Question 1

Question 2Assertion (A): A series stores data row-wise.Reasoning (R): A series is a one-dimensional labelled data structure.Both A and R are true and R is the correct explanation of A.Both A and R are true but R is not the correct explanation of A.A is true but R is false.A is false but R is true.

Accepted Answer

A is false but R is true.ExplanationA series in pandas does not store data row-wise. It is a one-dimensional array like object containing an array of data and an associated array of data labels, called its index.

Question 2

Question 3Assertion (A): Dataframe has both a row and column index.Reasoning (R): Dataframe is a two-dimensional labelled data structure like a table of MySQL.Both A and R are true and R is the correct explanation of A.Both A and R are true but R is not the correct explanation of A.A is true but R is false.A is false but R is true.

Accepted Answer

Both A and R are true and R is the correct explanation of A.ExplanationA DataFrame in Pandas has both a row index and a column index. It is a two-dimensional labeled data structure, similar to a table in MySQL, each value is identifiable with the combination of row and column indices.

Question 3

Question 4Assertion (A): While creating a series using scalar values, index must be provided.Reasoning (R): The scalar value is repeated to match the length of index.Both A and R are true and R is the correct explanation of A.Both A and R are true but R is not the correct explanation of A.A is true but R is false.A is false but R is true.

Accepted Answer

Both A and R are true and R is the correct explanation of A.ExplanationWhen creating a Series using scalar values, we must provide an index. This is because a Series requires an index to associate with each value. This scalar value shall be repeated to match the length of the index.

Question 4

Question 6Assertion (A): After running the following code:df = pd.DataFrame([11,46], index = ['True', 'False'])
print(df[True])A key error will be produced.Reasoning (R): Dataframe does not support Boolean Indexing.Both A and R are true and R is the correct explanation of A.Both A and R are true but R is not the correct explanation of A.A is true but R is false.A is false but R is true.

Accepted Answer

A is true but R is false.ExplanationDataFrames do support Boolean Indexing, which allows to select rows based on a Boolean condition. The code df[True] is trying to access a column named True, which does not exist in the DataFrame. The index of the DataFrame is ['True', 'False']. To access the row where the index is 'True', we would use df.loc['True']. This is an example of label-based indexing, where we are selecting a row based on its index label.

Question 5

Question 10Which of the following commands shows the information with city="Delhi" from dataframe SHOP?print(SHOP[City == 'Delhi'])print(SHOP[SHOP.City == 'Delhi])print(SHOP[SHOP.'City' == 'Delhi'])print(SHOP[SHOP[City] == 'Delhi'])

Accepted Answer

print(SHOP[SHOP.City == 'Delhi'])Reason — The correct code print(SHOP[SHOP.City == 'Delhi']) filters the SHOP DataFrame to show only the rows where the City column is equal to 'Delhi'. It does this by creating a boolean mask SHOP.City == 'Delhi' that returns True for rows where the city is 'Delhi' and False otherwise, and then using this mask to select the corresponding rows from the original DataFrame using SHOP[]. The resulting DataFrame, which contains only the rows that match the condition, is then printed to the console.

Question 6

Question 1What is the significance of Pandas Library?

Accepted Answer

The significance of Python Pandas library is as follows:It can read or write in many different data formats (integer, float, double, etc.).It can calculate in all the possible ways data is organized i.e., across rows and down columns.It can easily select subsets of data from bulky data sets and even combine multiple datasets together. It has functionality to find and fill missing data.It allows to apply operations to independent groups within the data.It supports reshaping of data into different forms.It supports advanced time-series functionality.It supports visualization by integrating matplotlib and seaborn etc. libraries.

Question 7

Question 7How can we create CSV file? Explain with steps.

Accepted Answer

The steps to create CSV files are as follows:Launch Microsoft Excel.Type the data given in Table in the Excel sheet.Save the file with a proper name by clicking File -> Save or Save As or press Ctrl + S to open the Save As window.Type the name of the file and select file type as CSV from the drop-down arrow.Click the Save button. Excel will ask for confirmation to select CSV format.Click OK.It will display a dialog box asking permission to keep comma as delimiter for CSV file.Lastly, click Yes to retain and save the Excel file in CSV format.

Question 8

Question 8How do you iterate over a dataframe? Explain with the help of code snippet.

Accepted Answer

Iterating over rows in the DataFrame:The iterrows() method is used to iterate over each row in the DataFrame. In this method, each horizontal subset is in the form of (row-index, series), where the series contains all column values for that row-index.For example :import pandas as pd
total_sales = {2015 : {'Qtr1' : 34500, 'Qtr2' : 45000}, 
               2016 : {'Qtr1' : 44500, 'Qtr2' : 65000}}
df = pd.DataFrame(total_sales)
for (row, rowseries) in df.iterrows():
    print("RowIndex :", row)
    print('Containing :')
    print(rowseries)OutputRowIndex : Qtr1
Containing :
2015    34500
2016    44500
Name: Qtr1, dtype: int64
RowIndex : Qtr2
Containing :
2015    45000
2016    65000
Name: Qtr2, dtype: int64
Iterating over columns in the DataFrame:The iteritems() method is used to iterate over each column in the DataFrame. In this method, each vertical subset is in the form of (column-index, series), where the series contains all row values for that column-index.For example :import pandas as pd
total_sales = {2015 : {'Qtr1' : 34500, 'Qtr2' : 45000}, 
               2016 : {'Qtr1' : 44500, 'Qtr2' : 65000}}
df = pd.DataFrame(total_sales)
for (col, colseries) in df.iteritems():
    print("Column Index :", col)
    print('Containing :')
    print(colseries)OutputColumn Index : 2015
Containing :
Qtr1    34500
Qtr2    45000
Name: 2015, dtype: int64
Column Index : 2016
Containing :
Qtr1    44500
Qtr2    65000
Name: 2016, dtype: int64

Question 9

Question 9Write commands to print the following details of a series object seal.(a) If the series is empty(b) Indexes of the series(c) The data type of underlying data(d) If the series stores any NaN values

Accepted Answer

(a)seal.empty(b)seal.index(c)seal.dtype(d)seal.hasnans

Question 10

Question 11Consider the following tables Item and Customer and answer the questions that follow:Table: ItemItem_IDItemNameManufacturerPricePC01Personal ComputerHCL India42000LCO5LaptopHP USA55000PCO3Personal ComputerDell USA32000PC06Personal ComputerZenith USA37000LCO3LaptopDell USA57000Table: CustomerItem_IDCustomerNameCityLCO3N RoyDelhiPCO3H SinghMumbaiPC06R PandeyDelhiLCO3C SharmaChennaiPC01K AgarwalBengaluruAssume that the Pandas has been imported as pd.(a) Create a dataframe called dfI for table Item.(b) Create a dataframe called dfC for table Customer.(c) Perform the default join operation on item_ID using two dataframes: dfI and dfC.(d) Perform the left join operation on item_ID using two dataframes: dfI and dfC.(e) Perform the right join operation on Item_ID using two dataframes: dfI and dfC.(f) Perform the default operation on Item_ID using two dataframes: dfI and dfC with the left index as true.(g) Perform the outer join operation on item_ID using two dataframes: dfI and dfC.(h) Create a new dataframe dfN using dataframes: dfI and dfC. The new dataframe data will hold both left index and right index true values.(i) Arrange the dataframe dfN in descending order of Price.(j) Arrange the dataframe dfN in descending order of City and Price.

Accepted Answer

(a)dfI = pd.DataFrame({
    'Item_ID': ['PC01', 'LCO5', 'PCO3', 'PC06', 'LCO3'],
    'ItemName': ['Personal Computer', 'Laptop', 'Personal Computer', 'Personal Computer', 'Laptop'],
    'Manufacturer': ['HCL India', 'HP USA', 'Dell USA', 'Zenith USA', 'Dell USA'],
    'Price': [42000, 55000, 32000, 37000, 57000]
})Output  Item_ID           ItemName Manufacturer  Price
0    PC01  Personal Computer    HCL India  42000
1    LCO5             Laptop       HP USA  55000
2    PCO3  Personal Computer     Dell USA  32000
3    PC06  Personal Computer   Zenith USA  37000
4    LCO3             Laptop     Dell USA  57000
(b)dfC = pd.DataFrame({
    'Item_ID': ['LCO3', 'PCO3', 'PC06', 'LCO3', 'PC01'],
    'CustomerName': ['N Roy', 'H Singh', 'R Pandey', 'C Sharma', 'K Agarwal'],
    'City': ['Delhi', 'Mumbai', 'Delhi', 'Chennai', 'Bengaluru']
})Output  Item_ID CustomerName       City
0    LCO3        N Roy      Delhi
1    PCO3      H Singh     Mumbai
2    PC06     R Pandey      Delhi
3    LCO3     C Sharma    Chennai
4    PC01    K Agarwal  Bengaluru
(c)df_default_join = pd.merge(dfI, dfC, on = 'Item_ID')Output  Item_ID           ItemName Manufacturer  Price CustomerName       City
0    PC01  Personal Computer    HCL India  42000    K Agarwal  Bengaluru
1    PCO3  Personal Computer     Dell USA  32000      H Singh     Mumbai
2    PC06  Personal Computer   Zenith USA  37000     R Pandey      Delhi
3    LCO3             Laptop     Dell USA  57000        N Roy      Delhi
4    LCO3             Laptop     Dell USA  57000     C Sharma    Chennai
(d)df_left_join = pd.merge(dfI, dfC, left_on='Item_ID', right_on='Item_ID', how='left')Output  Item_ID           ItemName Manufacturer  Price CustomerName       City
0    PC01  Personal Computer    HCL India  42000    K Agarwal  Bengaluru
1    LCO5             Laptop       HP USA  55000          NaN        NaN
2    PCO3  Personal Computer     Dell USA  32000      H Singh     Mumbai
3    PC06  Personal Computer   Zenith USA  37000     R Pandey      Delhi
4    LCO3             Laptop     Dell USA  57000        N Roy      Delhi
5    LCO3             Laptop     Dell USA  57000     C Sharma    Chennai
(e)df_right_join = pd.merge(dfI, dfC, left_on='Item_ID', right_on='Item_ID', how='right')Output  Item_ID           ItemName Manufacturer  Price CustomerName       City
0    LCO3             Laptop     Dell USA  57000        N Roy      Delhi
1    PCO3  Personal Computer     Dell USA  32000      H Singh     Mumbai
2    PC06  Personal Computer   Zenith USA  37000     R Pandey      Delhi
3    LCO3             Laptop     Dell USA  57000     C Sharma    Chennai
4    PC01  Personal Computer    HCL India  42000    K Agarwal  Bengaluru
(f) This operation is not possible because the left_index parameter is used for merging on the index of the dataframes, not on a specific column. The default merge operation is an inner join, and it requires a common column to merge on.(g)df_outer_join = pd.merge(dfI, dfC, on='Item_ID', how='outer')Output  Item_ID           ItemName Manufacturer  Price CustomerName       City
0    LCO3             Laptop     Dell USA  57000        N Roy      Delhi
1    LCO3             Laptop     Dell USA  57000     C Sharma    Chennai
2    LCO5             Laptop       HP USA  55000          NaN        NaN
3    PC01  Personal Computer    HCL India  42000    K Agarwal  Bengaluru
4    PC06  Personal Computer   Zenith USA  37000     R Pandey      Delhi
5    PCO3  Personal Computer     Dell USA  32000      H Singh     Mumbai
(h)dfN = pd.merge(dfI, dfC, left_index=True, right_index=True)OutputItem_ID_x           ItemName Manufacturer  Price Item_ID_y CustomerName       City
0      PC01  Personal Computer    HCL India  42000      LCO3        N Roy      Delhi
1      LCO5             Laptop       HP USA  55000      PCO3      H Singh     Mumbai
2      PCO3  Personal Computer     Dell USA  32000      PC06     R Pandey      Delhi
3      PC06  Personal Computer   Zenith USA  37000      LCO3     C Sharma    Chennai
4      LCO3             Laptop     Dell USA  57000      PC01    K Agarwal  Bengaluru
(i)dfN_sorted_price = dfN.sort_values(by='Price', ascending=False)Output  Item_ID_x           ItemName Manufacturer  Price Item_ID_y CustomerName       City
4      LCO3             Laptop     Dell USA  57000      PC01    K Agarwal  Bengaluru
1      LCO5             Laptop       HP USA  55000      PCO3      H Singh     Mumbai
0      PC01  Personal Computer    HCL India  42000      LCO3        N Roy      Delhi
3      PC06  Personal Computer   Zenith USA  37000      LCO3     C Sharma    Chennai
2      PCO3  Personal Computer     Dell USA  32000      PC06     R Pandey      Delhi
(j)dfN_sorted_city_price = dfN.sort_values(by=['City', 'Price'], ascending=[False, False])Output  Item_ID_x           ItemName Manufacturer  Price Item_ID_y CustomerName       City
1      LCO5             Laptop       HP USA  55000      PCO3      H Singh     Mumbai
0      PC01  Personal Computer    HCL India  42000      LCO3        N Roy      Delhi
2      PCO3  Personal Computer     Dell USA  32000      PC06     R Pandey      Delhi
3      PC06  Personal Computer   Zenith USA  37000      LCO3     C Sharma    Chennai
4      LCO3             Laptop     Dell USA  57000      PC01    K Agarwal  Bengaluru

Question 11

Question 12Consider the following series object namely S:0 0.430271 
1 0.617328 
2 0.265421 
3 0.836113 
dtype: float64
What will be returned by the following statements?(a) S * 100(b) S > 0(c) S1 = pd.Series(S)(d) S3 = pd.Series(S1) + 3

Accepted Answer

(a) S * 100Output0    43.0271
1    61.7328
2    26.5421
3    83.6113
dtype: float64
(b) S > 0Output0    True
1    True
2    True
3    True
dtype: bool
(c) S1 = pd.Series(S)Output0    0.430271
1    0.617328
2    0.265421
3    0.836113
dtype: float64
(d) S3 = pd.Series(S1) + 3Output0    3.430271
1    3.617328
2    3.265421
3    3.836113
dtype: float64

Question 12

Question 13What will be the output produced by the following code?Stationery = ['pencils', 'notebooks', 'scales', 'erasers']
S = pd.Series([20, 33, 52, 10], index = Stationery)
S2 = pd.Series([17, 13, 31, 32], index = Stationery)
print(S == S2)
S = S + S2
print(S)

Accepted Answer

Outputpencils      False
notebooks    False
scales       False
erasers      False
dtype: bool
pencils      37
notebooks    46
scales       83
erasers      42
dtype: int64
ExplanationThe code creates two pandas Series, S and S2, with the index Stationery which is a list. The code then compares S and S2 element-wise using the "==" operator, which returns a boolean Series indicating whether each pair of values is equal. Finally, the code adds S and S2 element-wise using the "+" operator, which returns a new Series with the summed values, and assigns the result back to S. The resulting S Series will have the same index as before i.e., Stationery.

Question 13

Question 14What will be the output produced by the following codes, considering the Series object S given in Q.13?(a) print(S[1:4])(b) print(S[:1])(c) print(S[0:2])(d) S[0:2] = 12     print(S)(e) print(S.index)(f) print(S.values)The Series object 'S' is as follows:pencils      20
notebooks    33
scales       52
erasers      10
dtype: int64

Accepted Answer

(a) print(S[1:4])Outputnotebooks    33
scales       52
erasers      10
dtype: int64
ExplanationThe slice S[1:4] starts at index 1 and ends at index 3, hence, it includes three elements i.e., elements from index 1 and 3.(b) print(S[:1])Outputpencils    20
dtype: int64
ExplanationThe slice S[:1] starts at index 0 and ends at index 1, but because the end index is exclusive, it includes only one element i.e., the element at index 0.(c) print(S[0:2])Outputpencils      20
notebooks    33
dtype: int64
ExplanationThe slice S[0:2] starts at index 0 and ends at index 1, hence, it includes two elements i.e., elements from index 0 and 1.(d) S[0:2] = 12     print(S)Outputpencils      12
notebooks    12
scales       52
erasers      10
dtype: int64
ExplanationThe slice S[0:2] = 12 assigns the value 12 to indices 0 and 1 in Series S, directly modifying those elements. The updated Series is then printed.(e) print(S.index)OutputIndex(['pencils', 'notebooks', 'scales', 'erasers'], dtype='object')
ExplanationThe code print(S.index) displays the indices of Series S.(f) print(S.values)Output[12 12 52 10]
ExplanationThe code print(S.values) displays the values of Series S.

Question 14

Question 15Write a program to iterate and print a dataframe column-wise 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 15

Question 16Write a program to iterate and print a dataframe row-wise 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 16

Question 17(a)Find the error in the following code fragments:S2 = pd.Series([101, 102, 1-2, 104])
print (S2.index)
S2.index = [0.1.2.3, 4, 5]
S2[5] = 220
print (S2)

Accepted Answer

S2 = pd.Series([101, 102, 1-2, 104]) 
print(S2.index)
S2.index = [0.1.2.3, 4, 5] #Error 1
S2[5] = 220
print(S2)Error 1 — In the line S2.index = [0.1.2.3, 4, 5], the index values are not separated by commas. It should be S2.index = [0, 1, 2, 3, 4, 5]. Additionally, the Series S2 initially has four elements, so assigning a new index list of six elements ([0, 1, 2, 3, 4, 5]) to S2.index will raise a ValueError because the new index list length does not match the length of the Series.The corrected code is:S2 = pd.Series([101, 102, 1-2, 104])
print(S2.index)
S2.index = [0, 1, 2, 3]
S2[5] = 220
print(S2)

Question 17

Question 17(b)Find the error in the following code fragments:S = pd.Series(2, 3, 4, 55, index = range (4))

Accepted Answer

In the above code fragment, the data values should be enclosed in square brackets [] to form a list.The corrected code is:S = pd.Series([2, 3, 4, 55], index = range(4))

Question 18

Question 17(c)Find the error in the following code fragments:S1 = pd.Series(1, 2, 3, 4, index = range(7))

Accepted Answer

In the above code fragment, the data values should be enclosed in square brackets to form a list and the specified index range range(7) is out of range for the provided data [1, 2, 3, 4]. Since there are only four data values, the index should have a length that matches the number of data values.The corrected code is:S1 = pd.Series([1, 2, 3, 4], index = range(4))

Question 19

Question 17(d)Find the error in the following code fragments:S2 = pd.Series([1, 2, 3, 4, 5], index = range(4))

Accepted Answer

The error in the code fragment is that the length of the data list [1, 2, 3, 4, 5] does not match the length of the index range(4). Since there are only five data values, the index should have a length that matches the number of data values.The corrected code is:S1 = pd.Series([1, 2, 3, 4, 5], index = range(5))

Question 20

Question 18Find the error:data = np.array(['a', 'b', 'c', 'd', 'e', 'f'])
s = pd.Series (data, index=[100, 101, 102, 103, 104, 105])
print(s[102,103,104])

Accepted Answer

The error in the above code is in the line print(s[102, 103, 104]). When accessing elements in a pandas Series using square brackets, we should use a list of index values, not multiple separate index values separated by commas.The corrected code is:data = np.array(['a', 'b', 'c', 'd', 'e', 'f'])  
s = pd.Series(data, index = [100, 101, 102, 103, 104, 105])  
print(s[[102, 103, 104]])

Data Handling using Pandas

Assertions And Reasons

Multiple Choice Questions

Solutions To Unsolved Questions

State True Or False