching method, which means that it aligns the DataFrames based on a common index or set of keys, and then combines them.

Question 6

Question 11Functions sum() and cumsum() produce the same result.

Accepted Answer

FalseReason — The function sum() returns the sum of the values for the requested axis. On the other hand, cumsum() calculates the cumulative sum. In the output of this function, the value of each row is replaced by the sum of all prior rows, including this row.

Question 7

Question 17Why is missing data filled in DataFrame with some value ?

Accepted Answer

The dropna() method in pandas can be used to remove rows or columns with missing values from a DataFrame. However, this can also lead to a loss of non-null data, which may not be desirable for data analysis. To avoid losing non-null data, we can use the fillna() method to fill missing values with a specified value. This can help to ensure that the dataset is complete and can be used for data analysis.

Question 8

Question 6Name and explain some cumulative functions provided by Pandas.

Accepted Answer

The cumulative functions provided by Pandas are cumsum(), cumprod(), cummax(), cummin().cumsum() — It calculates cumulative sum i.e., in the output of this function, the value of each row is replaced by sum of all prior rows including this row. The syntax is <DF>.cumsum([axis = None]).cumprod() — It calculates cumulative product of values in a DataFrame object.cummax() — It calculates cumulative maximum of value from a DataFrame object.cummin() — It calculates cumulative minimum of value from a DataFrame object.

Question 9

Question 12Assume that required libraries (Pandas and Numpy) are imported and DataFrame ndf has been created as shown in solved problem 16. Predict the output produced by following code fragment :print(ndf[ndf["age"] > 30])
print(ndf.head(2)) 
print(ndf.tail(3))The following DataFrame ndf is from solved problem 16 :       Name Sex    Position       City  age  Projects  Budget
0    Rabina   F     Manager  Bangalore   30        13      48
1      Evan   M   Programer  New Delhi   27        17      13
2       Jia   F     Manager    Chennai   32        16      32
3     Lalit   M     Manager     Mumbai   40        20      21
4  Jaspreet   M  Programmer    Chennai   28        21      17
5      Suji   F  Programmer  Bangalore   32        14      10

Accepted Answer

Output    Name Sex    Position       City  age  Projects  Budget
2    Jia   F     Manager    Chennai   32        16      32
3  Lalit   M     Manager     Mumbai   40        20      21
5   Suji   F  Programmer  Bangalore   32        14      10
     Name Sex   Position       City  age  Projects  Budget
0  Rabina   F    Manager  Bangalore   30        13      48
1    Evan   M  Programer  New Delhi   27        17      13
       Name Sex    Position       City  age  Projects  Budget
3     Lalit   M     Manager     Mumbai   40        20      21
4  Jaspreet   M  Programmer    Chennai   28        21      17
5      Suji   F  Programmer  Bangalore   32        14      10
Explanationprint(ndf[ndf["age"] > 30]) — The ndf["age"] > 30 creates a boolean mask where True indicates rows where the "age" column has values greater than 30. Then ndf[...] uses this boolean mask to filter the DataFrame ndf, returning only rows where the condition is True.print(ndf.head(2)) — It prints the first 2 rows of the DataFrame ndf.print(ndf.tail(3)) — It prints the last 3 rows of the DataFrame ndf.

Question 10

Question 13Given the two DataFrames as :>>> dfc1        
    -   -         
    0   1        
0   2   a      
1   3   b      
2   4   c   
>>> dfc2
    -   -   
    0   1   2
0   2   3   4
2   p   q   r
Why are following statements giving errors ?(a) print(dfc1 + dfc2)(b) print(dfc1.sub(dfc2))(c) print(dfc1 * dfc2)

Accepted Answer

(a) print(dfc1 + dfc2) — This statement tries to add two DataFrames dfc1 and dfc2. However, they have different shapes (dfc1 has 3 rows and 2 columns, while dfc2 has 2 rows and 3 columns), and their indices do not match. DataFrame addition requires both DataFrames to have the same shape and compatible indices, which is not the case here.(b) print(dfc1.sub(dfc2)) — This statement attempts to subtract dfc1 from dfc2 using the sub() method. Similar to addition, subtraction between DataFrames requires them to have the same shape and compatible indices, which is not satisfied here due to the mismatch in shapes and indices.(c) print(dfc1 * dfc2) — This statement tries to perform element-wise multiplication between dfc1 and dfc2. Again, this operation requires both DataFrames to have the same shape and compatible indices, which is not the case here due to the mismatched shapes and indices.

Question 11

Question 14Consider the following code that creates two DataFrames :ore1 = pd.DataFrame(np.array([[20, 35, 25, 20], [11, 28, 32, 29]]),
       columns = ['iron', 'magnesium', 'copper', 'silver'])
ore2 = pd.DataFrame(np.array([[14, 34, 26, 26], [33, 19, 25, 23]]),
       columns = ['iron', 'magnesium', 'gold', 'silver'])What will be the output produced by the following code fragments ?(a) print(ore1 + ore2)     ore3 = ore1.radd(ore2)     print(ore3)(b) print(ore1 - ore2)     ore3 = ore1.rsub(ore2)     print(ore3)(c) print(ore1 * ore2)     ore3 = ore1.mul(ore2)     print(ore3)(d) print(ore1 / ore2)     ore3 = ore1.rdiv(ore2)     print(ore3)

Accepted Answer

(a)Output   copper  gold  iron  magnesium  silver
0     NaN   NaN    34         69      46
1     NaN   NaN    44         47      52
   copper  gold  iron  magnesium  silver
0     NaN   NaN    34         69      46
1     NaN   NaN    44         47      52
Explanationprint(ore1 + ore2): This line attempts to add the DataFrames ore1 and ore2 using the '+' operator. When adding DataFrames with different shapes and column names, pandas aligns the DataFrames based on indices and columns, resulting in NaN values where elements are missing in either DataFrame.ore3 = ore1.radd(ore2): This line uses the radd() method, which reverses the addition operation between DataFrames ore1 and ore2.The radd() function and '+' operator produce the same result in pandas, as the order of operands does not affect addition due to its commutative property.(b)Output  copper  gold  iron  magnesium  silver
0     NaN   NaN     6          1      -6
1     NaN   NaN   -22          9       6
   copper  gold  iron  magnesium  silver
0     NaN   NaN    -6         -1       6
1     NaN   NaN    22         -9      -6
Explanationprint(ore1 - ore2): This line performs a subtraction operation between corresponding elements in DataFrames ore1 and ore2. When subtracting DataFrames with different shapes and column names, pandas aligns the DataFrames based on indices and columns, resulting in NaN values where elements are missing in either DataFrame.ore3 = ore1.rsub(ore2): This line uses the rsub() method, which reverses the subtraction operation between DataFrames ore1 and ore2.The rsub() function and the '-' operator in pandas do not produce the same result because the rsub() function performs reverse subtraction, which means it subtracts the left operand from the right, while the '-' subtracts the right operand from the left operand.(c)Output   copper  gold  iron  magnesium  silver
0     NaN   NaN   280       1190     520
1     NaN   NaN   363        532     667
   copper  gold  iron  magnesium  silver
0     NaN   NaN   280       1190     520
1     NaN   NaN   363        532     667
Explanationprint(ore1 * ore2): This line attempts to perform element-wise multiplication between the DataFrames ore1 and ore2 using the '*' operator. When multiplying DataFrames with different shapes and column names, pandas aligns the DataFrames based on indices and columns, resulting in NaN values where elements are missing in either DataFrame.ore3 = ore1.mul(ore2): This line uses the mul() method to perform element-wise multiplication between DataFrames ore1 and ore2.(d)Output   copper  gold      iron  magnesium    silver
0     NaN   NaN  1.428571   1.029412  0.769231
1     NaN   NaN  0.333333   1.473684  1.260870
   copper  gold  iron  magnesium    silver
0     NaN   NaN   0.7   0.971429  1.300000
1     NaN   NaN   3.0   0.678571  0.793103
Explanationprint(ore1 / ore2): This line attempts to perform element-wise division between the DataFrames ore1 and ore2 using the '/' operator. When dividing DataFrames with different shapes and column names, pandas aligns the DataFrames based on indices and columns, resulting in NaN values where elements are missing in either DataFrame.ore3 = ore1.rdiv(ore2): This line uses the rdiv() method to perform reciprocal division between DataFrames ore1 and ore2.

Question 12

Question 15Consider the DataFrame wdf as shown below : minTempmaxTempRainfallEvaporation02.98.024.30.013.114.026.93.626.213.723.43.635.313.315.539.846.317.616.12.855.418.216.90.065.521.118.20.274.818.317.00.083.620.819.50.097.719.422.816.2109.924.125.20.01111.828.527.30.21213.229.127.90.01316.824.130.90.01419.428.131.20.01521.634.432.10.01620.433.831.20.01718.526.730.01.21818.832.432.30.61917.628.633.40.02019.730.333.40.0(a) Write statement(s) to calculate minimum value for each of the columns.(b) Write statement(s) to calculate maximum value for each of the rows.(c) Write statement(s) to calculate variance for column Rainfall.(d) Write statement(s) to compute mean , mode median for last 10 rows.

Accepted Answer

(a)>>> wdf.min()OutputMinimum values for each column:
minTemp         2.9
maxTemp         8.0
Rainfall       15.5
Evaporation     0.0
dtype: float64
(b)>>> wdf.max(axis=1)OutputMaximum values for each row:
0     24.3
1     26.9
2     23.4
3     39.8
4     17.6
5     18.2
6     21.1
7     18.3
8     20.8
9     22.8
10    25.2
11    28.5
12    29.1
13    30.9
14    31.2
15    34.4
16    33.8
17    30.0
18    32.4
19    33.4
20    33.4
dtype: float64
(c)>>> wdf['Rainfall'].var()OutputVariance for column Rainfall: 38.852999999999994
(d)>>> last_10_rows = wdf.tail(10)
>>> last_10_rows.mean()
>>> last_10_rows.mode()
>>> last_10_rows.median()OutputMean for last 10 rows:
minTemp        17.78
maxTemp        29.60
Rainfall       30.97
Evaporation     0.20
dtype: float64

Mode for last 10 rows:
   minTemp  maxTemp  Rainfall  Evaporation
0     11.8     24.1      31.2          0.0
1     13.2     26.7      33.4          NaN
2     16.8     28.1       NaN          NaN
3     17.6     28.5       NaN          NaN
4     18.5     28.6       NaN          NaN
5     18.8     29.1       NaN          NaN
6     19.4     30.3       NaN          NaN
7     19.7     32.4       NaN          NaN
8     20.4     33.8       NaN          NaN
9     21.6     34.4       NaN          NaN

Median for last 10 rows:
minTemp        18.65
maxTemp        28.85
Rainfall       31.20
Evaporation     0.00
dtype: float64

Question 13

Question 1Write a program to print a DataFrame one column at a time and print only first three columns.Solutionimport pandas as pd

data = {
    'Name': ['Aliya', 'Hemanth', 'Charlie'],
    'Age': [25, 30, 35],
    'City': ['Bangalore', 'Chennai', 'Mumbai'],
    'Salary': [50000, 60000, 70000]
}

df = pd.DataFrame(data)
first_three_columns = df.iloc[:, :3]

print("Each column:")
for column_name in first_three_columns:
    column_data = first_three_columns[column_name]
    print(column_name)
    print(column_data)OutputEach column:
Name
0      Aliya
1    Hemanth
2    Charlie
Name: Name, dtype: object
Age
0    25
1    30
2    35
Name: Age, dtype: int64
City
0    Bangalore
1      Chennai
2       Mumbai
Name: City, dtype: object

Accepted Answer

importpandasaspddata={'Name': ['Aliya','Hemanth','Charlie'],'Age': [25,30,35],'City': ['Bangalore','Chennai','Mumbai'],'Salary': [50000,60000,70000]}df=pd.DataFrame(data)first_three_columns=df.iloc[:, :3]print("Each column:")forcolumn_nameinfirst_three_columns:column_data=first_three_columns[column_name]print(column_name)print(column_data)OutputEach column:Name0      Aliya1    Hemanth2    CharlieName: Name, dtype: objectAge0    251    302    35Name: Age, dtype: int64City0    Bangalore1      Chennai2       MumbaiName: City, dtype: object

Question 14

Question 2Write a program to print a DataFrame one row at a time and print only first five rows.Solutionimport pandas as pd
data = {
    'Name': ['Amruta', 'Harsh', 'Yogesh', 'Shreya', 'Zoya', 'Nyra'],
    'Age': [25, 30, 35, 40, 45, 28],
    'City': ['Chandigarh', 'Jaipur', 'Dehradun', 'Delhi', 'Vadodara', 'Guwahati']
}

df = pd.DataFrame(data)
first_five_rows = df.head(5)
print("Each row:")
for index, row in first_five_rows.iterrows():
    print("Index:", index)
    print(row)OutputEach row:
Index: 0
Name        Amruta
Age             25
City    Chandigarh
Name: 0, dtype: object
Index: 1
Name     Harsh
Age         30
City    Jaipur
Name: 1, dtype: object
Index: 2
Name      Yogesh
Age           35
City    Dehradun
Name: 2, dtype: object
Index: 3
Name    Shreya
Age         40
City     Delhi
Name: 3, dtype: object
Index: 4
Name        Zoya
Age           45
City    Vadodara
Name: 4, dtype: object

Accepted Answer

importpandasaspddata={'Name': ['Amruta','Harsh','Yogesh','Shreya','Zoya','Nyra'],'Age': [25,30,35,40,45,28],'City': ['Chandigarh','Jaipur','Dehradun','Delhi','Vadodara','Guwahati']}df=pd.DataFrame(data)first_five_rows=df.head(5)print("Each row:")forindex,rowinfirst_five_rows.iterrows():print("Index:",index)print(row)OutputEach row:Index: 0Name        AmrutaAge             25City    ChandigarhName: 0, dtype: objectIndex: 1Name     HarshAge         30City    JaipurName: 1, dtype: objectIndex: 2Name      YogeshAge           35City    DehradunName: 2, dtype: objectIndex: 3Name    ShreyaAge         40City     DelhiName: 3, dtype: objectIndex: 4Name        ZoyaAge           45City    VadodaraName: 4, dtype: object

Question 15

Question 3Write a program that performs count, sum, max, and min functions :On rowsOn columnsSolutionimport pandas as pd
data = {
    'A': [1, 2, 3],
    'B': [4, 5, 6],
    'C': [7, 8, 9]
}

df = pd.DataFrame(data)
print("Operations on rows:")
print("Count:")
print(df.count(axis=1))
print("Sum:")
print(df.sum(axis=1))
print("Max:")
print(df.max(axis=1))
print("Min:")
print(df.min(axis=1))

print("
Operations on columns:")
print("Count:")
print(df.count())
print("Sum:")
print(df.sum())
print("Max:")
print(df.max())
print("Min:")
print(df.min())OutputOperations on rows:
Count:
0    3
1    3
2    3
dtype: int64
Sum:
0    12
1    15
2    18
dtype: int64
Max:
0    7
1    8
2    9
dtype: int64
Min:
0    1
1    2
2    3
dtype: int64

Operations on columns:
Count:
A    3
B    3
C    3
dtype: int64
Sum:
A     6
B    15
C    24
dtype: int64
Max:
A    3
B    6
C    9
dtype: int64
Min:
A    1
B    4
C    7
dtype: int64

Accepted Answer

importpandasaspddata={'A': [1,2,3],'B': [4,5,6],'C': [7,8,9]}df=pd.DataFrame(data)print("Operations on rows:")print("Count:")print(df.count(axis=1))print("Sum:")print(df.sum(axis=1))print("Max:")print(df.max(axis=1))print("Min:")print(df.min(axis=1))print("
Operations on columns:")print("Count:")print(df.count())print("Sum:")print(df.sum())print("Max:")print(df.max())print("Min:")print(df.min())OutputOperations on rows:Count:0    31    32    3dtype: int64Sum:0    121    152    18dtype: int64Max:0    71    82    9dtype: int64Min:0    11    22    3dtype: int64Operations on columns:Count:A    3B    3C    3dtype: int64Sum:A     6B    15C    24dtype: int64Max:A    3B    6C    9dtype: int64Min:A    1B    4C    7dtype: int64

Question 16

Question 4Take a DataFrame of your choice. Write a program to calculate count of values only in a selective column.Solutionimport pandas as pd
data = {
    'Name': ['Deepika', 'Simran', 'Shivang', 'Anurag', 'Ankith'],
    'Age': [25, 30, None, 40, 45],
    'City': ['Goa', 'Kochi', 'Pondicherry', 'Rohtak', 'Kasol']
}

df = pd.DataFrame(data)
selected_column = 'Age'
count_values = df[selected_column].count()
print("Count of values in", selected_column, ":" , count_values)OutputCount of values in Age : 4

Accepted Answer

importpandasaspddata={'Name': ['Deepika','Simran','Shivang','Anurag','Ankith'],'Age': [25,30,None,40,45],'City': ['Goa','Kochi','Pondicherry','Rohtak','Kasol']}df=pd.DataFrame(data)selected_column='Age'count_values=df[selected_column].count()print("Count of values in",selected_column,":",count_values)OutputCount of values in Age : 4

Question 17

Question 5Give two identical DataFrames Sales16 and Sales17. But Sales17 has some values missing. Write code so that Sales17 fills its missing values from the corresponding entries of Sales16.Solutionimport pandas as pd
data_sales16 = {
    'Product': ['A', 'B', 'C', 'D'],
    'Sales': [100, 150, 120, 180]
}
Sales16 = pd.DataFrame(data_sales16)
data_sales17 = {
    'Product': ['A', 'B', 'C', 'D'],
    'Sales': [100, None, 120, None]
}
Sales17 = pd.DataFrame(data_sales17)
Sales17 = Sales16.fillna({'B': 150, 'D': 180})
print("Sales16:")
print(Sales16)
print("
Sales17 (after filling missing values):")
print(Sales17)OutputSales16:
  Product  Sales
0       A    100
1       B    150
2       C    120
3       D    180

Sales17 (after filling missing values):
  Product  Sales
0       A    100
1       B    150
2       C    120
3       D    180

Accepted Answer

importpandasaspddata_sales16={'Product': ['A','B','C','D'],'Sales': [100,150,120,180]}Sales16=pd.DataFrame(data_sales16)data_sales17={'Product': ['A','B','C','D'],'Sales': [100,None,120,None]}Sales17=pd.DataFrame(data_sales17)Sales17=Sales16.fillna({'B':150,'D':180})print("Sales16:")print(Sales16)print("
Sales17 (after filling missing values):")print(Sales17)OutputSales16:  Product  Sales0       A    1001       B    1502       C    1203       D    180Sales17 (after filling missing values):  Product  Sales0       A    1001       B    1502       C    1203       D    180

Question 18

Question 7Four Series objects Temp1, Temp2, Temp3 and Temp4 store the temperatures of week1, week2, week3 and week4 respectively. Create a DataFrame from these four series objects where the indexes should be 'Sunday', 'Monday', ... , 'Saturday', and columns should be 'Week1', 'Week2', 'Week3' and 'week4'.Solutionimport pandas as pd
weekdays = ['Sunday', 'Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday']
week1 = [25.4, 26.7, 27.2, 28.9, 29.8, 30.2, 31.5]  
week2 = [22.3, 23.2, 24.6, 25.4, 26.8, 27.9, 28.4]  
week3 = [20.1, 21.2, 22.4, 23.9, 24.7, 25.57, 26.78]  
week4 = [18.4, 19.1, 20.3, 21.7, 22.67, 23.4, 24.09]  
Temp1 = pd.Series(week1, index=weekdays)
Temp2 = pd.Series(week2, index=weekdays)
Temp3 = pd.Series(week3, index=weekdays)
Temp4 = pd.Series(week4, index=weekdays)
df = pd.DataFrame({'Week1': Temp1, 'Week2': Temp2, 'Week3': Temp3, 'Week4': Temp4})
print(df)Output           Week1  Week2  Week3  Week4
Sunday      25.4   22.3  20.10  18.40
Monday      26.7   23.2  21.20  19.10
Tuesday     27.2   24.6  22.40  20.30
Wednesday   28.9   25.4  23.90  21.70
Thursday    29.8   26.8  24.70  22.67
Friday      30.2   27.9  25.57  23.40
Saturday    31.5   28.4  26.78  24.09

Accepted Answer

importpandasaspdweekdays=['Sunday','Monday','Tuesday','Wednesday','Thursday','Friday','Saturday']week1=[25.4,26.7,27.2,28.9,29.8,30.2,31.5]week2=[22.3,23.2,24.6,25.4,26.8,27.9,28.4]week3=[20.1,21.2,22.4,23.9,24.7,25.57,26.78]week4=[18.4,19.1,20.3,21.7,22.67,23.4,24.09]Temp1=pd.Series(week1,index=weekdays)Temp2=pd.Series(week2,index=weekdays)Temp3=pd.Series(week3,index=weekdays)Temp4=pd.Series(week4,index=weekdays)df=pd.DataFrame({'Week1':Temp1,'Week2':Temp2,'Week3':Temp3,'Week4':Temp4})print(df)OutputWeek1  Week2  Week3  Week4Sunday      25.4   22.3  20.10  18.40Monday      26.7   23.2  21.20  19.10Tuesday     27.2   24.6  22.40  20.30Wednesday   28.9   25.4  23.90  21.70Thursday    29.8   26.8  24.70  22.67Friday      30.2   27.9  25.57  23.40Saturday    31.5   28.4  26.78  24.09

Question 19

Question 8Given a DataFrame that stores the details of past 25 years' monthly sales. Some old data is however missing. Write a script to calculate average :Monthly sales across yearsYearly salesMake sure that missing values do not hamper the overall result.Solutionimport pandas as pd
data = {
    'Year': [],
    'Month': [],
    'Sales': []
}

n = int(input("Enter the number of rows in the DataFrame: "))
for i in range(n):
    year = int(input("Enter the year: "))
    month = int(input("Enter the month (1-12): "))
    sales = float(input("Enter the sales amount: "))
    data['Year'].append(year)
    data['Month'].append(month)
    data['Sales'].append(sales)

df = pd.DataFrame(data)

df.fillna(0, inplace=True)

avg_monthly_sales = df.groupby('Month')['Sales'].mean()

yearly_sales = df.groupby('Year')['Sales'].mean()

print("Average Monthly Sales Across Years:")
print(avg_monthly_sales)
print("
Yearly Sales:")
print(yearly_sales)OutputAverage Monthly Sales Across Years:
Month
1     543.25
2     644.25
3     486.50
4     583.50
5     376.50
6     644.75
7     847.00
8     497.75
9     564.50
10    701.00
11    524.75
12    522.25
Name: Sales, dtype: float64

Yearly Sales:
Year
2000    781.082333
2001    480.030233
2002    390.530000
2003    682.917767
2004    421.250050
2005    408.333333
2006    509.757500
2007    634.333667
2008    907.250000
2009    876.222222
2010    940.757578
2011    690.900067
2012    623.250043
2013    890.088888
2014    491.500000
2015    601.919997
2016    623.250055
2017    489.086633
2018    523.750000
2019    789.910067
2020    456.250000
2021    481.083333
2022    590.750000
2023    618.916667
2024    621.250000
Name: Sales, dtype: float64

Accepted Answer

importpandasaspddata={'Year': [],'Month': [],'Sales': []}n=int(input("Enter the number of rows in the DataFrame: "))foriinrange(n):year=int(input("Enter the year: "))month=int(input("Enter the month (1-12): "))sales=float(input("Enter the sales amount: "))data['Year'].append(year)data['Month'].append(month)data['Sales'].append(sales)df=pd.DataFrame(data)df.fillna(0,inplace=True)avg_monthly_sales=df.groupby('Month')['Sales'].mean()yearly_sales=df.groupby('Year')['Sales'].mean()print("Average Monthly Sales Across Years:")print(avg_monthly_sales)print("
Yearly Sales:")print(yearly_sales)OutputAverage Monthly Sales Across Years:Month1     543.252     644.253     486.504     583.505     376.506     644.757     847.008     497.759     564.5010    701.0011    524.7512    522.25Name: Sales, dtype: float64Yearly Sales:Year2000    781.0823332001    480.0302332002    390.5300002003    682.9177672004    421.2500502005    408.3333332006    509.7575002007    634.3336672008    907.2500002009    876.2222222010    940.7575782011    690.9000672012    623.2500432013    890.0888882014    491.5000002015    601.9199972016    623.2500552017    489.0866332018    523.7500002019    789.9100672020    456.2500002021    481.0833332022    590.7500002023    618.9166672024    621.250000Name: Sales, dtype: float64

Question 20

Question 9Given two identical DataFrames Sales16 and Sales17. But Sales17 has some values missing. Write code so that Sales17 fills its missing values from corresponding entries of Sales16.Solutionimport pandas as pd
data_sales16 = {
    'Product': ['A', 'B', 'C', 'D'],
    'Sales': [100, 150, 120, 180]
}
Sales16 = pd.DataFrame(data_sales16)
data_sales17 = {
    'Product': ['A', 'B', 'C', 'D'],
    'Sales': [100, None, 120, None]
}
Sales17 = pd.DataFrame(data_sales17)
Sales17 = Sales16.fillna({'B': 150, 'D': 180})
print("Sales16:")
print(Sales16)
print("
Sales17 (after filling missing values):")
print(Sales17)OutputSales16:
  Product  Sales
0       A    100
1       B    150
2       C    120
3       D    180

Sales17 (after filling missing values):
  Product  Sales
0       A    100
1       B    150
2       C    120
3       D    180

Accepted Answer

importpandasaspddata_sales16={'Product': ['A','B','C','D'],'Sales': [100,150,120,180]}Sales16=pd.DataFrame(data_sales16)data_sales17={'Product': ['A','B','C','D'],'Sales': [100,None,120,None]}Sales17=pd.DataFrame(data_sales17)Sales17=Sales16.fillna({'B':150,'D':180})print("Sales16:")print(Sales16)print("
Sales17 (after filling missing values):")print(Sales17)OutputSales16:  Product  Sales0       A    1001       B    1502       C    1203       D    180Sales17 (after filling missing values):  Product  Sales0       A    1001       B    1502       C    1203       D    180

Python Pandas — II

Multiple Choice Questions

Truefalse Questions

Type A Very Short Answer Questions

Type B Short Answer Questionsconceptual Questions

Type C Long Answer Questions